KR20200019919A - Method for detecting anomaly of data - Google Patents

Abstract

According to one embodiment of the present disclosure, disclosed is a method for detecting an anomaly of image data. The method comprises the steps of: receiving image data by a pre-learned network function; classifying the image data using the pre-learned network function; obtaining a scoring value for each class for classifying the image data outputted from the pre-learned network function; and determining whether an anomaly exists in the image data based on the scoring value for each class.

Description

Anomaly Detection Method of Data {METHOD FOR DETECTING ANOMALY OF DATA}

The present invention relates to artificial intelligence data processing, and more particularly, to a method for determining whether anomaly exists in image data using an artificial neural network.

Targets that are inspection targets in the process are imaged to check for defects. On the other hand, human detection and analysis of image data is limited. There is a need in the art for a method of determining the state of high accuracy image data. A neural network may be used to implement a method for determining a state of high accuracy image data.

An anomaly sensing method and apparatus using artificial neural networks have been introduced to the industry. Normal data is easily secured, but abnormal data, including anomaly, is not easily secured. There is a need in the art for a solution to reduce the demand for abnormal data, including anomalies for learning artificial neural networks.

Korean registered patent KR1847874B1 discloses an image recognition method using a neural network.

The present disclosure has been made in response to the above-described background, and an object thereof is to provide a method for anomaly determination of image data.

An object of the present disclosure is to provide information on whether anomaly of image data exists through an anomaly determination method of image data.

In accordance with an embodiment of the present disclosure for realizing the above-described problem, a method for determining anomaly of image data is disclosed. The method for determining anomaly of image data may include: receiving image data by a pre-learned network function; Classifying the image data using the pre-learned network function; Obtaining a scoring value for each class for classifying the image data output from the pre-learned network function; And determining whether anomaly exists in the image data based on the scoring value for each class.

In an alternative embodiment of the anomaly determination method of image data, determining whether anomaly exists in the image data based on the scoring value for each class, comparing the magnitude of each scoring value for each class. ; And determining whether anomaly exists in the image data based on the comparison result.

In an alternative embodiment of the anomaly determination method of image data, determining whether anomaly exists in the image data based on the scoring value for each class comprises: obtaining a variance of the scoring value; And determining whether anomaly exists in the image data based on the dispersion.

In an alternative embodiment of the method for anomaly determination of image data, determining whether anomaly exists in the image data based on the scoring value for each class comprises: number of classes whose scoring value is greater than or equal to a first threshold value; Determining; And determining that there is no anomaly in the image data when the number of classes whose scoring value is greater than or equal to a first threshold is less than or equal to a first predetermined number.

In an alternative embodiment of the anomaly determination method of image data, determining whether anomaly exists in the image data based on the scoring value for each class, wherein the number of classes whose scoring value is greater than or equal to a second threshold value; Determining; And determining that anomaly exists in the image data when the number of classes whose scoring value is greater than or equal to a second threshold is greater than or equal to a second predetermined number, wherein the second threshold is less than or equal to a first threshold; Can be.

In an alternative embodiment of the method for determining anomaly of image data, if anomaly exists in the image data, the method may further include displaying a portion of the image data in the image data where the anomaly exists. .

In an alternative embodiment of an anomaly determination method of image data, classifying the image data using the pre-learned network function may include classifying each portion of the image data to segment the image data. It may include.

In an alternative embodiment of the method for anomaly determination of image data, segmenting the image data by segmenting each portion of the image data may include segmenting the image data into respective classes for each pixel. It may include.

In an alternative embodiment of the method for anomaly determination of image data, acquiring a class-specific scoring value for classifying the image data output from the pre-learned network function may be performed for each pixel of the image data, respectively. Obtaining a scoring value for each class of may include.

In an alternative embodiment of the method for anomaly determination of image data, the image data may comprise one or more components classified into each class.

A computing device for anomaly determination of data is disclosed in accordance with one embodiment of the present disclosure for realizing the above problems. The computing device includes one or more processors; And a memory storing instructions executable in the processor; Wherein the processor is configured to input image data into a pre-learned network function, classify the image data using the pre-learned network function, and classify the image data output from the pre-learned network function. A scoring value for each class may be obtained, and it may be determined whether anomaly exists in the image data based on the scoring value for each class.

A computer readable storing medium comprising a plurality of instructions executed by one or more processors of an anomaly determination device of image data according to an embodiment of the present disclosure for realizing the above problem. The computer program stored in the is disclosed. The computer program includes: instructions for a pre-learned network function to receive image data; Classifying the image data using the pre-learned network function; Obtaining a scoring value for each class for classifying the image data output from the pre-learned network function; And determining whether anomaly exists in the image data based on the scoring value for each class.

The present disclosure can provide a method for determining anomaly of image data.

1 is a block diagram of a computing device for performing a method of determining anomaly of image data according to an embodiment of the present disclosure.
2 is a schematic diagram illustrating a convolutional neural network according to an embodiment of the present disclosure.
FIG. 3 is a diagram schematically illustrating classifying image data through a network function and obtaining a scoring value for each class for classifying image data according to an exemplary embodiment of the present disclosure.
4 is a diagram illustrating an example of normal image data according to an embodiment of the present disclosure.
5 is a diagram illustrating an example of image data in which anomaly exists according to an embodiment of the present disclosure.
FIG. 6 is a diagram illustrating an anomaly portion of image data in which anomaly exists according to an embodiment of the present disclosure.
FIG. 7A is a diagram schematically illustrating a scoring value for each class when no anomaly exists in image data according to an embodiment of the present disclosure.
FIG. 7B is a diagram schematically illustrating a scoring value for each class when anomaly exists in image data according to an embodiment of the present disclosure.
8 is a block diagram illustrating a means for implementing a method for determining anomaly of image data according to an embodiment of the present disclosure.
9 is a block diagram illustrating a module for implementing a method for determining anomaly of image data according to an embodiment of the present disclosure.
10 is a block diagram illustrating logic for implementing a method for determining anomaly of image data according to an embodiment of the present disclosure.
11 is a block diagram illustrating a circuit for implementing a method of determining anomaly of image data according to an embodiment of the present disclosure.
12 is a block diagram of a computing device according to an embodiment of the present disclosure.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. It may be evident, however, that such embodiments may be practiced without these specific details.

As used herein, the terms “component”, “module”, “system” and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or the execution of software. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a processor and / or thread of execution. One component can be localized within one computer. One component may be distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may be connected via a network such as the Internet and other systems via, for example, signals with one or more data packets (e.g., data and / or signals from one component that interacts with other components in a local system, distributed system). Data may be communicated via local and / or remote processes.

In addition, the term “or” is intended to mean an implicit “or” rather than an exclusive “or”. That is, unless specified otherwise or unambiguously in context, "X uses A or B" is intended to mean one of the natural implicit substitutions. That is, X uses A; X uses B; Or where X uses both A and B, "X uses A or B" may apply in either of these cases. Also, it is to be understood that the term "and / or" as used herein refers to and includes all possible combinations of one or more of the listed related items.

In addition, the terms "comprises" and / or "comprising" should be understood to mean that the corresponding features and / or components are present. It is to be understood, however, that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Also, unless otherwise specified or in the context of indicating a singular form, the singular in the specification and claims should generally be construed as meaning "one or more."

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be electronic hardware, computer software, or a combination of both. It should be appreciated that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in a variety of ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those of ordinary skill in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. The invention should be construed in the broadest scope consistent with the principles and novel features set forth herein.

1 is a block diagram of a computing device for performing a method of determining anomaly of image data according to an embodiment of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is merely an example for simplicity. In one embodiment of the present disclosure, computing device 100 may include other components for performing the computing environment of computing device 100.

The computing device 100 may include a processor 110, a memory 120, a communication module 130, and a camera module 140.

The processor 110 may include one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. and a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 120 to perform a method for anomaly determination of image data according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform calculation for learning a neural network. The processor 110 may process neural networks, such as processing input data for learning in deep learning (DN), extracting features from the input data, calculating errors, and weighting neural networks using backpropagation. Can perform calculations for learning. At least one of the CPU, GPGPU, and TPU of the processor 110 may handle the learning of the network function. For example, the CPU and GPGPU can work together to learn network functions and classify data using network functions. In addition, in an embodiment of the present disclosure, a processor of a plurality of computing devices may be used together to process learning of a network function and data classification using the network function. In addition, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, a GPGPU, or a TPU executable program.

In an embodiment of the present disclosure, the computing device 100 may distribute and process network functions using at least one of a CPU, a GPGPU, and a TPU. In addition, in an embodiment of the present disclosure, the computing device 100 may distribute and process network functions along with other computing devices.

In one embodiment of the present disclosure, an image processed using a network function may include an image stored in a storage medium of the computing device 100, an image captured by the camera module 140 of the computing device 100, and / or a communication module ( 130 may be an image transmitted from another computing device such as an image database. In addition, in one embodiment of the present disclosure, an image processed using a network function may be an image stored in a computer-readable storage medium (eg, but may not include, but not limited to, flash memory). The computing device 100 may receive an image file stored in a computer readable storage medium through an input / output interface (not shown).

In an embodiment of the present disclosure, the computing device 100 may receive image data and classify the image data using a pre-learned network function. In an embodiment of the present disclosure, the computing device 100 may determine whether anomaly exists in the image data based on a class-specific scoring value for classifying the image data output from the function of the pre-learned network.

According to an embodiment of the present disclosure, when the computing device may determine the image as one classification based on the scoring value for each class, the computing device may determine the image as normal data. The computing device may determine the image as abnormal data in which anomaly exists when the image cannot be determined by any one classification based on the scoring value for each class. The computing device may determine that the pixel is normal data when it is possible to determine the corresponding pixel as one classification based on the scoring value for each class. The computing device may determine that the anomaly exists as abnormal data when the corresponding pixel cannot be determined by any one classification based on the scoring value for each class. That is, in one embodiment of the present disclosure, since the computing device performs image segmentation training using the normal data as the training data, when the computing device can classify the input data based on the scoring value, the corresponding input data is normal data. Can be. However, if the computing device cannot classify the input data based on the scoring value, then the input data may contain new patterns that the computing device did not learn, and new patterns that the computing device did not learn would be anomaly. There is a possibility. Therefore, the computing device may classify input data that cannot be classified through the learned model as abnormal data including anomalies.

According to an embodiment of the present disclosure, when the computing device may determine a corresponding pixel as two or more classifications based on the scoring value for each class, the corresponding pixel may be determined as normal data including an edge in which two or more components exist. .

In an embodiment of the present disclosure, the computing device determines whether anomaly exists in the image data based on each of the scoring values for each class, and thus whether or not anomalies exist in the image data based on the scoring values for each class. Can be determined. The scoring value is a value representing a value corresponding to the probability that the corresponding part belongs to a specific class. The target portion of the scoring value may include the entire image received by the computing device, each pixel of the image received by the computing device, and the like. It may be determined whether anomaly exists by comparing magnitude values corresponding to the probabilities of each of the scoring values for each class indicated by the value corresponding to the probability. When there is only one value larger than a predetermined predetermined threshold value among the magnitude values corresponding to the probability of each class, it may be determined that normal image data having a normal pattern without anomaly is received. When there is at least one value larger than a predetermined predetermined threshold value among the magnitude values corresponding to the probability of each class, it may be determined that the anomaly receives abnormal image data that is outside the normal pattern.

　의 기댓값에서

　의 기댓값의 제곱을 뺀 것이다. 즉,

의 분산

는

이다. 스코어링 값들의 분산이 크면 클래스별 스코어링 값들이 고르지 않게 분포되어 있다는 것을 의미한다. 스코어링 값들의 분산이 크다는 것은, 특정 클래스에 대한 스코어링 값이 다른 클래스들의 스코어링 값보다 압도적으로 높다는 것을 의미한다. 하나의 클래스의 스코어링 값이 압도적으로 큰 값을 가지면 컴퓨팅 장치가 사전 학습된 네트워크를 통해 이미지 데이터를 어느 하나의 분류로 분류할 수 있다. 하나의 클래스의 스코어링 값이 압도적으로 큰 값을 가지면 해당 데이터는 어노말리가 존재하지 않는 정상 데이터로 판단될 수 있다. 스코어링 값들의 분산이 작으면 클래스별 스코어링 값들이 고르게 분포되어 있다는 것을 의미한다. 스코어링 값들의 분산이 작으면 하나의 클래스의 스코어링 값이 다른 어느 클래스의 스코어링 값보다 압도적으로 크지 않다는 것을 의미한다. 압도적인 스코어링 값을 가지는 클래스가 없으면 컴퓨팅 장치가 사전 학습된 네트워크를 통해 이미지 데이터를 어느 하나의 분류로 분류할 수 없어, 해당 데이터는 어노말리가 존재하는 비정상 데이터로 판단될 수 있다. 컴퓨팅 장치는 상기 스코어링 분포에 기초하여 구한 분산 값이 사전결정된 임계 값 이상이면 어노말리가 존재하지 않는 정상 이미지 데이터로 판단할 수 있다. 컴퓨팅 장치는 상기 스코어링 분포에 기초하여 구한 분산 값이 사전결정된 임계 값 이하이면 어노말리가 존재하는 비정상 이미지 데이터로 판단할 수 있다.In another embodiment of the present disclosure, the computing device obtains a variance of scoring values for each class, and determines whether there is anomaly in the image data based on the variance value, thereby determining the image data based on the scoring value for each class. It can be determined whether or not air anomaly exists. The variance of scoring values for each class indicated by a value corresponding to the probability may be obtained. The variance of X

At the expected value of

Minus the expected square of. In other words,

Dispersion

Is

to be. If the variance of the scoring values is large, it means that the scoring values for each class are distributed unevenly. A large variance of scoring values means that the scoring value for one class is overwhelmingly higher than the scoring values of other classes. If the scoring value of one class has an overwhelmingly large value, the computing device may classify the image data into any one classification through a pre-learned network. If a scoring value of one class has an overwhelmingly large value, the corresponding data may be determined as normal data in which no anomaly exists. If the variance of the scoring values is small, it means that the scoring values for each class are evenly distributed. A small variance of scoring values means that the scoring value of one class is not overwhelmingly greater than the scoring value of any other class. If there is no class having an overwhelming scoring value, the computing device cannot classify the image data into any one class through the pre-learned network, and thus the data may be determined to be abnormal data in which anomaly exists. The computing device may determine that the anomaly is normal image data in which no anomaly exists when the variance value obtained based on the scoring distribution is equal to or greater than a predetermined threshold value. The computing device may determine that the anomaly exists as abnormal image data when the variance value obtained based on the scoring distribution is equal to or less than a predetermined threshold value.

In another embodiment of the present disclosure, a computing device determines a number of classes whose scoring value is greater than or equal to a first threshold value, and if the number of classes whose scoring value is greater than or equal to a first threshold value is less than or equal to a first predetermined number, By determining that anomaly does not exist, it may be determined whether anomaly exists in the image data based on the scoring value for each class. Detailed description thereof will be described later with reference to FIG. 7A.

In another embodiment of the present disclosure, the computing device determines the number of classes whose scoring value is greater than or equal to a second threshold value, and if the number of classes whose scoring value is greater than or equal to a second threshold value is greater than or equal to a second predetermined number, By determining that anomaly exists, it may be determined whether anomaly exists in the image data based on the scoring value for each class. Detailed description thereof will be described later with reference to FIG. 7B.

In another embodiment of the present disclosure, the computing device may determine whether anomalies exist in the input data using another pre-learned other network function (second network function). Another network function may be a network function that takes as input a scoring value for each class that is an output of a pre-learned network function. Another network function may be a network function connected in series with a dictionary network function. Here, serial may mean that at least a part of the output of the dictionary network function may be an input of another network function, or a part of the dictionary network function may overlap with another network function. Another network function is a network function trained by inputting a scoring value for each class, which is an output of a pre-learned network function of the present disclosure, and a network learning pattern of a scoring value for each class of normal data and a scoring value for each class of abnormal data. It can be a function. That is, the other network function may be a network function learned by a supervised learning method using both the abnormal data and the scoring values for the normal data as the training data. The computing device may obtain a class-specific scoring value for classifying the image data in the first network function (ie, the network function for performing image segmentation in one embodiment of the present disclosure). The second network function of the computing device (that is, the network function for determining whether the data of another embodiment of the present disclosure is anomalous) receives a scoring value for each class from the first network function to determine whether anomaly exists. can do. The number of output nodes of the first network function may be greater than or equal to the number of classes. The number of input nodes of the second network function may be greater than or equal to the number of output nodes of the first network function. A remaining node that does not match the output node of the first network function among the number of input nodes of the second network function may be set to a null value. The second network function may include a kind of network function for determining whether anomaly exists in the input data based on a scoring value for each class, for example, a convolutional neural network (CNN). , Multi-layer perceptrons (MLPs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), Q networks, U networks , Siamese network, and the like, but the present disclosure is not limited thereto. When the first network function and the second network function are serially connected, two or more serially connected network functions may constitute one network function.

After determining whether anomaly exists, the computing device may display a portion of the image data where the anomaly exists in the image data when anomaly exists in the image data. The portion displaying anomaly on the image data may be the entirety of the image data, or may be a portion of a pixel unit of the image data.

The memory 120 may store a computer program for performing an anomaly determination method of image data according to an embodiment of the present disclosure, and the stored computer program may be read and driven by the processor 110.

The communication module 130 may transmit / receive data for performing an anomaly determination method of image data according to an exemplary embodiment of the present disclosure and the like with other computing devices, servers, and the like. The communication module 130 may transmit / receive data necessary for an embodiment of the present disclosure, such as image data, to another computing device, a server, and the like. For example, the communication module 130 may receive training image data from a training image database. In addition, the communication module 130 may enable communication between a plurality of computing devices so that learning of network functions is distributed in each of the plurality of computing devices. The communication module 130 may enable communication between a plurality of computing devices to enable distributed processing of data classification using a network function.

The camera module 140 may photograph the inspection object to generate image data in order to perform the anomaly determination method of the image data according to an embodiment of the present disclosure. In one embodiment of the present disclosure, computing device 100 may include one or more camera modules 140.

2 is a schematic diagram illustrating a convolutional neural network according to an embodiment of the present disclosure.

Throughout this specification, neural networks, network functions, neural networks may be used in the same sense. A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to identify latent structures of data. In other words, you can identify the potential structures of photos, texts, videos, voices, and music (e.g., what objects are in the photos, what the content and emotions of the text are, and what the content and emotions of the voice are). . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs) and deep belief networks (DBNs). , Q network, U network, Siamese network and the like.

The convolutional neural network shown in FIG. 2 is a type of deep neural network and includes a neural network including a convolutional layer. Convolutional neural networks are a type of multilayer perceptrons designed to use minimal preprocessing. A CNN may consist of one or several convolutional layers and artificial neural network layers associated with them. The CNN may further utilize weights and pooling layers. This structure allows CNNs to take full advantage of the two-dimensional input data. Convolutional neural networks can be used to recognize objects in an image. Convolutional neural networks can process image data by representing them in a matrix with dimensions. For example, in the case of image data encoded in red-green-blue (RGB), each of R, G, and B colors may be represented by a 2-dimensional (for example, 2-dimensional image) matrix. That is, the color value of each pixel of the image data may be a component of the matrix, and the size of the matrix may be equal to the size of the image. Therefore, the image data can be represented by three two-dimensional matrices (three-dimensional data array).

In a convolutional neural network, a convolutional process (input and output of a convolutional layer) can be performed by moving a convolutional filter and multiplying the convolutional filter with matrix components at each position of an image. The convolutional filter may be configured as an n * n matrix. Convolutional filters can generally consist of a fixed type of filter that is smaller than the total number of pixels in the image. That is, when an m * m image is input into a convolutional layer (for example, a convolutional layer having a convolutional filter size of n * n), a matrix representing n * n pixels including each pixel of the image This convolutional filter can be a component product (i.e., the product of each component of the matrix). The component matching the convolutional filter may be extracted from the image by the product with the convolutional filter. For example, a 3 * 3 convolutional filter for extracting up and down linear components from an image may be constructed as [[0,1,0], [0,1,0], [0,1,0]] Can be. When a 3 * 3 convolutional filter for extracting vertical linear components from an image is applied to an input image, the vertical linear components matching the convolutional filter may be extracted from the image and output. The convolutional layer may apply a convolutional filter to each matrix (ie, R, G, and B colors for R, G, and B coded images) for each channel representing the image. The convolutional layer may apply a convolutional filter to the input image to extract a feature that matches the convolutional filter from the input image. The filter value of the convolutional filter (ie, the value of each component of the matrix) may be updated by backpropagation during the learning process of the convolutional neural network.

The subsampling layer is connected to the output of the convolutional layer, which simplifies the output of the convolutional layer, thereby reducing memory usage and calculation amount. For example, if you input the output of a convolutional layer to a pooling layer that has a 2 * 2 max pooling filter, you can compress the image by outputting the maximum value included in each patch for every 2 * 2 patch in each pixel of the image. Can be. The above-mentioned pooling may be a method of outputting a minimum value in a patch or an average value of patches, and any pooling method may be included in the present disclosure.

The convolutional neural network may include one or more convolutional layers and subsampling layers. The convolutional neural network may extract a feature from an image by repeatedly performing a convolutional process and a subsampling process (eg, the aforementioned max pooling). Through an iterative convolutional process and a subsampling process, the neural network can extract the global features of the image.

The output of the convolutional layer or subsampling layer may be input to a fully connected layer. A fully connected layer is a layer where all the neurons in one layer and all the neurons in neighboring layers are connected. The full connected layer may refer to a structure in which all nodes of each layer are connected to all nodes of another layer in the neural network.

In an embodiment of the present disclosure, the neural network may include a deconvolutional neural network (DCNN) to perform segmentation of image data. The deconvolutional neural network performs an operation similar to calculating the convolutional neural network in the reverse direction. The deconvolutional neural network may output a feature extracted from the convolutional neural network as a feature map associated with the original data. A description of the specific configuration for the convolutional neural network is discussed in more detail in US registered patent US9870768, which is incorporated herein by reference in its entirety.

FIG. 3 is a diagram schematically illustrating classifying image data through a network function and obtaining a scoring value for each class for classifying image data according to an exemplary embodiment of the present disclosure.

The pre-learned network function 320 of the present disclosure may be learned in at least one manner of supervised learning, unsupervised learning, and semi supervised learning. Learning of network functions is intended to minimize errors in the output. In learning the network function, the training data is repeatedly input to the network function. In learning the network function, we calculate the output of the network function and the target error for the training data. In order to reduce errors in the learning of network functions, the weight of each node of the network function can be updated by back-propagating the error of the network function from the output layer of the network function to the input layer.

In detail, the pre-learned network function 320 of the present disclosure may be a teacher-learned network function using only labeled normal data as learning data. The labeled training data may be a label directly to a component of normal data. According to an embodiment of the present disclosure, an image processing algorithm technique may be used when a user directly labels a component of normal data. The image processing algorithm may include canny edge detection, Harris corner detection, and the like, but the present disclosure is not limited thereto. Through Canny Edge Detection, the computing device blurs the image to remove noise, detects edges using mask edges, removes Non-Maximum Values, and connects the edges by dividing the size with double thresholds. By this, the edge can be extracted. Image processing algorithms can be used to extract edges of constant pixel thickness from normal data. Labeling may be a method in which a user selects an area based on an edge of a constant pixel thickness extracted from normal data. For example, the image may be separated into various parts by extracting the edge of the image through the canny edge detection. Part of the separated image can be labeled with trees, walks, sky, etc. The labeled training data may be labeled with components of normal data. The labeled training data may be labeled with components of normal data using another artificial neural network. The training data may be labeled with the entire area of the image. The labeled training data can be input to the network function, and the error can be calculated by comparing the label of the training data with the classification, which is the output of the network function. The calculated error can be backpropagated in the direction of the input layer in the output layer that is reverse in the network function. The connection weight of each node of each layer may be updated according to the backpropagation backpropagated from the output layer toward the input layer. The connection weight of each node to be updated may be changed according to a learning rate. Calculation of network functions for input data and backpropagation of errors can constitute a learning cycle. The learning rate may be applied differently according to the number of repetitions of the learning cycle of the network function. For example, in the early stages of network function learning, higher learning rates can be used to increase efficiency by allowing network functions to quickly gain some level of performance. For example, lower learning rates can be used for later learning of network functions to increase accuracy. Image segmentation may include image processing that separates a portion of the image from other portions based on, for example, pixels, edges, colors, etc. extracted from the image. The classification that is the output of the network function may be pixel by pixel image segmentation. Scoring values may be obtained in pixel units of an image, and image data may be classified by segmenting based on each pixel scoring value. Image segmentation may refer to image processing that separates a part of an image from another part.

The method of determining anomaly of image data of the present disclosure may be performed by one or more processors 110 of the computing device 100. One or more processors 110 of computing device 100 of the present disclosure may perform a computational process of network function 320 of the present disclosure. All computational processes (ie, pre-learning of network functions, scoring by class, classifying image data, etc.) of the anomaly determination method of the image data of the present disclosure may be performed by a processor of the computing device 100. That is, the expression processing data in the network function 320 may mean a process in which the processor 110 of the computing device 100 processes the data by executing a network function.

In the anomaly determination method of the image data of the present disclosure, the computing device 100 may input the image data to the network function 320. In the anomaly determination method of the image data of the present disclosure, the computing device 100 may output a result based on an anomaly calculation result of the image data calculated by the network function 320.

The network function 320 of the present disclosure can be used for classification of data. For example, the network functions of the present disclosure can be used for Anomaly Detection.

Anomaly data may refer to abnormal data that deviates from a normal pattern of data. The data may have an atypical pattern. Anomaly data may refer to data that deviates from this atypical pattern. For example, data about the image of a product in a production process may have an atypical pattern of normal product. The anomaly data may be data that deviates from the atypical pattern (ie, an image of a defective product, etc.). The description of the normal data, the atypical pattern, and the anomaly data of the present disclosure is only an example, and the present disclosure is not limited thereto.

The image data 310 received by the pre-learned network function 320 is an image that is an object of anomaly determination. In the present disclosure, anomaly data may be abnormal data that deviates from a normal pattern of data. The image data 310 received by the pre-learned network function 320 may be normal data having a normal pattern. The image data 310 received by the pre-learned network function 320 may be abnormal data that deviates from the normal pattern. The image data 310 received by the pre-learned network function 320 may be an image of a product to be inspected.

Image data received by the pre-learned network function 320 may include one or more components classified into each class. The image data input to the network function 320 may be an image of a product to be inspected. The image data input to the network function 320 may be an image including a plurality of components, such as a PCB, a hole, copper, and a conductor, which may be classified into respective classes. The description of the above-described class and the product to be inspected is merely an example, and the present disclosure is not limited thereto.

The pre-learned network function 320 calculates the received image data as a network function and outputs a class-specific scoring value 330 for each pixel. The class may mean each component of the received image data. The output value may represent a scoring value corresponding to a class such as a PCB substrate, holes, copper, and conductive lines for each pixel. The foregoing class is exemplary only and the present disclosure is not limited thereto.

The pre-learned network function 320 may classify the image data based on the scoring value of the class for each pixel. The scoring value of the class may be to classify which component each pixel corresponds to. If there is an overwhelmingly high class in the class of the pixel, the pixel may be classified into a class having an overwhelmingly high scoring value. Each pixel can be segmented based on the result value classified by class. By combining pixels of the same classification, you can separate them so that they are distinguished from other pixel parts of the image. Image segmentation may also include a process of visualizing and displaying a part of the image data separated from the other part in the image data. If a scoring value of a class corresponding to copper among the class of pixels is overwhelmingly greater than the scoring values of other classes, such as PCB, hole, and conductive line, the pixel may be classified as copper. The image segmentation process, which combines the pixels classified as copper, separates them from other parts of the image, such as PCBs, holes, and conductors. The copper part of the image data, which is a separated part, can be visualized and displayed.

The pre-learned network function 320 may obtain a scoring value for each class and then determine anomaly based on the score. Anomaly can be determined based on whether the scoring value for each class is appropriate to classify as a specific class. It is determined whether or not it is appropriate to classify a specific class according to the distribution of scoring values for each class. Anomalies can be determined based on whether they are appropriate to classify into a particular class. If it is unclear to classify a specific class according to the distribution of scoring values for each class, it can be determined as anomaly. Normal cases can be determined to classify specific classes according to the distribution of scoring values for each class. Since the pre-learned network function is trained only with labeled normal data, the normal data can be classified clearly. However, abnormal data has a difference from normal data. Based on the difference from the normal data, the scoring value of each class is different from the scoring value of the normal data. The scoring value of the normal data is overwhelmingly high when the scoring value of one class is compared with the scoring value of another class, or there is no class having a similar scoring value. Unlike the learning data of the pre-learned network function, the abnormal data may not have an overwhelming value when the scoring value of one class is compared with the scoring value of another class. The abnormal data may have similar values to scoring values of a plurality of classes, unlike learning data of a pre-learned network function. The abnormal data may have a small variance value of classes, unlike the learning data of a pre-learned network function. The computing device may determine anomaly based on the scoring value of the class. The computing device may extract location information of the anomaly portion through image segmentation. The computing device may go through the process of identifying the location of the anomaly data from another portion of the image. The image segmentation may also include a process of identifying the location of the anomaly data and visualizing and displaying a part of the image separated from the other part.

4 is a diagram illustrating an example of normal image data according to an embodiment of the present disclosure.

The training data of the dictionary network function of one embodiment of the present disclosure may be labeled normal data. Such labeling may be the labeling of the component of the normal data directly by the user. Such labeling may be a labeling component of normal data using another artificial neural network. The training data may be labeled with the PCB 400, the hole 410, the copper wire 420, the element 430, and the like. The components are examples only and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, if the normal image data is received by a pre-learned network function and outputs a scoring value of a class for each pixel, a scoring value as shown in FIG. 7A is obtained. When the scoring value of a class is output on the normal image data, the scoring value of one class is overwhelmingly higher than the scoring value of another class. When the scoring value for each class of the pixel of the portion corresponding to the hole 410 is output, the scoring value as shown in FIG. 7A may be extracted. A class having a scoring value greater than the first threshold value 712 may correspond to the hole 410. The class having a scoring value smaller than the first threshold value 712 may be the plate 400, the copper wire 420, and the device 430. Since the scoring value of the pixel value of the portion corresponding to the hole 410 is overwhelmingly high when one class is compared with the other class, the pre-learned network function may be determined as normal image data.

5 is a diagram illustrating an example of image data in which anomaly exists according to an embodiment of the present disclosure.

According to an exemplary embodiment of the present disclosure, when a pre-trained network function receives image data in which anomaly exists and outputs a scoring value of a class for each pixel, a scoring value as shown in FIG. 7B is obtained. When the scoring value of a class is output to image data in which anomalies exist, the scoring value of one class does not have an overwhelmingly larger value than the scoring value of another class, or the scoring values of a plurality of classes have similar values. If a scoring value for each class of a pixel corresponding to the hole 510 is output, a scoring value as shown in FIG. 7B may be extracted. There may be a plurality of classes having a scoring value greater than the second threshold value 722. A scoring value in which a plurality of classes have similar values may be output. The pre-learned network function may be determined as image data including anomaly because there is no class having one overwhelming scoring value.

FIG. 6 is a diagram illustrating an anomaly portion of image data in which anomaly exists according to an embodiment of the present disclosure.

The pre-learned network function may determine whether anomalies exist in the image data received by the network function based on the scoring value. Anomaly may be displayed on an image result value that is output by segmenting an image to which anomaly exists and separating it from other parts of the image.

7A is a diagram schematically illustrating a scoring value for each class when no anomaly exists in image data according to an exemplary embodiment of the present disclosure.

FIG. 7B is a diagram schematically illustrating a scoring value for each class when anomaly exists in image data according to an embodiment of the present disclosure.

In another embodiment of the present disclosure, the computing device determines the number of classes whose scoring value is greater than or equal to a first threshold value, and wherein the image when the number of classes whose scoring value is greater than or equal to a first threshold value is less than or equal to a first predetermined number. By determining that no anomaly exists in the data, it may be determined whether anomaly exists in the image data based on the scoring value for each class. When the image data input to the network function is normal image data, the scoring distribution for each class may be represented as a distribution as shown in FIG. 7A. When the network function outputs a scoring distribution for each class of the pixels of the hole 410 of FIG. 4, the distribution of FIG. 7A may appear. The scoring value having the value corresponding to the largest probability may be the hole 410, and the scoring value having the value corresponding to the other small probability may be the PCB board 400 and the element 430 of the copper wire 420. When the first predetermined number is one, there may be one scoring value greater than the first threshold value 712 in FIG. 7A. Therefore, when the first predetermined number is 1, the network function may determine the image data received by the network function as image data having a normal pattern. When the first predetermined number is 1, the network function may determine that the image data received by the network function is normal image data not including anomaly. As a result of the pixel-by-pixel segmentation of FIG. 4, an edge may be included in one pixel. For example, since the edge is included in one pixel as a result of the segmentation, both the hole 410 and the PCB plate 400 may be included. When the first predetermined number is two, if an edge is included in one pixel, then a scoring value greater than the first threshold value 712 in FIG. 7A may be two holes and a PCB plate. If the number of scoring values greater than the first threshold value is two, the first predetermined number is two or less, so that the network function determines that the image data received by the network function is normal image data that does not include anomaly having a normal pattern. can do.

In another embodiment of the present disclosure, the computing device determines the number of classes whose scoring value is greater than or equal to a second threshold, and wherein the image when the number of classes whose scoring value is greater than or equal to a second threshold is greater than or equal to a second predetermined number. By determining that anomaly exists in the data, it may be determined whether anomaly exists in the image data based on the scoring value for each class, and the second threshold value may be equal to or less than a first threshold value. If the image data input to the network function is abnormal image data including anomaly, the scoring distribution for each class may be represented as a distribution as shown in FIG. 7B. When the network function outputs a scoring distribution for each class of the pixels of the hole 510 of FIG. 5, the distribution of FIG. 7B may appear. The deviation of the magnitude of the scoring value for each pixel for the corresponding pixel may be small. The second threshold 722 may be less than or equal to the first threshold 712. The second predetermined number may be two or more. Since the number of classes greater than or equal to the second threshold value 722 is equal to or greater than 2, which is the second predetermined number, the network function may determine that the image data received by the network function is abnormal image data that does not include anomaly having an abnormal pattern. .

8 is a block diagram illustrating a means for implementing a method for determining anomaly of image data according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a method for determining anomaly of image data may be implemented by the following means.

According to one embodiment of the present disclosure, a method for determining anomaly of image data includes: means 810 for receiving a pre-trained network function from the image data; Means (820) for classifying the image data using the pre-learned network function; Means (830) for obtaining a class-specific scoring value for classifying the image data output from the pre-learned network function; And means 840 for determining whether anomaly exists in the image data based on the scoring value for each class.

In an alternative embodiment of the method for anomaly determination of image data, the means for determining whether anomaly exists in the image data based on the scoring value for each class includes: comparing the magnitude of each scoring value for each class; Means for; And means for determining whether anomaly exists in the image data based on the comparison result.

In an alternative embodiment of the anomaly determination method of image data, the means for determining whether anomaly exists in the image data based on the scoring value for each class comprises: means for obtaining a variance of the scoring value; And means for determining whether anomaly exists in the image data based on the variance value.

In an alternative embodiment of the method for anomaly determination of image data, the means for determining whether anomaly exists in the image data based on the scoring value for each class is based on a class of which the scoring value is equal to or greater than a first threshold value. Means for determining a number; And means for determining that no anomaly exists in the image data when the number of classes whose scoring value is greater than or equal to a first threshold is less than or equal to a first predetermined number.

In an alternative embodiment of the method of anomaly determination of image data, the means for determining whether anomaly exists in the image data based on the scoring value for each class is based on a class of which the scoring value is greater than or equal to a second threshold value. Means for determining a number; And means for determining that anomaly is present in the image data when the number of classes whose scoring value is greater than or equal to a second threshold value is greater than or equal to a second predetermined number, wherein the second threshold value is a first threshold value; It may be:

In an alternative embodiment of the method for anomaly determination of image data, the method may further include means for displaying, in the image data, a portion where the anomaly exists in the image data when anomaly exists in the image data. have.

In an alternative embodiment of the method for anomaly determination of image data, the means for classifying the image data using the pre-learned network function comprises: classifying each portion of the image data to segment the image data. Means may be further included.

In an alternative embodiment of the method of anomaly determination of image data, the means for segmenting the respective portions of the image data to segment the image data comprises: classifying the image data into respective classes for each pixel and segmenting the image data. It may include a means for.

In an alternative embodiment of the method for anomaly determination of image data, the means for obtaining a class-specific scoring value for classifying the image data output from the pre-learned network function comprises: for each pixel of the image data; Means for obtaining a scoring value for each class.

In an alternative embodiment of the method for anomaly determination of image data, the image data may comprise one or more components classified into each class.

9 is a block diagram illustrating a module for implementing a method for determining anomaly of image data according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a method for determining anomaly of image data may be implemented by the following module.

According to an embodiment of the present disclosure, a method for determining anomaly of image data may include: a module 910 for receiving a pre-trained network function from the image data; A module 920 for classifying the image data using the pre-learned network function; A module (930) for obtaining a class-specific scoring value for classifying the image data output from the pre-learned network function; And a module 940 for determining whether anomaly exists in the image data based on the scoring value for each class.

In an alternative embodiment of the method for anomaly determination of image data, a module for determining whether anomaly exists in the image data based on the scoring value for each class is further configured to compare the magnitude of each scoring value for each class. Module for; And a module for determining whether anomaly exists in the image data based on the comparison result.

In an alternative embodiment of the anomaly determination method of image data, the module for determining whether anomaly exists in the image data based on the scoring value for each class comprises: a module for obtaining a variance of the scoring value; And a module for determining whether anomaly exists in the image data based on the dispersion value.

In an alternative embodiment of the method for anomaly determination of image data, the module for determining whether anomaly exists in the image data based on the scoring value for each class is further configured to determine whether the scoring value is equal to or greater than a first threshold value. A module for determining a number; And a module for determining that no anomaly exists in the image data when the number of classes whose scoring value is greater than or equal to a first threshold is less than or equal to a first predetermined number.

In an alternative embodiment of the method for anomaly determination of image data, the module for determining whether anomaly exists in the image data based on the scoring value for each class further includes: determining that the scoring value is equal to or greater than a second threshold value. A module for determining a number; And a module for determining that anomaly exists in the image data when the number of classes whose scoring value is greater than or equal to a second threshold value is greater than or equal to a second predetermined number, wherein the second threshold value is a first threshold value; It may be:

In an alternative embodiment of the anomaly determination method of the image data, when anomaly exists in the image data, the module may further include a module for displaying the anomaly-existing portion of the image data on the image data. have.

In an alternative embodiment of the anomaly determination method of image data, the module for classifying the image data using the pre-learned network function may be configured to classify each part of the image data to segment the image data. The module may further include.

In an alternative embodiment of the method for anomaly determination of image data, the module for classifying each portion of the image data and segmenting the image data may include classifying the image data into respective classes for each pixel and segmenting the image data. It may include a module for.

In an alternative embodiment of the method for anomaly determination of image data, the module for obtaining a class-specific scoring value for classifying the image data output from the pre-learned network function may be provided for each pixel of the image data. It may include a module for obtaining a scoring value for each class.

In an alternative embodiment of the method for anomaly determination of image data, the image data may comprise one or more components classified into each class.

10 is a block diagram illustrating logic for implementing a method for determining anomaly of image data according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a method for determining anomaly of image data may be implemented by the following logic.

According to an embodiment of the present disclosure, a method for determining anomaly of image data may include: logic 1010 for pre-trained network function to receive image data; Logic (1020) for classifying the image data using the pre-learned network function; Logic 1030 for obtaining a class-specific scoring value for classifying the image data output from the pre-learned network function; And logic 1040 for determining whether anomaly exists in the image data based on the scoring value for each class.

In an alternative embodiment of the method for anomaly determination of image data, logic for determining whether anomaly exists in the image data based on the scoring value for each class is to compare the magnitude of each scoring value for each class. Logic for; And logic for determining whether anomaly exists in the image data based on the comparison result.

In an alternative embodiment of the method for anomaly determination of image data, logic for determining whether anomaly exists in the image data based on the scoring value for each class comprises: logic for obtaining a variance of the scoring value; And logic for determining whether anomaly exists in the image data based on the dispersion value.

In an alternative embodiment of the method for anomaly determination of image data, logic for determining whether anomaly exists in the image data based on the scoring value for each class is based on a class of which the scoring value is equal to or greater than a first threshold value. Logic to determine a number; And logic for determining that there is no anomaly in the image data when the number of classes whose scoring value is greater than or equal to a first threshold is less than or equal to a first predetermined number.

In an alternative embodiment of the method for anomaly determination of image data, logic for determining whether anomaly exists in the image data based on the scoring value for each class is based on a class of which the scoring value is greater than or equal to a second threshold. Logic to determine a number; And logic for determining that anomaly exists in the image data when the number of classes whose scoring value is greater than or equal to a second threshold value is greater than or equal to a second predetermined number, wherein the second threshold value is a first threshold value; It may be:

In an alternative embodiment of the anomaly determination method of the image data, if anomaly exists in the image data, the method may further include logic for displaying in the image data the portion where the anomaly exists in the image data. have.

In an alternative embodiment of the anomaly determination method of image data, logic for classifying the image data using the pre-learned network function is configured to classify each portion of the image data to segment the image data. The logic may further include.

In an alternative embodiment of the method for anomaly determination of image data, logic for classifying each portion of the image data and segmenting the image data may include classifying the image data into respective classes for each pixel and segmenting the image data. It may include logic for.

In an alternative embodiment of the method for anomaly determination of image data, logic for obtaining a class-specific scoring value for classifying the image data output from the pre-learned network function is performed for each pixel of the image data. It may include logic for obtaining a scoring value for each class.

In an alternative embodiment of the method for anomaly determination of image data, the image data may comprise one or more components classified into each class.

11 is a block diagram illustrating a circuit for implementing a method of determining anomaly of image data according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a method for determining anomaly of image data may be implemented by the following circuit.

According to one embodiment of the present disclosure, a method for determining anomaly of image data includes: a circuit 1210 for receiving a pre-learned network function by the image data; Circuitry 1220 for classifying the image data using the pre-learned network function; A circuit 1230 for obtaining a scoring value for each class for classifying the image data output from the pre-learned network function; And a circuit 1240 for determining whether anomaly exists in the image data based on the scoring value for each class.

In an alternative embodiment of the anomaly determination method of image data, a circuit for determining whether anomaly exists in the image data based on the scoring value for each class is further configured to compare the magnitude of each scoring value for each class. Circuit for; And a circuit for determining whether anomaly exists in the image data based on the comparison result.

In an alternative embodiment of the anomaly determination method of image data, the circuit for determining whether anomaly exists in the image data based on the scoring value for each class comprises: a circuit for obtaining a variance of the scoring value; And a circuit for determining whether anomaly exists in the image data based on the dispersion value.

In an alternative embodiment of the method for anomaly determination of image data, the circuit for determining whether anomaly exists in the image data based on the scoring value for each class includes: determining that the scoring value is equal to or greater than a first threshold value. Circuitry for determining a number; And circuitry for determining that no anomaly exists in the image data when the number of classes whose scoring value is greater than or equal to a first threshold is less than or equal to a first predetermined number.

In an alternative embodiment of the method for anomaly determination of image data, the circuit for determining whether anomaly exists in the image data based on the scoring value for each class includes: determining that the scoring value is equal to or greater than a second threshold value. Circuitry for determining a number; And circuitry for determining that anomaly exists in the image data when the number of classes whose scoring value is greater than or equal to a second threshold value is greater than or equal to a second predetermined number, wherein the second threshold value is a first threshold value; It may be:

In an alternative embodiment of the method for determining anomaly of image data, the method may further include a circuit for displaying a portion of the image data in the image data when the anomaly exists in the image data. have.

In an alternative embodiment of the anomaly determination method of image data, the circuit for classifying the image data using the pre-learned network function is configured to classify each portion of the image data to segment the image data. The circuit may further include.

In an alternative embodiment of the method for anomaly determination of image data, a circuit for classifying each portion of the image data and segmenting the image data may include classifying the image data into respective classes for each pixel and segmenting the image data. It may include a circuit for.

In an alternative embodiment of the method for anomaly determination of image data, circuitry for obtaining a class-specific scoring value for classifying the image data output from the pre-learned network function is provided for each pixel of the image data. It may include a circuit for obtaining a scoring value for each class.

In an alternative embodiment of the method for anomaly determination of image data, the image data may comprise one or more components classified into each class.

12 is a block diagram of a computing device according to an embodiment of the present disclosure.

12 shows a brief general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above generally with respect to computer executable instructions that may be executed on one or more computers, those skilled in the art will appreciate that the present disclosure may be implemented in combination with other program modules and / or as a combination of hardware and software. will be.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods of the present disclosure may include uniprocessor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like (each of which And other computer system configurations, including one or more associated devices.

The described embodiments of the present disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Any medium accessible by a computer can be a computer readable medium. Computer readable media includes volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer readable storage media and computer readable transmission media. Computer-readable storage media are volatile and nonvolatile media, temporary and non-transitory media, removable and non-removable implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data. Media. Computer storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROMs, digital video disks or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, Or any other medium that can be accessed by a computer and used to store desired information.

Computer-readable transmission media typically embody computer readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and the like. Includes all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed to encode information in the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above should also be included within the scope of computer readable transmission media.

An exemplary environment 1100 is illustrated that implements various aspects of the present disclosure, including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 connects system components, including but not limited to system memory 1106, to processing unit 1104. Processing unit 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1104.

The system bus 1108 may be any of several types of bus structures that may be further interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input / output system (BIOS) is stored in nonvolatile memory 1110, such as ROM, EPROM, EEPROM, etc., and the BIOS provides a basic aid for transferring information between components in the computer 1102, such as during startup. Contains routines. RAM 1112 may also include fast RAM, such as static RAM, for caching data.

Computer 1102 also includes an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) —this internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes, magnetic floppy disk drive (FDD) 1116 (eg, for reading from or writing to removable diskette 1118), and optical disk drive 1120 (eg, CD-ROM Disc 1122 for reading from or writing to or writing from other high capacity optical media such as DVD). The hard disk drive 1114, the magnetic disk drive 1116, and the optical disk drive 1120 are connected to the system bus 1108 by the hard disk drive interface 1124, the magnetic disk drive interface 1126, and the optical drive interface 1128, respectively. ) Can be connected. Interface 1124 for external drive implementation includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide nonvolatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate zip drives, magnetic cassettes, flash memory cards, cartridges, and the like. Other types of media readable by the computer, etc. may also be used in the exemplary operating environment and it will be appreciated that any such media may include computer executable instructions for performing the methods of the present disclosure.

Multiple program modules may be stored in the drive and RAM 1112, including operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or a portion of the operating system, applications, modules and / or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 through one or more wired / wireless input devices, such as a keyboard 1138 and a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. These and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, but the parallel port, IEEE 1394 serial port, game port, USB port, IR interface, Etc. can be connected by other interfaces.

A monitor 1144 or other type of display device is also connected to the system bus 1108 via an interface such as a video adapter 1146. In addition to the monitor 1144, the computer generally includes other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer (s) 1148, via wired and / or wireless communications. Remote computer (s) 1148 can be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and typically is associated with computer 1102. Although many or all of the components described above are included, for simplicity, only memory storage 1150 is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) 1152 and / or a larger network, such as a telecommunications network (WAN) 1154. Such LAN and WAN networking environments are commonplace in offices and businesses, facilitating enterprise-wide computer networks such as intranets, all of which may be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 via a wired and / or wireless communication network interface or adapter 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed therein for communicating with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158, connect to a communications computing device on the WAN 1154, or establish communications over the WAN 1154, such as over the Internet. Other means. The modem 1158, which may be an internal or external and wired or wireless device, is connected to the system bus 1108 via the serial port interface 1142. In a networked environment, program modules or portions thereof described with respect to computer 1102 may be stored in remote memory / storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

Computer 1102 is associated with any wireless device or entity disposed and operating in wireless communication, such as a printer, scanner, desktop and / or portable computer, portable data assistant, communications satellite, wireless detectable tag. Communicate with any equipment or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technology. Thus, the communication can be a predefined structure as in a conventional network or simply an ad hoc communication between at least two devices.

Wireless Fidelity (Wi-Fi) allows you to connect to the Internet without wires. Wi-Fi is a wireless technology such as a cell phone that allows a device, for example, a computer, to transmit and receive data indoors and outdoors, ie anywhere within the coverage area of a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, high-speed wireless connections. Wi-Fi may be used to connect computers to each other, to the Internet, and to a wired network (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). have.

Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols, and chips that may be referenced in the above description may include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. Or particles, or any combination thereof.

One of ordinary skill in the art of the disclosure will appreciate that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, It will be appreciated that for purposes of the present invention, various forms of program or design code, or combinations thereof, may be implemented. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. One skilled in the art of the present disclosure may implement the described functionality in various ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The various embodiments presented herein may be embodied in a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media may include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical discs (eg, CDs, DVDs, etc.), smart cards, and flash memory. Devices, such as, but not limited to, EEPROM, cards, sticks, key drives, and the like. In addition, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based upon design priorities, it is understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those of ordinary skill in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure should not be limited to the embodiments presented herein but should be construed in the broadest scope consistent with the principles and novel features presented herein.

Claims (1)

As a method of classifying input data,
Computing the input data using a pre-learned network function to classify the input data;
Identifying a distribution of scoring for at least two classes, in accordance with a result of the operation on at least some of the input data; And
Determining whether the input data is abnormal based on the distribution;
Containing,
How to classify input data.

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