Search Results (15)

Due to its capacity to unveil the dynamic characteristics of time series data, entropy has attracted growing interest. However, traditional entropy feature extraction methods, such as permutation entropy, fall short in concurrently considering both the absolute amplitude information of signals and the temporal correlation between sample points. Consequently, this limitation leads to inadequate differentiation among different time series and susceptibility to noise interference. In order to augment the discriminative power and noise robustness of entropy features in time series analysis, this paper introduces a novel method called Tsallis entropy-based complexity-improved permutation entropy casualty plane (TC-IPE-CP). TC-IPE-CP adopts a novel symbolization approach that preserves both absolute amplitude information and inter-point correlations within sequences, thereby enhancing feature separability and noise resilience. Additionally, by incorporating Tsallis entropy and weighting the probability distribution with parameter q, it integrates with statistical complexity to establish a feature plane of complexity and entropy, further enriching signal features. Through the integration of multiscale algorithms, a multiscale Tsallis-improved permutation entropy algorithm is also developed. The simulation results indicate that TC-IPE-CP requires a small amount of data, exhibits strong noise resistance, and possesses high separability for signals. When applied to the analysis of heart rate signals, fault diagnosis, and underwater acoustic signal recognition, experimental findings demonstrate that TC-IPE-CP can accurately differentiate between electrocardiographic signals of elderly and young subjects, achieve precise bearing fault diagnosis, and identify four types of underwater targets. Particularly in underwater acoustic signal recognition experiments, TC-IPE-CP achieves a recognition rate of 96.67%, surpassing the well-known multi-scale dispersion entropy and multi-scale permutation entropy by 7.34% and 19.17%, respectively. This suggests that TC-IPE-CP is highly suitable for the analysis of complex time series. Full article

In modern warfare, frequency-hopping communication serves as the primary method for battlefield information transmission, with its significance continuously growing. Fighting for the control of electromagnetic power on the battlefield has become an important factor affecting the outcome of war. As communication electronic warfare evolves, jammers employing deep neural networks (DNNs) to decode frequency-hopping communication parameters for smart jamming pose a significant threat to communicators. This paper proposes a method to generate adversarial samples of frequency-hopping communication signals using adaptive whitening and feature gradient smoothing. This method targets the DNN cognitive link of the jammer, aiming to reduce modulation recognition accuracy and counteract smart interference. First, the frequency-hopping signal is adaptively whitened. Subsequently, rich spatiotemporal features are extracted from the hidden layer after inputting the signal into the deep neural network model for gradient calculation. The signal’s average feature gradient replaces the single-point gradient for iteration, enhancing anti-disturbance capabilities. Simulation results show that, compared with the existing gradient symbol attack algorithm, the attack success rate and migration rate of the adversarial samples generated by this method are greatly improved in both white box and black box scenarios. Full article

The conformation and dynamics of metropolitanisation act as propulsive elements of territorial transformations. The deficiency of infrastructural equipment, the heterogeneity of urban fabric and the lack of services and public spaces contribute to severing the identity ties between settled communities and territories. In light of this, within the more general reflection concerning urban regeneration, we recall the role that cultural heritage plays in the physical and functional organisation of the city, as a reflection of the interaction between community and context. The contribution is contextualized in the research work on the activities related to Thematic Line 4 of the Extended Partnership 5—CHANGES (NRRP). The thematic line activities follow three phases: 1. contextualization; 2. operational phase; 3. experimentation. The research work presented here is part of the ‘operational phase’, to identify strategies and projects for heritage-led regeneration. The article analyses the pilot case of CSOA Forte Prenestino as a starting point for thinking about expanding the research activity to other similar cases. It is a self-managed community centre in Rome (Italy) located in the nineteenth-century Forte, which has become a symbol of collective identity. The case study was identified following three levels of investigation: 1. identification of the municipality, first-level administrative subdivision of the city of Rome (Italy); 2. identification of the main historical, archaeological and architectural emergencies of the municipality; 3. identification of an asset to be analysed as “Heritage by designation” (involvement of experts) and “Heritage by appropriation” (involvement of communities). The research results show the “Recognition Path” of Forte Prenestina: according to what has been ‘designated’ by urban planning instruments, project instruments, legislative instruments and authors of scientific publications and conferences; and on the basis of the bottom-up ‘appropriation’ process of the asset that has allowed its management, assessment of its cultural and social potential and its development. The research results allow us to reflect on heritage-led urban regeneration as a strategy capable of capturing and promoting the links between social integration and cultural–historical identity. Full article

When a construction disaster occurs, the first-line rescue personnel often enter the disaster site immediately, and every second counts in rescuing the people who need help. However, the rescue personnel may not be familiar with the indoor layouts of different buildings. If the indoor paths are complicated, or when the fire smoke obstructs the line of sight, the rescue personnel are prone to spatial disorientation, which usually causes the rescue personnel to fall into danger. Therefore, we have developed the “Wearable Augmented reality Seeking System” (WASS) to assist rescue personnel in reading the information provided by the “Building Information Guiding System”. This system allows them to enter an unfamiliar space and reach the target rescue position, retreat to the entrance, or find an alternative escape route. The WASS is based on the HoloLens augmented reality system, which displays 3D digital information such as indoor layouts, one’s current location, spatial images captured by an infrared camera and a depth camera, and 3D virtual guiding symbols or text. The WASS includes two modules: First, the augmented reality gesture interaction module allows one to read the positioning anchor information of the “Building Information Guiding System” (BIGS). The rescue personnel can communicate via gestures, select the task target, and follow the 3D virtual guidance symbols in the air to reach the relay anchor points and finally arrive at the target position. Second, the service support module, including a lighting source and backup power, ensures that the QR code recognition process and long-term operation of the WASS are successful. Full article

Misinterpreting maps can have serious consequences, especially in situations requiring quick decisions like using car navigation systems. Studies indicate that a map reader’s experience is crucial for understanding maps, but factors such as age, education, and gender can also influence interpretation. However, understanding only the proportion of correctly interpreted information is not enough. It is essential to investigate the types of mistakes made and their causes. To address this, we conducted a study available in six languages with 511 participants who completed an online questionnaire testing their map reading skills. The questions focused on scale usage, mental rotation, and recognizing map categories (relief, line and point symbols, and geographic names). Gender had significant relation with one skill, qualification with two and age with three. Experience was associated to the highest number of skills, a total of four, confirming previous findings. When making mistakes, participants tended to overestimate distances and struggled with conceptual similarities in symbol recognition. Experienced readers often misplaced reference locations of geographic names. The results of the research could be used in the design of large-scale maps (e.g., car navigation), as they allow to reduce typical map reading errors by careful selection of symbol types and placements. Full article

The research presented in this paper refers to the possibility to understand the information presented by symbols, which indicate safety status and possible troubles regarding automobiles and driving. The testing included sixteen symbols, six of which were ISO-verified and recommended symbols. The study included 204 youth respondents. The study used a multidisciplinary, ergonomic approach, based on research of the usability of symbols. The basic task of subjects was to recognize the symbols and to rate their confidence on the five-point scale that they gave exact answers. For each symbol, hypotheses about the proportion of correct answers at the population level were tested, applying the inferential statistics technique. The ISO criterion of 67% successful symbol recognition was adopted as the limit value for the justification of the use of symbols. The test results showed that eight out of sixteen symbols did not meet this criterion. The results of this research indicate that it is necessary to take certain measures in order to improve the understanding of the function of symbols on vehicle dashboards. One of the proposed measures consists in the improvement of training courses in driving schools, which from a theoretical but also a practical aspect should include education about symbols on car dashboards, primarily those responsible for informing about the safety status. In addition, a redesign of the symbols that had the lowest recognition rate can be recommended. Full article

Deep learning approaches have sparked much interest in the AI community during the last decade, becoming state-of-the-art in domains such as pattern recognition, computer vision, and data analysis. However, these methods are highly demanding in terms of training data, which is often a major issue in the geospatial and remote sensing fields. One possible solution to this problem comes from the Neuro-Symbolic Integration field (NeSy), where multiple methods have been defined to incorporate background knowledge into the neural network’s learning pipeline. One such method is KENN (Knowledge Enhanced Neural Networks), which injects logical knowledge into the neural network’s structure through additional final layers. Empirically, KENN showed comparable or better results than other NeSy frameworks in various tasks while being more scalable. Therefore, we propose the usage of KENN for point cloud semantic segmentation tasks, where it has immense potential to resolve issues with small sample sizes and unbalanced classes. While other works enforce the knowledge constraints in post-processing, to the best of our knowledge, no previous methods have injected inject such knowledge into the learning pipeline through the use of a NeSy framework. The experiment results over different datasets demonstrate that the introduction of knowledge rules enhances the performance of the original network and achieves state-of-the-art levels of accuracy, even with subideal training data. Full article

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems. Full article

Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address this problem, we investigated the potential of a deep learning-based method for point symbol recognition and proposed a deep convolutional neural network (DCNN)-based model for this task. We created point symbol datasets from different sources for training and prediction models. Within this framework, atrous spatial pyramid pooling (ASPP) was adopted to handle the recognition difficulty owing to the differences between point symbols and natural objects. To increase the positioning accuracy, the k-means++ clustering method was used to generate anchor boxes that were more suitable for our point symbol datasets. Additionally, to improve the generalization ability of the model, we designed two data augmentation methods to adapt to symbol recognition. Experiments demonstrated that the deep learning method considerably improved the recognition accuracy and efficiency compared with classical algorithms. The introduction of ASPP in the object detection algorithm resulted in higher mean average precision and intersection over union values, indicating a higher recognition accuracy. It is also demonstrated that data augmentation methods can alleviate the cross-domain problem and improve the rotation robustness. This study contributes to the development of algorithms and the evaluation of geographic elements extracted from STMs. Full article

This study is oriented towards machine autonomous mapping and the need to improve the efficiency of map point symbol recognition and configuration. Therefore, an intelligent recognition method for point symbols was developed using the You Only Look Once Version 3 (YOLOv3) algorithm along with the Convolutional Block Attention Module (CBAM). Then, the recognition results of point symbols were associated with the point of interest (POI) to achieve automatic configuration. To quantitatively analyze the recognition effectiveness of this study algorithm and the comparison algorithm for map point symbols, the recall, precision and mean average precision (mAP) were employed as evaluation metrics. The experimental results indicate that the recognition efficiency of point symbols is enhanced compared to the original YOLOv3 algorithm, and that the mAP is increased by 0.55%. Compared to the Single Shot MultiBox Detector (SSD) algorithm and Faster Region-based Convolutional Neural Network (Faster RCNN) algorithm, the precision, recall rate, and mAP all performed well, achieving 97.06%, 99.72% and 99.50%, respectively. On this basis, the recognized point symbols are associated with POI, and the coordinate of point symbols are assigned through keyword matching and enrich their attribute information. This enables automatic configuration of point symbols and achieves a relatively good effect of map configuration. Full article

Offline hand-drawn diagram recognition is concerned with digitizing diagrams sketched on paper or whiteboard to enable further editing. Some existing models can identify the individual objects like arrows and symbols, but they become involved in the dilemma of being unable to understand a diagram’s structure. Such a shortage may be inconvenient to digitalization or reconstruction of a diagram from its hand-drawn version. Other methods can accomplish this goal, but they live on stroke temporary information and time-consuming post-processing, which somehow hinders the practicability of these methods. Recently, Convolutional Neural Networks (CNN) have been proved that they perform the state-of-the-art across many visual tasks. In this paper, we propose DrawnNet, a unified CNN-based keypoint-based detector, for recognizing individual symbols and understanding the structure of offline hand-drawn diagrams. DrawnNet is designed upon CornerNet with extensions of two novel keypoint pooling modules which serve to extract and aggregate geometric characteristics existing in polygonal contours such as rectangle, square, and diamond within hand-drawn diagrams, and an arrow orientation prediction branch which aims to predict which direction an arrow points to through predicting arrow keypoints. We conducted wide experiments on public diagram benchmarks to evaluate our proposed method. Results show that DrawnNet achieves $2.4\%$, $2.3\%$, and $1.7\%$ recognition rate improvements compared with the state-of-the-art methods across benchmarks of FC-A, FC-B, and FA, respectively, outperforming existing diagram recognition systems on each metric. Ablation study reveals that our proposed method can effectively enable hand-drawn diagram recognition. Full article

This article deals with the issue of computer vision on a rolling mill. The main goal of this article is to describe the designed and implemented algorithm for the automatic identification of the character string of billets on the rolling mill. The algorithm allows the conversion of image information from the front of the billet, which enters the rolling process, into a string of characters, which is further used to control the technological process. The purpose of this identification is to prevent the input pieces from being confused because different parameters of the rolling process are set for different pieces. In solving this task, it was necessary to design the optimal technical equipment for image capture, choose the appropriate lighting, search for text and recognize individual symbols, and insert them into the control system. The research methodology is based on the empirical-quantitative principle, the basis of which is the analysis of experimentally obtained data (photographs of billet faces) in real operating conditions leading to their interpretation (transformation into the shape of a digital chain). The first part of the article briefly describes the billet identification system from the point of view of technology and hardware resources. The next parts are devoted to the main parts of the algorithm of automatic identification—optical recognition of strings and recognition of individual characters of the chain using artificial intelligence. The method of optical character recognition using artificial neural networks is the basic algorithm of the system of automatic identification of billets and eliminates ambiguities during their further processing. Successful implementation of the automatic inspection system will increase the share of operation automation and lead to ensuring automatic inspection of steel billets according to the production plan. This issue is related to the trend of digitization of individual technological processes in metallurgy and also to the social sustainability of processes, which means the elimination of human errors in the management of the billet rolling process. Full article

This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of which an embedding space can be built by means of symbolic histograms. In the embedding space, any Euclidean pattern recognition system can be used, possibly equipped with feature selection capabilities in order to select the most informative symbols. The selected symbols can be analysed by field-experts in order to extract further knowledge about the process to be modelled by the learning system, hence the proposed modelling strategy can be considered as a grey-box. The proposed embedding has been tested on thirty benchmark datasets for graph classification and, further, we propose two real-world applications, namely predicting proteins’ enzymatic function and solubility propensity starting from their 3D structure in order to give an example of the knowledge discovery phase which can be carried out starting from the proposed embedding strategy. Full article

The center method, which was first proposed in Rev. Stat. Appl. 1997 by Cazes et al. and Stat. Anal. Data Mining 2011 by Douzal-Chouakria et al., extends the well-known Principal Component Analysis (PCA) method to particular types of symbolic objects that are characterized by multivalued interval-type variables. In contrast to classical data, symbolic data have internal variation. The authors who originally proposed the center method used the center of a hyper-rectangle in ${\mathbb{R}}^m$ as a base point to carry out PCA, followed by the projection of all vertices of the hyper-rectangles as supplementary elements. Since these publications, the center point of the hyper-rectangle has typically been assumed to be the best point for the initial PCA. However, in this paper, we show that this is not always the case, if the aim is to maximize the variance of projections or minimize the squared distance between the vertices and their respective projections. Instead, we propose the use of an optimization algorithm that maximizes the variance of the projections (or that minimizes the distances between the squares of the vertices and their respective projections) and finds the optimal point for the initial PCA. The vertices of the hyper-rectangles are, then, projected as supplementary variables to this optimal point, which we call the “Best Point” for projection. For this purpose, we propose four new algorithms and two new theorems. The proposed methods and algorithms are illustrated using a data set comprised of measurements of facial characteristics from a study on facial recognition patterns for use in surveillance. The performance of our approach is compared with that of another procedure in the literature, and the results show that our symbolic analyses provide more accurate information. Our approach can be regarded as an optimization method, as it maximizes the explained variance or minimizes the squared distance between projections and the original points. In addition, the symbolic analyses generate more informative conclusions, compared with the classical analysis in which classical surrogates replace intervals. All the methods proposed in this paper can be executed in the RSDA package developed in R. Full article

Recent work has shown that recognizing point symbols is an essential task in the field of map digitization. For the identification of symbols, it is generally necessary to compare the symbols with a specific criterion and find the most similar one with each known symbol one by one. Most of the works can only identify a single symbol, a small number of works are to deal with multiple symbols simultaneously with a low recognition accuracy. Given the two deficiencies, this paper proposes a deep transfer learning architecture, where the task is to learn a symbol classifier with AlexNet. For the insufficient dataset, we develop a method for transfer learning that uses a MNIST dataset to pretrain the model, which makes up for the problem of small training dataset and enhances the generalization of the model. Before the recognition process, preprocessing the point symbols in the map to coarse screening out the areas suspected of point symbols. We show a significant improvement over using point symbol images to keep a high performance in being able to deal with many more categories of symbols simultaneously. Full article