Search Results (222)

The combination of distributed digital factories (D²Fs) with sustainable practices has been proposed as a revolutionary technique in modern manufacturing. This review paper explores the convergence of D²F with innovative sensor technology, concentrating on the role of Field Programmable Gate Arrays (FPGAs) in promoting this paradigm. A D²F is defined as an integrated framework where digital twins (DTs), sensors, laser additive manufacturing (laser-AM), and subtractive manufacturing (SM) work in synchronization. Here, DTs serve as a virtual replica of physical machines, allowing accurate monitoring and control of a given manufacturing process. These DTs are supplemented by sensors, providing near-real-time data to assure the effectiveness of the manufacturing processes. FPGAs, identified for their re-programmability, reduced power usage, and enhanced processing compared to traditional processors, are increasingly being used to develop near-real-time monitoring systems within manufacturing networks. This review paper identifies the recent expansions in FPGA-based sensors and their exploration within the D²Fs operations. The primary topics incorporate the deployment of eco-efficient data management and near-real-time monitoring, targeted at lowering waste and optimizing resources. The review paper also identifies the future research directions in this field. By incorporating advanced sensors, DTs, laser-AM, and SM processes, this review emphasizes a path toward more sustainable and resilient D²Fs operations. Full article

As the combination of panoramic images and laser point clouds becomes more and more widely used as a technique, the accurate determination of external parameters has become essential. However, due to the relative position change of the sensor and the time synchronization error, the automatic and accurate matching of the panoramic image and the point cloud is very challenging. In order to solve this problem, this paper proposes an automatic and accurate registration method for panoramic images and point clouds of urban large scenes based on line features. Firstly, the multi-modal point cloud line feature extraction algorithm is used to extract the edge of the point cloud. Based on the point cloud intensity orthoimage (an orthogonal image based on the point cloud’s intensity values), the edge of the road markings is extracted, and the geometric feature edge is extracted by the 3D voxel method. Using the established virtual projection correspondence for the panoramic image, the panoramic image is projected onto the virtual plane for edge extraction. Secondly, the accurate matching relationship is constructed by using the feature constraint of the direction vector, and the edge features from both sensors are refined and aligned to realize the accurate calculation of the registration parameters. The experimental results show that the proposed method shows excellent registration results in challenging urban scenes. The average registration error is better than 3 pixels, and the root mean square error (RMSE) is less than 1.4 pixels. Compared with the mainstream methods, it has advantages and can promote the further research and application of panoramic images and laser point clouds. Full article

Impact monitoring is an effective approach to ensuring the safety of composite structures. The accuracy of current algorithms mostly depends on the number of physical sensors, which is not an economical way for large-area composite structures. In order to combine the advantages of sparse and dense arrays, a two-stage collaborative approach is proposed to locate the general areas and precise positions of impacts on composite structures. In Stage I, the steering vector information of the possible position is simulated according to the principle of array sensor signal processing, and a virtual array sparse feature map is constructed. When an actual impact arrives, a similarity algorithm is then used to find the suspected area in the map, which narrows down the search area to a large extent. In Stage II, a compensated two-dimensional multiple signal classification (2D-MUSIC) algorithm-based imaging method is applied to estimate the precise position of the impact in the suspected area. Finally, the accuracy and effectiveness of the proposed method are validated by numerical simulation and experiments on a carbon fiber composite panel. Both numerical and experimental results verify that the two-stage impact location method can effectively monitor composite structures with sufficient accuracy and efficiency. Full article

With the advancement of technology, the Single-Edge Nibble Transmission (SENT) protocol has become increasingly prevalent in automotive sensor applications, highlighting the need for a robust SENT signal simulation and acquisition system. This paper presents a real-time SENT signal acquisition system based on NI Field Programmable Gate Array (FPGA) technology. The system supports a range of message and data frame formats specified by the SAE J2716 SENT protocol, operates autonomously within a LabVIEW self-compiled environment, and is compatible with NI hardware-in-the-loop (HIL) systems for virtual electronic control units (ECU) calibration. This innovative, self-developed SENT system accommodates four message formats, seven data frame formats, and three pause pulse modes. Benchmarking tests were conducted by integrating this system with the dSPACE SCALEXIO HIL (located in Paderborn, Germany) system for SENT signal simulation and acquisition. The results confirm that the system effectively simulates and acquires SENT signals in accordance with SAE J2716 standards, establishing it as an invaluable asset in the electronification, intelligentization, informatization, and smart sensing of automotive and agricultural machinery. Full article

The design of medical-grade compression garments is essential for therapeutic efficacy, requiring precise pressure distribution on specific body areas. This study evaluates the effectiveness of virtual fit technology, focusing on CLO3D, in designing these garments. Simulated strain and pressure values from CLO3D were compared to experimental measurements, alongside the development of a CP model using CLO3D’s digitized stretch stiffness (Youn’s CP model). Using a 3D-scanned manikin, the mechanical behavior of eight knit fabrics, including composite structures, was assessed under strain of 5%, 10%, 15%, and 20%. The results showed that CLO3D’s built-in pressure simulation overestimated the pressure, especially in plaited fabrics such as SJP and INTP, with discrepancies of up to 10 kPa at strain levels above 15%. In contrast, the experimental pressure measurements using the Kikuhime and PPS sensors varied within 0.13 to 2.59 kPa. Youn’s CP model provided a closer fit to the experimental data, with deviations limited to within 1.9 kPa. This finding highlights the limitations of CLO3D for precision-required applications and underscores the need for more advanced, customized algorithms in virtual fit technology to ensure reliable compression garment design, particularly in medical contexts, where precise pressure control is critical for patient outcomes. Full article

Underwater wireless sensor networks (UWSNs) have been applied in lots of fields. However, coverage holes are usually caused by complex underwater environment. Coverage holes seriously affect UWSNs’ performance and quality of service; thus, their recovery is crucial for 3D UWSNs. Although most of the current research recovery algorithms demand hole detection, the number of additional mobile nodes is too large, the communication and computing costs are high, and the coverage and energy balance are poor. Therefore, these methods are not suitable for UWSN hole repairing. In order to enhance the performance of hole recovery, a coverage hole recovery method for 3D UWSNs in complex underwater environments based on virtual force guidance and energy balance is proposed. The proposed method closely combines the node energy and considers complex environmental factors. A series of multi-dimensional virtual force models are established based on energy between nodes, area boundaries, zero-energy holes, low-energy coverage holes, underwater terrain, and obstacle forces. Then, a coverage hole recovery method for 3D UWSNs based on virtual force guidance and energy balance (CHRVE) is proposed. In this method, the direction and step size of mobile repairing node movement is guided by distributed computation of virtual forces, and the nodes are driven towards the target location by means of AUV or other carrier devices. The optimal position to improve coverage rate and node force balance is obtained. Simulation experiments show good adaptability and robustness to complex underwater terrain and different environments. The algorithm does not require precise coverage hole boundary detection. Furthermore, it balances network energy distribution significantly. Therefore, this method reduces the frequency of coverage hole emergence and network maintenance costs. Full article

This study deals with the color reproduction of a work of art to digitize it into a 3D realistic model. The experiment aims to digitize a work of art for application in a virtual reality environment concerning faithful color reproduction. Photogrammetry and scanning with a LiDAR sensor are used to compare the methods and work with colors during the reconstruction of the 3D model. An innovative tablet with a camera and LiDAR sensor is used for both methods. At the same time, current findings from the field of color vision and colorimetry are applied to 3D reconstruction. The experiment focuses on working with the RGB and L*a*b* color models and, simultaneously, on the sRGB, CIE XYZ, and Rec.2020(HDR) color spaces for transforming colors into a virtual environment. For this purpose, the color is defined in the Hex Color Value format. This experiment is a starting point for further research on color reproduction in the digital environment. This study represents a partial contribution to the much-discussed area of forgeries of works of art in current trends in forensics and forgery. Full article

With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices. Full article

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot’s workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology. Full article

A variety of methods for 3D object recognition and registration based on a deep learning pipeline have recently emerged. Nonetheless, these methods require large amounts of data that are not easy to obtain, sometimes rendering them virtually useless in real-life scenarios due to a lack of generalization capabilities. To counter this, we propose a novel local descriptor that takes advantage of the fractal dimension. For each 3D point, we create a descriptor by computing the fractal dimension of the neighbors at different radii. Our redmethod has many benefits, such as being agnostic to the sensor of choice and noise, up to a level, and having few parameters to tinker with. Furthermore, it requires no training and does not rely on semantic information. We test our descriptor using well-known datasets and it largely outperforms Fast Point Feature Histogram, which is the state-of-the-art descriptor for 3D data. We also apply our descriptor to a registration pipeline and achieve accurate three-dimensional representations of the scenes, which are captured with a commercial sensor. Full article

The rapid growth in technologies for 3D sensors has made point cloud data increasingly available in different applications such as autonomous driving, robotics, and virtual and augmented reality. This raises a growing need for deep learning methods to process the data. Point clouds are difficult to be used directly as inputs in several deep learning techniques. The difficulty is raised by the unstructured and unordered nature of the point cloud data. So, machine learning models built for images or videos cannot be used directly on point cloud data. Although the research in the field of point clouds has gained high attention and different methods have been developed over the decade, very few research works directly with point cloud data, and most of them convert the point cloud data into 2D images or voxels by performing some pre-processing that causes information loss. Methods that directly work on point clouds are in the early stage and this affects the performance and accuracy of the models. Advanced techniques in classical convolutional neural networks, such as the attention mechanism, need to be transferred to the methods directly working with point clouds. In this research, an attention mechanism is proposed to be added to deep convolutional neural networks that process point clouds directly. The attention module was proposed based on specific pooling operations which are designed to be applied directly to point clouds to extract vital information from the point clouds. Segmentation of the ShapeNet dataset was performed to evaluate the method. The mean intersection over union (mIoU) score of the proposed framework was increased after applying the attention method compared to a base state-of-the-art framework that does not have the attention mechanism. Full article

In the evolving realm of ergonomics, there is a growing demand for enhanced comfortability, visibility, and accessibility in the operation of engineering machinery. This study introduces an innovative approach to assess the ergonomics of a driller’s cabin by utilizing a digital human. Through the utilization of inertial motion capture sensors, the method enables the operation of a virtual driller animated by real human movements, thereby producing more precise and realistic human–machine interaction data. Additionally, this study develops a simplified model for the human upper limbs, facilitating the calculation of joint forces and torques. An ergonomic analysis platform, encompassing a virtual driller’s cabin and a digital human model, is constructed using Unity 3D. This platform enables the quantitative evaluation of comfortability, visibility, and accessibility. Its versatility extends beyond the current scope, offering substantial support for product development and enhancement. Full article

This study investigates the integration of Building Information Models (BIMs) and Virtual Tour (VT) environments in the Architecture, Engineering and Construction (AEC) industry, focusing on Computer-Aided Facility Management (CAFM), Computerized Maintenance Management Systems (CMMSs), and data Life-Cycle Assessment (LCA). The interconnected nature of tasks throughout a building’s life cycle increasingly demands a seamless integration of real-time monitoring, 3D models, and building data technologies. While there are numerous examples of effective links between IoT and BIMs, as well as IoT and VTs, a research gap exists concerning VT-BIM integration. This article presents a technical solution that connects BIMs and IoT data using VTs to enhance workflow efficiency and information transfer. The VT is developed upon a pilot based on the Controlled Environments for Living Lab Studies (CELLS), a unique facility designed for flexible monitoring and remote-control processes that incorporate BIMs and IoT technologies. The findings offer valuable insights into the potential of VTs to complement and connect to BIMs from a life-cycle perspective, improving the usability of digital twins for beginner users and contributing to the advancement of the AEC and CAFM industries. Our technical solution helps complete the connectivity of BIMs-VT-IoT, providing an intuitive interface (VT) for rapid data visualisation and access to dashboards, models and building databases. The practical field of application is facility management, enhancing monitoring and asset management tasks. This includes (a) sensor data monitoring, (b) remote control of connected equipment, and (c) centralised access to asset-space information bridging BIM and visual (photographic/video) data. Full article

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user’s reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user’s color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas. Full article

Localization has emerged as an important and critical component of research in Wireless Sensor Networks (WSNs). WSN is a network of numerous sensors distributed across broad areas of the world to conduct numerous activities, including sensing the data and transferring it to various devices. Most applications, like animal tracking, object monitoring, and innumerable resources put in the interior as well as outdoor locations, need to identify the position of the occurring incident. The primary objective of localization is to identify the locality of sensor nodes installed in a network so that the location of a particular event can be traced. Different optimization approaches are observed in the work for solving the localization challenge in WSN and assigning the apt positions to undiscovered sensor nodes. This research employs the approach of localizing sensor nodes in a 2D platform utilizing an exclusive static anchor node and virtual anchors to detect dynamic target nodes by projecting these six virtual anchors hexagonally at different orientations and then optimizing the estimated target node co-ordinates employing Whale Optimization-based Naked Mole Rat Algorithm (WONMRA). Moreover, the effectiveness of a variety of optimization strategies employed for localization is compared to the WONMRA strategy concerning localization error and the number of nodes being localized, and it has been investigated that the average error in localization is 0.1999 according to WONMRA and is less than all other optimization techniques. Full article