Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by allowing the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, they also attract cyberattacks due to the value of sensitive information. A cyberattack on manufacturing big data can lead to a significant loss of profits and unprecedented business disruption. Moreover, the increasing use of artificial intelligence (AI) in smart factories means that manufacturing equipment is now vulnerable to cyberattacks, posing a critical threat to smart manufacturing systems. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence this paper presents the development of a new approach called Mosaic Neuron Perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, enabling a trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the CNC turning process, demonstrate that the proposed method significantly improves the prevention of inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.
{"title":"Privacy-preserving Neural Networks for Smart Manufacturing","authors":"Hankang Lee, Daniel Finke, Hui Yang","doi":"10.1115/1.4063728","DOIUrl":"https://doi.org/10.1115/1.4063728","url":null,"abstract":"Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by allowing the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, they also attract cyberattacks due to the value of sensitive information. A cyberattack on manufacturing big data can lead to a significant loss of profits and unprecedented business disruption. Moreover, the increasing use of artificial intelligence (AI) in smart factories means that manufacturing equipment is now vulnerable to cyberattacks, posing a critical threat to smart manufacturing systems. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence this paper presents the development of a new approach called Mosaic Neuron Perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, enabling a trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the CNC turning process, demonstrate that the proposed method significantly improves the prevention of inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haedong Kim, Tyler Hartleb, Khalid Bello, Faisal Aqlan, Richard Zhao, Hui Yang
Abstract Engineering is an inherently creative and collaborative endeavor to solve real-world problems, in which collaborative problem solving (CPS) is considered one of the most critical professional skills. Hands-on practices and assessment methods are essential to promote deeper learning and foster the development of professional skills. However, most existing approaches are based on out-of-process procedures such as surveys, tests, or interviews that measure the effectiveness of learning activity in an aggregated way. It is desirable to quantify CPS dynamics during the learning process. Advancements in virtual reality (VR) provide great opportunities to realize digital learning environments to facilitate a learning-by-doing curriculum. In addition, sensors in VR systems allow us to collect in-process user behavioral data. This paper presents a multiplayer VR manufacturing simulation game for virtual hands-on learning experiences, as well as a behavioral modeling method for monitoring the CPS skills of participants. First, we developed the Virtual Learning Factory, where users play simulation games of various manufacturing paradigms. Second, we collected action logs from a sample of participants and used the same pattern to generate more data. Third, the behavioral data are modeled as dynamic networks for each player. Last, network features are calculated, and a CPS scoring method is driven from them. Experimental results show that the proposed behavioral modeling successfully captures different patterns of CPS dynamics according to manufacturing paradigms and individuals. This detailed assessment contributes to the development of appropriate student-specific interventions to improve learning outcomes.
{"title":"Behavioral Modeling of Collaborative Problem Solving in Multiplayer Virtual Reality Manufacturing Simulation Games","authors":"Haedong Kim, Tyler Hartleb, Khalid Bello, Faisal Aqlan, Richard Zhao, Hui Yang","doi":"10.1115/1.4063089","DOIUrl":"https://doi.org/10.1115/1.4063089","url":null,"abstract":"Abstract Engineering is an inherently creative and collaborative endeavor to solve real-world problems, in which collaborative problem solving (CPS) is considered one of the most critical professional skills. Hands-on practices and assessment methods are essential to promote deeper learning and foster the development of professional skills. However, most existing approaches are based on out-of-process procedures such as surveys, tests, or interviews that measure the effectiveness of learning activity in an aggregated way. It is desirable to quantify CPS dynamics during the learning process. Advancements in virtual reality (VR) provide great opportunities to realize digital learning environments to facilitate a learning-by-doing curriculum. In addition, sensors in VR systems allow us to collect in-process user behavioral data. This paper presents a multiplayer VR manufacturing simulation game for virtual hands-on learning experiences, as well as a behavioral modeling method for monitoring the CPS skills of participants. First, we developed the Virtual Learning Factory, where users play simulation games of various manufacturing paradigms. Second, we collected action logs from a sample of participants and used the same pattern to generate more data. Third, the behavioral data are modeled as dynamic networks for each player. Last, network features are calculated, and a CPS scoring method is driven from them. Experimental results show that the proposed behavioral modeling successfully captures different patterns of CPS dynamics according to manufacturing paradigms and individuals. This detailed assessment contributes to the development of appropriate student-specific interventions to improve learning outcomes.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicholas Wu, Brendan Whalen, Ji Ma, Prasanna V. Balachandran
Abstract In this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach towards inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar-Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack of fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.
{"title":"Probabilistic Printability Maps for Laser Powder Bed Fusion via Functional Calibration and Uncertainty Propagation","authors":"Nicholas Wu, Brendan Whalen, Ji Ma, Prasanna V. Balachandran","doi":"10.1115/1.4063727","DOIUrl":"https://doi.org/10.1115/1.4063727","url":null,"abstract":"Abstract In this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach towards inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar-Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack of fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Habibor Rahman, Erfan Yazdandoost Hamedani, Young-Jun Son, Mohammed Shafae
Abstract Identifying, analyzing, and evaluating cybersecurity risks is essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems is lacking. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors' techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.
{"title":"Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment","authors":"Md Habibor Rahman, Erfan Yazdandoost Hamedani, Young-Jun Son, Mohammed Shafae","doi":"10.1115/1.4063729","DOIUrl":"https://doi.org/10.1115/1.4063729","url":null,"abstract":"Abstract Identifying, analyzing, and evaluating cybersecurity risks is essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems is lacking. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors' techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper presents an improved method for optimizing the singularity structure of hexahedral meshes using various dual operations. Our approach aims at reducing element distortion by decomposing complex singular nodes into singular curves using high-quality sheet insertion at proper locations. Then, singular curves that meet the topological parallel requirements are paired to perform the semantic column operation, which eliminates the singular curves. Finally, the topological structure is further optimized by collapsing sheets, resulting in a valid hex mesh with a simpler structure. Compared to existing hexahedral mesh simplification methods, our approach can generate higher quality meshes. Experimental results demonstrate the effectiveness of the proposed method.
{"title":"Singularity structure optimization for hexahedral mesh via dual operations","authors":"Chun Shen, Rui Wang","doi":"10.1115/1.4063402","DOIUrl":"https://doi.org/10.1115/1.4063402","url":null,"abstract":"Abstract This paper presents an improved method for optimizing the singularity structure of hexahedral meshes using various dual operations. Our approach aims at reducing element distortion by decomposing complex singular nodes into singular curves using high-quality sheet insertion at proper locations. Then, singular curves that meet the topological parallel requirements are paired to perform the semantic column operation, which eliminates the singular curves. Finally, the topological structure is further optimized by collapsing sheets, resulting in a valid hex mesh with a simpler structure. Compared to existing hexahedral mesh simplification methods, our approach can generate higher quality meshes. Experimental results demonstrate the effectiveness of the proposed method.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation of coupler curves in the context of neural networks has not been fully explored. This study compares four commonly used features or representations of four-bar coupler curves: Fourier descriptors, wavelets, point coordinates, and images. The results demonstrate that these diverse representations can be unified using a generative AI framework called Variational Autoencoder (VAE). This study shows that a VAE can provide a standalone representation of a coupler curve, regardless of the input representation, and that the compact latent dimensions of the VAE can be used to describe coupler curves of four-bar linkages. Additionally, a new approach that utilizes a VAE in conjunction with a fully connected neural network to generate dimensional parameters of four-bar linkage mechanisms is proposed. This research presents a novel opportunity for automated conceptual design of mechanisms for robots and machines.
{"title":"An Invariant Representation of Coupler Curves using a Variational AutoEncoder: Application to Path Synthesis of Four-Bar Mechanisms","authors":"Anar Nurizada, Anurag Purwar","doi":"10.1115/1.4063726","DOIUrl":"https://doi.org/10.1115/1.4063726","url":null,"abstract":"Abstract This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation of coupler curves in the context of neural networks has not been fully explored. This study compares four commonly used features or representations of four-bar coupler curves: Fourier descriptors, wavelets, point coordinates, and images. The results demonstrate that these diverse representations can be unified using a generative AI framework called Variational Autoencoder (VAE). This study shows that a VAE can provide a standalone representation of a coupler curve, regardless of the input representation, and that the compact latent dimensions of the VAE can be used to describe coupler curves of four-bar linkages. Additionally, a new approach that utilizes a VAE in conjunction with a fully connected neural network to generate dimensional parameters of four-bar linkage mechanisms is proposed. This research presents a novel opportunity for automated conceptual design of mechanisms for robots and machines.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135095618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amin Yousefpour, Mehdi Shishehbor, Zahra Zanjani Foumani, Ramin Bostanabad
Abstract Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty detection. The majority of existing anomaly detection methods either are exclusively developed for (semi) supervised settings, or provide poor performance in unsupervised applications where there is no training data with labeled anomalous samples. To bridge this research gap, we introduce a robust, efficient, and interpretable methodology based on nonlinear manifold learning to detect anomalies in unsupervised settings. The essence of our approach is to learn a low-dimensional and interpretable latent representation (aka manifold) for all the data points such that normal samples are automatically clustered together and hence can be easily and robustly identified. We learn this low-dimensional manifold by designing a learning algorithm that leverages either a latent map Gaussian process (LMGP) or a deep autoencoder (AE). Our LMGP-based approach, in particular, provides a probabilistic perspective on the learning task and is ideal for high-dimensional applications with scarce data. We demonstrate the superior performance of our approach over existing technologies via multiple analytic examples and real-world datasets.
{"title":"Unsupervised Anomaly Detection via Nonlinear Manifold Learning","authors":"Amin Yousefpour, Mehdi Shishehbor, Zahra Zanjani Foumani, Ramin Bostanabad","doi":"10.1115/1.4063642","DOIUrl":"https://doi.org/10.1115/1.4063642","url":null,"abstract":"Abstract Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty detection. The majority of existing anomaly detection methods either are exclusively developed for (semi) supervised settings, or provide poor performance in unsupervised applications where there is no training data with labeled anomalous samples. To bridge this research gap, we introduce a robust, efficient, and interpretable methodology based on nonlinear manifold learning to detect anomalies in unsupervised settings. The essence of our approach is to learn a low-dimensional and interpretable latent representation (aka manifold) for all the data points such that normal samples are automatically clustered together and hence can be easily and robustly identified. We learn this low-dimensional manifold by designing a learning algorithm that leverages either a latent map Gaussian process (LMGP) or a deep autoencoder (AE). Our LMGP-based approach, in particular, provides a probabilistic perspective on the learning task and is ideal for high-dimensional applications with scarce data. We demonstrate the superior performance of our approach over existing technologies via multiple analytic examples and real-world datasets.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marco Rossoni, Matteo Pozzi, Giorgio Colombo, Marco Gribaudo, Pietro Piazzolla
Abstract Point cloud 3D models are becoming more and more popular thanks to the spreading of scanning systems employed in many fields. When used for rendering purposes, point clouds are usually displayed with their original color acquired at scan time, without considering the lighting condition of the scene where the model is placed. This leads to a lack of realism in many contexts, especially in the case of animated point clouds employed in eXtended Reality applications where it would be desirable to have the model reacting to incoming light and integrating with the surrounding environment. This paper proposes the application of Physically Based Rendering (PBR), a rendering technique widely used in Real-Time Computer Graphics applications, to animated point cloud models for reproducing specular reflections, and achieving a photo-realistic and physically accurate look under any lighting condition. Firstly, we consider the extension of commonly used animated point cloud formats, to include normal vectors, and PBR parameters, as well as the encoding of the animated environment maps required by the technique. Then, an animated point cloud model is rendered with a shader implementing the proposed PBR method. Finally, the PBR pipeline is compared to traditional renderings of the same point cloud obtained with commonly used shaders, under different lighting conditions and environments. It will be shown how the point cloud better integrates visually with its surroundings.
随着扫描系统在各个领域的广泛应用，点云三维模型越来越受欢迎。当用于渲染目的时，点云通常以扫描时获得的原始颜色显示，而不考虑模型所在场景的照明条件。这导致在许多情况下缺乏真实感，特别是在扩展现实应用中使用的动画点云的情况下，它希望模型对入射光做出反应并与周围环境相结合。本文提出了一种广泛应用于实时计算机图形学应用的基于物理的渲染技术(physical Based Rendering, PBR)，用于动画点云模型，以再现镜面反射，并在任何光照条件下实现逼真的物理精确外观。首先，我们考虑了常用的动画点云格式的扩展，包括法向量和PBR参数，以及该技术所需的动画环境图的编码。然后，使用实现PBR方法的着色器渲染动画点云模型。最后，在不同的光照条件和环境下，将PBR管道与使用常用着色器获得的相同点云的传统渲染图进行比较。它将展示点云如何在视觉上更好地与周围环境融为一体。
{"title":"Physically-based Rendering of Animated Point Clouds for eXtended Reality","authors":"Marco Rossoni, Matteo Pozzi, Giorgio Colombo, Marco Gribaudo, Pietro Piazzolla","doi":"10.1115/1.4063559","DOIUrl":"https://doi.org/10.1115/1.4063559","url":null,"abstract":"Abstract Point cloud 3D models are becoming more and more popular thanks to the spreading of scanning systems employed in many fields. When used for rendering purposes, point clouds are usually displayed with their original color acquired at scan time, without considering the lighting condition of the scene where the model is placed. This leads to a lack of realism in many contexts, especially in the case of animated point clouds employed in eXtended Reality applications where it would be desirable to have the model reacting to incoming light and integrating with the surrounding environment. This paper proposes the application of Physically Based Rendering (PBR), a rendering technique widely used in Real-Time Computer Graphics applications, to animated point cloud models for reproducing specular reflections, and achieving a photo-realistic and physically accurate look under any lighting condition. Firstly, we consider the extension of commonly used animated point cloud formats, to include normal vectors, and PBR parameters, as well as the encoding of the animated environment maps required by the technique. Then, an animated point cloud model is rendered with a shader implementing the proposed PBR method. Finally, the PBR pipeline is compared to traditional renderings of the same point cloud obtained with commonly used shaders, under different lighting conditions and environments. It will be shown how the point cloud better integrates visually with its surroundings.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135344464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Due to the fact that rotors usually operate in a non-stationary mode with changing speeds, the conventional rotor unbalance detection method based on the stationary signal will produce a major “spectrum ambiguity issue” and affect the accuracy of rotor unbalance detection. To this end, a tacholess order tracking method based on the STFTSC algorithm is suggested in this study, where the STFTSC algorithm is developed by combining the short-time Fourier transform and the seam carving algorithm. Firstly, the STFTSC algorithm is utilized to accurately extract the instantaneous frequency (IF) of the rotor and calculate the instantaneous phase under variable-speed conditions. Subsequently, the original signal is resampled in the angular domain to transform the non-stationary time domain signal into a stable angle domain signal, eliminating the effect of the speed variations. Finally, the angular domain signal is transformed into the order domain signal, which uses the discrete Fourier transform and the discrete spectrum correction method to identify the amplitude and phase corresponding to the fundamental frequency component of the signal. The simulation results show that the IF extracted by the STFTSC algorithm has higher extraction accuracy compared with the traditional STFT spectral peak detection method and effectively eliminates the effect of speed fluctuations. A rotor dynamic-balancing experiment shows that the unbalance correction effect based on the STFTSC algorithm is remarkable, with the average unbalance amount decrease rate on the left and right sides being 90.02% and 92.56%, respectively, after a single correction.
{"title":"A tacholess order tracking method based on the STFTSC algorithm for rotor unbalance fault diagnosis under variable-speed conditions","authors":"Binyun Wu, Liang Hou, Shaojie Wang, Xiaozhen Lian","doi":"10.1115/1.4063401","DOIUrl":"https://doi.org/10.1115/1.4063401","url":null,"abstract":"Abstract Due to the fact that rotors usually operate in a non-stationary mode with changing speeds, the conventional rotor unbalance detection method based on the stationary signal will produce a major “spectrum ambiguity issue” and affect the accuracy of rotor unbalance detection. To this end, a tacholess order tracking method based on the STFTSC algorithm is suggested in this study, where the STFTSC algorithm is developed by combining the short-time Fourier transform and the seam carving algorithm. Firstly, the STFTSC algorithm is utilized to accurately extract the instantaneous frequency (IF) of the rotor and calculate the instantaneous phase under variable-speed conditions. Subsequently, the original signal is resampled in the angular domain to transform the non-stationary time domain signal into a stable angle domain signal, eliminating the effect of the speed variations. Finally, the angular domain signal is transformed into the order domain signal, which uses the discrete Fourier transform and the discrete spectrum correction method to identify the amplitude and phase corresponding to the fundamental frequency component of the signal. The simulation results show that the IF extracted by the STFTSC algorithm has higher extraction accuracy compared with the traditional STFT spectral peak detection method and effectively eliminates the effect of speed fluctuations. A rotor dynamic-balancing experiment shows that the unbalance correction effect based on the STFTSC algorithm is remarkable, with the average unbalance amount decrease rate on the left and right sides being 90.02% and 92.56%, respectively, after a single correction.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135878952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Augmented Reality (AR) enhances the user's perception of the real environment by superimposing virtual images generated by computers. These virtual images provide additional visual information that complements the real-world view. AR systems are rapidly gaining popularity in various manufacturing fields such as training, maintenance, assembly, and robot programming. In some AR applications, it is crucial for the invisible virtual environment to be precisely aligned with the physical environment to ensure that human users can accurately perceive the virtual augmentation in conjunction with their real surroundings. The process of achieving this accurate alignment is known as calibration. During some robotics applications using AR, we observed instances of misalignment in the visual representation within the designated workspace. This misalignment can potentially impact the accuracy of the robot's operations during the task. Based on previous research on AR-assisted robot programming systems, this work investigates the sources of misalignment errors and presents a simple and efficient calibration procedure to reduce the misalignment accuracy in general video see-through AR systems. To accurately superimpose virtual information onto the real environment, it is necessary to identify the sources and propagation of errors. In this work, we outline the linear transformation and projection of each point from the virtual world space to the virtual screen coordinates. An offline calibration method is introduced to determine the offset matrix from the Head-Mounted Display (HMD) to the camera, and experiments are conducted to validate the improvement achieved through the calibration process.
{"title":"A Global Correction Framework for Camera Registration in Video See-Through Augmented Reality Systems","authors":"Wenhao Yang, Yunbo Zhang","doi":"10.1115/1.4063350","DOIUrl":"https://doi.org/10.1115/1.4063350","url":null,"abstract":"\u0000 Augmented Reality (AR) enhances the user's perception of the real environment by superimposing virtual images generated by computers. These virtual images provide additional visual information that complements the real-world view. AR systems are rapidly gaining popularity in various manufacturing fields such as training, maintenance, assembly, and robot programming. In some AR applications, it is crucial for the invisible virtual environment to be precisely aligned with the physical environment to ensure that human users can accurately perceive the virtual augmentation in conjunction with their real surroundings. The process of achieving this accurate alignment is known as calibration. During some robotics applications using AR, we observed instances of misalignment in the visual representation within the designated workspace. This misalignment can potentially impact the accuracy of the robot's operations during the task. Based on previous research on AR-assisted robot programming systems, this work investigates the sources of misalignment errors and presents a simple and efficient calibration procedure to reduce the misalignment accuracy in general video see-through AR systems. To accurately superimpose virtual information onto the real environment, it is necessary to identify the sources and propagation of errors. In this work, we outline the linear transformation and projection of each point from the virtual world space to the virtual screen coordinates. An offline calibration method is introduced to determine the offset matrix from the Head-Mounted Display (HMD) to the camera, and experiments are conducted to validate the improvement achieved through the calibration process.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48143893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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