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Latin American Test Workshop, LATW

作者： Edward-W. Caro-Anzola Giuseppe Esposito Juan-David Guerrero-Balaguera Andrés-F. Jiménez-López Fabián-R. Jiménez-López Robert Limas-Sierra Gustavo Ramirez-Espinosa Edoardo Giusto Bartolomeo Montrucchio Oscar-F. Vera-Cely Josie E. Rodriguez Condia Universidad Pedagógica y Tecnológica de Colombia Faculty of Engineering Tunja Colombia Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Universidad de los Llanos Villavicencio Colombia Department of Electronics Pontificia Universidad Javeriana Bogotá Colombia University of Naples Federico II DIETI Naples Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

Nowadays, the Internet of Things (IoT) and edge computing paradigms drive innovation in several fields, including autonomous systems, environmental monitoring, smart farming, and surveillance. The wide adoption of IoT and edge computing through commercial-off-the-shelf (COTS) provides adequate computational resources under limited energy consumption and affordable prices, which allow the development of versatile, scalable, and adaptable systems for several domains. Both features (versatility and scalability) have brought attention to domains with strict dependability requirements. However, ensuring effective, reliable, fault-tolerant IoT systems is challenging and mostly neglected in some domains. This special session briefly overviews strategies for the reliable design of IoT and edge computing systems for environmental monitoring in smart farming and urban scenarios. Then, we analyze an emerging paradigm (Split computing) and its reliability features. Further-more, the session discusses and focuses on design challenges, learned lessons, reliability implications, and open questions.

关键词： Smart agriculture Data integrity Reliability engineering Real-time systems Hardware Calibration Reliability Internet of Things Environmental monitoring Edge computing

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Attention Mechanism-Based Multilayer Pruned Graph Convolutional Network for Industrial Fault Diagnosis

SSRN

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SSRN 2024年

作者： Zhao, Qiang Chen, Libin Chen, Qiyue Han, Yinghua Sun, Jie College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China The State Key Laboratory of Rolling and Automation Northeastern University United States Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

Fault diagnosis in complex industrial processes has become a vital issue drawing the attention in the industrial field. With the increasing deployment of sensors in industrial process equipment, deep learning (DL) based approaches have become more feasible. However, most existing DL-based approaches have two main drawbacks. Firstly, there is complex coupling between variables in industrial processes. Most existing approaches have only one metric to build the network, which cannot fully explore the potential relationships between variables. Secondly, there are few studies on aggregating critical features of different variables and reducing the interference of irrelevant and redundant information. To overcome these challenges, this paper presents a new approach for industrial process troubleshooting, called the variational modal decomposition-multi-head graph pruning network (VMD-MHGPN). This approach draws inspiration from the attention mechanism and combines it with graph construction and pruning. Specifically, we use multiple attention mechanisms to learn the interactions between nodes. Previously, variational modal decomposition (VMD) is applied to extract fault features at different time scales and use them as subsequent graph nodes. To reduce the impact of irrelevant and redundant nodes on the fault feature representation, we introduce the graph pruning operation, which uses an attention mechanism to score nodes and discard relatively unimportant ones. The final representation of fault features is generated by computing the weights of different interaction graphs based on the attention mechanism and aggregating multiple interaction graphs according to the weights. The proposed approach for diagnosing faults in industrial processes proved efficient and superior based on experimental findings from a three-phase flow apparatus and a Tennessee Eastman process. © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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CS-Net: Contribution-based Sampling Network for Point Cloud Simplification

arXiv

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arXiv 2025年

作者： Guo, Tian Chen, Chen Yuan, Hui Mao, Xiaolong Hamzaoui, Raouf Hou, Junhui School of Control Science and Engineering Shandong University Ji’nan250061 China School of Software Shandong University Ji’nan250061 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computer Science City University of Hong Kong Hong Kong

Point cloud sampling plays a crucial role in reducing computation costs and storage requirements for various vision tasks. Traditional sampling methods, such as farthest point sampling, lack task-specific information and, as a result, cannot guarantee optimal performance in specific applications. Learning-based methods train a network to sample the point cloud for the targeted downstream task. However, they do not guarantee that the sampled points are the most relevant ones. Moreover, they may result in duplicate sampled points, which requires completion of the sampled point cloud through post-processing techniques. To address these limitations, we propose a contribution-based sampling network (CS-Net), where the sampling operation is formulated as a Top-k operation. To ensure that the network can be trained in an end-to-end way using gradient descent algorithms, we use a differentiable approximation to the Top-k operation via entropy regularization of an optimal transport problem. Our network consists of a feature embedding module, a cascade attention module, and a contribution scoring module. The feature embedding module includes a specifically designed spatial pooling layer to reduce parameters while preserving important features. The cascade attention module combines the outputs of three skip connected offset attention layers to emphasize the attractive features and suppress less important ones. The contribution scoring module generates a contribution score for each point and guides the sampling process to prioritize the most important ones. Experiments on the ModelNet40 and PU147 showed that CS-Net achieved state-of-the-art performance in two semantic-based downstream tasks (classification and registration) and two reconstruction-based tasks (compression and surface reconstruction). CS-Net also achieved high average precision for objection detection on the KITTI LiDAR point cloud dataset, demonstrating its effectiveness in three-dimensional object detection. Co

关键词： Importance sampling

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An Optimal Pricing Strategy for Ethereum Gasprice Based on Non-Preemptive Priority Queuing Game

SSRN

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SSRN 2023年

作者： Ma, Yu Wang, Xingya Gu, Xin Zhao, Zhihong College of Computer and Information Engineering College of Artificial Intelligence Nanjing Tech University Jiangsu Nanjing211816 China College of Command and Control Engineering Army Engineering University of PLA Jiangsu Nanjing210001 China College of Computing Science and Artificial Intelligence Suzhou City University Jiangsu Suzhou215104 China

In Ethereum, a game-theoretic relationship exists among transactions competing to be included in blocks. Miners prioritize transactions in the memory pool by their gasPrice, and select higher-ranked transactions for confirmation in blocks firstly. Consequently, from the perspective of transactions, this process of selection and packaging can be likened to a queuing system. To assist users in saving transaction fees and reducing waiting time, this paper utilizes a queuing game model to describe the transaction confirmation competition process and proposes an optimal pricing strategy for Ethereum transaction gasPrice that satisfies the Nash equilibrium. Specifically, the paper first models the queuing competition for transaction confirmation as a non-preemptive queuing model with priority. Then, it analyzes the impact of gasPrice on waiting time, deducing a functional relationship between waiting time and gasPrice, thereby deriving the users’ Nash equilibrium payment strategy. Experimental results demonstrate that compared to two state of the art machine learning-based pricing strategies and one based on Poisson regression, applying the pricing strategy proposed in this paper increases the Packing Rates of transactions being included in the next block by 56.21%, 50.95%, and 39.15%, respectively, and achieves cost savings of 21 times, 297 times, and 3 times for users. The results indicate that the gasPrice optimal pricing strategy proposed in this paper helps users effectively balance waiting time and cost. © 2023, The Authors. All rights reserved.

关键词： Game theory

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An Approach for Improving Automatic Mouth Emotion Recognition

arXiv

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arXiv 2022年

作者： Biondi, Giulio Franzoni, Valentina Gervasi, Osvaldo Perri, Damiano University of Florence Dept. of Mathematics and Computer Science Florence Italy University of Perugia Dept. of Mathematics and Computer Science Perugia Italy Sapienza University of Rome Department of Computer Control and Management Engineering "Antonio Ruberti" Rome Italy

The study proposes and tests a technique for automated emotion recognition through mouth detection via Convolutional Neural Networks (CNN), meant to be applied for supporting people with health disorders with communication skills issues (e.g. muscle wasting, stroke, autism, or, more simply, pain) in order to recognize emotions and generate real-time feedback, or data feeding supporting systems. The software system starts the computation identifying if a face is present on the acquired image, then it looks for the mouth location and extracts the corresponding features. Both tasks are carried out using Haar Feature-based Classifiers, which guarantee fast execution and promising performance. If our previous works focused on visual micro-expressions for personalized training on a single user, this strategy aims to train the system also on generalized faces data sets. © 2022, CC BY-NC-ND.

关键词： Emotion Recognition

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Nonlinear Active Noise control with Tap-Decomposed Robust Volterra

SSRN

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SSRN 2023年

作者： Yin, Kai-Li Zhao, Hao-Ran Pu, Yi-Fei Lu, Lu College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266520 China College of Computer Science Sichuan University Chengdu610065 China School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Electronics and Information Engineering Sichuan University Chengdu610065 China

The Volterra filter is proven to be a structurally simple method for nonlinear active noise control (NLANC). However, its wide application is deeply constrained by computational complexity. This paper proposes a tapdecomposed Volterra filter, in which a long-tap Volterra filter is approximated as multiple short-tap filters by the nearest Kronecker decomposition. Since the number of taps of decomposed filters is much less than that of the original counterpart, the proposed method greatly reduces the implementation cost of the Volterra filter. Moreover, for impulsive noise sources in NLANC, decomposed filters are updated using the maximum correntropy criterion derived from information theory. Simulation experiments with different noise source types highlight the noise control performance of the proposed approach. © 2023, The Authors. All rights reserved.

关键词： Adaptive filters

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scIAC: clustering scATAC-seq data based on Student’s t-distribution similarity imputation and denoising autoencoder

scIAC: clustering scATAC-seq data based on Student’s t-dist...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Lan Jin Ye Xun Sun Xiaoshu Zhu Qingfeng Chen Yi Pan School of computer electronic and information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of computer electronic and information Guangxi University Nanning China School of Computer Science and Engineering Yulin Normal University Yulin China School of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Assay of single cell transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) have enabled massively profiling of the chromatin accessibility landscape at the single-cell level. The essential step in analyzing scATAC-seq data is to cluster the cells into different clusters and utilize the clustering information in the subsequent downstream analysis. However, there are some challenges in the clustering analysis of scATAC-seq data. For example, scATAC-seq data are often high-dimensional and extremely sparse, as well as featuring high loss rate or noise. In this study, we proposed the scIAC to address these challenges of scATACseq data. In particular, scIAC combines the Student’s t-distribution similarity imputation and the denoising autoencoder based on the Zero-inflated Negative Binomial (ZINB) distribution. The Student’s t-distribution similarity imputation is used to solve the problem of high sparsity and high loss rate. The denoising autoencoder is employ to extract features which are useful for clustering and to reduce data noises. In addition, the self-training soft K-means and pairwise constraints are utilized in the clustering phase to enhance clustering performance. The experimental validation on several datasets shows that the proposed method performed better than other state-of-the-art methods. In conclusion, scIAC is an effective method to accurately cluster and identify cell types in scATAC-seq data.

关键词： Sequential analysis Runtime Clustering methods Noise reduction Neural networks Feature extraction Data models

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Kernel-Based Learning of Stable Nonlinear State-Space Models

Kernel-Based Learning of Stable Nonlinear State-Space Models

引用

IEEE Conference on Decision and control

作者： M.F. Shakib R. Tóth A.Y. Pogromsky A. Pavlov N. van de Wouw Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands Institute for Computer Science and Control Budapest Hungary Department of Geoscience and Petroleum NTNU Trondheim Norway

This paper presents a kernel-based learning approach for black-box nonlinear state-space models with a focus on enforcing model stability. Specifically, we aim to enforce a stability notion called convergence which guarantees that, for any bounded input from a user-defined class, the model responses converge to a unique steady-state solution that remains within a positively invariant set that is user-defined and bounded. Such a form of model stability provides robustness of the learned models to new inputs unseen during the training phase. The problem is cast as a convex optimization problem with convex constraints that enforce the targeted convergence property. The benefits of the approach are illustrated by a simulation example.

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Global Finite Time Stabilization of SISO Hurwitz Linear Systems Subject to Actuator Saturation: The Case of Real Eigenvalues

Global Finite Time Stabilization of SISO Hurwitz Linear Syst...

引用

American control Conference (ACC)

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A.

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers the global finite time stabilization of linear systems subject to actuator saturation. We show that a controllable SISO system whose eigenvalues are real and negative can be globally stabilized in finite time by a homogeneous feedback law. To obviate numerical issues, this homogeneous feedback law is implemented by discretizing a compact set. Numerical simulation validates the effectiveness of the proposed approach.

关键词： Linear systems Actuators Numerical simulation control systems Eigenvalues and eigenfunctions

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Full Point Encoding for Local Feature Aggregation in 3D Point Clouds

arXiv

引用

arXiv 2023年

作者： He, Yong Yu, Hongshan Yang, Zhengeng Liu, Xiaoyan Sun, Wei Mian, Ajmal National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China Department of Computer Science The University of Western Australia WA6009 Australia

Point cloud processing methods exploit local point features and global context through aggregation which does not explicity model the internal correlations between local and global features. To address this problem, we propose full point encoding which is applicable to convolution and transformer architectures. Specifically, we propose Full Point Convolution (FPConv) and Full Point Transformer (FPTransformer) architectures. The key idea is to adaptively learn the weights from local and global geometric connections, where the connections are established through local and global correlation functions respectively. FPConv and FPTransformer simultaneously model the local and global geometric relationships as well as their internal correlations, demonstrating strong generalization ability and high performance. FPConv is incorporated in classical hierarchical network architectures to achieve local and global shape-aware learning. In FPTransformer, we introduce full point position encoding in self-attention, that hierarchically encodes each point position in the global and local receptive field. We also propose a shape aware downsampling block which takes into account the local shape and the global context. Experimental comparison to existing methods on benchmark datasets show the efficacy of FPConv and FPTransformer for semantic segmentation, object detection, classification, and normal estimation tasks. In particular, we achieve state-of-the-art semantic segmentation results of 76% mIoU on S3DIS 6-fold and 72.2% on S3DIS Area 5. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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