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Resilient distributed optimization under mobile malicious attacks

IFAC-PapersOnLine 2023年第2期56卷 997-1002页

作者： Yuan Wang Changxin Liu Hideaki Ishii Karl Henrik Johansson Department of Robotics Hunan University Changsha China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer Science Tokyo Institute of Technology Yokohama Japan

This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.

关键词： Fault-tolerant distributed optimization multi-agent systems network security mobile adversary agents

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Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

arXiv

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arXiv 2024年

作者： Ismaeel, Ayad Ghany Krishnadas, J. Manishankar, S. Natarajan, Yuvaraj Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kodakara680684 India Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore641062 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk36001 Iraq Computer Center University of Anbar Ramadi55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk36001 Iraq

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. © 2024, CC BY-NC-ND.

关键词： Classification (of information)

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A Novel SCD11 CNN Model Performance Evaluation with Inception V3, VGG16 and ResNet50 Using Surface Crack Dataset

A Novel SCD11 CNN Model Performance Evaluation with Inceptio...

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International Conference on Unmanned Vehicle Systems-Oman (UVS)

作者： Adil Hussain Kashif Naseer Qureshi Raja Waseem Anwar Ayesha Aslam School of Electronics and Control Engineering Chang'an University Xi'An China Department of Computer Engineering and Electronics University of Limerick Limerick Ireland Dep of Computer Science German University of Technology in Oman Muscat Oman School of Information Engineering Chang'an University Xi'An China

Image processing techniques can be used to detect potential defects in images of infrastructure elements that have been collected or scanned. Aside from image processing, machine learning technologies are increasingly being applied to increase crack detection performance and resilience. A novel Surface Crack Detection Convolutional Neural Network (CNN) named the SCD11 CNN model is proposed. The main aim of this research paper is to improve efficiency and minimize the loss rate. The proposed CNN model is compared with the CNNs, including Inception V3, VGG16, and ResNet50, using a Surface Cracks Dataset. The accuracy and loss of the proposed model are compared. The results show that the proposed SCD11 CNN model performs better in test accuracy than Inception V3 and VGG16 and has a slightly lower accuracy than ResNet50.

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Causal Discovery from Shifted Multiple Environments 25

Causal Discovery from Shifted Multiple Environments

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Dezhi Yang Guoxian Yu Jun Wang Jinglin Zhang Carlotta Domeniconi School of Software Shandong University Jinan Shandong China School of Control Science and Engineering Shandong University Jinan Shandong China Department of Computer Science George Mason University Fairfax VA USA

ISBN: (纸本)9798400712456

A fundamental problem in many science domains is learning the causal structure of a system from observed data. The observed data canonically come from multiple environments (i.e. different times, locations, and measurements), and causal models may have unobserved shifts. Although the causal graphs can be identified by modeling the distribution changes among different environments, existing solutions can only learn causal structures when given environmental information. In contrast, we propose a causal discovery approach (CausalSME) which automatically identifies pseudo environments and unobserved distribution shifts. Specifically, CausalSME learns a causal model containing unobserved variables, which can correct the distribution shifts with mixed environments. The heart of CausalSME is a variational autoencoder that infers shifted causal effects of unobserved variables and guides the identification of environment information. It further divides the shifted samples by the identified environments to jointly learn an invariant causal model. We prove the structure identifiability of CausalSME with the causal additive model. In our extensive experiments we show that CausalSME achieves state-of-the-art performance.

关键词： adversarial learning

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Differentially Private Singular Value Decomposition for Training Support Vector Machines

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Computational Intelligence and Neuroscience 2022年 2022卷 2935975页

作者： Sun, Zhenlong Yang, Jing Li, Xiaoye College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Computer and Control Engineering Qiqihar University Qiqihar161006 China

Support vector machine (SVM) is an efficient classification method in machine learning. The traditional classification model of SVMs may pose a great threat to personal privacy, when sensitive information is included in the training datasets. Principal component analysis (PCA) can project instances into a low-dimensional subspace while capturing the variance of the matrix A as much as possible. There are two common algorithms that PCA uses to perform the principal component analysis, eigenvalue decomposition (EVD) and singular value decomposition (SVD). The main advantage of SVD compared with EVD is that it does not need to compute the matrix of covariance. This study presents a new differentially private SVD algorithm (DPSVD) to prevent the privacy leak of SVM classifiers. The DPSVD generates a set of private singular vectors that the projected instances in the singular subspace can be directly used to train SVM while not disclosing privacy of the original instances. After proving that the DPSVD satisfies differential privacy in theory, several experiments were carried out. The experimental results confirm that our method achieved higher accuracy and better stability on different real datasets, compared with other existing private PCA algorithms used to train SVM. © 2022 Zhenlong Sun et al.

关键词： Singular value decomposition

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Two Factors that Influence Our Selection of Digital Avatars: Gender Performativity and Historical Culture 5th

Two Factors that Influence Our Selection of Digital Avatars:...

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5th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Liu, Shutang Li, Yongyu Li, Minggui Guan, Yin Jiang, Hang Jiang, Lei Faculty of Humanities and Arts Macau University of Science and Technology 999078 China Academic Affairs Office Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China Culture and Creation Department Fuzhou Polytechnic Fuzhou China

ISBN: (纸本)9789819908479

In this study, 74 college student volunteers were recruited as experimental participants, and 108 game 3D images of the Arena of Valor game were used as the selected samples. The digital avatar is presented to the subject through AR technology. Then, the participants were asked to choose five digital avatar that they would probably go on to use in the future metaverse. Finally, after excluding the effects of game familiarity and other socio-statistical variables, it was found that the gender of the selected Digital avatar was positively correlated with the actual participants’ gender, and it was further found that females were more gender specific and more aggregated in terms of Gender Expression than males, while males were less gender specific and more dispersed. This study found that although there was certain influence of historical culture on the selection aspect of the digital avatar, it was not significant. Local game characters and Japanese game characters have equally important influence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Real-Time Trajectory Generation via Dynamic Movement Primitives for Autonomous Racing

Real-Time Trajectory Generation via Dynamic Movement Primiti...

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American control Conference (ACC)

作者： Catherine Weaver Roberto Capobianco Peter R. Wurman Peter Stone Masayoshi Tomizuka Sony AI Department of Mechanical Engineering University of California Berke-ley CA USA Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Computer Science The University of Texas at Austin TX USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We employ sequences of high-order motion primitives for efficient online trajectory planning, enabling competitive racecar control even when the car deviates from an offline demonstration. Dynamic Movement Primitives (DMPs) utilize a target-driven nonlinear differential equation combined with a set of perturbing weights to model arbitrary motion. The DMP's target-driven system ensures that online trajectories can be generated from the current state, returning to the demonstration. In racing, vehicles often operate at their handling limits, making precise control of acceleration dynamics essential for gaining an advantage in turns. We introduce the Acceleration goal (Acc. goal) DMP, extending the DMP's target system to accommodate accelerating targets. When sequencing DMPs to model long trajectories, our Acc. goal DMP explicitly models acceleration at the junctions where one DMP meets its successor in the sequence. Applicable to DMP weights learned by any method, the proposed DMP generates trajectories with less aggressive acceleration and jerk during transitions between DMPs compared to second-order DMPs. Our proposed DMP sequencing method can recover from trajectory deviations, achieve competitive lap times, and maintain stable control in autonomous vehicle racing within the high-fidelity racing game Gran Turismo Sport. Video available: https://***/***/racingdmp/home

关键词： Sequential analysis Trajectory planning Dynamics Games Streaming media Real-time systems Trajectory

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SSIE-Diffusion: Personalized Generative Model for Subject-Specific Image Editing

SSIE-Diffusion: Personalized Generative Model for Subject-Sp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lin Guo Meiling Liu Kaiqun Fu Jiyun Zhou College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Electrical Engineering and Computer Science South Dakota State University South Dakota USA d Lieber Institute Johns Hopkins University Baltimore USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Large-scale Text-to-Image (T2I) models can generate high-quality images by controlling the synthesis based on textual prompts. However, concepts related to specific subjects are often challenging to accurately describe through text, leading to the model's inability to generate diverse images containing these specific concepts. Current methods exhibit shortcomings, including stringent requirements for input images, poor model generalization, uncontrollable image generation quality, lack of editing flexibility and unbalanced semantic consistency. In this paper, we propose a personalized generation method for targeted subject image editing, referred to as SSIE-Diffusion. We address these challenges by training an image encoder to invert original images into text embeddings, ensuring the controllability and accuracy of concept generation. To mitigate overfitting issues during training, we devise a mask loss. Additionally, building upon the T2I model, we propose fine-tuning the U-Net architecture in the model using a regularized dataset. This involves improving the projection matrices mapping to key and value features in the cross-attention layers, enhancing the visual fidelity of personalized images of the subject, and simultaneously addressing language drift issues to balance semantic consistency. Experimental results demonstrate the effectiveness of our proposed method in editing specific subjects within individual images, surpassing current SOTA methods. Across various scenarios, the generated images exhibit superior visual fidelity and editing flexibility, while maintaining high computational efficiency.

关键词： Training Visualization Image synthesis Computational modeling Semantics Neural networks Text to image

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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