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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Antonio Alliegro Francesco Cappio Borlino Tatiana Tommasi Department of Control and Computer Engineering. Politecnico di Torino Italy and Italian Institute of Technology Italy

ISBN: (纸本)9781713871088

In recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies focus on canonical closed-set conditions, neglecting the intrinsic open nature of the real-world. This limits the abilities of robots and autonomous systems involved in safety-critical applications that require managing novel and unknown signals. In this context exploiting 3D data can be a valuable asset since it provides rich information about the geometry of perceived objects and scenes. With this paper we provide the first broad study on 3D Open Set learning. We introduce 3DOS: a novel testbed for semantic novelty detection that considers several settings with increasing difficulties in terms of semantic (category) shift, and covers both in-domain (synthetic-to-synthetic, real-to-real) and cross-domain (synthetic-to-real) scenarios. Moreover, we investigate the related 2D Open Set literature to understand if and how its recent improvements are effective on 3D data. Our extensive benchmark positions several algorithms in the same coherent picture, revealing their strengths and limitations. The results of our analysis may serve as a reliable foothold for future tailored 3D Open Set methods.

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Distributed Online Bandit Nonconvex Optimization with One-Point Residual Feedback via Dynamic Regret

arXiv

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arXiv 2024年

作者： Hua, Youqing Liu, Shuai Hong, Yiguang Johansson, Karl Henrik Wang, Guangchen School of Control Science and Engineering Shandong University Jinan China Nanyang Technological University Singapore Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper considers the distributed online bandit optimization problem with nonconvex loss functions over a time-varying digraph. This problem can be viewed as a repeated game between a group of online players and an adversary. At each round, each player selects a decision from the constraint set, and then the adversary assigns an arbitrary, possibly nonconvex, loss function to this player. Only the loss value at the current round, rather than the entire loss function or any other information (e.g. gradient), is privately revealed to the player. Players aim to minimize a sequence of global loss functions, which are the sum of local losses. We observe that traditional multi-point bandit algorithms are unsuitable for online optimization, where the data for the loss function are not all a priori, while the one-point bandit algorithms suffer from poor regret guarantees. To address these issues, we propose a novel one-point residual feedback distributed online algorithm. This algorithm estimates the gradient using residuals from two points, effectively reducing the regret bound while maintaining O(1) sampling complexity per iteration. We employ a rigorous metric, dynamic regret, to evaluate the algorithm's performance. By appropriately selecting the step size and smoothing parameters, we demonstrate that the expected dynamic regret of our algorithm is comparable to existing algorithms that use two-point feedback, provided the deviation in the objective function sequence and the path length of the minimization grows sublinearly. Finally, we validate the effectiveness of the proposed algorithm through numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Worst-Scenario Prediction for Field-to-Cable Coupling Under Continuous Intentional Electromagnetic Interference Attack

Worst-Scenario Prediction for Field-to-Cable Coupling Under ...

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Asia-Pacific Symposium on Electromagnetic Compatibility (APEMC)

作者： Huamin Jie Eng Kee Chua Zhenyu Zhao Yongqi Chang Changdong Wang Kye Yak See School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9784885523472

ISBN: (纸本)9798350349498

Studying intentional electromagnetic interference (IEMI) is crucial for safeguarding hardware safety, as it helps to address the deliberate disruption of electronic devices. The field-to-cable coupling serves as a prevalent attack mechanism of IEMI, precipitating unexpected failures of the device under test. Many efforts focus on the analysis of field-to-cable coupling under high-power electromagnetic pulse, but less target on the phenomenon illuminated with continuous waves. This paper proposes a novel method to predict the worst-case for field-tocable coupling under continuous IEMI attack. By employing the transmission line theory to characterize the cables and obtaining the impedance information of the devices at both ends of the cable, an analytical model is built for determining the terminal voltage frequency responses induced by IEMI. Subsequently, the sensitive frequency region of this cable-connected electrical system can be determined, facilitating purposive immunity improvements. Using an unmanned aerial vehicle (UAV) as an example, the analytical calculations are compared with the radiated susceptibility test. The results exhibit a good agreement with experiments and validate the effectiveness of the proposed method.

关键词： Couplings Analytical models Systematics Electromagnetic interference Voltage Immunity testing Autonomous aerial vehicles

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Node-Decomposition Privacy-Preserving for Finite-Time Secondary control of DC Microgrids

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IEEE Transactions on Smart Grid 2025年

作者： Wu, Han Zhu, Si-Min Chai, Li Zhu, Zhen-Hua Tian, Yu-Chu Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology School of Artificial Intelligence and Automation Wuhan430081 China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou China Queensland University of Technology School of Computer Science BrisbaneQLD4001 Australia

In the distributed secondary control of DC microgrids, achieving both rapid convergence and robust information security is critical for enhancing control performance and maintaining power quality. However, current privacy-preserving approaches often compromise either control performance or privacy preservation performance. To address this challenge, this paper presents a node-decomposition privacy-preserving (NDPP) method for fully distributed, finite-time secondary control in DC microgrids. By splitting each node in the communication graph into multiple sub-nodes, the method enhances both convergence speed and privacy preservation without sacrificing control accuracy. Sub-nodes communicate within groups, and the topology of the generated graph is concealed from malicious nodes and eavesdroppers. Theoretical analysis is provided to prove finite-time voltage regulation and current sharing with privacy preservation against malicious nodes and external eavesdroppers. Simulations are conducted to verify the effectiveness of the method in maintaining control performance and power quality. © 2010-2012 IEEE.

关键词： Privacy by design

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Differentially Private Online Federated Learning with Correlated Noise

Differentially Private Online Federated Learning with Correl...

引用

IEEE Conference on Decision and control

作者： Jiaojiao Zhang Linglingzhi Zhu Mikael Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Systems Engineering and Engineering Management Chinese University of Hong Kong

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an $(\epsilon, \delta)$ DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm.

关键词： Privacy Federated learning Heuristic algorithms Noise Signal processing algorithms Servers

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Investigation of Mode Transformation in Motor control System of Unmanned Aerial Vehicle Exposed to Intentional Electromagnetic Interference

Investigation of Mode Transformation in Motor Control System...

引用

Asia-Pacific Symposium on Electromagnetic Compatibility (APEMC)

作者： Huamin Jie Zhenyu Zhao Yongqi Chang Fei Fan Changdong Wang Kye Yak See School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9784885523472

ISBN: (纸本)9798350349498

Susceptibility acts as a critical metric for assessing an electrical apparatus’s performance and reliability amidst the external electromagnetic environment, especially the intentional electromagnetic interference (IEMI). Given that the IEMI often invades electrical systems in common-mode (CM) and converts to the differential-mode (DM) signal to disrupt the normal operation of devices, investigating the mode transformation in the system under attack is pivotal for its susceptibility analysis. This paper first investigates the CM to DM conversion in the motor control system of the unmanned aerial vehicle (UAV) illuminated by an external IEMI. Experimental studies explore the influence of the distance between UAV and copper ground on mode conversion, revealing that adjusting the UAV’s landing gear height have the potential to enhance its susceptibility.

关键词： Performance evaluation Motor drives Electric potential Gears Electromagnetic interference RF signals Immunity testing

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Correlation Analysis and Predictive Factors for Building a Mathematical Model 1

引用

7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Nelyub, V.A. Tynchenko, V.S. Gantimurov, A.P. Degtyareva, K.V. Kukartseva, O.I. Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia

ISBN: (数字)9783031535499

ISBN: (纸本)9783031535482

The study, published in the journal Nature Medicine, looked at data on 1,000 people from China who were tracked over an average period of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution. The study analyzed data on patients with lung cancer, including their age, gender, exposure to air pollution, alcohol consumption, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoking, chest pain, cough, hemoptysis, fatigue, weight loss, shortness of breath, wheezing, difficulty swallowing, nail thickening and snoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Air pollution

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Finite Sample Analysis for a Class of Subspace Identification Methods

arXiv

引用

arXiv 2024年

作者： He, Jiabao Ziemann, Ingvar Rojas, Cristian R. Hjalmarsson, Håkan The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The University of Pennsylvania PhiladelphiaPA19104 United States

While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical analysis of SIMs remains an open problem, especially in the non-asymptotic regime. In this work, we provide a finite sample analysis for a class of SIMs, which reveals that the convergence rates for estimating Markov parameters and system matrices are O(1/√N), in line with classical asymptotic results. Based on the observation that the model format in classical SIMs becomes non-causal because of a projection step, we choose a parsimonious SIM that bypasses the projection step and strictly enforces a causal model to facilitate the analysis, where a bank of ARX models are estimated in parallel. Leveraging recent results from finite sample analysis of an individual ARX model, we obtain an overall error bound of an array of ARX models and proceed to derive error bounds for system matrices via robustness results for the singular value decomposition. Copyright © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhanced Performance

Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhan...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Wafa Akermi Nouha Alyaoui Awatef Benfradj Guiloufi Karim Chabir Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Departement of Computer Engineering College of Computer Qassim University Buraidh Saudi Ababia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

High node mobility, rapid topology changes provide specific challenges for vehicular ad hoc networks (VANETs), which have an immediate impact on the routing protocols' performance. Traditional approaches, like the Optimized Link State Routing (OLSR) protocol, provide proactive route management but fail to fully account for critical dynamic parameters like link stability, relative vehicle speed, node distance, and bandwidth availability. This work proposes a hybrid method combining OLSR with Q-learning to facilitate real-time adaptive routing. The model leverages dynamic metrics to proactively evaluate links while employing Q-learning to optimize routing decisions based on rewards computed from performance factors like delay, packet loss rate, and link duration. Simulation results demonstrate that our approach significantly outperforms classic OLSR. The improvements include reduced packet loss rates, increased average throughput, lower average latency, and a reduction in control overhead. These findings confirm that integrating dynamic metrics and adaptive learning effectively addresses the challenges posed by VANETs.

关键词： Q-learning Simulation Vehicular ad hoc networks Packet loss Routing Throughput Routing protocols Stability analysis Topology Vehicle dynamics

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Masquerading Email Detection 8

Masquerading Email Detection

引用

8th International Conference on Information technology and Nanotechnology, ITNT 2022

作者： Saurabh, Raman Bhasme, Sarang Balasaheb Saeed, Muhammad Salman Kumar, Chanchal Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Jamia Millia Islamia Department of Computer Science New Delhi India Ciena India Pvt. Ltd Haryana India Federal Research Centre Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The email which contains suspicious links or malicious files is one of the considerable threats in the digital world nowadays and because of these types of emails, enormous social and financial losses occur. The different approaches of detection of masquerading emails, which are already proposed, are unceasingly being renovated, the outcomes of that are not more impressive. Moreover, the masquerading email crisis is intensifying at a higher rate in the past few years. Because of the rapid increase in masquerading email threats, there is a need to develop new techniques to eliminate these types of masquerading email which is the present need of the hour. In our proposed work, the structure of the email was thoroughly analysed and then fed on recurrent convolutional neural networks. After that the Recurrent convolutional neural network model processes the email header and body of the email for detecting the suspicious or malicious files, elements and codes at character and word level of particular email;if the suspicious elements, file extension is detected then it will automatically transfer to the spam folder. For increasing the veracity of the design, the training of the model is performed with a balanced and unbalanced data set due to which improves the efficiency to distinguish between the legitimate and non-legitimate emails. Thus, the result of the experiment implies that the filtering or recognition probability of non-legitimate email is much closer to the value one, which also symbolizes that certainty of the proposed model enhances. As a result, classification of the spam and non-spam email is done by using the neural network classifier. The masquerading emails contain the suspicious links when the user clicks on the link the redirection takes place and the user reaches the suspicious website where the confidential information is asked from the user. The details shared by users results in financial and social losses. © 2022 IEEE.

关键词： Support vector machines

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