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arXiv 2025年

作者： Liu, Chengen Tenorio, Victor M. Marques, Antonio G. Isufi, Elvin Faculty of Electrical Engineering Mathematics and Computer Science Netherlands Department of Intelligent Systems Delft University of Technology Netherlands Department of Signal Theory and Communications King Juan Carlos University Spain

—Topological spaces, represented by simplicial complexes, capture richer relationships than graphs by modeling interactions not only between nodes but also among higher-order entities, such as edges or triangles. This motivates the representation of information defined in irregular domains as topological signals. By leveraging the spectral dualities of Hodge and Dirac theory, practical topological signals often concentrate in specific spectral subspaces (e.g., gradient or curl). For instance, in a foreign currency exchange network, the exchange flow signals typically satisfy the arbitrage-free condition and hence are curl-free. However, the presence of anomalies can disrupt these conditions, causing the signals to deviate from such subspaces. In this work, we formulate a hypothesis testing framework to detect whether simplicial complex signals lie in specific subspaces in a principled and tractable manner. Concretely, we propose Neyman-Pearson matched topological subspace detectors for signals defined at a single simplicial level (such as edges) or jointly across all levels of a simplicial complex. The (energy-based projection) proposed detectors handle missing values, provide closed-form performance analysis, and effectively capture the unique topological properties of the data. We demonstrate the effectiveness of the proposed topological detectors on various real-world data, including foreign currency exchange networks. © 2025, CC BY.

关键词： Network coding

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A Linear Degradation Model-Based Remaining Useful Life Prediction Framework of Supercapacitors Considering Non-Consistency of Degradation Trends

SSRN

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SSRN 2024年

作者： Zhang, Huaizhi Lin, Wenwen Zhang, Yuejun Xiang, Wei Ren, Yaping Faculty of Mechanical Engineering and Mechanics Ningbo University Zhejiang Ningbo315211 China Faculty of Mechatronic Engineering Zhejiang Business Technology Institute Zhejiang Ninghai315699 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

A supercapacitor is a new type of electrochemical energy storage device. Accurately predicting the remaining useful life (RUL) of a supercapacitor is crucial and important for the energy storage management system. This article presents a framework for predicting RUL, integrating a linear degradation model with an adaptive forgetting factor recursive least squares algorithm. Given the degradation trends' nonlinear intrinsic characteristics, the Box-Cox transformation (BCT) is applied to transform the degradation data. Enhancements to the BCT are made using a stepwise linear regression algorithm to enhance its effectiveness. Subsequently, the degradation path of the supercapacitor is characterized using this linear model. From this, a spatial state model of the system is formulated, deriving its basis from the linear degradation approach. The parameters of the linear degradation model are considered as state variables. Iterative updating of the state variables is performed using an adaptive forgetting factor algorithm to minimize the least squares. The algorithm dynamically adjusts the forgetting factor through an incremental PID control algorithm. The incremental PID control algorithm can response to the identification errors of capacitance. After iterations, the linear degradation model is extrapolated to predict RUL. The results indicate that the linear degradation model can explain more than 98% of the capacity fluctuations. The average deviation of RUL predictions for the test set was initially about 5%, decreasing to 0.57% towards the end. Furthermore, the proposed framework offers the advantage of low additional computational costs compared to a nonlinear approach, while still allowing for competitive prediction accuracy. © 2024, The Authors. All rights reserved.

关键词： Supercapacitor

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A Residual Multi-Scale Convolutional Neural Network with Transformers for Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 915-932页

作者： Yan, Tianhao Meng, Hao Parada-Cabaleiro, Emilia Tao, Jianhua Li, Taihao Schuller, Bjorn W. Zhejiang Lab Research Center for Data Hub and Security Hangzhou311121 China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China Johannes Kepler University Linz Institute of Computational Perception Linz4040 Austria Tsinghua University Department of Automation Beijing100084 China UCAS Hangzhou Institute for Advanced Study Hangzhou310024 China University of Augsburg Chair of Embedded Intelligence for Health Care & Wellbeing Augsburg86159 Germany Imperial College London Group on Language Audio & Music LondonSW7 2AZ United Kingdom

The great variety of human emotional expression as well as the differences in the ways they perceive and annotate them make Speech Emotion Recognition (SER) an ambiguous and challenging task. With the development of deep learning, long-term progress has been made in SER systems. However, the existing convolutional neural networks present certain limitations, such as their inability to well capture features, which contain important emotional information. Moreover, the position encoding in the Transformer structure is relatively fixed and only encodes the time domain dimension, which cannot effectively obtain the position information of discriminative features in the frequency domain dimension. In order to overtake these limitations, we propose an end-to-end Residual Multi-Scale Convolutional Neural Networks (RMSCNN) with Transformer model network. Simultaneously, to further validate the effectiveness of RMSCNN in extracting multi-scale features and delivering pertinent emotion localization data, we developed the RMSC_down network in conjunction with the Wav2Vec 2.0 model. The results of the prediction of Arousal, Valence and Dominance on the popular corpora demonstrate the superiority and robustness of our approach for SER , showing an improvement of the recognition accuracy in the public dataset MSP-Podcast 1.9 version. The code is available at this GitHub. © 2010-2012 IEEE.

关键词： Convolutional neural networks

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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data, Decision and systems (ICDDS), IEEE International Conference on

作者： Rajendra Hegadi Syam Sankar Karthik Akella Koushik Reddy Pavan Kumar C Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka India Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka India

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology.

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Pothole Detection in Asphalt Roads: A Comprehensive Approach for Enhanced Road Maintenance and Safety with AlexNet Model

Pothole Detection in Asphalt Roads: A Comprehensive Approach...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Abdelmalak, Mark Emad Sobhi Khodadadi, Nima Zaki, Ahmed Mohamed Eid, Marwa M. Rizk, Faris H. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Bahrain Polytechnic Faculty of Engineering Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara University Jadara Research Center Irbid Jordan

ISBN: (纸本)9798350351408

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies. © 2024 IEEE.

关键词： Digital storage

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition

arXiv

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arXiv 2024年

作者： Xiang, Qiuchi Cheng, Jintao Luo, Jiehao Wu, Jin Fan, Rui Chen, Xieyuanli Tang, Xiaoyu School of Data Science and Engineering Xingzhi College South China Normal University Shanwei516600 China School of Electronic and Information Engineering South China Normal University Guangdong Foshan528225 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong College of Intelligence Science and Technology National University of Defense Technology Changsha China College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Place recognition is the foundation for enabling autonomous systems to achieve independent decision-making and safe operations. It is also crucial in tasks such as loop closure detection and global localization within SLAM. Previous methods utilize mundane point cloud representations as input and deep learning-based LiDAR-based Place Recognition (LPR) approaches employing different point cloud image inputs with convolutional neural networks (CNNs) or transformer architectures. However, the recently proposed Mamba deep learning model, combined with state space models (SSMs), holds great potential for long sequence modeling. Therefore, we developed OverlapMamba, a novel network for place recognition, which represents input range views (RVs) as sequences. In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation. Evaluated on three different public datasets, our method effectively detects loop closures, showing robustness even when traversing previously visited locations from different directions. Relying on raw range view inputs, it outperforms typical LiDAR and multi-view combination methods in time complexity and speed, indicating strong place recognition capabilities and realtime efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

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Energy and AI 2022年第2期8卷 177-212页

作者： Daniel Mitchell Jamie Blanche Sam Harper Theodore Lim Ranjeetkumar Gupta Osama Zaki Wenshuo Tang Valentin Robu Simon Watson David Flynn Smart Systems Group Institute of SensorsSignals and SystemsSchool of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghEH144ASUnited Kingdom Centre for Mathematics and Computer Science Intelligent and Autonomous Systems GroupCWIAmsterdam1098 XG Netherlands Algorithms Group Faculty of Electrical EngineeringMathematics and Computer Science(EEMCS)Delft University of Technology(TU Delft)Delft2628 XE Netherlands The University of Manchester Department of Electrical and Electronic EngineeringOxford RoadManchesterM139PL United Kingdom James Watt School of Engineering University of GlasgowG128QQUnited Kingdom

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

关键词： Artificial intelligence Autonomous systems Digitalization Offshore renewable energy Offshore wind farms Robotics

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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Computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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A Fast and Effective Multiple Kernel Clustering Method on Incomplete data

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Computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

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