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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 4826-4830页

作者： Jingqiu Guo Long Chen Lingxi Li Xiaoxiang Na Ljubo Vlacic Fei-Yue Wang Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University–Purdue University Indianapolis (IUPUI) Indianapolis IN USA Department of Engineering University of Cambridge Cambridge U.K. Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith University Nathan QLD Australia State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China

Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, we discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders’ insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.

关键词： Active appearance model Costs Economics Transportation Helicopters Aircraft propulsion Public transportation

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Sinusoidal-Assisted Synchrosqueezing Transform: Algorithms and Biomedical Applications

SSRN

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SSRN 2023年

作者： Chen, Tao Zheng, Qian Xie, Lei Su, Hongye State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Flexolink Technology Co. Ltd. Hangzhou China

Intermittent oscillation signals (IOSs) exist widely in biomedical systems. They are always contaminated by various randomness like noise and artifacts. Compared with traditional time-frequency analysis (TFA) methods, adaptive TFA methods show the significant advantages in separating IOSs. However, these methods are implemented by assuming that the signal is in a weakly noisy environment and have narrow-band limitation. Besides, it is difficult for these methods to alleviate the noise in the gap among time where IOSs happen. Therefore, in this paper, a new method called the sinusoidal-assisted synchrosqueezing transform (SASST) is proposed for separating IOSs in strongly noisy environment. The SASST method locates the IOSs with the aid of a sinusoidal signal. Such a sinusoidal signal fills the energy blank in the gap between time where IOSs happen. This enables that the ridge detection approach, which is the necessary tool for mode retrieval of SST-based methods, focuses on the assisted sinusoidal signal, rather than noise. Thus, the background noise in the gap is suppressed. Moreover, this sinusoidal-assisted framework can be introduced into any SST-based TFA methods. Depending on the characteristics of different biomedical signals, different SST-based methods are chosen to achieve better separation. As a result, SASST is capable of extracting both narrow- and wide-band IOSs in a strongly noisy environment. The effectiveness and advantages of the proposed algorithm are verified by both numerical simulations and real-world cases. Even in a strong noise environment of -10dB, SASST achieve output SNR of 12.66dB and 10.53dB in narrow- and wide-band simulation, respectively. © 2023, The Authors. All rights reserved.

关键词： Medical applications

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A Survey of Fault Tolerance in Cloud Computing

A Survey of Fault Tolerance in Cloud Computing

引用

Systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Om Kolthoum Elhajjej Nouha Alyaoui Samia Dardouri Karim Chabir Research Laboratory Modeling Aanalysis Control of System (MACS) LR16ES22 National engineering school of Gabes (ENIG) University of Gabes Tunisia Computer Science and Telecommunications Department Laboratory of Modeling Analysis and Control of Systems (MACS) Higher Institute of Applied Sciences and Technology Zarroug Gafsa Gafsa University Tunisia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV’COM Laboratory-Sup’Com University of Carthage Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.

关键词： Measurement Surveys Cloud computing Fault tolerance Technological innovation Scalability Fault tolerant systems computer architecture Software Software reliability

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Asymptotic properties of distributed social sampling algorithm

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Science China(Information sciences) 2020年第1期63卷 152-166页

作者： Qian LIU Xingkang HE Haitao FANG The Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences ACCESS Linnaeus Centre School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology

Social sampling is a novel randomized message passing protocol inspired by social communication for opinion formation in social networks. In a typical social sampling algorithm, each agent holds a sample from the empirical distribution of social opinions at initial time, and it collaborates with other agents in a distributed manner to estimate the initial empirical distribution by randomly sampling a message from current distribution estimate. In this paper, we focus on analyzing the theoretical properties of the distributed social sampling algorithm over random networks. First, we provide a framework based on stochastic approximation to study the asymptotic properties of the algorithm. Then, under mild conditions, we prove that the estimates of all agents converge to a common random distribution, which is composed of the initial empirical distribution and the accumulation of quantized error. Besides, by tuning algorithm parameters, we prove the strong consistency, namely, the distribution estimates of agents almost surely converge to the initial empirical distribution. Furthermore, the asymptotic normality of estimation error generated by distributed social sample algorithm is addressed. Finally, we provide a numerical simulation to validate the theoretical results of this paper.

关键词： social networks opinion formation social sampling stochastic approximation random networks asymptotic normality

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Artificial intelligence in cancer target identification and drug discovery

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Signal Transduction and Targeted Therapy 2022年第6期7卷 1951-1974页

作者： Yujie You Xin Lai Yi Pan Huiru Zheng Julio Vera Suran Liu Senyi Deng Le Zhang College of Computer Science Sichuan UniversityChengdu 610065China Laboratory of Systems Tumor Immunology Friedrich-Alexander-Universität Erlangen-Nürnberg(FAU)and Universitätsklinikum ErlangenErlangen 91052Germany Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesRoom D5131068 Xueyuan AvenueShenzhen University TownShenzhen 518055China School of Computing Ulster UniversityBelfast BT151EDUK Institute of Thoracic Oncology Department of Thoracic SurgeryWest China HospitalSichuan UniversityChengdu 610065China Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as ***,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover ***,we describe the scope of artificial intelligence biology analysis for novel anticancer target ***,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence ***,we showcase the applications of artificial intelligence approaches in cancer target identification and drug *** together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

关键词： drugs cancer drug

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STFormer: A dual-stage transformer model utilizing spatio-temporal graph embedding for multivariate time series forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第3期46卷 6951-6967页

作者： Xiao, Yuteng Liu, Zhaoyang Yin, Hongsheng Wang, Xingang Zhang, Yudong Jinan China Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Xuzhou University of Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing China

Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not effectively tackle the challenges posed by noise and unordered data. Drawing inspiration from advancing the Transformer model, we introduce a transformer-based method called STFormer to address this predicament. The STFormer utilizes a two-stage Transformer to capture spatio-temporal relationships and tackle the issue of noise. Furthermore, the MTS incorporates adaptive spatio-temporal graph structures to tackle the issue of unordered data specifically. The Transformer incorporates graph embedding to combine spatial position information with long-term temporal connections. Experimental results based on typical finance and environment datasets demonstrate that STFormer surpasses alternative baseline forecasting models and achieves state-of-the-art results for single-step horizon and multistep horizon forecasting. © 2024 - IOS Press. All rights reserved.

关键词： Recurrent neural networks

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automatic Identification for Projector Brand and Model Number 1

引用

6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Li, Zuoyong Wang, Weice Zhang, Fuquan Fan, Haoyi Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

The projector production process needs to pack manufactured projectors. The key step of projector packing is to check whether the brand and model number of the projector is correct or not, for avoiding the projector’s packing error. To achieve automation of identifying projector brand and model number for improving the efficiency of the projector packing, we proposed an automatic identification method by using image processing, character recognition, and string matching. Specifically, image grayscale processing, image gradient calculation, morphological operations, and image thresholding are sequentially performed to determine candidate target regions located by a projector's brand and model number. Then, two shape prior-based image features are designed to perform target region screening, i.e., excluding fake target regions as far as possible. Next, the character recognition technology is used to extract the texts in the target regions. Finally, string matching is performed to obtain identification results of the projector's brand and model number. Experimental results on several projectors demonstrate the effectiveness of the proposed method on automatic identification of projector brand and model number. The proposed method provides a general framework for identifying the brand and model number of other similar electronic products. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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The diversity and ecological significance of microbial traits potentially involved in B_(12) biosynthesis in the global ocean

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mLife 2023年第4期2卷 416-427页

作者： Jiayin Zhou Wei Qin Xinda Lu Yunfeng Yang David Stahl Nianzhi Jiao Jizhong Zhou Jihua Liu Qichao Tu Institute of Marine Science and Technology Shandong UniversityQingdaoChina Joint Lab for Ocean Research and Education at Dalhousie University Shandong University and Xiamen UniversityQingdaoChina School of Biological Sciences University of OklahomaNormanOklahomaUSA Department of Civil and Environmental Engineering Massachusetts Institute of TechnologyCambridgeMassachusettsUSA State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijingChina Department of Civil and Environmental Engineering University of WashingtonSeattleWashingtonUSA Institute of Marine Microbes and Ecospheres Xiamen UniversityXiamenChina Earth and Environmental Sciences Lawrence Berkeley NationalLaboratoryBerkeley CaliforniaUSA Institutefr Environmental Genomics University of OklahomaNormanOklahomaUSA School of Civil Engineering and Environmental Sciences University of OklahomaNormanOklahomaUSA School of Computer Sciences University of OklahomaNormanOklahomaUSA DermBiont Inc. BostonMassachusettsUSA

Cobalamin(B_(12)),an essential nutrient and growth cofactor for many living organisms on Earth,can be fully synthesized only by selected prokaryotes in ***,microbial communities related to B_(12) biosynthesis could serve as an example subsystem to disentangle the underlying ecological mechanisms balancing the function and taxonomic make-up of complex functional *** anchoring microbial traits potentially involved in B_(12) biosynthesis,we depict the biogeographic patterns of B_(12) biosynthesis genes and the taxa harboring them in the global ocean,despite the limitations of detecting de novo B_(12) synthesizers via metagenomes *** the taxonomic and functional composition of B_(12) biosynthesis genes were strongly shaped by depth,differentiating the epipelagic zones from the mesopelagic *** genes related to B_(12) biosynthesis were relatively stably distributed across different oceans,but the taxa harboring them varied considerably,showing clear functional redundancy among microbial *** taxa carrying B_(12) biosynthesis genes in the surface water were influenced by environmental factors such as temperature,oxygen,and ***,the composition of functional genes was only weakly associated with these environmental *** model analyses demonstrated that determinism governed the variations in B_(12) biosynthesis genes,whereas a higher degree of stochasticity was associated with taxonomic *** associations were observed between the chlorophyll a concentration and B_(12) biosynthesis,confirming its importance in primary production in the global *** results of this study reveal an essential ecological mechanism governing the assembly of microbes in nature:the environment selects for function rather than taxonomy;functional redundancy underlies stochastic community assembly.

关键词： B_(12)biosynthesis community assembly functional genes functional redundancy ocean primary production

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Improving routing efficiency in vehicular AD Hoc Networks

Improving routing efficiency in vehicular AD Hoc Networks

引用

International Conference on control, Decision and Information Technologies (CoDIT)

作者： W. Akermi N. Alyaoui A. B. Guiloufi K. Chabbir N. Nasri Automatic Industrial Systems Department 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 Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Preparatory Institute for Engineering Studies of Gabes Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science Department-Gafsa Gafsa University Gafsa Tunisia Laboratory of Smart Systems for Engineering and E-Health Based on Technologies of Image and Telecommunications (SETIT) Sfax University Sfax Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Vehicular networks, also known as Vehicular Ad Hoc Networks (VANETs), have become a crucial technology for vehicle communication, providing wireless connectivity among vehicles and between vehicles and infrastructure. However, the dynamic and complex nature of these networks presents unique challenges in routing and resource management. In this study, we explore the use of artificial intelligence (AI) to improve the performance of vehicular networks, focusing on two widely used routing protocols: Optimized Link State Routing (OLSR) and Destination-Sequenced Distance Vector (DSDV).We aim to evaluate the performance of these traditional routing protocols within the context of vehicular networks by integrating machine learning techniques such as neural network regression (MLRegression). Our goal is to understand how the integration of artificial intelligence can enhance the prediction and optimization of vehicular network performance. Using MLRegression, we aim to develop accurate prediction models for performance metrics such as latency, throughput, packet delivery rate, and more, based on network parameters and environmental conditions. This comparative evaluation, which involves incorporating an AI-based approach, will allow us to better understand the strengths and weaknesses of each protocol and provide recommendations for improving vehicular networks.

关键词： Measurement Wireless communication Adaptation models Adaptive systems Vehicular ad hoc networks Routing Throughput Routing protocols Vectors Vehicle dynamics

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