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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Payam Aboutalebi Dong Trong Nguyen Aitor J. Garrido Izaskun Garrido Department of Automatic Control and Systems Engineering Engineering School of Bilbao - EIB/BIE Automatic Control Group - ACG University of the Basque Country - UPV/EHU Bilbao Spain Department of Marine Technology Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

This paper addresses the challenge of reducing undesired oscillations in barge-type floating wind turbines (FWTs) caused by wind and wave forces, which result in fluctuations in generator power and system oscillations. These oscillations can negatively impact the system’s lifespan, power generation, maintenance costs, and component stress. To mitigate these issues, oscillating water columns (OWCs) are integrated within the FWT substructure. A control strategy is proposed for the OWCs based on response amplitude operators, aiming to minimize oscillations in a 5 MW wind turbine structure. A considered case study is conducted for above-rated wind speeds to evaluate the effectiveness of the controllers and to compare the performance of an uncontrolled FWT against a controlled FWT integrated with OWCs. The findings demonstrate the efficacy of the control strategy in pitch oscillations reducing for 29.9% and generated power fluctuations reducing for 23.03% and highlight the advantages of integrating OWCs in floating offshore wind turbines.

关键词： Vibrations Fluctuations Wind speed Switches Wind power generation Valves control systems

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Deep Learning ECG Signal Analysis: Description and Preliminary Results 18th

Deep Learning ECG Signal Analysis: Description and Prelimin...

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Dependable computer Systems and Networks - Proceedings of the Eighteenth International Conference on Dependability of computer Systems DepCoS-RELCOMEX

作者： Surowiec, Mateusz Ciskowski, Piotr Kluwak, Kondrad Jeleń, Lukasz Department of Computer Engineering Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9783031377198

Electrocardiography is one of the most commonly performed examinations when a patient experiences symptoms of heart disease. A detailed analysis of the problem shows that it can also be used to detect and recognize emotions and activity. In this paper, we present several architectures for the analysis of electrocardiogram (ECG) data. Such architectures could be used for activity and emotion detection in augmentative and alternative communication (AAC) schemes. The use of deep learning algorithms shows the potential of using popular wearable devices and their usefulness for this analysis. Here, we present the preliminary results of our analysis, which provide promising conclusions on the application of a complete AAC framework. The presented methodology is applicable for the classification of the ECG data and can be further used with wearable devices. The conclusions and results of the study presented allow us to investigate real-time data analysis with a wearable device to predict certain cardiac conditions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wearable technology

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Internet of things-based automation design and organizational innovation of manufacturing enterprises

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Materials Today: Proceedings 2022年 56卷 1769-1775页

作者： Babu, T. Roopa, H. Shukla, Arvind Kumar Stalin David, D. Jayadatta, S. Rajesh, A.S. Department of Instrumentation and Control Engineering St. Joseph's College of Engineering Chennai India Department of Information Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Application IFTM University Moradabad India Department of CSE IFET College of Engineering Villupuram India Department of Management Studies KLE's IMSR BVB Campus Hubli India Department of Mechanical Engineering JSS S&T University Mysore India

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing *** support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies. © 2021

关键词： Product design

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Fuquan Wu, Yu Xu, Xinxin Wang, Chuansheng Ma, Minjia Grau, Antoni Huang, Jiayan School of Computer and Big Data Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (纸本)9798350386226

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices. © 2024 IEEE.

关键词： Electrocardiography

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Qiang Zhang, Shuxin Dong, Suyu Xu, Long Dong, Weihe Li, Xiaokun Sun, Pengzhong Jiang, Feng Zhang, Xianyu Luo, Gongning Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Medicine and Health Harbin China Heilongjiang University School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Medical University Cancer Hospital Department of Breast Surgery Harbin China King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences & Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9798350337488

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level. © 2023 IEEE.

关键词： gene identification graph convolutional network inductive matrix completion lncRNA-disease association

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Static Deadlock Detection for Interrupt-driven Embedded Software

Static Deadlock Detection for Interrupt-driven Embedded Soft...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Yu Zhang Kaiwen Zhang Xiaofeng Li Yi Xie Jian Guan Bin Gu Guanjun Liu Department of Computer Science and Technology Tongji University Shanghai China Beijing Control Engineering Institute Beijing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

Interrupt-driven embedded software is widely used in safety-critical systems, where any occurrence of errors can lead to serious consequences. Deadlock is a common concurrency error, and deadlock detection methods are mainly designed for multi-threaded programs. However, due to the differences in concurrency control and preemption mechanisms between interrupt-driven programs and multi-threaded programs, the existing deadlock detection methods for multi-threaded programs cannot be directly applied to interrupt-driven programs. A static deadlock detection method is proposed for interrupt-driven programs to enhance the reliability and safety of interrupt-driven embedded software. Building upon the traditional context-sensitive lockset analysis, this method takes into account the concurrent semantics of interrupts and extends the lock graph model. Our method effectively reduces false positives by analyzing the concurrent relationships of cycles in a lock graph. Based on these methods, we develop a deadlock detection tool, and experiments are conducted with it. The results demonstrate that the proposed method can effectively detect deadlocks related to interrupts.

关键词： Concurrent computing Analytical models Semantics Buildings System recovery Software reliability Safety Social intelligence Embedded software Context modeling

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A Collaborative and Distributed Learning-Based Solution to Autonomously Plan computer Networks

A Collaborative and Distributed Learning-Based Solution to A...

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Doriana Monaco Alessio Sacco Enrico Alberti Guido Marchetto Flavio Esposito Department of Control and Computer Engineering Politecnico di Torino Italy Computer Science Department Saint Louis University USA

The high programmability provided by Software Defined Networking (SDN) paradigm facilitated the integration of Machine Learning (ML) methods to design a new family of network management schemes. Among them, we can cite self-driving networks, where ML is used to analyze data and define strategies that are then translated into network configurations by the SDN controllers, making the networks autonomous and capable of auto-scaling decisions based on the network’s needs. Despite their attractiveness, the centralized design of the majority of proposed solutions cannot keep up with the increasing size of the network. To this end, this paper investigates the use of a multi-agent reinforcement learning (MARL) model for auto-scaling decisions in an SDN environment. In particular, we study two possible alternatives for distributing operations: a collaborative one, where controllers share the same observations, and an individual one, where controllers make decisions according to their own logic and share only some basic information, such as the network topology. After an experimental campaign performed both on Mininet and GENI, results showed that both approaches can guarantee high throughput while minimizing the set of active resources.

关键词： Technological innovation computer aided instruction Network topology Distance learning Design methodology Conferences Collaboration

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

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engineering 2023年第1期20卷 77-95页

作者： Jun Liu Kangli Dong Yi Sun Ioannis Kakkos Fan Huang Guozheng Wang Peng Qi Xing Chen Delin Zhang Anastasios Bezerianos Yu Sun Key Laboratory for Biomedical Engineering of Ministry of Education of China Zhejiang UniversityHangzhou 310007China Department of Neurology Sir Run Run Shaw HospitalSchool of MedicineZhejiang UniversityHangzhou 310020China School of Electrical and Computer Engineering National Technical University of AthensAthens 15780Greece Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 200092China Department of Anesthesiology The First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China The N1 Institute for Health Center for Life SciencesNational University of SingaporeSingapore 117456Singapore

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

关键词： Anesthesia Brain network Connectivity Graph theoretical analysis Clinical monitoring system

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An Integrated Observer Framework Based Mechanical Parameters Identification for Adaptive control of Permanent Magnet Synchronous Motor

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Complex System Modeling and Simulation 2022年第4期2卷 354-367页

作者： Zhong Liao Zhaohua Liu Lei Chen Mingyang Lyu Dian Wang Faming Wu Zhengheng Wang Hualiang Wei the School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China the Wind Power Business Division CRRC Zhuzhou Institute Co.Ltd.Zhuzhou 412001China. the Department of Automatic Control and Systems Engineering University of SheffieldSheffieldS13JDUK.

An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this ***,an integrated observer framework is established for mechanical parameters’estimation,which consists of an extended sliding mode observer(ESMO)and a Luenberger *** at minimizing the influence of parameters coupling,the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer *** obtaining estimates of the mechanical parameters,the optimal proportional integral(PI)parameters of the speed-loop are determined according to third-order best design *** a result,the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the ***,the disturbance is compensated according to the ***,the experiments were carried out on simulation platform,and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.

关键词： parameter identification permanent magnet synchronous motor adaptive control extended sliding mode observer Luenberger observer

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