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An Antiswing Positioning controller for Rotary Cranes 7

An Antiswing Positioning Controller for Rotary Cranes

7th IEEE Annual International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2017

作者： Yang, Tong Sun, Ning Qian, Yuzhe Fang, Yongchun College of Computer and Control Engineering China

ISBN: (纸本)9781538604892

In this paper, a new gravity terms compensated proportional-derivative controller (GTC-PD) is proposed for underactuated rotary crane systems. In particular, an energy storage function, consisting of kinetic and potential energies of rotary crane systems, is constructed, and on the basis of that, the controller is developed which can achieve accurate rotation positioning and load swing suppression at the same time without any approximation or linearization for the nonlinear dynamics. Furthermore, rigorous stability analysis is proved in theory. Numerical simulations are presented to validate the satisfactory performance of the new nonlinear GTC-PD controller for rotary crane systems. © 2017 IEEE.

关键词： controllers

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Iterative learning based fault detection and estimation in nonlinear systems

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Journal of Systems engineering and Electronics 2012年第3期23卷 419-424页

作者： Wei Cao Wang Cong Ming Sun College of Automation Harbin Engineering University Harbin 150001 E R. China College of Computer and Control Engineering Qiqihar University Qiqihar 161006 E R. China

Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detec- tion, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the difference- value signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking con- vergent speed than the proportional-type （P-type） algorithm, but also calculates more simply than the proportional-derivative-type （PD-type） algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.

关键词： iterative learning nonlinear system fault detection fault estimation.

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Application of detecting algorithm based on network

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Journal of Harbin Institute of Technology(New Series) 2004年第3期11卷 275-280页

作者：张凤斌杨永田江子扬孙冰心 College of Computer Science and Technology Harbin Engineering University Harbin 150001China Computer and Control College Harbin University of Science and Technology Harbin 150080China College of Computer Science and Technology Harbin Engineering University Harbin 150001China Computer and Control College Harbin University of Science and Technology Harbin 150080China

Because currently intrusion detection systems cannot detect undefined intrusion behavior effectively, according to the robustness and adaptability of the genetic algorithms, this paper integrates the genetic algorithms into an intrusion detection system, and a detection algorithm based on network traffic is proposed. This algorithm is a real-time and self-study algorithm and can detect undefined intrusion behaviors effectively.

关键词： network security intrusion detection anomaly

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Leaderless and leader-following consensus of linear multi-agent systems

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Chinese Physics B 2014年第11期23卷 136-144页

作者：张晓娇崔宝同娄柯 School of Computer Science and Control Engineering North University of China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University College of Electrical Engineering Anhui Polytechnic University

This paper studies the consensus problems for multi-agent systems with general linear and nonlinear dynamics. The leaderless and leader-following consensus problems are investigated respectively. Contraction theory is employed to gen- erate some sufficient conditions for testing the agents reaching consensus. Under these conditions and certain assumptions, the trajectories of multi-agent systems in directed topology will converge to each other. Finally, two numerical examples are given to illustrate the effectiveness of the proposed results,

关键词： linear multi-agent systems contraction theory virtual system partial contraction

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SSIG: Single-Sample Information Gain Model for Integrating Multi-Omics Data to Identify Cancer Subtypes

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Chinese Journal of Electronics 2021年第2期30卷 303-312页

作者： ZHANG Yuanyuan WANG Ziqi WANG Shudong KOU Chuanhua School of Information and Control Engineering Qingdao University of Technology College of Computer Science and Technology China University of Petroleum (East China)

Different living environments of cancer samples lead to different molecular mechanisms of cancer development, which in turn leads to different cancer subtypes. How to identify cancer subtypes is a key issue for the realization of precision medicine. With the development of high-throughput technologies, multi-omics data which can better understand different causes of cancer have emerged. However, the current methods of analyzing cancer subtypes using multi-omics data is mostly derived from population cancer sample data and ignores the differences between different cancer ***, the joint analysis of multi-omics based on a single sample may reveal more information about the differences between individual cancers. A strategy for identifying cancer subtypes is proposed based on Single-sample information gain(SSIG) which construct sample feature matrix by considering the heterogeneity of sample. Applying this strategy to current popular subtype identification methods, cancer subtypes can be identified more accurately and the mechanism of cancer can be found from the perspective of a single sample. By comparing different methods in different clustering measure, and using survival analysis, it is shown that SSIG is more suitable for cancer subtype identification than the original multi-omics data, and it is easier to mine the cancer subtype classification mechanism hidden behind the data.

关键词： identify cancer subtypes SSIG medical computing cancer subtype classification mechanism Single-sample information gain model different cancer subtypes original multiomics data genetics cancer subtype identification current popular subtype identification methods population cancer sample data different cancer samples data analysis cancer development genomics cancer sample feature matrix different molecular mechanisms different living environments bioinformatics individual cancers different clustering measure

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Observer-based finite-time control of conformable Takagi–Sugeno fuzzy systems with time delay

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Asian Journal of control 2025年

作者： Kahouli, Omar Gassara, Hamdi Aloui, Ali Elleuch, Zied Bouteraa, Yassine Department of Electronics Engineering Community College University of Ha'il Ha'il Saudi Arabia Laboratory of Science and Technology of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Department of Computer Science Community College University of Ha'il Ha'il Saudi Arabia Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

This paper examines finite-time (F-T) observer-based control of fractional-order nonlinear systems with time delay, employing the Takagi–Sugeno fuzzy (T-SF) approach. This study utilizes the conformable fractional derivative (CFD) as its foundational framework. Sufficient conditions are formulated to guarantee that the augmented vector, consisting of the state estimates and the state estimation errors, stays within a ball centered at the origin. This applies not only to specified initial conditions but also to predefined disturbances. These conditions are then reformulated as linear matrix inequalities (LMIs) conditions, which can be efficiently solved using the LMI toolbox. An illustrative example is given to illustrate the proposed result. © 2025 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Delay-sensitive applications

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A MATHEMATICAL APPROACH TO DESYNCHRONIZATION OF COUPLED OSCILLATORS： APPLICATION TO A NEURONAL ENSEMBLE

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International Journal of Biomathematics 2013年第2期6卷 173-189页

作者： GHAZAL MONTASERI ARAS ADHAMI-MIRHOSSEINI MOHAMMAD JAVAD YAZDANPANAH Advanced Control Systems Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Advanced Control Systems LaboratoryControl and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Synchronization of neurons plays an important role in vision, movement and memory. However, many neurological disorders such as epilepsies, Parkinson disease and essen- tial tremor are related to excessive synchronization of neurons. In the line of therapy, stimulations to these pathologically synchronized neurons should be capable of breaking synchrony. As the first step of designing an effective stimulation, we consider desynchro- nization problem of coupled limit-cycle oscillators ensemble. First, the desynchronization problem is redefined in a nonlinear output regulation framework. Then, we design an output regulator （stimulation） which forces limit-cycle oscillators to track exogenous sinusoidal references with different phases. The proposed stimulation is robust against variations of oscillators＇ frequencies. Mathematical analysis and simulation results reveal the efficiency of the proposed technique.

关键词： Limit-cycle oscillators nonlinear output regulation synchrony suppression robust stimulation.

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Evolutionary Games with Different Time Scales of Strategy Updating 29

Evolutionary Games with Different Time Scales of Strategy Up...

引用

第29届中国控制与决策会议

作者： Jianlei Zhang Yuying Zhu Chunyan Zhang Department of Automation College of Computer and Control EngineeringNankai University Tianjin Key Laboratory of Intelligent Robotics College of Computer and Control EngineeringNankai University

ISBN: (纸本)9781509046584

Variation in learning rates within populations suggests that evolutionary game theory may not necessarily be restricted to uniform time scales associated with the game interaction and strategy adaption evolution. Using agent-based evolutionary simulation in the framework of prisoner's dilemma game, we demonstrate the sustainability of cooperation and the emergence of different macro-effects, when equipping agents with non-uniform time scale preferences. We employ a spatial random regular grid to describe the social interactions among agents. We conclude that the cooperation level has a strong dependence on the population composition, and the suitable fraction of the fast-updating players in the system which is associated with the maximal cooperation frequency has been found out. Besides, the extent of the promotive effect of diversifying time scales is also closely related with the payoff adoption rules in strategy updating,especially when we invent a past history for each agent. Summing up the gained results, a general conclusion can be drawn, saying that the combination of these factors(e.g. time scales and memory) gives rise to rich dynamic behavior of the system.

关键词： Evolutionary Game Theory Complex Networks Evolution Dynamics

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