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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Su, Cheng-Hua Ko, Li-Wei Jung, Tzyy-Ping Onton, Julie Tzou, Shey-Cherng Juang, Jia-Chi Hsu, Chung-Yao Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Department of Biomedical Science and Environmental Biology Kaohsiung807 Taiwan Institute for Neural Computation Institute of Engineering in Medicine University of California San Diego La Jolla CA92093 United States College of Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute for Neural Computation University of California San Diego La Jolla CA92093 United States Kaohsiung Medical University Hospital Sleep Disorders Center Department of Neurology Kaohsiung807 Taiwan College of Medicine Kaohsiung Medical University Department of Neurology Kaohsiung807 Taiwan

ISBN: (纸本)9781665410205

Sleep quality is critical for human well-being. Lack of sleep and poor sleep quality impair daily cognitive functions and health. While stress has been recognized as a detrimental factor on sleep quality, the relationship between pre-sleep stress level, resting EEG and subsequent sleep structure remains to be explored. This study presents a novel approach that evaluates pre-sleep stress levels using a 2-channel EEG to predict slow-wave sleep (SWS) deficiency. We recorded forehead EEG immediately before sleep onset, then utilized power spectra and entropy analysis to extract stress-related neurological features, including beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn), and spectral entropy (SpEn). We found that individuals with SWS deficiency exhibited signs of stress, such as a robust beta/delta correlation, higher alpha asymmetry, and increased FuzzEn. Conversely, individuals with ample SWS displayed weak beta/delta correlation and reduced FuzzEn in EEG recordings. Finally, we tested the robustness of the selected neuro markers with two supervised learning models and found that the selected markers predict SWS deficiency with an accuracy above 70%. Our study demonstrated that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. The proposed method can be integrated with a portable EEG device and sleep-improving interventions to develop a personalized sleep-improvement solution. © 2024 IEEE.

关键词： Sleep research

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A predictive model of death from cerebrovascular diseases in intensive care units

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Intelligent Medicine 2023年第4期3卷 267-279页

作者： Mohammad Karimi Moridani Seyed Kamaledin Setarehdan Ali Motie Nasrabadi Esmaeil Hajinasrollah Department of Biomedical Engineering Faculty of HealthTehran Medical SciencesIslamic Azad UniversityTehranIran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer EngineeringCollege of EngineeringUniversity of TehranTehranIran Departments of Biomedical Engineering Shahed UniversityTehranIran Loghman Medical Center Shahid Beheshti University of Medical SciencesTehranIran

Objective This study aimed to explore the mortality prediction of patients with cerebrovascular diseases inthe intensive care unit(ICU)by examining the important signals during different periods of admission in theICU,which is considered one of the new topics in the medical *** approaches have been proposed forprediction in this *** of these methods has been able to predict mortality somewhat,but many of thesetechniques require recording a large amount of data from the patients,where recording all data is not possiblein most cases;at the same time,this study focused only on heart rate variability(HRV)and systolic and diastolicblood *** The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring inIntensive Care II(MIMIC-II)Clinical *** proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients,48 men and 40 women,during their first 48 hours of ICU *** electrocardiogram(ECG)signals are related to lead II,and the sampling frequency is 125 *** time of admission and time ofdeath are labeled in all *** this study,the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood *** the patient’s future condition,the combination of features extracted from the return mapping generatedby the HRV signal,such as angle(𝛼),area(A),and various parameters generated by systolic and diastolic bloodpressure,including DBPMax−Min SBPSD have been ***,to select the best feature combination,the geneticalgorithm(GA)and mutual information(MI)methods were *** sample t-test statistical analysis was usedto compare the results of two episodes(death and non-death episodes).The P-value for detecting the significancelevel was considered less than *** The results indicate that the new approach presented in this paper can be compared with other methodsor l

关键词： Death prediction Cerebrovascular diseases Intensive care unit Heart rate variability Systolic and diastolic blood pressure Return map

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An Actor-Critic Reinforcement Learning Scheme for Reactive 3D Optimal Motion Planning Based on Fluid Dynamics

An Actor-Critic Reinforcement Learning Scheme for Reactive 3...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Marios Malliaropoulos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing a closed-loop, smooth and natural-looking navigation solution. Special emphasis is given to an inherent challenge of artificial potential field (APF) methods, namely establishing proofs of safety and stability over the entire optimization process. A model-based actor-critic reinforcement learning algorithm is introduced to approximate the optimal solution to the Hamilton-Jacobi-Bellman equation and update the controller parameters in a deterministic manner. Through a series of ROS-Gazebo software-in-the-loop simulations the proposed methodology demonstrates robustness and outperforms widely used methods such as the RRT ∗ , highlighting its contribution to the field of 3D optimal motion planning.

关键词： Solid modeling Three-dimensional displays Navigation Reinforcement learning Mathematical models Stability analysis Robustness Planning Safety Optimization

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Compressive Sensing-Based Reconstruction of Lissajous-Like Nodding Lidar Data

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ASME Letters in Dynamic Systems and control 2021年第1期1卷 011009页

作者： Benson, Michael T. Sathishchandra, Harish Clayton, Garrett M. Andersson, Sean B. Department of Mechanical Engineering Villanova University VillanovaPA19085 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Center for Nonlinear Dynamics and Control Department of Mechanical Engineering Villanova University VillanovaPA United States Department of Mechanical Engineering Division of Systems Engineering Boston University BostonMA United States

In this article, a compressive sensing-based reconstruction algorithm is applied to data acquired from a nodding multibeam Lidar system following a Lissajous-like trajectory. Multibeam Lidar systems provide 3D depth information of the environment, but the vertical resolution of these devices may be insufficient in many applications. To mitigate this issue, the Lidar can be nodded to obtain higher vertical resolution at the cost of increased scan time. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally subsample the imaged area. In this article, a compressive sensing-based reconstruction algorithm is applied to the data collected during a relatively fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare the reconstructions to those made using simple nearest-neighbor interpolation. © 2021 ASME Letters in Dynamic Systems and control. All rights reserved.

关键词： Compressed sensing

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Adaptive Active Fault-Tolerant Attitude control for Spacecraft with Adaptive Prescribed Performance

Adaptive Active Fault-Tolerant Attitude Control for Spacecra...

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International Symposium on Autonomous Systems (ISAS)

作者： Ze Yang Baoqing Yang Ruihang Ji Jie Ma Control and Simulation Center Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering National University of Singapore Singapore

For spacecraft attitude control affected by environmental disturbance, parameter uncertainty and actuator fault, a novel composite active fault-tolerant scheme, combining a strong tracking Cubature Kalman filter (STCKF) with adaptive prescribed performance control (APPC), is investigated in this paper. The proposed STCKF is capable of estimating lumped fault rapidly but accurately, and it is robust to model uncertainty. An adaptive finite-time prescribed performance function (APPF) whose boundaries can be flexibly adjusted in the case of actuator faults is proposed. Then an active fault tolerant controller is designed using nonsingular terminal sliding mode control (NTSMC) in conjunction with APPF. Simulation experiments and comparisons show that the proposed strategy exhibits better fault tolerance, lower conservatism and better steady-state performance.

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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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Game Theory for Autonomy: From Min-Max Optimization to Equilibrium and Bounded Rationality Learning

Game Theory for Autonomy: From Min-Max Optimization to Equil...

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American control Conference (ACC)

作者： Kyriakos G. Vamvoudakis Filippos Fotiadis João P. Hespanha Raphael Chinchilla Guosong Yang Mushuang Liu Jeff S. Shamma Lacra Pavel Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Center for Control Dynamical Systems and Computation University of California Santa Barbara CA Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA Department of Mechanical and Aerospace Engineering University of Missouri Columbia MO USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign IL USA Department of Electrical and Computer Engineering University of Toronto Canada

Finding Nash equilibria in non-cooperative games can be, in general, an exceptionally challenging task. This is owed to various factors, including but not limited to the cost functions of the game being nonconvex/nonconcave, the players of the game having limited information about one another, or even due to issues of computational complexity. The present tutorial draws motivation from this harsh reality and provides methods to approximate Nash or min-max equilibria in non-ideal settings using both optimization- and learning-based techniques. The tutorial acknowledges, however, that such techniques may not always converge, but instead lead to oscillations or even chaos. In that respect, tools from passivity and dissipativity theory are provided, which can offer explanations about these divergent behaviors. Finally, the tutorial highlights that, more frequently than often thought, the search for equilibrium policies is simply vain; instead, bounded rationality and non-equilibrium policies can be more realistic to employ owing to some players’ learning imperfectly or being relatively naive – "bounded rational." The efficacy of such plays is demonstrated in the context of autonomous driving systems, where it is explicitly shown that they can guarantee vehicle safety.

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Infrastructure-dependent ramp-metering control for METANET-s

Infrastructure-dependent ramp-metering control for METANET-s

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IEEE Conference on Decision and control

作者： Ayda Kamalifar Carlo Cenedese Michele Cucuzzella Antonella Ferrara Department of Electrical Computer Science and Biomedical Engineering University of Pavia Pavia Italy Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Nijenborgh 4 Groningen the Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we propose a novel infrastructure-dependent ramp-metering control for the recently proposed METANET with service station (METANET-s) model, i.e., a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. We study the effect of a ramp-metering control scheme on a highway stretch with a service station and show that it is capable of actively regulate internal traffic demand attempting to exit the service station via its on-ramp, on top of contributing to decrease the traffic congestion on the mainstream. In fact, the proposed control scheme effectively prevents the backlog of vehicles attempting to merge back onto the mainstream. This dynamic control mechanism is further endowed by a route guidance control strategy increasing the share of vehicles stopping at the service station during main-stream congestion periods, e.g. via incentives. The combined effect of our control schemes allows to take full advantage of the presence of service stations, reducing the overall traffic congestion. Simulation results demonstrate the effectiveness of the proposed control strategies.

关键词： Simulation Merging Numerical models Vehicle dynamics Traffic congestion

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A survey on user-centric cell-free massive MIMO systems

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Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ke Chen Xiaosong Han Xiaoran Li Yanchun Liang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai College of Science and Technology Zhuhai China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

关键词： Drugs Deep learning Adaptation models Visualization Biological system modeling Noise Knowledge graphs Predictive models Fingerprint recognition Transformers

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