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Cyber-Enabled Well-Being Oriented Daily Living Support Based on Personal Data analysis

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2020年第2期8卷 493-502页

作者： Kasuya, Seiji Zhou, Xiaokang Tago, Kiichi Nishimura, Shoji Jin, Qun Waseda Univ Grad Sch Human Sci 2-579-15 Mikajima Tokorozawa Saitama 3591192 Japan Shiga Univ Fac Data Sci 1-1-1 Banba Hikone Shiga 5228522 Japan Waseda Univ Fac Human Sci 2-579-15 Mikajima Tokorozawa Saitama 3591192 Japan

We are living in a cyber-physical-social environment with a variety of lifestyles and values. Living support has become important in such a diverse society. Owing to the ability to collect a large amount of personal data or life logs in the cyber-physical-social environment, it is now possible for us to provide living support based on personal data analysis. Moreover, analyzing such data can facilitate a deep understanding of an individual. In this study, we focus on the provision of cyber-enabled well-being oriented daily living support for an individual based on personal data analysis. Three categories of personal data are identified from an individual's daily life data. In this paper, we discuss the basic concept, model, and framework for well-being oriented personal data analysis in order to offer suggestions and advices to improve the living quality of an individual. Finally, we report a feasibility study with an application scenario by using personal and environmental data.

关键词： Data analysis Prediction algorithms Feature extraction Data mining Collaboration Sensors algorithm design and analysis Personal data analytics well-being oriented living support data-driven behavior analysis cyber-enabled application

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Objective Assessment of Beat Quality in Transcranial Doppler Measurement of Blood Flow Velocity in Cerebral Arteries

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第3期67卷 883-892页

作者： Jalaleddini, Kian Canac, Nicolas Thorpe, Samuel G. O'Brien, Michael J. Ranjbaran, Mina Delay, Benjamin Dorn, Amber Y. Scalzo, Fabien Thibeault, Corey M. Wilk, Seth J. Hamilton, Robert B. Neural Analyt Inc Los Angeles CA 90064 USA Univ Calif Berkeley Dept Neurol Berkeley CA 94720 USA

Objective: Transcranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. Similar to other real-time measurement modalities, especially in healthcare, the identification of high-quality signals is essential for clinical interpretation. Our goal is to identify poor quality beats and remove them prior to further analysis of the TCD signal. Methods: We selected objective features for this purpose including Euclidean distance between individual and average beat waveforms, cross-correlation between individual and average beat waveforms, ratio of the high-frequency power to the total beat power, beat length, and variance of the diastolic portion of the beat waveform. We developed an iterative outlier detection algorithm to identify and remove the beats that are different from others in a recording. Finally, we tested the algorithm on a dataset consisting of more than 15 h of TCD data recorded from 48 stroke and 34 in-hospital control subjects. Results: We assessed the performance of the algorithm in the improvement of estimation of clinically important TCD parameters by comparing them to that of manual beat annotation. The results show that there is a strong correlation between the two, that demonstrates the algorithm has successfully recovered the clinically important features. We obtained significant improvement in estimating the TCD parameters using the algorithm accepted beats compared to using all beats. Significance: Our algorithm provides a valuable tool to clinicians for automated detection of the reliable portion of the data. Moreover, it can be used as a pre-processing tool to improve the data quality for automated diagnosis of pathologic beat waveforms using machine learning.

关键词： Signal processing algorithms Euclidean distance Arteries Iterative algorithms Correlation Morphology Biomedical signal processing ultrasonography signal processing algorithms change detection algorihms heuristic algorithms algorithm design and analysis

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Sparse Bayesian Learning With Dynamic Filtering for Inference of Time-Varying Sparse Signals

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 388-403页

作者： O'Shaughnessy, Matthew R. Davenport, Mark A. Rozell, Christopher J. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

Many signal processing applications require estimation of time-varying sparse signals, potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an algorithm for dynamic filtering of time-varying sparse signals based on the sparse Bayesian learning (SBL) framework. The key idea underlying the algorithm, termed SBL-DF, is the incorporation of a signal prediction generated from a dynamics model and estimates of previous time steps into the hyperpriors of the SBL probability model. The proposed algorithm is online, robust to imperfect dynamics models (due to the propagation of dynamics information through higher-order statistics), robust to certain undesirable dictionary properties such as coherence (due to properties of the SBL framework), allows the use of arbitrary dynamics models, and requires the tuning of fewer parameters than many other dynamic filtering algorithms do. We also extend the fast marginal likelihood SBL inference procedure to the informative hyperprior setting to create a particularly efficient version of the SBL-DF algorithm. Numerical simulations show that SBL-DF converges much faster and to more accurate solutions than standard SBL and other dynamical filtering algorithms. In particular, we show that SBL-DF outperforms state of the art algorithms when the dictionary contains the challenging coherence and column scaling structure found in many practical applications.

关键词： algorithm design and analysis signal processing algorithms Kalman filters heuristic algorithms system dynamics

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An Efficient Single-Parameter Scaling Memoryless Broyden-Fletcher-Goldfarb-Shanno algorithm for Solving Large Scale Unconstrained Optimization Problems

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IEEE ACCESS 2020年 8卷 85664-85674页

作者： Lv, Jing Deng, Songhai Wan, Zhong Cent South Univ Sch Math & Stat Changsha 410083 Peoples R China

In this paper, a new spectral scaling memoryless Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is developed for solving large scale unconstrained optimization problems, where the scaling parameter is chosen so as to minimize all the eigenvalues of search direction matrices. The search directions in this algorithm are proved to satisfy the approximate Dai-Liao conjugate condition. With this advantage of the search directions, a scaling memoryless BFGS update formula is constructed and an algorithm is developed by incorporating acceleration strategy of line search and restart criterion. Under mild assumptions, global convergence of the algorithm is proved. Numerical tests demonstrate that the developed algorithm is more robust and efficient in solving large scale benchmark test problems than the similar ones in the literature.

关键词： Computational efficiency convergence of numerical methods optimization methods algorithm design and analysis

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Optimum Partition for Wireless Charging Scheduling in Wireless Sensor Networks With Applications

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INTERNATIONAL JOURNAL OF GRID AND HIGH PERFORMANCE COMPUTING 2022年第1期14卷 1页

作者： Zhang, Zuoli Hu, Wenfei Peng, TungHsien Zheng, Zexiang Wenzhou Polytech Dept Elect & Elect Engn Wenzhou Peoples R China Wenzhou Polytech Dept Comp & Informat Engn Wenzhou Peoples R China Chung Hua Univ Informat Coll Hsinchu Taiwan Wenzhou Polytech Wenzhou Peoples R China

Previous research on wireless charging did not discuss the problem of using optimal allocation to improve the network life cycle. The authors designed and analyzed a variety of network area cutting methods: rectangular cutting, square cutting, concentric circle cutting, sector cutting, and mixed cutting. Through simulation experiments, the difference between the survival time and the number of received packets is compared. The experimental results show that mixed cutting can calculate the expected energy consumption according to the energy consumption rate, and then allocate chargers according to the expected energy consumption, making the energy consumption burden of each charger more equitable. Compared with other partition methods, the load capacity distribution of each charger is more uniform. In terms of survival time and receiving message packets, the network can have a longer survival time, receive more message packets, and use the power consumption of each block more evenly and effectively.

关键词： algorithm design and analysis automatic charging energy network lifetime partition scheduling wireless charging wireless power transmission wireless sensor networks

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AI-Driven Cybersecurity Threats to Future Networks

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IEEE VEHICULAR TECHNOLOGY MAGAZINE 2020年第3期15卷 5-6页

作者： Senouci, Sidi-Mohammed Sedjelmaci, Hichem Liu, Jiajia Rehmani, Mubashir Husain Bou-Harb, Elias Univ Paris XI Comp Sci Orsay France Inst Natl Polytech Toulouse Toulouse France Orange Labs Cybersecur & Artificial Intelligence AI Lannion France Orange Labs Lannion France Northwestern Polytech Univ Sch Cybersecur Xian Shaanxi Peoples R China Cork Inst Technol Dept Comp Sci Cork Ireland Univ Texas San Antonio Cyber Ctr Secur & Analyt San Antonio TX USA

The articles in this special section focus on artificial intelligence-driven cybersecurity threats to future networks. Future-generation networks (5G and beyond 5G) will include a variety of services for various verticals, such as enhanced mobile broadband, health monitoring, Industry 4.0, smart energy distribution, and automotive networks. These vertical services and the critical components that comprise 5G architecture (e.g., radio access and edge and core networks) exhibit several cybersecurity vulnerabilities that attract attackers to use all their capabilities to exploit and hence shut down these networks. Recently, a new generation of smart threats, defined as artificial intelligence (AI ) attacks, has appeared. These smart attacks either turn AI into weapons to attack 5G services or hack the AI algorithms used by 5G components. In the first misbehavior, attackers take advantage of AI’s improved ability to launch lethal and stealthy threats against attractive targets, e.g., autonomous vehicles, drones, or manufacturing machinery. In the second misbehavior, attackers hack machine learning (ML) algorithms by modifying, for instance, the labels of the ML classification functions and altering the training data, causing a decrease in the accuracy of the classification rate.

关键词： Special issues and sections Artificial intelligence Computer security Computer hacking Machine learning algorithm design and analysis 5G mobile communication Man-machine systems Human computer interaction Autonomous vehicles

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Introduction to the Special Issue on Nature-Inspired Optimization Methods in Fuzzy Systems

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第6期28卷 1019-1022页

作者： Slowik, Adam Cpalka, Krzysztof Jin, Yaochu Koszalin Univ Technol Dept Elect & Comp Sci PL-75453 Koszalin Poland Czestochowa Tech Univ Inst Computat Intelligence PL-42201 Czestochowa Poland Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

The papers in this special issue focus on nature-inspired optimization methods in fuzzy systems. These methods which are very often used to solve complex optimization problems that cannot be efficiently solved by traditional optimization algorithms. Optimization of fuzzy systems is also a complex optimization task involving continuous, integer, and combinatorial problems. For example, selection of input attributes of the fuzzy system, design of fuzzy system structure, selection of membership functions, and selection of inference operators can be seen as combinatorial optimization problems, whereas selection of the parameters in membership functions and fuzzy rules are continuous optimization problem. In addition, optimization of a fuzzy system becomes a multiobjective optimization problems when we take both interpretability and accuracy of the fuzzy systems into account. Thus, applications of nature-inspired optimization.

关键词： Special issues and sections Fuzzy systems algorithm design and analysis Optimization methods Fuzzy logic Inference algorithms Particle swarm optimization

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Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress

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Chronic Stress 2023年 7卷

作者： Goel, Rahul Tse, Teresa Smith, Lia J. Floren, Andrew Naylor, Bruce Williams, M. Wright Salas, Ramiro Rizzo, Albert S. Ress, David Department of Neuroscience Baylor College of Medicine Houston TX United States Department of Psychology University of Houston Houston TX United States Center for Translational Research on Inflammatory Diseases Michael E. DeBakey VA Medical Center Houston TX United States Department of Electrical and Computer Engineering University of Texas at Austin Austin TX United States Department of Psychiatry and Behavioral Sciences Baylor College of Medicine Houston TX United States The Menninger Clinic Houston TX United States Institute for Creative Technologies University of Southern California Los Angeles CA United States

Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically supported treatments have demonstrated reductions in PTSD symptomatology, there remains a need to improve treatment effectiveness. Functional magnetic resonance imaging (fMRI) neurofeedback has emerged as a possible treatment to ameliorate PTSD symptom severity. Virtual reality (VR) approaches have also shown promise in increasing treatment compliance and outcomes. To facilitate fMRI neurofeedback-associated therapies, it would be advantageous to accurately classify internal brain stress levels while Veterans are exposed to trauma-associated VR imagery. Methods: Across 2 sessions, we used fMRI to collect neural responses to trauma-associated VR-like stimuli among male combat Veterans with PTSD symptoms (N = 8). Veterans reported their self-perceived stress level on a scale from 1 to 8 every 15 s throughout the fMRI sessions. In our proposed framework, we precisely sample the fMRI data on cortical gray matter, blurring the data along the gray-matter manifold to reduce noise and dimensionality while preserving maximum neural information. Then, we independently applied 3 machine learning (ML) algorithms to this fMRI data collected across 2 sessions, separately for each Veteran, to build individualized ML models that predicted their internal brain states (self-reported stress responses). Results: We accurately classified the 8-class self-reported stress responses with a mean (± standard error) root mean square error of 0.6 (± 0.1) across all Veterans using the best ML approach. Conclusions: The findings demonstrate the predictive ability of ML algorithms applied to whole-brain cortical fMRI data collected during individual Veteran sessions. The framework we have developed to preprocess whole-brain cortical fMRI data and train ML models across sessions would provide a valuable tool to enable individualized real-

关键词： algorithm design and analysis behavioral sciences functional magnetic resonance imaging machine learning algorithms mental disorders posttraumatic stress disorder (PTSD) psychiatry veterans

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Automatic ECG-Based Emotion Recognition in Music Listening

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2020年第1期11卷 85-99页

作者： Hsu, Yu-Liang Wang, Jeen-Shing Chiang, Wei-Chun Hung, Chien-Han Feng Chia Univ Dept Automat Control Engn Taichung 40724 Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan 701 Taiwan

This paper presents an automatic ECG-based emotion recognition algorithm for human emotion recognition. First, we adopt a musical induction method to induce participants' real emotional states and collect their ECG signals without any deliberate laboratory setting. Afterward, we develop an automatic ECG-based emotion recognition algorithm to recognize human emotions elicited by listening to music. Physiological ECG features extracted from the time-, and frequency-domain, and nonlinear analyses of ECG signals are used to find emotion-relevant features and to correlate them with emotional states. Subsequently, we develop a sequential forward floating selection-kernel-based class separability-based (SFFS-KBCS-based) feature selection algorithm and utilize the generalized discriminant analysis (GDA) to effectively select significant ECG features associated with emotions and to reduce the dimensions of the selected features, respectively. Positive/negative valence, high/low arousal, and four types of emotions (joy, tension, sadness, and peacefulness) are recognized using least squares support vector machine (LS-SVM) recognizers. The results show that the correct classification rates for positive/negative valence, high/low arousal, and four types of emotion classification tasks are 82.78, 72.91, and 61.52 percent, respectively.

关键词： Electrocardiography Music Emotion recognition Physiology Feature extraction Multiple signal classification algorithm design and analysis Electrocardiogram emotion recognition music machine learning

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Regret Lower Bounds for Unbiased Adaptive Control of Linear Quadratic Regulators

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IEEE CONTROL SYSTEMS LETTERS 2020年第3期4卷 785-790页

作者： Ziemann, Ingvar Sandberg, Henrik KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-10044 Stockholm Sweden

We present lower bounds for the regret of adaptive control of the linear quadratic regulator. These are given in terms of problem specific expected regret lower bounds valid for unbiased policies linear in the state. Our approach is based on the insight that the adaptive control problem can, given our assumptions, be reduced to a sequential estimation problem. This enables the use of the Cramer-Rao information inequality which yields a scaling limit lower bound of logarithmic order. The bound features both information-theoretic and control-theoretic quantities. By leveraging existing results, we are able to show that the bound is tight in a special case.

关键词： Adaptive control Regulators Convergence Control theory Reinforcement learning Riccati equations Adaptive control machine learning estimation error algorithm design and analysis

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