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Converse negative imaginary theorems

Automatica 2024年 165卷

作者： Khong, Sei Zhen Zhao, Di Lanzon, Alexander Department of Electrical Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Department of Control Science and Engineering & National Key Laboratory of Autonomous Intelligent Unmanned Systems & Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Control Systems Centre Department of Electrical and Electronic Engineering School of Engineering University of Manchester Oxford Road ManchesterM13 9PL United Kingdom

Converse negative imaginary theorems for linear time-invariant systems are derived. In particular, we provide necessary and sufficient conditions for a feedback system to be robustly stable against various types of negative imaginary (NI) uncertainty. Uncertainty classes of marginally stable NI systems and stable strictly NI systems with restrictions on their static or instantaneous gains are considered It is shown that robust stability against the former class entails the strictly NI property, whereas the latter class entails the NI property. We also establish a non-existence result that no stable system can robustly stabilise all marginally stable NI uncertainty, thereby showing that the uncertainty class of NI systems is too large as far as robust feedback stability is concerned, thus justifying the consideration of subclasses of NI systems with constrained static or instantaneous gains. © 2024 Elsevier Ltd

关键词： Linear time-invariant system

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L-band tunable mode-locked fiber laser achieving up to 19 kHz wavelength sweeping rate

L-band tunable mode-locked fiber laser achieving up to 19 kH...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Ye, Guanyu Chow, Kin Kee Liu, Bowen Ma, Yifan Dai, Maolin Shirahata, Takuma Yamashita, Shinji Set, Sze Yun Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo153-8904 Japan Department of Electrical Engineering and Information Systems The University of Tokyo Bunkyo-ku Tokyo113-8656 Japan Department of Engineering Manchester Metropolitan University Chester Street ManchesterM1 5GD United Kingdom

For the first time, we introduce a rapid wavelength-swept L-band tunable mode-locked fiber laser, boasting an exceptional wavelength sweeping rate of up to 19 kHz, enabled by external modulation of the pump current. &... 详细信息

ISBN: (纸本)9781957171395

关键词： Mode-locked fiber lasers

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Machine learning-based evaluation of cancer pain from functional brain images

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological engineering 2021年第Proc期Annual59卷 780-782页

作者： Ogawa, Mayuko Ben, Hui Ono, Yumie Lin, Wenying Electrical Engineering Program Graduate School Science and Technology Meiji University Japan School of Science and Technology Meiji University Japan National Taiwan University Hospital Cancer Center Taiwan

Pain management is one of the important treatments of cancer patients. This study aims to develop a classifier of chronic cancer pain patients from their brain metabolic activity measured by FDG-PET. We compared FDG-PET brain images of 74 painful and 29 painless cancer patients to clarify the brain activity specific to chronic pain of cancer. Using the detected brain activity pattern, we further developed a classifier that determines the presence or absence of chronic pain from PET brain images by machine learning methods. The painful cancer patients showed significantly increased activity in the amygdala, hippocampus, and decreased activity in the cingulate gyrus and precuneus (p © 2021, Japan Soc. of Med. Electronics and Biol. engineering. All rights reserved.

关键词： Diseases

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Evaluation of Chest CT-Scan Anomaly Detection Models Using Principal Component Analysis 25

Evaluation of Chest CT-Scan Anomaly Detection Models Using P...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alyabrodi, Ala Al-Daja, Shrouq Injadat, MohammadNoor Moubayed, Abdallah Elrashidi, Ali Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Data Science & Ai Department Zarqa Jordan King Fahd University of Petroleum & Minerals Research Center for Intelligent Secure Systems Computer Engineering Department and Interdisciplinary Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Machine learning-based improvements in anomaly detection, visualization, and segmentation are made possible by the growing digitization of medical imaging, which reduces the workload for medical specialists. Nevertheless, trustworthy labeled data is necessary for supervised machine learning and is sometimes expensive, time-consuming, or impossible to get. Consequently, techniques that need either little labeling (unsupervised methods) or partial labeling (semi-supervised methods) have been used increasingly been used. One approach that can be used to tackle medical imaging tasks like segmentation and classification is anomaly detection, which makes use of both unsupervised and semi-supervised methods. This study investigates the anomaly detection capabilities of deep learning and machine learning models trained on chest CT scans. To adopt this approach, firstly, by evaluating several feature extraction techniques (Principal Component Analysis (PCA), Autoencoder, and variance threshold), PCA achieves the best results on the selected dataset. After that, different machine learning and deep learning models (Isolation Forest, K-means, feed-forward neural networks, and convolutional neural networks) are assessed to detect anomalies using PCA for dimensionality reduction. The experiments show that the NN-Feedforward model with PCA outperformed the other models, also attains remarkably low false alarm rate, demonstrating its effectiveness in reducing false positives and precisely detecting anomalies. © 2024 IEEE.

关键词： Computerized tomography

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A Novel Metaverse-as-a-Service Architecture from an Operator View

A Novel Metaverse-as-a-Service Architecture from an Operator...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Vesal Ahsani Ali Rahimi Mehdi Letafati Babak Hossein Khalaj Department of Electrical Engineering and Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

In this research paper, the authors present a comprehensive overview of three key elements of Metaverse-as-a-service (MaaS) platforms: privacy and security, edge computing, and blockchain technology. The study begins by examining security concerns for wireless access to the Metaverse. It then delves into privacy and security issues within the Metaverse from data-centric, learning-centric, and human-centric perspectives. The authors propose novel and less-explored methods to aid mobile network operators and Metaverse service providers in creating secure and private MaaS platforms across various layers of the Metaverse, from access to social interactions. The article also explores the potential of edge computing to enhance various aspects of the Metaverse and the challenges associated with its implementation. Furthermore, the study examines the 10 main challenges of MaaS platforms and how blockchain technology can address them. Finally, the authors present future visions and recommendations, such as content-centric security and zero-trust Metaverse, as well as unresolved challenges in blockchain technology to provide insights for network designers in the Metaverse era.

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Fundamental limits of distributed covariance matrix estimation under communication constraints 24

Fundamental limits of distributed covariance matrix estimati...

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Proceedings of the 41st International Conference on Machine Learning

作者： Mohammad Reza Rahmani Mohammad Hossein Yassaee Mohammad Ali Maddah-Ali Mohammad Reza Aref Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran and Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings.

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Criteria-based Unsupervised Feature Selection Techniques: A Comparative Study Over Chest CT Scan 25

Criteria-based Unsupervised Feature Selection Techniques: A ...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Daja, Shrouq Alyabrodi, Ala' Moubayed, Abdallah Injadat, MohammadNoor Elrashidi, Ali Zarqa University Data Science & Ai Department Zarqa Jordan Zarqa University Cyber Security Department Zarqa Jordan King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Research on classifying chest CT scans as normal or abnormal using machine learning and deep learning has garnered significant attention. To address this, various feature selection (FS) methods are employed to reduce dimensionality and identify key features. This paper evaluates criteria tailored to unsupervised binary datasets, such as feature subsets, redundancy, and diversity, by comparing Mutual Information-Based Methods (MI) with Clustering-Based Feature Selection (Agglomerative Clustering) ones. Additionally, it examines Graph Structure, Spectral Properties, and Quality criteria by comparing Graph-Based Methods with Spectral Feature Selection-be (SPEC). For feature reduction, Agglomerative Clustering may offer a slight advantage due to better handling of diversity and redundancy, though the difference is minimal. If these aspects are crucial, Agglomerative Clustering might be preferred. Conversely, for Graph-Based vs. SPEC, SPEC Method is more suitable for feature reduction due to its coherent feature selection and focus on capturing significant patterns related to medical interventions. © 2024 IEEE.

关键词： Contrastive Learning

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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Impaired default mode network activity in cancer pain patients

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological engineering 2021年第Proc期Annual 59卷 644-646页

作者： Ben, Hui Ogawa, Mayuko Ono, Yumie Lin, Wenying Electrical Engineering Program Graduate School Science and Technology Meiji University Japan School of Science and Technology Meiji University Japan National Taiwan University Hospital Cancer Center Taiwan

The mechanism of chronic cancer pain has yet fully elucidated even though nearly 80% of cancer patients suffer from chronic pain. We investigated the difference in brain activities between painful and painless cancer patients from 103 FDG-PET brain images of the head-neck or esophageal cancer patients. Painful cancer patients showed enhanced activity in the bilateral amygdala, hippocampus, and pons, and inhibited activity in the precuneus and middle cingulate gyrus relative to the painless patients. The metabolic connectivity related to the above regions showed a significant relationship with brain regions associated with the default mode network (DMN) in the painful cancer patients, while no significant metabolic connectivity to the DMN regions was found in the painless patients. These results indicate that in cancer pain patients, chronic pain may increase limbic system activity and affect the DMN, leading to changes in the cognition of pain and visceral sensation. © 2021, Japan Soc. of Med. Electronics and Biol. engineering. All rights reserved.

关键词： Brain

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Dual-Objective Mixed Integer Linear program and Memetic Algorithm for an Industrial Group Scheduling Problem

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1199-1209页

作者： Ziyan Zhao Shixin Liu MengChu Zhou Abdullah Abusorrah State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern UniversityShenyang 110819China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

Group scheduling problems have attracted much attention owing to their many practical *** work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due *** is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production *** objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the *** obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy *** computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its *** high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

关键词： Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling

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