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6th International Scientific Conference on information, Control, and Communication Technologies, ICCT 2022

作者： Bogatyrev, Vladimir A. Bogatyrev, Stanislav V. Bogatyrev, Anatoly V. Security Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Saint Petersburg Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia Yadro Cloud Storage Development Center Saint Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

An approach to substantiating the choice of a discipline for the maintenance of a redundant computer system, with the possible use of node resources saved after failures, is considered. The choice is aimed at improving the reliability and profitability of the system, taking into account the operational costs of restoring nodes. Models of reliability of systems with service disciplines are proposed, providing both the possibility of immediate recovery of nodes after failures, and allowing degradation of the system when using node resources stored after failures in it. The models take into account the conditions of the admissibility or inadmissibility of the loss of information accumulated during the operation of the system. The operating costs are determined, taking into account the costs of restoring nodes for the system maintenance disciplines under consideration © 2022 IEEE.

关键词： Fault tolerance

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ReACT: Reinforcement Learning for Controller Parametrization Using B-Spline Geometries

ReACT: Reinforcement Learning for Controller Parametrization...

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IEEE International Conference on systems, Man and Cybernetics

作者： Thomas Rudolf Daniel Flögel Tobias Schürmann Simon Süß Stefan Schwab Sören Hohmann Embedded Systems and Sensors Engineering department FZI Research Center for Information Technology Karlsruhe Germany Institute of Control Systems of the Karlsruhe Institute of Technology Karlsruhe Germany

Robust and performant controllers are essential for industrial applications. However, deriving controller parameters for complex and nonlinear systems is challenging and time-consuming. To facilitate automatic controller parametrization, this work presents a novel approach using deep reinforcement learning (DRL) with N-dimensional B-spline geometries (BSGs). We focus on the control of parameter-variant systems, a class of systems with complex behavior which depends on the operating conditions. For this system class, gain-scheduling control structures are widely used in applications across industries due to well-known design principles. Facilitating the expensive controller parametrization task regarding these control structures, we deploy an DRL agent. Based on control system observations, the agent autonomously decides how to adapt the controller parameters. We make the adaptation process more efficient by introducing BSGs to map the controller parameters which may depend on numerous operating conditions. To preprocess time-series data and extract a fixed-length feature vector, we use a long short-term memory (LSTM) neural networks. Furthermore, this work contributes actor regularizations that are relevant to real-world environments which differ from training. Accordingly, we apply dropout layer normalization to the actor and critic networks of the truncated quantile critic (TQC) algorithm. To show our approach's working principle and effectiveness, we train and evaluate the DRL agent on the parametrization task of an industrial control structure with parameter lookup tables.

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A K -Band Four-Beam Transmitter with Decoupled Phase/Gain Control and Enhanced Power Back off Efficiency for SATCOM

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IEEE Transactions on Microwave Theory and Techniques 2024年第11期72卷 6443-6459页

作者： Gao, Huiyan Li, Nayu Xie, Xinhong Wang, Shaogang Lu, Hang Kuan, Yen-Cheng Yu, Xiaopeng Song, Chunyi Jane Gu, Qun Xu, Zhiwei Institute of Marine Electronics and Intelligent Systems Ocean College Zhejiang University Zhoushan316021 China Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China Donghai Laboratory Zhoushan316021 China National Yang Ming Chiao Tung University International College of Semiconductor Technology Hsinchu30010 Taiwan Institute of VLSI Design and Research College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

This article presents a 17.7-20.2 GHz eight-element four-beam RF-beamforming transmitter in 65-nm CMOS for satellite communication (SATCOM). The transmitter utilizes an analog scheme in the variable-gain amplifier (VGA) to achieve dB-in-linear gain control with a high dynamic range (DR), together with a current combining crossbar for a compact footprint. The vector-modulation-based (VM-based) phase shifter (PS) adopts the cancellation-based Gm units for low switching variation. Furthermore, eight PSs of each beam are split into two groups, with each group sharing the same tunable I/Q generator for low power consumption. Considering the increased peak-to-average power ratio (PAPR) caused by high-order modulation schemes and multibeam operation, the transmitter incorporates compact transformer-based Doherty power amplifiers (DPAs) with adaptive bias control to improve the power efficiency at back-off. The chip achieves a 31-dB gain-tuning range with a 0.283-dB step and a 360° phase-shifting range with a calibrated 7-bit resolution. The measured root-mean-square (RMS) phase/gain errors induced by the VGA/PS are below 2.05°/0.147 dB, respectively, which decouples phase/gain control. The transmitter achieves a maximum output 1-dB gain compression point (OP1dB) of 17.9 dBm and a maximum saturated power (Psat) of 18.9 dBm. The output element exhibits 29.3% drain efficiency (DE) at 6-dB power back off (PBO) from Psat , which is 1.56/3.12 times over the normalized class-B/class-A implementations, demonstrating Doherty PBO efficiency enhancement. The chip occupies an area of 8.59 × 4.17 mm2 and consumes 398 mW per element at OP1dB. Additionally, a 16-element linear array has been developed to showcase the capabilities of the multibeam operation and gain tapering. © 1963-2012 IEEE.

关键词： Efficiency

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Bicolour Pulse Encoding in a Polarization-Maintaining Laser by a Twistable Tapered-Fiber Lyot Filter

Bicolour Pulse Encoding in a Polarization-Maintaining Laser ...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Bowen Liu Maolin Dai Yifan Ma Shinji Yamashita Sze Yun Set Department of Electrical Engineering and Information Systems the University of Tokyo Tokyo Japan Research Center for Advanced Science and Technology the University of Tokyo Tokyo Japan

An effective method to control dichromatic pulse encodings is reported. The spectral ripples are enhanced via a twistable tapered-fiber Lyot filter, thereby degrading the actual saturable absorption and thus causing m... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Optical fiber polarization Degradation Absorption Switches Solitons Lasers and electrooptics Encoding

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Pump-Controlled Mode-Locking Switching in an All-Polarization-Maintaining Fiber Laser

Pump-Controlled Mode-Locking Switching in an All-Polarizatio...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Maolin Dai Bowen Liu Guanyu Ye Yifan Ma Naoki Yamaguchi Takuma Shirahata Sze Yun Set Shinji Yamashita Department of Electrical Engineering and Information Systems University of Tokyo Tokyo Japan Research Center for Advanced Science and Technology University of Tokyo Tokyo Japan

We demonstrate the pump-controlled mode-locking switching in an all-polarization-maintaining, CNT-mode-locked fiber laser. By simply changing the injected pump power, the mode-locking can be switched between two wavel... 详细信息

ISBN: (纸本)9798350369311

关键词： Multiplexing Spectroscopy Laser mode locking Switches Laser stability Lasers and electrooptics Erbium-doped fiber lasers Laser excitation Sensors Pump lasers

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Multiagent Autonomous Source Search Using Submodularity and Branch-and-Bound

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无人系统（英文） 2024年第1期12卷 19-28页

作者： Xiaoling Xu Damián Marelli Wei Meng Qianqian Cai Minyue Fu School of Automation Guangdong University of TechnologyGuangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou 510006P.R.China School of Automation Guangdong University of TechnologyGuangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou 510006P.R.China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Engineering University of Newcastle NSW 2308Australia

This paper is concerned with near-optimal source search problem using a multiagent system in cluttered indoor *** goal of the problem is to maximize the detection probability within the minimum search *** propose a two-stage strategy to achieve this *** the first stage,a greedy approach is used to define a set of grid cells with the aim of maximizing the detection *** the second stage,an iterative branch-and-bound procedure is used to design the search paths of all agents so that all grid cells are visited by one agent and the largest search path among all agents is *** results show that the proposed search algorithm has better performance in terms of exploration time compared to other existing methods.

关键词： Multiagent system source search submodularity branch-and-bound indoor exploration

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Biosignal-based Control of a Robotic Gait Training Lifter

Biosignal-based Control of a Robotic Gait Training Lifter

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Xiaoxi Zhang Hideki Kadone Tomohiro Konishi Yang Chen Modar Hassan Kenji Suzuki Artificial Intelligence Laboratory University of Tsukuba Tsukuba Japan Center for Innovative Medicine and Engineering University of Tsukuba Hospital Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this paper, we present a robotic walker that aims to encourage the patient’s voluntary movement by enabling intention-based control of the mobile base. We proposed two variants of biosignal-based control methods for the robotic gait training lifter (GTL): one method to control the speed of the walker directly from Electromyography (EMG) signals of the muscles of the lower limb, and the other method to control the acceleration of the walker from EMG. We implemented these methods in a robotic walker and conducted experiments with one healthy male and one female participant to evaluate the feasibility of the proposed method. The results showed that the velocity of the human and walker can be synchronized with both controllers in the walking phase. The proposed method allows users to use together with robotic exoskeletons for rehabilitation sessions and has the potential to promote the active participation of the patient.

关键词： Legged locomotion Training Exoskeletons Muscles Electromyography Synchronization engineering in medicine and biology Robots

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CLIENT RECRUITMENT FOR FEDERATED LEARNING IN ICU LENGTH OF STAY PREDICTION

arXiv

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arXiv 2023年

作者： Scheltjens, Vincent Momo, Lyse Naomi Wamba Verbeke, Wouter De Moor, Bart KU Leuven STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium KU Leuven LIRIS Research Centre for Information Systems Engineering Belgium

Background: Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast amounts of training data which are available in the medical sector, albeit decentralized. Medical institutions generate vast amounts of data for which sharing and centralizing remains a challenge as the result of data and privacy regulations. The federated learning technique is well-suited to tackle these challenges. However, federated learning comes with a new set of open problems related to communication overhead, efficient parameter aggregation, client selection strategies and more. Methods: In this work, we address the step prior to the initiation of a federated network for model training, client recruitment. By intelligently recruiting clients, communication overhead and overall cost of training can be reduced without sacrificing predictive performance. Client recruitment aims at pre-excluding potential clients from partaking in the federation based on a set of criteria indicative of their eventual contributions to the federation. In this work, we propose a client recruitment approach using only the output distribution and sample size at the client site. Results: We show how a subset of clients can be recruited without sacrificing model performance whilst, at the same time, significantly improving computation time. By applying the recruitment approach to the training of federated models for accurate patient Length of Stay prediction using data from 189 Intensive Care Units, we show how the models trained in federations made up from recruited clients significantly outperform federated models trained with the standard procedure in terms of predictive power and training time. © 2023, CC BY.

关键词： Intensive care units

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systems Modeling of Trust in AI-Enabled Medical Diagnosis

Systems Modeling of Trust in AI-Enabled Medical Diagnosis

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Annual IEEE systems Conference

作者： Negin Moghadasi Misagh Piran Rupa S. Valdez Negar Moghaddasi Davis C. Loose Thomas L. Polmateer James H. Lambert Dept. of Systems and Information Engineering University of Virginia Johnson & Johnson Medtech Charlottesville USA Dept. of Radiology Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Ruhr University of Bochum Bad Oeyhausen Germany Dept. of Systems and Information Engineering School of Data Science University of Virginia Charlottesville USA Dept. of Dentistry Western University of Health Sciences Pomona USA Dept. of Engineering Systems and Environment University of Virginia Charlottesville USA Dept. of Engineering Systems and Environment CCALS University of Virginia Charlottesville USA Dept. of Systems and Information Engineering University of Virginia Charlottesville USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

The progression of artificial intelligence, particularly in healthcare domain, has brought significant progress to treatment, drug discovery, disease diagnosis and more. The main contribution of this paper is the development of a scenario-based preferences risk register framework for quantifying AI related risks in disease diagnosis. Two case studies, one involving physician and the other one with involving patients as the main stakeholders, are compared to evaluate the effectiveness of the framework and to evaluate how the initiatives ranking orders will evolve based on the most and least disruptive scenarios for each cases. The framework is applied to realistic case studies on cardiac sarcoidosis, identifying success criteria, initiatives, emergent conditions, and the most and least disruptive scenarios. The success criteria align with the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF) seven trustworthy AI characteristics. Finally, the framework identifies the most and least disruptive scenarios and the most important initiatives for both cases. Results show that Non-Interpretable AI and Lack of Human-AI Communications, Privacy Attacks, and Human Errors in Design, Develop, Measurement and Implementation are the most disruptive scenarios in both cases, however, scenario Cyber Security Threats also counted as other most disruptive scenarios to the initiatives ranked by patients. This adaptable framework has the potential not only to transfer findings to global healthcare systems but also to extend its effectiveness to various other domains, such as transportation, finance, environment, and design.

关键词： Privacy Measurement uncertainty Transportation NIST systems modeling Registers Medical diagnosis

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IoT Cloud Computing Middleware for crowd monitoring and evacuation

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International Journal of Circuits, systems and Signal Processing 2021年 15卷 1790-1802页

作者： Gazis, Alexandros Katsiri, Eleftheria Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece Institute for the Management of Information Systems Athena Research & Innovation Center in Information Communication & Knowledge Technologies Marousi15125 Greece

Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the contrary, wireless sensor networks (WSNs) are distributed systems that scale and feature large numbers of small, computationally limited, low-power, unreliable nodes. In this article, we provide a top-down approach explaining the architecture, implementation and rationale of a distributed fault-tolerant IoT middleware. Specifically, this middleware consists of multiple mini-computing devices (Raspberry Pi) connected in a WSN which implement the Map-Reduce algorithm. First, we explain the tools used to develop this system. Second, we focus on the Map-Reduce algorithm implemented to overcome common network connectivity issues, as well as to enhance operation availability and reliability. Lastly, we provide benchmarks for our middleware as a crowd tracking application for a preserved building in Greece (i.e., M. Hatzidakis’ residence). The results of this study show that IoT middleware with low-power and low-cost components are viable solutions for medium-sized cloud computing distributed and parallel computing centres. Potential uses of this middleware apply for monitoring buildings and indoor structures, in addition to crowd tracking to prevent the spread of COVID-19. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： Middleware

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