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P-127 Spiking network models of developing mouse prefrontal cortex

Clinical Neurophysiology 2023年 148卷 e65-e66页

作者： Lorenz, M. Bitzenhofer, S. Chini, M. Martínez-Cañada, P. Hanganu-Opatz, I. Panzeri, S. Department of Excellence for Neural Information Processing Center for Molecular Neurobiology (ZMNH) University Hospital Eppendorf Germany Center for Neuroscience and Cognitive Systems Istituto Italiano di Tecnologia Italy Department of Pharmacy and Biotechnology University of Bologna Italy Institute of Developmental Neurophysiology Center for Molecular Neurobiology (ZMNH) University Hospital Eppendorf Germany Department of Computer Architecture and Technology University of Granada Spain

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An evolutionary deep learning method for short-term wind speed prediction: A case study of the lillgrund offshore wind farm

arXiv

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arXiv 2020年

作者： Neshat, Mehdi Nezhad, Meysam Majidi Abbasnejad, Ehsan Tjernberg, Lina Bertling Garcia, Davide Astiaso Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Sapienza University of Rome Italy Australian Institute for Machine Learning The University of Adelaide Australia School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy

Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper-parameters of two long-term-short-term (LSTM) ANN models for wind prediction. The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea. Two forecasting horizons including ten-minutes ahead (absolute short term) and one-hour ahead (short term) are considered in our experiments. Our experimental results indicate that the new approach is superior to five other applied machine learning models, i.e., polynomial neural network (PNN), feed-forward neural network (FNN), nonlinear autoregressive neural network (NAR) and adaptive neuro-fuzzy inference system (ANFIS), as measured by five performance criteria. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications

arXiv

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arXiv 2020年

作者： Yang, Helin Xiong, Zehui Zhao, Jun Niyato, Dusit Xiao, Liang Wu, Qingqing School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Information and Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)’s beamforming and the IRS’s reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Hybrid neuro-evolutionary method for predicting wind turbine power output

arXiv

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arXiv 2020年

作者： Neshat, Mehdi Abbasnejad, Ehsan Heydari, Azim Garcia, Davide Astiaso Nezhad, Meysam Majidi Groppi, Daniele Tjernberg, Lina Bertling Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Australian Institute for Machine Learning University of Adelaide Australia Sapienza University of Rome Italy Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine Learning (ML) methods have been successfully applied in a wide range of domains, including renewable energy. However, due to the challenging nature of power prediction in wind farms, current models are far short of the accuracy required by industry. In this paper, we deploy a composite ML approach-namely a hybrid neuro-evolutionary algorithm-for accurate forecasting of the power output in wind-turbine farms. We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. At the beginning stage, the k-means clustering method and an Autoencoder are employed, respectively, to detect and filter noise in the SCADA measurements. Next, with the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm as a hyper-parameter optimizer, and a recurrent neural network (RNN) called Long Short-term memory (LSTM) to model the power curve of a wind turbine in a farm. Two short time forecasting horizons, including ten-minutes ahead and one-hour ahead, are considered in our experiments. We show that our approach outperforms its counterparts. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

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Machine Learning Approach to Enhance the Performance of MNP?Labeled Lateral Flow Immunoassay

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Nano-Micro Letters 2019年第1期11卷 132-146页

作者： Wenqiang Yan Kan Wang Hao Xu Xuyang Huo Qinghui Jin Daxiang Cui Department of Instrument Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentKey Laboratory of Thin Film and Microfabrication (Ministry of Education) Shanghai Jiao Tong University School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Department of Biomedical Engineering JiLin Medical University State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo University

The use of magnetic nanoparticle(MNP)-labeled immunochromatography test strips(ICTSs) is very important for point-ofcare testing(POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin(HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL^(-1) and the ideal detection limit was 0.014 mIU mL^(-1), which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

关键词： Point-of-care testing Immunochromatography test strips Magnetic nanoparticles Machine learning Support vector machine

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States-Based Energy Management System for an EV Hybrid Charging Station with Z-Source Converters

States-Based Energy Management System for an EV Hybrid Charg...

引用

Power Tech Conference

作者： Enrique González-Rivera Pablo García-Triviño Luis M. Fernández-Ramirez Raúl Sarrias-Mena Juan P. Torreglosa Francisco Jurado Department of Electrical Engineering Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP-023) University of Cadiz Algeciras Spain Department of Engineering in Automation Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP-023) Electronics and Computer Architecture & Networks University of Cadiz Algeciras Spain Electrical Engineering Department University of Huelva Palos de la Frontera Spain Department of Electrical Engineering Research Group in Research and Electrical Technology (PAIDI- TEP-152) University of Jaen Linares Spain

ISBN: (数字)9781665435970

ISBN: (纸本)9781665411738

This work presents a new energy management system (EMS) based on states for a hybrid charging station of electric vehicles (EV). The hybrid charging station is composed of a photovoltaic (PV) system, a complete hydrogen system based on fuel cell (FC), electrolyzer (EZ) and tank as energy storage system (ESS), a battery energy storage system (BES), a grid connection, and six fast charging units, all of them connected to a common MVDC bus through Z-sources converters (ZSC). The states-based EMS is designed to control the power flow among the energy sources of the hybrid charging station. The viability of the EMS is proved under a long-term simulation of 25 years in Simulink by using an EV load profile taken from the Strategy technology Energy Plan of the EU and real data for the sun irradiance.

关键词： Photovoltaic systems Renewable energy sources Costs Software packages Hydrogen Charging stations Hybrid power systems

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Stability analysis of gradient-based distributed formation control with heterogeneous sensing mechanism: Two and three robot case

arXiv

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arXiv 2020年

作者： Chan, Nelson P.K. Jayawardhana, Bayu de Marina, Hector Garcia Engineering and Technology Institute Groningen University of Groningen Nijenborgh 4 Groningen9747AG Netherlands Department of Computer Architecture and Automatic Control at the Faculty of Physics Universidad Complutense de Madrid Madrid28040 Spain

This paper focuses on the stability analysis of a formation shape displayed by a team of mobile robots that uses heterogeneous sensing mechanism. Depending on the convenience and reliability of the local information, each robot utilizes the popular gradient-based control law which, in this paper, is either the distance-based or the bearing-only formation control. For the two and three robot case, we show that the use of heterogeneous gradient-based control laws can give rise to an undesired invariant set where a distorted formation shape is moving at a constant velocity. The (in)stability of such an invariant set is dependent on the specified distance and bearing constraints. For the two robot case, we prove almost global stability of the desired equilibrium set while for the three robot case, we guarantee local asymptotic stability for the correct formation shape. We also derive conditions for the three robot case in which the undesired invariant set is locally attractive. Numerical simulations are presented for illustrating the theoretical results in the three robot *** Codes 34H05, 70E60, 93C10, 93C85 © 2020, CC-BY-NC-SA.

关键词： Control theory

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Angle-Constrained Formation Control for Circular Mobile Robots

arXiv

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arXiv 2020年

— In this letter, we investigate the formation control problem of mobile robots moving in the plane where, instead of assuming robots to be simple points, each robot is assumed to have the form of a disk with equal radius. Based on interior angle measurements of the neighboring robots’ disk, which can be obtained from low-cost vision sensors, we propose a gradient-based distributed control law and show the exponential convergence property of the associated error system. By construction, the proposed control law has the appealing property of ensuring collision avoidance between neighboring robots. We also present simulation results for a team of four circular mobile robots forming a rectangular *** Codes 34H05, 70E60, 93C10, 93C85 Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

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Soft Error Mitigation for Deep Convolution Neural Network on FPGA Accelerators

Soft Error Mitigation for Deep Convolution Neural Network on...

引用

IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Wenshuo Li Guangjun Ge Kaiyuan Guo Xiaoming Chen Qi Wei Zhen Gao Yu Wang Huazhong Yang Department of Electronic Engineering BNRist Tsinghua University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China Tianjin International Engineering Institute Tianjin University Tianjin China

ISBN: (数字)9781728149226

ISBN: (纸本)9781728149233

Convolution neural networks (CNNs) have been widely used in many applications. Field-Programmable Gate Array (FPGA) based accelerator is an ideal solution for CNNs in embedded systems. However, the single event upset (SEU) effect in FPGA device may have a significant influence on the performance of CNNs. In this paper, we analyze the sensibility of CNNs to SEU and present a fault-tolerant design for CNN accelerators. First, we find that SEU in processing elements (PEs) has the worst effects on CNNs since it produces proportional errors and will not get refreshed. Furthermore, it is indicated that the large positive perturbation contributes almost all of the performance loss. Based on such observations, we propose an error detecting scheme to locate incorrect PEs and give an error masking method to achieve fault-tolerance. Experiments demonstrate that the proposed method achieves similar fault-tolerant performance with the triple modular redundancy (TMR) scheme while the overhead is much lower than it.

关键词： Table lookup Adders Field programmable gate arrays Single event upsets Neural networks Fault tolerance

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Impact of NBTI on Increasing the Susceptibility of FinFET to Radiation

Impact of NBTI on Increasing the Susceptibility of FinFET to...

引用

Annual International Symposium on Reliability Physics

作者： Frank Sill Torres Hussam Amrouch Jörg Henke Rolf Drechsler Group of Computer Architecture University of Bremen Germany Department of Computer Science Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

This work investigates the interaction between Neg-ative Bias Temperature Instability (NBTI)and radiation effects in 14nm FinFET devices. Due to the complex interaction between traps generated by NBTI and induced charges by strikes of ionizing particles, we opted for a complete physical-based analysis using TCAD mixed-mode simulations. This enables an accurate estimation and then modeling of the duration a circuit requires to recover from a particle strike and, thus, return to correct operation under the effects of NBTI. This a crucial aspect, because the longer the recovery time, the higher the probabilities of a soft-error and that this error remains undetected. Further, our employed setup enables an accurate determination of the critical charge (Q crit ), i. e. the minimum collected charge that results into a faulty transition of a circuit's output node. Our investigation reveals that there is indeed a strong relation between NBTI and the time a circuit remains in faulty state. Consequently, detection schemes must be adapted during circuit's operation to take aging into account in order to avoid that errors remain undetected.

关键词： Negative bias temperature instability Thermal variables control Inverters Ions Circuit faults FinFETs Load modeling

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