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arXiv 2022年

作者： Giammarino, Vittorio Dunne, Matthew F. Moore, Kylie N. Hasselmo, Michael E. Stern, Chantal E. Paschalidis, Ioannis Ch. Division of Systems Engineering Boston University BostonMA02446 United States Dept. of Electrical and Computer Engineering Boston University BostonMA02446 United States Dept. of Biomedical Engineering Boston University BostonMA02215 United States Cognitive Neuroimaging Center Boston University BostonMA02215 United States Graduate Program for Neuroscience Boston University BostonMA02215 United States Center for Systems Neuroscience Boston University BostonMA02215 United States

We develop a simple framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL) using the on-policy Proximal Policy Optimization (PPO) algorithm which shows better stability than other algorithms and can steadily improve the policies pretrained with IL. We show that the combination of IL and RL can match human results and that good performance strongly depends on combining the allocentric information with an egocentric representation of the environment. © 2022, CC BY.

关键词： Reinforcement learning

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An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

arXiv

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arXiv 2021年

作者： Marcelino, Carolina Gil Leite, G.M.C. Delgado, C.A.D.M. De Oliveira, L.B. Wanner, E.F. Jiménez-Fernández, S. Salcedo-Sanz, S. Department of Signal Processing and Communications Universidad de Alcalá Spain Institute of Computing Federal University of Rio de Janeiro Brazil Post-Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Computing Department Federal Center of Technology Education of Minas Gerais Brazil

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multiobjective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. © 2021, CC BY-NC-ND.

关键词： Multiobjective optimization

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Vacuum Ultraviolet Light-Generating Metalens

Vacuum Ultraviolet Light-Generating Metalens

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

作者： Ming Lun Tseng Michael Semmlinger Ming Zhang Catherine Arndt Tzu-Ting Huang Jian Yang Hsin Yu Kuo Vin-Cent Su Mu Ku Chen Cheng Hung Chu Benjamin Cerjan Din Ping Tsai Peter Nordlander Naomi J. Halas Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Applied Sciences Academia Sinica Taipei Taiwan Department of Electrical and Computer Engineering Rice University Houston Texas USA Laboratory for Nanophotonics Rice University Houston Texas USA Applied Physics Graduate Program Rice University Houston Texas USA Department of Physics and Astronomy Rice University Houston Texas USA Department of Physics National Taiwan University Taipei Taiwan Department of Electrical Engineering National United University Miaoli Taiwan Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong Department of Chemistry Rice University Houston Texas USA

Vacuum ultraviolet (VUV) light is of great importance to many applications such as photochemistry and nanolithography. In this talk, a nonlinear metalens composed of ZnO nanoantennas for VUV generation and focusing wi...

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Effects of Hydrogen on Endurance Characteristics in NAND Flash Memories

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ECS Advances 2022年第4期1卷 042001-042001页

作者： Chiu, Yung-Yueh Liu, Wen-Chien Chen, Yu-Jung Kumaria, Roshni Takeshita, Toshiaki Yano, Masaru Shirota, Riichiro Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan College of Semiconductor Research National Tsing Hua University Hsinchu 300 Taiwan EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Winbond Electronics Corporation Kanagawa 222-0033 Japan

This study comprehensively investigates the effect of hydrogen content in forming gas annealing (FGA) on the endurance of NAND flash memories by statistically analyzing the transconductance (G_m,max) characteristics. The G_m,max degradation (ΔG_m,max) worsened with higher cycling temperature, delayed time period from erasing to programming (t_EP) operation, and higher word-line bias (V_A) during t_EP. Moreover, these effects become more pronounced as the hydrogen content in FGA increases. Using the measured G_m,max distributions and Monte -Carlo TCAD technology, the activation energy (E_A) of oxide damage creation can be extracted. The extracted E_A values were 50 meV and 160 meV for the diluted (4%) and pure (100%) hydrogen samples, respectively. This suggests that a higher hydrogen concentration results in more ionized hydrogen atoms remaining in the oxide layer. During t_EP, these hydrogen ions can drift near the Si/SiO₂ surface, where they may react to form trap states. E_A is revealed to have a linear relationship with respect to V_A, where the slope is independent of the hydrogen content in FGA. © 2022 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.

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ZETA: a library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems

arXiv

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arXiv 2025年

作者： Rego, Brenner S. Scott, Joseph K. Raimondo, Davide M. Terra, Marco H. Raffo, Guilherme V. Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Engineering and Architecture University of Trieste Trieste34127 Italy Department of Electronics Engineering Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely zonotopes, constrained zonotopes, and line zonotopes, in addition to a basic implementation of interval arithmetic. This library has capabilities starting from the basic set operations with these sets, including propagations through nonlinear functions using various approximation methods. The features of ZETA allow for reachability analysis and state estimation of discrete-time linear, nonlinear, and descriptor systems, in addition to active fault diagnosis of linear systems. Efficient order reduction methods are also implemented for the respective set representations. Some examples are presented in order to illustrate the functionalities of the new library. © 2025, CC BY.

关键词： Digital arithmetic

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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Renewable Biodiesel Production from Oleaginous Yeast Biomass Using Industrial Wastes

Renewable Biodiesel Production from Oleaginous Yeast Biomass...

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2019 Research, Invention, and Innovation Congress, RI2C 2019

作者： Kongruang, Sasithorn Roytrakul, Sittiruk Sriariyanun, Malinee Bioprocess Engineering and Biotechnology Center Department of Biotechnology Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut' S University of Technology North Bangkok Bangkok Thailand Chemical and Process Engineering Program Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

The accumulation lipid from oleaginous microorganisms is recognized as a second generation fuel. Biooil is known to as intracellular product of oily yeast utilizing various carbon substrates and converting different quantities of lipids in the form of triacylglycerols. This second generation fuel can be used to make biodiesel via a transesterification process. This study investigated the morphological characteristics of eight strains of Thai oleaginous yeasts via microscopy and analyzed the fatty acid profiling of yeasts cultured in three carbon sources: glucose, sugar cane molasses and crude glycerol in order to estimate biodiesel properties. To approach this goal, batch fermentations were used to culture eight yeast strains, Rhodosporidium toruloides TISTR 5123, TISTR 5154, TISTR 5149, Yarrowia lipolytica TISTR 5054, TISTR 5151, TISTR 5621, Rhodotorula glutinis TISTR 5159 and Rhodotorula graminis TISTR 5124 for 96 h under 30°C at 250 rpm. Result revealed that eight yeast strains contained significant amounts of fatty acids and lipids and accumulated mainly palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C 18:1) and linoleic acid (C18:2), and they are suitable for the production of biodiesel. Fatty acid productions and profiles indicated that these yeast strains can be potentially used as the triacylglycerols producers for biodiesel production. © 2020 The Authors, published by EDP Sciences.

关键词： Lipids

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Voltage Levels and Reactive Power in Distribution Networks with High Integration of Distributed Photovoltaic Generation

Voltage Levels and Reactive Power in Distribution Networks w...

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2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019

作者： Darui, Caroline B. F. Lucca, Tiago G. Abaide, Alzenira Da R. Da Veiga, Matheus F. Da Cruz, Matheus S. Knak Neto, Nelson Wüst, Lucas L. Figueiró, Iuri C. Federal University of Santa Maria Graduate Program in Electrical Engineering Santa Maria Brazil URI Santo Ângelo Department of Engineering and Computer Science Santo Ângelo Brazil

ISBN: (纸本)9781538695678

The Brazilian electrical system is undergoing many changes due to the exponential growth of photovoltaic systems connected to the grid (PV). This increase may affect power quality parameters, such as reduction of power factor and increase of voltage levels. This work aims to model and simulate a low voltage distribution network with high levels of integration of PV systems in order to verify how the voltage and reactive parameters behave in these cases. It was used new load curve models, the Monte Carlo method and the OpenDSS software. © 2019 IEEE.

关键词： Monte Carlo methods

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Null space-based formation control with leader change possibility 19

Null space-based formation control with leader change possib...

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19th International Conference on Advanced Robotics, ICAR 2019

作者： Netol, Valentim Ernandes Sarcinelli-Filho, Mario Brandao, Alexandre Santos Núcleo de Especialização em Robótica Federal University of Viçosa Department of Electrical Engineering and Graduate Program on Computer Science Viçosa MG Brazil Graduate Program on Electrical Engineering Federal University of Espírito Santo Vitória - ES Brazil

ISBN: (纸本)9781728124674

This work discusses the implementation and experimentation of a control system designed to guide a multi-robot formation of one aerial and three ground robots, in a trajectory tracking task. The implementation considers three partial formations of two robots, each one associated to a virtual structure, a straight line in the 3D space with its own formation controller. Two of the partial line formations are homogeneous formations of two ground vehicles, and the third one is a heterogeneous formation, involving a ground and an aerial vehicles. All the partial formations have the same reference for position, which is one of the ground vehicles. This also allows to discuss what would happen if this ground vehicle, the reference for all the line formations, and thus for the whole formation, presents a failure, what is considered here, in connection to an escorting mission. The result is that the designed control system is effectively able to accomplish the mission, even upon a failure of the reference robot, what is validated by experimental results also discussed here. © 2019 IEEE.

关键词： Ground vehicles

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Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

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Nature biomedical engineering 2025年第2期9卷 283页

作者： Hamidreza Abbaspourazad Eray Erturk Bijan Pesaran Maryam M Shanechi Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. Departments of Neurosurgery Neuroscience and Bioengineering University of Pennsylvania Philadelphia PA USA. Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. shanechi@usc.edu. Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA USA. shanechi@usc.edu.

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