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Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control

Procedia Computer Science 2023年 216卷 213-220页

作者： Santo Wijaya Yaya Heryadi Yulyani Arifin Wayan Suparta Lukas Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta 55281 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance.

关键词： Model-based Reinforcement Learning Random-Shooting Policy LSTM MPC Nonlinear System

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Adaptive continuous barrier function-based super-twisting global sliding mode stabilizer for chaotic supply chain systems

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Chaos, Solitons and Fractals 2024年 182卷

作者： Sepestanaki, Mohammadreza Askari Rezaee, Hamidreza Soofi, Mohammad Fayazi, Hossein Rouhani, Seyed Hossein Mobayen, Saleh Department of Electrical Engineering Faculty of Engineering University of Zanjan Zanjan Iran Power Electronics Research Institute Malek-Ashtar University of Technology Isfahan Iran Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung City807618 Taiwan Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan

Supply chains face various uncertainties due to the dynamic and unstable nature of today's worldwide market. Introducing these uncertainties to both the upstream and downstream elements of supply chains adds complexity and nonlinearity to them. This paper investigates the dynamic behavior of a chaotic three-echelon supply chain system and designs a finite-time global nonlinear super-twisting sliding mode controller based on an adaptive continuous barrier function technique for stabilizing the defined system under external disturbances and model uncertainties. This method dynamically adapts the controller parameters according to the system's current state using parameter adaptation algorithms. The adaptive barrier function is used to ensure the system's stability, while the super-twisting algorithm is used to speed up the convergence rate and robustness of the system. Moreover, the genetic optimization algorithm has been employed to attain the optimal values of parameters, thereby enhancing the performance of the designed controller. Ultimately, the effectiveness of this research's proposed approach has been evaluated through simulation in the MATLAB-Simulink environment and experimental testing using the Baseline Real-Time Speedgoat test platform. These analyses are paramount and helpful for strategic decision-makers because they allow visualization and control of the states/dynamics of the entire supply chain. The internal parameters of the supply chain are used as control parameters, and different significant states are discovered to achieve synchronization. © 2024 Elsevier Ltd

关键词： Supply chains

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Vertical CNT-Si Photodiode Array

arXiv

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arXiv 2024年

作者： Ahnood, Arman Zhou, Hang Dai, Qing Vygranenko, Yuri Suzuki, Yuji Esmaeili-Rad, M.R. Amaratunga, Gehan Nathan, Arokia Electrical Engineering Division Department of Engineering University of Cambridge Cambridge United Kingdom School of Physics University of Melbourne Melbourne3010 Australia Peking University Shenzhen Graduate School Peking University Shenzhen China Electronics Telecommunications and Computer Engineering Instituto Superior de Engenharia de Lisboa Lisboa Portugal London Centre for Nanotechnology University College London London United Kingdom Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

A photodiode consisting nanopillars of thin film silicon p-i-n on an array of vertically aligned carbon nanotubes (CNT) with a non-continuous cathode electrode is demonstrated. The structure exploits the intrinsic enhancement of the CNTs’ electric field, which leads to reduction in the photodiode’s operating voltage and response time, and enhancement of optical coupling due to better light trapping, as compared with the conventional planar photodiode. These improvements translate to higher resolution and higher frame rate flat panel imaging systems for a broad range of applications, including computed tomography and particle detection. © 2024, CC BY-SA.

关键词： Photodiodes

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Jitter Due to Random Telegraph Noise in Voltage-Controlled Oscillators Based on Ring Oscillators

Jitter Due to Random Telegraph Noise in Voltage-Controlled O...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Caroline P. Garcia Bernardo Lourosa Gilson I. Wirth Graduate Program in Microelectronics (PGMicro) Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Electrical Engineering Department (DELET) Universida de Federal do Rio Grande do Sul Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

As transistors dimensions shrink to nanometer sizes, the variability of their electrical parameters becomes a significant factor in the performance of integrated circuits. Random Telegraph Noise (RTN) is a time-dependent variability that changes the drain current (or threshold voltage) of metal-oxide-semiconductor field-effect transistors (MOSFETs) from one instant in time to the other, leading to jitter in oscillating circuits, such as ring and voltage-controlled (VCO) oscillators. This work evaluates the time-domain jitter caused by the RTN in a capacitive and a current-controlled ring oscillator-based VCO. Different concepts of jitter (absolute jitter, period jitter, and cycle-to-cycle jitter) are studied, focusing on the influence of time window sizes on the jitter amplitude. Monte Carlo simulations are employed for both oscillators, considering a modified SPICE simulator to properly include the RTN impact on the threshold voltage of the transistors.

关键词： Ring oscillators Monte Carlo methods Voltage-controlled oscillators Noise Jitter SPICE Threshold voltage Transistors Autocorrelation Time-domain analysis

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ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation 32

ContourletNet: A Generalized Rain Removal Architecture Using...

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32nd British Machine Vision Conference, BMVC 2021

作者： Chen, Wei-Ting Tsai, Cheng-Che Fang, Hao-Yu Chen, I. Hsiang Ding, Jian-Jiun Kuo, Sy-Yen Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan ASUS Intelligent Cloud Services Asustek Computer Inc Taipei Taiwan

Images acquired from rainy scenes usually suffer from bad visibility which may damage the performance of computer vision applications. The rainy scenarios can be categorized into two classes: moderate rain and heavy rain scenes. Moderate rain scene mainly consists of rain streaks while heavy rain scene contains both rain streaks and the veiling effect (similar to haze). Although existing methods have achieved excellent performance on these two cases individually, it still lacks a general architecture to address both heavy rain and moderate rain scenarios effectively. In this paper, we construct a hierarchical multi-direction representation network by using the contourlet transform (CT) to address both moderate rain and heavy rain scenarios. The CT divides the image into the multi-direction subbands (MS) and the semantic subband (SS). First, the rain streak information is retrieved to the MS based on the multi-orientation property of the CT. Second, a hierarchical architecture is proposed to reconstruct the background information including damaged semantic information and the veiling effect in the SS. Last, the multi-level subband discriminator with the feedback error map is proposed. By this module, all subbands can be well optimized. This is the first architecture that can address both of the two scenarios effectively. The code is available in https://***/cctakaet/ContourletNet-BMVC2021. © 2021. The copyright of this document resides with its authors.

关键词： Rain

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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IEEE International Conference on Consumer electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Jirayu Petchhan Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science Dept. of Electrical Engineering National Taiwan University of Science & Technology Taiwan R. O. C. Department of Computer Science and Information Engineering National Taipei University of Technology Taiwan R. O. C.

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle.

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Automated Image Captioning with Multi-layer Gated Recurrent Unit

Automated Image Captioning with Multi-layer Gated Recurrent ...

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European Signal Processing Conference (EUSIPCO)

作者： Özge Taylan Moral Volkan Kiliç Aytug Onan Wenwu Wang Electrical and Electronics Engineering Graduate Program Izmir Katip Celebi University Turkey Department of Computer Engineering Izmir Katip Celebi University Turkey Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey UK

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

Describing the semantic content of an image via natural language, known as image captioning, has recently attracted substantial interest in computer vision and language processing communities. Current image captioning approaches are mainly based on an encoder-decoder framework in which visual information is extracted by an image encoder and captions are generated by a text decoder, using convolution neural net-works (CNN) and recurrent neural networks (RNN), respectively. Although this framework is promising for image captioning, it has limitations in utilizing the encoded visual information for generating grammatically and semantically correct captions in the RNN decoder. More specifically, the RNN decoder is ineffective in using the contextual information from the encoded data due to its limited ability in capturing long-term complex dependencies. Inspired by the advantage of gated recurrent unit (GRU), in this paper, we propose an extension of conventional RNN by introducing a multi-layer GRU that modulates the most relevant information inside the unit to enhance the semantic coherence of captions. Experimental results on the MSCOCO dataset show the superiority of our proposed approach over the state-of-the-art approaches in several performance metrics.

关键词： Measurement Visualization Recurrent neural networks Semantics Natural languages Europe Logic gates

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Performance Analysis of Interleaved Bidirectional DC-DC Converters for Battery Control in Islanded Microgrid

Performance Analysis of Interleaved Bidirectional DC-DC Conv...

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IEEE General Meeting Power& Energy Society

作者： Lindemberg Roberto de Lima Pedro A. de Alcântara Luana C. S. Soares Camila M. V. Barros Luciano S. Barros Mathematics and Computational Modeling Graduate Program João Pessoa Brazil Electrical Engineering Graduate Program Federal University of Paraíba João Pessoa Brazil Computer Systems Department Federal University of Paraíba João Pessoa Brazil

In this paper, considering photovoltaic/battery sourced microgrids with grid-forming droop control in the load interface converter for power management, two alternative converter topologies for the battery are tested: dual boost interleaved converter and three-phase interleaved converter. These topologies are based on the conventional bidirectional buckboost converter and their employment is to allow a reduction in component sizing, improvement in power quality, and higher output voltage. Analysis of the operation of these converters in different operation modes of the microgrid is carried out, including battery normal charging, battery normal discharging, battery charging limit, and maximum state-of-charge, through simulations in MATLAB/Simulink environment. Results suggest advantages such as reduced stress on switches and battery charging current ripple for the two interleaved converters tested and the potential implementation of higher output voltage for the dual boost converter.

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Auxiliary Classifier based Residual RNN for Image Captioning

Auxiliary Classifier based Residual RNN for Image Captioning

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European Signal Processing Conference (EUSIPCO)

作者： Özkan Çaylı Volkan Kılıç Aytuğ Onan Wenwu Wang Electrical and Electronics Engineering Graduate Program İzmir Katip Çelebi University Turkey Department of Computer Engineering İzmir Katip Çelebi University Turkey Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey UK

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

Image captioning aims to generate a description of visual contents with natural language automatically. This is useful in several potential applications, such as image understanding and virtual assistants. With recent advances in deep neural networks, natural and semantic text generation has been improved in image captioning. However, maintaining the gradient flow between neurons in consecutive layers becomes challenging as the network gets deeper. In this paper, we propose to integrate an auxiliary classifier in the residual recurrent neural network, which enables the gradient flow to reach the bottom layers for enhanced caption generation. Experiments on the MSCOCO and VizWiz datasets demonstrate the advantage of our proposed approach over the state-of-the-art approaches in several performance metrics.

关键词： Measurement Deep learning Visualization Recurrent neural networks Virtual assistants Semantics Neurons

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Adaptive Control Applied to Half-Bridge Synchronous Buck Converter using LCL Filter for Charging Batteries

Adaptive Control Applied to Half-Bridge Synchronous Buck Con...

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Power electronics and Control (SEPOC), Seminar on

作者： Vinícius Descovi Rodrigues João Vitor Merlo Jonas Roberto Tibola Rodrigo Varella Tambara Research and Development Center in Electrical Engineering - NUPEDEE Federal University of Santa Maria Santa Maria Brasil Graduate Program in Electrical Engineering - PPGEE Federal University of Santa Maria Santa Maria Brasil Research and Development Center in Electrical and Computational Systems - GSEC Federal University of Santa Maria Santa Maria Brasil Group of Power Electronics and Control - GEPOC Federal University of Santa Maria Santa Maria Brasil

ISBN: (数字)9798350379419

ISBN: (纸本)9798350379426

This paper proposes a discrete-time Model Reference Adaptive Controller (MRAC), applied to a Half-Bridge synchronous buck converter for charging batteries. The system uses an LCL filter that interfaces the power converter and the batteries. A virtual plant method is proposed for better transitory response of the adaptive control system. The model reference utilizes a reduced order, to simplify the project of the adaptive controller. Simulation results are presented for validation of the control project.

关键词： Seminars Adaptation models Buck converters Simulation Power electronics Batteries Topology Adaptive control

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