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A resilient and intelligent multi-objective energy management for a hydrogen-battery hybrid energy storage system based on MFO technique

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RENEWABLE ENERGY 2024年 222卷

作者： Elkholy, M. H. Senjyu, Tomonobu Metwally, Hamid Farahat, M. A. Irshad, Ahmad Shah Hemeida, Ashraf M. Lotfy, Mohammed Elsayed Univ Ryukyus Dept Elect & Elect Engn Okinawa 9030213 Japan Zagazig Univ Fac Engn Elect Power & Machines POB 44519 Zagazig Egypt Kandahar Univ Engn Fac Dept Energy Engn Kandahar 3801 Afghanistan Aswan Univ Fac Energy Engn Dept Elect Engn Aswan 81528 Egypt

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe.

关键词： Hydrogen-battery hybrid energy storage system Moth-flame Optimization Operating reserve field programmable gate array Backup energy management system

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Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期136卷

作者： Sahani, Mrutyunjaya Choudhury, Sasmita Siddique, Marif Daula Parida, Tanmoy Dash, Pradipta Kishore Panda, Sanjib Kumar Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Siksha O Anusandhan Deemed Be Univ Multidisciplinary Res Cell Bhubaneswar 751030 India

Accurate prediction of photovoltaic (PV) power can significantly alleviate energy crises. However, the inherent randomness and intermittency of PV power pose challenges to the stability and safety of PV-penetrated grid systems. To address this, we have developed a novel hybrid model: a reduced deep convolutional stack autoencoder with a minimum variance multikernel random vector functional link network (RDCSAEMVMRVFLN). This model enhances grid efficacy and safety. We extract the most informative band-limited intrinsic mode functions (BLIMFs) of highly nonlinear and nonstationary solar energy parameters using an entropy, kurtosis, and correlation coefficient-based information-oriented variational mode decomposition (IOVMD). These efficient BLIMFs are concatenated and input into the RDCSAE for rich, abstract, and discriminative representation computation. A less computationally complex MVMKRVFLN regression method, incorporating these refined representations, is proposed for superior prediction accuracy and reduced computational complexity. Our method shows exceptional performance in predicting solar temperature, irradiation, and power for multi-horizon forecasts with minimal error metrics (correlation coefficients of 0 . 999 +/- 0 . 001, 0 . 992 +/- 0 . 001, 0 . 986 +/- 0 . 02 and 0 . 978 +/- 0 . 02, and RMSE of 0 . 016 +/- 0 . 001, 0 . 024 +/- 0 . 001, 0 . 034 +/- 0 . 001 and 0 . 045 +/- 0 . 001 for the interval of 10 minutes, 30 minutes, 1 hour and 3 hours respectively) in both single-step and multistep forecasting compared to conventional methods. The RDCSAE-MVMKRVFLN model is implemented on a high-speed Xilinx Virtex-5 FPGA embedded processor to validate its simplicity, robustness, and practicability. Additionally, we examine the prediction performance using real-time data from a 1 MW solar farm in Odisha, India, demonstrating the model's effectiveness and superiority.

关键词： Reduced deep convolutional stack autoencoder Information-oriented variational mode decomposition Minimum variance multikernel random vector functional link network Single step and multistep forecasting field programmable gate array

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Design of Polar Turbo Product Codes for Optical Fiber Communications Based on FPGA

Design of Polar Turbo Product Codes for Optical Fiber Commun...

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Asia Communications and Photonics Conference (ACP) / International Conference on Information Photonics and Optical Communications (IPOC)

作者： Chen, Xinda Qian, Weifeng Tao, Kai Wang, Yongben Wang, Weiming Wang, Yinghao ZTE Corp Key Lab Mobile Network & Mobile Multimedia Techno Wuhan 430000 Peoples R China Inner Mongolia Mobile Commun Grp Co Ltd Dept Project Construct Hohhot Henan Peoples R China

ISBN: (纸本)9781665481557

We propose a dynamic parameter decoding algorithm for polar turbo product codes (polar-TPC), obtaining performance gains of up to 0.1 dB without additional complexity. Employing field programmable gate array (FPGA) emulations, the performance of polar-TPC at ultra-low bit error rate (BER) is verified. In addition, we construct a 16.7% concatenated polar-TPC and Reed-Solomon (RS) code with a pre-forward-error-correction BER of 2.05x10(-2), applicable to optical fiber communications.

关键词： forward error correction polar turbo product code Reed-Solomon code error-floor field programmable gate array optical fiber communication

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Low Phase Shift Filter for Current Sensing based on the Difference between AC Machine Models with and without Iron Losses 24

Low Phase Shift Filter for Current Sensing based on the Diff...

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24th European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Himker, Niklas Kruempelmann, Marcel Mertens, Axel Leibniz Univ Hannover Inst Dr Syst & Power Elect Welfengarten 1 Hannover Germany

ISBN: (纸本)9789075815399

For a current measurement with the goals of a high bandwidth and a high sampling rate, a filter with minimal phase shift is proposed. The filter is analytically derived using the differential equation of an induction machine (IM) model and is based on the difference in the stator current behaviour of machines with and without iron losses. To use this filter, only two parameters need to be set.

关键词： AC machine Vector control Current sensor field programmable gate array

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Rapid Prototyping and Hardware-In-the-Loop Verification of Enhanced Sliding Mode Control of an Asynchronous Machine Using a Xilinx System Generator and an FPGA-Zynq Board

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年

作者： Tarkhani, Rakia Krim, Saber Mimouni, Mohamed Faouzi Univ Monastir Natl Engn Sch Monastir Lab Automat Elect Syst & Environm Monastir Tunisia Univ Monastir Fac Sci Monastir Monastir Tunisia Univ Kairouan Higher Inst Appl Sci & Technol Kasserine Kairouan Tunisia

This article describes rapid prototyping using the Xilinx system generator (XSG) of a direct field oriented control (DFOC) strategy based on improved super twisting sliding mode controllers (ISTSMCs) of an asynchronous machine to succeed a hardware implementation on a field programmable gate array (FPGA) board. The main goal is to enhance regulation loops governing rotor speed, rotor flux, and stator current components within the classical DFOC structure. These ISTSMCs replace integral proportional controllers, resulting in improved system dynamics, faster speed and torque responses, and heightened robustness against load and rotor resistance variations, surpassing other control methods. Additionally, the article aims to implement this DFOC-ISTSMC method on an FPGA board to reduce control system sampling time and loop delays, leveraging the FPGA's parallel processing capabilities. The hardware architecture is designed using XSG in the MATLAB/Simulink environment, enabling effective simulation, testing, discrete algorithm creation, and VHDL code generation for FPGA implementation via the hardware-in-the-loop (HIL) process. Assessment involves digital simulation studies and HIL processes using XSG with a Xilinx FPGA Zynq 7000 under MATLAB/Simulink, demonstrating improved system performance, reduced time delays, and efficient FPGA utilization. This approach validates the effectiveness of the proposed DFOC-ISTSMC algorithm in enhancing control strategy for asynchronous machines.

关键词： rapid prototyping Xilinx system generator field programmable gate array asynchronous machine super twisting sliding mode controller

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Design of an FPGA-Based Controller for Fast Scanning Probe Microscopy

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SENSORS 2024年第18期24卷 6108-6108页

作者： Gregorat, Leonardo Cautero, Marco Carrato, Sergio Giuressi, Dario Panighel, Mirco Cautero, Giuseppe Esch, Friedrich Univ Trieste DIA Dipartimento Ingn & Architettura I-34127 Trieste Italy Elettra Sincrotrone Trieste SCpA Sci PkStr Statale 14Km 163 5 I-34149 Basovizza Italy Univ Trieste Dipartimento Fis I-34127 Trieste Italy CNR IOM Ist Officina Mat Lab TASC Str Statale 14Km 163 5 I-34149 Basovizza Italy INFN Ist Nazl Fis Nucl Sez Trieste Via Valerio 2 I-34127 Trieste Italy Tech Univ Munich TUM Sch Nat Sci Chair Phys Chem Lichtenbergstr 4 D-85748 Garching Germany Tech Univ Munich TUM Sch Nat Sci Catalysis Res Ctr Dept Chem Lichtenbergstr 4 D-85748 Garching Germany Univ Luxembourg Dept Phys & Mat Sci Scanning Probe Microscopy Lab Luxembourg Luxembourg

Atomic-scale imaging using scanning probe microscopy is a pivotal method for investigating the morphology and physico-chemical properties of nanostructured surfaces. Time resolution represents a significant limitation of this technique, as typical image acquisition times are on the order of several seconds or even a few minutes, while dynamic processes-such as surface restructuring or particle sintering, to be observed upon external stimuli such as changes in gas atmosphere or electrochemical potential-often occur within timescales shorter than a second. In this article, we present a fully redesigned field programmable gate array (FPGA)-based instrument that can be integrated into most commercially available standard scanning probe microscopes. This instrument not only significantly accelerates the acquisition of atomic-scale images by orders of magnitude but also enables the tracking of moving features such as adatoms, vacancies, or clusters across the surface ("atom tracking") due to the parallel execution of sophisticated control and acquisition algorithms and the fast exchange of data with an external processor. Each of these measurement modes requires a complex series of operations within the FPGA that are explained in detail.

关键词： scanning probe microscopy field programmable gate array digital signal processing fast scanning tunneling microscopy atom tracking

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Heterogeneous coexistence of extremely many attractors in adaptive synapse neuron considering memristive EMI

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CHAOS SOLITONS & FRACTALS 2024年 178卷

作者： Zhang, Jianlin Bao, Han Yu, Xihong Chen, Bei Changzhou Univ Sch Microelect & Control Engn Changzhou 213159 Peoples R China

Heterogeneous coexistence of multiple attractors was exhibited by a two-dimensional (2-D) non-autonomous model of adaptive synapse neuron with external excitation. Considering that electromagnetic induction (EMI) is an unavoidable interference in the electrophysiological environment, and memristors are often used to simulate the EMI induced by neuron membrane potentials, can the memristive EMI current be used instead of the external excitation current in the 2-D non-autonomous adaptive synapse neuron model? To this end, this paper proposes a three-dimensional (3-D) autonomous model of memristor-based adaptive synapse neuron (MASN) considering EMI. The MASN model has extremely many equilibrium points with complicated stability evolutions, resulting in the heterogeneous coexistence of extremely many attractors. The heterogeneously coexisting behaviors of the MASN model are investigated through some numerical methods, and the globally coexisting bifurcation behaviors, initials-relied kinetic distributions, and initials-sensitive riddled basins of attraction are thereby demonstrated. Furthermore, based on field programmable gate array (FPGA) platform, the MASN model is digitally implemented and the correctness of the numerical results is verified by hardware experiments.

关键词： Memristor-based adaptive synapse neuron Electromagnetic induction Heterogeneous coexistence Equilibrium point field programmable gate array

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An explainable embedded neural system for on-board ship detection from optical satellite imagery

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Ieracitano, Cosimo Mammone, Nadia Spagnolo, Fanny Frustaci, Fabio Perri, Stefania Corsonello, Pasquale Morabito, Francesco C. Univ Mediterranea Reggio Calabria DICEAM Via Zehender Loc Feo Vito I-89122 Reggio Di Calabria Italy Univ Calabria DIMES Via P Bucci Arcavacata Rende I-87036 Arcavacata Di Rende Italy Univ Calabria DIMEG Via P BucciArcavacata Rende I-87036 Arcavacata Di Rende Italy

Automatic ship detection from spaceborne systems such as satellites or aircrafts, raises considerable attention in sea surface monitoring because of the several applications in military and civilian field. In this context, processing satellite images on-board would reduce the latency time especially for emergency situations. In this paper, an hardware-oriented (HO) ship detection system based on a customized Convolutional Neural Network (CNN), here referred to as HO-ShipNet, , is proposed and tested on a revised version of the "Ships in Satellite Imagery"(SSI) Kaggle dataset, reporting detection accuracy of up to 95%. Furthermore, the explainability of HO-ShipNet is investigated by means of explainable Artificial Intelligence (xAI) techniques (i.e., Local Interpretable Model-Agnostic Explanation (LIME) and Occlusion Sensitivuty Analysis (OSA)), in order to understand the reasoning behind the HO-ShipNet decisions by detecting the most important input features and consequently ensure the trustworthiness of the model itself. Finally, HO-ShipNet is also implemented on the heterogeneous Xilinx xc7z045ffg900-2 SoC field programmable gate array (FPGA) outperforming stateof-the-art FPGA-based accelerators dealing with high-resolution frames. The promising results encourage the potential deployment of the proposed system for on-board applications.

关键词： Ship detection Explainable artificial intelligence Optical satellite images field programmable gate array

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Modeling and simulation of three-phase IGBT full-bridge inverter circuit based on FPGA

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期118卷

作者： Zhang, Junchu Yan, Jianjun Zhang, Yongming East China Univ Sci & Technol Sch Mech & Power Engn Shanghai 200237 Peoples R China East China Univ Sci & Technol Shanghai Key Lab Intelligent Sensing & Detect Tech Shanghai 200237 Peoples R China Shanghai Inst Elect Engn Sch Elect Engn Shanghai 201306 Peoples R China

The field of motor drive makes extensive use of electronic power modeling and simulation of three-phase IGBT full-bridge inverter circuits. The accuracy and computational efficiency of these models have a direct impact on the dependability of the motor control system. The majority of earlier research focused solely on static processes of turning on and off in IGBTs, disregarding the transient proesses that occur when three-phase IGBT full-bridge inverter circuits are switched at high frequencies. This has an impact on the circuits' accuracy in real-time simulation. Therefore, this paper proposes and builds a field-programmable logic gate array (FPGA)-based steady-state and transient dual-phase three-phase IGBT full-bridge inverter circuit model for the static and transient characteristics of the insulated gate bipolar transistor (IGBT) element in the circuit. Depending on whether or not the switching states of the six IGBTs in the three-phase IGBT fullbridge inverter circuit are altered, the simulation process is split into steady state and transient phases. In the steady state phase with large step size, the circuit is d iscretized using the binary L/ C approach. In the transient phase, the transient process is divided into several small-step-long time domains. Real-time simulation waveforms are generated by interleaving and combining the multistage fitting method's solution of the circuit's transient waveforms at tiny step lengths with the steady state phase. Finally, in order to demonstrate the accuracy of the circuit model in this work, the simulation results of the two-stage three-phase IGBT full-bridge inverter circuit model based on FPGA are compared with those of the conventional ideal model for waveform comparison and data analysis.

关键词： field programmable gate array Three-phase inverter circuit Switch characteristics Real-time simulation

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Radar-Jamming Decision-Making Based on Improved Q-Learning and FPGA Hardware Implementation

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REMOTE SENSING 2024年第7期16卷 1190页

作者： Zheng, Shujian Zhang, Chudi Hu, Jun Xu, Shiyou Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China

In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar-jamming decision-making algorithm based on an improved Q-Learning algorithm. This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a field programmable gate array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using a hardware description language. Then, the reinforcement learning algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtains considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar-jamming decision-making algorithm implementation.

关键词： radar-jamming decision-making reinforcement learning field programmable gate array

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