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Solar Power Estimation Using GIS Considering Shadow Effects for Distribution system Planning

Solar Power Estimation Using GIS Considering Shadow Effects ...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Bunme, Pawita Shiota, Atsushi Mitani, Yasunori Kyushu Inst Technol Dept Elect & Elect Engn Kitakyushu Fukuoka Japan Gen Affairs Bur City Kitakyushu Dept Informat Technol Kitakyushu Fukuoka Japan

ISBN: (数字)9781728174556

ISBN: (纸本)9781728174556

During the last decades, photovoltaic (PV) panels have been widely installed in Japanese residential areas to achieve clean and renewable energy. this work employs DSM (Digital Surface Model) layers to extract important elevations, such as trees and buildings, in conjunction with assumptions of cloud movement processes from GIS (Geographic Information system) to obtain realistic solar power affected by solar radiation shading on the PV panel. With GIS technology, the solar radiation shading effects are combined with distribution system modeling to observe the significant impact of shadows in the overall distribution system. Moreover, this research can facilitate a more effective distribution system planning by evaluating the influence of PV power variations, assuming a large number of installations.

关键词： Solar Radiation Photovoltaic Geographic Information system (GIS) Digital Surface Model (DSM) Distribution system

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Application of two-point boundary problem with optimization of a candidate function 20

Application of two-point boundary problem with optimization ...

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20th International conference on Control, Automation and systems (ICCAS)

作者： Docekal, Tomas Ozana, Stepan VSB Tech Univ Ostrava Dept Cybernet & Biomed Engn FEECS 17 Listopadu 15-2172 Ostrava 70833 Czech Republic

ISBN: (数字)9788993215205

ISBN: (纸本)9788993215199

the paper deals with the control design of nonlinear systems. It presents the application of the presented methodology on a swing-up of a single inverted pendulum. It is aimed at planning the reference state trajectories and the appropriate feedforward control signal and formulated by a two-point boundary value problem. One of the complications during the solution tackles the problem of providing a good initial guess for state trajectories. the additional optimization procedure for dealing with this issue is described in this paper. there is an emphasis on the universality of the described solution of the boundary value problem because it can be applied for a variety of different nonlinear systems.

关键词： boundary value problem optimization inverted pendulum

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thermoelectric system Economics - the Apex: New Paradigms in Manufacturing and Interface Performance Relationships Driving system Cost optimizations 20

Thermoelectric System Economics - The Apex: New Paradigms in...

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20th InterSociety conference on thermal and thermomechanical Phenomena in Electronic systems (Itherm)

作者： Hendricks, Terry J. CALTECH Jet Prop Lab NASA Prop Thermal & Mat Syst 4800 Oak Grove Dr Pasadena CA 91109 USA

ISBN: (纸本)9781728185392

thermoelectric energy recovery systems require cost-optimized designs with high thermal and thermoelectric (TE) performance to surmount common commercialization barriers controlling the acceptance of thermoelectric generator (TEG) systems. Recent work has addressed this requirement with new analytic tools and paradigms that allow integrated TEG cost-performance analysis and optimization. New work herein has identified the existence of optimized TE element design (i.e., TE element length) for minimizing TEG cost and provides design guidelines associated with critical thermal and electrical contact resistance effects and TE manufacturing sensitivities. Once optimum hot-side heat flux criteria are satisfied, this work highlights the tradeoffs between interface resistance effects and manufacturing cost effects in determining optimum-cost TE element lengths, and then quantifies the increase in optimum-cost TE lengths as manufacturing costs become more sensitive to TE element lengths. Optimized TE element design criteria and key non-dimensional design parameters ( i.e., [C ''' L-TE/C ''], [kappa(TE)/kappa(con)], [rho(con)/(rho L-TE,ave(TE))]) driving the design are presented and explained showing the interdependencies between manufacturing cost parameters and thermal and electrical interface impacts on integrated TE cost-performance analysis and design. Comprehensive TEG system cost-performance relationships clearly demonstrate the two parameters that most greatly impact the TEG system cost are heat exchanger costs ($/(W/K)) and TE hot side heat flux. Reducing heat exhanger cost from $1/(W/K) to $0.5/(W/K) can decrease TEG system cost by $2/W to $4/W depending on TE hot side heat fluxes from 4-10 W/cm(2). the impact of TE hot side heat flux on TEG system cost is larger;reducing TE system cost by factors of 2-4 as TE hot side heat flux increases from 4 W/cm2 to high levels around 18 W/cm2. the levels of thermal contact resistance and electrical contact resistance required

关键词： thermoelectric systems economics energy recovery interface effects manufacturing costs cost optimization

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optimization Model of Electric Vehicle Charging Station Layout 20

Optimization Model of Electric Vehicle Charging Station Layo...

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20th COTA International conference of Transportation Professionals (CICTP) - Advanced Transportation, Enhanced Connection

作者： Zhao, Jiayi Huang, Linshuying Lv, Xinli Wang, Jinzhao Yin, Jianyuan Changan Univ Sch Automobile Xian 710064 Shanxi Peoples R China

ISBN: (数字)9780784482933

ISBN: (纸本)9780784482933

the paper proposes a new method, which is based on the superposition of the road network and power distribution network, for modeling the location of an electric vehicle charging station. the construction cost, operating cost, voltage deviation, unreliable penalty cost, and driving distance cost are taken as the objective function. Because the characteristic of the location and volume model established for the paper is non-linear, large-scale and multi-constraint, to ensure the fast and accurate solution of the model, we used the improved Chicken Swarm optimization Algorithm (CSO)to solve the model. then, determining the layout plan for electric vehicle charging stations in the example area. the result shows that the solution based on the improved Chicken Swarm optimization Algorithm is suitable.

关键词： Electric vehicle Charging station Optimal layout CSO

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Research on a Wargaming system for Deep Reinforcement Learning

Research on a Wargaming System for Deep Reinforcement Learni...

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International conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Zelong Sun Yanfang Fu Zijian Cao Kailin Lei Zihan Li Bo Lu Xin Liang School of Weapon Scince and Technological Xi^an Technological University Xi^an China School of Computer Scince and Engineering Xi^an Technological University Xi^an China The 20th Research Institute of China Electronics Technology Corporation China

the automatic decision-making confrontation for wargames is faced with the related difficulties of large amount of information, incomplete information and high decision-making complexity. To overcome these problems, this paper sorts out the basic concepts and process framework of combat mission planning in wargames, and analyzes the application method of deep reinforcement learning in the intelligent game process based on wargaming system, and proposes the optimization scheme of deep reinforcement learning in wargaming system, which provides a new research idea for the intelligent development of wargaming. Experiments show that the intelligent wargaming system based on deep reinforcement learning has significant advantages over the traditional wargaming system in data processing, decision-making speed and strategies.

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Data Mining in system-Level Design Space Exploration of Embedded systems 1

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20th International conference on Embedded Computer systems - Architectures, modeling, and Simulation (SAMOS)

作者： Richthammer, Valentina Scheinert, Tobias Glass, Michael Ulm Univ Inst Embedded Syst Real Time Syst Ulm Germany

ISBN: (数字)9783030609399

ISBN: (纸本)9783030609399;9783030609382

With increasingly complex applications and architectures, the task of determining Pareto-optimal implementations at the system level becomes a challenge even for state-of-the-art Design Space Exploration (DSE) methodologies. In this field, nature-inspired techniques such as Evolutionary Algorithms (EAs) are frequently employed, since they are well-suited to the multi-objective and hard-constrained nature of the DSE optimization problem. On the other hand, meta-heuristic approaches are problem-agnostic and are often observed to converge relatively quickly. Furthermore, this type of optimization lacks explainability, i.e. the way in which the optimization algorithm arrives at improved solutions as well as the individual contributions of design decisions to the resulting quality of a solution are not at all clear - and are consequently not utilized during DSE as of yet. To remedy this, we propose the integration of automated data-mining techniques into state-of-the-art DSE flows. Data mining is, thereby, used for (a) the automatic extraction and generation of previously untapped information from the optimization process to be (b) incorporated into the DSE to enhance optimization quality. We present a variety of ways to extract and include relevant knowledge during DSE, as well as (c) several possibilities to gain insight into the interdependence between decision variables and optimization objectives. Experimental results for benchmark systems for large-scale many-cores to networked embedded systems demonstrate the potential of the proposed techniques to improve the quality of the optimized implementations at no DSE-time overhead.

关键词： Design automation Knowledge discovery

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Power Battery Reverse Logistics Network optimization Model Under Tax Relief Policy 7th

Power Battery Reverse Logistics Network Optimization Model U...

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7th International conference on Life system modeling and Simulation, LSMS 2021, and 7th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Guan, Qian Yang, Yuxiang China Jiliang University Hangzhou310018 China

ISBN: (纸本)9789811672095

In this paper, a two-stage mixed integer linear programming model is established based on 1) the uncertainty of quantity and quality of used power battery recycling and 2) the government's tax reduction policy. A solution strategy combining SAA with genetic algorithm is proposed. the effectiveness of the stochastic model is verified in numerical experiments, where the model results are compared and analyzed under settings with both certain and uncertain parameters. In addition, this paper investigates the impact of tax relief policy on the layout decision and cost of logistics network. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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20th International conference on Artificial Intelligence and Soft Computing, ICAISC 2021

20th International Conference on Artificial Intelligence and...

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20th International conference on Artificial Intelligence and Soft Computing, ICAISC 2021

ISBN: (纸本)9783030878962

the proceedings contain 90 papers. the special focus in this conference is on Artificial Intelligence and Soft Computing. the topics include: Efficient Recurrent Neural Network Architecture for Musical Style Transfer;Impact of ELM Parameters and Investment Horizon for Currency Exchange Prediction;spectroscopy-Based Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks;possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks;convolutional Autoencoder Based Textile Defect Detection Under Unconstrained Setting;clustering-Based Adaptive Self-Organizing Map;applying Machine Learning Techniques to Identify Damaged Potatoes;Quantifying the Severity of Common Rust in Maize Using Mask R-CNN;federated Learning Model with Augmentation and Samples Exchange Mechanism;factor Augmented Artificial Neural Network vs Deep Learning for Forecasting Global Liquidity Dynamics;flexible Data Augmentation in Off-Policy Reinforcement Learning;polynomial Neural Forms Using Feedforward Neural Networks for Solving Differential Equations;quantum-Hybrid Neural Vector Quantization – A Mathematical Approach;A Graphic CNN-LSTM Model for Stock Price Predication;applying Convolutional Neural Networks for Stock Market Trends Identification;the Extreme Value Evolving Predictor in Multiple Time Series Learning;towards Synthetic Multivariate Time Series Generation for Flare Forecasting;the Streaming Approach to Training Restricted Boltzmann Machines;abrupt Change Detection by the Nonparametric Approach Based on Orthogonal Series Estimates;Recommendation system for Signal Processing in SHM;integrate-and-Fire Neurons for Low-Powered Pattern Recognition;monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines;a Proposal for Hybrid Memories Management Exploring Fuzzy-Based Page Migration Policy;a Learning Automata-Based Approach to Lifetime optimization in Wireless Sensor Networks.

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20th International conference on Artificial Intelligence and Soft Computing, ICAISC 2021

20th International Conference on Artificial Intelligence and...

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20th International conference on Artificial Intelligence and Soft Computing, ICAISC 2021

ISBN: (纸本)9783030879853

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A Deep Learning-based Pest Insect Monitoring system for Ultra-low Power Pocket-sized Drones

A Deep Learning-based Pest Insect Monitoring System for Ultr...

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International conference on Distributed Computing in Sensor systems (DCOSS)

作者： Luca Crupi Luca Butera Alberto Ferrante Daniele Palossi Dalle Molle Institute for Artificial Intelligence (IDSIA) USI and SUPSI Lugano Switzerland Integrated Systems Laboratory (IIS) ETH Zürich Zürich Switzerland

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Smart farming and precision agriculture represent game-changer technologies for efficient and sustainable agribusiness. Miniaturized palm-sized drones can act as flexible smart sensors inspecting crops, looking for early signs of potential pest outbreaking. However, achieving such an ambitious goal requires hardware-software codesign to develop accurate deep learning (DL) detection models while keeping memory and computational needs under an ultra-tight budget, i.e., a few MB on-chip memory and a few 100s mW power envelope. this work presents a novel vertically integrated solution featuring two ultra-low power system-on-Chips (SoCs), i.e., the dual-core STM32H74 and a multi-core GWT GAP9, running two State-of-the-Art DL models for detecting the Popillia japonica bug. We fine-tune both models for our image-based detection task, quantize them in 8-bit integers, and deploy them on the two SoCs. On the STM32H74, we deploy a FOMO-MobileNetV2 model, achieving a mean average precision (mAP) of 0.66 and running at 16.1 frame/s within 498 mW. While on the GAP9 SoC, we deploy a more complex SSDLite-MobileNetV3, which scores an mAP of 0.79 and peaks at 6.8 frame/s within 33 mW. Compared to a top-notch RetinaNet-ResNet101-FPN full-precision baseline, which requires 14.9× more memory and 300× more operations per inference, our best model drops only 15% in mAP, paving the way toward autonomous palm-sized drones capable of lightweight and precise pest detection.

关键词： Smart agriculture Quantization (signal) Computational modeling Memory management Computer bugs Low power electronics throughput

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