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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1136 条记录，以下是441-450 订阅

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Energy Storage System Control Scheme based on Pretrained Adaptive Dynamic programming for PSOs

Energy Storage System Control Scheme based on Pretrained Ada...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： R. Rajkumar Chudamani Ramineni D. Sateesh Kumar S. Kaliappan S Farook Christu Paul Ramaian Department of Electronics and Instrumentation Engineering Kongu Engineering College Erode Tamil Nadu India Department of Mathematics Prasad V. Potluri Siddhartha Institute of Technology Vijayawada Andhra Pradesh India Department of Engineering Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi Andhra Pradesh India Department of Automobile Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

With the increasing requirement of power the storage of energy becomes more and more challenging. To tackle this problem control schemes for energy storage systems are introduced. Over the years, various techniques were implemented in control schemes to improve its performance. In recent years, machine learning is widely used in control schemes for energy storage systems, one of those machine learning techniques is Adaptive Dynamic programming (ADP). ADP is a type of algorithm that allows the subject to improve its behavior in an unknown domain while interacting over a period of time. ADP is an online self-learning algorithm that uses the data collected over a period of time to improve itself. this study proposes an optimal energy control scheme for energy storage systems using ADP. the algorithm will take the parameters such as load demand, power consumed in the time period, and the cost of electricity in the given location as its input and the algorithm’s final objective is to improve the performance index of the energy storage system, the better the performance index the lower the electricity cost. this method will provide an optimum performance index for the energy storage system based on the input parameters presented to the algorithm. To improve the efficiency and to reduce the time consumption for self-learning of ADP algorithm pretraining was required. To retrain the ADP algorithm another algorithm is used i.e. Particle Swarm Optimization (PSO) was used. the PSO algorithm can find the optimum solution in a defined solution space. the pretrained ADP model provides an optimum performance index for the energy storage systems which reduces the cost of electricity.

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Neural Network-Accelerated Trajectory Optimization for Launch Vehicle Landing

Neural Network-Accelerated Trajectory Optimization for Launc...

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IEEE international conference on Control science and Systems engineering (CCSSE)

作者： Zhipeng Shen Shiyu Zhou Jianglong Yu Hailong Huang Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong China Department of Biomedical Engineering City University of Hong Kong Hong Kong China School of Automation Science and Electrical Engineering Beihang University Beijing China

this paper presents a novel trajectory optimization method for the 6-degrees-of-freedom powered landing problem in aerospace guidance and control. the method combines machine learning and convex optimization to achieve real-time performance. Specifically, we formulate the powered landing problem as an optimal control problem and transform it into a convex optimization problem. To enhance the state-of-the-art sequential convex programming (SCP) algorithm, we use a deep neural network as an initial trajectory generator to provide a satisfactory initial guess for the SCP algorithm. Simulation results show that the proposed method achieves precise guidance of the vehicle to the landing site. Monte Carlo tests demonstrate that it can save an average of 40.8% of the computation time compared to the SCP method. therefore, the proposed scheme is suitable for real-time applications in the aerospace industry.

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A Compact-Structured Convolutional Neural Network for Single Image Denoising and Super-Resolution 7th

A Compact-Structured Convolutional Neural Network for Single...

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7th international conference on Computer Vision and Image Processing, CVIP 2022

作者： Deivalakshmi, S. Sandana, J. Sudaroli Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031314063

In this work, a simple and compact-structured convolutional neural network (CNN) entitled as Denoising and Super-Resolution (DnSR) network for single image denoising and super resolution (SISR) is presented. Using sparsity property (a smaller number of large intensity values and a larger number of small intensity values), the training of DnSR is made to converge early. We obtain the sparsity by learning residuals of image pixels instead of learning raw image pixels particularly for SR. We use a smaller receptive field of the image patch for faster training. We use gradient clipping to train DnSR net with an extremely large learning rate to overcome gradient inconsistency during training. An end-to-end mapping is done by learning the complex mapping between low-resolution (LR) image patches and the corresponding residual of high resolution (AHR) image patches using DnSR net. the proposed DnSR net uses a smaller number of parameters (approximately 3 times lesser) than the existing state-of-the-art (SOTA) SRCNN method. the proposed DnSR provides superior reconstruction accuracy with less artifacts when compared to other SOTA methods. the performance of the DnSR is assessed qualitatively and quantitatively using different image quality metrics. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Mapping

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A Precision-Compensated Linear Power Flow Model Based on Mapping Voltage Magnitudes from Logarithmic Space to Squared Space

A Precision-Compensated Linear Power Flow Model Based on Map...

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IEEE international conference on Power and Energy Applications (ICPEA)

作者： Haipeng Lai Zhigang Li Jiahui Zhang Xiang Bai Huajian Li Hongyuan Liang J. H. Zheng School of Electric Power Engineering South China University of Technology Guangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shanxi Energy Internet Research Institute Taiyuan China

ISBN: (数字)9798350356113

ISBN: (纸本)9798350356120

Linear power flow is widely applied in various areas of power systems. the DC power flow model is a classical linear power flow model, but its main limitation is the inability to account for voltage magnitude. Other classical models that consider voltage magnitude are typically linearized in the original voltage magnitude space, leading to a computational accuracy bottleneck. To address this issue, this study first proposes an improved linear power flow model. the model remaps the voltage magnitude from logarithmic space to square space, preserving the nonlinear characteristics of the original current equation and compensating for the voltage phase angle. this approach overcomes the accuracy bottleneck of classical models. then, through matrix partitioning and transformation, the model achieves complete decoupling of the voltage magnitude and phase angle, avoiding nonconvex quadratic programming problems in optimization and enhancing computational efficiency. Finally, the accuracy and speed of the proposed model are validated through simulations across multiple test systems.

关键词： Visualization Accuracy Computational modeling Voltage Mathematical models Numerical models Power systems Quadratic programming Optimization Load flow

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Speed Planning Strategy in Urban Environments Considering Passenger’s Preferences, Traffic Signals and Road Limits

Speed Planning Strategy in Urban Environments Considering Pa...

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conference on Vehicle Control and Intelligence (CVCI)

作者： Yongjun Yan Dongming Han Qixiang Zhang Zhenwu Fang Jinxiang Wang Guodong Yin School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Southeast University Nanjing China

A personalized speed planning strategy for autonomous vehicles (AVs) is proposed in this paper, which can generate individual speed trajectories for passengers with different driving preferences in typical urban environments with multiple traffic signals, road terrain, and speed limits. Firstly, the K-means method is used to analyze and cluster the drivers’ data in the case of free driving (not following the front vehicle) in the NGSIM dataset, and then the actual longitudinal speeds and accelerations preference of different drivers’ groups in the urban environments can be obtained. Secondly, according to the information on road restrictions, traffic signals phase, and driving preferences of the passengers in AVs, a decision-making mechanism is designed to calculate the personalized traveling time of each traffic signal section, in which the preferences of speeds are transformed into the preferences of traveling time. then, the dynamic programming (DP) algorithm and the bisection method are integrated to plan personalized speed trajectories for each traffic signal road section, with consideration of the factors such as preferred traveling time in this road section, road speed limits, passenger comfort, driving economy, etc. Finally, a typical urban scenario is designed for algorithm verification and performance evaluation. the test results show that the strategy can generate safe, comfortable, energy-saving, and personalized speed trajectories for different groups of passengers.

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A Lower Bound Guided Hierarchical Approach for a Two-Stage Scheduling Problem

A Lower Bound Guided Hierarchical Approach for a Two-Stage S...

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IEEE international conference on Automation science and engineering (CASE)

作者： Jiaxi Wu Ye Liang Leyuan Shi Department of Industrial Engineering & Management Peking University China China Academy of Information and Communications Technology China Department of Industrial & Systems Engineering University of Wisconsin-Madison WI USA

Motivated by a challenging and common production scenario in the discrete manufacturing industry, we study a two-stage scheduling problem where sub-components are processed in the first stage and then grouped together to complete the final products. Our goal is to minimize the completion time of the last product, i.e., the makespan. We first propose a novel Mixed Integer Linear programming (MILP) model to formally define the problem, which is capable of finding optimum solutions for small-scale problems. To tackle the practical challenge of large-scale problems, we design an efficient lower-bound guided hierarchical heuristic (LBGH) to produce high-quality solutions within a reasonable amount of time. Numerical experiments demonstrate that the LBGH outperforms state-of-the-art meta-heuristics in terms of solution quality and computational efficiency.

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Smart Garbage Monitoring System with Dynamic programming Model

Smart Garbage Monitoring System with Dynamic Programming Mod...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： N. Sugitha Mirrahashini K Harish M K Devakumar G Praveen Kumar S Department of Electronics & communication Engineering Saveetha Engineering college Chennai India Department of Medical Electronics Saveetha Engineering college Chennai India

the most significant issue in cities is garbage collection. the average amount of waste a person produces daily rises as the population grows, creating severe waste management issues. Any municipality's most pressing issues are bin status monitoring, locating the shortest route, and vehicle costs. the problem's consequences result in inadequate waste monitoring and management procedures. An effective garbage collection system that periodically measures the amount of garbage in the bins is used to solve these issues. the shortest route can be determined using this bin's status. the vehicle only went to the places where the bins in the city are full. the Wi-Fi module will transmit all of the information to the municipality. the main objective of the proposed work is to create a flexible application that dynamically solve the issue of late bin clearance, poor management of bins etc. the path selection is made by using the Travelling Salesman Algorithm in the Dynamic programming approach. the sensor network is utilized to gather information regarding the garbage contents in the bins and remotely trigger the request for a cleaning process using the Internet of things. the internet is connected with microcontroller hardware using a Node-MCU device, or ESP8266, a small internet-supporting module that works as a plug-and-play module to any microcontroller. the primary objective of this system is to send information to the municipality and the mobile application regarding the status of the bin, the minimum cost path, the total travel distance, and the kind of vehicle required. this system aims to shorten travel time, save money on diesel and human resources for garbage collection, and improve waste collection and monitoring.

关键词： Waste management Wireless sensor networks Costs Microcontrollers Urban areas Sociology Mobile applications

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Parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators 7th

Parallel Pileup Correction for Nuclear Spectrometric Data o...

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7th international conference on Smart Computing and Communication, SmartCom 2022

作者： Chen, Zikang Kong, Xiangcong Zheng, Xiaoying Zhu, Yongxin Trigano, Tom Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Shamoon College of Engineering Ashdod Israel

ISBN: (纸本)9783031281235

Spectroscopy devices suffer from the pulse pileup phenomenon, caused by overlapping of the signals. the energy-domain based pileup correction algorithm estimates the pulse energy distribution by measuring the duration and energies of pileups directly and does not need to identify each individual pulses. the correction algorithm can efficiently recovers the energy spectrum even under a very high photon arrival rate. However, the correction algorithm is sequential in nature and is slow when the energy resolution is high. A fast parallel implementation of the original correction algorithm is proposed in this paper. the parallel counterpart leverages state-of-the-art many-core system technology and achieves a nearly linear acceleration when the problem size scales. the speedup ratio exceeds 1,000 when the energy spectrum is split into 2,048 bins. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： X ray spectroscopy

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MMFA-Net: A New Brain Tumor Segmentation Method Based on Multi-modal Multi-scale Feature Aggregation 7th

MMFA-Net: A New Brain Tumor Segmentation Method Based on Mu...

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7th Asian conference on Pattern Recognition, ACPR 2023

作者： Dong, Yulin Jiang, Zhizhuo Liu, Yu Tsinghua Shenzhen International Graduate School Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9783031476365

Precise brain tumor segmentation is crucial for brain cancer diagnosis. Magnetic resonance imaging (MRI) plays an essential role in tumor segmentation due to the abundant information in multi-model data. However, current deep learning-based image segmentation methods expose their limitations in fully extracting and utilizing the comprehensive information from multi-modal MRI. To tackle this issue, we propose a multi-modal multi-scale feature aggregation network (MMFA-Net) for brain tumor segmentation in MRI images. A well-designed dual-branch framework employing spatial location activation (SPA) module is proposed in MMFA-Net to share the location information of the lesion area extracted by each branch. Moreover, the multi-scale feature aggregation (MFA) module is developed to fuse the global and local information to improve the performance of brain tumor segmentation. Extensive experimental results based on the public BraTS2020, BraTS2021, and ISBI datasets indicate that the proposed MMFA-Net exhibits superior performance compared to several state-of-the-art methods in the domain of multi-modal brain tumor segmentation. this superiority is most evident in the Dice Score and 95 % -Hausdorff Distance (HD95) metrics, where MMFA-Net demonstrates an average improvement of 2.5 % in the Dice score on tumor segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Magnetic resonance imaging

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Robust Optimal Adaptive Cruise Control Based on ADP for Intelligent Vehicles under Bounded Disturbances

Robust Optimal Adaptive Cruise Control Based on ADP for Inte...

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conference on Vehicle Control and Intelligence (CVCI)

作者： Yixiao Zhan Chunjie Zhai Xiang Long Bo Wang Huiqin Chen Chuqiao Chen School of Automation Hangzhou Dianzi University Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Mechanical Engineering Hangzhou Dianzi University Hangzhou China School of Cyberspace Security Hangzhou Dianzi University Hangzhou China

In the Adaptive Cruise Control mode, intelligent vehicles often encounter various matching disturbances that can impact the effectiveness of the Adaptive Cruise Control system. To achieve tracking performance and driving comfort of adaptive cruise control for intelligent vehicles under bounded matching disturbances, this paper proposes a robust optimal Adaptive Cruise Control strategy. First, an optimal controller is designed to realize the driving safety and comfort. then, to solve the optimal control law, we constructed a critic network to approximate the unknown parameter by Adaptive Dynamic programming, and the asymptotically stability and Uniformly Ultimately Bounded stability are analyzed using Lyapunov analysis. Finally, the effectiveness of the control strategy is verified by simulations.

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