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检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

共 1644 条记录，以下是661-670 订阅

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Single-Ended Primary Inductor Converter (SEPIC) Converter, DC-DC Boost, and Cuk Converters with a Self-Guided Reimanuian Residual network: Optimisation of Solar Energy

Single-Ended Primary Inductor Converter (SEPIC) Converter, D...

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Automation, Computing and Renewable Systems (ICACRS), International conference on

作者： M. Sivaram Krishnan V. Govindaraj G. Jerin D. Yuvanandhini N. Sandhiya A Asha Begum Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Tamilnadu

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Finding solutions to reduce emissions of carbon dioxide, which is a toxic gas that produces negative changes on a worldwide scale, is the difficulty that the greenhouse effect faces in the present day. Solar power, which takes advantage of a solar array system that is made up of a variety of components, is one of the environmentally beneficial energy sources. The Maximum Power Point Tracker (MPPT), which ensures that the system generates the most amount of electricity possible, is an essential component of this system. The Insulated Gate Bipolar Transistor (IGBT), which modifies the system's resistance, receives a signal from the MPPT via a Pulse Width Modulator (PWM). The Perturbation and Observation (P&O) algorithm was utilized by conventional controllers; however, it was unable to cope with the rapid changes that occurred in the environment. The new Self-guided Reimannian residual network (SGRRNN) controller produced to enhances efficiency by rapidly adjusting to alterations. The hyperparameters of SGRRNN is optimized using the Energy Valley Optimizer to improve the efficiency. Boost, Cuk, and Single-Ended Primary Inductor Converter (SEPIC) converters are the three DC-DC converters that are compared in this work with the SGRRNN controller on the basis of three data sets consisting of 104, 201, and 1001 respectively. This outcomes in an increase in the effectiveness of solar photovoltaic (PV) systems, with the SGRRNN controller obtaining a peak efficiency of 96.2275% when that controller is integrated with a DC-DC Boost converter.

关键词： Maximum power point trackers Photovoltaic systems Insulated gate bipolar transistors Resistance Renewable energy sources Solar energy Inductors Voltage control optimization Residual neural networks

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Enabling Hyperparameter Tuning of Machine Learning Classifiers in Production 3

Enabling Hyperparameter Tuning of Machine Learning Classifie...

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3rd IEEE International conference on Cognitive Machine Intelligence (IEEE CogMI)

作者： Sandha, Sandeep Singh Aggarwal, Mohit Saha, Swapnil Sayan Srivastava, Mani Univ Calif Los Angeles Los Angeles CA 90095 USA Arm Res Austin TX USA

ISBN: (纸本)9781665416214

Machine learning (ML) classifiers are widely adopted in the learning-enabled components of intelligent Cyber-physical Systems (CPS) and tools used in designing integrated circuits. Due to the impact of the choice of hyperparameters on an ML classifier performance, hyperparameter tuning is a crucial step for application success. However, the practical adoption of existing hyperparameter tuning frameworks in production is hindered due to several factors such as inflexible architecture, limitations of search algorithms, software dependencies, or closed source nature. To enable state-of-the-art hyperparameter tuning in production, we propose the design of a lightweight library (1) having a flexible architecture facilitating usage on arbitrary systems, and (2) providing parallel optimization algorithms supporting mixed parameters (continuous, integer, and categorical), handling runtime failures, and allowing combined classifier selection and hyperparameter tuning (CASH). We present Mango, a black-box optimization library, to realize the proposed design. Mango is currently used in production at Arm for more than 25 months and is available open-source (https://***/ARM-software/mango). Our evaluation shows that Mango outperforms other black-box optimization libraries in tuning hyperparameters of ML classifiers having mixed parameter search spaces. We discuss two use cases of Mango deployed in production at Arm, highlighting its flexible architecture and ease of adoption. The first use case trains ML classifiers on the Dask cluster using Mango to find bugs in Arm's integrated circuits designs. As a second use case, we introduce an AutoML framework deployed on the Kubernetes cluster using Mango. Finally, we present the third use-case of Mango in enabling neural architecture search (NAS) to transfer deep neural networks to TinyML platforms (microcontroller class devices) used by CPS/IoT applications.

关键词： Hyperparameter tuning Machine learning in production Parallel Bayesian optimization

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Faster Policy Learning with Continuous-Time Gradients 3

Faster Policy Learning with Continuous-Time Gradients

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Ainsworth, Samuel Lowrey, Kendall Thickstun, John Harchaoui, Zaid Srinivasa, Siddhartha Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA

We study the estimation of policy gradients for continuous-time systems with known dynamics. By reframing policy learning in continuous-time, we show that it is possible to construct a more efficient and accurate gradient estimator. The standard back-propagation through time estimator (BPTT) computes exact gradients for a crude discretization of the continuous-time system. In contrast, we approximate continuous-time gradients in the original system. With the explicit goal of estimating continuous-time gradients, we are able to discretize adaptively and construct a more efficient policy gradient estimator which we call the Continuous-Time Policy Gradient (CTPG). We show that replacing BPTT policy gradients with more efficient CTPG estimates results in faster and more robust learning in a variety of control tasks and simulators.

关键词： differentiable physics neural ODEs reinforcement learning optimal control policy optimization

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Falcon Optimized CNN for the Detection of Alzheimer's Disease in MRI Image

Falcon Optimized CNN for the Detection of Alzheimer's Diseas...

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Smart Systems for applications in Electrical Sciences (ICSSES), International conference on

作者： S. Febeena Ezhil Jothi L. Mahalakshmi P. Elamurugan Naveenkumar Kaliyan J. Swapna R. Rajeshwari Department of Artificial Intelligence and Data Science Adhi College of Engineering And Technology Kanchipuram Department of Biomedical Engineering Kongunadu College of Engineering and Technology Trichy Department of Instrumentation and Control Engineering Sri Manakula Vinayagar Engineering College Puducherry Department of Computer science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electrical and Electronics Engineering J.P. College of Engineering Ayikudi Tenkasi

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Degenerative illnesses that lead to the eventual death of brain neurons afflict a large number of people. Compared to the traditional Machine Learning (ML) approach, the recent success in the medical community of classifying Alzheimer's Disease (AD) utilized a Deep Learning (DL) technique eliminates the necessity for manual feature extraction techniques. Although DL methods are widely used, there is currently a dearth of trustworthy and comprehensive research on the quantitative analysis of brain Magnetic Resonance Image (MRI) data. In this paper, an effective AD classification approach is proposed that use MRI to classify brain structure and diagnose AD. Gaussian filter is used to eliminate noises at different degrees of complexity, noisy backgrounds, low contrast, and partial volume effect in the original input image. Next, K-Means clustering segmentation is useful for dividing image into different regions based on pixel intensity values. Key features for categorization are extracted using the Gray Level Co-Occurrence Matrix (GLCM) method. A Convolutional Neural network (CNN), enhanced by Falcon optimization Algorithm (FOA), is employed for AD categorization. The proposed system, implemented on the Python platform using the best Alzheimer's MRI dataset, demonstrates an enhanced accuracy of 93.21 %. Additionally, this model-based approach provides a capable avenue for advancing research in the early detection of AD and holds substantial potential in differentiating AD patients from healthy individuals.

关键词： Training Accuracy Magnetic resonance imaging Neurons Magnetic resonance Feature extraction Convolutional neural networks Alzheimer's disease optimization Testing

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Multi-time scale optimal operation of virtual power plants with integrated EV charging and swapping stations

Multi-time scale optimal operation of virtual power plants w...

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Power Systems and Electrical Technology (PSET), IEEE International conference on

作者： Hongtao Yuan Xiaoyuan Xu Zheng Yan Jinsong Liu Bing Shen Bingyan Xu Key Laboratory of Control of Power Transmission and Conversion Shanghai Jiao Tong University Shanghai China Shanghai Electric Power Company Electric Power Science Research Institute State Grid Shanghai Electric Power Company Shanghai China Shanghai Electric Power Company Jinshan Power Supply Company State Grid Shanghai Electric Power Company Shanghai China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

In response to deal with the challenges for power system by the increasing EV penetration rate, this paper proposes an optimal operation strategy for virtual power plants which comprehensively considers the adjustment capabilities of regional adjustable resources. Based on the dynamic characteristics of road traffic flow, this paper takes the spatial and temporal distribution characteristics of EV charging load in account and establishes a mathematical model of EV charging and swapping integrated station (CSIS). Moreover, an optimal scheduling model of virtual power plant(VPP) is proposed considering renewable energy sources (RES), micro gas turbines (MT), CSIS, energy storage power stations, static var compensators (SVC) and other components. To reduce the prediction errors' impact on regional RES output, load demand, and road traffic flow, the multi-time scale optimization scheduling strategy based on model predictive control technology is proposed in this paper to improve the adaptability of the model in practice. The case study takes the actual road conditions of bus lines in a city and the improved 12-node transportation network as an example to verify the proposed strategy. The calculation results prove the rationality and effectiveness of the proposed model.

关键词： Adaptation models Power system dynamics Urban areas Optimal scheduling Virtual power plants Power grids Electric vehicle charging Road traffic Mathematical models Load modeling

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Research on optimization control strategy of vehicle-road-grid charging and exchanging based on user demand guidance 3

Research on optimization control strategy of vehicle-road-gr...

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2021 3rd International conference on Artificial Intelligence and Computer Science, AICS 2021

作者： Zhang, Yuanxing Li, Bin Cao, Boyuan Li, Taoyong Huang, Xingde Yang, Xingang China Electric Power Research Institute Beijing EV Charging and Battery Swapping Engineering Technology Research Center Beijing100192 China State Grid Shanghai Electric Power Company Shanghai200437 China

Considering the time-space uncertainty of electric vehicle charging and changing demand, the uneven distribution and random diversity of charging stations, the big data cloud network is used to realize the real-time information interaction between charging and changing power station and electric vehicle. Based on the user demand orientation, an intelligent charging and changing optimization control strategy of "Vehicle-Road-Grid" integrating cloud vehicle operation data is proposed. Under the discrete electric vehicle charging demand, this strategy quickly organizes the supply information around the discrete point. On the basis of the minimum energy consumption between the electric vehicle charging request point and the supply point, considering the multi-dimensional factors such as the optimal interests of the electric vehicle users, the shortest waiting time for charging and changing, and the shortest driving distance for charging and changing, the multi-objective optimal configuration strategy is realized. The simulation results show that the strategy can stably select the charging and swapping scheme that best meets the actual needs of users, effectively improve the charging and swapping service experience of users, and enhance the adhesion and participation of users. © Journal of Physics: conference Series 2021.

关键词： Multiobjective optimization

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IGDT-based Robust Power Dispatch for Active Distribution network Considering Demand Response 3

IGDT-based Robust Power Dispatch for Active Distribution Net...

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3rd International conference on Energy Resources and Sustainable Development, ICERSD 2020

作者： Zhuang, Huihua Zhuang, Huimin Locomotive and Rolling Stock Department Kunming Railway Vocational and Technical College Kunming China Department of Control Engineering Chengdu University of Information Technology Chengdu China

Considering increasing uncertain renewable energy sources (RES) and flexible loads in active distribution network (ADN), this study proposes a novel optimal model for robust hourly energy scheduling of ADN. Firstly, a deterministic optimal dispatching model is formulated, which aims at minimizing the total operation cost of distribution network;Secondly, the information gap decision theory (IGDT) is employed to handle uncertainties of RES generation. One of the features of the proposed model is to take into account the impact of demand response of flexible loads and energy storage system (ESS) as the effective tools to reduce unintended costs due to uncertainty of RESs. Also, the uncertainty of RESs is handled in a way that maximum tolerable uncertainty is achieved for a given worsening of total operation cost. The model is formulated as a mixed integer nonlinear optimization problem and solved in the genetic algorithm. Numerical simulation on the IEEE 33-bus system has been performed. Comparisons with two types of probabilistic techniques demonstrate the effectiveness and benefits of the proposal. © The Authors, published by EDP Sciences 2021.

关键词： Renewable energy resources

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Particle Swarm optimization and Genetic Mutation Based Routing Technique for IoT-Based Homogeneous Software-Defined WSNs 3rd

Particle Swarm Optimization and Genetic Mutation Based Routi...

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3rd International conference on Recent Innovations in Computing, ICRIC 2020

作者： Ramteke, Rohit Singh, Samayveer Department of Computer Science & Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India

ISBN: (纸本)9789811582967

Recent advancement in wireless sensor network has evolved as an open system which can be reconfigured dynamically. Generally, these networks have different limitations and challenges such as energy consumption in data collection, control node election, load balancing, etc. An efficient load balancing in terms of data collection and forwarding is depended on the routing techniques which are responsible to provide an effective path to transmit the collected data such that the minimum amount of energy should be consumed in the process. The control nodes are responsible for assigning the task and data transmission in the cluster-based routing techniques. The selection of the control node is an NP-hard problem. In this paper, an adaptive particle swarm optimization (PSO) ensemble with genetic mutation-based routing is introduced to select control nodes for IOT-based software-defined WSN. The proposed method plays a significant role in selecting the control nodes based on the fitness value. Fitness value takes energy and distance parameters into consideration. First, the proposed work is implemented for homogeneous nodes that can be deployed with single and multiple sinks. Further, the proposed work can also implement for the heterogeneous sensor nodes having different computing power accompanied by single and multiple sinks. The simulation result of the proposed method outperforms over some other existing algorithms under the different arrangements of the network. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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Research on the Detection Method of Transformer Oil Performance Parameters Based on Multi-frequency Ultrasonic Technology and IMFO-BP 3

Research on the Detection Method of Transformer Oil Performa...

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3rd International conference on control and Robots (ICCR)

作者： Xin, Zhang Xin, Wang State Grid Jilin Elect Power Co Ltd Yanbian Power Supply Co Yanbian Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai Peoples R China

ISBN: (纸本)9781728175621

Transformer oil is an important part of transformer insulation, and some important performance parameters of transformer oil need to be quickly and accurately tested. A transformer oil performance detection model based on multi-frequency ultrasonic (MFU) technology and improved Moth-Flame optimization algorithm (IMFO) optimized Back Propagation (BP) Neural network is proposed. Let the ultrasonic waves of multiple frequencies pass through the transformer oil, and measure the transmission speed and attenuation coefficient at different frequencies. Introduce adaptive weight and Levy flight to improve the Moth-Flame optimization algorithm to optimize the BP neural network, and use the IMFO-BP algorithm with strong optimization ability and high precision to train the samples to obtain the transformer oil performance parameter detection model. Verify its feasibility through experimental testing.

关键词： transformer oil multi-frequency ultrasonic technology IMFO levy flight BP neural network

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A Priority-Based Congestion control Algorithm for Wireless Sensor networks

A Priority-Based Congestion Control Algorithm for Wireless S...

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International conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： Tshimangadzo M. Tshilongamulenzhe Topside E. Mathonsi DP DuPlessis Maredi I. Mphahlele Department of Information Technology Tshwane University of Technology Pretoria South Africa

Wireless Sensor networks (WSNs) is an area that has generated an increasing interest currently worldwide. WSNs fall under IEEE 802.11 standard in which wireless network is formed by sensor nodes (SNs) that interconnect with each other through wireless links. SNs are capable of sensing, processing, and communicating via a wireless channel in a harsh environment, and most of them are battery powered. WSNs are mostly used for traffic communication in different environments such as irrigations, healthcare, home, and the military. WSNs are implemented to monitor temperature, humidity, pressure, among others within the network environment. The implementation of WSNs in various environment came with different challenges such as traffic congestion. The traffic congestion that occurs during packet transmission is normally occurs when there is buffer overflow. As a result, packet loss, packet delay, and network throughput impairment occurred during packet transmission within the network. This paper proposed a Priority-Based Congestion control (PBCC) algorithm in order to manage packet distribution to avoid the buffer overflow in WSNs while improving Quality of Service (QoS). The Particle Swarm optimization Gravitational Search Algorithm (PSOGSA) and Weighted Priority based Fair Queue Gradient Rate control (WPFQGRC) were integrated in order to develop the proposed PBCC algorithm. network Simulator 2 (NS-2) was used to test the effectiveness of the proposed PBCC algorithm. The simulation results showed that the proposed PBCC algorithm reduced the packet loss by 29.4%, packet delay by 32.6%, and improved network throughput by 96.8% when compared with PSOGSA and WPFQGRC algorithms.

关键词： Temperature sensors Temperature measurement Wireless sensor networks Irrigation Simulation Packet loss Quality of service

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