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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711-720 订阅

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Two-Stage Distributionally Robust Voltage Regulation of Active Distribution network with Hybrid Energy Storage

Two-Stage Distributionally Robust Voltage Regulation of Acti...

引用

3rd IEEE Student conference on Electrical Machines and Systems (SCEMS)

作者： Zhao, Wantong Cao, Zhaojing Wan, Can Xu, Yuqi Zhao, Changfei Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9781728156224

Due to inherent volatility and intermittence, the increasing penetration of distributed renewable energies brings significant voltage deviation problem in active distribution network. In this paper, a novel two-stage distributionally robust optimization (TSDRO) strategy of hybrid energy storage systems is proposed for voltage regulation in active distribution network. The prediction errors of distributed renewable generation with different time-scales are considered in constructing Wasserstein ambiguity sets. In the first stage, distributed battery energy storage systems are scheduled day-ahead in distributionally robust optimization with chance constraints. In the second stage, model predictive control is utilized to roll optimize the intraday charging/discharging strategy of the supercapacitor energy storage systems. The simulation results show the effectiveness of the proposed TSDRO strategy.

关键词： distributionally robust optimization hybrid energy storage voltage regulation active distribution network

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Elite Parallel Cuckoo Search Algorithm for Regional Coverage control Problem in High-Density Wireless Sensor networks

Elite Parallel Cuckoo Search Algorithm for Regional Coverage...

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International conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Min Tian Jinbin Bai Jiangquan Li Mingqi Huang Chenyang Zhan Lingjie Ren College of Mechanical and Electrical Engineering Shihezi University Shihezi China

ISBN: (纸本)9781665417334

With the growing application of high-density wireless sensor network (HDWSN), coverage control technology has been a key and paramount problem in different scenarios of HDWSN. A regional coverage optimization algorithm is very significant to monitor the network, reduce the waste of resources and improve the lifetime of HDWSNs. Regional coverage control is a technique providing a method that avoids duplicate coverage in HDWSN. However, the regional coverage control is a nonlinear constrained problem whose complexity increases with a quantity of nodes. In this paper, an elite parallel cuckoo search algorithm (EPCSA), a randomized swarm optimization algorithm for regional coverage control in HDWSNs, motivated by elite selection and parallel theory is proposed. To assess the EPCSA's overall efficiency, the EPCSA flow of regional coverage optimization is designed. The model of the regional coverage control problem and the objective function is given. In the simulation, in order to further verify the effectiveness of EPCSA, particle swarm optimization (PSO) and ant colony optimization (ACO) are compared with EPCSA under the same conditions as EPCSA proposed in this paper. The performance of coverage optimization of three algorithms is compared and analyzed. Results show that the regional coverage rate and the deployment of sensor nodes of the network are optimized effectively by the EPCSA.

关键词： Wireless communication Wireless sensor networks Simulation Search problems Linear programming Mathematical models Particle swarm optimization

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Robust Integrated optimization of Active Distribution network Based on System Risk Index

Robust Integrated Optimization of Active Distribution Networ...

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3rd IEEE Student conference on Electrical Machines and Systems (SCEMS)

作者： Zhang, Yumin Gao, Haishu Ji, Xingquan Liu, Qi Yu, Yixiao Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781728156224

This paper presents a comprehensive distribution robust optimization model based on the distribution network risk index to address the distributional uncertainties of distributed generations (DGs) and loads. To analyze the security of active distribution network (ADN) systematically, a risk assessment method based on improved semi-invariant method and clustering technique is proposed. The risk assessment method is performed on the integrated dynamic reconfiguration and reactive power optimization model. This method can consider the correlation between distributed power sources, and when the injected power is large, the calculation results with higher accuracy can be obtained Case studies on the PG&E 69-bus system verify the robustness and feasibility of the proposed model.

关键词： active distribution network distributional robust optimization probability distribution risk assessment

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Parallel Distributed Implementation of Neuroevolution of Augmenting Topologies in Continuous control Tasks

Parallel Distributed Implementation of Neuroevolution of Aug...

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IEEE International conference on Advanced Trends in Information Theory (ATIT)

作者： Illia Achour Anatoliy Doroshenko National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine

ISBN: (纸本)9781665406673

This paper proposes a novel distributed implementation of neuroevolution of augmenting topologies method, which, considering the availability of sufficient computational resources, allows drastically speed up the process of optimal neural network configuration search. The proposed solution includes batch genome evaluation for the purpose of performance optimization, fair, and even computational resources usage. The benchmarking shows that the generated neural networks evaluation process can give orders of magnitude increase of efficiency on the demonstrated continuous control task and computational environment.

关键词： network topology Neural networks Process control Genomics Benchmark testing Topology Computational efficiency

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Integration of Machine Learning Techniques in Virtual Wireless Sensor network for insect monitoring 3

Integration of Machine Learning Techniques in Virtual Wirele...

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3rd International conference on Smart and Intelligent Learning for Information optimization, CONSILIO 2021

作者： Hussain, Mohammad Equebal Hussain, Rashid Depar tment of Computer Science Suresh Gyan Vihar University Jaipur India

Agriculture industries are comparatively slow in adopting emerging technologies than any other industries despite lot of exciting research. The use of Wireless sensor network (WSN) is very important role in agriculture for more productive and sustainable growth. The structure of WSN is tightly application dependent. Every WSN have sensors, processing unit, low frequency radio wave transmitter and power supply using battery. With increasing number of interconnected WSN devices, there is substantial increase in data generation. It contains both control messages as well as application dependent data, collected by the sensors. The collected data are frequently sent to the nearest centralized controller for further processing and decision making. For continuous functioning of WSN, uninterrupted power supply is needed. Many researches are carried out to overcome these challenges. In this manuscript we are proposing a simple and effective machine learning techniques combined with pause and play method to increase battery life of WSN. This could be achieved in three stages play-pause-play (PPP) model. First by gathering data for some time (play mode), Second by putting WSN to sleep (pause mode), in the backend, apply machine learning algorithm that helps to build model from training data to predict the future data. At the final stage, put WSN back to play mode. Compare the result with actual data and fine tune the model to reduce the error. Using this method WSN will get enough sleep time to increase overall life by simulating the normal behaviour of sensor node. The sleep time will be calculated dynamically. © Published under licence by IOP Publishing Ltd.

关键词： Agriculture

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optimization problem of external dynamic influences for the elimination of vibration phenomena in composite elastic building structures 3

Optimization problem of external dynamic influences for the ...

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3rd International Scientific conference on Applied Physics, Information Technologies and Engineering, APITECH-III 2021

作者： Provotorov, V.V. Lebedeva, Y.O. Tran, D. Borisoglebskaya, L.N. Vorobyov, E.I. Voronezh State University 1 University sq. Voronezh394006 Russia Baltic State Technical University "Voenmeh" named after D.F. Ustinov 1st Krasnoarmeyskaya str. St. Petersburg190005 Russia Vietnam education ministry of education and training 49 Dai Co Viet str Viet Nam Orel State university 95 Komsomol'skaya str. Orel302026 Russia Voronezh State Technical University 14 Moscow ave. Voronezh394026 Russia

The paper considers the actual task of construction engineering: the elimination of harmful (undesirable) vibration phenomena in composite metal building structures and bridge cable-stayed systems – complete damping and vibration of the bridge cable-stayed system entails the calming of the entire bridge structure. Similar problems arise when designing devices that lead to dynamic rest of network antenna structures. The presentation of the material is based on the main provisions of the modern theory of optimal control of distributed differential systems with network carriers. An extensive literature is devoted to the sanctification of the research of the mentioned problem, however, an exhaustive solution of the problems of controlling elastic structures by external influences of various types (distributed, boundary, starting, and point control) and a comparative analysis of their properties did not find their place in these studies. In addition, the ways of algorithmization of previously obtained results are not always specified. The paper uses a mathematical model describing small oscillations of a spatial string system. It is convenient to use such a system and approaches to the analysis of its mathematical model as a fairly simple fundamental object when considering a more complex object, which is an elastic system of more complex elements. The results of the work are presented in a form that makes it possible to formalize the computational procedure, which is convenient for algorithmizing the process of determining the solution, and therefore for the modern application of computer technology. It remains to add that the work indicates ways to transfer the obtained results to the case of analyzing optimal control problems with carriers on multidimensional network-like domains. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Cables

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Topology Identification for Distribution network Driven by Diverse Measurement Data

Topology Identification for Distribution Network Driven by D...

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Electrical Engineering and control Technologies (CEECT), conference on

作者： Zijiing Wang Keyou Wang Jin Xu Shanghai Jiao Tong University Shanghai China

The analysis, optimization and control of the power grid becomes more complicated as the scale of renewable energy continues to expand. There are two factors causing the scarcity of accurate distribution network topology and parameters. Firstly, with lots of distributed equipment attached, the distribution network changes topology frequently. Secondly, the amount of monitoring equipment is limited. However, inferring the correct network topology through historical operating data becomes possible crediting to data-driven technology. Current identification methods require full sets of measurements. Unfortunately, actual measurements are generally incomplete and are from various sources. Data sets with this property are defined as diverse measurement data in this paper. Driven by diverse measurement data, a distribution network topology identification algorithm is proposed. Regression method and the branch power flow equation are employed to perform the identification. The analysis of IEEE 7-node and 33-node networks shows that accurate topology identification results are achieved, which indicates the algorithm proposed is robust and suitable for a variety of identification scenarios.

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Depth Estimation for Small Obstacles Based on Monocular Vision 3

Depth Estimation for Small Obstacles Based on Monocular Visi...

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3rd International conference on Unmanned Systems (ICUS)

作者： Gao, Wang Wang, Changqing Li, Lei Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent Beijing Peoples R China

ISBN: (纸本)9781728180250

In order to solve the problem of small obstacles avoidance in ultra-low altitude flying of unmanned aerial vehicles (UAVs), a depth estimation algorithm based on monocular visual for small tower obstacles is proposed by combining deep learning and traditional methods. With the advantage of convolutional neural networks to extract the salient features of the images, a lightweight and multi-level residual convolution neural network for obstacle segmentation is proposed. The cross-entropy loss function is optimized by adding the weight of the background and obstacles area. A method based on pairs of coplanar points is introduced to complete the depth estimation of a single frame image. Moreover, the recursive formula of sequence images depth results is derived, and a multi-frame voting optimization algorithm is proposed. Finally, the simulation results show that the proposed algorithm can effectively realize the fast segmentation and accurate depth estimation of the small tower obstacles in the sequence images.

关键词： ultra-low altitude flight monocular vision small obstacles depth estimation

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Design and Simulation Evaluation of Pneumatic Tool Handle 3

Design and Simulation Evaluation of Pneumatic Tool Handle

引用

IEEE 3rd International conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Ge Zhenghao Wei Siyuan Wei Tao Shaanxi Univ Sci & Technol Mech & Elect Engn Xian Peoples R China

ISBN: (数字)9781728190327

ISBN: (纸本)9781728190327

With the development of economy and the improvement of people's living standard, the civil demand for pneumatic tools is strong, and the market potential is huge. In this paper, air nail gun is taken as an example to make a simple analysis of its general working posture. Combined with the structure of human upper limbs and hands, it provides a reference basis for the structural design of the handle and a more reasonable scheme is obtained. Then, ANSYS Workbench is used for stress analysis of the hand part to further improve the handle modeling. At the same time, based on the design principles of hand tools, according to the results obtained from the analysis, combined with the anatomy of the hand, the design of the handle was improved and analyzed again. The results verify that the improved handle shape is more suitable for people to grasp, disperses the stress of the hand, distributes the stress more evenly in each area, and avoids the stress concentration.

关键词： pneumatic tools ergonomics handle optimization design

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Soft Robot control With a Learned Differentiable Model 3

Soft Robot Control With a Learned Differentiable Model

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3rd IEEE International conference on Soft Robotics (RoboSoft)

作者： Bern, James M. Schnider, Yannick Banzet, Pol Kumar, Nitish Coros, Stelian ETH Zurich Switzerland Inst Intelligent Interact Syst IIIS Computat Robot Lab Zurich Switzerland

ISBN: (纸本)9781728165707

Soft robots are inherently safe and comply readily to their environment. They are therefore exciting for applications like search and rescue or medicine, which involve a high degree of uncertainty, and require interacting with humans. However, the best way to model and control soft robots largely remains an open question. One promising approach is to leverage physically-based modeling techniques such as the finite element method. However, such techniques are inherently limited by their physical assumptions. Indeed, real-world soft robots are often made from unpredictable materials, using imprecise techniques. Data-driven approaches provide an exciting alternative, as they can learn real-world fabrication defects and asymmetries. In this paper we present our first investigation into using machine learning to do soft robot control. We learn a differentiable model of a soft robot's quasi-static physics, and then perform gradient-based optimization to find optimal open-loop control inputs. We find that our learned model captures phenomena that would be absent from an idealized physically-based simulation. We also present practical techniques for acquiring high-quality motion capture data, and observations the effect of network complexity on model accuracy.

关键词： Soft robots Modeling control Learning

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