检索结果-内蒙古大学图书馆

IEEE 23rd Workshop on Control and Modeling for Power Electronics (COMPEL)

作者： Elizondo, David Barrios, Ernesto L. Sanchis, Pablo Publ Univ Navarre UPNA Inst Smart Cities ISC Dept Elect Elect & Commun Engn Campus Arrosadia Pamplona 31006 Spain

ISBN: (纸本)9781665410816

Inductor filters, such as the ones implemented in DC-DC buck-boost converters for electric vehicle chargers, have a major impact on the converter weight, volume and cost. Thus, their design is key in order to obtain an optimal design of the whole converter. This paper proposes a design methodology for powder core toroidal inductors, which is based on a holistic approach of the design of the inductor, where losses due to high-frequency effects are computed by means of specific loss model for toroidal windings, and saturation, geometrical and thermal constraints are considered. The convenience of the design tool is shown through an analysis over a wide variation of parameters, including converter topology, parallelization, switching frequency and inductance. The analysis demonstrates the relevance of high-frequency effects on the inductor design, so certain misconceptions can be avoided, such as that the inductor volume monotonically decreases when the inductance value is decreased or that paralleling inductors always results in more compact designs. A design example is presented for a 15-kW, three-level electric vehicle battery charger. The algorithm is used to obtain an optimal design of the converter, including the inductors and SiC MOSFET devices. Finally, an easy method to obtain a commercial inductor design from the theoretical one provided by the algorithm is presented.

关键词： toroidal inductor high-frequency effects winding loss optimization algorithm multilevel dc-dc converter electric vehicle

来源：评论

学校读者我要写书评

暂无评论

Reliable Islanded Microgrid Operation Using Dynamic Optimal Power Management

引用

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第2期57卷 1755-1766页

作者： Harasis, Salman Sozer, Yilmaz Elbuluk, Malik Univ Akron Dept Elect & Comp Engn Akron OH 44325 USA

The tradeoff between the operating cost and the reliability of microgrids imposes challenges of having cost-effective power dispatch and reliable operations. The work presented in this article optimally solves this issue in a microgrid that contains photovoltaic (PV), dispatchable generator, and storage battery. Flexible control algorithms are developed to enable high penetration of PV power in islanded microgrids such that the challenge of having a cost-effective and reliable operation can be mitigated. This is achieved by creating a power flow optimization framework that considers all variables associated with the system operation. Particle swarm optimization is utilized to effectively optimize the power flow among the integrated sources and to dynamically schedule the battery state of charge. The proposed controller of each source works to fulfill the requirements of the optimization. The energy index of reliability is employed as a direct measure to quantify the reliability level of the microgrid. The effectiveness of the proposed approach is demonstrated by simulations and validated by experimental studies.

关键词： Batteries Microgrids Reliability State of charge optimization Generators Control systems Economic power-sharing microgrid optimal battery state of charge (SOC) optimal power optimization algorithm particle swarm optimization (PSO) photovoltaic (PV) power flow reliability

来源：评论

学校读者我要写书评

暂无评论

Prediction and analysis of domestic water consumption based on optimized grey and Markov model

引用

WATER SUPPLY 2021年第7期21卷 3887-3899页

作者： Wang, Zhaocai Wu, Xian Wang, Huifang Wu, Tunhua Shanghai Ocean Univ Coll Informat Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Food Shanghai 201306 Peoples R China Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China

With the rapid development of urbanization and the continuous improvement of living standards, China's domestic water consumption shows a growing trend. However, in some arid and water deficient areas, the shortage of water resources is a crucial factor affecting regional economic development and population growth. Therefore, it is essential important to reliably predict the future water consumption data of a region. Aiming at the problems of poor prediction accuracy and overfitting of non-growth series in traditional grey prediction, this paper uses residual grey model combined with Markov chain correction to predict domestic water consumption. Based on the traditional grey theory prediction, the residual grey prediction model is established. Combined with the Markov state transition matrix, the grey prediction value is modified, and the model is applied to the prediction of domestic water consumption in Shaanxi Province from 2003 to 2019. The fitting results show that the accuracy grade of the improved residual grey prediction model is "good". This shows that the dynamic unbiased grey Markov model can eliminate the inherent error of the traditional grey GM (1,1) model, improve the prediction accuracy, have better reliability, and can provide a new method for water consumption prediction.

关键词： grey model Markov chain optimization algorithm state transition matrix water consumption prediction

来源：评论

学校读者我要写书评

暂无评论

Optimal Dynamic Recharge Scheduling for Two-Stage Wireless Power Transfer

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第8期17卷 5719-5729页

作者： Pandiyan, Akshayaa Y. S. Boyle, David E. Kiziroglou, Michail E. Wright, Steven W. Yeatman, Eric M. Imperial Coll London Dept Elect & Elect London SW7 2AZ England Int Hellenic Univ Dept Ind Engn & Management Nea Moudania 57001 Greece Imperial Coll London Dyson Sch Design Engn London SW7 2AZ England

Many industrial-Internet-of-Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles (UAV) and embedded devices with subsystems to facilitate WPT allows a UAV to become a viable mobile power delivery vehicle (PDV) and data collection agent. A key challenge is, therefore, to ensure that a PDV can optimally schedule power delivery across the network, such that it is as reliable and resource efficient as possible. To achieve this and out-perform naive on-demand recharging strategies, in this article, we propose a two-stage wireless power network (WPN) approach in which a large network of devices may be grouped into small clusters, where packets of energy inductively delivered to each cluster by the PDV are acoustically distributed to devices within the cluster. In this article, we describe a novel dynamic recharge scheduling algorithm that combines genetic weighted clustering with nearest neighbor search to jointly minimize PDV travel distance and WPT losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces, and the algorithm is shown to achieve similar to 90% throughput for large, dense networks.

关键词： Dynamic recharge scheduling genetic algorithm (GA) optimization algorithm wireless power network (WPN) wireless rechargeable sensor network

来源：评论

学校读者我要写书评

暂无评论

optimization of Elastic Properties of Composite Laminates Using Fiber-Optic Strain Sensors

引用

APPLIED COMPOSITE MATERIALS 2020年第5期27卷 491-509页

作者： Tashkinov, Mikhail A. Matveenko, Valeriy P. Perm Natl Res Polytech Univ Komsomolsky Ave 29 Perm 614990 Russia Russian Acad Sci Inst Continuous Media Mech Academician Korolev St 1 Perm 614013 Russia

The paper is devoted to combined computational and experimental approach for estimation of the elastic mechanical properties of structures made of laminate polymer composite materials (PCM). The computational component of the technique is connected with numerical simulation of mechanical behavior during quasistatic deformation of structures made of PCM. The experimental component is based on measurement of strains by fiber-optic strain sensors (FOSS) with Bragg gratings (FBG sensors), embedded in composite laminates or attached to them. The principle of the proposed method is based on the comparison of the data from FBG sensors, placed in the predetermined control points in the composite structure, with the data of numerical finite element modelling of the same structure. To refine the elastic constants in accordance with the information received from the FBG sensors, an algorithm is proposed, according to which the inverse problems are solved in order to ensure that the numerical and experimental results coincide with the specified accuracy. The implementation of the algorithm is demonstrated on the example case studies.

关键词： Mechanical characteristics Fiber-optic strain sensors Bragg gratings optimization algorithm Finite element method

来源：评论

学校读者我要写书评

暂无评论

Differential evolution algorithm with a complementary mutation strategy and data Fusion-Based parameter adaptation

引用

INFORMATION SCIENCES 2024年 668卷

作者： Chen, Bozhen Ouyang, Haibin Li, Steven Zou, Dexuan Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China RMIT Univ Grad Sch Business & Law Melbourne 3000 Australia Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Peoples R China

As an excellent optimization algorithm widely used to solve various practical problems, differential evolution (DE) algorithm has few parameters, yet its performance is significantly affected by these parameters. To address this issue, this paper presents a novel variant of DE called DACDE, which utilizes data fusion-based parameter adaptation and a complementary mutation strategy. Most parameter adaptation methods based on successful history only analyze the mean of parameters and ignore their degree of dispersion. In DACDE, the successful parameter distribution is recorded and described by both the mean and variance. Data fusion is then used to combine records and generate an estimated distribution, which is applied to a Gaussian distribution to generate new parameters. Inspired by opposition-based learning, we introduce a complementary mutation strategy. This strategy employs a symmetric selection mechanism to adapt to the varying search abilities required by the algorithm at different stages. The new variant is verified on 32 single-objective functions from CEC 2011 and 2014 benchmark suites, and the results show that DACDE is competitive compared to other 29 evolutionary algorithms.

关键词： Differential evolution optimization algorithm Data fusion Novel mutation strategy Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

An effective framework of human abnormal behaviour recognition and tracking using multiscale dilated assisted residual attention network

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Vidya, Queen Mary Selvakumar, S. Sathyabama Inst Science&Technol Dept Comp Sci & Engn Chennai 600119 Tamilnadu India Visvesvaraya Coll Engn & Technol Dept Comp Sci & Engn Hyderabad 501510 Telangana India

The prevention of certain unwanted crime events and eliminating them even before their execution can be done by automatic identification of abnormal behavior in humans. Hence automatic prediction of abnormal human behavior is a difficult task to perform. Some of the automated model has been implemented and provided the most promising results. The manual intervention is being the greatest approach in earlier time, yet it brings with numerous errors, consumes more time and more cost effective. Henceforth, the automated model is suggested for identifying the activities. As the scholar focus on machine and deep learning, this classifier may extract the handcrafted features. But it fails to yield the appropriate solution for finding the activities. Since it belongs to the video frames, the object detection is highly Ineffective feature vector and inadequate scale measures of the learning model paves the way for performance degradation. This issue can be resolved by including an attention mechanism in the deep learning model for both monitoring and classification purposes. The recommended Human Abnormal Behavior Recognition and Tracking (HABRT) model performs the following operations, such as the collection of video, categorizing the behavior in the video as normal or abnormal, monitoring, extraction of the object, and classification of the abnormality. The input video with such frames is initially gathered from publically available databases. By using these frames, the abnormal behavior classification is done by Multiscale Dilated assisted Residual Attention Network (MD -RAN), For further enhancement, the hyper-parameters in the MD -RAN are optimally selected by novel Modified Random Parameter-based Chimp optimization algorithm (MRP-ChOA). Once the abnormal frames are obtained, the activity tracking is achieved by Adaptively Modified You Only Look Once (YOLO) V3 (AM-YOLO V3). This model encompasses with multiple layers, so that utilized number of layers are determined op

关键词： Human Abnormal Behavior Recognition Human Activity Tracking Adaptively Modified You Only Look Once V3 Modified Random Parameter-based Chimp optimization algorithm Multiscale Dilated assisted Residual Attention Network

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Computation in Social Propagation over Complex Networks: A Survey

引用

International Journal of Automation and computing 2021年第4期18卷 503-520页

作者： Tian-Fang Zhao Wei-Neng Chen Xin-Xin Ma Xiao-Kun Wu School of Computer Science&Engineering South China University of TechnologyGuangzhou 510006China School of Journalism and Communication South China University of TechnologyGuangzhou 510006China

Social propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simulation helps to analyze and understand the social contagion, and problem-oriented optimization is devoted to contain or improve the infection results. Though there have been many models and optimization techniques, the matter of concern is that the increasing complexity and scales of propagation processes continuously refresh the former conclusions. Recently, evolutionary computation(EC) shows its potential in alleviating the concerns by introducing an evolving and developing perspective. With this insight, this paper intends to develop a comprehensive view of how EC takes effect in social propagation. Taxonomy is provided for classifying the propagation problems, and the applications of EC in solving these problems are reviewed. Furthermore, some open issues of social propagation and the potential applications of EC are *** paper contributes to recognizing the problems in application-oriented EC design and paves the way for the development of evolving propagation dynamics.

关键词： Evolutionary computation complex network propagation dynamics social diffusion evolution model optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Short-term industrial load forecasting based on error correction and hybrid ensemble learning

引用

ENERGY AND BUILDINGS 2024年 313卷

作者： Fan, Chaodong Nie, Shanghao Xiao, Leyi Yi, Lingzhi Li, Gongrong Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan 411100 Peoples R China

Accurate industrial load forecasting is a prerequisite for ensuring the smooth operation of the power system. Due to the strong fluctuation and complex characteristics of industrial loads, it is difficult to accurately predict shortterm power demand. To address this issue, this paper proposes a deep learning prediction model based on hybrid ensemble and error correction. The proposed model is divided into two phases: in the first phase, deep power features are extracted from multivariate data through a hybrid ensemble strategy consisting of Random Subspace, Boosting, Ensemble Pruning, and Multi-Objective Molecular Dynamics Theory optimization algorithm (MMDTOA). First, the strategy splits high-dimensional industrial data into multiple sub-datasets. Subsequently, for the features of each sub-dataset, the proposed MMDTOA is applied to perturb the parameters of GRU to generate base learning machines that balance accuracy and diversity. Finally, these base learning machines are integrated by kernel ridge regression stacking. Among them, the two-stage selection strategy and co-evolutionary strategy are embedded into the MMDTOA to enhance the optimization searching effect;in the second stage, a combined error correction strategy is proposed by utilizing the residual information in the prediction results. By combining the dynamic Gaussian error correction function and GRU error correction model, the prediction accuracy of the ensemble model is further improved;Experimental results on real Korean datasets show that the proposed method achieves a minimum value of 3.684% and 7.266% in NMAE and NRMSE, which outperforms eight comparative models, such as SVM, ELM, and CNN, with higher accuracy and robustness.

关键词： Industrial Load Multi -objective Molecular Dynamics Theory optimization algorithm Ensemble Learning Error Correction

来源：评论

学校读者我要写书评

暂无评论

Updating assembly parameters for spacecraft assembly process state changes: based on data fusion method

引用

FRONTIERS IN PHYSICS 2024年 12卷

作者： Liu, Yue Li, Lijuan Lin, Xuezhu Guo, Lili Sun, Jing Wang, Hao Changchun Univ Sci & Technol Sch Optoelect Engn Changchun Peoples R China Changchun Univ Sci & Technol Zhongshan Inst Zhongshan Peoples R China

Thermal protection systems (TPSs) are important components of reusable spacecraft, and their assembly quality has a crucial impact on flight safety. Owing to the complex assembly process and variable states of spacecraft thermal protection systems, assembly parameters may vary under different assembly states. Therefore, to obtain assembly parameters accurately and efficiently under different assembly states, in this study, 3D point cloud data and fiber optic sensor data were fused to develop an assembly parameter update method for assembly process state changes. Firstly, based on the measured data of thermal protection components and load-bearing structure, the gap, flush and matching parameters solution model are proposed. Secondly, to address the deformation problem of the load-bearing structure caused by changes in assembly status, a fusion method based on laser scanning and sensor detection was devised to achieve deformation prediction of the assembly structure during the assembly process. Thirdly, based on the assembly parameter solution model and point cloud prediction model, a constraint-based assembly parameter optimisation model was established, and an improved quantum particle swarm optimisation (LQPSO) algorithm was employed to achieve assembly parameter updates oriented toward changes in assembly status. Finally, an experimental system for array-based thermal protection structure simulation was established to validate the proposed method. The results show that the proposed parameter update method can achieve ideal results for different assembly state simulation components.

关键词： thermal protection system fiber optic sensor data 3D point cloud data data fusion state change assembly parameters model establishment optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：