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检索条件"任意字段=International Conference on Complex Systems: Control and Modeling Problems"

共 12182 条记录，以下是121-130 订阅

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Research on Coordinated control Strategy for Photovoltaic-Storage-EV Charging of Local Power Network

Research on Coordinated Control Strategy for Photovoltaic-St...

引用

Smart Grid and Artificial Intelligence (SGAI), international conference on

作者： Ding Li Xiaoyan Lv Xuzhi Luo Jiaming Wang China Electric Power Planning & Engineering Institute Beijing China North China Electric Power Design Institute Co. Ltd of China Power Engineering Consulting Group Beijing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

With the improvement of distribution network reliability requirements and the increase in the penetration rate of distributed adjustable source-load resources, traditional centralized control methods have issues like long scheduling cycles, slow response speeds, and difficulty in efficiently controlling complex power grids. This paper proposes a collaborative model, a dual-objective optimization control model, to reduce the load fluctuation degree of distribution transformers and increase the consumption of distributed photovoltaic power in distribution station areas. By comparing pre- and post-optimization results, it's concluded that accessing an appropriate capacity of energy storage and controllable charging piles is a prerequisite for collaborative control of distribution station areas to increase new energy consumption and reduce transformer load fluctuations. A reasonable collaborative control strategy for the distribution network can still change the load situation to some extent, achieve peak shaving and valley filling, and improve the load condition of distribution transformers.

关键词： Photovoltaic systems Fluctuations Power supplies Collaboration Distribution networks Transformers Filling Smart grids Load modeling Energy storage

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Attitude Balance control of a Wheeled Quadruped Robot Utilizing an Inertial Measurement Unit

Attitude Balance Control of a Wheeled Quadruped Robot Utiliz...

引用

Robotics, control and Automation (ICRCA), international conference on

作者： Xiyuan Li Yunsong Li Jihong Zhang Changshuai Sun School of Electromechanical and Automotive Engineering Yantai University Yantai China School of Information Science and Technology Beijing University of Technology Beijing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

The Wheeled Quadruped Robot (WQR), a novel hybrid mobile platform that combines the advantages of wheeled and legged locomotion, has attracted significant research interest due to its enhanced mobility and stability in complex terrains. However, achieving efficient attitude stabilization during dynamic operations remains a critical technical challenge. This study initiates with a thorough kinematic modeling of the WQR mechanism. Next, we present a novel attitude stabilization methodology employing closed-loop control architecture driven by real-time Inertial Measurement Unit (IMU) data streams. Our approach integrates kinematic constraints and real-time IMU data with Proportional-Integral (PI) regulators to establish an inverse kinematics-guided adaptive adjustment mechanism. This mechanism dynamically adjusts leg movements in response to deviations in attitude. To assess the effectiveness of our control strategy under diverse terrain conditions and motion patterns, extensive simulations were conducted, comparing it with fixed joint angle control and traditional inverse kinematics-based control methods. The simulation results indicate that the WQR's attitude stability is markedly improved across various terrains and motion states after implementing our proposed control strategy. This research contributes to robotic control theory by innovatively integrating sensor fusion and adaptive kinematics, thereby significantly impacting the development of robust mobile platforms for unstructured environments.

关键词： Legged locomotion Adaptation models Measurement units Attitude control Kinematics Inertial navigation Robot sensing systems Stability analysis Real-time systems Quadrupedal robots

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CKKS Homomorphic Encryption Scheme for Financial Dataset

CKKS Homomorphic Encryption Scheme for Financial Dataset

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Bhawana S. Dakhare Lata L. Ragha Information Technology Research Scholar Terna College of Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Mumbai University Navi Mumbai India Computer Engineering Fr. C. Rodrigues Institute of Technology Vashi Navi Mumbai Mumbai University Mumbai India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Homomorphic Encryption(HE) makes it possible for highly regulated sectors to collaborate securely and share data. To achieve different objectives of financial services and preventing malicious activities financial institutions use various confidential data. Across a range of application areas detecting anomalies concerns the risk since unprotected data could contain important, crucial, and useful information and the use of machine learning models is essential for performing these operations. Models for machine learning are particularly vulnerable to different types of attacks at different phases of implementation due effective application in different domains based on sensitive data. The machine learning model can be protected using HE and HE is a cryptography technique that promises to provide privacy to data for sharing along with performing complex computations on confidential data. In this paper anomaly detection is performed using Multilayer perceptron, Random Forest, KNN and SVR. The performance of models is compared with the models working on encrypted data and pain text data as well.

关键词： Pain Biological system modeling Computational modeling Process control Nearest neighbor methods Multilayer perceptrons Data models Robustness Homomorphic encryption Random forests

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Research on Optimization Algorithm of Multi-Objective Cost control Model in Virtual Design of Intelligent Construction

Research on Optimization Algorithm of Multi-Objective Cost C...

引用

Electronics, Energy systems and Power Engineering (EESPE), IEEE international conference on

作者： Yujie Yang Changchun University of Architecture and Civil Engineering Changchun China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This study introduces a multi-objective cost management framework grounded in a multi-objective particle swarm optimization (MOPSO) approach. Initially, by establishing a virtual design framework for intelligent construction that incorporates diverse aspects such as financial expenditure, timelines, and resource allocation, a comprehensive multi-objective cost management structure is developed. Subsequently, a refined version of the MOPSO algorithm is crafted to perform complex multi-dimensional optimizations encompassing cost, duration, and quality across various project scenarios, thereby fulfilling the distinct demands of numerous projects. The results from simulation experiments indicate that this algorithm achieves superior convergence precision and solution equilibrium in virtual design contexts, notably enhancing both the convergence rate and optimization precision of the model. Comparative analysis with traditional algorithms and alternative parameter configurations highlights the proposed algorithm's pronounced benefits in terms of objective function performance, allowing for a balanced optimization of multiple objectives and, consequently, achieving a more effective cost management outcome. This research offers an innovative and efficient approach to multi-objective cost management within intelligent construction's virtual design sphere, demonstrating strong potential for practical use and promising application opportunities.

关键词： Power engineering Costs Computational modeling Linear programming Data models Resource management Particle swarm optimization Optimization Convergence Context modeling

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An Adaptive Discrete Human Learning Optimization for TSP-Like problems 8th

An Adaptive Discrete Human Learning Optimization for TSP-Lik...

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8th international conference on Life System modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Ling Kang, Xing Wang, Mengzhen Yao, Jun Pfaender, Fabien Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Sino-European School of Technology Shanghai University Shanghai200444 China

ISBN: (纸本)9789819602933

Human learning optimization (HLO) is a novel inborn binary meta-heuristic algorithm inspired by a simple human learning model. Although HLO, as a binary algorithm, can directly solve discrete (integer) problems, the efficiency may be significantly spoiled for the large-scale discrete problems due to the curse of dimension. Therefore, an adaptive discrete human learning optimization for TSP-like problems (ADHLOT) is proposed in which the random learning operator, individual learning operator, social learning operator and the re-learning operation of HLO are extended or re-designed to improve the efficiency of the algorithm for TSP-like problems. Furthermore, an adaptive strategy is used in ADHLOT to dynamically adjust the social learning rate for achieving a better balance between exploration and exploitation. Finally, ADHLOT is evaluated by solving TSP benchmark problems and compared with recent meta-heuristic algorithms. The experimental results show that the ADHLOT has obvious advantages and shows promising potential for applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Neural Network based Identification of 3-Φ Battery Energy Storage systems with Different Capacities

Neural Network based Identification of 3-Φ Battery Energy S...

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Power, control and Computing Technologies (ICPC2T), conference on

作者： Anju Yadav Nand Kishor Richa Negi Riccardo Lazzari Department of Electrical Engineering MNNIT Allahabad India Department of Engineering Østfold University College Fredrikstad Norway Materials and Generation Technologies Department Ricerca sul Sistema Energetico - RSE SpA Milan Italy

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Microgrids, as self-contained networks within the distribution grid, possess ample distributed generation resources, enabling them to operate independently or in connection with the main grid. However, maintaining stable voltage regulation at each node remains a complex challenge due to fluctuations in generation and load demands. This paper presents a study focused on modeling node voltages using neural networks (NN) for different battery types and capacities connected to a voltage source converter (VSC). The data is derived from an experimental microgrid setup, where active and reactive power regulation is applied at the VSC inputs. By using neural networks, the model predicts local node bus voltage as the output variable, with changes in active and reactive power serving as input variables. This approach allows for a dynamic and responsive voltage control model, addressing the unique operational needs of microgrids. The discussion is given as comparative performance of NN models, optimized using difference of log-sup-exp and Levenberg-Marquardt algorithms.

关键词： Reactive power Computational modeling Fitting Artificial neural networks Microgrids Mathematical models Regulation Batteries Voltage control Biological neural networks

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Systematic Review on Chronic Kidney Disease Detection Using Deep Learning

Systematic Review on Chronic Kidney Disease Detection Using ...

引用

international conference on Inventive Computation Technologies (ICICT)

作者： Y Surekha J Arul Valan Department of CSE NIT Nagaland

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Chronic Kidney Disease remains a public health problem despite advances in surgical treatments and treatments, and the recent rise in CKD has led to increased diagnosis among researchers worldwide. There is great interest in developing powerful methods for healing and preventive therapy. It can improve performance by learning features that deal with problems. In addition to laboratory tests, analyzing a patient's medical data can help medical partners predict early stages of disease. Numerous attempts have been made to develop intelligent systems that analyze health data and predict CKD, but the performance of these systems still needs improvement. In this analysis, a hybrid technique was used to create the models. The model that Pearson correlation uses to choose features. Critical literature review served as the foundation for the initial selection of the best model. This analysis's goal is to assess the top Deep Learning classification techniques and produce better outcomes. This achieves the highest accuracy that provides better results.

关键词： Deep learning Accuracy Computational modeling Prevention and mitigation Surgery Chronic kidney disease Public healthcare Medical diagnostic imaging Systematic literature review Predictive control

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Universal Readability: Simplification of complex Text Using Deep Learning

Universal Readability: Simplification of Complex Text Using ...

引用

Smart systems for applications in Electrical Sciences (ICSSES), international conference on

作者： B. P. Pradeep Kumar Sneha R Raksha R Shrajan Shetty Dept. of Computer Science and Design Atria Institute of Technology Bangalore India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The proposed framework implements text simplification through Bidirectional Encoder Representations from Transformers (BERT) model structures together with Generative Pre-trained Transformer-2 (GPT-2) deep learning elements. Traditional rule-based and statistical methods cannot accurately achieve the balance between simplification while retaining the original meaning. The evaluation results yielded a Bilingual Evaluation Understudy (BLEU) score of 0.95 while demonstrating superior readability performance. The research evaluation established successful text simplification because the Simple Measure of Gobbledygook (SMOG) reading difficulty decreased while the Flesch Reading Ease increased. The sophisticated technique delivers better outcomes than standard testing systems because it works efficiently with various types of text documents. The method benefits educational institutions and technical tasks with outstanding practicality and solves accessibility requirements effectively. Future research should integrate diverse data with domain-specific features to improve performance of the model.

关键词： Deep learning Statistical analysis Computational modeling Bidirectional control User interfaces Transformers Readability metrics Encoding User experience Testing

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Cloud and Fog-Based Traffic control System for Social Internet of Vehicles

Cloud and Fog-Based Traffic Control System for Social Intern...

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international conference on Web Research (ICWR)

作者： Masoumeh Rashidi Narges Yadegarifard Kowsar Ravandeh Maryam Tajvidi Ehsan Amiri Department of computer engineering Jahrom University Jahrom Iran Department of computer engineering Larestan higher education complex Lar Iran Department of computer engineering Payame Noor University of Jahrom Jahrom Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

The Internet of Things (IoT) and the Internet of Vehicles (IoV) are crucial for optimizing resources and enhancing the efficiency of urban transportation and traffic management systems. As these technologies expand, real-time data from billions of connected devices is collected, processed, and analyzed, leading to better resource utilization, improved safety, and reduced traffic congestion. Implementing fog and cloud computing systems effectively reduces latency and optimizes resource consumption. This paper also explores the use of the Tiny CNN model to enhance performance through feature extraction, resulting in increased accuracy in traffic detection and management. With ongoing technological advancements, IoT and robotics are poised to create smarter, more efficient systems across various sectors, significantly impacting daily life and urban infrastructure. This paper proposes a Tiny CNN model for traffic management in cloud and fog environments. Compared to existing methods like AlexNet, the Tiny CNN model excels in input processing and performs optimally under diverse conditions, making it a strong candidate for intelligent traffic management systems.

关键词： Training Cloud computing Computational modeling Transportation Feature extraction Real-time systems Safety Resource management Robots Internet of Vehicles

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Advanced Energy Management Strategies for Hybrid Energy Storage systems in Electric Vehicles: A Comprehensive Review

Advanced Energy Management Strategies for Hybrid Energy Stor...

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Power, control and Computing Technologies (ICPC2T), conference on

作者： Sneha Tibude Gaurav Goyal Alok Ranjan Sanjay Bodkhe Dept. of Electrical Engineering Shri Ramdeobaba College of Engineering and Management MH India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

An increasing need for sustainable transportation and the emergence of system HESS (hybrid energy storage systems) with supercapacitors and batteries have motivated the research and development of electric vehicles (EVs). In this review paper, different energy management strategies implemented in HESS for electric vehicles are investigated for improved efficiency and performance. The main goal is to compare some control methods like fuzzy logic control, model predictive control and genetic algorithms and to evaluate their performance in the optimization of the energy distribution among storage components. This study brings together multiple research articles to offer a complete review of these strategies. It is found from key findings that fuzzy logic control is adaptable to different driving conditions and model predictive control consistently provides precise domestic energy management with the aid of sophisticated predictive algorithms. In contrast, genetic algorithms excel at optimizing complex, multi objective problems; but at a large computational cost. In summary, it is shown that these advanced control strategies can be combined to combine high energy efficiency and long-life spans of HESS systems in electric vehicles. Some valuable insights are provided which could be useful reference for research in future as well as development in this field. This review seeks to help researchers and engineers select and refine energy management strategies that enable more sustainable and efficient electric transportation solutions.

关键词： Reviews Heuristic algorithms Transportation Supercapacitors Prediction algorithms Batteries Energy management Optimization Genetic algorithms Predictive control

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