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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101-110 订阅

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Path Planning for Distribution Network Robots with Adaptive Simulated Annealing-Enhanced Artificial Potential Field Method 26

Path Planning for Distribution Network Robots with Adaptive ...

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26th international conference on Industrial Technology, ICIT 2025

作者： Zhang, Baichao Chen, Xin Chen, Jinge Li, Jinbin Wang, Xiaokai Jian, Xu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan430077 China Hubei Fangyuan Dongli Electric Power Science Research Co. Ltd Wuhan430077 China

ISBN: (纸本)9798331521950

In this paper, we propose a hybrid algorithm that combines an improved Artificial Potential Field (APF) method with the Simulated Annealing (SA) algorithm for path planning of an electric power operation robot manipulator in complex distribution grid environments. To address the unreachable target issue inherent in traditional APF, we introduce a distance regulation factor to optimize the repulsive function. This modification allows the manipulator to smoothly approach the target point as it nears, while the repulsion from obstacles gradually decreases. Additionally, to overcome the limitations of the traditional SA algorithm, such as its tendency to get trapped in local minimum solutions and its inefficiency in complex environments, we propose an adaptive temperature rise strategy. This strategy increases the temperature, enhancing the probability of escaping local optimal solutions. When the APF algorithm becomes trapped in a local optimum, the improved SA algorithm is applied to escape the local minimum. Once the local optimum is avoided, the algorithm switches back to APF to continue the path planning process. Simulation results demonstrate that the proposed improved APF-SA algorithm adapts effectively to various complex environments, achieving shorter planning times and higher success rates compared to traditional APF and SA algorithms. It successfully resolves the unreachable target and local minimum problems associated with APF. Finally, the feasibility of the proposed APF-SA fusion algorithm is validated through experiments conducted on an electric power operation robot experimental platform for distribution grid applications. © 2025 IEEE.

关键词： Motion planning

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A Review of Ensemble Learning Techniques and Public Dataset for Intrusion Detection systems

A Review of Ensemble Learning Techniques and Public Dataset ...

引用

international conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Fifto Nugroho Ema Utami Kusrini Kusrini Arief Setyanto Doctoral of Informatics Engineering Universitas AMIKOM Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

As cyber threats grow increasingly complex, advanced Intrusion Detection systems (IDS) development has become a critical focus. Ensemble learning methods have shown great potential in enhancing IDS performance by improving accuracy, increasing robustness, and minimizing false positive rates. IDS public datasets are widely used as benchmarks, providing diverse attack scenarios and realistic traffic patterns for evaluating these techniques. This paper reviews advancements in ensemble learning methods for IDS, highlighting their effectiveness in addressing key challenges such as data imbalance and computational complexity. Additionally, it identifies the limitations of current approaches and proposes future research directions, including real-time IDS optimization and the development of explainable ensemble learning models.

关键词： Adaptation models Reviews Computational modeling Intrusion detection Traffic control Robustness Real-time systems Ensemble learning Computer security Computational complexity

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Investigation of Casting Quality control Through Deep Learning Techniques

Investigation of Casting Quality Control Through Deep Learni...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： R Sivakumar Dept. of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai INDIA

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The present manufacturing scene calls for a change towards improved assurance mechanisms to protect product quality. The complex problems given by high-throughput production techniques and flaws make this change important. Using traditional quality control techniques, attempts to sufficiently handle these problems usually fall short of expectations. This study advances the combination of industrial quality assurance with deep learning significantly. We offer exact defect identification using the VGG16 architecture under enhancement by dilated convolutions. Deep learning techniques—especially convolutional neural networks (CNNs)—have demonstrated quite great success in automating fault detection systems. Renowned for depth and expressive capacity, the VGG16 architecture provides a strong foundation. The use of dilated convolutions, used to solve spatial resolution restrictions, strengthens the basis. Dilated convolutions, often known as aurous convolutions, are a kind of convolution wherein the receptive field is enlarged without appreciably changing the parameters of the model. This strategic augmentation could help the VGG16 model to obtain a wider spectrum of contextual information, thus greatly improving its capacity to detect subtle patterns related with faults. This paper clarifies the benefits of the VGG16 architecture in fault discovery by analysing its technical details using dilated convolutions. The method is thoroughly introduced, empirical findings on a test dataset are shown, and a discussion of the broad ramifications for intelligent manufacturing is included. Through investigating the confluence of deep learning and manufacturing quality control, this work seeks to clarify the potential of VGG16 with dilated convolutions in improving defect detection accuracy. The work will especially look at the consequences of this intersection. This study provides understanding of the direction intelligent manufacturing will take, marked by the integration of cutti

关键词： Deep learning Fault diagnosis Accuracy Quality assurance Sensitivity Quality control Manufacturing Spatial resolution Defect detection Context modeling

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Collaborative Optimization Algorithm of Deep Learning and Compliant control in Online Trajectory Planning

Collaborative Optimization Algorithm of Deep Learning and Co...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Junting Liao Chunjian Zhao Hui Xie School of Computer and Artificial Intelligence Xiangnan University Chenzhou China China Telecom Hunan Chenzhou Branch Chenzhou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

With the rapid development of robotics and automation, online trajectory planning faces problems such as poor adaptability to dynamic environments and low computational efficiency. This study aims to propose a collaborative optimization algorithm that combines deep learning and compliant control to improve the efficiency and accuracy of online trajectory planning. First, CNN is used for feature extraction and a prediction model is established. Then a compliant control strategy is designed to deal with dynamic obstacles to ensure the flexibility and stability of the robot in trajectory planning. The trained CNN model is then used to generate real-time trajectory predictions. The robot motion is adjusted through a fuzzy logic controller to optimize the relative position of the trajectory and obstacles. The average obstacle avoidance success rate of the algorithm in dynamic environments reaches 97%. The collaborative optimization algorithm of deep learning and compliant control effectively improves the real-time and accuracy of online trajectory planning, providing a new solution for the application of intelligent robots in complex environments.

关键词： Deep learning Trajectory planning Heuristic algorithms Collaboration Predictive models Prediction algorithms Real-time systems Trajectory Convolutional neural networks Optimization

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Multi-objective Optimization and Dynamic Water Level Regulation Algorithms a case study of the US Great Lakes system

Multi-objective Optimization and Dynamic Water Level Regulat...

引用

international conference on Big Data and Smart Computing (BIGCOMP)

作者： Junlin Zeng Tao Wang Yinhao Wang Xiaogang Pan College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Global water resource management, particularly for large lakes, faces significant challenges in balancing ecological, economic, and flood control needs. Current methods struggle to address the diverse demands of stakeholders due to the complex hydrological and environmental factors involved. This paper proposes a solution using a multi-objective optimization model combined with dynamic feedback control. By applying hierarchical analysis (AHP), we assign stakeholder demand weights and develop a real-time water level prediction model using particle swarm optimization (PSO) with a shrinkage factor. The system also incorporates multidimensional feedback control via a spatio-temporal dynamic network to regulate water levels in real-time based on predictive data. Validation using the Great Lakes’ hydrological data from 2000 to 2022 shows the model’s effectiveness in handling environmental changes and maintaining water stability. The model proves to be a robust and practical tool for large-scale water resource management.

关键词： Heuristic algorithms Biological system modeling Predictive models Dynamic scheduling Stability analysis Regulation Real-time systems Feedback control Stakeholders Water resources

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Decentralized Cognitive Swarm-Based control systems: A Novel Approach for Autonomous Decision-Making and Adaptation

Decentralized Cognitive Swarm-Based Control Systems: A Novel...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Pavel A. Panilov Tatiana Y. Tsibizova Computer science and control systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The emergence of decentralized systems has precipitated a paradigm shift in the realm of autonomous control across diverse domains, ranging from robotics to critical infrastructure management. One such approach that is gaining traction is the cognitive swarm-based control system (CSBCS), which applies principles from cognitive science and swarm intelligence to decentralized decision-making processes. The present paper explores the design and application of CSBCS, emphasizing its ability to foster adaptability, robustness, and scalability in dynamic environments. The CSBCS framework integrates cognitive models that simulate human-like thinking, enabling individual agents within the swarm to operate autonomously, collaborate effectively, and adapt to changing conditions without centralized oversight. The experimental findings, derived from simulations and real-world applications demonstrate the potential of CSBCS in augmenting both individual and collective performance across a spectrum of domains, ranging from multi-robot coordination to critical infrastructure protection. CSBCS provides an framework for addressing complex issues that traditional centralized approaches are unable to tackle effectively.

关键词： Adaptation models Scalability Robot kinematics Heuristic algorithms Computational modeling Decision making Computer architecture Distance measurement Critical infrastructure Resource management

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Adaptive Simulated Binary Crossover with Bayesian Optimization for Industrial Applications

Adaptive Simulated Binary Crossover with Bayesian Optimizati...

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international Russian Smart Industry conference (SmartIndustryCon)

作者： Ali Deeb Vladimir Khokhlovskiy Viacheslav Shkodyrev Higher School of Cyberphysical Systems and Control Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Genetic Algorithms are widely used as global optimization solvers for complex and non-convex problems in industrial applications (resource and load planning, workshop logistics, process control, etc.). Simulated Binary Crossover (SBX) is a common operator in real-coded Genetic Algorithms for continuous optimization, but it typically relies on a fixed distribution index parameter that may not stay optimal throughout the evolutionary process. To address this, we propose a Transfer Learning-Based Adaptive Simulated Binary Crossover approach, which employs Bayesian Optimization to dynamically update the parameter during the Genetic Algorithm run. The parameter is periodically re-optimized based on the population's current dynamics. Tested on benchmark functions including Rosenbrock, Rastrigin, Ackley, and Griewank, our method achieves faster convergence and higher solution quality than standard Genetic Algorithms with a fixed parameter.

关键词： Industries Adaptive systems Benchmark testing Bayes methods Indexes Optimization Tuning Standards Genetic algorithms Convergence

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Integration and Application of Artificial Intelligence and Virtual Reality Technology in Garden Planning

Integration and Application of Artificial Intelligence and V...

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

作者： Xiongyaxiang Zhang Sichuan International Vocational College of Arts and Design Chengdu Sichuan China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

With the acceleration of urbanization, garden planning is facing increasingly complex design needs. While traditional design methods meet the requirements of personalization and efficiency, they often have problems such as information asymmetry and long design cycle, resulting in a gap between planning effects and actual needs. In order to solve these problems, this study proposes a garden planning fusion application solution based on artificial intelligence and virtual reality technology, aiming to improve the effect and efficiency of garden planning through intelligent design and immersive experience. Firstly, a convolutional neural network (CNN) based on deep learning is used to automatically analyze and recognize patterns in image data used in garden design. Big data technology is used to conduct in-depth mining of garden historical data, geographic information, and ecological environment, providing accurate basic information for garden design. Finally, virtual reality technology (VR) is used to create an immersive garden design model, which is visualized through a virtual reality platform. Designers can adjust the design plan in real time and perform interactive operations in the virtual environment. The experimental results show that in the design efficiency evaluation, the complex projects (projects 7-9) were reduced from 19.91 hours to 12.01 hours, saving about 39.7 %. In the evaluation of creative expression and innovation, the creativity score based on CNN and VR was 8.39, and the innovation score was 8.66. In the evaluation of scene adaptability and function optimization, the scores based on CNN and VR were 8.66 and 8.48 respectively. In the above data conclusion, the design method combining CNN and VR has significant advantages in improving the efficiency, innovation and adaptability of garden design.

关键词： Technological innovation Solid modeling Accuracy Design methodology Virtual environments Virtual reality Planning Convolutional neural networks Artificial intelligence Creativity

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NeuralODE-based Parameter Identification of the Three Chamber Model of the Circulatory System

NeuralODE-based Parameter Identification of the Three Chambe...

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international conference on Computational Cybernetics (ICCC)

作者： Ákos Antal Bálint Szabó Ákos Szlávecz Katalin Kovács J. Geoffrey Chase Balázs Benyó Dept. of Control Eng. and Inf. Tech. Faculty of Elect. Eng. and Informatics Budapest Univ. of Tech. and Eco. Budapest Hungary Dept. of Informatics Faculty Informatics and Elect. Eng. Széchenyi István Univ. Győr Hungary Dept. of Mech. Eng. University of Canterbury Christchurch NZ

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

In cases of Acute Circulatory Failure, fluid therapy is a commonly used intervention to stabilize heart function. However, the effectiveness of fluid therapy is not directly predictable, and the therapy can also be harmful. Physiological models can be used to predict fluid responsiveness - describing the effectiveness of fluid therapy for the patient - but require solving complex parameter identification problems. The current study aims to develop a Physics Informed Neural Network, specifically a NeuralODE-based parameter identification method for the Three Chamber cardiovascular model, which has the potential to be used to define a novel perfusion marker. The method is developed and validated on a clinical data set collected in model animal experiments.

关键词： Adaptation models Fluids Parameter estimation Animals Computational modeling Volume measurement Neural networks Medical treatment Physiology Physics

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Design of Novel Converter using ANN Algorithm for Industrial Loads

Design of Novel Converter using ANN Algorithm for Industrial...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sathish Kumar Shanmugam Kanimozhi Venkatesan Barath Srinivasan Kumar Gowtham Venkatachalam Karthik Ramasamy Electrical And Electroincs Engineering M.Kumarasamy College of Enginnering Karur Tamil Nadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The proposed project deals with the integrated Renewable energy system that uses wind (AC) and solar (DC) power sources will be designed and put into operation that is applicated to the industrial loads in the intension to optimize its operation. The proposed model involves in the design of the novel converter that converters DC source into AC source and supply it to the load. To control power generation, transform energy, and store it in a battery for later use, the system makes use of a controller. Pulse Width Modulation (PWM) is used to control the battery's energy so that to control the voltage that is given to the Industrial load. The control of the project model is managed by feeding ANN Algorithm to the PIC Micro controller. In order to power an external load, the system additionally has an inverter that transforms the DC energy that has been stored into AC. By integrating an LCD display, real-time monitoring is made easier, guaranteeing defect identification and operating efficiency. The project's goal is to an intelligent and flexible control mechanism is provided by Artificial Neural Networks (ANNs), which present a promising answer to these problems. Because ANN-based algorithms can learn and adapt to changing load circumstances in real time, unlike traditional controllers, they are especially well-suited for converters operating in complex and varied industrial environments. Because of their nonlinearity and flexibility, Artificial Neural Networks (ANNs) can effectively simulate the behavior of industrial loads and optimize power converter control to minimize losses, lower harmonic distortion, and improve response time, reliance on conventional power sources and it is achieved through the design of the novel converter.

关键词： Renewable energy sources Artificial neural networks Transforms Pulse width modulation Real-time systems Stability analysis Batteries Time factors Voltage control Load modeling

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