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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171-180 订阅

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Beyond Single Emotions: A Dual-Label Classification System for Enhanced Emotion Understanding in NLP Using Transformer-Based Models

Beyond Single Emotions: A Dual-Label Classification System f...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Monicadevi M S Gokila Department of Computer Applications Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Emotion detection in text is a significant subfield of natural language processing (NLP), with applications ranging from sentiment analysis to tracking mental health. Although single-label emotion classification has been extensively researched, multi-label emotion classification, especially dual-label classification, is not well explored. This paper explores dual-label emotion classification theoretically grounded in Plutchik's emotion dyads, suggesting that dyads of emotions are combined to produce more complex emotions. The transformer models Bidirectional Encoder Representations from Transformers (BERT), XLNet, and Robustly Optimized BERT Pre-training Approach (RoBERTa) are used to predict dual emotions on the GoEmotions dataset containing 28 classes of emotions. For better performance additional sentiment features Text Blob and Vader are included. Some emotions, like gratitude and admiration, are relatively easy to classify, but others, like grief and relief, are much more complicated. BERT achieved the highest accuracy (63.81%), while XLNet and RoBERTa performed better for subtle emotions. Overall, this approach gives a balance between emotional complexity and model practicality and a better understanding of the emotional nuance of the text without complicating the model excessively.

关键词： Emotion recognition Sentiment analysis Accuracy Computational modeling Bidirectional control Predictive models Transformers Encoding Real-time systems complexity theory

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Enhanced controller Placement Strategies in SDN: A Gravitational Search Algorithm Perspective

Enhanced Controller Placement Strategies in SDN: A Gravitati...

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international Communication systems and Networks and Workshops, COMSNETS

作者： Khushi Kalra Abha Kumari Akash Yadav Susham Biswas Dept. of CSE RGIPT Jais India Dept of CSE IIT Patna India Dept. of CSE RGIPT Jias India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

The controller Placement Problem (CPP) in Software-defined Networking (SDN) is outlined as finding the optimal number of controllers, their placement and identification of the set of switches to be managed by each controller. The CPP is pivotal for ensuring optimal network performance, scalability, and fault tolerance. This paper presents a novel approach to solving the CPP using the Gravitational Search Algorithm (GSA), a heuristic inspired by gravitational laws and mass interactions. Our study explores the effectiveness of GSA in minimizing both switch-to-controller latency and controller-to-controller latency within SDN environments. Simulation results demonstrate that GSA provides efficient solutions for CPP, outperforming traditional placement strategies, including Particle Swarm Optimization (PSO), across several critical metrics. GSA’s gravitational-based search mechanism provides a superior balance between exploration and exploitation, allowing it to escape local optima and achieve more globally optimal controller placements than PSO. Additionally, GSA’s adaptive pull mechanism better distributes controllers, ensuring load balance and minimizing both switch-to-controller and controller-to-controller latency, particularly in large-scale and complex network topologies. These results affirm GSA as a promising alternative for CPP in SDN, offering robust performance and scalability.

关键词： Measurement Scalability Simulation Heuristic algorithms Switches control systems Search problems Topology Software defined networking Particle swarm optimization

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Locating Faults on Overhead Power Transmission Lines of 110 kV and Above Using Emergency Mode Parameters

Locating Faults on Overhead Power Transmission Lines of 110 ...

引用

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

作者： Dmitriy Sharygin Galina Filatova Andrey Yablokov Department of electric power systems automatic control Ivanovo State Power Engineering University (ISPU) Ivanovo Russia

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article is devoted to the development of new fault location algorithms based on emergency mode parameters at instantaneous values on overhead power transmission lines of 110 kV and above. Increasing the accuracy of fault location algorithms is an important and urgent task. The methodological and simulation errors of fault location are directly related to the configuration of the power transmission line. The presence of parallel power transmission lines, mutual induction or branch substations affects the accuracy of fault location. In this work, the authors modernize their own previously developed fault location algorithms based on instantaneous values for complex power transmission line configurations. The developed fault location algorithms do not require additional corrections or means for processing primary signals and can be successfully applied when using data from digital in-strumental transformers. The studies of the algorithms were carried out on the simulation models of the overhead power lines in the Simulink/MATLAB software package. The remote fault location of the damage was carried out according to the parameters of the emergency mode. The assignment of parameters for the simulation modeling is carried out using the Monte Carlo method with subsequent statistical processing of the results and the construction of a justified inspection zone.

关键词： Instrument transformers Power transmission lines Accuracy Substations Voltage measurement Software packages Software algorithms Fault location Inspection Transmission line measurements

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Advancing Smart Transportation via AI for Sustainable Traffic Solutions in Saudi Arabia's

Advancing Smart Transportation via AI for Sustainable Traffi...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Gopich Bandarupalli Campbellsville university Texas USA

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The Saudi Arabian government has committed more than $100 billion (USD) to improving the country's transportation infrastructure, in line with Vision 2030 and the Sustainable Development Goals (SDGs) of the United Nations. The National Center for Transportation Safety (NCTS), which focuses on road safety, and the “Rental Contracts” initiative are two examples of the infrastructure development projects for which the FY2022 budget allotted 42 billion SAR. On the other hand, as cities become more populated, traffic congestion has worsened, making living more difficult. In response to these issues, the government is putting in place intelligent transportation systems that use Artificial Intelligence (AI) methods to predict traffic patterns and provide drivers with other routes that cut down on travel time. These AI-driven forecasts are anticipated to lessen traffic-related problems like pollution and health hazards, supporting the country's larger objectives for sustainable infrastructure. AI models, such as Random Forest (RF), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM), have been shown to be useful in traffic prediction based on empirical results. With a Mean Square Error (MSE) of 10.5, a Root Mean Square Error (RMSE) of 3.24, a Mean Absolute Error (MAE) of 2.15, and a Mean Absolute Percentage Error (MAPE) of 3.9%, the LSTM model outperformed both the RF and GRU models. These findings demonstrate how AI-driven models may help Saudi Arabia create transportation systems that are reliable, effective, and sustainable.

关键词： Radio frequency Computational modeling Government Urban areas Traffic control Artificial intelligence Sustainable development Long short term memory Traffic congestion Vehicles

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One-dimensional and three-dimensional simulation of aircraft equipment cabin combined with spray cooling 24

One-dimensional and three-dimensional simulation of aircraft...

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Proceedings of the 2024 4th international conference on Computational modeling, Simulation and Data Analysis

作者： Ying Zhao Qing Cao Yuanxun Zhu Xiaobin Shen Peng Liu Shuai Su Yu Sun Guiping Lin AVIC Shenyang Aircraft Design and Research Institute Shenyang Liaoning China Hangzhou International Innovation Institute Beihang University Hangzhou Zhejiang China School of Aeronautic Science and Engineering Beihang University Beijing China Hangzhou International Innovation Institute Beihang University Hangzhou Zhejiang China

ISBN: (纸本)9798400711831

Aircraft, especially hypersonic aircraft, are subjected to harsh thermal environments during flight, which makes thermal safety problems particularly prominent. The coupling action of aerodynamic heat from the outside and heat generated by internal equipment poses a severe challenge to the design of structural thermal protection systems and in-flight thermal control systems. The increasing power density of various electronic devices in the aircraft equipment cabin requires certain means to ensure that the thermal environment in the equipment compartment meets the operating conditions of electronic equipment. After conducting research, this study selected liquid nitrogen as a low-temperature consumable working fluid for the spray cooling of the equipment cabin. A one-dimensional simulation network model was built to calculate the liquid nitrogen flow required for aircraft equipment cabin spray cooling. The three-dimensional flow field simulation was used to verify the accuracy of the one-dimensional calculation. At the same time, the accuracy of the Fluent software simulation was verified using existing data. The results of the calculation were visualized and compared for typical flight conditions, and the flow field and temperature field in the equipment cabin, as well as the temperature of the equipment wall, were obtained. One-dimensional and three-dimensional iterations were achieved, and the feasibility and rationality of the working fluid evaporation cooling in the equipment cabin of the aircraft were determined.

关键词： Aircraft equipment cabin

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A Multi-Agent Symbiotic Organisms Search based Modified Fractional-Order PID controller for Magnetic Levitation Plant

A Multi-Agent Symbiotic Organisms Search based Modified Frac...

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

作者： Harideo Ram Deep Shekhar Acharya Shradha Kishore Electrical & Electronics Engineering Birla Institute of Technology Mesra Ranchi Jharkhand India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

A non-integer-order PID controller outperforms its integer-order counterpart in terms of better control when used for either integer-order or non-integer-order systems. However, due to a large number of tunable variables, tuning the FOPID controller is a challenging and complex task. This paper proposes a modified FOPID controller (mFOPID) in such a way as to offer comparable performance with a reduced number of parameters. The proposed modification reduces the tunable variables to three, without compromising the performance. The optimal set of parameters for the proposed controller has been estimated using the multi-agent based symbiotic organisms search (MASOS) algorithm by formulating and optimization problem choosing the objective function to be the root mean square error (RMSE). The performance of MASOS is compared with that of Grey Wolf Optimzer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The efficacy of the proposed control scheme has been assessed by means of implementing it on a magnetic levitation system (MLP), which is a nonlinear second-order open-loop unstable plant and has been compared with that of integer-order PID (IOPID) and FOPID controllers. The numerical results confirm the enhanced performance of the proposed controller and superiority of MASOS over the other algorithms. It was inferred that the proposed control scheme restricts both settling time and peak overshoot in the closed loop response, by more than 90% when compared to the other controllers. Additionally, the proposed scheme makes the system robust against parameter variations by imparting the isodamping property.

关键词： Symbiosis System performance Transfer functions Search problems Organisms Robustness Tuning Optimization Magnetic levitation Genetic algorithms

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Application of Particle Swarm Optimization Algorithm Based on Adaptive Inertia Adjustment Strategy in Scheduling of the Coking Process

Application of Particle Swarm Optimization Algorithm Based o...

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IEEE international conference on Industrial Technology (ICIT)

作者： Yiheng Chen Fan Yin Xiaochong Chen Yi Ren Jie Hu Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

The sccheduling for pushing plan during the coking process critically affects the efficiency and stability of production. However, the complexity with mutiple-stage during production makes it difficult to design an efficient coke pushing plan. To address this issue, this paper proposes a scheduling method based on the particle swarm optimization algorithm. Firstly, the fuzzy c-means clustering is utilized to categorize actual operating conditions as either normal or abnormal, thereby facilitating the scheduling of pushing plan under disparate conditions. Subsequently, the scheduling problem for the pushing plan is transformed into a traveling salesman problem, and scheduling models under various conditions are established. Finally, to accelerate the convergence and enhance the algorithm's global search capability, an adaptive inertia adjustment strategy is employed to dynamically regulate the velocity and position of particles. The proposed method has been implemented in the coking process. Through the analysis of application results, the completion coefficient of pushing plan has been increased by 4.25%, demonstrating that the proposed has advantages in scheduling the pushing plan during the actual coking process.

关键词： Job shop scheduling Heuristic algorithms Clustering algorithms Production Traveling salesman problems Stability analysis Robustness complexity theory Particle swarm optimization Convergence

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Research on Engineering Location of Rocket Debris Based on Multilateral Measurement Technology

Research on Engineering Location of Rocket Debris Based on M...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe conference on

作者： Siyi Du Sihan Qiu Dalian Jiaotong University Dalian China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

With the increasing number of space missions, the accurate location and recovery of rocket debris have become increasingly important. Traditional GPS and radar systems struggle to ensure the accuracy and reliability of positioning in complex and changeable environments, making the task of locating rocket debris highly challenging. This study aims to accurately locate the sonic booms of rocket debris using multilateral measurement technology and multiple monitoring devices. Based on the basic positioning of a single piece of debris, we first constructed a three-dimensional positioning model using the Time Difference of Arrival (TDOA). By applying the least squares method, the sonic boom time data captured by the monitoring device was obtained and traced back to the debris location. To simulate real-world scenarios more closely, this study introduced the Hungarian algorithm to solve the data association problem of multi-source signals in multi-rocket debris scenes and achieved the locking of each debris location through an optimization framework. Furthermore, to address the positioning problem when four rocket remnants exist simultaneously, positioning accuracy was improved by introducing a joint optimization strategy for position and time, along with a random error processing mechanism. When the model deals with multiple debris locations, the time difference is less than 5 seconds, and the positioning error is less than 200 meters. Our research has explored a set of methodologies for the difficult technical problem of rocket debris location, which is also applicable to similar multi-source location problems, and will contribute to improving the efficiency and safety of rocket recovery.

关键词： Rockets Solid modeling Accuracy Three-dimensional displays Time difference of arrival Process control Safety Monitoring Optimization Least mean squares methods

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

Path Planning for Distribution Network Robots with Adaptive ...

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IEEE international conference on Industrial Technology (ICIT)

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

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

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.

关键词： Electric potential Adaptation models Force Distribution networks Simulated annealing Manipulators Path planning Planning Collision avoidance Robots

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Attention-Augmented YOLO11 for High-Precision Aircraft Detection in Synthetic Aperture Radar Imagery

Attention-Augmented YOLO11 for High-Precision Aircraft Detec...

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Digital Signal Processing and its Applications (DSPA), international conference on

作者： Irem Bayraktar Murat Bakirci Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin Turkey

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Synthetic Aperture Radar (SAR) imaging has emerged as a pivotal technology in remote sensing, offering unparalleled capabilities for high-resolution imaging under all-weather conditions, day or night. This versatility has rendered SAR indispensable for various critical applications, including ground-based aircraft monitoring. Despite its advantages, the complex nature of SAR imagery, characterized by speckle noise and intricate geometric distortions, presents significant challenges for object detection tasks. In response, advanced object detection algorithms have become essential for extracting actionable insights from SAR data. This study focuses on ground-based aircraft detection using the latest addition to the YOLO family, YOLO11, a state-of-the-art object detection algorithm. While YOLO11 exhibits cutting-edge performance in standard detection tasks, its built-in attention mechanisms were found insufficient for addressing the unique complexities of SAR imagery. To overcome this limitation, we augmented the neck architecture of YOLO11 with Enhanced Channel Attention (ECA) modules, achieving significant performance improvements. The ECA-enhanced YOLO11 demonstrated an average increase in precision of 1.7% and mAP50 of 2.30% across various input image resolutions, with gains of up to 3.4% in lower-resolution scenarios $({256}\times {256}\ \text{pixels})$ . Furthermore, the F1 score consistently improved by up to 2.40%, showcasing the model's ability to balance precision and recall effectively. The results highlight the potential of integrating ECA modules into detection architectures to mitigate the challenges of SAR imagery, making it possible to achieve more reliable and accurate aircraft detection under complex imaging conditions.

关键词： Attention mechanisms Computational modeling Object detection Computer architecture Military aircraft Radar polarimetry complexity theory Air traffic control Aircraft Synthetic aperture radar

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