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A Novel Multi-Objective Non-Dominated Sorting Beluga Whale Optimization Algorithm for WFGD Process

A Novel Multi-Objective Non-Dominated Sorting Beluga Whale O...

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data driven control and learning systems (ddcls)

作者： Junheng Xu Kang Wang Xiaoli Li Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the operation of coal-fired power plants emits a significant amount of SO 2 , causing a considerable environmental challenge. Wet flue gas desulfurization (WFGD) is the mostly used desulfurization method in current practice for address this issue, however, the simultaneous optimization of desulfurization efficiency and cost remains challenging. Aiming to achieve optimal performance and reduce the computational complexity, this paper proposes a novel multi-objective non-dominated sorting beluga whale optimization (NSBWO) algorithm to concurrently optimize desulfurization efficiency and cost. Derived from the NSGA-II framework, the NSBWO algorithm adopts the beluga whale algorithm to generate offspring instead of the conventional genetic algorithm used in the original algorithm. this modification alleviates the concerns about performance dependence on user-defined parameters, enhancing its performance in multi-objective optimization. Experimental results demonstrate that, compared to classical algorithms, NSBWO exhibits superior overall performance and faster convergence speed. Finally, the algorithm is applied to real-world data sourced from the flue gas desulfurization system of a 1000MW coal-fired power plant unit. the obtained results validate the algorithm's effectiveness in optimizing both the efficiency and cost of the wet flue gas desulfurization system.

关键词： learning systems Costs Whales control systems Whale optimization algorithms Optimization Power generation

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A Bottom-Up Human Pose Estimation Correction Method Based on Object Detection

A Bottom-Up Human Pose Estimation Correction Method Based on...

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data driven control and learning systems (ddcls)

作者： Xiaoping Zhang Xiangpeng Meng Kai-Fung Chu Li Wang School of Electrical and Control Engineering North China University of Technology Beijing China Department of Engineering University of Cambridge Cambridge U.K.

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

As computer vision technology is widely applied in various domains, it inevitably faces complex and crowded scenes. However, human images in these scenes are more susceptible to occlusion, which degrades the performance of human pose estimation. Since human pose estimation methods typically represent the human pose feature as the skeleton of the human keypoints, the keypoint set estimation significantly influences the overall accuracy. To improve the human pose estimation accuracy in crowded scenes, a bottom-up human pose estimation correction method based on object detection is proposed in this paper. the keypoint set estimated by the bottom-up human pose estimation is corrected by introducing an object detection algorithm to reduce estimation errors and fill in missing keypoints, so as to alleviate the decline of detection effect caused by superimposed multilevel errors in occlusion scenes. the method is verified by our self-built human pose dataset in the bus compartment scene. the results show that the OpenPose human pose estimation algorithm with the method in this paper can obtain 4.41 mAP improvement, especially for the keypoints with strong local information.

关键词： learning systems Heating systems Estimation error Accuracy Quantization (signal) Pose estimation Object detection

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data-driven Soft Sensor Modelling with Time Delay Estimation through Bayesian Optimization

Data-Driven Soft Sensor Modelling with Time Delay Estimation...

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data driven control and learning systems (ddcls)

作者： Jun Yang Bo Wang Yangyang Liang Weiyong Zhang Peng Ding Chao Shang Methanol Branch Ningxia Coal Co. Ltd. Yinchuan China Tsingyun Intelligence (Beijing) Technologies Co. Ltd. Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the soft sensor technology is an effective solution to online estimation of primary variable that is difficult to measure via hardware sensors in industrial processes. By selecting several easy-to-measure auxiliary variables that are closely related to the primary variable, a mathematical model can be established to predict the primary variable online. However, there is typically a time-delayed correlation between auxiliary variables and primary variable, which is usually neglected in state-of-the-art soft sensing approaches. In this study, we propose a new data-driven soft sensing approach by considering time-delayed inputs in Support Vector Machine (i.e. TD-SVM). Both discrete-valued time delays and hyperparameters of SVM are jointly optimized by using Bayesian Optimization (BO) algorithm based on Gaussian process. We carry out case studies on process data from a real-world methanol plant in China, which demonstrate the usefulness of the proposed soft sensor model and parameter optimization strategy. It is shown that the BO can effectively optimize the estimation of time delays and hyperparameters of SVM jointly, thereby helping to improve the model accuracy.

关键词： Support vector machines Accuracy Soft sensors Delay effects Estimation Predictive models data models

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Modeling and Simulation of PV Parallel Arrays under Multiple Illuminations

Modeling and Simulation of PV Parallel Arrays under Multiple...

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data driven control and learning systems (ddcls)

作者： Wenwen Xiao Na Dong Kesen He Xiaoming Mai Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Building-integrated photovoltaics (BIPV) provide great flexibility for energy conservation and emission reduction. However, uneven illumination distribution can result in multi-peak characteristics, negatively impacting the system's efficiency. therefore, it is crucial to establish a photovoltaic (PV) cell model that is adaptable to environmental changes. the existing wildly-used seven-parameter circuit model faces big challenges in real engineering applications due to its unmatched parameters to the ones provided by manufactures. To address this issue, this study utilizes the maximum power point matching method and investigates parameter optimizations, including photocurrent (Ipv), reverse saturation current (Io), as well as ideality factor (A). An optimized seven-parameter model is established and the output characteristics of two different kinds of PV panels are presented. To maximize the output power, it is necessary to optimize the array configuration. Based on the enhanced even-parameter model, this paper does in-depth investigations on the maximum output of the six typical structures under three typical shading patterns, which are Series-Parallel (SP), Bridge-Linked (BL), Total-Cross- Tied (TCT), Honey-Comp (HC), Long Strings (LS), and Multi Strings (MS). the research findings will contribute to the selection of the optimal structure for PV arrays under various shading scenarios.

关键词： learning systems Adaptation models Energy conservation Lighting data models Photoconductivity Arrays

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An Effective Crop Diseases Detector via CropDetDiff Model with Multi-Sized Selective Kernels

An Effective Crop Diseases Detector via CropDetDiff Model wi...

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data driven control and learning systems (ddcls)

作者： Chenghao Hua Jianlei Kong National Engineering Research Center for Agri-Product Quality Traceability Beijing Technology and Business University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Crop disease detection is an integral aspect of smart agricultural practices, instrumental in augmenting crop yield and quality. Contemporary mainstream approaches are heavily reliant on deep learning paradigms whose enhancements are contingent upon substantial volumes of meticulously annotated datasets. However, in the domain of agricultural pathology detection, the arduous task of dataset creation and the inherent challenges associated with the detection of small targets pose formidable obstacles to existing methodologies. In response to these challenges, we introduce a detector named CropDetDiffthat employs diffusion learning to minimize reliance on training samples, thus delivering superior detection performance in agricultural settings with limited training data availability. To address the detection of small-sized crop targets, we have developed a lightweight graph neural network, LCSP-ViG, with an integrated Large-Sized Selective Kernel (LSK) network for sparse detection of small objects. We have also designed a One-to-Few (O2F) head featuring a label assignment strategy to enhance the precision of small object bounding boxes. Finally, a new bounding box loss function, HIoU, has been introduced to focus the model's supervised learning specifically on small objects. We conducted a series of experiments that verified the optimal detection performance of our model compared to other leading detectors. In particular, on the agricultural object detection datasets Strawberry, our model demonstrated APs of 73.16%, notably, there is a pronounced enhancement in detection performance of small targets. the performance of this model proves its effectiveness for the operation on agricultural robots.

关键词： Training Supervised learning Training data Process control Detectors Object detection data models

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Fault Diagnosis of Quadrotor UAV Propeller with Local-Global Attention-Based Graph Neural Networks

Fault Diagnosis of Quadrotor UAV Propeller with Local-Global...

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data driven control and learning systems (ddcls)

作者： Zhiwei Wang Shaojun Liang Ye Sun Yi Yang Yanwei Wang Ying Zheng Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Ordnance Sergeant Army Engineering University Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the wide application of Quadrotor Unmanned Aerial Vehicles (QUAV) in military and civilian fields, its flight reliability problem has become a hot research topic. the propellers, which are the important components relating to the performance of QUAV, play a central role in safe flight. Traditional data-driven fault diagnosis methods of QUAV are based on Euclidean distance, which cannot effectively capture the interrelationship of multiple variables. In this paper, we treat the fault diagnosis of QUAV propeller as a node classification problem in non-Euclidean infinite dimensional space by introducing the method of Local-Global Attention-based Graph Neural Network (LGA-GNN). First, the graph topology is constructed using the KNN method based on the real flight log of the QUAV. then, the local and global attention strategies are integrated to form a graph-based collaborative feature extraction model. Finally, a unified end-to-end fault diagnosis framework tailored for QUAV is proposed. In the experiment section, real flight data of QUAV with different propeller fault conditions are collected, and the effectiveness and superiority of the proposed method are verified by comparing with other methods based on real data.

关键词： Fault diagnosis learning systems Propellers Network topology Nearest neighbor methods Autonomous aerial vehicles Graph neural networks

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Distributed control of Heterogeneous Modular Flight Arrays under the Limited Space Constraints

Distributed Control of Heterogeneous Modular Flight Arrays u...

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data driven control and learning systems (ddcls)

作者： Jianhui Liu Chunxi Yang Xiufeng Zhang Yiming Li Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Modularized aerial vehicles are now receiving more and more attention for its flexibility and scalability. How to achieve fast and safe traversal among modular unmanned aerial vehicle (UAV) swarms in the limited space is a critical issue for them to accomplish complex missions. Motivated by cooperative control method of UAV swarms in structured airspace, this paper takes the modular flight array platform developed by our research team as an object. And then an advection model is established to discuss the dynamic process of multiple modular flight arrays. By designing two regions, a type of collision avoidance schedule is proposed. Moreover, a distributed controller based on the group of heterogeneous modular flight arrays is developed to solve air traffic problem among them under limited space constraints. Formal analyses are made to show that all modular flight arrays can reach their respective destination lines and avoid collisions. In order to make the modular flight arrays complete their respective missions quickly, a collision avoidance strategy is proposed, which shorten the convergence time and effectively avoids the oscillation phenomenon. Finally, simulation example is given to verify the effectiveness of the proposed method.

关键词： Space vehicles Schedules Space missions Scalability Aerospace electronics Autonomous aerial vehicles Safety

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Collaborative Decision-Making Technique for Wireless Communication Means Based on Multi-Agent Reinforcement learning

Collaborative Decision-Making Technique for Wireless Communi...

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data driven control and learning systems (ddcls)

作者： Kun Zhang Chao Hu Deqing Huang Jiachen Shen School of Electrical Engineering Southwest Jiaotong University Chengdu P. R. China Key Laboratory of Advanced Energy Traction and comprehensive energy saving railway industry Southwest Jiaotong University Chengdu P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Wireless communication is a pivotal domain within modern information and communication technology. With the proliferation of mobile devices and the Internet of things, wireless communication methods have become ubiquitous across diverse industries. Challenges arise in collaborative decision-making technology that leverages wireless communication, including heightened information variability, increased time sensitivity, and a significant need for manual intervention. this paper introduces, for the first time, the application of multi -agent reinforcement learning technology to develop wireless communication agents. this approach involves distributed training, real-time simulation interaction, and other key technologies rooted in the pertinent wireless communication mechanism. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method.

关键词： Wireless communication Training Technological innovation Target tracking Sensitivity Decision making Collaboration

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T-S Fuzzy $H_{i}/H_\infty$ Optimization Based Fault Detection Method for Traction Motor in High-Speed Train

T-S Fuzzy $H_{i}/H_\infty$ Optimization Based Fault Detectio...

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data driven control and learning systems (ddcls)

作者： Guangdong Sun Jiahong Xu Maiying Zhong Lu Zhang College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper focuses on the problem of speed sensor fault detection (FD) for traction motor in high-speed train subject to multi-source interference. An observer-based fault detection filter (FDF) is employed to generate a residual signal for speed sensor fault described by a Takagi-Sugeno (T-S) fuzzy model. the robustness to multi-source interference is measured by the $H_{\infty}$ -norm of the interferences to the residual, while the sensitivity to fault is measured by the $H_{\infty}$ -norm or the $H\_$ criteria of the fault to the residual. As a result, the design of the FDF is formulated as an $H_{i} /H_{\infty}$ optimization problem, aiming to achieve an optimal balance between the sensitivity to fault and the robustness to interference. A unified solution is proposed to obtain the observer gain matrix and post-filter. To illustrate the effectiveness of the developed method in this paper, two types of faults are considered in the simulation, including sudden-change and slow-change faults. the simulated results are shown that the developed method in this paper can achieve effective detection of speed sensor fault for traction motor in high-speed train.

关键词： learning systems Sensitivity Fault detection Simulation Interference Observers Traction motors

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Dynamic Optimization for SP of control Loops Based on Both Rapid Intelligent Optimization and UniSim/EPKS Environments

Dynamic Optimization for SP of Control Loops Based on Both R...

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data driven control and learning systems (ddcls)

作者： Hehui Zhang Zhu Wang Shao Xian Wang Department of automation China University of Petroleum (Beijing) Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Advanced Process control is frequently applied in industrial chemical processes, where Dynamic Matrix control is widely employed for its capability to handle complex optimization control problems with multiple variables and constraints. However, during prolonged operation of industrial processes, factors such as changes in operating conditions and external environmental variations may lead to a mismatch between the predictive model and the actual plant in DMC. To address the Model-plant Mismatch, this paper proposes a dynamic trajectory rapid intelligent optimization method based on online reconstruction of the predictive model. Constructing the system model based on the known linear dynamic model of the control loop and PID parameters, the new dynamic matrix is obtained using the closed-loop step response of the system model. then, updating the prediction model online and simultaneously updating the optimization-constrained conditions in real time ensures the long-term effectiveness of the advanced control scheme. To rapidly solve the optimization objective, this paper utilizes an improved GA-PSO algorithm to compute the Set Point trajectory of the control loop. Finally, this study conducted simulations by integrating Honeywell's UniSim Design simulation platform with the EPKS DCS control system, verifying the effectiveness of dynamic matrix online updating and demonstrating the long-term effectiveness of the advanced control scheme.

关键词： Monte Carlo methods Computational modeling Simulation Process control Optimization methods Predictive models control systems

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