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Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm 13

Trajectory Optimization of Manipulator for Loading and Unloa...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Zhao, Dongliang Fang, Yiming Yang, Chen Xin, Jingliang Yanshan University Key Lab of Industrial Computer Control Engineering Hebei Province Qinhuangdao China Eng. Res. Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Hebei Province Qinhuangdao China Hebei Iron and Steel Group Hebei Province Tangshan China

ISBN: (纸本)9798350315196

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. Then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. The improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. Then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. The algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. The DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter. © 2023 IEEE.

关键词： Trajectories

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An Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization Algorithm with Its Application in PID Parameter Optimization

An Improved Hybrid Aquila Optimizer and Pigeon-Inspired Opti...

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International Symposium on computer Science and Intelligent controls (ISCSIC)

作者： Dongning Chen Xinwei Du Haowen Wang Qinggui Xian Jianhao Sha Chengyu Yao School of Mechanical Engineering Yanshan University Qinhuangdao China Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

To improve the optimization performance of the standard Aquila Optimizer (AO), an Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization (IHAOPIO) algorithm based on stochastic balancing factor and Dimension-by-dimension Centroid Opposition-Based Learning (DCOBL) strategy is proposed. Wilcoxon test is introduced to statistically analyze the data of six classical benchmark functions. Compared with several other optimization algorithms, the proposed IHAOPIO algorithm has better optimization capability in accuracy. The improved algorithm is used to the PID parameter optimization of one electro-hydraulic position servo system and achieves good control effect.

关键词： computer science Accuracy Benchmark testing Servomotors Optimization Standards Intelligent control

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EAVFormer: an end-to-end audio and visual emotion recognition network based on transformers

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Multimedia Systems 2025年第3期31卷 1-22页

作者： Liu, Haoran Sun, Zijian Li, Haibin Li, Yaqian Zhang, Wenming Song, Tao School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan University Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Shenzhen Research Institute of Yanshan University Shenzhen518057 China Key Lab of Measurement Technology & Instrumentation of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Audio-visual emotion recognition, integrating information from various sources, enhances the comprehensive understanding and analysis of human emotions. However, existing methods in this field primarily focus on feature fusion, often overlooking the equally crucial stage of feature extraction. Typically, these methods employ pre-trained models to extract features during the initial phase, complicating modality-specific feature extraction and increasing system complexity and coordination challenges, which hinder effective audio-visual feature fusion. To address these issues, we propose an end-to-end audio-visual emotion recognition model, EAVFormer. For the audio modality, we input raw audio signals along with a mix of mel spectrograms and Mel Frequency Cepstral Coefficients (MFCCs), employing a hierarchical transformer technique that facilitates both shallow local and deep global feature extraction to fully harness complementary acoustic information, significantly enhancing the acoustic modality’s contribution. For the visual modality, we input raw video frames and employ a hierarchical transformer feature extraction method that integrates three-dimensional convolution and spatio-temporal self-attention mechanisms to address spatial and temporal redundancies and dependencies, thereby enhancing the expression of dynamic visual information. In the modal fusion stage, we construct dual-modality representations and encode inter-modal information while preserving modality-specific and meaningful intra-modal features. Testing on three popular datasets RAVDESS, CREMA-D, and CMU-MOSEI demonstrates that EAVFormer surpasses existing state-of-the-art methods in audio-visual emotion recognition accuracy, achieving rates of 95.17%, 86.13%, and 78.22%, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Image coding

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An Improved NSGAII Algorithm for Flexible Job-shop Scheduling Problem Based on A New Decoding Mechanism

An Improved NSGAII Algorithm for Flexible Job-shop Schedulin...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Man Li Kai Ma Shiliang Guo Shuai Li Bin Yang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

The flexible job-shop scheduling problem (FJSP) is a famous combinatorial optimization problem. FJSP is widely used in process manufacturing industry, so it is of great significance to study FJSP to improve production efficiency. In this paper, a non-dominated sorting genetic algorithm II based on a new decoding mechanism (NSGAII_NDM) is proposed. Firstly, the opposite-based learning strategy is introduced on the basis of NSGAII, and the sufficiency of global search is improved by generating the opposite value. Secondly, an improved adaptive crossover and mutation probability strategy is added, which can dynamically adjust the inherent crossover and mutation probability with the number of iterations and improve the convergence speed of the algorithm. Furthermore, based on fully active scheduling strategy, a new decoding mechanism is proposed, which can get a better scheduling scheme by arranging the machines and starting time of each process more reasonably. Finally, the improved algorithm is tested in a standard test case, which verifies the superiority of NSGAII_NDM in solving the flexible job-shop scheduling problem.

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An Improved Seagull Optimization Algorithm for Flexible Job-shop Scheduling Problem

An Improved Seagull Optimization Algorithm for Flexible Job-...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Kai Ma Shijie Yao Shiliang Guo Bin Yang Shuai Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

Job-shop scheduling problem is the core link and key technology to realize smart factory and develop smart manufacturing technology. Among them, the flexible job-shop scheduling problem (FJSP) which is more consistent with the actual production and processing mode has been focused. This paper proposes an improved seagull optimization algorithm(ISOA) for solving the FJSP. The ISOA uses chaos initialization and reverse learning to optimize initial population, and introduces Q-learning to change the fitting parameters to balance global search and local search. In addition, we propose a local search model that is adaptive iterative process to enhance the algorithm's optimization search capability. The results of the experiment proved that the algorithm is superior in terms of optimization speed and scheduling results.

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Relaxed Stability Analysis and Event-Triggered controller Design for Discrete-Time Positive T-S Fuzzy Networked control Systems

Relaxed Stability Analysis and Event-Triggered Controller De...

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Annual Conference of industrial Electronics Society

作者： Xiaoxiao Wang Xiaomiao Li Zhiyong Bao Fucai Liu Yuehao Du Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

This paper deals with the relaxation stability analysis and event-triggered controller design of discrete-time positive Takagi-Sugeno (T-S) fuzzy networked control systems (FNCSs). The constraints of positive conditions in NCSs bring significant challenges to stability analysis and controller design. A 1-norm event-triggered scheme is proposed for the positive system, which can effectively reduce the frequency of network communication. Since the event-triggered T-S fuzzy controller (ETTSFC) can only observe the latest transmission state after the triggering action, the ETTSFC selects the premise variables different from the T-S fuzzy model, improving design and implementation flexibility. Using the linear copositive Lyapunov function (LCLF), the design criteria of the event-triggered controller to ensure the positivity and stability of the closed-loop system are derived. To relax the sum-of-squares (SOS) based conservative condition, we adopt the Taylor series membership functions (TSMFs) and introduce TSMFs and their approximation error into the stability conditions. Finally, an example is given to illustrate the effectiveness of the design.

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G-Anomaly: A Pyramid Graph Transformer-based Vision-Language Model for General industrial Anomaly Detection

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IEEE Transactions on Automation Science and engineering 2025年

作者： Li, Jiaqi Wen, Shuhuan Fang, Bin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Beiing University of Posts and Telecommunications Department of Artificial intelligence Beijing China

Visual-language alignment is crucial for enhancing the domain adaptability of industrial anomaly detection models. However, the existing methods overlook the importance of structured image representation, fail to further distinguish topological differences between anomalies and the inherent textures of products, which reduces the accuracy of semantic matching. To address this problem, we propose a novel industrial anomaly detection model G-Anomaly, to preserve the topological structure of the sample images and further enhance the model’s domain adaptability. We designed Pyramid Graph Transformer as a visual encoder to extract multi-scale visual features, which can directly preserve the structural relationships between different regions of the image, and also optimize the over-smoothing issue present in deep graph networks, thereby retaining the distinguishability of anomalous nodes. Additionally, we design a Multi-level Domain Adapter that ensures semantic consistency of anomalous features across different scales and contexts by performing visual-language matching at various resolutions and levels of abstraction. This enhances the model’s domain adaptability for anomaly detection for a wide range of industrial products. We collect and craft an actual solar panel dataset PV_actual AD, and conduct extensive experiments on the public dataset MVTec AD as well as the actual solar panel dataset PV_actual AD. This has demonstrated that G-Anomaly not only performs well in standard testing environments but also exhibits robustness and domain adaptability for anomaly detection tasks in real-world scenarios. © 2025 IEEE. All rights reserved.

关键词： Semantics

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Face Inpainting Based on Gan Multi-Dimensional Consistency Guidance

SSRN

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SSRN 2024年

作者： Li, Yaqian Zhan, Xin Li, Haibin Zhang, Wenming Hebei Provincial Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao066004 China

A B S T R A C TThe averageness of low-frequency features and the unreliability of high-frequency features result in identity deviations in the inpainted faces. To address this problem, a two-stage generative model is proposed, which includes structural prior guidance and multidimensional consistency guidance. Firstly, a parsing inpainting module was designed to predict structural information. Subsequently, a multidimensional information guidance module was constructed, which, based on semantic consistency guidance, extracts low-frequency confidence information of the inpainted image to suppress the averageness of facial inpainting. Frequency-domain information is extracted from the inpainted image, combined with a facial recognition network, and the frequency-domain identity feature consistency loss is calculated to aid in the inpainting of high-frequency information, including identity features, thus achieving identity consistency in facial inpainting. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Event-Triggered Robust Model Predictive control for Multi-Agent Systems Under Hybrid Attacks

Event-Triggered Robust Model Predictive Control for Multi-Ag...

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第42届中国控制会议

作者： Yongxin Yin Jianxiong Li Chongyi Gao Yiming Fang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Department of Electromechanical Engineering Tangshan University

In this paper,the consensus control problem for multi-agent systems（MASs） suffering hybrid attacks is discussed,in which the hybrid attacks include randomly occurring deception attacks on system dynamic behavior and denial-of-service attacks（DoS）.An event-triggered robust model predictive control（RMPC） method is presented to achieve the consensus of MASs under hybrid *** can be proved that the consensus errors can be asymptotic with prescribed H performance,the sufficient condition for stability can be given in terms of linear matrix inequalities（LMIs）,and the controller gains are obtained by solving an optimization problem involving some *** the event-triggered mechanism with maximum trigger interval,not only can not only save computation time and facilitate the efficiency of the shared resources but also can effectively be responsible for the negative impact of cyber-attacks on system ***,simulation results are given to illustrate the validity and effectiveness of the proposed method.

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A TCN-Transformer Model of Acceleration Prediction for Preceding Vehicle in CACC Systems

A TCN-Transformer Model of Acceleration Prediction for Prece...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Wenzhao Tang Wenbin Tang Xiaohong Jiao Yahui Zhang Dongxue Zhang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China School of Mechanical Engineering Yanshan University Qinhuangdao China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

This study establishes an acceleration prediction model of the preceding vehicle tailored for cooperative adaptive cruise control (CACC) systems that address the common challenges posed by V2V communication delays or no communication from the preceding vehicle. The designed model integrates a temporal convolutional network (TCN) with the Transformer architecture block to create a sophisticated mechanism for temporal data analysis. This integration leverages the strengths of TCN in capturing spatial features and the multi-attention mechanism of Transformers to enhance the recognition of long-term dependencies in the time series of data. The synergy between these techniques not only effectively narrows the identification gap between short-term and long-term dependencies but also helps to process spatial information for accurate time-series prediction, significantly improving prediction accuracy. Simulations using the NGSIM dataset confirm the model's prediction performance over the traditional model. Further, the potential of the method to solve the communication delay problem is verified, highlighting the effectiveness of TCN and Transformer multi-attention mechanism in improving the CACC capability.

关键词： Adaptation models Accuracy Uncertainty Adaptive systems Time series analysis Vehicular ad hoc networks Predictive models Transformers Delays Cruise control

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