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Joint Feature Encoding and Task Alignment Mechanism for Emotion-Cause Pair Extraction

computers, Materials & Continua 2025年第1期82卷 1069-1086页

作者： Shi Li Didi Sun College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant ***-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying *** comprehension is crucial for making informed strategic decisions in various business and societal ***,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause *** address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment *** model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause ***,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among *** proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 *** results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its *** research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-c

关键词： Emotion-cause pair extraction interactive information enhancement joint feature encoding label consistency task alignment mechanisms

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Open Architecture for computer-Aided control engineering

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IEEE control Systems 1993年第2期13卷 17-27页

作者： Barker, H. Anthony Chen, Min Grant, Philip W. Jobling, Chris P. Townsend, Peter Control and Computer-Aided Engineering Research Group University of Wales Swansea SA2 8PP Singleton Park United Kingdom

Open systems are currently being used successfully in many areas of information technology. Some guiding principles for the design of open systems suitable for computer-aided control engineering are presented. Some possible architectures are considered and an assessment is made of the available technologies which might be suitable for the basis of an open environment for computer-aided control engineering. An analogy is made with the requirements for computer-aided software engineering, for which a reference model has already been developed, and it is concluded that such a model might well provide a clear definition of what is needed in computer-aided control engineering. © 1993 IEEE

关键词： computer architecture control engineering computing control engineering Packaging machines Open systems Software packages User interfaces Bridges Error correction Information technology

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Physical Layer Security Scheme With AoI-Awareness for Industrial IoT Based on Covert Communications

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 276-278页

作者： Yaping Li Zhi-Xin Liu Jia-Wei Su Ya-Zhou Yuan the Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

Dear Editor,Industrial Internet of things(IIoT) is a typical application of cyberphysical system(CPS). In the IIoT, wireless communication is an inevitable trend to replace the deployment-limited wired transmission for cases with large-scale and mobile devices. However, wireless communication gives rise to critical issues related to physical security, such as malicious detections and attacks [1].

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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

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Science China(Information Sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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Using join learning of multi-level features for automated essay scoring

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Neural Computing and Applications 2025年 1-18页

作者： Li, Shi Gao, Yan College of Computer and Control Engineering Northeast Forestry University Wenzheng Street Heilongjiang Province Harbin150006 China

Recently, the approach of extracting features of essays at different levels for joint learning and scoring using hybrid models has achieved excellent results. However, there are still some issues that need to be improved: (1) Splitting the essays into sentences of the same length when extracting sentence-level features may result in the truncation of sentence-level semantics;(2) multi-level representations of essays obtained through pre-trained language models (PLMs) are not sufficiently comprehensive and in depth to provide limited improvement in model performance;and (3) in extracting features from multi-level essay representations, finer-grained local features in sentences and dependencies among features are ignored. To address these issues, we propose an improved method for joint learning of multi-level features based on PLMs. Specifically, we first split the essays according to different semantic scales through a text splitter to maximize the retention of semantic and structural features at sentence level;secondly, we integrate the essay representations derived from the PLM and mine more comprehensive and deeper multi-level features from it;and finally, we aggregate sentence-level features and construct dependencies among them by a feature extraction fusion method. The value of average quadratic weighted kappa (QWK) on the Kaggle ASAP competition dataset is 0.809, which validates the effectiveness of our proposed method in the automated essay scoring (AES) task. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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A multi-lesion segmentation method for diabetic retinopathy based on location information guidance

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Multimedia Tools and Applications 2025年第15期84卷 15205-15222页

作者： Chen, Yu Xu, Shibao Long, Jun Xie, Yining College of Computer and Control Engineering Northeast Forestry University Harbin150040 China College of Mecheanical and Electrical Engineering Northeast Forestry University Harbin150040 China

In order to solve the problem that the segmentation model does not pay enough attention to the lesion information and loses the lesion information, this paper proposes the MLS-Net (Multiple-Lesions-Segmentation-Net) segmentation model. Firstly, the lesions in the image are detected and reconstructed using the lesions information descriptor extraction module to preserve the location, scale and features of the lesions information (Lesion information descriptor). Secondly, the lesions information descriptors are combined with the downsampling part of the semantic segmentation module, so that the lesions information descriptors can play a role in guiding the location and features in the downsampling process of the model, in order for the segmentation module to pay more attention to the regions of the image where lesions exist. Compared with recent representative studies Ffu-net, the evaluation metrics of each lesions were improved by more than 2%. The problem of the model losing lesions information are effectively solved, and the performance of the DR semantic segmentation model is improved. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Location

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Conditional Feature Generative Adversarial Network for Fault Diagnosis of Axial Piston Pump

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IEEE Sensors Journal 2025年第11期25卷 20907-20919页

作者： Chen, Dong-Ning Hu, Dong-Bo Wang, Hao-Wen Xian, Qing-Gui Yao, Cheng-Yu Deng, Ran-Yang Yanshan University School of Mechanical Engineering Qinhuangdao066004 China Yanshan University Hebei Key Laboratory of Industrial Computer Control Engineering Qinhuangdao066004 China

A conditional feature generative adversarial network (CFGAN) for small sample data augmentation was proposed in this paper to address the issue of the scarcity of fault samples in axial piston pump fault diagnosis. With one-dimensional data as input, a feature extractor was constructed in this method to extract features, bringing about an original deep feature dataset. Subsequently, this dataset was employed to train the generation model for enhancing the training efficiency. Additionally, the Wasserstein distance was introduced to improve the stability of the model training. Conditional labels were added to the inputs of the generator and discriminator to guide the model's training and generate high-quality feature datasets. Afterward, the effectiveness of the proposed data generation method was verified through the data obtained from the Paderborn Bearing Dataset and an axial piston pump fault simulation testbed. The fault diagnosis accuracy of the augmented dataset was significantly improved compared to the non-augmented dataset. Furthermore, a multi-scale convolutional neural network was established and utilized different-sized convolutional kernels to extract and fuse multi-scale information from the samples. This approach enabled the capture of more comprehensive fault state features, contributing to enhanced diagnostic accuracy. The experimental data of the axial piston pump validate that the diagnostic accuracy with fused multi-sensor signals was higher compared to a single sensor. © 2001-2012 IEEE.

关键词： Generative adversarial networks

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Harnessing Trend Theory to Enhance Distributed Proximal Point Algorithm Approaches for Multi-Area Economic Dispatch Optimization

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computers, Materials & Continua 2025年第3期82卷 4503-4533页

作者： Yaming Ren Xing Deng College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China

The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly *** the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization *** proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs *** study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the *** PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence ***,the convergence efficiency of the PPA is significantly influenced by the parameter *** address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational *** computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch *** simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.

关键词： Multi-area economic dispatch problem proximal point algorithm trend theory

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An enhanced neuro-adaptive PID sliding mode control for robot manipulators:promoting sustainable automation

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Neural Computing and Applications 2025年第9期37卷 6877-6898页

作者： Elmogy, Ahmed Alhemaly, Nagah El-Ghaish, Hany Elawady, Wael Computer Engineering Deptartment Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Computers & Control Engineering Department Tanta University Tanta31527 Egypt

The control of robot manipulators presents significant challenges, primarily due to their complex, nonlinear dynamics. Another major difficulty arises from environmental and operational disturbances. Numerous control frameworks and approaches have been proposed in the literature to address these issues. However, many of these approaches are discontinuous or non-adaptive, which negatively affects their performance and makes them unsuitable for real-time applications. This paper proposes a novel integrated control scheme that combines adaptive control with an improved continuous second-order sliding mode control (CSOS), utilizing generalized artificial neural networks (GANN) for enhanced performance in robot manipulators. The proposed control method consists of two key components: An adaptive proportional-integral-derivative (PID) controller and an adaptive CSOS-based control module (CSOSSD-APID), designed to deliver superior transient and steady-state performance. In this approach, the adaptive CSOS benefits from GANN’s strong noise handling capabilities and its ability to estimate uncertainties effectively. This integration significantly enhances the robustness of robot manipulator control across various tracking tasks, using a single pre-trained GANN model with fine-tuned weights tailored to each task. Numerical simulations demonstrate the effectiveness and versatility of the proposed control scheme, particularly in managing highly time-varying trajectories while contributing to more sustainable and efficient automation practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adaptive control systems

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