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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Event-based nonsingular fixed-time containment control for nonlinear multiagent systems with dynamic uncertainties

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Science China(Information Sciences) 2025年第5期68卷 413-414页

作者： Yuanbo SU Qihe SHAN Tieshan LI C.L.Philip CHEN Navigation College Dalian Maritime University School of Automation Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering South China University of Technology

Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear multiagent systems (MASs) has received considerable attention,for example [1,2].Although the valued studies in [1,2] investigate containment control problems for MASs subject to nonlinearities,the proposed distributed nonlinear protocols only achieve the asymptotic *** a crucial performance indicator for distributed containment control of MASs,the fast convergence is conducive to achieving better control accuracy [3].The work in [4] first addresses the backstepping-based adaptive fuzzy fixed-time containment tracking problem for nonlinear high-order MASs with unknown external ***,the designed fixedtime control protocol [4] cannot escape the singularity problem in the backstepping-based adaptive control *** is well known,the singularity problem has become an inherent problem in the adaptive fixed-time control design,which may cause the unbounded control inputs and even the instability of controlled ***,how to solve the nonsingular fixed-time containment control problem for nonlinear MASs is still open and awaits breakthrough to the best of our knowledge.

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Hybrid Model and Data-Driven Optimal PID Control with Enhanced Disturbance Rejection

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IEEE Transactions on automation Science and Engineering 2025年 22卷 15286-15298页

作者： Liu, Ning Zhang, Yajun Wang, Zhuqing Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Taiyuan University of Technology College of Computer Science and Technology Taiyuan030600 China

This paper proposes an optimal proportional-integral-derivative (PID) control method with improved disturbance rejection for nonlinear uncertain systems. The controller achieves optimal closed-loop dynamics by integrating constant and time-varying control gains. Initially, the system is transformed into a linear model with a nonlinear uncertainty term that lumps unmodeled dynamics and disturbances. Constant PID gains are subsequently derived using an optimal tuning rule based on the frequency and gain of the linear model at −180° and −120° phase lag points for systems with and without ultimate points, respectively, by employing a fractional-order integrator and Tchebyshev polynomials to simplify computation. Then an extended state observer (ESO)-assisted recursive optimization approach is introduced to adaptively adjust the time-varying PID gains in real time based on the uncertainty estimation from the ESO and operational data, improving adaptation under nonlinear uncertainties. This hybrid tuning enhances the robustness of the closed-loop system to uncertain dynamics and mitigates the instability caused by poor initial parameters, outperforming traditional model and data-driven methods. The stability of the proposed approach is proven, and its effectiveness is validated through simulations and an industrial case study on the pressure control of the hematite regrinding process. © 2004-2012 IEEE.

关键词： Disturbance rejection

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Saliency Feature Learning for Multi-modality Person Retrieval in Visual Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 15883-15896页

作者： You, Shuai Feng, Yujian Wang, Shitao Wu, Fei Sha, Yuchen Ji, Yimu Jing, Xiao-Yuan School of Internet of Things Nanjing China Jiangsu University of Technology School of Computer Engineering Changzhou China NJUPT School of Internet of Things Nanjing China NJUPT School of Computer Science Nanjing China NJUPT College of Automation College of Artificial Intelligence Nanjing China Wuhan University School of Computer Wuhan430072 China

In the Visual Internet of Things (VIoT), smart surveillance is an important component of the Multi-modality Person Retrieval (MPR) task. Capturing discriminative pedestrian information from images aids in identifying target individuals through sketch or text descriptions, improving the reliability of VIoT systems. Current granularity-level matching methods face challenges with information redundancy and modality differences in MPR tasks. In this paper, we propose a novel approach for intra-and inter-modality saliency feature learning by Multi-granularity Feature Selection and Relation (MFSR). Firstly, to reduce redundant information within each modality, the Intra-modality Feature Selection Module (IFSM) employs the adaptive weighting mechanism to enhance salient pedestrian features while suppressing irrelevant features. Secondly, the Multi-granularity Feature Relation Module (MFRM) aligns cross-modality salient person features by increasing the similarity scores of local and global features across modalities, to reduce differences between multi-modality visual-text pairs. Finally, the Cross-modality Similarity Matching (CSM) loss is designed to enhance consistency in visual-text pairs of the same identity, ensuring compact intra-class features by minimizing discrepancies between cross-modality similarity distributions and identity-matching distributions. Experimental results show that our approach achieves state-of-the-art performance on benchmark datasets. © 2014 IEEE.

关键词： Feature Selection

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Improved YOLOv8-Based Obstacle Detection Algorithm for Waterborne Transportation 2

Improved YOLOv8-Based Obstacle Detection Algorithm for Water...

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2nd International Conference on the Frontiers of Intelligent Control, Measurement, and Signal Systems, FICMSS 2025

作者： Zhang, Haozhi Yuan, Chang Wang, Ke Wang, Yudi Liu, Qinghua Shi, Jinlong College of Computer Science Jiangsu University of Science and Technology Jiangsu Province Zhenjiang212003 China College of Automation Jiangsu University of Science and Technology Jiangsu Province Zhenjiang212003 China

Waterborne obstacle detection plays a critical role in the environmental perception of intelligent cargo ship transportation, directly influencing both the efficiency and safety of the transportation process. However, current approaches still encounter challenges regarding detection accuracy, real-time performance, and model lightweighting. So this paper proposes a novel lightweight waterborne obstacle detection algorithm based on an improved YOLOv8 framework. The algorithm integrates an advanced Multi-Branch Fusion (MBF) and Attentional Feature (AF) module with the Cross Stage Feature Fusion (C2f) module to form a new C2fPPAC module, enhancing small-object feature representation while minimizing computational overhead. A feature fusion module, C2f-REPC, is introduced based on structural re-parameterization techniques, which reduces feature redundancy and improves detection accuracy. Additionally, a 160×160 detection head is added to enhance small object detection in challenging backgrounds. Experimental results on a ship-port logistics waterborne obstacle dataset show that the proposed algorithm outperforms the original YOLOv8 by achieving a 3.4% increase in mAP50 and a 3.6% increase in mAP50-95. The computational cost is reduced to 7.9G, and the model size is decreased by 2MB, allowing for real-time and precise detection of waterborne obstacles. The results highlight the effectiveness and advantages of the proposed approach. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Freight transportation

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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