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A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 369-380页

作者： Tianyu Wang Fan Zhou Yangjie Wu Jun Zhao Wei Wang IEEE the School of Control Science and Engineering Dalian University of Technology the School of Information Science and Engineering Dalian Polytechnic University

As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.

关键词： Ensemble learning industrial energy prediction multi-working condition structural deep network transfer learning

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Quantum color image encryption: Dual scrambling scheme based on DNA codec and quantum Arnold transform

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Chinese Physics B 2025年第1期34卷 235-244页

作者： Tao Cheng Run-Sheng Zhao Shuang Wang Kehan Wang Hong-Yang Ma School of Sciences Qingdao University of TechnologyQingdao 266033China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quantum Arnold transform(QAr T) to propose a new double encryption algorithm for quantum color images to improve the security and robustness of image encryption. First, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Second, we use QAr T to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of “Monkey”, “Flower”, and “House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average normalized pixel change rate(NPCR) of RGB three-channel is 99.61%, the average uniform average change intensity(UACI) is 33.41%,and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.

关键词： DNA codec quantum Arnold transform quantum image encryption algorithm

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Global receptive field transformer decoder method on quantum surface code data and syndrome error correction

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Chinese Physics B 2025年第3期34卷 267-276页

作者： Ao-Qing Li Ce-Wen Tian Xiao-Xuan Xu Hong-Yang Ma Jun-Qing Liang School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China School of Sciences Qingdao University of TechnologyQingdao 266033China

Quantum computing has the potential to solve complex problems that are inefficiently handled by classical ***,the high sensitivity of qubits to environmental interference and the high error rates in current quantum devices exceed the error correction thresholds required for effective algorithm ***,quantum error correction technology is crucial to achieving reliable quantum *** this work,we study a topological surface code with a two-dimensional lattice structure that protects quantum information by introducing redundancy across multiple qubits and using syndrome qubits to detect and correct ***,errors can occur not only in data qubits but also in syndrome qubits,and different types of errors may generate the same syndromes,complicating the decoding task and creating a need for more efficient decoding *** address this challenge,we used a transformer decoder based on an attention *** mapping the surface code lattice,the decoder performs a self-attention process on all input syndromes,thereby obtaining a global receptive *** performance of the decoder was evaluated under a phenomenological error *** results demonstrate that the decoder achieved a decoding accuracy of 93.8%.Additionally,we obtained decoding thresholds of 5%and 6.05%at maximum code distances of 7 and 9,*** results indicate that the decoder used demonstrates a certain capability in correcting noise errors in surface codes.

关键词： quantum error correction surface code transformer decoder

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Quantum decoder design for subsystem surface code based on multi-head graph attention and edge weighting

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Chinese Physics B 2025年第2期34卷 165-176页

作者： Nai-Hua Ji Hui-Qian Sun Bo Xiao Ping-Li Song Hong-Yang Ma School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China School of Sciences Qingdao University of TechnologyQingdao 266033China

Quantum error-correcting codes are essential for fault-tolerant quantum computing,as they effectively detect and correct noise-induced errors by distributing information across multiple physical *** subsystem surface code with three-qubit check operators demonstrates significant application potential due to its simplified measurement operations and low logical error ***,the existing minimum-weight perfect matching(MWPM)algorithm exhibits high computational complexity and lacks flexibility in large-scale ***,this paper proposes a decoder based on a graph attention network(GAT),representing error syndromes as undirected graphs with edge weights,and employing a multihead attention mechanism to efficiently aggregate node features and enable parallel *** to MWPM,the GAT decoder exhibits linear growth in computational complexity,adapts to different quantum code structures,and demonstrates stronger robustness under high physical error *** experimental results demonstrate that the proposed decoder achieves an overall accuracy of 89.95%under various small code lattice sizes(L=2,3,4,5),with the logical error rate threshold increasing to 0.0078,representing an improvement of approximately 13.04%compared to the MWPM *** result significantly outperforms traditional methods,showcasing superior performance under small code lattice sizes and providing a more efficient decoding solution for large-scale quantum error correction.

关键词： quantum error correction graph attention network subsystem surface code circuit-level noise

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MKC-LSSVM: A Robust Motion Classification for ZUPT of Foot-Mounted Inertial Navigation

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IEEE Sensors Journal 2025年第9期25卷 15796-15808页

作者： Wang, Guoqing Zhou, Dan Yang, Chunyu Ma, Lei China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China

The zero-velocity update (ZUPT) technique is crucial for mitigating the drift in foot-mounted inertial navigation systems (FMINSs). However, the accuracy of zero-velocity detection (ZVD) is highly sensitive to motion types, which limits its effectiveness in dynamic scenarios. To address this challenge, we propose a novel motion classifier called the least-squares support vector machine based on multikernel correntropy (MKC-LSSVM), which adjusts the detection threshold of the widely used stance hypothesis optimal estimation (SHOE) detector according to motion types. Unlike traditional LSSVM, which uses a quadratic loss function, our approach employs a multikernel-correntropy-based loss function, enhancing robustness against sensor noise and outliers. The new nonconvex optimization problem is transformed into two convex subproblems using the half-quadratic optimization method and solved via an alternating iterative algorithm. Furthermore, the whale optimization algorithm (WOA) is utilized to optimize the free parameters of MKC-LSSVM, ensuring optimal performance. The experimental results on public mixed-motion datasets demonstrate that the proposed MKC-LSSVM achieves superior motion classification accuracy compared to existing methods. By incorporating MKC-LSSVM, the new FMINS framework significantly enhances positioning accuracy through improved ZUPT capability. This work not only advances the motion classification for FMINS, but also provides a robust classifier for complex dynamic applications. © 2025 IEEE.

关键词： Inertial navigation systems

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Interpretable Data-Driven Learning With Fast Ultrasonic Detection for Battery Health Estimation

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 267-269页

作者： Kailong Liu Yuhang Liu Qiao Peng Naxin Cui Chenghui Zhang the School of Control Science and Engineering Shandong University the Information Technology Analytics & Operations GroupQueen's University Belfast

Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.

关键词： Electronic health record

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Global Satellite Navigation Distributed Synchronous Spoofing Monitoring Method based on Spatial Cross-correlation Spectrum

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IEEE Communications Letters 2025年第5期29卷 903-907页

作者： Wang, Chun Huang, Yan Huang, Yuxuan Kou, Yanhong Xi'an University of Architecture Technology School of Information and Control Engineering Xi'an710055 China Beihang University School of Electronics and Information Engineering Beijing100083 China

A novel distributed global satellite navigation synchronous spoofer, which is deployed as a pseudo-satellite constellation, sends several synchronous spoofing signals from different directions of arrival (DOAs). In this letter, we propose a synchronous spoofing monitoring method based on a spatial cross-correlation spectrum. The power and corresponding DOA measurements of those correlated sources are extracted by searching for the peak of the proposed cross-correlation spectrum. Experimental results demonstrate that the proposed method enables the simultaneous estimation of three essential features, such as DOAs, received power levels, and cross-correlations among different navigation signals, and effectively detects the synchronous spoofing signal at the moment when the monitoring correlation coefficients reach the maximum of 1. © 2025 IEEE.

关键词： Direction of arrival

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Hierarchical Cross-Modal Image Generation for Multimodal Biometric Recognition With Missing Modality

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IEEE Transactions on information Forensics and Security 2025年 20卷 4308-4321页

作者： Pan, Zaiyu Jiang, Shuangtian Yang, Xiao Yuan, Hai Wang, Jun China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China

Multimodal biometric recognition has shown great potential in identity authentication tasks and has attracted increasing interest recently. Currently, most existing multimodal biometric recognition algorithms require test samples with complete multimodal data. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practical scenarios. To this end, we proposed a hierarchical cross-modal image generation for palmprint and palmvein based multimodal biometric recognition with missing modality. First, a hierarchical cross-modal image generation model is designed to achieve the pixel alignment of different modalities and reconstruct the image information of missing modality. Specifically, a cross-modal texture transfer network is utilized to implement the texture style transformation between different modalities, and then a cross-modal structure generation network is proposed to establish the correlation mapping of structural information between different modalities. Second, multimodal dynamic sparse feature fusion model is presented to obtain more discriminative and reliable representations, which can also enhance the robustness of our proposed model to dynamic changes in image quality of different modalities. The proposed model is evaluated on three multimodal biometric benchmark datasets, and experimental results demonstrate that our proposed model outperforms recent mainstream incomplete multimodal learning models. © 2005-2012 IEEE.

关键词： Biometrics

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Adaptive control of a Flexible Manipulator With Unknown Hysteresis and Intermittent Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 148-158页

作者： Shouyan Chen Weitian He Zhijia Zhao Yun Feng Zhijie Liu Keum-Shik Hong School of Mechanical and Electrical Engineering Guangzhou University the School of Control Science and Engineering Shandong University IEEE the College of Electrical and Information Engineering Hunan University the National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University the School of Intelligence Science and Technology University of Science and Technology Beijing (USTB) the Institute for Future the School of AutomationQingdao University the School of Mechanical Engineering Pusan National University

In this study, we consider a single-link flexible manipulator in the presence of an unknown Bouc-Wen type of hysteresis and intermittent actuator faults. First, an inverse hysteresis dynamics model is introduced, and then the control input is divided into an expected input and an error compensator. Second,a novel adaptive neural network-based control scheme is proposed to cancel the unknown input hysteresis. Subsequently,by modifying the adaptive laws and local control laws, a fault-tolerant control strategy is applied to address uncertain intermittent actuator faults in a flexible manipulator system. Through the direct Lyapunov theory, the proposed scheme allows the state errors to asymptotically converge to a specified interval. Finally,the effectiveness of the proposed scheme is verified through numerical simulations and experiments.

关键词： Adaptive control flexible manipulator intermittent actuator faults inverse hysteresis dynamics vibration control

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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