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

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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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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Deep Feature Unsupervised Domain Adaptation for Time-Series Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 725-737页

作者： Lu, Nannan Yan, Tong Zhu, Song Qian, Jiansheng Han, Min China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology School of Mathematics Xuzhou221116 China Dalian University of Technology School of Control Science and Engineering Dalian116023 China

Unsupervised domain adaptation (UDA) for time series classification (TSC) is an important but challenging task. In the process of UDA, feature learning is most critical. Most of the existing works in this area are based on learning domain-invariant feature representation of data with help of some restriction such as MMD. However, they ignored that the mutual effects between the pretrained network and the downstream target network was also conducive to the learning of domain-invariant features. In this article, we propose a deep feature unsupervised domain adaptation (DFUDA) for time series classification. First, we pretrain a network based on consistency learning to ensure the invariant feature extraction from the source and target domains. Then, we propose an end-to-end unsupervised domain adaptation, which includes the layer matching and the unsupervised domain adaptation to promote more confident knowledge transfer. Finally, the pretrained network receives feedback of the domain adaptation's performance. To verify the effectiveness of the proposed method, we perform the comprehensive experiments on fault diagnosis datasets and human activity recognition datasets. The results show that DFUDA outperforms the state of the arts methods for both scenarios. © 2024 IEEE.

关键词： Contrastive Learning

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Ciprofloxacin(CIP)-polluted water treatment via a facile mechanochemical route:Influencing factors and mechanism insights

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水科学与水工程 2025年第1期18卷 21-29页

作者： Xiao-yi Wang Cheng-liang Dong Kun Xu Ri-long Xiao Xiu-juan Feng School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Mechano-chemistry Research Institute China University of Mining and TechnologyXuzhou 221116China Mechano-chemistry Research Institute China University of Mining and TechnologyXuzhou 221116China School of Mines China University of Mining and TechnologyXuzhou 221116China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Mines China University of Mining and TechnologyXuzhou 221116China Rare Earth Research Institute China University of Mining and TechnologyXuzhou 221116China

Degrading ciprofloxacin(CIP)-polluted water has recently emerged as an urgent environmental *** study introduced mechano-chemical treatment(MCT)as an innovative and underexplored approach for the degradation of CIP in *** influence of various additives(CaO,Fe2O3,SiO2,Al,and Fe)on CIP degradation efficiency was ***,six types of composite additives(Fe-CaO,Fe-Fe2O3,Fe-SiO2,Fe-Al,Al-SiO2,and Al-CaO)were explored,with the composite of 20％Fe and 80％SiO2 exhibiting notable *** impacts of additive content,pH value,and co-existing ions on CIP degradation efficiency were ***,the effectiveness of MCT in degrading other medical pollutants(norfloxacin,ofloxacin,and enrofloxacin)was *** transformations and changes in the crystal structure,oxidation state,microstructure,and morphology of the Fe-SiO2 composite additive were characterized using X-ray diffraction,X-ray photoelectron spectroscopy,and scanning electron microscopy *** study proposed a sigmoid trend kinetic model(the Delogu model)that better elucidates the MCT *** plausible degradation pathways were discussed based on intermediate substance identification and pertinent *** study not only establishes a pathway for the facile degradation of CIP pollutants through MCT but also contributes to advancements in wastewater treatment methodologies.

关键词： Mechanochemistry Degradation of ciprofloxacin Fe-SiO2 additive Medical pollutants Wastewater treatment

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DCL-depth: monocular depth estimation network based on iam and depth consistency loss

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Multimedia Tools and Applications 2025年第8期84卷 4773-4787页

作者： Han, Chenggong Lv, Chen Kou, Qiqi Jiang, He Cheng, Deqiang The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

The self-supervised monocular depth estimation algorithm obtains excellent results in outdoor environments. However, traditional self-supervised depth estimation methods often suffer from edge blurring in complex textured regions and the loss of depth information in pixels within weakly-textured areas. To enhance the perception ability of the deep network for complex textured areas and the accuracy of depth estimation in weakly-textured regions, the following methods are proposed in this paper. First of all, the image activity measure (IAM) is used to segment the image features. Based on the multi-directional distribution of image contours, the network's perception ability has been improved, resulting in effective enhancement of depth estimation in complex regions. Furthermore, a new loss function called depth consistency loss (DCL) is proposed, which is based on recursive recurrent networks. The DCL aims to measure the similarity between the output images of the first-order network and the second-order network, and the network's constraint on weak-texture regions has been strengthened. By employing this approach, the accuracy of estimating depth information in weakly-textured regions can be enhanced. Through adequate experimentation on the public indoor datasets, the results show that our network outperforms the compared algorithms in terms of accuracy and visualization of predicted depth. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Complex networks

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Event-triggered leader-follower bipartite consensus control for nonlinear multi-agent systems under DoS attacks

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Science china(information Sciences) 2025年第3期68卷 286-301页

作者： Wei SU Chaoxu MU Song ZHU Ben NIU Changyin SUN School of Control Science and Engineering Tianjin University School of Mathematics China University of Mining and Technology School of Control Science and Engineering Dalian University of Technology School of Automation Southeast University

Aiming at the consensus control problem of nonlinear multi-agent systems(MASs) under directed topology, a leader-follower bipartite consensus control strategy is proposed. This strategy takes into account the potential for denial-of-service(DoS) attacks and completely unknown system dynamics. Specifically, the bipartite consensus dynamics describes the cooperation and competition relationship between followers and the leader, that is, the follower chooses to move in accordance with or opposite to the leader according to its trajectory. In order to optimize the communication bandwidth and mitigate the impact of DoS attacks, the proposed consensus control scheme integrates the DoS attack detection mechanism and event-triggered mechanism. In addition, neural networks(NNs) are used to solve the nonlinear problem, and a speed function is designed to achieve the desired tracking performance, ensuring that all agents' tracking errors converge to a predefined set in a finite time. With the help of backstepping, graph theory, and Lyapunov stability theory, sufficient conditions for achieving bipartite consensus without Zeno behavior are established. Finally, the accuracy and feasibility of the theoretical analysis are verified by simulation cases.

关键词： multi-agent systems neural networks bipartite consensus event-trigger denial-of-service attack

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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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Reconfigurable Janus metasurface with chiral meta-atoms for multi-channel vortex beams and holography multiplexing

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Optics Express 2025年第1期33卷 309-321页

作者： Gao, Peng Chen, Cong Dai, Yaowei Wang, Xinyan Cui, Hongzhong Zhu, Xiaojun Liu, Hai Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Microelectronics School of Integrated Circuits Nantong University Nantong226019 China

Multi-channel multiplexing metasurfaces have attracted considerable interest with the growing demand for multifunctional integration and enhanced communication capabilities. Dynamic tuning of electromagnetic waves with multiple degrees of freedom is a key approach to improving information processing capabilities. Metasurfaces with chiral meta-atoms and Janus metasurfaces with asymmetric transmission properties introduce new degrees of freedom for multiplexing technologies. It is a challenge to combine these two methods to achieve more degrees of freedom of multi-channel multiplexed metasurface. In this study, we present reconfigurable Janus metasurfaces with chiral meta-atoms based on vanadium dioxide (VO2). Our results demonstrate that chiral meta-atoms enable dynamic switching between asymmetric transmission and reflection at varying temperatures. In the design of multi-channel vortex beams, this reconfigurable Janus metasurface can generate vortex beams with different spins by active control of propagation direction or temperature. In multi-channel metasurface holography designs, this chiral unit-based reconfigurable Janus metasurface can actively produce eight-channel metasurface holograms by manipulating chiral characteristics, propagation direction, or temperature. Additionally, in the design of metasurfaces for information encryption, four distinct metasurface holograms have been generated, which can be synthesized into a unique decrypted image. This research not only offers a novel approach for the design of multi-path multiplexing metasurfaces but also paves the way for applications in multi-channel structured light and promoting high-capacity and high-secure holographic applications. Consequently, it provides new possibilities for terahertz optical information encryption, data storage, and intelligent information processing. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Holograms

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