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A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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Science China(Information Sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU institute of Information Science School of Computer and Information TechnologyBeijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and network technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last ***, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： $\Phi$-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM

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Unleashing potentials with deep learning: decoding the complex events for distributed fiber optic sensing applications

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Science China(Information Sciences) 2024年第5期67卷 337-338页

作者： Yujiao LI Liqin HU Kuanglu YU institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and network technology Beijing Jiaotong University

After decades of research, distributed fiber optic sensors have become essential tools for precise long-distance measurements. They are applied across various sectors such as railway transportation, gas and petroleum, and smart grids. Signal processing, particularly in phase-sensitive optical time domain reflectometry(Φ-OTDR) event recognition and classification, is a primary research focus.

关键词： Deep learning

HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and technology Wuhan University of Science and Technology School of Future technology South China University of Technology Shenzhen Research institute of Hunan University School of Computer Science Hunan University of Technology and Business institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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Deep Guided Attention network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of Computer Science and technology Beijing Institute of Technology Advanced Reasearch institute of Multidisciplinary Science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Training Visualization Inverse problems Green products Noise reduction Pipelines Signal processing

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

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Journal of Energy Chemistry 2024年第4期91卷 417-432页

作者： Sijia Yang Caiping Zhang Haoze Chen Jinyu Wang Dinghong Chen Linjing Zhang Weige Zhang National Active Distribution network technology Research Center(NANTEC) Beijing Jiaotong UniversityBeijing 100044China

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered ***,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity *** address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled *** traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory *** generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate *** only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating *** high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

关键词： Lithium-ion battery pack Capacity estimation Label generation Multi-machine learning model Real-world operating

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Terahertz wide-angle metalens with nearly ideal object-image relation

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Chinese Optics Letters 2024年第1期22卷 147-152页

作者：王宇程洁嵘冀允允范飞常胜江 institute of Modern Optics Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Micro-scale Optical Information Science and technology Tianjin 300350China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing network technology Tianjin 300350China

Metalenses are essential components in terahertz imaging ***,without careful design,they show limited field of view and their practical applications are ***,a wide-angle metalens is proposed whose structure is optimized for focusing within the incident angles of±25°.Simulation and experiment results show that the focusing efficiency,spot size,and modulation transfer function of this lens are not sensitive to the incident *** importantly,this wide-angle metalens follows the ideal Gaussian formula for the object-image relation,which ensures a wider field of view and better contrast in the imaging experiment.

关键词： terahertz metalens wide-angle Gaussian formula

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Method and Practice of Trusted Embedded Computing and Data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for network Computing and Security technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： Embedded computing Protocols Data security Computer architecture Encryption Internet Data communication

Identity-Preserving Adversarial Training for Robust network Embedding

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Journal of Computer Science & technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of network Data Science and technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

Spatially Coupled Codes via Bidirectional Block Markov Superposition Transmission

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Tsinghua Science and technology 2024年第3期29卷 656-670页

作者： Gaoyan Li Shancheng Zhao Haiqiang Chen Jinming Wen College of Information Science and technology Jinan UniversityGuangzhou 510632China Guangxi Key Laboratory of Multimedia Communications and network technology Guangxi UniversityNanning 530004China

In this paper,we present a new class of spatially coupled codes obtained by using both non-recursive and recursive block-oriented *** resulting codes are termed as bidirectional block Markov superposition transmission(BiBMST)***,we perform an iterative decoding threshold analysis according to protograph-based extrinsic information transfer(PEXIT)charts for the BiBMST codes over the binary erasure channels(BECs).Secondly,we derive the generator and parity-check matrices of the BiBMST ***,extensive numerical results are presented to show the advantages of the proposed BiBMST ***,our numerical results show that,under the constraint of an equal decoding latency,the BiBMST codes perform better than the recursive BMST(rBMST)***,the simulation results show that,in finite-length regime,negligible performance gain is obtained by increasing the encoding *** solve this limitation by introducing partial superposition,and the resulting codes are termed as partially-connected BiBMST(PC-BiBMST)*** results have confirmed the advantages of the PC-BiBMST codes over the original BiBMST *** also present extensive simulation results to show the performance advantages of the PC-BiBMST codes over the spatially coupled low-density parity-check(SC-LDPC)codes,spatially coupled generalized LDPC(SC-GLDPC)codes,and the original BiBMST codes in the finite-length regime.

关键词： block Markov superposition transmission(BMST) protograph-based extrinsic information transfer(PEXIT) capacity-approaching codes spatial coupling