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IEEE International Conference on systems, Man and Cybernetics

作者： Ruijie Xu Shichao Chen Wenqiao Sun Yisheng Lv Jialiang Luo Ying Tang The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems The Center of National Railway Intelligent Transportation System Engineering and Technology Institute of Automation Chinese Academy of Sciences China Academy of Railway Sciences Corporation Limited Beijing China China Academy of Railway Sciences Corporation Limited Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing Beijing China Department of Electrical and Computer Engineering Rowan University Glassboro USA

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation system (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.

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DiagLLM: multimodal reasoning with large language model for explainable bearing fault diagnosis

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Science China(Information Sciences) 2025年第6期 41-55页

作者： Jie WANG Tianrui LI Yan YANG Shiqian CHEN Wanming ZHAI School of Computing and Artificial Intelligence Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education State Key Laboratory of Rail Transit Vehicle System

Accurate and reliable bearing fault diagnosis is critical for ensuring the safe operation of mechanical *** data-driven methods encounter challenges in training advanced deep learning models, primarily due to the scarcity of fault data and inconsistencies in data distributions. Additionally, these methods often suffer from limited interpretability and reliability, as they lack constraint-guided learning based on the physical mechanisms underlying bearing failures, which hampers their utility in machine condition monitoring. Recent advancements in large language models(LLMs) demonstrate their potential to address these challenges. To this end, we aim to enhance the generalization and interpretability of bearing fault diagnosis by leveraging the capabilities of multimodal LLMs. Specifically, a novel framework called DiagLLM is designed to achieve this goal. DiagLLM leverages the powerful reasoning capabilities of large language models and incorporates contextual information from both envelope spectrum images and expert knowledge to accurately diagnose bearing faults. To effectively tune DiagLLM, diagnostic visual instruction-following data are constructed to link fault feature descriptions with signal characteristics, and the entire model is fine-tuned using a parameter-efficient training pipeline. Extensive experiments are conducted on two publicly available bearing fault diagnosis datasets, and the results show that DiagLLM outperforms leading baselines, particularly in scenarios with limited data and cross-data generalization.

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A Grouping Strategy-Based Progressive Fusion Network for Hyperspectral Image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Baodong Zhang Yongbiao Gao Guixin Zhao Guotao Wang Juncan Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively.

关键词： Convolution Superresolution Feature extraction Acoustics Data mining Speech processing Hyperspectral imaging

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chiming Duan Tong Jia Yong Yang Guiyang Liu Jinbu Liu Huxing Zhang Qi Zhou Ying Li Gang Huang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%.

关键词： Training Industries systems operation Large language models Retrieval augmented generation Active learning Noise Software systems Speech processing Anomaly detection

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Design of echo state network with multi-objective optimization algorithm and l1 regularization

Design of echo state network with multi-objective optimizati...

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第34届中国控制与决策会议

作者： Zhanhong Wu Cuili Yang Beijing University of Technology Faculty of Information TechnologyBeijing Key Laboratory of Computational Intelligence and Intelligent SystemEngineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education

In this paper,a new approach for optimizing the structure and prediction error of echo state network（ESN） is *** is a kind of recurrent neural network with simple training and strong generalization *** is an important structure of ESN,which determine network ***,multi-objective optimization algorithm is used to optimize network structure and training error ***,a local search algorithm based on l regularization is used to accelerate *** experiment results of time series prediction and standard classification show that MESN can improve the network prediction performance while sparse network structure.

关键词： Echo state network multi-objective optimization algorithm l1 regularization

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A Novel DCNN Based MI-EEG Classification Method Using Spatio-Frequency Information

A Novel DCNN Based MI-EEG Classification Method Using Spatio...

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2021 China Automation Congress, CAC 2021

作者： Wang, Linlin Li, Mingai Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781665426473

Deep convolutional neural network (DCNN) has been successfully applied to improve the classification performance of motor imagery (MI) tasks in the electroencephalogram (EEG) based brain computer interface (BCI). However, there is still a great challenge about how to extract effective features from EEG signals. So, a novel parallel DCNN structure-based MI-EEG classification method is proposed in this paper. Fast Fourier transform is utilized to transform EEG signals from time domain to frequency domain. Then, μ and β rhythms, which are related to MI tasks, are redivided into three sub-bands. The averaged power is calculated for each sub-band. In order to utilize the position information of electrodes, azimuthal equidistant projection is adopted to convert the 3-D location of each electrode into 2-D position. Then, Clough-Tocher interpolation algorithm is employed to generate a spatio-frequency image for each sub-band. Furthermore, a parallel DCNN (pDCNN), which includes three DCNNs correspondings to three sub-bands respectively, is designed to extract and classify spatio-frequency features. The BCI2000 dataset is adopted, and the average accuracy and Kappa value reach 90.25% and 0.81 respectively, which are greater than that of the existing methods. These experimental results show that the effectiveness of the proposed MI-EEG classification method. © 2021 IEEE

关键词： Electroencephalography

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

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arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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A LSTM Model with Attention Mechanism for Soft Sensor of SO2 Conversion Rate in Flue Gas Acid Production Process

A LSTM Model with Attention Mechanism for Soft Sensor of SO2...

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Data Driven Control and Learning systems (DDCLS)

作者： Yongzheng Zhang Xiaoli Li Kang Wang Yang Li Faculty of Information Technology Beijing University of Technology Beijing Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing Engineering Research Center of Digital Community Beijing University of Technology Beijing School of international studies Communication University of China Beijing

The sulfur dioxide (SO2) concentration of flue gas in metal smelting industry is high and difficult to recycle. The commonly used SO2 desulfurization treatment is acid production from flue gas, however, it is difficult to directly measure the conversion rate of SO2. In this paper, we present a soft sensor method based on Long Short-Term Memory (LSTM) which integrates the attention mechanism for predicting SO2 conversion rate to tackle such problem. Considering that the change of SO2 conversion rate is affected by many external factors, the attention mechanism is used to quickly and accurately predict the change results by considering the system data of several past moments. The proposed attention mechanism uses LSTM units to encode the hidden state of the look back time data, obtains different attention weights, and then decodes and predicts SO2 conversion rate according to the hidden state. The experimental results indicate that LSTM model with attention mechanism has lower training cost compared with LSTM model. The training accuracy and soft sensor accuracy are also improved owing to the attention mechanism. It is instructive for SO2 conversion rate soft sensor in acid production from flue gas.

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VGRISys: A Vision-Guided Robotic intelligent system for Autonomous Instrument Calibration*

VGRISys: A Vision-Guided Robotic Intelligent System for Auto...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhenqi Li Hewei Wang Yijie Li Soumyabrata Dev Guoyu Zuo Beijing-Dublin International College Beijing University of Technology Beijing China School of Computer Science University College Dublin Dublin Ireland Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China

With the increasing sophistication of musical instruments and the importance of precise tuning, a novel and efficient approach to instrument calibration is of significant necessity. This paper presents VGRISys, an innovative system leveraging robotics and artificial intelligence to facilitate the intricate process of instrument tuning. VGRISys incorporates a multi-axis robotic arm, meticulously engineered for fine-tuned physical interaction with various musical instruments, alongside an intelligent system that guides the robotic arm using real-time audio feedback analysis. In addition, it employs advanced signal processing and machine learning algorithms, allowing the system to identify pitch discrepancies and respond with appropriate corrective action. VGRISys remarkably reduces the time required for instrument tuning, thus improving efficiency and democratizing access to high-quality tuning. Furthermore, the system’s machine learning capabilities allow it to refine its tuning process over repeated operations, providing a scalable solution to the ever-evolving complexities of instrument tuning. The exploration and development of VGRISys contribute significantly to the wider fields of robotics, artificial intelligence, and music technology.

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A Hierarchy Early Warning Method based on RBF Neural Network for BOD in WWTP

A Hierarchy Early Warning Method based on RBF Neural Network...

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第34届中国控制与决策会议

作者： Junkai Zhang Wenjing Li Faculty of Information Technology Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education

As an indicator of the organic pollution of waste water,the early warning for the Biochemical Oxygen Demand(BOD) is vital and could prevent substandard waste water from environment *** paper presents a BOD early warning method combining multi-step time series prediction and hierarchy ***,an MIMO strategy based on an RBF neural network is designed to predict BOD concentration with multiple steps ***,a hierarchy alarm method based on a double-check from limit-value and tendency is proposed to recognize alarms from the predicted BOD time series ***,the BOD data from the Benchmark Simulation Model 1(BSM1) platform is used to test the effectiveness of the proposed *** experimental results show that the proposed method has a reliable early warning performance in different alarming level.

关键词： Early warning method Multi-step Prediction RBF neural network Biochemical Oxygen Demand(BOD) Waste Water Treatment Process

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