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arXiv 2020年

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Tencent Youtu Lab Sun Yat-sen University China

In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. Our project is available at https://***/X-BrainLab/PI-ReID. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Calibration of the internal and external parameters of wheeled robot mobile chasses and inertial measurement units based on nonlinear optimization

arXiv

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arXiv 2020年

作者： Peng, Gang Lu, Zezao Tan, Zejie He, Dingxin Li, Xinde Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan School of Automation South East University Nanjing

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit (IMU) to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an IMU arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal reference calibration methods available for IMUs, external reference calibration methods between the IMU and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel chassis-IMU internal and external parameter calibration algorithm based on nonlinear optimization, which is designed for robots equipped with cameras, IMUs, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the IMU and the internal and external parameters of the camera. All of the internal and external reference calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a Mecanum wheel omnidirectional mobile platform as an example, as well as suitable for other type chassis of mobile robots. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

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High Confidence Tracking with Offline Historical Learning and Online Correlation Filter Updating

High Confidence Tracking with Offline Historical Learning an...

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作者： Shou-dong HAN Hong-wei WANG Xin-xin XIA Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Research Institute of Huazhong University of Science and Technology in Shenzhen

Target tracking is one of the most challenging tasks in computer vision. In this paper, the high confidence tracking(HCT) algorithm is proposed by combining the offline historical learning network with online correlation filter updating model. First, the weighted historical targets are introduced into the offline learning network, which solves the problem of target loss caused by inaccurate tracking of the previous frame. Second, the target's confidence detection mechanism is proposed, and added to the correlation filter tracking algorithm, so that the model drift is avoided. Finally, we form a new high confidence tracking algorithm with offline learning. Compared with the state-of-the-art tracking algorithm, our algorithm performs outstandingly on benchmark OTB13 and OTB15, while ensuring real-time performance.

关键词： High confidence Target tracking Offline learning Online updating

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Joint multi-task cascade for instance segmentation

Joint multi-task cascade for instance segmentation

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作者： Wen, Yaole Hu, Fuyuan Ren, Jinchang Shang, Xinru Li, Linyan Xi, Xuefeng School of Electronic & Information Engineering Suzhou University of Science and Technology SuzhouJiangsu215009 China Centre for Signal and Image Processing University of Strathclyde Glasgow United Kingdom Suzhou Institute of Trade & Commerce SuzhouJiangsu215009 China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou University of Science and Technology SuzhouJiangsu215009 China Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology SuzhouJiangsu215009 China

Instance segmentation requires both pixel-level classification accuracy and high-level semantic features at the target instance level, which is very challenging, and the cascade structure can effectively improve both of these problems. To make full use of the relationship between detection and segmentation, this paper proposes a joint multi-tasking cascade structure, which is not simply to cascade the two tasks of detection and segmentation, but to unitedly put them into multi-stage processing, and especially to integrate the information at different stages of the mask branch. The entire structure can effectively utilize the superior characteristics of each stage in the matter of detection and segmentation, thus improving the quality of mask prediction. The feature fusion process is introduced in the full convolution networks (FCN) branch, and the high-level and low-level features are effectively fused to enhance the contextual information of the picture semantic features. The experiments demonstrate the better results on the COCO dataset. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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Parameter Estimation of Redundant System 15th

Parameter Estimation of Redundant System

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15th International Conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information Technology, Applications and Tools, FITAT 2019

作者： Xie, Chao-Fan Xu, Lin Zhang, Fuquan Xu, Lu-Xiong Department of Electrical Engineering I-Shou University KaohsiungTaiwan Taiwan Electronic Information and Engineering Institute Fuqing Branch of Fujian Normal University Fuqing FuzhouFuJian China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuqing FuzhouFuJian China

ISBN: (纸本)9789811397134

The components usually are redundant and they are often connected to larger systems in series or in parallel. For large systems, the order estimation formulas of different types of components are very important which are deduced by collecting the data of overall faults. The estimation properties in the sense of probability are given. It is the asymptotic convergence of probability. Continue to improve variance estimator to achieve asymptotically minimum uniform variance estimator. This ensures the practicability of the theoretical formula. Further study uses computer simulation to estimate series and parallel systems in which the components are redundant, and it gives the specific handing framework. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Computer simulation Estimation Redundant

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Modified image dehazing method based on dark channel prior 2nd

Modified image dehazing method based on dark channel prior

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2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2018

作者： Hu, Junpeng Cao, Xinrong Chen, Xinwei Li, Zuoyong Zhang, Fuquan College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9783030045845

image dehazing is an important research topic in the field of image processing and computer vision. image dehazing aims to remove haze in images and make image scenes clearer. image dehazing based on dark channel prior is a currently popular type of methods. However, image dehazing results obtained by existing methods based on dark channel prior usually have color distortion and low brightness causing partial image details invisible. To alleviate this issue, we presented a modified method based on dark channel prior. First, the proposed method estimates the value of atmospheric light by using a quadtree algorithm, and uses the dark channel prior to pixelwisely estimate and optimize medium transmission. Second, the proposed method uses a classic atmospheric scattering model to generate an initial image dehazing result, and transforms the result from RGB (Red, Green, and Blue) color space to HSV (Hue, Saturation, and Value) color space. Finally, the proposed method conducts the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm on the V component of the initial result, and maps the result into the RGB space to obtain final image dehazing result. Experimental results showed that the proposed method effectively alleviated color distortion and made image scenes clearer in image dehazing results. © Springer Nature Switzerland AG 2019.

关键词： Demulsification

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Vibration fatigue analysis and research of URV shunt reactor 14

Vibration fatigue analysis and research of URV shunt reactor

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14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019

作者： Li, Shengmin Zheng, Zhicong Liang, Jining Xiao, Yamin Faculty of Automation and Information Engineering Xi'an University Ot Technology Xi'an710048 China Intelligent Information Processing Shaanxi Key Laboratory of Complex System Control Xi' an710048 China

ISBN: (纸本)9781538694909

Problems of vibration fatigue in UHV reactor is mentioned, comparative analysis of several commonly used fatigue analysis methods, finally, the modal superposition method is used to calculate the structural fatigue caused by vibration. Through analysis of reactor vibration data, the reason for using frequency domain signals as fatigue frequency domain load spectrum to calculate the service life of reactors. In order to perform reactor fatigue analysis, a finite element model of the reactor was first built using ANSYS Workbench finite element integration software, the frequency response function FRF is obtained by harmonic response analysis, and then the vibration time domain signal are transformed into frequency signal as load spectrum input with the power density spectrum, finally, nCode fatigue analysis software is used to estimate the service life of the reactor. © 2019 IEEE.

关键词： Finite element method

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Modelling brain based on canonical ensemble with functional MRI: A thermodynamic exploration on neural system

arXiv

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arXiv 2021年

作者： Li, Wei Zhou, Chenxi Yang, Bin Fan, Wenliang Chen, Xi School of Artificial Intelligence and Automation Huazhong University of Science and Technology WuhanHubei430074 China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China WuhanHubei430074 China Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology WuhanHubei430074 China

Objective. Modelling is an important way to study the working mechanism of brain. While the characterization and understanding of brain are still inadequate. This study tried to build a model of brain from the perspective of thermodynamics at system level, which brought a new thinking to brain modelling. Approach. Regarding brain regions as systems, voxels as particles, and intensity of signals as energy of particles, the thermodynamic model of brain was built based on canonical ensemble theory. Two pairs of activated regions and two pairs of inactivated brain regions were selected for comparison in this study, and the analysis on thermodynamic properties based on the model proposed were performed. In addition, the thermodynamic properties were also extracted as input features for the detection of Alzheimer's disease. Main results. The experiment results verified the assumption that the brain also follows the thermodynamic laws. It demonstrated the feasibility and rationality of brain thermodynamic modelling method proposed, indicating that thermodynamic parameters could be applied to describe the state of neural system. Meanwhile, the brain thermodynamic model achieved much better accuracy in detection of Alzheimer's disease, suggesting the potential application of thermodynamic model in auxiliary diagnosis. Significance. (1) Instead of applying some thermodynamic parameters to analyze neural system, a brain model at system level was proposed from perspective of thermodynamics for the first time in this study. (2) The study discovered that the neural system also follows the laws of thermodynamics, which leads to increased internal energy, increased free energy and decreased entropy when system is activated. (3) The detection of neural disease was demonstrated to be benefit from thermodynamic model, implying the immense potential of thermodynamics in auxiliary *** Codes 80-10, 82-10, 92B99 Copyright © 2021, The Authors. All rights reserved.

关键词： Brain

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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Probabilistic Robust Parity Relation based Fault Detection Using Biased Minimax Probability Machine ⁎

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IFAC-PapersOnLine 2020年第2期53卷 646-651页

作者： Yujia Ma Yiming Wan Maiying Zhong Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China College of Electrical Engineering and Automation Shangdong University of Science and Technology Qingdao 266590 China

This paper proposes a probabilistic robust parity relation based approach to fault detection of stochastic linear systems. Instead of assuming exact knowledge of disturbance distribution, the uncertainty of distribution information is taken into account by considering an ambiguity set of disturbance distributions. The biased minimax probability machine scheme is exploited to formulate an integrated design of the parity vector/matrix and the detection threshold. It maximizes the worst-case fault detection rate (FDR) with respect to selected reference faults, while ensuring a predefined worst-case false alarm rate. Firstly, a scalar residual design is derived in an analytical form. The analysis of its FDR in the presence of an arbitrary fault shows its limitation due to using a single reference fault. This issue is further addressed by proposing a vector residual design with a systematic method to select multiple reference faults. The efficacy of the proposed approach is illustrated by a simulation example.

关键词： Fault detection parity relation biased minimax probability machine false alarm rate fault detection rate

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