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Robust object tracking with RGBD-based sparse learning Article

Zi-ang MA, Zhi-yu XIANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 989-1001 doi: 10.1631/FITEE.1601338

Abstract: Robust object tracking has been an important and challenging research area in the field of computer visionIn this paper, a novel RGBD and sparse learning based tracker is proposed.The range data is integrated into the sparse learning framework in three respects.Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate timealgorithms, including both sparse learning and RGBD based methods.

Keywords： Object tracking     Sparse learning     Depth view     Occlusion templates     Occlusion detection    

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A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1472-9

Abstract:

• A spectral machine learning approach is proposed for predicting mixed

Keywords： Antibiotic contamination     Spectral detection     Machine learning    

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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1,   Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: This paper presents a new approach for automatical classification of structural state through deep learningdesigned to fuse both the feature extraction and classification blocks into an intelligent and compact learningCNN can extract the structural state information from the vibration signals and classify them; 2) the detection

Keywords： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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A robust object tracking framework based on a reliable point assignment algorithm Article

Rong-feng ZHANG, Ting DENG, Gui-hong WANG, Jing-lun SHI, Quan-sheng GUAN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4,   Pages 545-558 doi: 10.1631/FITEE.1601464

Abstract: Visual tracking, which has been widely used in many vision fields, has been one of the most active researchHowever, there are still challenges in visual tracking, such as illumination change, object occlusionMoreover, a Kalman filter is applied to the detection step to speed up the detection processing and reducefalse detection.Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the

Keywords： Local maximal wavelet coefficients     Reliable point assignment     Object tracking     Tracking learning detection    

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: Data-based methods of supervised learning have gained popularity because of available Big Data and computingHowever, the common paradigm of the loss function in supervised learning requires large amounts of labeledscarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detectionTherefore, a fault detection method based on self-supervised feature learning was proposed to addressThe self-supervised representation learning uses a sequence-based Triplet Loss.

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

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Multiple fault separation and detection by joint subspace learning for the health assessment of wind

Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 333-347 doi: 10.1007/s11465-017-0435-0

Abstract: Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) techniqueThe superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigatedJSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection

Keywords： joint subspace learning     multiple fault diagnosis     sparse decomposition theory     coupling feature separation    

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A multi-sensor-system cooperative scheduling method for ground area detection and target tracking Research Article

Yunpu ZHANG, Qiang FU, Ganlin SHAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 245-258 doi: 10.1631/FITEE.2200121

Abstract: First, an model was built, and the method of calculating the detection risk was proposed to quantifythe detection benefits in scheduling.model was established, in which the posterior Carmér-Rao lower bound was applied to evaluate future trackingFinally, an objective function was developed which considers the requirements of detection, tracking,

Keywords： Sensor scheduling     Area detection     Target tracking     Road constraints     Doppler blind zone    

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Machine vision-based automatic fruit quality detection and grading

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2023532

Abstract:

● A machine vision-based prototype system was developed for fruit grading.

Keywords： Computer and machine vision     convolution neural network     deep learning     defective fruit detection     fruit    

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EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum Research Article

Yunzhan ZHOU, Tian FENG, Shihui SHUAI, Xiangdong LI, Lingyun SUN, Henry Been-Lirn DUH,yunzhan.zhou@durham.ac.uk,t.feng@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 1,   Pages 101-112 doi: 10.1631/FITEE.2000318

Abstract: Explorations toward development of a mechanism using eye-tracking data have so far been limited to 2DWe present the first 3D Eye-tracking Dataset for modeling in a virtual Museum, known as the EDVAM.

Keywords： Visual attention     Virtual museums     Eye-tracking datasets     Gaze detection     Deep learning    

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Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking Article

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Engineering 2021, Volume 7, Issue 9,   Pages 1248-1261 doi: 10.1016/j.eng.2021.04.027

Abstract:

This paper synchronizes control theory with computer vision by formalizing object tracking as a sequentialA reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is oftenUnlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requiresThe methodology showcases RL for real-time object-tracking applications in the oil sands industry.Along with a presentation of the interface tracking problem, this paper provides a detailed review of

Keywords： Interface tracking     Object tracking     Occlusion     Reinforcement learning     Uniform manifold approximation    

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An Ultracompact Spoof Surface Plasmon Sensing System for Adaptive and Accurate Detection of Gas Using

Xuanru Zhang,Jia Wen Zhu,Tie Jun Cui,

Engineering doi: 10.1016/j.eng.2023.05.013

Abstract: For applications in the Internet of Things (IoT), accurate detection of resonance frequency shifts usingultracompact integrated sensing system that merges a spoof surface plasmon resonance sensor with signal detectionscheme was developed to track the resonance shift, which minimized the hardware circuit and made the detectionto a smartphone wirelessly through Bluetooth, working in both frequency scanning mode and resonance tracking

Keywords： Spoof surface plasmons     Internet of Things     Integrated sensing     Resonance tracking     Microwave sensing    

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Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Frontiers of Structural and Civil Engineering   Pages 1281-1294 doi: 10.1007/s11709-023-0975-9

Abstract: Concrete is widely used in various large construction projects owing to its high durability, compressive strength, and plasticity. However, the tensile strength of concrete is low, and concrete cracks easily. Changes in the concrete structure will result in changes in parameters such as the frequency mode and curvature mode, which allows one to effectively locate and evaluate structural damages. In this study, the characteristics of the curvature modes in concrete structures are analyzed and a method to obtain the curvature modes based on the strain and displacement modes is proposed. Subsequently, various indices for the damage diagnosis of concrete structures based on the curvature mode are introduced. A damage assessment method for concrete structures is established using an artificial bee colony backpropagation neural network algorithm. The proposed damage assessment method for dam concrete structures comprises various modal parameters, such as curvature and frequency. The feasibility and accuracy of the model are evaluated based on a case study of a concrete gravity dam. The results show that the damage assessment model can accurately evaluate the damage degree of concrete structures with a maximum error of less than 2%, which is within the required accuracy range of damage identification and assessment for most concrete structures.

Keywords： hydraulic structure     curvature mode     damage detection     artifical neural network     artificial bee colony    

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of two periodic leaky-wave antennas with sum and difference beam scanning for application in target detectionand tracking Research Article

Mianfeng HUANG, Juhua LIU,liujh33@mail.sysu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 4,   Pages 567-581 doi: 10.1631/FITEE.2200473

Abstract: An array of two periodic leaky-wave s (LWAs) with scanning is proposed for application in . The array is composed of two periodic LWAs with different periods, in which each LWA generates a narrow beam through the =-1 space harmonic. Due to the two different periods for the two LWAs, two beams with two different directions can be realized, which can be combined into a sum beam when the array is fed in phase or into a difference beam when the array is fed 180° out of phase. The array integrated with 180° hybrid is designed, fabricated, and measured. Measurement results show that the sum beam can reach a gain up to 15.9 dBi and scan from -33.4° to 20.8°. In the scanning range, the direction of the null in the difference beam is consistent with the direction of the sum beam, with the lowest null depth of -40.8 dB. With the excellent performance, the provides an alternative solution with low complexity and low cost for .

Keywords： Antenna     Leaky-wave antenna (LWA)     Substrate-integrated waveguide (SIW)     Sum and difference beam     Target detectionand tracking    

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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Frontiers of Structural and Civil Engineering 2015, Volume 9, Issue 1,   Pages 1-16 doi: 10.1007/s11709-014-0277-3

Abstract: large amount of researches and studies have been recently performed by applying statistical and machine learningtechniques for vibration-based damage detection.data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning

Keywords： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic    

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Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 470-487 doi: 10.1007/s11684-020-0782-9

Abstract: deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasksterms of feature extraction, DL approaches are less labor intensive compared with conventional machine learningstudies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection

Keywords： pathology     deep learning     segmentation     detection     classification    

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Title Author Date Type Operation

Robust object tracking with RGBD-based sparse learning

Zi-ang MA, Zhi-yu XIANG

Journal Article

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Journal Article

Digital image correlation-based structural state detection through deep learning

Journal Article

A robust object tracking framework based on a reliable point assignment algorithm

Rong-feng ZHANG, Ting DENG, Gui-hong WANG, Jing-lun SHI, Quan-sheng GUAN

Journal Article

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Journal Article

Multiple fault separation and detection by joint subspace learning for the health assessment of wind

Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN

Journal Article

A multi-sensor-system cooperative scheduling method for ground area detection and target tracking

Yunpu ZHANG, Qiang FU, Ganlin SHAN

Journal Article

Machine vision-based automatic fruit quality detection and grading

Journal Article

EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum

Yunzhan ZHOU, Tian FENG, Shihui SHUAI, Xiangdong LI, Lingyun SUN, Henry Been-Lirn DUH,yunzhan.zhou@durham.ac.uk,t.feng@zju.edu.cn

Journal Article

Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Journal Article

An Ultracompact Spoof Surface Plasmon Sensing System for Adaptive and Accurate Detection of Gas Using

Xuanru Zhang,Jia Wen Zhu,Tie Jun Cui,

Journal Article

Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Journal Article

of two periodic leaky-wave antennas with sum and difference beam scanning for application in target detectionand tracking

Mianfeng HUANG, Juhua LIU,liujh33@mail.sysu.edu.cn

Journal Article

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Journal Article

Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

Journal Article