Author name cluster

Chao Hu

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13 papers

2 author rows

AAAI Conference 2023 Conference Paper

Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition

Bingjun Luo
Junjie Zhu
Tianyu Yang
Sicheng Zhao
Chao Hu
Xibin Zhao
Yue Gao

Existing methods on facial expression recognition (FER) are mainly trained in the setting when multi-class data is available. However, to detect the alien expressions that are absent during training, this type of methods cannot work. To address this problem, we develop a Hierarchical Spatial One Class Facial Expression Recognition Network (HS-OCFER) which can construct the decision boundary of a given expression class (called normal class) by training on only one-class data. Specifically, HS-OCFER consists of three novel components. First, hierarchical bottleneck modules are proposed to enrich the representation power of the model and extract detailed feature hierarchy from different levels. Second, multi-scale spatial regularization with facial geometric information is employed to guide the feature extraction towards emotional facial representations and prevent the model from overfitting extraneous disturbing factors. Third, compact intra-class variation is adopted to separate the normal class from alien classes in the decision space. Extensive evaluations on 4 typical FER datasets from both laboratory and wild scenarios show that our method consistently outperforms state-of-the-art One-Class Classification (OCC) approaches.

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EAAI Journal 2021 Journal Article

A physics-informed deep learning approach for bearing fault detection

Sheng Shen
Hao Lu
Mohammadkazem Sadoughi
Chao Hu
Venkat Nemani
Adam Thelen
Keith Webster
Matthew Darr

In recent years, advances in computer technology and the emergence of big data have enabled deep learning to achieve impressive successes in bearing condition monitoring and fault detection. While existing deep learning approaches are able to efficiently detect and classify bearing faults, most of these approaches depend exclusively on data and do not incorporate physical knowledge into the learning and prediction processes—or more importantly, embed the physical knowledge of bearing faults into the model training process, which makes the model physically meaningful. To address this challenge, we propose a physics-informed deep learning approach that consists of a simple threshold model and a deep convolutional neural network (CNN) model for bearing fault detection. In the proposed physics-informed deep learning approach, the threshold model first assesses the health classes of bearings based on known physics of bearing faults. Then, the CNN model automatically extracts high-level characteristic features from the input data and makes full use of these features to predict the health class of a bearing. We designed a loss function for training and validating the CNN model that selectively amplifies the effect of the physical knowledge assimilated by the threshold model when embedding this knowledge into the CNN model. The proposed physics-informed deep learning approach was validated using (1) data from 18 bearings on an agricultural machine operating in the field, and (2) data from bearings on a laboratory test stand in the Case Western Reserve University (CWRU) Bearing Data Center.

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EAAI Journal 2014 Journal Article

Health diagnostics using multi-attribute classification fusion

Pingfeng Wang
Prasanna Tamilselvan
Chao Hu

This paper presents a classification fusion approach for health diagnostics that can leverage the strengths of multiple member classifiers to form a robust classification model. The developed approach consists of three primary steps: (i) fusion formulation using a k-fold cross validation model; (ii) diagnostics with multiple multi-attribute classifiers as member algorithms; and (iii) classification fusion through a weighted majority voting with dominance approach. State-of-the-art classification techniques from three broad categories (i. e. , supervised learning, unsupervised learning, and statistical inference) were employed as member algorithms. The diagnostics results from the fusion approach will be better than, or at least as good as, the best result provided by all individual member algorithms. The developed classification fusion approach is demonstrated with the 2008 PHM challenge problem and rolling bearing health diagnostics problem. Case study results indicated that, in both problems, the developed fusion diagnostics approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness.

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ICRA Conference 2013 Conference Paper

Adaptive visual tracking with reacquisition ability for arbitrary objects

Tianyu Yang
Baopu Li
Chao Hu
Max Q. -H. Meng

This paper introduces a novel tracking framework for robots that can adapt various appearance changes of object and also owns the ability of reacquisition after drift. Two classifiers, LaRank and Online Random Ferns, are adopted to realize this tracking algorithm. The former one maintains the adaptive tracking using a Condensation-based method with an online support vector machine (SVM) as observation model, which also provides the reliable image patch samples to detector for updating. The other one is in charge of the task of detection in order to redetect the object when the target drifts. We also present a refinement strategy to improve the tracker's performance by discarding the support vector corresponding to possible wrong updates by a matching template after re-initialization. The experiments on benchmark dataset compare our tracking method with several other state-of-the-art algorithms, demonstrating a promising performance of the proposed framework.

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ICRA Conference 2012 Conference Paper

A novel correspondence searching strategy in multiocular vision

Ning Wei
Baopu Li
Qing He
Chao Hu
Max Q. -H. Meng

Correspondence searching among different images is a fundamental problem in computer vision. It is important to find correspondences correctly and rapidly, especially for real-time tracking systems. Therefore, the definition of search areas in images is crucial. Traditional epipolar constraint is not noise-enduring; some reformative methods lack explicit geometric meanings. All of them cannot help defining rational search areas under noises. This paper proposes two new binocular imaging constraints with clear geometric meanings and strong restraining forces. Based on them, a novel searching strategy among multiimages is developed which can define optimal search areas with smallest sizes but best reliability. Practical algorithms for implementation are presented and experiments with real images are performed, validating the effectiveness of the proposed strategy.

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IROS Conference 2006 Conference Paper

The Calibration of 3-Axis Magnetic Sensor Array System for Tracking Wireless Capsule Endoscope

Chao Hu
Max Q. -H. Meng
Mrinal Mandal 0001

A magnetic localization and orientation system is proposed for tracking wireless capsule endoscope. This system uses a small magnet enclosed in the capsule to serve as excitation source. When the capsule moves, the magnet establishes a static magnetic field around. With the magnetic sensor array composed of Honeywell 3-axis magnetic sensors, HMC1053, the magnetic intensities in some pre-determined spatial points can be detected, and the magnet's position and orientation parameters can be computed based on an algorithm. To initiate the system and obtain better tracking accuracy, we propose a calibration technique for the magnetic tracking system. The calibration includes sensitivity determination and nonlinearity adjustment, sensor center position and orientation adjustment. Based on the calibration procedures, the system can achieve satisfactory tracking accuracy with the average localization error 3. 3 mm and the average orientation error 3. 0deg

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IROS Conference 2005 Conference Paper

Data gathering communication in wireless sensor networks using ant colony optimization

Niannian Ding
Peter X. Liu
Chao Hu

This paper introduces a centralized approach to data gathering and communication for wireless sensor networks. Inspired by the social behaviors of ants, we clearly partition the work for the base station and sensor nodes according to their different functions and capabilities. A near-optimal chain is achieved by using an ant colony optimization method ruing in the base station. The sensor nodes in the network then form a bi-direction chain structure, which is self-adaptive to any minor changes. The simulation results show that the developed AntChain algorithm performs much better than the LEACH and PEGASIS methods, in terms of energy-efficiency, data integrity and life time, when the base station.

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IROS Conference 2005 Conference Paper

Efficient magnetic localization and orientation technique for capsule endoscopy

Chao Hu
Max Q. -H. Meng
Mrinal Mandal 0001

To build a new wireless robotic capsule endoscope with external guidance for controllable and interactive GI tract examination, a sensing system is needed for tracking 3D location and 2D orientation of the capsule movement. An appropriate sensing approach is to enclose a small permanent magnet in the capsule. The magnet establishes a magnetic field around the patient's body. With the sensing data of magnetic sensor array outside the patient's body, the 3D location and 2D orientation of the capsule can be calculated. Higher localization and orientation accuracy can be obtained if more sensors and proper optimization algorithm are applied. In this paper, different nonlinear optimization algorithms are evaluated, and we have found that Levenberg-Marquardt method provides higher accuracy and faster speed. Simulations were done for investigating the de-noise ability of this algorithm based on different sensor arrays. Furthermore, the real experiment shows that the results are satisfactory with high accuracy.

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ICRA Conference 2004 Conference Paper

Image Distortion Correction for Wireless Capsule Endoscope

Chao Hu
Max Q. -H. Meng
Peter X. Liu
Xiang Wang

The images captured by wireless capsule endoscope might have nonlinear spatial distortion, which makes accurate medical examination difficult. So it is a prerequisite to have this distortion corrected. Typically, the correction uses a calibration pattern, which might be a chessboard, dot, grid, or circle pattern. Based on this pattern, enough characteristic samples can be extracted accurately and conveniently, and mathematic model can be built for the distortion in the captured image with respect to the original calibration pattern. Then the correction parameters, including image centers and mapping polynomials, could be found to realize the correction. If the model is too complicated to be accurately built, correction using neural network is a good choice, since it does not rely on the mathematic model of the distortion.

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ICRA Conference 2004 Conference Paper

On-line Data-driven Fuzzy Clustering with Applications to Real-time Robotic Tracking

Peter X. Liu
Max Q. -H. Meng
Chao Hu

Robotic target tracking has been used in a variety of applications. Due to limited sampling rate, sensory characteristics and processing delays, an important issue in such systems is thus to extrapolate ahead the trajectory (position, orientation, velocity and/or acceleration) of moving targets based on past observations. This paper introduces a novel on-line data-driven fuzzy clustering algorithm that is based on the maximum entropy principle for this particular task. In this algorithm, the fuzzy inference mechanism is extracted automatically from observed data without any human help, which thus eliminates the necessity of expert knowledge and a priori information on moving targets, as required by most traditional techniques. This algorithm does not require training, which enables it to work in a completely on-line fashion. Another important and distinct advantage of the algorithm exists in the fact that it is very fast and efficient in terms of computational cost and thus can be implemented in real time. In the mean time, the introduced algorithm has the ability to adapt quickly to the dynamics of moving targets. All these features make it especially suitable for the task to predict the trajectory of moving targets in robotic tracking. Simulation results show the effectiveness and efficiency of the presented algorithm.

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IROS Conference 2003 Conference Paper

A modular structure for Intemet mobile robots

Peter X. Liu
Max Q. -H. Meng
Chao Hu
Jie Sheng

In this paper we introduce a software and hardware structure for on-line mobile robotic systems. The system hardware configuration mainly consists of a commercially available Pioneer 2 PeopleBot mobile robot, a Sony PTZ video camera and a pair of BreezeNet indoor wireless Ethernet adaptors. The system employs a client-server software architecture in which the client server is insulated from the lower-level details of the mobile robot. This architecture is implemented on the real Internet and the preliminary result is promising. By adopting this modular structure, it will be very easy to construct an experimental platform for the research on diverse teleoperation topics such as remote control algorithms, interface designs, network protocols and applications etc.

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ICRA Conference 2003 Conference Paper

Control and data transmission for internet robots

Peter X. Liu
Max Q. -H. Meng
Jason Jianjun Gu
Simon X. Yang
Chao Hu

For Internet-based tele-robotic systems (Internet robots), the most challenging and distinct difficulties are associated with Internet transmission delays, delay jitter and not-guaranteed bandwidth availability, which might lead to dramatic performance degradation or even instability. In this paper, a new approach to dealing with these problems is explored and implemented. Specifically, a rate-based end-to-end transport protocol is developed for real-time data transmission and an adaptive control scheme is developed to control the robot remotely. A mobile robot teleoperation system, ArtBot-I, is developed to verify and test the solutions. In the experiments, the users successfully guided a Pioneer-2 mobile robot through a laboratory environment remotely via the Internet using a web browser.

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IROS Conference 2003 Conference Paper

Visual gesture recognition for human-machine interface of robot teleoperation

Chao Hu
Max Q. -H. Meng
Peter X. Liu
Xiang Wang

This paper presents a new visual gesture recognition method for the human-machine interface of mobile robot teleoperation. The interface uses seven static hand gestures, each of which represents an individual control command for the motion control of the remote robot. All the important aspects to develop such an interface are explored, including image acquisition, adaptive object segmentation with color image in RGB, HLS representation, morphological filtering, hand finding and labeling, and recognition with edge codes, template matching, and skeletonizing. By choosing processing methods and procedures properly, a higher ratio of correct recognition and a faster speed are achieved from the experiments.

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