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A partition approach for robust gait recognition based on gait template fusion Research Articles

Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu,heukejun@126.com,liuliangliang@hrbeu.edu.cn,dingxinnan@hrbeu.edu.cn,yukaiqiang@hrbeu.edu.cn,hugang@hrbeu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000377

Abstract: has significant potential for remote human identification, but it is easily influenced by identity-unrelated factors such as clothing, carrying conditions, and view angles. Many have been presented that can effectively represent gait features. Each gait template has its advantages and can represent different prominent information. In this paper, gait template fusion is proposed to improve the classical representative gait template (such as a ) which represents incomplete information that is sensitive to changes in contour. We also present a partition method to reflect the different gait habits of different body parts of each pedestrian. The fused template is cropped into three parts (head, trunk, and leg regions) depending on the human body, and the three parts are then sent into the convolutional neural network to learn merged features. We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones. The results show good accuracy and robustness of the proposed method for .

Keywords： 步态识别；分块算法；步态模板；步态分析；步态能量图；深度卷积神经网络；生物特征识别；模式识别    

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Pattern Recognition With Fuzzy Central Clustering Algorithms

Zen Huanglin,Yuan Hui,Liu Xiaofang

Strategic Study of CAE 2004, Volume 6, Issue 11,   Pages 33-37

Abstract:

Based on optimization of constrained nonlinear programming, an approach of clustering center and a fuzzy membership function of pattern classification are derived from an objective function of the constrained nonlinear programming. An unsupervised algorithm with recursive expression and a fuzzy central cluster neural network are suggested in this paper. The fuzzy central cluster neural network proposed here can realize crisp decision or fuzzy decision in pattern classification.

Keywords： fuzzy sets     central cluster     pattern recognition     neural network    

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Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks

Lu Shuang,Zhang Zida,Li Meng

Strategic Study of CAE 2004, Volume 6, Issue 2,   Pages 56-60

Abstract:

Radial basis function neural network is a type of three — layer feedforward network. It has many good properties, such as powerful ability for function approximation, classification and learning rapidly. In this paper, in the light of the merit of radial basis function neural network and on the basis of the feature analysis of vibration signal of rolling bearing, AR model is presented by using time series method. Radial basis function neural networks is established based on AR model parameters. In the light of the theory of radial basis function neural networks, fault pattern of rolling bearing is recognized correspondingly. Theory and experiment show that the recognition of fault pattern of rolling bearing based on radial basis function neural networks theory is available and its precision is high.

Keywords： rolling bearing     vibration signal     AR model     RBF neural networks     pattern recognition    

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Learning and Applications of Procedure Neural Networks

He Xingui,Liang Jiuzhen,Xu Shaohua

Strategic Study of CAE 2001, Volume 3, Issue 4,   Pages 31-35

Abstract:

This paper deals with learning algorithms for procedure neural networks (PNN) and its applications in aggregation chemical reaction and seepage test in oil geology. Weight bases selection rules and pattern curve standard problems are also discussed. These examples show that PNN have extensive applications.

Keywords： procedure neural networks     learning algorithm     pattern recognition     chemical reaction     seepage    

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Recognition of walking environments and gait period by surface electromyography Special Feature on Intelligent Robats

Seulki KYEONG, Wonseok SHIN, Minjin YANG, Ung HEO, Ji-rou FENG, Jung KIM

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3,   Pages 342-352 doi: 10.1631/FITEE.1800601

Abstract:

Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots, which measure and drive human movements, to interact with humans. Therefore, many different types of sensors are needed. When using various sensors, a data design is needed for effective sensing. An electromyographic (EMG) signal can be used to identify intended motion before the actual movement, and the delay time can be shortened via control of the exoskeleton robot. Before using a lower limb exoskeleton to help in walking, the aim of this work is to distinguish the walking environment and gait period using various sensors, including the surface electromyography (sEMG) sensor. For this purpose, a gait experiment was performed on four subjects using the ground reaction force, human–robot interaction force, and position sensors with sEMG sensors. The purpose of this paper is to show progress with the use of sEMG when recognizing walking environments and the gait period with other sensors. For effective data design, we used a combination of sensor types, sEMG sensor locations, and sEMG features. The results obtained using an individual mechanical sensor together with sEMG showed improvement compared to the case of using an individual sensor, and the combination of sEMG and position information showed the best performance in the same number of combinations of three sensors. When four sensor combinations were used, the environment classification accuracy was 96.1%, and the gait period classification accuracy was 97.8%. Vastus medialis (VM) and gastrocnemius (GAS) were the most effective combinations of two muscle types among the five sEMG sensor locations on the legs, and the results were 74.4% in pre-heel contact (preHC) and 71.7% in pre-toe-off (preTO) for environment classification, and 68.0% for gait period classification, when using only the sEMG sensor. The two effective sEMG feature combinations were “mean absolute value (MAV), zero crossings (ZC)” and “MAV, waveform length (WL)”, and the “MAV, ZC” results were 80.0%, 77.1%, and 75.5%. These results suggest that the sEMG signal can be effectively used to control an exoskeleton robot.

Keywords： Walking environment     Gait Period     Surface electromyography (sEMG)     Exoskeleton    

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A network security entity recognition method based on feature template and CNN-BiLSTM-CRF Research Papers

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 872-884 doi: 10.1631/FITEE.1800520

Abstract:

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template (FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.

Keywords： Network security entity     Security knowledge graph (SKG)     Entity recognition     Feature template     Neural network    

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Application of Fuzzy Pattern Recognition in the Measurement of Slurry Concentration

Li Dejun,Lv Runhua,Wang Runtian

Strategic Study of CAE 2007, Volume 9, Issue 5,   Pages 81-84

Abstract:

  Slurry is widely used in construction projects, and it is important to control the slurry's physical characteristic properly. The acoustic method is used,  which can effectively monitor the physical parameters of slurry,  such as concentration. Data processing affects directly the precision in the measurement of slurry concentration by the sound attenuation and velocity. Based on the fuzzy pattern recognition, data are sorted and further classified, with cooperative clustering algorithm.

Keywords： fuzzy pattern recognition     nearest neighbor(NN)     cooperative clustering algorithm(CCA)     slurry concentration    

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Sensor-guided gait-synchronization lower-extremity-exoskeleton for potential application on unilateral knee-injured people Research Articles

Donghai WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6,   Pages 920-936 doi: 10.1631/FITEE.2000465

Abstract:

This paper presents a gait-synchronization system to help potential unilateral knee-injured people walk normally with a ed (LEE). This relieves the body weight loading on the knee-injured leg and synchronizes its motion with that of the healthy leg during the swing phase of walking. The gait-synchronization system is integrated with a designed to sense the motion/gait of the healthy leg. Guided by the measured joint-angle trajectories, the motorized hip joint lifts the links during walking and synchronizes the knee-injured gait with the healthy gait by a half-cycle delay. The effectiveness of the LEE is illustrated experimentally. We compare the measured joint-angle trajectories between the healthy and knee-injured legs, the simulated knee forces, and the human-exoskeleton interaction forces. The results indicate that the motorized hip-controlled LEE can synchronize the motion/gait of the combined body-ed LEE and injured leg with that of the healthy leg.

Keywords： Sensor-guided     Lower-extremity-exoskeleton     Body sensor network     Gait synchronization     Weight-support    

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Binary neural networks for speech recognition Regular Papers

Yan-min QIAN, Xu XIANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5,   Pages 701-715 doi: 10.1631/FITEE.1800469

Abstract:

Recently, deep neural networks (DNNs) significantly outperform Gaussian mixture models in acoustic modeling for speech recognition. However, the substantial increase in computational load during the inference stage makes deep models difficult to directly deploy on low-power embedded devices. To alleviate this issue, structure sparseness and low precision fixed-point quantization have been applied widely. In this work, binary neural networks for speech recognition are developed to reduce the computational cost during the inference stage. A fast implementation of binary matrix multiplication is introduced. On modern central processing unit (CPU) and graphics processing unit (GPU) architectures, a 5–7 times speedup compared with full precision floatingpoint matrix multiplication can be achieved in real applications. Several kinds of binary neural networks and related model optimization algorithms are developed for large vocabulary continuous speech recognition acoustic modeling. In addition, to improve the accuracy of binary models, knowledge distillation from the normal full precision floating-point model to the compressed binary model is explored. Experiments on the standard Switchboard speech recognition task show that the proposed binary neural networks can deliver 3–4 times speedup over the normal full precision deep models. With the knowledge distillation from the normal floating-point models, the binary DNNs or binary convolutional neural networks (CNNs) can restrict the word error rate (WER) degradation to within 15.0%, compared to the normal full precision floating-point DNNs or CNNs, respectively. Particularly for the binary CNN with binarization only on the convolutional layers, the WER degradation is very small and is almost negligible with the proposed approach.

Keywords： Speech recognition     Binary neural networks     Binary matrix multiplication     Knowledge distillation     Population count    

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A Study on the Essence of Optimal Statistical Uncorrelated Discriminant Vectors

Wu Xiaojun,Yang Jingyu,Wang Shitong,Liu Tongming,Josef Kittler

Strategic Study of CAE 2004, Volume 6, Issue 2,   Pages 44-47

Abstract:

A study has been made on the essence of optimal set of uncorrelated discriminant vectors in this paper. A whitening transform has been constructed on the basis of the eigen decomposition of population scatter matrix, which makes the population scatter matrix an identity matrix in the transformed sample space. Thus, the optimal discriminant vectors solved by conventional LDA methods are statistical uncorrelated. The research indicates that the essence of the statistical uncorrelated discriminant transform is the whitening transform plus conventional linear discriminant transform. The distinguished characteristic of the proposed method is that the obtained optimal discriminant vectors are orthogonal and statistical uncorrelated. The proposed method suits for all the problems of algebraic feature extraction. The numerical experiments on facial database of ORL show the effectiveness of the proposed method.

Keywords： pattern recognition     feature extraction     disciminant analysis     generalized optimal set of discriminant vectors     face recognition    

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DDUC: an erasure-coded system with decoupled data updating and coding Research Article

Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI,chunruitang@126.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 5,   Pages 742-758 doi: 10.1631/FITEE.2200253

Abstract: To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results, researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques. To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios, we have designed a novel network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing. The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with . The two modules are skip-connected to work together to improve the robustness of the overall network. Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.

Keywords： Signal noise elimination     Deep adaptive threshold learning network     Multi-scale feature fusion     Modulation recognition    

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ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model Research Article

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12,   Pages 1551-1684 doi: 10.1631/FITEE.2000511

Abstract: Physiological signal based biometric analysis has recently attracted attention as a means of meeting increasing privacy and security requirements. The real-time nature of an electrocardiogram (ECG) and the hidden nature of the information make it highly resistant to attacks. This paper focuses on three major bottlenecks of existing deep learning driven approaches: the lengthy time requirements for optimizing the hyperparameters, the slow and computationally intense identification process, and the unstable and complicated nature of ECG acquisition. We present a novel deep neural network framework for learning feature representations directly from ECG time series. The proposed framework integrates deep bidirectional long short-term memory (BLSTM) and . The overall approach not only avoids the inefficient and experience-dependent search for hyperparameters, but also fully exploits the spatial information of ordinal local features and the memory characteristics of a recognition algorithm. The effectiveness of the proposed approach is thoroughly evaluated in two ECG datasets, using two protocols, simulating the influence of electrode placement and acquisition sessions in identification. Comparing four recurrent neural network structures and four classical machine learning and deep learning algorithms, we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series. The experimental results demonstrated an average identification rate of 97.71%, 99.41%, and 98.89% in training, validation, and test sets, respectively. Thus, this study proves that the application of APSO and LSTM techniques to biometric can achieve a lower algorithm engineering effort and higher capacity for generalization.

Keywords： 心电图生物特征；个体身份识别；长短期记忆网络；自适应粒子群优化算法    

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DARPA Robotics Grand Challenge Participation and Ski-Type Gait for Rough-Terrain Walking Article

Hongfei Wang, Shimeng Li, Yuan F. Zheng

Engineering 2015, Volume 1, Issue 1,   Pages 36-45 doi: 10.15302/J-ENG-2015006

Abstract:

In this paper, we briefly introduce the history of the Defense Advanced Research Projects Agency (DARPA) Grand Challenge programs with particular focus on the 2012 Robotics Challenge. As members of team DRC-HUBO, we propose different approaches for the Rough-Terrain task, such as enlarged foot pedals and a transformation into quadruped walking. We also introduce a new gait for humanoid robot locomotion to improve stability performance, called the Ski-Type gait. We analyze the stability performance of this gait and use the stability margin to choose between two candidate step sequences, Crawl-1 and Crawl-2. Next, we perform a force/torque analysis for the redundant closed-chain system in the Ski-Type gait, and determine the joint torques by minimizing the total energy consumption. Based on the stability and force/torque analysis, we design a cane length to support a feasible and stable Crawl-2 gait on the HUBO2 humanoid robot platform. Finally, we compare our experimental results with biped walking to validate the Ski-Type gait. We also present our team performance in the trials of the Robotics Challenge.

Keywords： humanoid robot     DARPA robotics challenge (DRC)     rough-terrain walking     Ski-Type gait    

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Control strategy for gait transition of an underactuated 3Dbipedal robot Research Articles

Hai-hui YUAN, Yi-min GE, Chun-biao GAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1026-1035 doi: 10.1631/FITEE.1800206

Abstract: Significant research interest has recently been attracted to the study of bipedal robots due to the wide variety of their potential applications. In reality, bipedal robots are often required to perform gait transitions to achieve flexible walking. In this paper, we consider the gait transition of a five-link underactuated three-dimensional (3D) bipedal robot, and propose a two-layer control strategy. The strategy consists of a unique, event-based, feedback controller whose feedback gain in each step is updated by an adaptive control law, and a transition controller that guides the robot from the current gait to a neighboring point of the target gait so that the state trajectory can smoothly converge to the target gait. Compared with previous works, the transition controller is parameterized and its control parameters are obtained by solving an optimization problem to guarantee the physical constraints in the transition process. Finally, the effectiveness of the control strategy is illustrated on the underactuated 3D bipedal robot.

Keywords： Gait transition     Underactuated three-dimensional biped     Event-based feedback controller     Adaptive control law    

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第二届中国模式识别与计算机视觉学术会议（Chinese Conference on Pattern Recognition and Computer Vision）

Conference Date: 8 Nov 2019

Conference Place: 中国/陕西/西安

Administered by: 中国图象图形学学会、中国计算机学会、中国自动化学会、中国人工智能学会

Title Author Date Type Operation

A partition approach for robust gait recognition based on gait template fusion

Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu,heukejun@126.com,liuliangliang@hrbeu.edu.cn,dingxinnan@hrbeu.edu.cn,yukaiqiang@hrbeu.edu.cn,hugang@hrbeu.edu.cn

Journal Article

Pattern Recognition With Fuzzy Central Clustering Algorithms

Zen Huanglin,Yuan Hui,Liu Xiaofang

Journal Article

Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks

Lu Shuang,Zhang Zida,Li Meng

Journal Article

Learning and Applications of Procedure Neural Networks

He Xingui,Liang Jiuzhen,Xu Shaohua

Journal Article

Recognition of walking environments and gait period by surface electromyography

Seulki KYEONG, Wonseok SHIN, Minjin YANG, Ung HEO, Ji-rou FENG, Jung KIM

Journal Article

A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Journal Article

Application of Fuzzy Pattern Recognition in the Measurement of Slurry Concentration

Li Dejun,Lv Runhua,Wang Runtian

Journal Article

Sensor-guided gait-synchronization lower-extremity-exoskeleton for potential application on unilateral knee-injured people

Donghai WANG

Journal Article

Binary neural networks for speech recognition

Yan-min QIAN, Xu XIANG

Journal Article

A Study on the Essence of Optimal Statistical Uncorrelated Discriminant Vectors

Wu Xiaojun,Yang Jingyu,Wang Shitong,Liu Tongming,Josef Kittler

Journal Article

DDUC: an erasure-coded system with decoupled data updating and coding

Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI,chunruitang@126.com

Journal Article

ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Journal Article

DARPA Robotics Grand Challenge Participation and Ski-Type Gait for Rough-Terrain Walking

Hongfei Wang, Shimeng Li, Yuan F. Zheng

Journal Article

Control strategy for gait transition of an underactuated 3Dbipedal robot

Hai-hui YUAN, Yi-min GE, Chun-biao GAN

Journal Article

第二届中国模式识别与计算机视觉学术会议（Chinese Conference on Pattern Recognition and Computer Vision）

8 Nov 2019

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