Jigang Tong

Tianjin University of Technology

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Enzeng Dong

Tianjin University of Technology

Shengzhi Du

Tshwane University of Technology

Sen Yang

Tianjin University of Technology

Wenyu Li

Nankai University

Feng Duan

Nankai University

Zhiheng Wu

University of Chinese Academy of Sciences

Zenghui Wang

University of South Africa

Zengqiang Chen

Nankai University

Sen Yang

Tianjin University of Technology

Zufeng Zhang

Tsinghua University

Cooperative Institutions

Robotic Technology (United States)

145

Tianjin University of Technology

Chinese Academy of Sciences

Peking University

University of Chinese Academy of Sciences

Shanghai Jiao Tong University

Zhejiang University

Huazhong University of Science and Technology

Nankai University

Harbin Institute of Technology

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Computer-guided system for design of factories

International Journal of Computer Integrated Manufacturing (2003)

Jigang Tong CA van Luttervelt Fw Wedman H. Bikker

To stay competitive at the start of the 21st century, manufacturing companies are in great need of support for designing/redesigning their production systems in accordance with the drastically, rapidly and frequently changing environment. Our aim is to develop a computer-guided system for the design of factories in the manufacturing industry. As a first step, a hybrid reference model for factory design is presented in this paper to describe the necessary design actions carried out during the process of factory design. According to the reference model, the general design actions together with their corresponding techniques are categorized. Different participants are working on these design activities, each representing the opinion from disciplines inside or outside the company in a distributed cooperative way with equal authorities but heterogeneous knowledge. To control effectively the interactions among the cooperating designers from different departments, a computer guidance method of the process of design of a factory based on the concept of a multi-agent system is presented. Several agents represent users (participants in the factory design process) to the system and the coordination mechanism of the system handles complex interactions with other cooperating agents and system resources. The aim is a better coordination of the interactions of design steps in each of the activities and a more effective management of the entire design process. A prototype software system is presented.

Factory (object-oriented programming)

10.1080/0951192031000115705

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A Enhanced Feature Extraction for 3D Object Detection

Qingao Meng Jigang Tong Sen Yang Tian Xie Xin Jin

Feature (linguistics)

10.1109/ccssta62096.2024.10691806

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An Engraving Character Recognition System Based on Machine Vision

DEStech Transactions on Computer Science and Engineering (2017)

Yu Wang Zhiheng Wu Hongbin Liu Qiyu Chen Xianyun Duan

In order to solve the problem of low efficiency, low quality and false detection of metal surface engraving character detection, we proposed a kind of engraving character recognition system based on machine vision. This system can identify and detect through the image acquisition of engraving area, and a series of mathematical and morphological treatments of binary image. It can extract the contour using optimized Laplace edge detection operator and combining with the convexity characteristic of engraved words. The system successfully carried out the experiment of engraving character recognition and it can detect the character recognition with high efficiency and high qualified rate. It has realized the automation of engraving character recognition test production line, and it has certain application value and promotion significance.

Engraving

Optical character recognition

Machine Vision

10.12783/dtcse/aiea2017/14919

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Analysis of dynamics properties of ankle joint during push-forward disturbance rejection

Yuping Li Mingwen Li Muye Pang Jinfeng Wang Jigang Tong

Ankle joint dynamics properties have been widely studied because of their important role in interaction with environment. In this paper, these properties were studied under the experimental condition of push-recovery. The disturbance was exerted on backs of subjects. The amplitude of disturbance was limited so that only ankle joint strategy was evoked, without foot tilting. A data-synchronized-collection system was developed to measure ankle joint kinematics, kinetics and sEMG information. Ankle joint mechanical impedance, i.e. torque adjustment corresponding to joint angle, was investigated together with sEMG of antagonistic and agonistic muscles. Despite of complication of muscle-skeleton structure and central nervous system, a simple control mechanism was observed, describing by linear and quadratic polynomials with high correlation coefficient. Experimental results also indicated that the reflex-like behavior takes charge with the resistant torque generation at the first moment, helping to adjust ankle joint mechanical impedance. Furthermore, SOL muscle activation level had a linear relationship with ankle joint kinematics data. This pilot study is as one part of our investigation of human push-recovery ability, which tries to reveal the mechanism of ankle dynamics and helps to improve bipedal robot balance capability.

10.1109/icinfa.2016.7831912

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Design of Wireless Sensor Network Node with Hyperchaos Encryption Based on FPGA

International Workshop on Chaos-fractals Theories and Applications (2009)

Jigang Tong Zhenxin Zhang Qinglin Sun Zengqiang Chen

Wireless sensor network has been widely used and its real-time data processing capability is very limited. This paper puts forward a new solution, which is the novel wireless sensor network node design with hyperchaos encryption based on FPGA. With the wide application on wireless sensor network based on ZigBee protocol, we encrypt the transported data in the network using hyperchaos by FPGA, which combines their flexibility in rapid real-time data processing with free configuration in FPGA and enhances the security of the transported data. Then we can make full use of their advantages and achieve a good design of sensor network node.

Wireless WAN

Sensor node

10.1109/iwcfta.2009.47

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Citations (14)

プッシュフォワード外乱除去時の足関節の動力学特性の解析【Powered by NICT】

IEEE Conference Proceedings (2016)

Li Yuping Li Mingwen Muye Pang Wang Jinfeng Jigang Tong

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Hybrid mental tasks based human computer interface via integration of pronunciation and motor imagery

Journal of Neural Engineering (2022)

Jigang Tong Xiaoying Wei Enzeng Dong Zhe Sun Shengzhi Du

Abstract Objective. Among the existing active brain–computer interfaces (BCI), the motor imagination (MI) is widely used. To operate the MI BCI effectively, subjects need to carry out trainings on corresponding imagining tasks. Here, we studied how to reduce the discomfort and fatigue of active BCI imaginary tasks and the inability to concentrate on them while improving the accuracy. Approach. This paper proposes a hybrid BCI composed of MI and pronunciation imagination (PI). The electroencephalogram signals of ten subjects are recognized by the adaptive Riemannian distance classification and the improved frequency selective filter-bank Common Spatial Pattern recognition. Main results. The results show that under the new paradigm with the combination of MI and PI, the recognition accuracy is higher than the MI alone. The highest recognition rate of the proposed hybrid system can reach more than 90%. Furthermore, through the subjects’ scoring results of the operation difficulty, it is concluded that the designed hybrid paradigm is more operable than the traditional BCI paradigm. Significance. The separable tasks in the active BCI are limited and the accuracy needs to be improved. The new hybrid paradigm proposed by us improves the accuracy and operability of the active BCI system, providing a new possibility for the research direction of the active BCI.

Motor Imagery

Pronunciation

Interface (matter)

10.1088/1741-2552/ac9a01

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Memristive synaptic crosstalk effects on Hopfield neural network

2022 IEEE International Conference on Mechatronics and Automation (ICMA) (2022)

Yapeng Zhang Enzeng Dong Jigang Tong Ronghao Li Sen Yang

Due to the transmission of neurotransmitters, crosstalk occurs between the synapses connecting neurons in Hopfield neural network (HNN). Synaptic crosstalk can affect the signal transmission and even some functions of the brain. In order to study the dynamical behavior of neural networks under the action of synaptic crosstalk, a hyperbolic memristor is used to simulate the synapses of HNN in this paper. The dynamical behaviours were revealed by analytical tools such as bifurcation diagram, Lyapunov exponential spectrum, local attraction basin, and phase plane diagram. In addition, the average Hamiltonian energy of the system was analysed based on Helmholtz's theorem, and the firing activity of neurons depends on the release and absorption of energy, and the study of energy in neurons helps to better understand the neural dynamics.

Memristor

Crosstalk

10.1109/icma54519.2022.9856308

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Fatigue Driving Detection of EEG Signals by LSTM Deep Neural Network with LPSD and DE

2022 IEEE International Conference on Mechatronics and Automation (ICMA) (2023)

Hao Zhang Enzeng Dong Jigang Tong Sen Yang Shengzhi Du

Fatigue driving detection has always been an urgent social problem to be solved, and many complex factors increase the difficulty of detection. Nowadays, more scholars choose to use multi-channel EEG signals for fatigue detection, which is accompanied by the development of deep learning networks. Here, we propose the deep Long Short Term Memory (LSTM) neural network with the Differential Entropy (DE) and Logarithm Power Spectrum Density (LPSD). We first divide the EEG signal into 8-s Hanning windows with 50% overlap, and then use the Short Time Fourier Transform(STFT) on the extracted time window to obtain 50 frequency sub-bands. Finally, LPSD and DE features are extracted from the frequency offspring obtained from the STFT and input into the deep LSTM neural network. The network can choose to learn more task related information by adding soft attention and extract effective time information from EEG signals,thereby improving the model's performance in detecting driving fatigue. In addition, we also collected EEG signals from 8 subjects in both awake and fatigued states,and used the PERCLOS index to judge the degree of fatigue. Using the collected dataset, we compared the effectiveness of the deep LSTM neural network with five other methods. The results indicate that the deep LSTM neural network has a better classification accuracy of 93.12%. It proves that the network has good performance in fatigue detection of EEG signals.

Deep Neural Networks

10.1109/icma57826.2023.10216090

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Citations (1)

Analysis of dynamics properties of ankle joint during push-forward disturbance rejection

IEEE Conference Proceedings (2016)

Yuping Li Mingwen Li Muye Pang Jinfeng Wang Jigang Tong

Dynamics

Inverse dynamics

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