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A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1222-1242页

作者： Ce Zhang Azim Eskandarian Virginia Tech Mechanical Engineering Department ASIM Lab Virginia 24061-0131 USA

The driver’s cognitive and physiological states affect his/her ability to control the ***,these driver states are essential to the safety of *** design of advanced driver assistance systems(ADAS)or autonomous vehicles will depend on their ability to interact effectively with the driver.A deeper understanding of the driver state is,therefore,***(EEG)is proven to be one of the most effective methods for driver state monitoring and human error *** paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three ***,the commonly used EEG system setup for driver state studies is ***,the EEG signal preprocessing,feature extraction,and classification algorithms for driver state detection are ***,EEG-based driver state monitoring research is reviewed in-depth,and its future development is *** is concluded that the current EEGbased driver state monitoring algorithms are promising for safety ***,many improvements are still required in EEG artifact reduction,real-time processing,and between-subject classification accuracy.

关键词： Advanced driver assistance systems(ADAS) data analysis electroencephalography(EEG) intelligent vehicles machine learning algorithms neural network.

Novel method for the evaluation of data quality based on fuzzy control

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Journal of Systems Engineering and Electronics 2008年第3期19卷 606-610页

作者： Ban Xiaojuan Ning Shurong Xu Zhaolin Cheng Peng School of Information Engineering Univ. of Science & Technology Beijing Beijing 100083 P. R. China Beijing Inst. of Electronic System Engineering Beijing 100854 P. R. China

One of the goals of data collection is preparing for decision-making, so high quality requirement must be satisfied. Rational evaluation of data quality is an effective way to identify data problem in time, and the quality of data after this evaluation is satisfactory with the requirement of decision maker. A fuzzy neural network.based research method of data quality evaluation is proposed. First, the criteria for the evaluation of data quality are selected to construct the fuzzy sets of evaluating grades, and then by using the learning ability of NN, the objective evaluation of membership is carried out, which can be used for the effective evaluation of data quality. This research has been used in the platform of ＇data report of national compulsory education outlay guarantee＇ from the Chinese Ministry of Education. This method can be used for the effective evaluation of data quality worldwide, and the data quality situation can be found out more completely, objectively, and in better time by using the method.

关键词： data quality evaluation system fuzzy control theory neural network.

Locating Impedance Change in Electrical Impedance Tomography Based on Multilevel BP neural network

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Journal of Shanghai University(English Edition) 2003年第3期7卷 251-255页

作者：彭源莫玉龙 School of Communication and Information Engineering of Shanghai University Shanghai 200072 China

Electrical impedance tomography(EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network.(MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.

关键词： image reconstruction electrical impedance tomography neural network.

Research on Tourism Resources Development Pattern Effect Evaluation based on Regression Analysis and neural network

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International Journal of Technology Management 2016年第8期 23-25页

作者： Yimei Xue Zhengzhou Henan Henan University of Animal Husbandry and Economy Zhengzhou Henan 450045 China

In this paper, we conduct research on the tourism resources development pattern effect evaluation based on regression analysis andneural network. Tourism resources development and the protection of all sorts of the contradictions, in the fi nal analysis is the confl ict betweenthe interests of stakeholders. Real life in China, and tourism resources development and protection of the interests of the relevant party basicallyhas the following six parties the local government, investment and the business practice, the original residents, tourists, tourism practitioners, thesocial organization. The survival and the development of general tourism industry is based on the premise of environmental protection, tourismlandscape are carefully protected, let the natural landscape and human landscape form a virtuous cycle, to attract more tourists, promote thesustainable development of scenic spots. Under this basis, this paper proposes the new idea on the issues that is meaningful.

关键词： Tourism Resources Effect Evaluation Regression Analysis neural network.

Learning-based force servoing control of a robot with vision in an unknown environment

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Journal of Systems Engineering and Electronics 2004年第2期15卷 171-178页

作者： XiaoNanfeng SchoolofComputerScienceandEngineering SouthChinaUniversityofTechnologyGuangzhou510641P.R.China

A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of the robot are derived and learned by a neural network. Secondly, a learning controller based on the neural network.is designed for the robot to trace the object. Thirdly, a discrete time impedance control law is obtained for the force servoing of the robot, the on-line learning algorithms for three neural network. are developed to adjust the impedance parameters of the robot in the unknown environment. Lastly, wiping experiments are carried out by using a 6 DOF industrial robot with a CCD camera and a force/torque sensor in its end effector, and the experimental results confirm the effecti veness of the approach.

关键词： robotics force servoing vision control learning algorithm neural network.

Pattern recognitionbased method for radar antideceptive jamming

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Journal of Systems Engineering and Electronics 2005年第4期16卷 802-805页

作者： Ma Xiaoyan Qin Jiangmin Li Jianxun Air Force Radar Academy Wuhan 430019 P. R. China

In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network.（NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.

关键词： angle deceptive jamming anti-jamming pattern recognition feature extraction neural network.

Towards Domain-agnostic Depth Completion

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Machine Intelligence Research 2024年第4期21卷 652-669页

作者： Guangkai Xu Wei Yin Jianming Zhang Oliver Wang Simon Niklaus Simon Chen Jia-Wang Bian Zhejiang University Hangzhou 310058China DaJiang Technology Shenzhen 518057China Adobe Research California 95110USA University of Oxford Oxford OX12JDUK

Existing depth completion methods are often targeted at a specific sparse depth type and generalize poorly across task *** present a method to complete sparse/semi-dense,noisy,and potentially low-resolution depth maps obtained by various range sensors,including those in modern mobile phones,or by multi-view reconstruction *** method leverages a data-driven prior in the form of a single image depth prediction network.trained on large-scale datasets,the output of which is used as an input to our *** propose an effective training scheme where we simulate various sparsity patterns in typical task *** addition,we design two new benchmarks to evaluate the generalizability and robustness of depth completion *** simple method shows superior cross-domain generalization ability against state-of-the-art depth completion methods,introducing a practical solution to highqualitydepthcapture onamobile device.

关键词： Monocular depth estimation depth completion zero-shot generalization scene reconstruction neural network.

Novel Newton’s learning algorithm of neural network.

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Journal of Systems Engineering and Electronics 2006年第2期17卷 450-454页

作者： Long Ning Zhang Fengli Coll. of Electronic Engineering Univ. of Electronic Science and Technology of China Chengdu 610054 P.R. China

Newton＇s learning algorithm of NN is presented and realized. In theory, the convergence rate of learning algorithm of NN based on Newton＇s method must be faster than BP＇s and other learning algorithms, because the gradient method is linearly convergent while Newton＇s method has second order convergence rate. The fast computing algorithm of Hesse matrix of the cost function of NN is proposed and it is the theory basis of the improvement of Newton＇s learning algorithm. Simulation results show that the convergence rate of Newton＇s learning algorithm is high and apparently faster than the traditional BP method＇s, and the robustness of Newton＇s learning algorithm is also better than BP method＇ s.

关键词： Newton's method Hesse matrix fast learning BP method neural network.

A novel chaotic optimization algorithm and its applications

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Journal of Harbin Institute of Technology(New Series) 2010年第2期17卷 254-258页

作者：费春国韩正之 College of Aeronautical Automation Civil Aviation University of China Dept. of Automation Shanghai Jiaotong University

This paper presents a chaos-genetic algorithm (CGA) that combines chaos and genetic algorithms. It can be used to avoid trapping in local optima profiting from chaos'randomness,ergodicity and regularity. Its property of global asymptotical convergence has been proved with Markov chains in this paper. CGA was applied to the optimization of complex benchmark functions and artificial neural network.s (ANN) training. In solving the complex benchmark functions,CGA needs less iterative number than GA and other chaotic optimization algorithms and always finds the optima of these functions. In training ANN,CGA uses less iterative number and shows strong generalization. It is proved that CGA is an efficient and convenient chaotic optimization algorithm.

关键词： chaotic optimization chaos-genetic algorithms (CGA) genetic algorithms neural network.