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2019 Jiangsu Annual Conference on automation, JACA 2019

作者： Li, Fan Niu, Dan Li, Tao Xue, Yufeng Huang, Xueying School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems Ministry of Education Nanjing China Jiangsu Juli Intelligent Machinery Corp. Ltd. Suzhou China

With the rapid development of the stereo garage, there are plenty of security problems appeared in stereo garage. The monitoring and management system of the stereo garage has attracted more and more attention. A cloud monitoring and management system for stereo garage is designed based on Web technology, which collects real-time data from the intelligent controller of the stereo garage, stores them in the cloud database. History and real-time data are studied to complete the functions of garage overview, real-time video monitoring, fault management, garage management, and system management. Deep confidence networks are proposed to conduct fault diagnosis for stereo garage, and the results are sent to the management interface of cloud portal (APP and website) for remote management. Test results show that the management efficiency of the stereo garage is significantly improved. The system guarantees the safe and reliable work of the stereo garage. It achieves better performance especially in the remote management and fault diagnosis. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Failure analysis

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An Enhanced Fault Diagnosis Method with Uncertainty Quantification Using Bayesian Convolutional Neural Network

An Enhanced Fault Diagnosis Method with Uncertainty Quantifi...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Qihang Fang Gang Xiong Xiuqin Shang Sheng Liu Bin Hu Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Fault diagnosis is a vital technique to pinpoint the machine malfunctions in manufacturing systems. In recent years, the deep learning techniques greatly improve the fault detection accuracy, but there still remain some problems. If one fault is absent in the training data or the fault signal is disturbed by severe noise interference, the fault classifier may misjudge the health state. This problem limits the reliability of the fault diagnosis in real applications. In this paper, we enhance the fault diagnosis method by using Bayesian Convolutional Neural Network (BCNN). A Shannon entropy-based method is presented to quantify the prediction uncertainty. The BCNN turns the deterministic predictions to probabilistic distributions and enhances the robustness of the fault diagnosis. The uncertainty quantification method helps to indicate the wrong predictions, detect unknown faults, and discover the strong disturbances. Then, a fine-tuning strategy is applied to enhance the model performance further. The potential usability of the proposed method in monitoring the motors of 3D printers is studied. And the experiment is conducted on a motor bearing dataset provided by Case Western Reserve University. The proposed BCNN achieves 99.82% fault classification accuracy over nine health conditions. Its robustness is verified by comparing the testing accuracy with three other methods on the noisy datasets. And the uncertainty quantification method successfully detects the outlier inputs.

关键词： Fault diagnosis Uncertainty Bayes methods Circuit faults Robustness Predictive models Noise measurement

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Dynamic Balance and Trajectory Tracking control of Quadruped Robots Based on Virtual Model control

Dynamic Balance and Trajectory Tracking Control of Quadruped...

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第三十九届中国控制会议

作者： Guangrong Chen Bowen Hou Sheng Guo Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

As for a motion control framework of robots, virtual model control（VMC） can use virtual components to create virtual forces/torques. Actually, the virtual forces/torques are generated by joint actuators when the virtual components interact between robots and environments. In this paper, a virtual model control is proposed to do the dynamic balance control of quadruped robots in trot gait. In each leg, virtual model control includes swing phase control of robots and stance phase counterparts. In whole body, based on the forces/torques distribution method between two stance legs, virtual model control is mainly about the attitude control containing roll, pitch and yaw. Then, an intuitive approach of velocity control is employed for the locomotion of quadruped robots. Based on the velocity planning and control, a trajectory tracking control approach is investigated by considering four factors: terrain complexity index, curvature radius of given trajectory, distance to terminal, and maximum velocity of quadruped robots. Finally, the effectiveness of proposed controllers is validated by co-simulations.

关键词： Virtual model control Dynamic balance control Trajectory tracking control Quadruped robots

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

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IEEE International Conference on Networking, Sensing and control

作者： Xiaoyan Gong Ying Tang Xiwei Liu Sifeng Jing Wei Cui Joleen Liang Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA Squirrel AI Learning Shanghai China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Nowadays, AI education from Kindergarten to 9 th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts.

关键词： Ethics Systematics Education Government Tools Sensors Artificial intelligence

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CBS-GAN: A Band Selection Based Generative Adversarial Net for Hyperspectral Sample Generation

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IOP Conference Series: Earth and Environmental Science 2021年第1期734卷

作者： Yulin Qiao Mostofa Zaman Mohammad Yi Li Xiaobo Liu Zhihua Cai School of Automation China University of Geosciences (Wuhan) 430074 Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems 430074 Wuhan China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan 430078 China School of Computer Science China University of Geosciences (Wuhan) 430078 Wuhan China

Sample generation is an effective method to improve the performance of hyperspectral image classification by generating virtual samples for training sample expansion in the training process of classification. However, there are some defects existing in the previous sample generation methods including the lack of spatial information, the redundant generation and the damage of the original spectral components. In this paper, we propose conditional band selection generative adversarial net, named CBS-GAN, to handle this problem. Firstly, the band selection net of CBS-GAN is utilized to avoid redundant bands and keep original spectral information, then the generation net of CBS-GAN generates the spatial-spectral data blocks by selected bands for sample generation. The experiments of classification are also used to demonstrate the availability of virtual samples generated by our method.

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A Parallel Education Based intelligent Tutoring systems Framework

A Parallel Education Based Intelligent Tutoring Systems Fram...

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IEEE International Conference on Networking, Sensing and control

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong Wei Cui Joleen Liang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA Squirrel AI Learning Shanghai China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

although online education has improved efficiency of learners' access to high-quality educational resources, real-time interaction between instructors and learners have not yet been achieved in online personalized learning. intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research & development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed.

关键词： Human computer interaction Conferences Education Market research Sensors Artificial intelligence

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The influence of color temperature mismatch in star simulator on positioning accuracy and magnitude measurement by star sensor

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Journal of Physics: Conference Series 2021年第1期2132卷

作者： Zhiyong Wang Shishen Liu Hao Sun Jingjing Zhang Ruyou Li Chen Liu School of Automation China University of Geosciences Wuhan Hubei 430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China

During the calibrating of star sensor, the calibration accuracy is greatly affected by the mismatch between the color temperature of the light and the to-be-measured star, which further affects the attitude measurement accuracy. This paper studied the near-infrared spectra of stars with different color temperatures, and analyzed the errors on star positioning and magnitude measurement of star sensor due to the color temperature mismatch. The results showed that in the central field of view, the spot centroid deviation caused by spectral mismatch is smaller than that in the edge field of *** the defocus of the imaging surface also affects the spot centroid deviation. Besides, when calibrating with 6000K color temperature light, the maximum measurement error can reach -1.9126 magnitude.

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Image Fusion based on Variational Method for Maintenance of Gradient and Intensity

Image Fusion based on Variational Method for Maintenance of ...

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Chinese control Conference (CCC)

作者： Jinglan Bai Liping Yan Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P. R. China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Infrared and visible image fusion technology helps to improve the spatial resolution. It mainly preserves the features and details of the source images and generates a fusion image with visual enhancement. In this paper, based on the gradient features and intensity information of the source images, an optimization model for image fusion is built. Firstly, the pre-fused gradient of the source images is obtained by combining the structure tensor and the proposed local gradient similarity, where local gradient similarity is used to make the fused gradient direction more accurately. Secondly, the source images are reconstructed into salient and non-salient images according to the comparison of the pixel intensity. A weight map before the non-salient image in the optimization model makes the effective details preserved, so that the pre-fused images consist of the salient image and the non-salient image with a weight map. Finally, an optimization model is constructed to constrain the gradient and intensity of the final fused image close to the pre-fused gradient and the pre-fused images. The final fused image is obtained from solving the optimization model by use of the variational method. The experimental results are evaluated from subjective and objective assessments, which show the effectiveness of the proposed algorithm.

关键词： Optimization Tensile stress Image fusion Mathematical model Matrix decomposition Eigenvalues and eigenfunctions Visualization

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Load curve modeling based on behavior analysis of distributed power customers 4

Load curve modeling based on behavior analysis of distribute...

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2019 4th Asia Conference on Power and Electrical Engineering, ACPEE 2019

作者： Han, Xinyang Zhang, Yu Jiao, Yuqiao Zhang, Yue Qu, Yanjing Ding, Min State Grid Energy Research Institute Beijing102200 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China State Grid Shanghai Municipal Electric Power Company Shanghai200122 China

As renewable power generation directly affects the customers' traditional electricity behavior and then offsets the power load, this paper proposes a load curve modeling method for renewable power customers based on the behavior analysis. Firstly, customers' active behavior is represented by the quantity of active customer households. Based on the analysis of customer behaviors, a modeling method for the quantity of active customer households is proposed based on Markov Chain Monte Carlo method. Then, with the inputs as the quantity of active customer households and time of photovoltaic power generation, an inference model based on fuzzy logic is proposed to get the quantity of customer household starting electrical appliances. By combing the average usage time of electrical appliances, load characteristics are analyzed based on usage state of electrical appliance of distributed power customers. Finally, the simulation results verify the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

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Large-scale 3D Semantic Mapping Using Stereo Vision

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International Journal of automation and computing 2018年第2期15卷 194-206页

作者： Yi Yang Fan Qiu Hao Li Lu Zhang Mei-Ling Wang Meng-Yin Fu School of Automation and National Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China Nanjing University of Science and Technology Nanjing 210094 China

In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping （SLAM） systems. This paper presents an approach to generate an outdoor large-scale 3D dense semantic map based on binocular stereo vision. The inputs to system are stereo color images from a moving vehicle. First, dense 3D space around the vehicle is constructed, and tile motion of camera is estimated by visual odometry. Meanwhile, semantic segmentation is performed through the deep learning technology online, and the semantic labels are also used to verify tim feature matching in visual odometry. These three processes calculate the motion, depth and semantic label of every pixel in the input views. Then, a voxel conditional random field （CRF） inference is introduced to fuse semantic labels to voxel. After that, we present a method to remove the moving objects by incorporating the semantic labels, which improves the motion segmentation accuracy. The last is to generate tile dense 3D semantic map of an urban environment from arbitrary long image sequence. We evaluate our approach on KITTI vision benchmark, and the results show that the proposed method is effective.

关键词： Semantic map stereo vision motion segmentation visual odometry simultaneous localization and mapping （SLAM）.

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