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检索条件"机构=Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems"

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Convolutional Neural Networks for Facial Expression Recognition with Few Training Samples

Convolutional Neural Networks for Facial Expression Recognit...

引用

Chinese control Conference

作者： Zhongzhao Xie Yongbo Li Xinmei Wang Wendi Cai Jing Rao Zhenzhu Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

Facial expression recognition (FER) plays an important role in human-machine interaction. An assistant robot having a close interaction with human being should be able to recognize human facial expression. FER is a non-trivial problem because each individual has his own way to reveal his emotion and the facial expressions of two different persons may not be totally identical. Hence, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps. The experiments employed to evaluate our technique were carried out using two largely used public databases(CK+, JAFFE). A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods-97.85% of accuracy in the CK+ database-it is fast to train, and it allows for real time facial expression recognition with standard computers.

关键词： Training Face recognition Convolutional neural networks Neurons Convolution Feature extraction

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Synchronous Detector for GMI Magnetic Sensor Based on Lock-in Amplifier

Synchronous Detector for GMI Magnetic Sensor Based on Lock-i...

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Chinese control Conference (CCC)

作者： Jinchao Wang Fang Jin Lei Zhu Zhi Zhao Hengchang Rao Junlei Song Kaifeng Dong M Wenqin Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

A new concept of a synchronous detector for Giant Magneto-Impedance (GMI) sensors is presented. This concept combines a lock-in amplifier, with outstanding capabilities, high speed and a feedback approach that ensures the amplitude detection with easily adjustable gain. The synchronous detector is capable of measuring high-frequency and very low amplitude signals without the use of diode-based active rectifiers or analog switches. In comparison with most of the commercially available diode-based peak detectors, the linearity of the synchronous detector is generally better, especially for low level amplitudes. The synchronous detector has been used for the amplitude measurement of single frequency sine signal and for the demodulation of amplitude-modulated signal. It has also been successfully integrated in a GMI sensor prototype. Magnetic field measurements in open- and closed-loop of this sensor have been conducted. The measured sensitivity was about 1.02 V/Oe in open-loop while it was 0.12V/Oe in close-loop. The above research provides technical accumulation for the design of sensor nodes based on GMI sensor wireless sensor networks.

关键词： Detectors Magnetic sensors Sensitivity Magnetic cores Voltage measurement Linearity

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Multi-Convolution Neural Networks-Based Deep Learning Model for Emotion Understanding

Multi-Convolution Neural Networks-Based Deep Learning Model ...

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第37届中国控制会议

作者： Luefeng Chen Min Wu Wanjuan Su Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Tokyo Institute of Technology

Multi-convolution neural networks-based deep learning model in combination with multimodal data for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using multi-convolution neural networks, where multilayer convolutions are connected in series and multiple networks are executed in parallel. Moreover,when optimizing the weights of deep neural network by traditional method, it is easy to fall into poor local optimal. To address this problem, a hybrid genetic algorithm with stochastic gradient descent is developed, which has the capacity of inherent implicit parallelism and better global optimization of genetic algorithm so that it can adaptively find the better weights of the *** in order to speed up the convergence of the proposal, the weights optimized by stochastic gradient descent will be taken as a chromosome of genetic algorithms initial population, and it also can be used as a priori knowledge. To verify the effectiveness of the proposal, experiments on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-the-art methods. Meanwhile, the preliminary application experiments are also carried out and the results indicate that the proposal can be extended to human-robot interaction.

关键词： Deep learning emotion understanding convolution neural network facial expression body gesture

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Bat Algorithm Optimizer for Drilling Trajectory Designing under Wellbore Stability Constraints

Bat Algorithm Optimizer for Drilling Trajectory Designing un...

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第37届中国控制会议

作者： Jiafeng Xu Xin Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

In the process of deep geological resources exploration, there are often some complex stratigraphic situations. A threedimension drilling trajectory is designed by using the geological environment data. The trajectory should be sure that the target position is reachable. And it can avoid the area prone to be in an accident. In order to solve this problem, this paper discusses a three-dimensional wellbore trajectory design optimization model suitable for shale gas horizontal mining. Considering the wellbore stability and dogleg severity as constraints, the minimum drilling length is taken as the objective function. Depending on the Mohr-Coulomb criterion, the range of azimuth angle and inclination angle of safety trajectory can be determined. It can be seen as a constraint on the wellbore derrick segment. As for the optimization algorithm, bat algorithm（BA） optimizer combined with a penalty function method is applied to this optimization model. Finally, the case study shows that the BA performs well in the wellbore optimization design. It obtains the trade-off result between the safe and efficient drilling demands. And, it provides a basic model idea for the real-time trajectory optimization while drilling.

关键词： Drilling Trajectory Designing Wellbore Stability Bat Algorithm

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A Detection System of Tool Parameter Using Machine Vision

A Detection System of Tool Parameter Using Machine Vision

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第37届中国控制会议

作者： Hantao Wang Hong Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

This paper suggests a novel technique for the tool parameter measurement based on machine vision. Tool images are captured by using a machine vision system and the outer contour image of the cutter is obtained by using the machine vision technology. The HALCON image processing library is used as the development platform to build the tool parameters test system. Several algorithms including image segmentation, edge extraction and fitting ellipse determination is used for image *** tool parameters such as external diameter and contour angle of tool edge can be obtained after rebuilding the contour of tool *** proposed scheme is shown to be reliable and effective for the automated tool parameter measurement.

关键词： Machine vision image processing tool parameter

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Optimal Rescue Path for Maritime Air Crash Based on Probability Density Distribution and Bayesian formula

Optimal Rescue Path for Maritime Air Crash Based on Probabil...

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第37届中国控制会议

作者： Jing Wang Ping Yu Zhen-Tao Liu State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Optimal rescue path for maritime air crash based on probability density distribution and Bayesian formula is proposed,where probable crash area is determined through surface search at a high altitude,and then the minimum wreckage floating zone is obtained by probability density ***,we divide it into regular hexagons based on Honeycomb Model to identify the point search area that is endowed with priority by Bayesian ***,the optimal path is worked out by transferring optimization problem to be a Traveling Salesman Problem(TSP).In simulations,some spots are randomly chosen on a Google map in which we can find debris in ocean through surface search at high *** a point search route is obtained by using Annealing algorithm,which strips out 4%of time compared with current *** intelligent search and rescue framework based on reinforcement learning is *** future work,search and rescue work will be free from manpower and bad weather constraints.

关键词： Point search Hexagonal regions Priority Bayesian formula Traveling Salesman Problem Reinforcement Learning

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Research on a Thermostatic Heating control System for Optically Pumped Cesium Magnetometer

Research on a Thermostatic Heating Control System for Optica...

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第37届中国控制会议

作者： Shuting Hu Jian Ge Haobin Dong Liyue Xue Zhiwen Yuan Jun Zhu HaiYang Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System

Since the operating temperature of absorption chamber affects the sensitivity of the optically pumped cesium magnetometer（OPCM） directly, it is necessary to control the temperature precisely. In this paper, by using the positive temperature coefficient（PTC） and the integral separation proportional-integral-derivative（PID） algorithm, a high-precision thermostatic heating system is designed for OPCM. Firstly, the PTC heating device and the TSic506 temperature sensor are used to form a closed loop control system. Then, the three parameters of P, I and D are adjusted for different temperature, and the temperature control system is realized by STM32 microcontroller. Finally, the integral separation PID algorithm is used to eliminate overshoot. Experiments show that the effective temperature control ranges are 45?C5?C, the accuracy is less than ±0.2?C, and the system stability time is 300 s. It is obviously that the designed system has reference value and guidance significance for OPCM.

关键词： Thermostatic heating control system Integral separation PID algorithm Optically pumped cesium magnetometer

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Design and Application of a Web-based Comprehensive Evaluation and Decision Support System for Tobacco Cultivation Process

Design and Application of a Web-based Comprehensive Evaluati...

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第37届中国控制会议

作者： Gong Chen Yonghua Xiong Wei Chen Hao Zhou School of Automation China University of Geoscience Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Guizhou Tobacco Science Institute

The evaluation and decision support system are widely used in agriculture with the development of information technology. In this paper, a Web-based comprehensive evaluation and decision support system（WCEDSS） is designed for flue-cured tobacco cultivation process. Using the artificial neural networks and some image processing methods, WCEDSS can predict various indicators of tobacco such as yield and flue-cured score, or give decisions at different stages of tobacco cultivation. As the system is developed by the Browser/Server（B/S） architecture, it is possible to make full use of the Internet resources and to facilitate users at any place with access to the Internet. Test results indicates that WCEDSS can achieve good performance. With other auxiliary functions, the system shows potential and extensive practicality in agricultural information management and guidance.

关键词： tobacco Browser/Server architecture evaluate decision support

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Hierarchical Prediction Model Based on BP Neural Network for Predicting CO/CO_2 in Iron Ore Sintering Process

Hierarchical Prediction Model Based on BP Neural Network for...

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Chinese control Conference

作者： Ben Xu Xin Chen Yang Zhou School of Automation China University of Geosciences Wuhan China Intelligent Automation for Complex Systems Hubei key Laboratory of Advanced Control Wuhan China

The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production process. To find ways of reducing the energy consumption, it is necessary to predict the carbon efficiency. The value of CO/CO_2 in the carbon emission can reflect the utilization of carbon combustion in sintering process. In this study, the CO/CO_2 is taken to be a measure of carbon efficiency and a hierarchical model is built to predict it. Firstly, the physical and chemical reactions and the carbon flow mechanism in the sintering process are analyzed, and the process parameters that affect the CO/CO_2 are determined. Then, the gray relational analysis method is used to analyze the influence factors to determine the relationship between the parameters, and a hierarchical predictive model for CO/CO_2 is established based on the relationship between the parameters. The hierarchical predictive model is divided into two parts: the predictive models for the thermal state parameters and the predictive model for CO/CO_2. The inputs of the predictive models for the thermal state parameters are the raw material parameters and the operating parameters, and the inputs of the predictive model for CO/CO_2 are the predicted values of the predictive models for the thermal state parameters. Finally, the simulation results verify the effectiveness of the proposed modeling method. This method can provide a theoretical basis for the optimization and control of carbon efficiency in the sintering process.

关键词： Predictive models Combustion Correlation Mathematical model Carbon dioxide Chemicals Raw materials

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Modeling and Maximizing Angle Coverage in Visual Sensor Networks

Modeling and Maximizing Angle Coverage in Visual Sensor Netw...

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第37届中国控制会议

作者： Yonghua Xiong Manjie Lu Wei Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Guizhou Tobacco Science Institute

Maintaining the quality of network coverage is a major concern in visual sensor networks. In this paper, we study the angle coverage problem in visual sensor networks, considering the target is very large, and each camera node can only monitor a portion of its perimeter. The goal of the proposed work is to schedule camera nodes to achieve maximum angle coverage in different period. Firstly, we establish a novel coverage model and formally prove that the problem is NP-hard in ***, we present a scheduling scheme based on greedy algorithm, to schedule camera nodes into disjoint cover sets working in ***, we conclude that the approximate ratio of the proposed algorithm is k, theoretically, and the time complexity of the algorithm is O（n）. Finally, extensive simulations have been conducted to evaluate the performance of the proposed algorithm.

关键词： Visual sensor network angle coverage camera node scheduling greedy algorithm

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