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Clutch control has a great effect on the starting quality and shifting quality of heavy-duty vehicles with automated mechanical transmission (AMT). The motion characteristics of a clutch actuator for heavy-duty vehicles with AMT are studied in this paper to investigate the clutch control strategy further. The modeling principle of the automatic clutch system is analyzed, and a simulation analysis is given to prove its validity and rationality. Normalized velocity and velocity modulation percentage are proposed as evaluation parameters for the clutch actuator driven by pulse width modulation (PWM) signals. Based on an AMT test bench, the actuator motion characteristics are analyzed. Experimental results show that the range of normalized velocity and velocity modulation percentage are obtained for the clutch engagement and disengagement processes. By analyzing the experimental data, the engaging velocity and disengaging velocity of the actuator are estimated using the solenoid valves in combination. The research results provide a fundamental basis for precise controlling of the clutch and improving the smoothness of heave-duty vehicles.
Yunxia Li; Zengcai Wang. Motion Characteristics of a Clutch Actuator for Heavy-Duty Vehicles with Automated Mechanical Transmission. Actuators 2021, 10, 179 .
AMA StyleYunxia Li, Zengcai Wang. Motion Characteristics of a Clutch Actuator for Heavy-Duty Vehicles with Automated Mechanical Transmission. Actuators. 2021; 10 (8):179.
Chicago/Turabian StyleYunxia Li; Zengcai Wang. 2021. "Motion Characteristics of a Clutch Actuator for Heavy-Duty Vehicles with Automated Mechanical Transmission." Actuators 10, no. 8: 179.
In this paper, a novel adaptive cruise control (ACC) algorithm based on model predictive control (MPC) and active disturbance rejection control (ADRC) is proposed. This paper uses an MPC algorithm for the upper controller of the ACC system. Through comprehensive considerations, the upper controller will output desired acceleration to the lower controller. In addition, to increase the accuracy of the predictive model in the MPC controller and to address fluctuations in the vehicle’s acceleration, an MPC aided by predictive estimation of acceleration is proposed. Due to the uncertainties of vehicle parameters and the road environment, it is difficult to establish an accurate vehicle dynamic model for the lower-level controller to control the throttle and brake actuators. Therefore, feed-forward control based on a vehicle dynamic model (VDM) and compensatory control based on ADRC is used to enhance the control precision and to suppress the influence of internal or external disturbance. Finally, the proposed optimal design of the ACC system was validated in road tests. The results show that ACC with APE can accurately control the tracking of the host vehicle with less acceleration fluctuation than that of the traditional ACC controller. Moreover, when the mass of the vehicle and the slope of the road is changed, the ACC–APE–ADRC controller is still able to control the vehicle to quickly and accurately track the desired acceleration.
Zengfu Yang; Zengcai Wang; Ming Yan. An Optimization Design of Adaptive Cruise Control System Based on MPC and ADRC. Actuators 2021, 10, 110 .
AMA StyleZengfu Yang, Zengcai Wang, Ming Yan. An Optimization Design of Adaptive Cruise Control System Based on MPC and ADRC. Actuators. 2021; 10 (6):110.
Chicago/Turabian StyleZengfu Yang; Zengcai Wang; Ming Yan. 2021. "An Optimization Design of Adaptive Cruise Control System Based on MPC and ADRC." Actuators 10, no. 6: 110.
The key technology to realize intelligent unmanned coal mining is the strapdown inertial navigation system (SINS); however, the gradual increase of cumulative error during the working process needs to be solved. On the basis of an SINS/odometer (OD)-integrated navigation system, this paper adds magnetometer (MAG)-aided positioning and proposes an SINS/OD/MAG-integrated shearer navigation system. The velocity observation equation is obtained from the speed constraints during shearer movement, and the yaw angle observation equation is obtained from the magnetometer output. The position information of the SINS output is calibrated using these two observations. In order to improve the fault tolerance of the integrated navigation system, an adaptive federated Kalman filter is established to complete the data fusion of the SINS. Experimental results show that the positioning accuracy of the SINS/OD/MAG-integrated navigation system is 75.64% and 74.01% higher in the east and north directions, respectively, than the SINS/OD-integrated navigation system.
Ming Yan; Zengcai Wang. Precise Shearer Positioning Technology Using Shearer Motion Constraint and Magnetometer Aided SINS. Mathematical Problems in Engineering 2021, 2021, 1 -12.
AMA StyleMing Yan, Zengcai Wang. Precise Shearer Positioning Technology Using Shearer Motion Constraint and Magnetometer Aided SINS. Mathematical Problems in Engineering. 2021; 2021 ():1-12.
Chicago/Turabian StyleMing Yan; Zengcai Wang. 2021. "Precise Shearer Positioning Technology Using Shearer Motion Constraint and Magnetometer Aided SINS." Mathematical Problems in Engineering 2021, no. : 1-12.
The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigue features and the time information of fatigue features, and this reduces the recognition accuracy. To solve these problems, we propose a driver sleepiness estimation model based on factorized bilinear feature fusion and a long- short-term recurrent convolutional network to detect driver drowsiness efficiently and accurately. The proposed framework includes three models: fatigue feature extraction, fatigue feature fusion, and driver drowsiness detection. First, we used a convolutional neural network (CNN) to effectively extract the deep representation of eye and mouth-related fatigue features from the face area detected in each video frame. Then, based on the factorized bilinear feature fusion model, we performed a nonlinear fusion of the deep feature representations of the eyes and mouth. Finally, we input a series of fused frame-level features into a long-short-term memory (LSTM) unit to obtain the time information of the features and used the softmax classifier to detect sleepiness. The proposed framework was evaluated with the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results showed that this method had better stability and robustness compared with other methods.
Shuang Chen; Zengcai Wang; Wenxin Chen. Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network. Information 2020, 12, 3 .
AMA StyleShuang Chen, Zengcai Wang, Wenxin Chen. Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network. Information. 2020; 12 (1):3.
Chicago/Turabian StyleShuang Chen; Zengcai Wang; Wenxin Chen. 2020. "Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network." Information 12, no. 1: 3.
The technology of coal-rock interface recognition is the core of realizing the automatic heightening technology of shearer’s rocker. Only by accurately and quickly identifying the interface of coal and rock can we realize the fully automatic control of shearer. As the only one used in the actual detection of coal mining machine drum cutting coal seam after the thickness of the remaining coal seam detection method, natural γ-ray has a very practical advantage. Based on the relationship between the attenuation of the natural γ-ray passing through the coal seam and the thickness of the coal seam, the mathematical model of the attenuation of the natural γ-ray penetrating coal seam is established. By comparing the attenuation intensity of γ-ray with or without brackets, it is verified that the hydraulic girders will absorb some natural γ-rays. Finally, this paper uses the ground simulation experiment and the field experiment to verify the correctness of the mathematical model and finally develop the natural γ-ray seam thickness sensor. The sensor has the function of indicating the thickness of the coal seam, measuring the natural γ-ray intensity, and storing and processing the data.
Zengfu Yang; Zengcai Wang; Ming Yan. Performance Analysis of Natural γ-Ray Coal Seam Thickness Sensor and Its Application in Automatic Adjustment of Shearer’s Arms. Journal of Electrical and Computer Engineering 2020, 2020, 1 -10.
AMA StyleZengfu Yang, Zengcai Wang, Ming Yan. Performance Analysis of Natural γ-Ray Coal Seam Thickness Sensor and Its Application in Automatic Adjustment of Shearer’s Arms. Journal of Electrical and Computer Engineering. 2020; 2020 ():1-10.
Chicago/Turabian StyleZengfu Yang; Zengcai Wang; Ming Yan. 2020. "Performance Analysis of Natural γ-Ray Coal Seam Thickness Sensor and Its Application in Automatic Adjustment of Shearer’s Arms." Journal of Electrical and Computer Engineering 2020, no. : 1-10.
Driver drowsiness is a major cause of road accidents. In this study, a novel approach that detects human drowsiness is proposed and investigated. First, driver face and facial landmarks are detected to extract facial region from each frame in a video. Then, a residual-based deep 3D convolution neural network (CNN) that learned from an irrelevant dataset is constructed to classify driver facial image sequences with a certain number of frames for obtaining its drowsiness output probability value. After that, a certain number of output probability values is concatenated to obtain the state probability vector of a video. Finally, a recurrent neural network is adopted to classify constructed probability vector and obtain the recognition result of driver drowsiness. The proposed method is tested and investigated using a public drowsy driver dataset. Experimental results demonstrate that similar to 2D CNN, 3D CNN can learn spatiotemporal features from irrelevant dataset to improve its performance obviously in driver drowsiness classification. Furthermore, the proposed method performs stably and robustly, and it can achieve an average accuracy of 88.6%.
Lei Zhao; Zengcai Wang; Guoxin Zhang; Huanbing Gao. Driver drowsiness recognition via transferred deep 3D convolutional network and state probability vector. Multimedia Tools and Applications 2020, 79, 26683 -26701.
AMA StyleLei Zhao, Zengcai Wang, Guoxin Zhang, Huanbing Gao. Driver drowsiness recognition via transferred deep 3D convolutional network and state probability vector. Multimedia Tools and Applications. 2020; 79 (35-36):26683-26701.
Chicago/Turabian StyleLei Zhao; Zengcai Wang; Guoxin Zhang; Huanbing Gao. 2020. "Driver drowsiness recognition via transferred deep 3D convolutional network and state probability vector." Multimedia Tools and Applications 79, no. 35-36: 26683-26701.
When a low-cost micro-electro-mechanical system inertial measurement unit is used for a vehicle navigation system, errors will quickly accumulate because of the large micro-electro-mechanical system sensor measurement noise. To solve this problem, an automotive sensor–aided low-cost inertial navigation system is proposed in this article. The error-state model of the strapdown inertial navigation system has been derived, and the measurements from the wheel speed sensor and steer angle sensor are used as the new observation vector. Then, the micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated system is established based on adaptive Kalman filtering. The experimental results show that the positioning error of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor is 94.67%, 98.88%, and 97.88% less than the values using pure strapdown inertial navigation system in the east, north, and down directions, respectively. The yaw angle error is reduced to less than 1°, and the vehicle velocity estimation of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated navigation system is closer to the reference value. These results show the precision of the integrated navigation solution.
Susu Fang; Zengcai Wang; Lei Zhao. Research on the automotive sensor–aided low-cost inertial navigation system for land vehicles. Advances in Mechanical Engineering 2019, 11, 1 .
AMA StyleSusu Fang, Zengcai Wang, Lei Zhao. Research on the automotive sensor–aided low-cost inertial navigation system for land vehicles. Advances in Mechanical Engineering. 2019; 11 (1):1.
Chicago/Turabian StyleSusu Fang; Zengcai Wang; Lei Zhao. 2019. "Research on the automotive sensor–aided low-cost inertial navigation system for land vehicles." Advances in Mechanical Engineering 11, no. 1: 1.
This paper presents a lane departure detection approach that utilizes a stacked sparse autoencoder (SSAE) for vehicles driving on motorways or similar roads. Image preprocessing techniques are successfully executed in the initialization procedure to obtain robust region-of-interest extraction parts. Lane detection operations based on Hough transform with a polar angle constraint and a matching algorithm are then implemented for two-lane boundary extraction. The slopes and intercepts of lines are obtained by converting the two lanes from polar to Cartesian space. Lateral offsets are also computed as an important step of feature extraction in the image pixel coordinate without any intrinsic or extrinsic camera parameter. Subsequently, a softmax classifier is designed with the proposed SSAE. The slopes and intercepts of lines and lateral offsets are the feature inputs. A greedy, layer-wise method is employed based on the inputs to pretrain the weights of the entire deep network. Fine-tuning is conducted to determine the global optimal parameters by simultaneously altering all layer parameters. The outputs are three detection labels. Experimental results indicate that the proposed approach can detect lane departure robustly with a high detection rate. The efficiency of the proposed method is demonstrated on several real images.
Zengcai Wang; Xiaojin Wang; Lei Zhao; Guoxin Zhang. Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder. Mathematical Problems in Engineering 2018, 2018, 1 -15.
AMA StyleZengcai Wang, Xiaojin Wang, Lei Zhao, Guoxin Zhang. Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder. Mathematical Problems in Engineering. 2018; 2018 ():1-15.
Chicago/Turabian StyleZengcai Wang; Xiaojin Wang; Lei Zhao; Guoxin Zhang. 2018. "Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder." Mathematical Problems in Engineering 2018, no. : 1-15.
The traditional adaptive cruise control system generally requires 25–40 km/h velocity to function. Moreover, the adaptive cruise control system cannot decelerate to the stop state, cannot adjust for stationary objects, and has limited scope of application. This study achieved the traffic jam tracking function of vehicles through the simultaneous use of millimeter wave and laser sensors and the analysis of the driving behavior of skilled drivers. The spacing and acceleration control of a vehicle is optimized based on the premise of ensuring safety and comfort by providing smooth, comfortable, safe, and radical control modes for driver selection, thereby increasing the probability that adaptive cruise control adopted by drivers. In addition, the collision avoidance function is added for safety reasons. Finally, actual vehicle experiments show that the distance and acceleration errors are in the expected range of errors of drivers. Moreover, the validity and practicability of the proposed adaptive cruise control algorithm are verified.
Guoxin Zhang; Zengcai Wang; Baiwang Fan; Lei Zhao; Yazhou Qi. Adaptive cruise control system with traffic jam tracking function based on multi-sensors and the driving behavior of skilled drivers. Advances in Mechanical Engineering 2018, 10, 1 .
AMA StyleGuoxin Zhang, Zengcai Wang, Baiwang Fan, Lei Zhao, Yazhou Qi. Adaptive cruise control system with traffic jam tracking function based on multi-sensors and the driving behavior of skilled drivers. Advances in Mechanical Engineering. 2018; 10 (9):1.
Chicago/Turabian StyleGuoxin Zhang; Zengcai Wang; Baiwang Fan; Lei Zhao; Yazhou Qi. 2018. "Adaptive cruise control system with traffic jam tracking function based on multi-sensors and the driving behavior of skilled drivers." Advances in Mechanical Engineering 10, no. 9: 1.
Manual calibration and testing on real vehicles are common methods of generating shifting schedules for newly developed vehicles. However, these methods are time-consuming. Shifting gear timing is an important operating parameter that affects shifting time, power loss, fuel efficiency, and driver comfort. The stacked autoencoder (SAE) algorithm, a type of artificial neural network, is used in this study to predict shifting gear timing on the basis of throttle percentage, vehicle velocity, and acceleration. Experiments are conducted to obtain training and testing data. Different neural networks are trained with experimental data on a real vehicle under different road conditions collected using the CANcaseXL device and control AMESim simulation model, which was constructed based on real vehicle parameters. The input number of SAE is determined through a comparison between two and three parameters. The output type of SAE is determined through a comparative experiment on pattern recognition and multifitting. Meanwhile, the network structure of SAE is determined through a comparative experiment on simple and deep-learning neural networks. Experimental results demonstrate that using the SAE intelligent shift control strategy to determine shift timing not only is feasible and accurate but also saves time and development costs.
Zengcai Wang; Yazhou Qi; Guoxin Zhang; Lei Zhao. Smart Shift Decision Method Based on Stacked Autoencoders. Journal of Control Science and Engineering 2018, 2018, 1 -13.
AMA StyleZengcai Wang, Yazhou Qi, Guoxin Zhang, Lei Zhao. Smart Shift Decision Method Based on Stacked Autoencoders. Journal of Control Science and Engineering. 2018; 2018 ():1-13.
Chicago/Turabian StyleZengcai Wang; Yazhou Qi; Guoxin Zhang; Lei Zhao. 2018. "Smart Shift Decision Method Based on Stacked Autoencoders." Journal of Control Science and Engineering 2018, no. : 1-13.
Driver drowsiness is a frequent cause of traffic accidents. Research on driver drowsiness detection methods is important to improve road traffic safety. Previous driving fatigue detection methods frequently extracted single features such as eye or mouth changes and trained shallow classifiers, which limit the generalisation capability of these methods. This study proposes a framework for recognising driver drowsiness expression by using facial dynamic fusion information and a deep belief network (DBN) to address the aforementioned problem. First, the landmarks and textures of the facial region are extracted from videos captured using a high-definition camera. Then, a DBN is built to classify facial drowsiness expressions. Finally, the authors’ method is tested on a driver drowsiness dataset, which includes different genders, ages, head poses and illuminations. Certain experiments are also carried out to investigate the effects of different facial subregions and temporal resolutions on the accuracy of driver fatigue recognition. Results demonstrate the validity of the proposed method, which has an average accuracy of 96.7%.
Lei Zhao; Zengcai Wang; Xiaojin Wang; Qing Liu. Driver drowsiness detection using facial dynamic fusion information and a DBN. IET Intelligent Transport Systems 2017, 12, 127 -133.
AMA StyleLei Zhao, Zengcai Wang, Xiaojin Wang, Qing Liu. Driver drowsiness detection using facial dynamic fusion information and a DBN. IET Intelligent Transport Systems. 2017; 12 (2):127-133.
Chicago/Turabian StyleLei Zhao; Zengcai Wang; Xiaojin Wang; Qing Liu. 2017. "Driver drowsiness detection using facial dynamic fusion information and a DBN." IET Intelligent Transport Systems 12, no. 2: 127-133.
Eye state recognition is widely used in many fields, such as driver drowsiness recognition, facial expression classification, and human–computer interface technology. This study proposes a novel framework based on the deep learning method to classify eye states in still facial images. The proposed method combines a deep neural network and a deep convolutional neural network to construct a deep integrated neural network for characterizing useful information in the eye region by use of the joint optimization method. A transfer learning strategy is applied to extract effective abstract eye features and improve the classification capability of the proposed model on small sample datasets. Experimental results on the Closed Eyes in the Wild (CEW) and Zhejiang University Eyeblink datasets show that the proposed approach outperforms other state-of-the-art methods. In addition, the effects of transfer learning methods with different pretraining datasets on classification accuracy are investigated with the CEW dataset. A driver drowsiness recognition dataset is constructed and used in an experiment to evaluate the effectiveness of the proposed method in driving environments. Experimental results demonstrate that the proposed method performs more stably and robustly than do other methods.
Lei Zhao; Zengcai Wang; Guoxin Zhang; Yazhou Qi; Xiaojin Wang. Eye state recognition based on deep integrated neural network and transfer learning. Multimedia Tools and Applications 2017, 77, 19415 -19438.
AMA StyleLei Zhao, Zengcai Wang, Guoxin Zhang, Yazhou Qi, Xiaojin Wang. Eye state recognition based on deep integrated neural network and transfer learning. Multimedia Tools and Applications. 2017; 77 (15):19415-19438.
Chicago/Turabian StyleLei Zhao; Zengcai Wang; Guoxin Zhang; Yazhou Qi; Xiaojin Wang. 2017. "Eye state recognition based on deep integrated neural network and transfer learning." Multimedia Tools and Applications 77, no. 15: 19415-19438.
This study employs the mechanical vibration and acoustic waves of a hydraulic support tail beam for an accurate and fast coal-rock recognition. The study proposes a diagnosis method based on bimodal deep learning and Hilbert-Huang transform. The bimodal deep neural networks (DNN) adopt bimodal learning and transfer learning. The bimodal learning method attempts to learn joint representation by considering acceleration and sound pressure modalities, which both contribute to coal-rock recognition. The transfer learning method solves the problem regarding DNN, in which a large number of labeled training samples are necessary to optimize the parameters while the labeled training sample is limited. A suitable installation location for sensors is determined in recognizing coal-rock. The extraction features of acceleration and sound pressure signals are combined and effective combination features are selected. Bimodal DNN consists of two deep belief networks (DBN), each DBN model is trained with related samples, and the parameters of the pretrained DBNs are transferred to the final recognition model. Then the parameters of the proposed model are continuously optimized by pretraining and fine-tuning. Finally, the comparison of experimental results demonstrates the superiority of the proposed method in terms of recognition accuracy.
Guoxin Zhang; Zengcai Wang; Lei Zhao; Yazhou Qi; Jinshan Wang. Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform. Shock and Vibration 2017, 2017, 1 -13.
AMA StyleGuoxin Zhang, Zengcai Wang, Lei Zhao, Yazhou Qi, Jinshan Wang. Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform. Shock and Vibration. 2017; 2017 ():1-13.
Chicago/Turabian StyleGuoxin Zhang; Zengcai Wang; Lei Zhao; Yazhou Qi; Jinshan Wang. 2017. "Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform." Shock and Vibration 2017, no. : 1-13.
MuDi Xiong; Lei Lu; Weili Zheng; Lin Liu; Xiaojin Wang; Zengcai Wang; Lei Zhao. Research on the detection system for dynamic ship draft on the basis of ultrasonic diffraction effect. Automotive, Mechanical and Electrical Engineering 2017, 419 -425.
AMA StyleMuDi Xiong, Lei Lu, Weili Zheng, Lin Liu, Xiaojin Wang, Zengcai Wang, Lei Zhao. Research on the detection system for dynamic ship draft on the basis of ultrasonic diffraction effect. Automotive, Mechanical and Electrical Engineering. 2017; ():419-425.
Chicago/Turabian StyleMuDi Xiong; Lei Lu; Weili Zheng; Lin Liu; Xiaojin Wang; Zengcai Wang; Lei Zhao. 2017. "Research on the detection system for dynamic ship draft on the basis of ultrasonic diffraction effect." Automotive, Mechanical and Electrical Engineering , no. : 419-425.
This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences. First, based on incremental formulation, discriminative deformable face alignment method is adapted to locate facial points to correct in-plane head rotation and break up facial region from background. Then, spatial-temporal motion local binary pattern (LBP) feature is extracted and integrated with Gabor multiorientation fusion histogram to give descriptors, which reflect static and dynamic texture information of facial expressions. Finally, a one-versus-one strategy based multiclass support vector machine (SVM) classifier is applied to classify facial expressions. Experiments on Cohn-Kanade (CK) + facial expression dataset illustrate that integrated framework outperforms methods using single descriptors. Compared with other state-of-the-art methods on CK+, MMI, and Oulu-CASIA VIS datasets, our proposed framework performs better.
Lei Zhao; Zengcai Wang; Guoxin Zhang. Facial Expression Recognition from Video Sequences Based on Spatial-Temporal Motion Local Binary Pattern and Gabor Multiorientation Fusion Histogram. Mathematical Problems in Engineering 2017, 2017, 1 -12.
AMA StyleLei Zhao, Zengcai Wang, Guoxin Zhang. Facial Expression Recognition from Video Sequences Based on Spatial-Temporal Motion Local Binary Pattern and Gabor Multiorientation Fusion Histogram. Mathematical Problems in Engineering. 2017; 2017 ():1-12.
Chicago/Turabian StyleLei Zhao; Zengcai Wang; Guoxin Zhang. 2017. "Facial Expression Recognition from Video Sequences Based on Spatial-Temporal Motion Local Binary Pattern and Gabor Multiorientation Fusion Histogram." Mathematical Problems in Engineering 2017, no. : 1-12.
Huazhu Wu; Zengcai Wang; Changyou Wang. Study on the recognition method of airport perimeter intrusion incidents based on laser detection technology. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 2017, 25, 2737 -2748.
AMA StyleHuazhu Wu, Zengcai Wang, Changyou Wang. Study on the recognition method of airport perimeter intrusion incidents based on laser detection technology. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES. 2017; 25 ():2737-2748.
Chicago/Turabian StyleHuazhu Wu; Zengcai Wang; Changyou Wang. 2017. "Study on the recognition method of airport perimeter intrusion incidents based on laser detection technology." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 25, no. : 2737-2748.
Zengcai Wang; Guoxin Zhang; Lei Zhao. Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders. Journal of Vibroengineering 2016, 18, 4261 -4275.
AMA StyleZengcai Wang, Guoxin Zhang, Lei Zhao. Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders. Journal of Vibroengineering. 2016; 18 (7):4261-4275.
Chicago/Turabian StyleZengcai Wang; Guoxin Zhang; Lei Zhao. 2016. "Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders." Journal of Vibroengineering 18, no. 7: 4261-4275.
. Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.
Lei Zhao; Zengcai Wang; Xiaojin Wang; Yazhou Qi; Qing Liu; Guoxin Zhang. Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning. Journal of Electronic Imaging 2016, 25, 53024 .
AMA StyleLei Zhao, Zengcai Wang, Xiaojin Wang, Yazhou Qi, Qing Liu, Guoxin Zhang. Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning. Journal of Electronic Imaging. 2016; 25 (5):53024.
Chicago/Turabian StyleLei Zhao; Zengcai Wang; Xiaojin Wang; Yazhou Qi; Qing Liu; Guoxin Zhang. 2016. "Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning." Journal of Electronic Imaging 25, no. 5: 53024.
Yunxia Li; Zengcai Wang; Weili Peng; Zhou Zheng. Experimental Study on Synchronous Shifting for AMT without Synchronizer Based on Three-phase Induction Motor. International Journal of Smart Home 2016, 10, 197 -212.
AMA StyleYunxia Li, Zengcai Wang, Weili Peng, Zhou Zheng. Experimental Study on Synchronous Shifting for AMT without Synchronizer Based on Three-phase Induction Motor. International Journal of Smart Home. 2016; 10 (2):197-212.
Chicago/Turabian StyleYunxia Li; Zengcai Wang; Weili Peng; Zhou Zheng. 2016. "Experimental Study on Synchronous Shifting for AMT without Synchronizer Based on Three-phase Induction Motor." International Journal of Smart Home 10, no. 2: 197-212.
B Wang; Y Wang; Zengcai Wang. Automatic recognition of waste rock in top coal caving based on digital image processing. Mechatronics Engineering and Electrical Engineering 2015, 5 -7.
AMA StyleB Wang, Y Wang, Zengcai Wang. Automatic recognition of waste rock in top coal caving based on digital image processing. Mechatronics Engineering and Electrical Engineering. 2015; ():5-7.
Chicago/Turabian StyleB Wang; Y Wang; Zengcai Wang. 2015. "Automatic recognition of waste rock in top coal caving based on digital image processing." Mechatronics Engineering and Electrical Engineering , no. : 5-7.