http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
NOVEL BENCH-BASED INSPECTION APPROACH FOR AUTOMOBILE ANTI-LOCK BRAKING SYSTEM
Xiangmo Zhao,Ruru Hao,Zhou Zhou,Amira Ashour,Nilanjan Dey 한국자동차공학회 2018 International journal of automotive technology Vol.19 No.5
Bench inspection approach for automobile Anti-lock Braking System (ABS) has gained research interests recently due to its high efficiency, small site occupation and insusceptibility to environment influences. The current work proposed a novel systematic bench inspection approach for ABS. In order to dynamically simulate various road adhesion coefficients, torque controllers are used for loading different torques to the drums. Furthermore, flywheels are adopted to simulate the translational inertia of the vehicle braking on road for compensating the inertial energy of ABS road experiment on the bench. The principal component analysis (PCA) is applied for accurate and efficient data analysis. The automatic evaluation of ABS is achieved by using the processed PCA data as an input to the back-propagation (BP) neural network classifier. The experiments established that the new approach can accurately simulate various road braking conditions. It can be carried out for the inspection of ABS installed in the car.
IEM-based Tone Injection for Peak-to-Average Power Ratio Reduction of Multi-carrier Modulation
( Yang Zhang ),( Xiangmo Zhao ),( Jun Hou ),( Yisheng An ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.9
Tone Injection (TI) scheme significantly reduces the peak-to-average power ratio (PAPR) of Multicarrier Modulation (MCM). However, the computational complexity of the TI scheme rises exponentially with the extra freedom constellation number. Therefore, a novel immune evolutionary mechanism-based TI scheme is proposed in this paper to reduce the computational complexity. By restraining undesirable degeneracy during the processing, this IEM scheme can dramatically increase the population fitness. Monte Carlo results show that proposed IEM-based TI scheme can achieve a significant PAPR and BER improvement with a low complexity.
( Haigen Min ),( Xiangmo Zhao ),( Zhigang Xu ),( Licheng Zhang ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.1
In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.
( Jun Hou ),( Xiangmo Zhao ),( Fei Hui ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.7
High peak-to-average power ratio (PAPR) of transmitted signals is a major drawback in Multicarrier modulation (MCM) systems. Companding transform is a well-known method to reduce the PAPR without restrictions on system parameters such as the number of subcarriers, frame format and constellation type. In this paper, a novel adaptive companding scheme, mainly focuses on compressing the large signals into the desirable distribution, is proposed to reduce the PAPR with low implementation complexity. In addition, formulas to calculate its PAPR and bit error rate (BER) performance are also derived. Simulation results confirm that the proposed scheme can achieve an effective tradeoff between PAPR reduction and BER performance by carefully choosing the companding parameter.
Panpan Yang,Xingwen Chen,Xiangmo Zhao,Maode Yan 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.8
The fixed time event-triggered tracking control for interconnected nonlinear uncertain systems, whose state variables are unmeasurable, is investigated via an observer-based approach. The unmeasurable states are estimated by the designed neural observer under the fixed time stability criterion, and the uncertain interconnections are compensated by a smooth function. Then, a novel tracking error-based event-triggered strategy is employed to reduce the communication frequency of the control signal. By means of radial basis function (RBF) neural networks (NNs) to approximate the unknown nonlinearities, a fixed time event-triggered controller is designed to guarantee the convergence of tracking error to the origin in a fixed time. Finally, the proposed technique is verified by some numerical simulations.
( Kenan Mu ),( Fei Hui ),( Xiangmo Zhao ) 한국정보처리학회 2016 Journal of information processing systems Vol.12 No.2
This paper presents a complete method for vehicle detection and tracking in a fixed setting based on computer vision. Vehicle detection is performed based on Scale Invariant Feature Transform (SIFT) feature matching. With SIFT feature detection and matching, the geometrical relations between the two images is estimated. Then, the previous image is aligned with the current image so that moving vehicles can be detected by analyzing the difference image of the two aligned images. Vehicle tracking is also performed based on SIFT feature matching. For the decreasing of time consumption and maintaining higher tracking accuracy, the detected candidate vehicle in the current image is matched with the vehicle sample in the tracking sample set, which contains all of the detected vehicles in previous images. Most remarkably, the management of vehicle entries and exits is realized based on SIFT feature matching with an efficient update mechanism of the tracking sample set. This entire method is proposed for highway traffic environment where there are no nonautomotive vehicles or pedestrians, as these would interfere with the results.
Mu, Kenan,Hui, Fei,Zhao, Xiangmo Korea Information Processing Society 2016 Journal of information processing systems Vol.12 No.2
This paper presents a complete method for vehicle detection and tracking in a fixed setting based on computer vision. Vehicle detection is performed based on Scale Invariant Feature Transform (SIFT) feature matching. With SIFT feature detection and matching, the geometrical relations between the two images is estimated. Then, the previous image is aligned with the current image so that moving vehicles can be detected by analyzing the difference image of the two aligned images. Vehicle tracking is also performed based on SIFT feature matching. For the decreasing of time consumption and maintaining higher tracking accuracy, the detected candidate vehicle in the current image is matched with the vehicle sample in the tracking sample set, which contains all of the detected vehicles in previous images. Most remarkably, the management of vehicle entries and exits is realized based on SIFT feature matching with an efficient update mechanism of the tracking sample set. This entire method is proposed for highway traffic environment where there are no non-automotive vehicles or pedestrians, as these would interfere with the results.
Detection of HF-ERW Status by Neural Network on Imaging
Hui-Feng Wang,Jing Cao,Xiangmo Zhao,Xiao-Meng Wang,Gui-Ping Wang 한국정밀공학회 2017 International Journal of Precision Engineering and Vol.18 No.7
To achieve online testing of high-frequency electric resistance welding (HF-ERW) tube quality, forecasting models were established for welding defect conditions with collected high-speed images of the joint melting phenomenon, based on a radial basis function neural network (RBFNN). Firstly, the dimensions of the collected image samples were deduced by principal component analysis (PCA). Then, the reduced-dimension image samples were set as inputs of both BPNN (back-propagation neural network) and, for comparison, RBFNN, which were trained so that the model parameters were optimized. Finally, the testing sample set was identified by trained networks. The experimental results show that RBFNN had better generalization ability for HF-ERW images than BPNN, which meant that the recognition rate of low-heat input status reached 100%, while the recognition rate of overheating input status reached 97.67%. They also show that the welding quality detection system based on a neural network is very effective and has a strong guiding significance for welding quality control.