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Huan Liu,Kuangrong Hao,Yongsheng Ding 한국정보과학회 2017 Journal of Computing Science and Engineering Vol.11 No.3
In this paper, the abdominal-deformation measurement scheme is conducted on a shape-flexible mannequin using the DIC technique in a stereo-vision system. Firstly, during the integer-pixel displacement search, a novel fractal dimension based on an adaptive-ellipse subset area is developed to track an integer pixel between the reference and deformed images. Secondly, at the subpixel registration, a new mutual-learning adaptive particle swarm optimization (MLADPSO) algorithm is employed to locate the subpixel precisely. Dynamic adjustments of the particle flight velocities that are according to the deformation extent of each interest point are utilized for enhancing the accuracy of the subpixel registration. A test is performed on the abdominal-deformation measurement of the shape-flexible mannequin. The experiment results indicate that under the guarantee of its measurement accuracy without the cause of any loss, the time-consumption of the proposed scheme is significantly more efficient than that of the conventional method, particularly in the case of a large number of interest points.
Trend-GRU Model based Time Series Data Prediction in Melt Transport Process
Dongqi Zhao,Kuangrong Hao,Lei Chen,Bing Wei,Xue-Song Tang,Xin Cai,Lihong Ren 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
Temperature and pressure are two important performance indicators during the melt transport of polyester fiber production, which can affect the overall properties of the melt. Therefore, the accurate prediction of these two indicators is crucial for the control of melt properties. This paper proposes a data prediction model, Trend-GRU (Gated Recurrent Unit), which can extract the feature of unstable change of the melt data. On the premise that the model is not over-complicated, a new structure is designed to extract the feature of unstable change to improve the prediction accuracy. Two data sets on temperature and pressure collected from the actual production process of a spinning factory are used for comparative experiments. The results show that the accuracy of the data prediction of the proposed model is better than the original one.
Liu, Huan,Hao, Kuangrong,Ding, Yongsheng Korean Institute of Information Scientists and Eng 2017 Journal of Computing Science and Engineering Vol.11 No.3
In this paper, the abdominal-deformation measurement scheme is conducted on a shape-flexible mannequin using the DIC technique in a stereo-vision system. Firstly, during the integer-pixel displacement search, a novel fractal dimension based on an adaptive-ellipse subset area is developed to track an integer pixel between the reference and deformed images. Secondly, at the subpixel registration, a new mutual-learning adaptive particle swarm optimization (MLADPSO) algorithm is employed to locate the subpixel precisely. Dynamic adjustments of the particle flight velocities that are according to the deformation extent of each interest point are utilized for enhancing the accuracy of the subpixel registration. A test is performed on the abdominal-deformation measurement of the shape-flexible mannequin. The experiment results indicate that under the guarantee of its measurement accuracy without the cause of any loss, the time-consumption of the proposed scheme is significantly more efficient than that of the conventional method, particularly in the case of a large number of interest points.
Identification of ARX Model with Multi-Gaussian Noises
Wentao Bai,Fan Guo,Lei Chen,Kuangrong Hao,Biao Huang 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
In most industrial environments, the real process data points are usually subject to contamination from a variety of noises. Most of the traditional methods assume that the process noise is Gaussian white noise, which can lead to poor robustness of the model to abnormal data. In this paper, we consider an ARX model with multi-Gaussian white noises, assuming that the collected data is affected by different noise models and switching of noise model follows Markov chain probability. The parameters of the model are estimated by Expectation-Maximization (EM) algorithm. A numerical example and a continuous stirred tank reactor are employed to verify the effectiveness of the proposed algorithm.