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Mingzhi Wang,Anqi Xu,Ruolin Peng,Liangsheng Qiu,Jie Tao 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.11
Al/Ni energetic laminates with various Al/Ni thickness ratios were prepared by electrodeposition and hot pressing method toinvestigate the effect of the thickness ratios on mechanical properties and exothermic energy of the composites. In this study,the different Al/Ni thickness ratios of 9:4, 9:6, 9:8 and 9:10 were determined by varying the initial thickness of Ni layers. Theresults showed that the Al/Ni laminate with the thickness ratio of 9:4 had the largest exothermic heat of 1139 J/g, while thelaminate with 9:10 possessed the optimum mechanical properties, such as the tensile strength of 395.8 MPa, the elongationof 13.3% and the bending strength of 706 MPa. It was found that the tensile properties and plastic forming properties of thelaminates were enhanced continuously with the increasing proportion of nickel layers due to higher strength and the improvingcollaborative deformation of Al/Ni multilayers, when the Al/Ni thickness ratio altered from 9:4 to 9:10. Meanwhile,the exothermic reaction energy was declined significantly because of the increasing proportion of the exothermic reactionto form AlNi3compounds, which possessed the lower energy density. In addition, the effect of Al/Ni thickness ratio on thereaction paths in the continuous heating process was also studied.
Carbon dioxide reforming of methane over MgO promoted Ni/CNT catalyst
Dehua Zhang,Guangcheng Wei,Yiru Wang,Jing Wang,Ping Ning,Qiulin Zhang,Mingzhi Wang,Tengfei Zhang,Kaixian Long 한국화학공학회 2018 Korean Journal of Chemical Engineering Vol.35 No.10
Carbon dioxide reforming of methane to syngas was investigated over a series of MgO promoted Ni/CNT catalysts. MgO played a critical role in improving the catalytic performance of Ni/CNT. The results showed that the addition of MgO strengthened the interaction of Ni and interior surface of CNT. Highly dispersed nickel particles with small size (less than 4.5nm) were also observed in MgO modified CNT. Otherwise, the NiO nanoparticles were facilely reduced over the catalyst prepared with a narrow size of CNT, as shown in H2-TPR. The reaction tests demonstrated that the Ni-based catalyst with an addition of MgO and narrow size of CNT exhibited better catalytic activity. Furthermore, the lifetime of Ni-based catalyst was prolonged effectively after adding MgO, attributed to the stabilization and dispersion of Ni particles and the effective restraint on the gasification of CNT.
Modeling of a paper-making wastewater treatment process using a fuzzy neural network
Mingzhi Huang,유창규,Jinquan Wan,Yan Wang,Yongwen Ma,Huiping Zhang,류홍빈,Zhanzhan Hu 한국화학공학회 2012 Korean Journal of Chemical Engineering Vol.29 No.5
An intelligent system that includes a predictive model and a control was developed to predict and control the performance of a wastewater treatment plant. The predictive model was based on fuzzy C-means clustering, fuzzy inference and neural networks. Fuzzy C-means clustering was used to identify model’s architecture, extract and optimize fuzzy rule. When predicting, MAPE was 4.7582% and R was 0.8535. The simulative results indicate that the learning ability and generalization of the model was good, and it can achieve a good predication of effluent COD. The control model was based on a fuzzy neural network model, taking into account the difference between the predicted value of COD and the setpoint. When simulating, R was 0.9164, MAPE was 5.273%, and RMSE was 0.0808, which showed that the FNN control model can effectively change the additive dosages. The control of a paper-making wastewater treatment process in the laboratory using the developed predictive control model and MCGS (monitor and control generated system) software shows the dosage was computed accurately to make the effluent COD remained at the setpoint,when the influent COD value or inflow flowrate was changed. The results indicate that reasonable forecasting and control performances were achieved through the developed system; the maximum error was only 3.67%, and the average relative error was 2%.
Huang, Mingzhi,Ma, Yongwen,Wan, Jinquan,Wang, Yan,Chen, Yangmei,Yoo, Changkyoo Ecomed 2014 Environmental Science and Pollution Research Vol.21 No.20
<P>Due to the inherent complexity, uncertainty, and posterity in operating a biological wastewater treatment process, it is difficult to control nitrogen removal in the biological wastewater treatment process. In order to cope with this problem and perform a cost-effective operation, an integrated neural-fuzzy control system including a fuzzy neural network (FNN) predicted model for forecasting the nitrate concentration of the last anoxic zone and a FNN controller were developed to control the nitrate recirculation flow and realize nitrogen removal in an anoxic/oxic (A/O) process. In order to improve the network performance, a self-learning ability embedded in the FNN model was emphasized for improving the rule extraction performance. The results indicate that reasonable forecasting and control performances had been achieved through the developed control system. The effluent COD, TN, and the operation cost were reduced by about 14, 10.5, and 17 %, respectively.</P>
Corrigendum Algorithm for Gesture Recognition Based on Multiple Information Fusion and Kinect
Jinghui Wang,Wenqun Cao,Mingzhi Niu 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.10
Gesture recognition is an important and challenging task in the field of computer vision. Starting from the 3D shape of coding gestures, it puts forward a new kind of gesture recognition framework based on depth image. It extracts the space characteristics of a variety of 3D point cloud based on Kinect, including local principal components analysis on point cloud to get the histogram of main component, gradient direction histogram based on local depth difference and depth distribution histogram of local point cloud. Principal component histogram and gradient direction histogram effectively coding the local shape of gestures, depth distribution histogram compensates the loss of the shaping descriptor information. Through preliminary training of random forest classifier to filter the characteristics, and characteristics with less influence on classification results are removed, thus the computational costs are reduced. The filtered characteristics are used for training of random forest classifier again to classify gestures. Experiment is carried on two large-scale gesture data sets, for more difficult ASL dataset, the proposed method has improved the recognition rate of 3.6% then the best previous algorithm.
Robust Gesture Recognition with Kinect Data Acquisition
Jinghui Wang,Mingzhi Niu 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.10
To realize the gesture recognition of high precision ratio, the gesture recognition method of multi-model data fusion based on Kinect depth image is proposed, to implement the automatic splicing of models. First of all, the feature package model uses the speeded up robust feature (SURF) algorithm to replace the scale invariant feature transform (SIFT) algorithm to extract features, improve the real-time performance. Secondly, Hu moment is introduced to describe the global gesture features, further improving the recognition rate, the ray casting is used finally, and the obtained coordinate information is used to solve the rigid transformation between two point cloud models. Finally, the proposed data fusion method is verified through two experiments, the algorithm in this paper is better than the traditional support vector machine (SVM) method both in real time performance and recognition rate, and obtains better model splicing effect.
A GA-Based Neural Fuzzy System for Modeling a Paper Mill Wastewater Treatment Process
Huang, Mingzhi,Wan, Jinquan,Ma, Yongwen,Zhang, Huiping,Wang, Yan,Wei, Chaohai,Liu, Hongbin,Yoo, ChangKyoo American Chemical Society 2011 INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH - Vol.50 No.23
<P>A genetic algorithm-based neural fuzzy system (GA-NFS) was presented for studying the coagulation process of wastewater treatment in a paper mill. In order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability, the GA-NFS was employed to model the nonlinear relationships between the effluent concentration of pollutants and the chemical dosages, and a hybrid learning algorithm divided into two stages was proposed for parameters learning. During the first learning stage, a genetic algorithm was used to optimize the structure of GA-NFS and the membership function of each fuzzy term due to its capability of parallel and global search. On the basis of an optimized training stage, the back-propagation algorithm (BP algorithm) was chosen to update the parameters of GA-NFS to improve the system precision. The GA-NFS proves to be very effective in modeling coagulation perform and performs better than adaptive-network-based fuzzy inference system (ANFIS). RMSE, MAPE, and <I>R</I> between the predicted and observed values for GA-NFS were only 0.01099, 2.3337, and 0.9375, respectively.</P>
Kenan Li,Mingzhi Wang,Feng Chen,Ning Yan,Qin Zou,Yucheng Zhao,Jianmin Li,Fu Zhao 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.7
During the planar lightwave circuit (PLC) splitter manufacturing process, high precision is essential. Controlling machining precision is extremely difficult during the high-speed-and-precision dicing process, which is empirically related to the rotational speed, static diameter and elastic modulus of diamond dicing blades and other factors. However, the effect of changes in the outer diameter of blades on machining precision has been disregarded in the PLC manufacturing process. In this research, we proposed a dynamic diameter (Dd) for describing changes in a blade’s outer diameter during the high-speed machining process. Dd is positively correlated with machining precision. Here, we derived a formula for calculating Dd that is related to the rotational speed, static diameter, elastic modulus, Poisson’s ratio, density, cutting length and radial wear rate of diamond dicing blades. Furthermore, a series of experimental Dd values was obtained by changing the rotational speed, static diameter and elastic modulus of diamond dicing blades. These values are highly consistent with the calculated results. Our findings not only provide clues to compensate for diameter during high-speed-and-precision dicing process, but also offer guidelines for a new design route of diamond tools.