Jiabin Yu
Beijing Technology and Business University
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Publication
Featured researches published by Jiabin Yu.
Saudi Journal of Biological Sciences | 2017
Li Wang; Xiaoyi Wang; Xuebo Jin; Jiping Xu; Huiyan Zhang; Jiabin Yu; Qian Sun; Chong Gao; Lingbin Wang
The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.
Neural Computing and Applications | 2018
Li Wang; Tianrui Zhang; Xuebo Jin; Jiping Xu; Xiaoyi Wang; Huiyan Zhang; Jiabin Yu; Qian Sun; Zhiyao Zhao; Yuxin Xie
The forecasting methods of water bloom in existence are hard to reflect nonlinear dynamic change in algal bloom formation mechanism, leading to poor forecasting accuracy of bloom. To solve this problem, this paper deeply analyzes the generation process of algal bloom, introduces the recursive time series algorithm into the deep belief network model and improves the model structure and training algorithm, and proposes a forecasting method based on the recursive timed deep belief network model. The model introduces the current moments and historical time values of the characterization factors and influencing factors at the input layer, and increases the connection between the input layer and the hidden layer of the deep belief network. A recursive algorithm is used to establish the relationship between the current time value of the characterization factor and the historical time value of the characterization factor, and the connection between the current time value of the hidden layer and the influencing factor is increased. By re-extracting the characteristics of the hidden layer at each moment, and then fine tuning the network parameters by the BP neural network, a recursive timing deep belief network model is finally constructed. The results show that compared with the existing forecasting methods, this method can extract the characteristics of time series data more accurately and completely to deal with the dynamic nonlinear process and can further improve the forecast accuracy of algal blooms.
Journal of Chemistry | 2017
Jiabin Yu; Zhaoyang Wang; Xiaoyi Wang; Jiping Xu; Jie Jia
The process of water eutrophication involves the interaction of external factors, nutrients, microorganisms, and other factors. It is complex and has not yet been effectively studied. To examine the formation process of water eutrophication, a set of orthogonal experiments with three factors and four levels is designed to analyze the key factors. At the same time, with the help of a large amount of monitoring data, the principal component analysis method is used to extract the main components of water eutrophication and determine the effective evaluation indicators of eutrophication. Finally, the Bayesian theory of uncertainty is applied to the evaluation of the eutrophication process to evaluate the sample data. The simulation results demonstrate the validity of the research method.
Cluster Computing | 2017
Li Wang; Tianrui Zhang; Jiping Xu; Jiabin Yu; Xiaoyi Wang; Huiyan Zhang; Zhiyao Zhao
Algae bloom outbreak is a dynamic nonlinear process with time-varying characteristics and it is difficult for existing algal bloom prediction method to consider the complex characteristics, which leads to low accuracy prediction. For the problem, a dynamic deep belief nets model that combines time series analysis with deep learning methods is proposed by analyzing algal bloom outbreak mechanism. The model introduces historical moment in input layer, increases connection between input layer and hidden layer, uses contrastive divergence algorithm to introduce historical moment in hidden layer and weight and bias algorithms are given timing characteristic in pre-training stage. At the same time, the model adopts dynamic learning rate to complete pre-training and the back-propagation algorithm is used to fine tune network parameters to complete the whole model training. The instance validation results show that the method can more accurately describe dynamic nonlinear process than other prediction methods and further improve prediction accuracy.
Advances in Mathematical Physics | 2017
Xiao-zhe Bai; Huiyan Zhang; Xiaoyi Wang; Li Wang; Jiping Xu; Jiabin Yu
Globally, cyanobacteria blooms frequently occur, and effective prediction of cyanobacteria blooms in lakes and reservoirs could constitute an essential proactive strategy for water-resource protection. However, cyanobacteria blooms are very complicated because of the internal stochastic nature of the system evolution and the external uncertainty of the observation data. In this study, an adaptive-clustering algorithm is introduced to obtain some typical operating intervals. In addition, the number of nearest neighbors used for modeling was optimized by particle swarm optimization. Finally, a fuzzy linear regression method based on error-correction was used to revise the model dynamically near the operating point. We found that the combined method can characterize the evolutionary track of cyanobacteria blooms in lakes and reservoirs. The model constructed in this paper is compared to other cyanobacteria-bloom forecasting methods (e.g., phase space reconstruction and traditional-clustering linear regression), and, then, the average relative error and average absolute error are used to compare the accuracies of these models. The results suggest that the proposed model is superior. As such, the newly developed approach achieves more precise predictions, which can be used to prevent the further deterioration of the water environment.
2015 5th International Conference on Computer Sciences and Automation Engineering (ICCSAE 2015) | 2016
Jiaji Yu; Li Wang; Xiaoyi Wang; Jiping Xu; Huiyan Zhang; Jiabin Yu
Currently, there are still many problems in food safety supervision of Beijing, more than 80% of the foods are depending on the outer supply which has increased difficulty in the effective control from the source of food safety. The planning levels of food safety management are still in need, the food safety risk evaluation and warning are scarcely used. With the development of the investment to the outer supply base, the food safety supervision mechanism should be perfected while the food input risk should be controlled. This paper build the food safety supervision information system in Beijing by using the HACCP theory based on the intelligent information processing technology and the food safety data platform, in order to improve the management level and efficiency, and then to realize the overall monitoring of food safety through the all-process supervision of the outer supply and food circulation.
Optik | 2016
Qian Sun; Xiaoyi Wang; Jiping Xu; Li Wang; Huiyan Zhang; Jiabin Yu; Tingli Su; Xun Zhang
Engineering review | 2014
Li Wang; Xiaoyi Wang; Jiping Xu; Yan Shi; Jiabin Yu
IEEE Access | 2018
Zhiyao Zhao; Xiaoyi Wang; Jiping Xu; Jiabin Yu
Desalination and Water Treatment | 2018
Li Wang; Tianrui Zhang; Xuebo Jin; Jiping Xua; Xiaoyi Wang; Huiyan Zhang; Jiabin Yu; Qian Sun; Zhiyao Zhao; Lei Zheng