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      저자 : Qiuju Xie (검색결과 2 건)
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      • A Method for Missing Data Recovery of Air Pollutants Monitoring in Henhouse Based on QGSA-SVM

        Jinming Liu,Qiuju Xie,Guiyang Liu,Yong Sun 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.3

        To solve the data missing problem caused by sensor faults during the air pollutants monitoring in henhouse, a method for missing data recovery was proposed based on support vector machine (SVM). Multiple factors that influence monitoring values of the air pollutants in henhouse, such as temporal, spatial and environmental, were considered to established a SVM regression model to estimate the missing data of the air pollutants monitoring. Meanwhile, to obtain better prediction accuracy, regression model parameters were optimized by a novel hybrid optimization algorithm which was combined standard genetic algorithm with quantum genetic strategy and simulated annealing tactics. Taking the data processing of the ammonia (NH3) concentration as an example, the proposed method was tested with the monitoring data of 3 days in a farm. The estimation results of missing data shown that there was a litter error between the estimated data and monitoring data, the maximal relative error was 5.87% (percent), the average relative error was 1.77% (percent). It is verified that this method of missing data recovery is feasible and valid.

      • A Method for Missing Data Recovery of Waste Gas Monitoring in Animal Building Based on GA-SVM

        Jinming Liu,Qiuju Xie,Yuanyuan Zhang 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.5

        In order to solve the data missing problem caused by sensor faults during the waste gas monitoring in animal building, a method for missing data recovery was presented based on support vector machine (SVM) combined with genetic algorithm (GA). Multiple factors that influence monitoring values of the waste gas in animal building such as temporal, spatial and environmental, were considered to established a SVM regression prediction model to estimate the missing data of the waste gas monitoring. Meanwhile, to obtain better prediction accuracy, model parameters were optimized by the GA. The data processing of the ammonia (NH3) concentration was taken as an example; monitoring data of 3 days were randomly selected in a farm to test the presented model in this paper. It is shown that there was a very little error between the estimated data and the monitoring data, the maximal relative error was 6.99 % (percent), and the average relative error was 2.15 % (percent). It is an effective method for missing data recovery and a practical way of data processing for waste gas monitoring in animal building.

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