漂洗是鱼糜加工中重要的步骤之一。鱼肉在漂洗过程中会损失大量水溶性蛋白质。对鱼糜漂洗液进行蛋白质回收,可以提高鱼糜利用率,降低污水处理成本,减少环境污染。本试验采用絮凝剂三氯化铁(FeCl_(3))对河鲀鱼糜漂洗液中的水溶性蛋白质...漂洗是鱼糜加工中重要的步骤之一。鱼肉在漂洗过程中会损失大量水溶性蛋白质。对鱼糜漂洗液进行蛋白质回收,可以提高鱼糜利用率,降低污水处理成本,减少环境污染。本试验采用絮凝剂三氯化铁(FeCl_(3))对河鲀鱼糜漂洗液中的水溶性蛋白质进行回收,根据不同的单因素变量,研究FeCl_(3)絮凝法回收鱼糜漂洗液中蛋白质的最佳条件。通过响应面分析法对试验条件进行优化,寻求最佳的组合条件。结果显示,FeCl_(3)法回收河鲀鱼糜漂洗液中水溶性蛋白质的最佳条件为pH 6、温度65℃、每10 mL鱼糜漂洗液添加1%1.4 mL FeCl_(3)溶液。展开更多
With the development of data age,data quality has become one of the problems that people pay much attention to.As a field of data mining,outlier detection is related to the quality of data.The isolated forest algorith...With the development of data age,data quality has become one of the problems that people pay much attention to.As a field of data mining,outlier detection is related to the quality of data.The isolated forest algorithm is one of the more prominent numerical data outlier detection algorithms in recent years.In the process of constructing the isolation tree by the isolated forest algorithm,as the isolation tree is continuously generated,the difference of isolation trees will gradually decrease or even no difference,which will result in the waste of memory and reduced efficiency of outlier detection.And in the constructed isolation trees,some isolation trees cannot detect outlier.In this paper,an improved iForest-based method GA-iForest is proposed.This method optimizes the isolated forest by selecting some better isolation trees according to the detection accuracy and the difference of isolation trees,thereby reducing some duplicate,similar and poor detection isolation trees and improving the accuracy and stability of outlier detection.In the experiment,Ubuntu system and Spark platform are used to build the experiment environment.The outlier datasets provided by ODDS are used as test.According to indicators such as the accuracy,recall rate,ROC curves,AUC and execution time,the performance of the proposed method is evaluated.Experimental results show that the proposed method can not only improve the accuracy and stability of outlier detection,but also reduce the number of isolation trees by 20%-40%compared with the original iForest method.展开更多
文摘漂洗是鱼糜加工中重要的步骤之一。鱼肉在漂洗过程中会损失大量水溶性蛋白质。对鱼糜漂洗液进行蛋白质回收,可以提高鱼糜利用率,降低污水处理成本,减少环境污染。本试验采用絮凝剂三氯化铁(FeCl_(3))对河鲀鱼糜漂洗液中的水溶性蛋白质进行回收,根据不同的单因素变量,研究FeCl_(3)絮凝法回收鱼糜漂洗液中蛋白质的最佳条件。通过响应面分析法对试验条件进行优化,寻求最佳的组合条件。结果显示,FeCl_(3)法回收河鲀鱼糜漂洗液中水溶性蛋白质的最佳条件为pH 6、温度65℃、每10 mL鱼糜漂洗液添加1%1.4 mL FeCl_(3)溶液。
基金supported by the State Grid Liaoning Electric Power Supply CO, LTDthe financial support for the “Key Technology and Application Research of the Self-Service Grid Big Data Governance (No.SGLNXT00YJJS1800110)”
文摘With the development of data age,data quality has become one of the problems that people pay much attention to.As a field of data mining,outlier detection is related to the quality of data.The isolated forest algorithm is one of the more prominent numerical data outlier detection algorithms in recent years.In the process of constructing the isolation tree by the isolated forest algorithm,as the isolation tree is continuously generated,the difference of isolation trees will gradually decrease or even no difference,which will result in the waste of memory and reduced efficiency of outlier detection.And in the constructed isolation trees,some isolation trees cannot detect outlier.In this paper,an improved iForest-based method GA-iForest is proposed.This method optimizes the isolated forest by selecting some better isolation trees according to the detection accuracy and the difference of isolation trees,thereby reducing some duplicate,similar and poor detection isolation trees and improving the accuracy and stability of outlier detection.In the experiment,Ubuntu system and Spark platform are used to build the experiment environment.The outlier datasets provided by ODDS are used as test.According to indicators such as the accuracy,recall rate,ROC curves,AUC and execution time,the performance of the proposed method is evaluated.Experimental results show that the proposed method can not only improve the accuracy and stability of outlier detection,but also reduce the number of isolation trees by 20%-40%compared with the original iForest method.
