Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to d...Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.展开更多
Drought events across the world are increasingly becoming a critical problem owing to its negative effects on water resources. There is need to understand on-site drought characteristics for the purpose of planning mi...Drought events across the world are increasingly becoming a critical problem owing to its negative effects on water resources. There is need to understand on-site drought characteristics for the purpose of planning mitigation measures. In this paper, meteorological drought episodes on spatial, temporal and trend domains were detected using Standardized Precipitation Index (SPI) and Effective Drought Index (EDI) in the upper Tana River basin. 41 years (1980-2016) monthly precipitation data from eight meteorological stations were used in the study. The SPI and EDI were used for reconstruction of the drought events and used to characterize the spatial, temporal and trend distribution of drought occurrence. Drought frequency was estimated as the ratio of a defined severity to its total number of events. The change in drought events was detected using a non-parametric man-Kendall trend test. The main drought conditions detected by SPI and EDI are severe drought, moderate drought, near normal, moderate wet, very wet and extremely wet conditions. From the results the average drought frequency between 1970 and 2010 for the south-eastern and north-western areas ranged from 12.16 to 14.93 and 3.82 to 6.63 percent respectively. The Mann-Kendall trend test show that drought trend increased in the south-eastern parts of the basin at 90% and 95% significant levels. However, there was no significant trend that was detected in the North-western areas. This is an indication that the south-eastern parts are more drought-prone areas compared to the North-western areas of the upper Tana River basin. Both the SPI and the EDI were effective in detecting the on-set of drought, description of the temporal variability, severity and spatial extent across the basin. It is recommended that the findings be adopted for decision making for drought-early warning systems in the river basin.展开更多
文摘Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.
文摘Drought events across the world are increasingly becoming a critical problem owing to its negative effects on water resources. There is need to understand on-site drought characteristics for the purpose of planning mitigation measures. In this paper, meteorological drought episodes on spatial, temporal and trend domains were detected using Standardized Precipitation Index (SPI) and Effective Drought Index (EDI) in the upper Tana River basin. 41 years (1980-2016) monthly precipitation data from eight meteorological stations were used in the study. The SPI and EDI were used for reconstruction of the drought events and used to characterize the spatial, temporal and trend distribution of drought occurrence. Drought frequency was estimated as the ratio of a defined severity to its total number of events. The change in drought events was detected using a non-parametric man-Kendall trend test. The main drought conditions detected by SPI and EDI are severe drought, moderate drought, near normal, moderate wet, very wet and extremely wet conditions. From the results the average drought frequency between 1970 and 2010 for the south-eastern and north-western areas ranged from 12.16 to 14.93 and 3.82 to 6.63 percent respectively. The Mann-Kendall trend test show that drought trend increased in the south-eastern parts of the basin at 90% and 95% significant levels. However, there was no significant trend that was detected in the North-western areas. This is an indication that the south-eastern parts are more drought-prone areas compared to the North-western areas of the upper Tana River basin. Both the SPI and the EDI were effective in detecting the on-set of drought, description of the temporal variability, severity and spatial extent across the basin. It is recommended that the findings be adopted for decision making for drought-early warning systems in the river basin.
