Construction and improvement of safety standardization was important measure to ensure hazardous chemicals enterprises developed sustainable and healthy.Based on problems existed in safety standardization of some hazardous chemicals enterprises presently,analysis on necessity cognition,normative connotation cognition,enterprise deployment and basic conditions were discussed,and countermeasures were carried out based on the discussion,including strengthen policy guidance and publicity,strengthen training intensity,and perfect safety standardization system.
It is wellknown that mine gas gushing forecasting is very significant to ensure the safety of mining. A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting is presented in the paper. Morlet wavelet function can be used as the kernel function of robust relevance vector machine. Mean percentage error has been used to measure the performance of the proposed method in this study. As the mean prediction error of mine gas gushing of the WRRVM model is less than 1.5%, and the mean prediction error of mine gas gushing of the RVM model is more than 2.5%, it can be seen that the prediction accuracy for mine gas gushing of the WRRVM model is better than that of the RVM model.
Incremental Attribute Learning (IAL) is a feasible machine learning strategy for solving high-dimensional pattern classification problems. It gradually trains features one by one, which is quite different from those conventional machine learning approaches where features are trained in one batch. Preprocessing, such as feature selection, feature ordering and feature extraction, has been verified as useful steps for improving classification performance by previous IAL studies. However, in the previous research, these preprocessing approaches were individually employed and they have not been applied for training simultaneously. Therefore, it is still unknown whether the classification results can be further improved by these different preprocess approaches when they are used at the same time. This study integrates different feature preprocessing steps for IAL, where feature extraction, feature selection and feature ordering are simultaneously employed. Experimental results indicate that such an integrated preprocessing approach is applicable for pattern classification performance improvement. Moreover, statistical significance testing also verified that such an integrated preprocessing approach is more suitable for the datasets with high-dimensional inputs.
This paper discusses the key issues of dynamic decision model and visualization technology of enterprise human resource management visualization and dynamic decision support system, based on the analysis of development trend and system objective of human resource management information system. And then gives a design method of this system based on B/S structure , according to the status of an enterprise in China. In this system, models mainly are built by dynamic decision and other mathematical methods, and visualization is completed by the technology of dimension of stratified. This system not only has a good forecast precision but also has a good effect by using the actual data in fact.
Leaf area index (LAI) is one of the most important canopy structure parameters utilized in process-based models of climate, hydrology, and biogeochemistry. In order to determine the reliability and applicability of satellite LAI products, it is critical to validate satellite LAI products. Due to surface heterogeneity and scale effects, it is difficult to validate the accuracy of LAI products. In order to improve the spatio-temporal accuracy of satellite LAI products, we propose a new multi-scale LAI product validation method based on a crop growth cycle. In this method, we used the PROSAIL model to derive Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LAI data and Gaofen-1 (GF-1) for the study area. The Empirical Bayes Kriging (EBK) interpolation method was used to perform a spatial multi-scale transformation of Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products, GF-1 LAI data, and ASTER LAI data. Finally, MODIS LAI satellite products were compared with field measured LAI data, GF-1 LAI data, and ASTER LAI data during the growing season of crop field. This study was conducted in the agricultural oasis area of the middle reaches of the Heihe River Basin in northwestern China and the Conghua District of Guangzhou in Guangdong Province. The results suggest that the validation accuracy of the multi-scale MODIS LAI products validated by ASTER LAI data were higher than those of the GF-1 LAI data and the reference field measured LAI data, showing a R2 of 0.758 and relative mean square error (RRMSE) of 28.73% for 15 m ASTER LAI and a R2 of 0.703 and RRMSE of 30.80% for 500 m ASTER LAI, which imply that the 15 m MODIS LAI product generated by the EBK method was more accurate than the 500 m and 8 m products. This study provides a new validation method for satellite remotely sensed products.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)