A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the mai...A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.展开更多
In recent years, with the increase of the depth of open-pit mining, the pollution level has been on the rise due to harmful gases and dust occurring in the process of mining. In order to accelerate the diffusion of th...In recent years, with the increase of the depth of open-pit mining, the pollution level has been on the rise due to harmful gases and dust occurring in the process of mining. In order to accelerate the diffusion of these air pollutants, the distributed regularity of the rock face temperature which is directly related to the air ventilation in deep open-pit mines should be studied. Here, we establish the key factors influencing the rock face temperature in a deep open-pit mine. We also present an empirical model of the rock face temperature variation in the deep open-pit mine, of which the performance is interestingly high compared with that of the field test. This study lays a foundation to study the ventilation thermodynamic theory in the deep open-pit mine, which is of great importance for theoretical studies and engineering applications of solving air pollution problem in deep open-pit mines.展开更多
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support ...A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.展开更多
Before diagnosed by DGA (dissolved gas analysis) methods, gas caution values, which index the level of gas formation, must be used to evaluate the possibility of incipient faults to reduce the misdiagnosis in the norm...Before diagnosed by DGA (dissolved gas analysis) methods, gas caution values, which index the level of gas formation, must be used to evaluate the possibility of incipient faults to reduce the misdiagnosis in the normal state. However, the calculation of these values is now only based on cumulative percentile method without taking into account operating conditions. To overcome this disadvantage, a new approach to calculate the transformer caution values is presented. This approach is based on statistical distribution and correlation analysis, and it takes the individual variation and fluctuation caused by internal and external factors into consideration. Then 6550 transformer DGA data collected from North China Power Grid are analyzed in this paper. The results show that the volume fraction of TH (total hydrocarbon) approximately obeys normal distribution when the 3-sigma rule is used to calculate its caution value. The volume fraction of CO has a strong positive correlation with oil temperature. For H2, the negative correlation with oil temperature is significant when the volume fraction is not very low. The caution value curves for CO and H2 are obtained by regression analyses. Thus, the gas caution values/curves obtained using the new method are not always constant, but vary with oil temperature, which is an advantage of the proposed method compared with cumulative percentile method. The variation of gas caution values/curves also reflects the influence of the external factors, for instance, va- rying with monitoring time ensures that the gas caution values are always consistent with operating status.展开更多
The carbon fiber reinforced composite is a new type of composite material with an excellent property in strength and elastic modulus,and has found extensive applications in aerospace,energy,automotive industry and so ...The carbon fiber reinforced composite is a new type of composite material with an excellent property in strength and elastic modulus,and has found extensive applications in aerospace,energy,automotive industry and so on.However,this composite has a strict requirement on processing techniques,for example,brittle damage or delamination often exists in conventional processing techniques.Abrasive water jet machining technology is a new type of green machining technique with distinct advantages such as high-energy and thermal distortion free.The use of abrasive water jet technique to process carbon fiber composite materials has become a popular trend since it can significantly improve the processing accuracy and surface quality of carbon fiber composite materials.However,there are too many parameters that affect the quality of an abrasive water jet machining.At present,few studies are carried out on the parameter optimization of such a machining process,which leads to the unstable quality of surface processing.In this paper,orthogonal design of experiment and regression analysis were employed to establish the empirical model between cutting surface roughness and machining process parameters.Then a verified model was used to optimize the machining process parameters for abrasive water jet cutting carbon fiber reinforced composites.展开更多
目的分析老年穿支动脉粥样硬化病患者血清微小RNA(micorRNA,miRNA)预测早期神经功能恶化的回归分析。方法选择2020年2月至2023年2月湖北医药学院附属随州市中心医院神经内科收治的老年穿支动脉粥样硬化病患者134例,依据早期神经功能恶...目的分析老年穿支动脉粥样硬化病患者血清微小RNA(micorRNA,miRNA)预测早期神经功能恶化的回归分析。方法选择2020年2月至2023年2月湖北医药学院附属随州市中心医院神经内科收治的老年穿支动脉粥样硬化病患者134例,依据早期神经功能恶化情况分为恶化组28例和未恶化组106例。入院时测定患者血清miR-130a、miR-210、miR-141-3p、miR-29a-3p水平,入院时及入院后7 d采用美国国立卫生研究院卒中量表(National Institutes of Health Stroke Scale,NIHSS)评分评估早期神经功能恶化情况。采用二元logistic回归分析法构建miR-130a、miR-210、miR-141-3p、miR-29a-3p预测老年穿支动脉粥样硬化病患者早期神经功能恶化模型,ROC曲线分析血清miR-130a、miR-210、miR-141-3p、miR-29a-3p水平对老年穿支动脉粥样硬化病患者早期神经功能恶化的预测价值。结果恶化组血清miR-130a、miR-210水平明显高于未恶化组,miR-141-3p、miR-29a-3p水平明显低于未恶化组,差异有统计学意义(P<0.01)。Logistic回归分析显示,血清miR-130a、miR-210、miR-141-3p、miR-29a-3p水平为老年穿支动脉粥样硬化病患者早期神经功能恶化的独立预测指标(P<0.05,P<0.01)。ROC曲线分析显示,血清miR-130a、miR-210、miR-141-3p、miR-29a-3p联合预测老年穿支动脉粥样硬化病患者早期神经功能恶化的曲线下面积为0.977(95%CI:0.936~0.995),敏感性为96.43%,特异性为90.57%,联合预测的效能明显优于各指标单独预测(P<0.01)。结论老年穿支动脉粥样硬化病患者血清miR-130a、miR-210、miR-141-3p、miR-29a-3p对预测早期神经功能恶化具有一定的价值,且四者联合检测可提高其预测效能。展开更多
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
基金Project(51274023) supported by the National Natural Science Foundation of ChinaProject(FRF-BD-17-007A) supported by Fundamental Research Funds for the Central Universities,China
文摘In recent years, with the increase of the depth of open-pit mining, the pollution level has been on the rise due to harmful gases and dust occurring in the process of mining. In order to accelerate the diffusion of these air pollutants, the distributed regularity of the rock face temperature which is directly related to the air ventilation in deep open-pit mines should be studied. Here, we establish the key factors influencing the rock face temperature in a deep open-pit mine. We also present an empirical model of the rock face temperature variation in the deep open-pit mine, of which the performance is interestingly high compared with that of the field test. This study lays a foundation to study the ventilation thermodynamic theory in the deep open-pit mine, which is of great importance for theoretical studies and engineering applications of solving air pollution problem in deep open-pit mines.
文摘A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
文摘A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.
基金Project supported by National Basic Research Program of China (973 Program) (2009CB724508)
文摘Before diagnosed by DGA (dissolved gas analysis) methods, gas caution values, which index the level of gas formation, must be used to evaluate the possibility of incipient faults to reduce the misdiagnosis in the normal state. However, the calculation of these values is now only based on cumulative percentile method without taking into account operating conditions. To overcome this disadvantage, a new approach to calculate the transformer caution values is presented. This approach is based on statistical distribution and correlation analysis, and it takes the individual variation and fluctuation caused by internal and external factors into consideration. Then 6550 transformer DGA data collected from North China Power Grid are analyzed in this paper. The results show that the volume fraction of TH (total hydrocarbon) approximately obeys normal distribution when the 3-sigma rule is used to calculate its caution value. The volume fraction of CO has a strong positive correlation with oil temperature. For H2, the negative correlation with oil temperature is significant when the volume fraction is not very low. The caution value curves for CO and H2 are obtained by regression analyses. Thus, the gas caution values/curves obtained using the new method are not always constant, but vary with oil temperature, which is an advantage of the proposed method compared with cumulative percentile method. The variation of gas caution values/curves also reflects the influence of the external factors, for instance, va- rying with monitoring time ensures that the gas caution values are always consistent with operating status.
基金National High-Tech R&D Program of China(863 Program)(2015AA043401)。
文摘The carbon fiber reinforced composite is a new type of composite material with an excellent property in strength and elastic modulus,and has found extensive applications in aerospace,energy,automotive industry and so on.However,this composite has a strict requirement on processing techniques,for example,brittle damage or delamination often exists in conventional processing techniques.Abrasive water jet machining technology is a new type of green machining technique with distinct advantages such as high-energy and thermal distortion free.The use of abrasive water jet technique to process carbon fiber composite materials has become a popular trend since it can significantly improve the processing accuracy and surface quality of carbon fiber composite materials.However,there are too many parameters that affect the quality of an abrasive water jet machining.At present,few studies are carried out on the parameter optimization of such a machining process,which leads to the unstable quality of surface processing.In this paper,orthogonal design of experiment and regression analysis were employed to establish the empirical model between cutting surface roughness and machining process parameters.Then a verified model was used to optimize the machining process parameters for abrasive water jet cutting carbon fiber reinforced composites.
文摘目的分析老年穿支动脉粥样硬化病患者血清微小RNA(micorRNA,miRNA)预测早期神经功能恶化的回归分析。方法选择2020年2月至2023年2月湖北医药学院附属随州市中心医院神经内科收治的老年穿支动脉粥样硬化病患者134例,依据早期神经功能恶化情况分为恶化组28例和未恶化组106例。入院时测定患者血清miR-130a、miR-210、miR-141-3p、miR-29a-3p水平,入院时及入院后7 d采用美国国立卫生研究院卒中量表(National Institutes of Health Stroke Scale,NIHSS)评分评估早期神经功能恶化情况。采用二元logistic回归分析法构建miR-130a、miR-210、miR-141-3p、miR-29a-3p预测老年穿支动脉粥样硬化病患者早期神经功能恶化模型,ROC曲线分析血清miR-130a、miR-210、miR-141-3p、miR-29a-3p水平对老年穿支动脉粥样硬化病患者早期神经功能恶化的预测价值。结果恶化组血清miR-130a、miR-210水平明显高于未恶化组,miR-141-3p、miR-29a-3p水平明显低于未恶化组,差异有统计学意义(P<0.01)。Logistic回归分析显示,血清miR-130a、miR-210、miR-141-3p、miR-29a-3p水平为老年穿支动脉粥样硬化病患者早期神经功能恶化的独立预测指标(P<0.05,P<0.01)。ROC曲线分析显示,血清miR-130a、miR-210、miR-141-3p、miR-29a-3p联合预测老年穿支动脉粥样硬化病患者早期神经功能恶化的曲线下面积为0.977(95%CI:0.936~0.995),敏感性为96.43%,特异性为90.57%,联合预测的效能明显优于各指标单独预测(P<0.01)。结论老年穿支动脉粥样硬化病患者血清miR-130a、miR-210、miR-141-3p、miR-29a-3p对预测早期神经功能恶化具有一定的价值,且四者联合检测可提高其预测效能。
