风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成...风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。展开更多
Designing of a multi-purpose plant as one of the well-known manufacturing systems is more challenging than other manufacturing systems. This paper applies a stochastic colored Petri net (CPN) to design and analyze mul...Designing of a multi-purpose plant as one of the well-known manufacturing systems is more challenging than other manufacturing systems. This paper applies a stochastic colored Petri net (CPN) to design and analyze multi-purpose plants. A simple approach is proposed to determine the utilization of shared resources and to reduce the equipment’s idle times. Three scenarios are presented to describe the proposed model. Generally, according to desire of a decision maker, different scenarios can be considered in the model to achieve to the expected design or plant configuration. The main characteristics of the proposed model are flexibility, the easiness of practical application and the simulation of the model in an easy way.展开更多
Composite water samples taken from Owena Multi-purpose Dam in six sampling campaigns covering the wet and dry seasons were analyzed for physico-chemical and microbial characteristics using standard methods for the exa...Composite water samples taken from Owena Multi-purpose Dam in six sampling campaigns covering the wet and dry seasons were analyzed for physico-chemical and microbial characteristics using standard methods for the examination of water and wastewater jointly published by the American Public Health Association, American Water Works Association and Water Pollution Control Federation. Results showed significant (p < 0.05) seasonal variations in most measured parameters with few showing significant spatial variation. The characteristics of the water from the dam lake revealed an acceptable quality for most measured parameters with low chemical pollutants burden when compared with drinking water standards and water quality for aquaculture. However, high values of turbidity, colour, iron, manganese and microbial load were recorded compared with drinking water standards, which call for proper treatment of the water before distribution for public consumption.展开更多
The effect of the positive bias on Reynolds stress （RS） and its effect on the radial turbulent transport at the edge plasma （r/a =0.9） and scrape-off layer （SOL） region of plasma in tokamak are investigated. The...The effect of the positive bias on Reynolds stress （RS） and its effect on the radial turbulent transport at the edge plasma （r/a =0.9） and scrape-off layer （SOL） region of plasma in tokamak are investigated. The radial and poloidal electric fields （Sr, Ep） and ion saturation current （Is） are measured by multi-purpose probe （MPP）. This probe is fabricated and constructed for the first time in the IR-T1 tokamak. The most advantage of this probe is that the variations of Er and Ep can be measured in different radii at the single shot. Thus the information of different radii can be compared with high precision. The bias voltage is fixed at Vbias = 200 V and it has been applied with the limiter bias that is fixed in r/a = 0.9. Moreover, the phase difference between radial and poloidal electric fields, and temporal evolution of the RS .spectrum detected by MPP are calculated. RS magnitude on the edge （r/a = 0.9） is more than its value in the SOL （r/a = 1.02）. With the applied bias 200 V, ItS and the magnitude of the phase difference between Er and Ep are increased, while the radial turbulent transport is decreased simultaneously. Thus it can be concluded that RS affects radial turbulence. Temporal evolution of the RS spectrum shows that the frequency of RS is increased and reaches its highest value at r/a=0.9 in the presence of bias.展开更多
This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine lear...This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.展开更多
Aimed at current deficiencies of multi-purpose guided missile kill probability model against gunship, the concept of the important coefficient of vulnerability blade unit is proposed in this paper. Laser fuze actuatio...Aimed at current deficiencies of multi-purpose guided missile kill probability model against gunship, the concept of the important coefficient of vulnerability blade unit is proposed in this paper. Laser fuze actuation model and warhead condition kill probability model of rotor blades are established by Monte Carlo method and kinetics theory with new ideas. Based on limited data, armor thickness of gunship is estimated, and a complete multi-purpose guided missile kill probability mathematical model is established, which provides necessary mathematical tool for the accurate and objective analysis of multi-purpose guided missile kill probability against gunship. Based on the establishment of the model, sensitivity analysis and optimal design of the main factors of multi-purpose guided missile kill probability are conducted, and the results show that the single multi-purpose guided missile lethality performance can be improved significantly by sensitivity analysis and optimization.展开更多
文摘风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。
文摘Designing of a multi-purpose plant as one of the well-known manufacturing systems is more challenging than other manufacturing systems. This paper applies a stochastic colored Petri net (CPN) to design and analyze multi-purpose plants. A simple approach is proposed to determine the utilization of shared resources and to reduce the equipment’s idle times. Three scenarios are presented to describe the proposed model. Generally, according to desire of a decision maker, different scenarios can be considered in the model to achieve to the expected design or plant configuration. The main characteristics of the proposed model are flexibility, the easiness of practical application and the simulation of the model in an easy way.
文摘Composite water samples taken from Owena Multi-purpose Dam in six sampling campaigns covering the wet and dry seasons were analyzed for physico-chemical and microbial characteristics using standard methods for the examination of water and wastewater jointly published by the American Public Health Association, American Water Works Association and Water Pollution Control Federation. Results showed significant (p < 0.05) seasonal variations in most measured parameters with few showing significant spatial variation. The characteristics of the water from the dam lake revealed an acceptable quality for most measured parameters with low chemical pollutants burden when compared with drinking water standards and water quality for aquaculture. However, high values of turbidity, colour, iron, manganese and microbial load were recorded compared with drinking water standards, which call for proper treatment of the water before distribution for public consumption.
文摘The effect of the positive bias on Reynolds stress （RS） and its effect on the radial turbulent transport at the edge plasma （r/a =0.9） and scrape-off layer （SOL） region of plasma in tokamak are investigated. The radial and poloidal electric fields （Sr, Ep） and ion saturation current （Is） are measured by multi-purpose probe （MPP）. This probe is fabricated and constructed for the first time in the IR-T1 tokamak. The most advantage of this probe is that the variations of Er and Ep can be measured in different radii at the single shot. Thus the information of different radii can be compared with high precision. The bias voltage is fixed at Vbias = 200 V and it has been applied with the limiter bias that is fixed in r/a = 0.9. Moreover, the phase difference between radial and poloidal electric fields, and temporal evolution of the RS .spectrum detected by MPP are calculated. RS magnitude on the edge （r/a = 0.9） is more than its value in the SOL （r/a = 1.02）. With the applied bias 200 V, ItS and the magnitude of the phase difference between Er and Ep are increased, while the radial turbulent transport is decreased simultaneously. Thus it can be concluded that RS affects radial turbulence. Temporal evolution of the RS spectrum shows that the frequency of RS is increased and reaches its highest value at r/a=0.9 in the presence of bias.
基金funded by Vietnam National Foundation for Science and Tech-nology Development(NAFOSTED)under Grant No.105.99-2019.309.
文摘This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.
文摘Aimed at current deficiencies of multi-purpose guided missile kill probability model against gunship, the concept of the important coefficient of vulnerability blade unit is proposed in this paper. Laser fuze actuation model and warhead condition kill probability model of rotor blades are established by Monte Carlo method and kinetics theory with new ideas. Based on limited data, armor thickness of gunship is estimated, and a complete multi-purpose guided missile kill probability mathematical model is established, which provides necessary mathematical tool for the accurate and objective analysis of multi-purpose guided missile kill probability against gunship. Based on the establishment of the model, sensitivity analysis and optimal design of the main factors of multi-purpose guided missile kill probability are conducted, and the results show that the single multi-purpose guided missile lethality performance can be improved significantly by sensitivity analysis and optimization.
