Baosteel＇ s Slag Short Flow（BSSF） is an innovative process for steelmaking slag treatment that was developed by Baosteel. The process principles, flow-chart, parameters and component systems of the BSSF for steelma...Baosteel＇ s Slag Short Flow（BSSF） is an innovative process for steelmaking slag treatment that was developed by Baosteel. The process principles, flow-chart, parameters and component systems of the BSSF for steelmaking slag treatment are presented. Characteristics of the finished BSSF slag are summarized by analyzing the slag＇ s physical and chemical performances. Several Utilization methods for the BSSF slag are given.展开更多
Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswil...Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.展开更多
Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanc...Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.展开更多
The coal-gas existing condition was ameliorated in the coal seams prone to coal-gas outburst adopting the mining method of protective strata.The gas volume and the gas pressure were reduced synchronously in the protec...The coal-gas existing condition was ameliorated in the coal seams prone to coal-gas outburst adopting the mining method of protective strata.The gas volume and the gas pressure were reduced synchronously in the protected coal seam,and the coal seam of high permeability prone to the coal-gas outburst was changed into that of low perme- ability with no proneness to the coal-gas outburst.The D_(15)coal seam was treated as the protective strata,and the D_(16-17)coal seam was treated as the protected strata in the Fifth coal mine in the Pingdingshan Coal Mining Group.The distance between the two coal seams was 5 m averagely,clarified into the extreme short-range protective strata.The numerical analysis was based on the theory of the porous media flow with the finite ele- ment method.The gas flow process and the change mechanism of the coal-gas pressure were analyzed in the process of mining the protective strata.展开更多
目的探讨呼吸困难指数气流受限程度指数(dyspnea index air flow restriction degree,ADO)在慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)患者近期预后评估中的价值。方法选取新疆医科大学第二附属医院呼吸内科自2021...目的探讨呼吸困难指数气流受限程度指数(dyspnea index air flow restriction degree,ADO)在慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)患者近期预后评估中的价值。方法选取新疆医科大学第二附属医院呼吸内科自2021年3月—2023年3月的COPD患者120例,并依照患者最终转归情况将其分为存活组(n=95)与死亡组(n=25)。观察2组患者的基础病情况及患者性别、年龄、第1秒用力呼气容积(first second forced expiratory volume,FEV1)占预计值的百分比和ADO指数等相关指标。比较ADO指数不同分数患者病死率。比较ADO指数预测180 d死亡的受试者工作特征(receiver operating characteristic,ROC)曲线面积。结果2组患者的高血压、冠心病、心律失常、糖尿病、慢性肝病、慢性肾病、亚临床甲减发生情况对比,差异无统计学意义(P>0.05)。死亡组患者的FEV1占预计值的百分比、FEV1占预计值的百分比评分、呼吸困难分[英国医学研究委员会(the Medical Research Council,MRC)]评分以及ADO指数均高于存活组患者(P<0.05)。ADO指数<5分者的死亡率高于ADO指数≥5分者(P<0.05)。ADO指数预测180 d死亡的ROC曲线面积为0.851(95%CI:0.767~0.928,P<0.001),ADO指数为5.5时,约登指数最大,为0.565。结论ADO可有效反映COPD病情严重程度,对于患者而言可准确反映其病情进展情况,帮助其获得良好的疾病治疗效果,对于患者近期预后而言也具有积极意义,临床应用效果良好。展开更多
文摘Baosteel＇ s Slag Short Flow（BSSF） is an innovative process for steelmaking slag treatment that was developed by Baosteel. The process principles, flow-chart, parameters and component systems of the BSSF for steelmaking slag treatment are presented. Characteristics of the finished BSSF slag are summarized by analyzing the slag＇ s physical and chemical performances. Several Utilization methods for the BSSF slag are given.
文摘Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.
基金Project(2012CB725403)supported by the National Basic Research Program of ChinaProjects(71210001,51338008)supported by the National Natural Science Foundation of ChinaProject supported by World Capital Cities Smooth Traffic Collaborative Innovation Center and Singapore National Research Foundation Under Its Campus for Research Excellence and Technology Enterprise(CREATE)Programme
文摘Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.
基金the Grants of National Scientific Funds of Control Mechanism of Geologic Hazards Induced by Coal-gas(50534070)
文摘The coal-gas existing condition was ameliorated in the coal seams prone to coal-gas outburst adopting the mining method of protective strata.The gas volume and the gas pressure were reduced synchronously in the protected coal seam,and the coal seam of high permeability prone to the coal-gas outburst was changed into that of low perme- ability with no proneness to the coal-gas outburst.The D_(15)coal seam was treated as the protective strata,and the D_(16-17)coal seam was treated as the protected strata in the Fifth coal mine in the Pingdingshan Coal Mining Group.The distance between the two coal seams was 5 m averagely,clarified into the extreme short-range protective strata.The numerical analysis was based on the theory of the porous media flow with the finite ele- ment method.The gas flow process and the change mechanism of the coal-gas pressure were analyzed in the process of mining the protective strata.
文摘目的探讨呼吸困难指数气流受限程度指数(dyspnea index air flow restriction degree,ADO)在慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)患者近期预后评估中的价值。方法选取新疆医科大学第二附属医院呼吸内科自2021年3月—2023年3月的COPD患者120例,并依照患者最终转归情况将其分为存活组(n=95)与死亡组(n=25)。观察2组患者的基础病情况及患者性别、年龄、第1秒用力呼气容积(first second forced expiratory volume,FEV1)占预计值的百分比和ADO指数等相关指标。比较ADO指数不同分数患者病死率。比较ADO指数预测180 d死亡的受试者工作特征(receiver operating characteristic,ROC)曲线面积。结果2组患者的高血压、冠心病、心律失常、糖尿病、慢性肝病、慢性肾病、亚临床甲减发生情况对比,差异无统计学意义(P>0.05)。死亡组患者的FEV1占预计值的百分比、FEV1占预计值的百分比评分、呼吸困难分[英国医学研究委员会(the Medical Research Council,MRC)]评分以及ADO指数均高于存活组患者(P<0.05)。ADO指数<5分者的死亡率高于ADO指数≥5分者(P<0.05)。ADO指数预测180 d死亡的ROC曲线面积为0.851(95%CI:0.767~0.928,P<0.001),ADO指数为5.5时,约登指数最大,为0.565。结论ADO可有效反映COPD病情严重程度,对于患者而言可准确反映其病情进展情况,帮助其获得良好的疾病治疗效果,对于患者近期预后而言也具有积极意义,临床应用效果良好。
