Recent studies have shown that synergistic precipitation of continuous precipitates(CPs)and discontinuous precipitates(DPs)is a promising method to simultaneously improve the strength and electrical conductivity of Cu...Recent studies have shown that synergistic precipitation of continuous precipitates(CPs)and discontinuous precipitates(DPs)is a promising method to simultaneously improve the strength and electrical conductivity of Cu-Ni-Si alloy.However,the complex relationship between precipitates and two-stage aging process presents a significant challenge for the optimization of process parameters.In this study,machine learning models were established based on orthogonal experiment to mine the relationship between two-stage aging parameters and properties of Cu-5.3Ni-1.3Si-0.12Nb alloy with preferred formation of DPs.Two-stage aging parameters of 400℃/75 min+400℃/30 min were then obtained by multi-objective optimization combined with an experimental iteration strategy,resulting in a tensile strength of 875 MPa and a conductivity of 41.43%IACS,respectively.Such an excellent comprehensive performance of the alloy is attributed to the combined precipitation of DPs and CPs(with a total volume fraction of 5.4%and a volume ratio of CPs to DPs of 6.7).This study could provide a new approach and insight for improving the comprehensive properties of the Cu-Ni-Si alloys.展开更多
Traditional heat treatment methods require a significant amount of time and energy to affect atomic diffusion and enhance the spheroidization process of carbides in bearing steel,while pulsed current can accelerate at...Traditional heat treatment methods require a significant amount of time and energy to affect atomic diffusion and enhance the spheroidization process of carbides in bearing steel,while pulsed current can accelerate atomic diffusion to achieve ultra-fast spheroidization of carbides.However,the understanding of the mechanism by which different pulse current parameters regulate the dissolution behavior of carbides requires a large amount of experimental data to support,which limits the application of pulse current technology in the field of heat treatment.Based on this,quantify the obtained pulse current processing data to create an important dataset that could be applied to machine learning.Through machine learning,the mechanism of mutual influence between carbide regulation and various factors was elucidated,and the optimal spheroidization process parameters were determined.Compared to the 20 h required for traditional heat treatment,the application of pulsed electric current technology achieved ultra-fast spheroidization of GCr15 bearing steel within 90 min.展开更多
To address problems in surface integrity and machining allowance distribution during combined electric arc-mechanical milling,this paper takes TC4 as the research object,examines the influence of electric arc milling(...To address problems in surface integrity and machining allowance distribution during combined electric arc-mechanical milling,this paper takes TC4 as the research object,examines the influence of electric arc milling(EAM)depth on recast layer thickness and surface roughness,alongside an analysis of the recast layer’s organization characteristics and sur-face morphology.A comparative evaluation of cutting forces,surface roughness,and surface hardening is conducted between combined milling and conventional mechanical milling.Key findings reveal that electric arc machining produces a recast layer with a hardness of 313.21 HV.As the EAM depth increases,the localized recast layer thickness and peak-to-valley(PV)differ-ences also rise.To ensure effective surface defect removal,the machining allowance for subsequent mechanical milling must exceed the combined thickness of the recast layer and the PV difference.Under identical parameters,combined milling yields higher surface roughness(0.584μm)and greater surface hardening(10.4%)compared to mechanical milling alone,alongside an 18.716 N increase in cutting force.Response surface methodology(RSM)analysis identifies feed per tooth as the most significant factor affecting surface roughness,followed by spindle speed,with milling depth having the least influence.展开更多
Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of...Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.展开更多
沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-...沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-热设备容量协同优化配置方法。首先,根据计算任务对时延的敏感性,精细化建模可推迟可中断、可推迟不可中断及不可推迟3类任务负载的时间约束,在此基础上综合源荷不确定性建立数据中心集群微网“并网-离网”2阶段分布鲁棒优化模型,采用列与约束生成(column and constraint generation,C&CG)算法求解。以青海某实际数据中心为案例的分析结果表明:所提出的方法可使微网容量配置成本下降约25.8%,弃风率下降约56%,并大幅提高数据中心集群微网离网运行可靠性。该文研究为沙戈荒区域绿色低碳数据中心建设提供了理论支撑。展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
基金financially supported by the National Key Research and Development Program of China(No.2023YFB3812601)the National Natural Science Foundation of China(Nos.51925401,92066205 and 92266301)the Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC001).
文摘Recent studies have shown that synergistic precipitation of continuous precipitates(CPs)and discontinuous precipitates(DPs)is a promising method to simultaneously improve the strength and electrical conductivity of Cu-Ni-Si alloy.However,the complex relationship between precipitates and two-stage aging process presents a significant challenge for the optimization of process parameters.In this study,machine learning models were established based on orthogonal experiment to mine the relationship between two-stage aging parameters and properties of Cu-5.3Ni-1.3Si-0.12Nb alloy with preferred formation of DPs.Two-stage aging parameters of 400℃/75 min+400℃/30 min were then obtained by multi-objective optimization combined with an experimental iteration strategy,resulting in a tensile strength of 875 MPa and a conductivity of 41.43%IACS,respectively.Such an excellent comprehensive performance of the alloy is attributed to the combined precipitation of DPs and CPs(with a total volume fraction of 5.4%and a volume ratio of CPs to DPs of 6.7).This study could provide a new approach and insight for improving the comprehensive properties of the Cu-Ni-Si alloys.
基金supported by the National Key R&D Program of China(2020YFA0714900,2023YFB3709903)the National Natural Science Foundation of China(U21B2082,52474410)+6 种基金the Key R&D Program of Shandong Province,China(2023CXGC010406)the Scientific Research Special Project for First-Class Disciplines in Inner Mongolia Autonomous Region(YLXKZX-NKD-001)the International Science and Technology Cooperation Project of Higher Education Institutions in Inner Mongolia Autonomous Region(GHXM-002)the Natural Science Foundation of Inner Mongolia Autonomous Region of China(2024ZD06)the Technology Support Project for the Construction of Major Innovation Platforms in Inner Mongolia Autonomous Region(XM2024XTGXQ16)the Beijing Municipal Natural Science Foundation(2222065)the Fundamental Research Funds for the Central Universities(FRF-TP-22-02C2).
文摘Traditional heat treatment methods require a significant amount of time and energy to affect atomic diffusion and enhance the spheroidization process of carbides in bearing steel,while pulsed current can accelerate atomic diffusion to achieve ultra-fast spheroidization of carbides.However,the understanding of the mechanism by which different pulse current parameters regulate the dissolution behavior of carbides requires a large amount of experimental data to support,which limits the application of pulse current technology in the field of heat treatment.Based on this,quantify the obtained pulse current processing data to create an important dataset that could be applied to machine learning.Through machine learning,the mechanism of mutual influence between carbide regulation and various factors was elucidated,and the optimal spheroidization process parameters were determined.Compared to the 20 h required for traditional heat treatment,the application of pulsed electric current technology achieved ultra-fast spheroidization of GCr15 bearing steel within 90 min.
基金supported by the National Natural Science Foundation of China“Study on the evolution law of discharge channel and deformation suppression method for low-pressure micro-arc milling machining of aerospace thin-walled parts”(52265061)The Tianshan Innovation Team“Robotics and intelligent equipment technology science and technology innovation team”(2022D14002).
文摘To address problems in surface integrity and machining allowance distribution during combined electric arc-mechanical milling,this paper takes TC4 as the research object,examines the influence of electric arc milling(EAM)depth on recast layer thickness and surface roughness,alongside an analysis of the recast layer’s organization characteristics and sur-face morphology.A comparative evaluation of cutting forces,surface roughness,and surface hardening is conducted between combined milling and conventional mechanical milling.Key findings reveal that electric arc machining produces a recast layer with a hardness of 313.21 HV.As the EAM depth increases,the localized recast layer thickness and peak-to-valley(PV)differ-ences also rise.To ensure effective surface defect removal,the machining allowance for subsequent mechanical milling must exceed the combined thickness of the recast layer and the PV difference.Under identical parameters,combined milling yields higher surface roughness(0.584μm)and greater surface hardening(10.4%)compared to mechanical milling alone,alongside an 18.716 N increase in cutting force.Response surface methodology(RSM)analysis identifies feed per tooth as the most significant factor affecting surface roughness,followed by spindle speed,with milling depth having the least influence.
文摘Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.
文摘沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-热设备容量协同优化配置方法。首先,根据计算任务对时延的敏感性,精细化建模可推迟可中断、可推迟不可中断及不可推迟3类任务负载的时间约束,在此基础上综合源荷不确定性建立数据中心集群微网“并网-离网”2阶段分布鲁棒优化模型,采用列与约束生成(column and constraint generation,C&CG)算法求解。以青海某实际数据中心为案例的分析结果表明:所提出的方法可使微网容量配置成本下降约25.8%,弃风率下降约56%,并大幅提高数据中心集群微网离网运行可靠性。该文研究为沙戈荒区域绿色低碳数据中心建设提供了理论支撑。
文摘目的评估时空电阻抗断层成像(spatiotemporal electrical impedance tomography,ST-EIT)在言语发声任务下,能否有效捕捉并区分腭裂(cleft palate,CP)患者与正常对照(normal control,NC)的言语呼吸功能特征。方法本研究纳入75名受试者(CP组37例,NC组38例)。在标准化发声任务中,同步采集电阻抗断层成像(electrical impedance tomography,EIT)图像与口鼻气流信号,构建涵盖时间、气流与空间维度的三域特征,采用曼-惠特尼U检验(MannWhitney U test)比较组间差异。基于肺量计法(spirometry)、鼻音计(nasometry)及ST-EIT等多源数据,分别训练极端梯度提升(extreme gradient boosting,XGBoost)分类模型,采用5折交叉验证评估性能,并引入Shapley加性解释(Shapley additive explanations,SHAP)方法进行特征贡献分析。结果CP组呈现显著的呼吸表型差异。时间域中,吸/呼相位时长均显著缩短(P<0.001),吸/呼时间比显著升高;气流域中,呼气期平均气流与峰值气流显著增强,吸气期无明显差异;空间域中,感兴趣区(region of interest,ROI)1和4的潮气阻抗变化(tidal impedance variation,TIV)显著升高,ROI2显著降低,全局不均一性(global inhomogeneity,GI)较低,通气中心(center of ventilation,CoV)呈轻度升高。ST-EIT模型分类性能最佳,曲线下面积(area under the curve,AUC)达0.915,准确率优于单一肺功能检测或鼻音计检测。SHAP结果表明,时空特征对分类决策贡献最大。结论ST-EIT技术能有效揭示CP患者言语呼吸功能在时间-气流-空间三域的特异性改变,为床旁筛查、康复评估及随访监测提供了优于常规检测的客观量化依据。
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
