The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ...The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.展开更多
Objective： The aims of this study were to assess the prognostic significance of WHO-based Prognostic Scoring System （WPSS） in myelodysplastic syndrome （MDS） from a single center institute and to compare WPSS with...Objective： The aims of this study were to assess the prognostic significance of WHO-based Prognostic Scoring System （WPSS） in myelodysplastic syndrome （MDS） from a single center institute and to compare WPSS with the international prognostic scoring system （IPSS）. Methods： A total of 100 cases with de novo MDS were reviewed and their karyotypes were detected. All of them were followed up and classified according to IPSS and WPSS risk groups. SPSS 13.0 software was applied to deal with all the data. The statistical methods included Kaplan - Meier, Log-rank test and cox regression. Results： Multivariate cox regression analysis indicated that WHO Classification （P=0.0190）, karyotype abnormalities categorized according to IPSS （P=0.0159） and red blood cell （RBC） transfusion （P=0.0009） were the three most important independent factors for predicting overall survival （OS） of MDS. WPSS and IPSS both had great capacity in predicting the OS of MDS at the time of diagnosis （P〈0.0001）. In time-dependent analysis, WPSS can predict the OS accurately in the following three years after diagnosis （P〈0.0001）, while IPSS failed to predict the OS 24 months after diagnosis （P=0.1094）. Conclusion： Our single center results proved that WPSS is a dynamic prognostic system which can predict the OS of MDS patients at any time during the course of their disease. This time-dependent prognostic scoring system may replace the IPSS in the near future.展开更多
基金The research project,“Research on Power Safety Assisted Decision System Based on Large Language Models”(Project Number:JSDL24051414020001)acknowledges with gratitude the financial and logistical support it has received.
文摘The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.
文摘Objective： The aims of this study were to assess the prognostic significance of WHO-based Prognostic Scoring System （WPSS） in myelodysplastic syndrome （MDS） from a single center institute and to compare WPSS with the international prognostic scoring system （IPSS）. Methods： A total of 100 cases with de novo MDS were reviewed and their karyotypes were detected. All of them were followed up and classified according to IPSS and WPSS risk groups. SPSS 13.0 software was applied to deal with all the data. The statistical methods included Kaplan - Meier, Log-rank test and cox regression. Results： Multivariate cox regression analysis indicated that WHO Classification （P=0.0190）, karyotype abnormalities categorized according to IPSS （P=0.0159） and red blood cell （RBC） transfusion （P=0.0009） were the three most important independent factors for predicting overall survival （OS） of MDS. WPSS and IPSS both had great capacity in predicting the OS of MDS at the time of diagnosis （P〈0.0001）. In time-dependent analysis, WPSS can predict the OS accurately in the following three years after diagnosis （P〈0.0001）, while IPSS failed to predict the OS 24 months after diagnosis （P=0.1094）. Conclusion： Our single center results proved that WPSS is a dynamic prognostic system which can predict the OS of MDS patients at any time during the course of their disease. This time-dependent prognostic scoring system may replace the IPSS in the near future.
