Based on the analysis of the basic principle of slope compensation, a high-precision adaptive slope compensation circuit for peak current mode boost DC/DC converter is designed. The circuit dynamically detects the inp...Based on the analysis of the basic principle of slope compensation, a high-precision adaptive slope compensation circuit for peak current mode boost DC/DC converter is designed. The circuit dynamically detects the input and output voltage of the boost circuit to realize automatic adjustment of the compensation amount with the change of duty ratio, which makes the ramp compensation slope optimized. The design uses a high-precision subtracter to improve the accuracy of slope compensation. While eliminating sub-slope oscillation and improving the stability of boost circuit, the negative impact of compensation on boost circuit is minimized, and the load capacity and transient response speed of boost circuit are guaranteed. The circuit is designed based on SMIC 0.18um CMOS technology, with simple structure, high reliability and easy engineering implementation. Spectre circuit simulator 17.1.0.124 64b simulation results show that the circuit has high compensation accuracy and wide input and output voltage range. When the working voltage is 3.3 V, the compensation slope can be adjusted adaptively under different duty cycles, and the minimum error between the compensation slope and the theoretical optimal compensation slope is only 0.42%.展开更多
Cardiotocography is one of the most widely used technique for recording changes in fetal heart rate (FHR) and uterine contractions. Assessing cardiotocography is crucial in that it leads to iden- tifying fetuses which...Cardiotocography is one of the most widely used technique for recording changes in fetal heart rate (FHR) and uterine contractions. Assessing cardiotocography is crucial in that it leads to iden- tifying fetuses which suffer from lack of oxygen, i.e. hypoxia. This situation is defined as fetal dis- tress and requires fetal intervention in order to prevent fetus death or other neurological disease caused by hypoxia. In this study a computer-based approach for analyzing cardiotocogram in- cluding diagnostic features for discriminating a pathologic fetus. In order to achieve this aim adaptive boosting ensemble of decision trees and various other machine learning algorithms are employed.展开更多
Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of it...Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of its seri-als-AdaBoost,analyzes the typical algorithms of AdaBoost.展开更多
Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Mac...Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.展开更多
文摘Based on the analysis of the basic principle of slope compensation, a high-precision adaptive slope compensation circuit for peak current mode boost DC/DC converter is designed. The circuit dynamically detects the input and output voltage of the boost circuit to realize automatic adjustment of the compensation amount with the change of duty ratio, which makes the ramp compensation slope optimized. The design uses a high-precision subtracter to improve the accuracy of slope compensation. While eliminating sub-slope oscillation and improving the stability of boost circuit, the negative impact of compensation on boost circuit is minimized, and the load capacity and transient response speed of boost circuit are guaranteed. The circuit is designed based on SMIC 0.18um CMOS technology, with simple structure, high reliability and easy engineering implementation. Spectre circuit simulator 17.1.0.124 64b simulation results show that the circuit has high compensation accuracy and wide input and output voltage range. When the working voltage is 3.3 V, the compensation slope can be adjusted adaptively under different duty cycles, and the minimum error between the compensation slope and the theoretical optimal compensation slope is only 0.42%.
文摘Cardiotocography is one of the most widely used technique for recording changes in fetal heart rate (FHR) and uterine contractions. Assessing cardiotocography is crucial in that it leads to iden- tifying fetuses which suffer from lack of oxygen, i.e. hypoxia. This situation is defined as fetal dis- tress and requires fetal intervention in order to prevent fetus death or other neurological disease caused by hypoxia. In this study a computer-based approach for analyzing cardiotocogram in- cluding diagnostic features for discriminating a pathologic fetus. In order to achieve this aim adaptive boosting ensemble of decision trees and various other machine learning algorithms are employed.
文摘Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of its seri-als-AdaBoost,analyzes the typical algorithms of AdaBoost.
基金This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government(MSIT)-NRF-2020R1A2B5B02002478.
文摘Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.
