In this paper,a novel intensifying-flux variable flux-leakage interior permanent magnet(IFVF-IPM)machine is proposed,in which flux barriers were designed deliberately between the adjacent poles to obtain intensifying-...In this paper,a novel intensifying-flux variable flux-leakage interior permanent magnet(IFVF-IPM)machine is proposed,in which flux barriers were designed deliberately between the adjacent poles to obtain intensifying-flux effect and variable flux-leakage property.The rotor topology and design principles of the proposed machine are also introduced.Then,a multi-objective optimization method is adopted based on the sensitivity analysis,and some design variables of IFVF-IPM machine with strong sensitivity are selected to optimization progress by using the non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ).Moreover,the electromagnetic characteristics of conventional IPM machine,conventional IFVF-IPM machine(CIFVF-IPM)and the novel IFVF-IPM machine are compared based on the finite element analysis(FEA)method which includes flux linkage,inductances characteristic,torque-speed envelops and power characteristic,as well as evaluation of the risk of irreversible demagnetization.Finally,the experiment results show that the IFVF-IPM machine has a better performance in flux weakening capability for wide speed range and a lower risk of irreversible demagnetization,which indicates the validity and feasibility of the proposed machine.展开更多
为了对黄河鲤体质量性状进行全基因组关联分析及全基因组选择模型的预测准确性比较,采用鲤250K高密度SNP芯片对613尾黄河鲤(Cyprinus carpio)进行基因分型,并通过测定其体质量性状的表型信息进行全基因组关联分析,以及基于体质量性状、...为了对黄河鲤体质量性状进行全基因组关联分析及全基因组选择模型的预测准确性比较,采用鲤250K高密度SNP芯片对613尾黄河鲤(Cyprinus carpio)进行基因分型,并通过测定其体质量性状的表型信息进行全基因组关联分析,以及基于体质量性状、全基因组关联分析(genome-wide association study,GWAS)的不同变异数据集对GBLUP、贝叶斯、RKHS和机器学习模型等10种全基因组选择模型的预测准确性进行比较,以筛选出适用于黄河鲤体质量性状的全基因组选择模型。结果表明:通过GWAS定位到与体质量性状相关的5个SNP,位于1号和21号染色体上,进一步筛选关联SNP所在区域的基因,定位到WBP1L、GPM6B、TIMMDC1、RCAN1、EOGT基因;当选取与黄河鲤体质量性状表型相关的前100个SNP作为数据集,分析全基因组选择模型预测准确性时,机器学习模型XGBoost的预测准确性最高,为0.26,当SNP的数量分别为500、1000、3000、5000、20000时,GBLUP模型的准确性均最高,分别为0.3084、0.3444、0.4393、0.4526、0.4007,而XGBoost、LightGBM和GBLUP模型的变异系数则较低,说明模型预测的稳定性相对可靠。研究表明,本研究中共鉴定到5个与黄河鲤体质量性状相关的候选基因,分别为WBP1L、GPM6B、TIMMDC1、RCAN1、EOGT,10种全基因组选择模型中GBLUP模型的预测准确性最高,可用于黄河鲤体质量性状的基因组选育。展开更多
The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless dat...The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless data transmission.End-nodes are connected to a gateway with a single hop.They consume very low-power,using very low data rate to deliver data.Since long transmission time is consequently needed for each data packet transmission in long range wide area networks,data transmission should be efficiently performed.Therefore,this paper proposes a multicast uplink data transmission mechanism particularly for bad network conditions.Transmission delay will be increased if only retransmissions are used under bad network conditions.However,employing multicast techniques in bad network conditions can significantly increase packet delivery rate.Thus,retransmission can be reduced and hence transmission efficiency increased.Therefore,the proposed method adopts multicast uplink after network condition prediction.To predict network conditions,the proposed method uses a deep neural network algorithm.The proposed method performance was verified by comparison with uplink unicast transmission only,confirming significantly improved performance.展开更多
Background:Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function.Efficient and precise AI algorithms may play a significant role in ...Background:Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function.Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia.In this study,we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts.Methods:We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend(WCHAT)study.For external validation,we used the Xiamen Aging Trend(XMAT)cohort.We compared the support vector machine(SVM),random forest(RF),eXtreme Gradient Boosting(XGB),and Wide and Deep(W&D)models.The area under the receiver operating curve(AUC)and accuracy(ACC)were used to evaluate the diagnostic efficiency of the models.Results:The WCHAT cohort,which included a total of 4057 participants for the training and testing datasets,and the XMAT cohort,which consisted of 553 participants for the external validation dataset,were enrolled in this study.Among the four models,W&D had the best performance(AUC=0.916±0.006,ACC=0.882±0.006),followed by SVM(AUC=0.907±0.004,ACC=0.877±0.006),XGB(AUC=0.877±0.005,ACC=0.868±0.005),and RF(AUC=0.843±0.031,ACC=0.836±0.024)in the training dataset.Meanwhile,in the testing dataset,the diagnostic efficiency of the models from large to small was W&D(AUC=0.881,ACC=0.862),XGB(AUC=0.858,ACC=0.861),RF(AUC=0.843,ACC=0.836),and SVM(AUC=0.829,ACC=0.857).In the external validation dataset,the performance of W&D(AUC=0.970,ACC=0.911)was the best among the four models,followed by RF(AUC=0.830,ACC=0.769),SVM(AUC=0.766,ACC=0.738),and XGB(AUC=0.722,ACC=0.749).Conclusions:The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness.It could be widely used in primary health care institutions or developing areas with an aging population.Trial Registration:Chictr.org,ChiCTR 1800018895.展开更多
This paper presented a novel wide-area nonlinear excitation control strategy for multi-machine power systems. A simple and effective model transformation method was proposed for the system's mathematical model in ...This paper presented a novel wide-area nonlinear excitation control strategy for multi-machine power systems. A simple and effective model transformation method was proposed for the system's mathematical model in the COI (center of inertia) coordinate system. The system was transformed to an uncertain linear one where deviation of generator terminal voltage became one of the new state variables. Then a wide-area nonlinear robust voltage controller was designed utilizing a LMI (linear matrix inequality) based robust control theory. The proposed controller does not rely on any preselected system operating point, adapts to variations of network parameters and system operation conditions, and assures regulation accuracy of generator terminal voltages. Neither rotor angle nor any variable's differentiation needs to be measured for the proposed controller, and only terminal voltages, rotor speeds, active and reactive power outputs of generators are required. In addition, the proposed controller not only takes into account time delays of remote signals, but also eliminates the effect of wide-area information's incompleteness when not all generators are equipped with PMU (phase measurement unit). Detailed tests were conducted by PSCAD/EMTDC for a three-machine and four-machine power systems respectively, and simulation results illustrate high performance of the proposed controller.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under grant no.52067008.
文摘In this paper,a novel intensifying-flux variable flux-leakage interior permanent magnet(IFVF-IPM)machine is proposed,in which flux barriers were designed deliberately between the adjacent poles to obtain intensifying-flux effect and variable flux-leakage property.The rotor topology and design principles of the proposed machine are also introduced.Then,a multi-objective optimization method is adopted based on the sensitivity analysis,and some design variables of IFVF-IPM machine with strong sensitivity are selected to optimization progress by using the non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ).Moreover,the electromagnetic characteristics of conventional IPM machine,conventional IFVF-IPM machine(CIFVF-IPM)and the novel IFVF-IPM machine are compared based on the finite element analysis(FEA)method which includes flux linkage,inductances characteristic,torque-speed envelops and power characteristic,as well as evaluation of the risk of irreversible demagnetization.Finally,the experiment results show that the IFVF-IPM machine has a better performance in flux weakening capability for wide speed range and a lower risk of irreversible demagnetization,which indicates the validity and feasibility of the proposed machine.
文摘为了对黄河鲤体质量性状进行全基因组关联分析及全基因组选择模型的预测准确性比较,采用鲤250K高密度SNP芯片对613尾黄河鲤(Cyprinus carpio)进行基因分型,并通过测定其体质量性状的表型信息进行全基因组关联分析,以及基于体质量性状、全基因组关联分析(genome-wide association study,GWAS)的不同变异数据集对GBLUP、贝叶斯、RKHS和机器学习模型等10种全基因组选择模型的预测准确性进行比较,以筛选出适用于黄河鲤体质量性状的全基因组选择模型。结果表明:通过GWAS定位到与体质量性状相关的5个SNP,位于1号和21号染色体上,进一步筛选关联SNP所在区域的基因,定位到WBP1L、GPM6B、TIMMDC1、RCAN1、EOGT基因;当选取与黄河鲤体质量性状表型相关的前100个SNP作为数据集,分析全基因组选择模型预测准确性时,机器学习模型XGBoost的预测准确性最高,为0.26,当SNP的数量分别为500、1000、3000、5000、20000时,GBLUP模型的准确性均最高,分别为0.3084、0.3444、0.4393、0.4526、0.4007,而XGBoost、LightGBM和GBLUP模型的变异系数则较低,说明模型预测的稳定性相对可靠。研究表明,本研究中共鉴定到5个与黄河鲤体质量性状相关的候选基因,分别为WBP1L、GPM6B、TIMMDC1、RCAN1、EOGT,10种全基因组选择模型中GBLUP模型的预测准确性最高,可用于黄河鲤体质量性状的基因组选育。
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2015-0-00403)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation)this work was supported by the Soonchunhyang University Research Fund.
文摘The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless data transmission.End-nodes are connected to a gateway with a single hop.They consume very low-power,using very low data rate to deliver data.Since long transmission time is consequently needed for each data packet transmission in long range wide area networks,data transmission should be efficiently performed.Therefore,this paper proposes a multicast uplink data transmission mechanism particularly for bad network conditions.Transmission delay will be increased if only retransmissions are used under bad network conditions.However,employing multicast techniques in bad network conditions can significantly increase packet delivery rate.Thus,retransmission can be reduced and hence transmission efficiency increased.Therefore,the proposed method adopts multicast uplink after network condition prediction.To predict network conditions,the proposed method uses a deep neural network algorithm.The proposed method performance was verified by comparison with uplink unicast transmission only,confirming significantly improved performance.
基金National Key R&D Program of China(No.2020YFC2005600)
文摘Background:Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function.Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia.In this study,we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts.Methods:We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend(WCHAT)study.For external validation,we used the Xiamen Aging Trend(XMAT)cohort.We compared the support vector machine(SVM),random forest(RF),eXtreme Gradient Boosting(XGB),and Wide and Deep(W&D)models.The area under the receiver operating curve(AUC)and accuracy(ACC)were used to evaluate the diagnostic efficiency of the models.Results:The WCHAT cohort,which included a total of 4057 participants for the training and testing datasets,and the XMAT cohort,which consisted of 553 participants for the external validation dataset,were enrolled in this study.Among the four models,W&D had the best performance(AUC=0.916±0.006,ACC=0.882±0.006),followed by SVM(AUC=0.907±0.004,ACC=0.877±0.006),XGB(AUC=0.877±0.005,ACC=0.868±0.005),and RF(AUC=0.843±0.031,ACC=0.836±0.024)in the training dataset.Meanwhile,in the testing dataset,the diagnostic efficiency of the models from large to small was W&D(AUC=0.881,ACC=0.862),XGB(AUC=0.858,ACC=0.861),RF(AUC=0.843,ACC=0.836),and SVM(AUC=0.829,ACC=0.857).In the external validation dataset,the performance of W&D(AUC=0.970,ACC=0.911)was the best among the four models,followed by RF(AUC=0.830,ACC=0.769),SVM(AUC=0.766,ACC=0.738),and XGB(AUC=0.722,ACC=0.749).Conclusions:The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness.It could be widely used in primary health care institutions or developing areas with an aging population.Trial Registration:Chictr.org,ChiCTR 1800018895.
文摘This paper presented a novel wide-area nonlinear excitation control strategy for multi-machine power systems. A simple and effective model transformation method was proposed for the system's mathematical model in the COI (center of inertia) coordinate system. The system was transformed to an uncertain linear one where deviation of generator terminal voltage became one of the new state variables. Then a wide-area nonlinear robust voltage controller was designed utilizing a LMI (linear matrix inequality) based robust control theory. The proposed controller does not rely on any preselected system operating point, adapts to variations of network parameters and system operation conditions, and assures regulation accuracy of generator terminal voltages. Neither rotor angle nor any variable's differentiation needs to be measured for the proposed controller, and only terminal voltages, rotor speeds, active and reactive power outputs of generators are required. In addition, the proposed controller not only takes into account time delays of remote signals, but also eliminates the effect of wide-area information's incompleteness when not all generators are equipped with PMU (phase measurement unit). Detailed tests were conducted by PSCAD/EMTDC for a three-machine and four-machine power systems respectively, and simulation results illustrate high performance of the proposed controller.
