Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of...Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of PMSM. Thus, an inter-turn short-circuit fault(ITSCF) diagnosis method based on high frequency(HF) voltage residual is proposed in this paper with proper HF signal injection. First, the analytical models of PMSM after the ITSCF are deduced. Based on the model, the voltage residual at low frequency(LF) and HF can be obtained. It is revealed that the HF voltage residual has a stronger ITSCF detection capability compared to the LF voltage residual. To obtain optimal fault signature, a 3-phase symmetrical HF voltage is injected into the machine drive system, and the HF voltage residuals are extracted. The fault indicator is defined as the standard deviation of the 3-phase HF voltage residuals. The effectiveness of the proposed ITSCF diagnosis method is verified by experiments on a triple 3-phase PMSM. It is worth noting that no extra hardware equipment is required to implement the proposed method.展开更多
Forecasting of ocean currents is critical for both marine meteorological research and ocean engineering and construction.Timely and accurate forecasting of coastal current velocities offers a scientific foundation and...Forecasting of ocean currents is critical for both marine meteorological research and ocean engineering and construction.Timely and accurate forecasting of coastal current velocities offers a scientific foundation and decision support for multiple practices such as search and rescue,disaster avoidance and remediation,and offshore construction.This research established a framework to generate short-term surface current forecasts based on ensemble machine learning trained on high frequency radar observation.Results indicate that an ensemble algorithm that used random forests to filter forecasting features by weighting them,and then used the AdaBoost method to forecast can significantly reduce the model training time,while ensuring the model forecasting effectiveness,with great economic benefits.Model accuracy is a function of surface current variability and the forecasting horizon.In order to improve the forecasting capability and accuracy of the model,the model structure of the ensemble algorithm was optimized,and the random forest algorithm was used to dynamically select model features.The results show that the error variation of the optimized surface current forecasting model has a more regular error variation,and the importance of the features varies with the forecasting time-step.At ten-step ahead forecasting horizon the model reported root mean square error,mean absolute error,and correlation coefficient by 2.84 cm/s,2.02 cm/s,and 0.96,respectively.The model error is affected by factors such as topography,boundaries,and geometric accuracy of the observation system.This paper demonstrates the potential of ensemble-based machine learning algorithm to improve forecasting of ocean currents.展开更多
Digital pulse width modulator is an integral part in digitally controlled Direct Current to Direct Current (DC-DC) converter utilized in modern portable devices. This paper presents a new Digital Pulse Width Modulator...Digital pulse width modulator is an integral part in digitally controlled Direct Current to Direct Current (DC-DC) converter utilized in modern portable devices. This paper presents a new Digital Pulse Width Modulator (DPWM) architecture for DC-DC converter using mealy finite state machine with gray code encoding scheme and one hot encoding method to derive the variable duty cycle Pulse Width Modulation (PWM) signal without varying the clock frequency. To verify the proposed DPWM technique, the architecture with control input of six, five and four bits are implemented and the maximum operating frequency along with power consumption results is obtained for different Field Programmable Gate Array (FPGA) devices. The post layout timing results are presented showing that architecture can work with maximum frequency of 326 MHz and derive PWM signal of 3.59 MHz. Experimental results show the implementation of the proposed architecture in low-cost FPGA (Spartan 3A) with on-board oscillator clock frequency of 12 MHz which is multiplied internally by two with Digital Clock Manager (DCM) and derive the PWM signal of 1.5 MHz with a time resolution of 1 ps.展开更多
对于广播电视制播工程项目,为确保直播内容安全播出和内容编辑的时效性,与直播相关的系统和内容编辑系统的核心设备需要接入不间断电源(Uninterruptible Power Supply,UPS)供电回路。因此,在供配电系统设计过程中,如何选择合适的UPS电...对于广播电视制播工程项目,为确保直播内容安全播出和内容编辑的时效性,与直播相关的系统和内容编辑系统的核心设备需要接入不间断电源(Uninterruptible Power Supply,UPS)供电回路。因此,在供配电系统设计过程中,如何选择合适的UPS电源设备技术架构成为一个关键问题。主要探讨了广播电视制播工程项目中UPS电源设备的选用,对UPS高频机和UPS工频机进行了比较分析,进而给出了广播电视制播工程中UPS电源设备的选用策略。展开更多
High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the ap...High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection(AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting(DIS) approach.The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine(SVM) and artificial neural network(ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics,information gain and ROC methods can achieve better prediction performance than the DIS approach.Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.展开更多
针对高速移动场景中人机混编通信模式下的安全问题展开研究,提出基于时延多普勒(Delay Doppler, DD)域密钥提取的正交时频空—物理层加密(Orthogonal Time Frequency Space-Physical Layer Encryption, OTFS-PLE)方法。该方法充分利用...针对高速移动场景中人机混编通信模式下的安全问题展开研究,提出基于时延多普勒(Delay Doppler, DD)域密钥提取的正交时频空—物理层加密(Orthogonal Time Frequency Space-Physical Layer Encryption, OTFS-PLE)方法。该方法充分利用快时变信道在DD域中的稀疏性,高效准确地估计信道路径的增益、多普勒频移和时延大小,生成安全可靠的初始密钥,再通过Tent序列将初始密钥扩展成加密密钥,根据密钥对OTFS的星座点进行相位扰乱,实现高效的加解密。该方法解决了高速移动场景人机混编通信中的密钥提取难的问题,能生成可靠的密钥并实现人机混编系统安全高效的加密通信。展开更多
基金supported in part by the Jiangsu Carbon Peak Carbon Neutralization Science and Technology Innovation Special Fund under Grant BE2022032-1National Natural Science Foundation of China under Grant 52277035, Grant 51937006 and Grant 51907028the “SEU Zhishan Young Scholars” Program of Southeast University。
文摘Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of PMSM. Thus, an inter-turn short-circuit fault(ITSCF) diagnosis method based on high frequency(HF) voltage residual is proposed in this paper with proper HF signal injection. First, the analytical models of PMSM after the ITSCF are deduced. Based on the model, the voltage residual at low frequency(LF) and HF can be obtained. It is revealed that the HF voltage residual has a stronger ITSCF detection capability compared to the LF voltage residual. To obtain optimal fault signature, a 3-phase symmetrical HF voltage is injected into the machine drive system, and the HF voltage residuals are extracted. The fault indicator is defined as the standard deviation of the 3-phase HF voltage residuals. The effectiveness of the proposed ITSCF diagnosis method is verified by experiments on a triple 3-phase PMSM. It is worth noting that no extra hardware equipment is required to implement the proposed method.
基金The fund from Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.SML2020SP009the National Basic Research and Development Program of China under contract Nos 2022YFF0802000 and 2022YFF0802004+3 种基金the“Renowned Overseas Professors”Project of Guangdong Provincial Department of Science and Technology under contract No.76170-52910004the Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention under contract No.2022491711the National Natural Science Foundation of China under contract No.51909290the Key Research and Development Program of Guangdong Province under contract No.2020B1111020003.
文摘Forecasting of ocean currents is critical for both marine meteorological research and ocean engineering and construction.Timely and accurate forecasting of coastal current velocities offers a scientific foundation and decision support for multiple practices such as search and rescue,disaster avoidance and remediation,and offshore construction.This research established a framework to generate short-term surface current forecasts based on ensemble machine learning trained on high frequency radar observation.Results indicate that an ensemble algorithm that used random forests to filter forecasting features by weighting them,and then used the AdaBoost method to forecast can significantly reduce the model training time,while ensuring the model forecasting effectiveness,with great economic benefits.Model accuracy is a function of surface current variability and the forecasting horizon.In order to improve the forecasting capability and accuracy of the model,the model structure of the ensemble algorithm was optimized,and the random forest algorithm was used to dynamically select model features.The results show that the error variation of the optimized surface current forecasting model has a more regular error variation,and the importance of the features varies with the forecasting time-step.At ten-step ahead forecasting horizon the model reported root mean square error,mean absolute error,and correlation coefficient by 2.84 cm/s,2.02 cm/s,and 0.96,respectively.The model error is affected by factors such as topography,boundaries,and geometric accuracy of the observation system.This paper demonstrates the potential of ensemble-based machine learning algorithm to improve forecasting of ocean currents.
文摘Digital pulse width modulator is an integral part in digitally controlled Direct Current to Direct Current (DC-DC) converter utilized in modern portable devices. This paper presents a new Digital Pulse Width Modulator (DPWM) architecture for DC-DC converter using mealy finite state machine with gray code encoding scheme and one hot encoding method to derive the variable duty cycle Pulse Width Modulation (PWM) signal without varying the clock frequency. To verify the proposed DPWM technique, the architecture with control input of six, five and four bits are implemented and the maximum operating frequency along with power consumption results is obtained for different Field Programmable Gate Array (FPGA) devices. The post layout timing results are presented showing that architecture can work with maximum frequency of 326 MHz and derive PWM signal of 3.59 MHz. Experimental results show the implementation of the proposed architecture in low-cost FPGA (Spartan 3A) with on-board oscillator clock frequency of 12 MHz which is multiplied internally by two with Digital Clock Manager (DCM) and derive the PWM signal of 1.5 MHz with a time resolution of 1 ps.
文摘对于广播电视制播工程项目,为确保直播内容安全播出和内容编辑的时效性,与直播相关的系统和内容编辑系统的核心设备需要接入不间断电源(Uninterruptible Power Supply,UPS)供电回路。因此,在供配电系统设计过程中,如何选择合适的UPS电源设备技术架构成为一个关键问题。主要探讨了广播电视制播工程项目中UPS电源设备的选用,对UPS高频机和UPS工频机进行了比较分析,进而给出了广播电视制播工程中UPS电源设备的选用策略。
基金Supported by the Philosophy and Social Science Fund of Higher Institutions of Jiangsu Province(2017SJB0234)Natural Science Foundation of Higher Education Institutions of Jiangsu Province(17KJB120004)+2 种基金MOE Layout Foundation of Humanities and Social Sciences(17YJA790101)the National Natural Science Foundation of China(71471081,71501088,71671082)MOE Project of Humanities and Social Sciences(17YJC630128)
文摘High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection(AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting(DIS) approach.The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine(SVM) and artificial neural network(ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics,information gain and ROC methods can achieve better prediction performance than the DIS approach.Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.
文摘针对高速移动场景中人机混编通信模式下的安全问题展开研究,提出基于时延多普勒(Delay Doppler, DD)域密钥提取的正交时频空—物理层加密(Orthogonal Time Frequency Space-Physical Layer Encryption, OTFS-PLE)方法。该方法充分利用快时变信道在DD域中的稀疏性,高效准确地估计信道路径的增益、多普勒频移和时延大小,生成安全可靠的初始密钥,再通过Tent序列将初始密钥扩展成加密密钥,根据密钥对OTFS的星座点进行相位扰乱,实现高效的加解密。该方法解决了高速移动场景人机混编通信中的密钥提取难的问题,能生成可靠的密钥并实现人机混编系统安全高效的加密通信。
