The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware...The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.展开更多
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.展开更多
The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differ...The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.展开更多
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates ...The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.展开更多
Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme ...Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-li...In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-link capacitors.The voltage pulses required by inductance-based initial position detection can cause unequal discharge of the series capacitors,shifting the neutral-point voltage away from half of DC-link voltage(U_(dc)/2).This neutral-point drift breaks the spatial symmetry of the inverter voltage vectors,so the 360°electrical period can no longer be evenly partitioned into six sectors during initial rotor position detection.To address this issue,this paper proposes a detection-pulse injection sequence that explicitly accounts for the asymmetric voltage vectors of the FSTP inverter.With the proposed sequence,the initial rotor position can be identified within a 30°electrical sector.The method requires no additional voltage or current sensors,and experimental results confirm its feasibility.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Definite-time zero-sequence over-current protection is presently used in systems whose neutral point is grounded by a low resistance(low-resistance grounding systems).These systems frequently malfunction owing to thei...Definite-time zero-sequence over-current protection is presently used in systems whose neutral point is grounded by a low resistance(low-resistance grounding systems).These systems frequently malfunction owing to their high settings of the action value when a high-impedance grounding fault occurs.In this study,the relationship between the zero-sequence currents of each feeder and the neutral branch was analyzed.Then,a grounding protection method was proposed on the basis of the zero-sequence current ratio coefficient.It is defined as the ratio of the zero-sequence current of the feeder to that of the neutral branch.Nonetheless,both zero-sequence voltage and zero-sequence current are affected by the transition resistance,The influence of transition resistance can be eliminated by calculating this coefficient.Therefore,a method based on the zero-sequence current ratio coefficient was proposed considering the significant difference between the faulty feeder and healthy feeder.Furthermore,unbalanced current can be prevented by setting the starting current.PSCAD simulation results reveal that the proposed method shows high reliability and sensitivity when a high-resistance grounding fault occurs.展开更多
Dual three-phase permanent-magnet synchronous machines(DTP-PMSM)connected with a single neutral point provide a loop for zero-sequence current(ZSC).This paper proposes a novel space vector pulse width modulation(SVPWM...Dual three-phase permanent-magnet synchronous machines(DTP-PMSM)connected with a single neutral point provide a loop for zero-sequence current(ZSC).This paper proposes a novel space vector pulse width modulation(SVPWM)strategy to suppress the ZSC.Five vectors are selected as basic voltage vectors in one switching period.The fundamental and harmonic planes and the zero-sequence plane are taken into consideration to synthesis the reference voltage vector.To suppress the ZSC,a non-zero zero-sequence voltage(ZSV)is generated to compensate the third harmonic back-EMF.Rather than triangular carrier modulation,the sawtooth carrier modulation strategy is used to generate asymmetric PWM signals.The modulation range is investigated to explore the variation of modulation range caused by considering the zero-sequence plane.With the proposed method,the ZSC can be considerably reduced.The simulated and experimental results are presented to validate the effectiveness of the proposed modulation strategy.展开更多
The arc-suppression coil(ASC)in parallel low resistance(LR)multi-mode grounding is adopted in the mountain wind farm to cope with the phenomenon that is misoperation or refusal of zero-sequence protection in LR ground...The arc-suppression coil(ASC)in parallel low resistance(LR)multi-mode grounding is adopted in the mountain wind farm to cope with the phenomenon that is misoperation or refusal of zero-sequence protection in LR grounding wind farm.If the fault disappears before LR is put into the system,it is judged as an instantaneous fault;while the fault does not disappear after LR is put into the system,it is judged as a permanent fault;the single-phase grounding fault(SLG)protection criterion based on zerosequence power variation is proposed to identify the instantaneous-permanent fault.Firstly,the distribution characteristic of zero-sequence voltage(ZSV)and zero-sequence current(ZSC)are analyzed after SLGfault occurs in multi-mode grounding.Then,according to the characteristics that zero-sequence power variation of non-fault collector line is small,while the zero-sequence power variation of fault collector line can reflect the active power component of fault resistance,the protection criterion based on zero-sequence power variation is constructed.The theoretical analysis and simulation results show that the protection criterion can distinguish the property of fault only by using the single terminal information,which has high reliability.展开更多
Compared with the traditional three-phase star connection winding,the open-end winding permanent magnet synchronous motor(OW-PMSM)system with a common direct current(DC)bus has a zero-sequence circuit,which makes the ...Compared with the traditional three-phase star connection winding,the open-end winding permanent magnet synchronous motor(OW-PMSM)system with a common direct current(DC)bus has a zero-sequence circuit,which makes the common-mode voltage and the back electromotive force(EMF)harmonic generated by the inverters produce the zero-sequence current in the zero-sequence circuit,and the zero-sequence current has great influence on the operation efficiency and stability of the motor control system.A zero-sequence current suppression strategy is presented based on model predictive current control for OW-PMSM.Through the mathematical model of OW-PMSM to establish the predictive model and the zero-sequence circuit model,the common-mode voltage under different voltage vector combinations is fully considered during vector selection and action time calculation.Then zero-sequence loop constraints are established,so as to suppress the zero-sequence current.In the end,the control strategy proposed in this paper is verified by simulation experiments.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
This paper reveals new contributions to the analysis and development of devices for harmonic distortion mitigation. Considering the sequential distribution of harmonic currents, zero-sequence components could be dimin...This paper reveals new contributions to the analysis and development of devices for harmonic distortion mitigation. Considering the sequential distribution of harmonic currents, zero-sequence components could be diminished using electromagnetic devices, particularly the eZSB （electromagnetic zero-sequence blocking）. One important characteristic of this device, which has received particular attention on this research, is its robustness and low cost of construction. Theoretical and experimental results related to the behavior of the electromagnetic blocking devices are presented. The results illustrate the consistence of the theoretical aspects related with the model in the frequency domain, as well as the performance of the blocking devices, reducing zero-sequence harmonic currents, mainly by the conjunct action of the eZSF （electromagnetic zero-sequence harmonic filter）, working as a impedance coupler. In this context, aiming the evaluation of the reliability of the results obtained through mathematical modeling, experimental tests have been carried out using a low-power prototype, highlighting particular aspects related to its function as a zero-sequence harmonic blocker.展开更多
Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion...Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion,however,it is an evolving field that has taken a new leap forward in recent years.A review and analysis of thrust-vectoring schemes for electric propulsion systems have been conducted.The scope of this review includes thrust-vectoring schemes that can be implemented for electrostatic,electromagnetic,and beam-driven thrusters.A classification of electric propulsion schemes that provide thrust-vectoring capability is developed.More attention is given to schemes implemented in laboratory prototypes and flight models.The final part is devoted to a discussion on the suitability of different electric propulsion systems with thrust-vectoring capability for modern space mission operations.The thrust-vectoring capability of electric propulsion is necessary for inner and outer space satellites,which are at a disadvantage with conventional unidirectional propulsion systems due to their limited maneuverability.展开更多
Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the eva...Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.展开更多
Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie...Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.展开更多
The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collect...The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collecting data in a near-equatorial orbit.Magnetic field data from MSS-1's onboard Vector Fluxgate Magnetometer(VFM),collected at a sample rate of 50 Hz,allows us to detect and investigate sources of magnetic data contamination,from DC to relevant Nyquist frequency.Here we report two types of artificial disturbances in the VFM data.One is V-shaped events concentrated at night,with frequencies sweeping from the Nyquist frequency down to zero and back up.The other is 5-Hz events(ones that exhibit a distinct 5 Hz spectrum peak);these events are always accompanied by intervals of spiky signals,and are clearly related to the attitude control of the satellite.Our analyses show that VFM noise levels in daytime are systematically lower than in nighttime.The daily average noise levels exhibit a period of about 52 days.The V-shaped events are strongly correlated with higher VFM noise levels.展开更多
基金co-supported by the National Natural Science Foundation of China(Grant Nos.62222404,T2450054,62304084,62504087,62361136587 and 92248304)the National Key Research and Development Plan of China(Grant No.2021YFB3601200)+3 种基金the Major Program of Hubei Province(Grant No.2023BAA009)the Research Grants Council of Hong Kong Postdoctoral Fellowship Scheme(Grant No.PDFS2223-4S06)the China Postdoctoral Science Foundation funded project(Grant No.2025M770530)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20250136).
文摘The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
基金supported by the National Key R&D Program of China under Grant No.2023YFA1008702the National Natural Science Foundation of China under Grant No.12571300。
文摘The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.
基金supported by grants PID2020-120308RB-I00 and PID2023-147802OB-I00 funded by MICIU/AEI/10.13039/501100011033FEDER,UE,by Aligning Science Across Parkinson’s(ref.ASAP-020505)through the Michael J.Fox Foundation for Parkinson’s Research+1 种基金by CiberNed Intramural Collaborative Projects(ref.PI2020/09)by the Spanish Fundación Mutua Madrile?a de Investigación Médica(to JLL)。
文摘The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.
文摘Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金supported in part by the National Natural Science Foundation of China under Grant 52477060in part by the Tianjin Natural Science Foundation Project under Grant 24JCZDJC00250in part by the Zhejiang Leading Innovation and Entrepreneurship Team Project under Grant 2024R01012.
文摘In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-link capacitors.The voltage pulses required by inductance-based initial position detection can cause unequal discharge of the series capacitors,shifting the neutral-point voltage away from half of DC-link voltage(U_(dc)/2).This neutral-point drift breaks the spatial symmetry of the inverter voltage vectors,so the 360°electrical period can no longer be evenly partitioned into six sectors during initial rotor position detection.To address this issue,this paper proposes a detection-pulse injection sequence that explicitly accounts for the asymmetric voltage vectors of the FSTP inverter.With the proposed sequence,the initial rotor position can be identified within a 30°electrical sector.The method requires no additional voltage or current sensors,and experimental results confirm its feasibility.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金supported in part by National Key Research and Development Program of China(2016YFB0900603)Technology Projects of State Grid Corporation of China(52094017000W).
文摘Definite-time zero-sequence over-current protection is presently used in systems whose neutral point is grounded by a low resistance(low-resistance grounding systems).These systems frequently malfunction owing to their high settings of the action value when a high-impedance grounding fault occurs.In this study,the relationship between the zero-sequence currents of each feeder and the neutral branch was analyzed.Then,a grounding protection method was proposed on the basis of the zero-sequence current ratio coefficient.It is defined as the ratio of the zero-sequence current of the feeder to that of the neutral branch.Nonetheless,both zero-sequence voltage and zero-sequence current are affected by the transition resistance,The influence of transition resistance can be eliminated by calculating this coefficient.Therefore,a method based on the zero-sequence current ratio coefficient was proposed considering the significant difference between the faulty feeder and healthy feeder.Furthermore,unbalanced current can be prevented by setting the starting current.PSCAD simulation results reveal that the proposed method shows high reliability and sensitivity when a high-resistance grounding fault occurs.
基金supported in part by the National Natural Science Foundation of China under Grant 51977099。
文摘Dual three-phase permanent-magnet synchronous machines(DTP-PMSM)connected with a single neutral point provide a loop for zero-sequence current(ZSC).This paper proposes a novel space vector pulse width modulation(SVPWM)strategy to suppress the ZSC.Five vectors are selected as basic voltage vectors in one switching period.The fundamental and harmonic planes and the zero-sequence plane are taken into consideration to synthesis the reference voltage vector.To suppress the ZSC,a non-zero zero-sequence voltage(ZSV)is generated to compensate the third harmonic back-EMF.Rather than triangular carrier modulation,the sawtooth carrier modulation strategy is used to generate asymmetric PWM signals.The modulation range is investigated to explore the variation of modulation range caused by considering the zero-sequence plane.With the proposed method,the ZSC can be considerably reduced.The simulated and experimental results are presented to validate the effectiveness of the proposed modulation strategy.
基金This paper is supported in part by the National Natural Science Foundations of China,and the Major Science and Technology Projects in Yunnan Province under Grant Nos.51667010,51807085,and 202002AF080001.
文摘The arc-suppression coil(ASC)in parallel low resistance(LR)multi-mode grounding is adopted in the mountain wind farm to cope with the phenomenon that is misoperation or refusal of zero-sequence protection in LR grounding wind farm.If the fault disappears before LR is put into the system,it is judged as an instantaneous fault;while the fault does not disappear after LR is put into the system,it is judged as a permanent fault;the single-phase grounding fault(SLG)protection criterion based on zerosequence power variation is proposed to identify the instantaneous-permanent fault.Firstly,the distribution characteristic of zero-sequence voltage(ZSV)and zero-sequence current(ZSC)are analyzed after SLGfault occurs in multi-mode grounding.Then,according to the characteristics that zero-sequence power variation of non-fault collector line is small,while the zero-sequence power variation of fault collector line can reflect the active power component of fault resistance,the protection criterion based on zero-sequence power variation is constructed.The theoretical analysis and simulation results show that the protection criterion can distinguish the property of fault only by using the single terminal information,which has high reliability.
基金Fundamental Research Funds for the Central Universities,China(No.2232019D3-53)Initial Research Funds for Young Teachers of Donghua University,China(104070053029)Shanghai Rising-Star Program,China(No.19QA1400400)。
文摘Compared with the traditional three-phase star connection winding,the open-end winding permanent magnet synchronous motor(OW-PMSM)system with a common direct current(DC)bus has a zero-sequence circuit,which makes the common-mode voltage and the back electromotive force(EMF)harmonic generated by the inverters produce the zero-sequence current in the zero-sequence circuit,and the zero-sequence current has great influence on the operation efficiency and stability of the motor control system.A zero-sequence current suppression strategy is presented based on model predictive current control for OW-PMSM.Through the mathematical model of OW-PMSM to establish the predictive model and the zero-sequence circuit model,the common-mode voltage under different voltage vector combinations is fully considered during vector selection and action time calculation.Then zero-sequence loop constraints are established,so as to suppress the zero-sequence current.In the end,the control strategy proposed in this paper is verified by simulation experiments.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
文摘This paper reveals new contributions to the analysis and development of devices for harmonic distortion mitigation. Considering the sequential distribution of harmonic currents, zero-sequence components could be diminished using electromagnetic devices, particularly the eZSB （electromagnetic zero-sequence blocking）. One important characteristic of this device, which has received particular attention on this research, is its robustness and low cost of construction. Theoretical and experimental results related to the behavior of the electromagnetic blocking devices are presented. The results illustrate the consistence of the theoretical aspects related with the model in the frequency domain, as well as the performance of the blocking devices, reducing zero-sequence harmonic currents, mainly by the conjunct action of the eZSF （electromagnetic zero-sequence harmonic filter）, working as a impedance coupler. In this context, aiming the evaluation of the reliability of the results obtained through mathematical modeling, experimental tests have been carried out using a low-power prototype, highlighting particular aspects related to its function as a zero-sequence harmonic blocker.
基金performed at large-scale research facility"Beam-M"of Bauman Moscow State Technical University following the government task by the Ministry of Science and Higher Education of the Russian Federation(No.FSFN-2024-0007).
文摘Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion,however,it is an evolving field that has taken a new leap forward in recent years.A review and analysis of thrust-vectoring schemes for electric propulsion systems have been conducted.The scope of this review includes thrust-vectoring schemes that can be implemented for electrostatic,electromagnetic,and beam-driven thrusters.A classification of electric propulsion schemes that provide thrust-vectoring capability is developed.More attention is given to schemes implemented in laboratory prototypes and flight models.The final part is devoted to a discussion on the suitability of different electric propulsion systems with thrust-vectoring capability for modern space mission operations.The thrust-vectoring capability of electric propulsion is necessary for inner and outer space satellites,which are at a disadvantage with conventional unidirectional propulsion systems due to their limited maneuverability.
基金primarily supported by the National Key R&D Program of China[grant number 2021YFC3000904]the Jiangsu Provincial Key Technology R&D Program[grant number BE2022851]National Natural Science Foundation of China[grant number 42405035]。
文摘Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.
基金supported by the National Key R&D Program of China(Grant2022YFF0503700)the National Natural Science Foundation of China(42474200 and 42174186)。
文摘The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collecting data in a near-equatorial orbit.Magnetic field data from MSS-1's onboard Vector Fluxgate Magnetometer(VFM),collected at a sample rate of 50 Hz,allows us to detect and investigate sources of magnetic data contamination,from DC to relevant Nyquist frequency.Here we report two types of artificial disturbances in the VFM data.One is V-shaped events concentrated at night,with frequencies sweeping from the Nyquist frequency down to zero and back up.The other is 5-Hz events(ones that exhibit a distinct 5 Hz spectrum peak);these events are always accompanied by intervals of spiky signals,and are clearly related to the attitude control of the satellite.Our analyses show that VFM noise levels in daytime are systematically lower than in nighttime.The daily average noise levels exhibit a period of about 52 days.The V-shaped events are strongly correlated with higher VFM noise levels.
