Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
1.Opportunities for electric motor drives in the low-altitude economy The implementation plan for the innovative application of general aviation equipment(2024–2030)outlines that by 2027,new general aviation equipmen...1.Opportunities for electric motor drives in the low-altitude economy The implementation plan for the innovative application of general aviation equipment(2024–2030)outlines that by 2027,new general aviation equipment will achieve commercial applications in urban air transport,logistics distribution and emergency rescue.展开更多
Electric vehicle(EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance...Electric vehicle(EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance leads to significant torsional vibrations and speed fluctuations, which not only compromise passenger comfort but also exert additional mechanical stress on the EVs. Conventional sensorless methods offer speed estimation and control;however, they provide suboptimal performance with sudden load torque disturbances and operational uncertainties, especially at low speeds and across diverse real-world driving cycles. To address these challenges and improve system robustness, this work proposes an advanced sensorless integral sliding mode control(ASISMC) that enhances performance under diverse operating conditions. The proposed ASISMC methodology shows robust performance across a wide speed range, effectively mitigating abrupt load torque disturbances while minimizing the effect of uncertainties within the system dynamics. The approach is experimentally validated for a wide range of speeds and periodic/non-periodic load torque disturbances. Additional validation through the new European driving cycle(NEDC) and urban dynamometer driving schedule(UDDS) demonstrates the method's effectiveness and reliability in real-world driving conditions.展开更多
Using path analysis, correlation analysis, partial correlation analysis and system dynamics method to study the driving force of cultivated land in Qinghai Lake Area, and using gradually regression analysis to establi...Using path analysis, correlation analysis, partial correlation analysis and system dynamics method to study the driving force of cultivated land in Qinghai Lake Area, and using gradually regression analysis to establish the driving force model of utilized change of cultivated land. Driving factors, action mechanism and process of utilized change of cultivated land were analyzed, and the differences during all factors were compared. The study provides some decision basis for sustainable utilization and management of land resources in Qinghai Lake Area.展开更多
A generic pulse width modulation（PWM） strategy is proposed for the multi-leg voltage source inverter（VSI）.First, the multi-leg VSI is modeled, which is independent from the load structure. Secondly, the proposed P...A generic pulse width modulation（PWM） strategy is proposed for the multi-leg voltage source inverter（VSI）.First, the multi-leg VSI is modeled, which is independent from the load structure. Secondly, the proposed PWM strategy is deduced by inverting the mathematical model of the multileg VSI. According to the relationship between the leg number of VSIs and the phase number of electrical machines, the multi-leg VSI-fed machine drives are classified into two types：matched and unmatched applications. The leg numbers of VSIs and the phase number of electrical machines are equal in matched applications while they are different in unmatched applications. The existing PWM strategies cannot be directly used for both matched and unmatched applications. However,the proposed PWM strategy can be general for both matched and unmatched applications, and no modifications are required. The effectiveness of the proposed PWM strategy is verified by experimental results.展开更多
This study investigates the reduction in polarization measurement accuracy caused by varying in-cident angles in a liquid crystal variable retarder(LCVR).The phase delay characteristics of the LCVR were examined,with ...This study investigates the reduction in polarization measurement accuracy caused by varying in-cident angles in a liquid crystal variable retarder(LCVR).The phase delay characteristics of the LCVR were examined,with particular emphasis on the influence of different two-dimensional incident angles on phase delay behavior.Building upon the calibration of phase delay under normal incidence,a phase delay calibra-tion model was developed to account for variations in incident angle and driving voltage.A mathematical re-lationship was established between phase delay and the azimuth angle(α)and pitch angle(β).Experimental validation was conducted under three conditions:α=20°,β=0°;α=0°,β=20°;and an arbitrary angle whereα=5°,β=15°.The results demonstrated that the maximum average deviation between theoretical pre-dictions and experimental measurements did not exceed 0.059 rad.The proposed calibration method proved to be both accurate and practical.This approach offers robust support for LCVR parameter calibration and performance optimization in optical systems,particularly in polarization imaging applications.展开更多
A diverse range of light and waves,spanning from near-infrared to ultraviolet,alongside ultrasound,have proven effective in propelling nanomotors.This review encapsulates the advancements in nanomotor research propell...A diverse range of light and waves,spanning from near-infrared to ultraviolet,alongside ultrasound,have proven effective in propelling nanomotors.This review encapsulates the advancements in nanomotor research propelled by waves of varying frequencies.It delves into the driving mechanisms and control methodologies of different nanomotor types,emphasizing the role of frequency.Nanomotors can be classified based on the frequency of the driving wave,encompassing ultraviolet light-driven,visible light-driven,near-infrared-driven,and ultrasounddriven variants.Each category corresponds to distinct propulsion mechanisms,including momentum transfer,photothermal effects,self-electrophoresis,and acoustic radiation force.Notably,visible light and near-infrared radiation predominantly propel momentum transfer nanomotors,while photothermal nanomotors are chiefly active within the infrared spectrum.Ultraviolet light drives most self-electrophoretic nanomotors,while ultrasound-driven nanomotors respond to acoustic radiation force.Furthermore,precise control over nanomotor speed and direction is achievable by adjusting the frequency of incident waves within a narrow range,modulating wave absorption rates.Lastly,this paper explores microwave nanomotors,an area yet to be reported,shedding light on potential driving mechanisms.展开更多
With the development of autonomous driving technologies,modern vehicles are confronted with two major challenges.On one hand,the rapid growth in electronic control units and data-intensive applications has led to a sh...With the development of autonomous driving technologies,modern vehicles are confronted with two major challenges.On one hand,the rapid growth in electronic control units and data-intensive applications has led to a sharp increase in invehicle data traffic,thereby demanding much higher communication bandwidth,lower latency,and enhanced security.On the other hand,ensuring driving safety calls for more advanced thermal management systems,as traditional point-type sensors face deployment challenges due to their limited monitoring range.展开更多
To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network ...To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network with attention mecha-nism(SS-SIRCN),inspired by the behavioral adaptation patterns of biological groups under external threats.First,by thoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed to risk,the study reveals the underlying patterns of trajectory changes influenced by external danger.Then,an attention-based spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features and potential driving risks.Finally,a semi-supervised learning framework is employed to enable risk assessment for autono-mous vehicles using only a small amount of labeled data.Experimental results on real-world vehicle trajectory datasets demonstrate that the proposed method achieves a risk prediction accuracy of 90.76%,outperforming other baseline models in performance.展开更多
This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematic...This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematical models of the tooth flanks for the face gear drives are established based on the gear skiving processes.Then,load tooth contact analysis(LTCA)model is established to calculate the contact performance data.Next,a two-stage optimization model is employed to determine the optimal parameters of the cutting edge with improved contact performances.The effectiveness of this method is validated through simulations and rolling tests.Compared with the traditional method,the proposed method can machine both the face gear and its mating pinion with a single cutter.Simulation results show that the proposed method avoids tooth surface edge contact,with the maximum tooth surface contact stress reduced by 31.7%,the contact ratio decreases by 21.5%,and the transmission error increases by 22.3%.Rolling tests verify the consistency of tooth surface contact patterns between simulations and experiments.The proposed method provides a reference for the cutting edge design of skiving cutters for face gear pairs.展开更多
Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies ha...Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.展开更多
This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limita...This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.展开更多
Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility ar...Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.展开更多
Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspect...Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspects of the driving decisions(strategic decision,tactical decision and operation decision)to analyze the economy of vehicle energy.The analytic hierarchy process(AHP)is used to assign the weight of the internal evaluation indexes,so as to form a complete assessment for drivers'eco-driving behaviors.The research result can not only quantitatively describe the energy-saving effect of drivers'decisions,but also put forward targeted driving suggestions to optimize drivers'eco-driving behaviors.This assessment model helps to clarify the potential of eco-driving on energy economy of transportation in a hierarchical way,and provides a valuable theoretical basis for the further promotion and application of eco-driving education.展开更多
Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster...Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.展开更多
Exploring hydroclimatic variability and its driving mechanisms during the Holocene is essential for comprehending both historical and prospective responses of water resources to climatic shifts in Arid Central Asia(AC...Exploring hydroclimatic variability and its driving mechanisms during the Holocene is essential for comprehending both historical and prospective responses of water resources to climatic shifts in Arid Central Asia(ACA)region.However,debate persists regarding whether dryland lakes in this region exhibited aridification or humidification during the Holocene.Lopnur serves as the terminal lake of Tarim rivers during the Holocene,which offers an ideal natural laboratory to address the questions.In this study,a high-resolution chronological framework was established through precise radiocarbon dating.Multi-proxy analyses,including geochemical composition,grain size distributions,MS,LOI,and C/N ratios were conducted from a lacustrine profile in the core area of“Great ear”in the southern part of Lopnur catchment.These analyses enabled the reconstruction of hydrological dynamics and facilitated the disentanglement of independent signals linked to climate variability,runoff fluctuations,and lake-level changes.The results demonstrate that the MidHolocene(7800–4000 cal yr B.P.)was characterized by cold and humid conditions,resulting in elevated surface runoff and lake level.The Late Holocene(4000–1300 cal yr B.P.)experienced intensified aridification,characterized by reduced runoff and declining lake level.These evidences suggested a climatic regime of a distinctive alternation between“cold-wet”and“warm-dry”climatic regimes during the Mid-to-Late Holocene.Compared with the previous studies from adjacent regions,we speculate that the hydroclimatic evolution of Lopnur catchment possibly influenced by a complex interplay of large spatial scale forcings,including variations in annual insolation,greenhouse gas concentrations,and ice sheets,as well as the localized controls such as topographic features,vegetation cover,and cloud-radiative feedbacks.Our findings enhance the understanding of past climatic complexity and provide valuable insights for future water resource management strategies in drylands.展开更多
As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and dr...As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and driving mechanisms of such landslides remains relatively limited.To address this knowledge gap,the present study integrated data analysis,field investigations,and remote sensing interpretation to construct a landslide database for the core area of the Qinling Mountains,and systematically analyzed the spatial patterns,development characteristics,and environmental driving factors of shallow landslides.The results reveal that shallow landslides are predominantly small-to-medium in scale,concentrated in regions with an altitude of 800–1000 m and a slope gradient of approximately 30°,with a distinct tendency to develop on sunny(southfacing)slopes.The occurrence frequency of these landslides exhibits a significant positive correlation with the soil moisture content of the weathered layer and the degree of groundwater enrichment in the study area.Specifically,these landslides are mainly developed in bedrock fissure water zones and karst fissure water zones with favorable water-bearing capacity,indicating that rainfall and surface hydrological processes are the key triggering factors for shallow landslides.Notably,vegetation exerts a mediating role in the"vegetation-hydrology-landslide"system:shallow landslides occur most frequently in areas with artificial or shrub-grass vegetation,peaking at a moderate coverage of 50%–60%.This peak suggests that vegetation within this range is ineffective at regulating soil moisture,while the interaction between specific vegetation types and hydrological enrichment further exacerbates landslide risk.By prioritizing the weights of vegetation and hydrological factors,we enhanced the information quantity model,which significantly improved its performance and increased the AUC value to 0.83.These findings confirm the pivotal roles of vegetation and hydrological factors,thereby providing a robust scientific basis for targeted landslide prevention and control in this region.展开更多
Understanding the evolution and mechanisms of livestock industry agglomeration provides valuable policy insights for reconciling growing meat demand with constrained resource endowments. This study analyzes the spatia...Understanding the evolution and mechanisms of livestock industry agglomeration provides valuable policy insights for reconciling growing meat demand with constrained resource endowments. This study analyzes the spatial agglomeration of livestock industry at the county level across China from 2000 to 2022 using the localization quotient and Moran's I. An interpretable machine learning approach is employed to test hypotheses concerning the driving mechanisms underlying the spatial distribution of livestock industry. The results show that the agglomeration of China's livestock industry is intensifying, with the agro-pastoral transitional zone(APTZ) emerging as a prominent agglomeration area and distinct agglomeration patterns observed within the zone as well as in its eastern and western regions. Proximity to markets has become an increasingly important determinant of livestock industry agglomeration in China. This market-driven shift has heightened the demand for agricultural feed, prompting the livestock industry to relax its dependence on local natural resource endowments and gradually relocate eastward. Regionally, the agglomeration within the APTZ is shaped by the joint effects of natural and social factors. Natural factors dominate agglomeration dynamics in the western regions of the zone, whereas social factors are more influential in its eastern regions.展开更多
The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronou...The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronounced spatiotemporal heterogeneity and fragmented distribution patterns,rendering them highly sensitive to environmental change.This study integrated Sentinel-2 remote sensing imagery with the SedInConnect model to delineate wetland patch distributions and calculate the Index of Connectivity(IC)values across the basin.Based on IC values,we stratified field sampling sites into high-,moderate-,and lowconnectivity gradient groups to analyze the relationships among plant community characteristics,vegetation spatial patterns,and wetland connectivity in the Qaidam Basin.Partial Least Squares Path Modeling(PLSPM)was further employed to quantify the driving mechanisms underlying wetland vegetation characteristics.The results revealed that wetland connectivity across the basin was generally low,with IC values up to 1.32 and displaying a west-to-east decreasing gradient.The west and northwest were characterized by relatively continuous high-connectivity wetland networks,while fragmented and low-connectivity wetlands predominated in the east and southeast.Connectivity regulated wetland vegetation patterns primarily by affecting patch size,fragmentation,and internal adjacency.High-connectivity areas had higher class area(CA),largest patch index(LPI),and area-weighted mean patch size(AREA_AM)than low-connectivity areas.Connectivity had the strongest effect on vegetation coverage,which declined sharply from 87.577%in highconnectivity areas to 12.152%in low-connectivity areas.Meanwhile,species diversity showed a moderately negative response to connectivity changes,whereas species evenness remained relatively unaffected.PLS-PM explained 78.300%and 67.500%of the variance in vegetation community and vegetation pattern,respectively.Climate played a dominant role in shaping vegetation characteristics,with significant negative effects on both vegetation community and pattern.Topography influenced vegetation indirectly through climate,and connectivity was influenced by both drivers and exerted positive effects on vegetation community and pattern.This study reveals the multi-pathway driving mechanisms underlying vegetation pattern formation in alpine wetlands,providing a theoretical foundation and decision-support framework for the scientific conservation and adaptive management of wetlands in the Qaidam Basin.展开更多
This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee dr...This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee driving safety,which is always ignored in existing works.Therefore,we propose a vehicular image segmentation-oriented semantic communication system,termed VIS-SemCom,focusing on transmitting and recovering image semantic features of high-important objects to reduce transmission redundancy.First,we develop a semantic codec based on Swin Transformer architecture,which expands the perceptual field thus improving the segmentation accuracy.To highlight the important objects'accuracy,we propose a multi-scale semantic extraction method by assigning the number of Swin Transformer blocks for diverse resolution semantic features.Also,an importance-aware loss incorporating important levels is devised,and an online hard example mining(OHEM)strategy is proposed to handle small sample issues in the dataset.Finally,experimental results demonstrate that the proposed VIS-SemCom can achieve a significant mean intersection over union(mIoU)performance in the SNR regions,a reduction of transmitted data volume by about 60%at 60%mIoU,and improve the segmentation accuracy of important objects,compared to baseline image communication.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
基金supported by the National Natural Science Foundation of China(No.52407064)。
文摘1.Opportunities for electric motor drives in the low-altitude economy The implementation plan for the innovative application of general aviation equipment(2024–2030)outlines that by 2027,new general aviation equipment will achieve commercial applications in urban air transport,logistics distribution and emergency rescue.
文摘Electric vehicle(EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance leads to significant torsional vibrations and speed fluctuations, which not only compromise passenger comfort but also exert additional mechanical stress on the EVs. Conventional sensorless methods offer speed estimation and control;however, they provide suboptimal performance with sudden load torque disturbances and operational uncertainties, especially at low speeds and across diverse real-world driving cycles. To address these challenges and improve system robustness, this work proposes an advanced sensorless integral sliding mode control(ASISMC) that enhances performance under diverse operating conditions. The proposed ASISMC methodology shows robust performance across a wide speed range, effectively mitigating abrupt load torque disturbances while minimizing the effect of uncertainties within the system dynamics. The approach is experimentally validated for a wide range of speeds and periodic/non-periodic load torque disturbances. Additional validation through the new European driving cycle(NEDC) and urban dynamometer driving schedule(UDDS) demonstrates the method's effectiveness and reliability in real-world driving conditions.
基金Supported by The Regional Sustainable Development of the Qing-TibetPlateau(2004)~~
文摘Using path analysis, correlation analysis, partial correlation analysis and system dynamics method to study the driving force of cultivated land in Qinghai Lake Area, and using gradually regression analysis to establish the driving force model of utilized change of cultivated land. Driving factors, action mechanism and process of utilized change of cultivated land were analyzed, and the differences during all factors were compared. The study provides some decision basis for sustainable utilization and management of land resources in Qinghai Lake Area.
基金The National Natural Science Foundation of China(No.51607038)the Natural Science Foundation of Jiangsu Province(No.BK20160673)+1 种基金the National Basic Research Program of China(973 Program)(No.2013CB035603)China Postdoctoral Science Foundation(No.2015M581697,2016T90401)
文摘A generic pulse width modulation（PWM） strategy is proposed for the multi-leg voltage source inverter（VSI）.First, the multi-leg VSI is modeled, which is independent from the load structure. Secondly, the proposed PWM strategy is deduced by inverting the mathematical model of the multileg VSI. According to the relationship between the leg number of VSIs and the phase number of electrical machines, the multi-leg VSI-fed machine drives are classified into two types：matched and unmatched applications. The leg numbers of VSIs and the phase number of electrical machines are equal in matched applications while they are different in unmatched applications. The existing PWM strategies cannot be directly used for both matched and unmatched applications. However,the proposed PWM strategy can be general for both matched and unmatched applications, and no modifications are required. The effectiveness of the proposed PWM strategy is verified by experimental results.
文摘This study investigates the reduction in polarization measurement accuracy caused by varying in-cident angles in a liquid crystal variable retarder(LCVR).The phase delay characteristics of the LCVR were examined,with particular emphasis on the influence of different two-dimensional incident angles on phase delay behavior.Building upon the calibration of phase delay under normal incidence,a phase delay calibra-tion model was developed to account for variations in incident angle and driving voltage.A mathematical re-lationship was established between phase delay and the azimuth angle(α)and pitch angle(β).Experimental validation was conducted under three conditions:α=20°,β=0°;α=0°,β=20°;and an arbitrary angle whereα=5°,β=15°.The results demonstrated that the maximum average deviation between theoretical pre-dictions and experimental measurements did not exceed 0.059 rad.The proposed calibration method proved to be both accurate and practical.This approach offers robust support for LCVR parameter calibration and performance optimization in optical systems,particularly in polarization imaging applications.
基金supported by the National Key Research and Development Program of China(2021YFA1401103)the National Natural Science Foundation of China(52473109,52073071)+3 种基金China Scholarship Council(CSC)(202306790056)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX22_2301)111 Project(B23008)the Innovative Leading Talent Team supported by 2022Wuxi Taihu Talent Program(1096010241230120)。
文摘A diverse range of light and waves,spanning from near-infrared to ultraviolet,alongside ultrasound,have proven effective in propelling nanomotors.This review encapsulates the advancements in nanomotor research propelled by waves of varying frequencies.It delves into the driving mechanisms and control methodologies of different nanomotor types,emphasizing the role of frequency.Nanomotors can be classified based on the frequency of the driving wave,encompassing ultraviolet light-driven,visible light-driven,near-infrared-driven,and ultrasounddriven variants.Each category corresponds to distinct propulsion mechanisms,including momentum transfer,photothermal effects,self-electrophoresis,and acoustic radiation force.Notably,visible light and near-infrared radiation predominantly propel momentum transfer nanomotors,while photothermal nanomotors are chiefly active within the infrared spectrum.Ultraviolet light drives most self-electrophoretic nanomotors,while ultrasound-driven nanomotors respond to acoustic radiation force.Furthermore,precise control over nanomotor speed and direction is achievable by adjusting the frequency of incident waves within a narrow range,modulating wave absorption rates.Lastly,this paper explores microwave nanomotors,an area yet to be reported,shedding light on potential driving mechanisms.
文摘With the development of autonomous driving technologies,modern vehicles are confronted with two major challenges.On one hand,the rapid growth in electronic control units and data-intensive applications has led to a sharp increase in invehicle data traffic,thereby demanding much higher communication bandwidth,lower latency,and enhanced security.On the other hand,ensuring driving safety calls for more advanced thermal management systems,as traditional point-type sensors face deployment challenges due to their limited monitoring range.
基金the Jilin Provincial Department of Science and Technology Youth Science and Technology Talent Cultivation Project(20250602051RC)Fundamental Research Funds for the Central Universities(2025-JCXK-19)National Natural Science Foundation of China under Grant 52272417.
文摘To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network with attention mecha-nism(SS-SIRCN),inspired by the behavioral adaptation patterns of biological groups under external threats.First,by thoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed to risk,the study reveals the underlying patterns of trajectory changes influenced by external danger.Then,an attention-based spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features and potential driving risks.Finally,a semi-supervised learning framework is employed to enable risk assessment for autono-mous vehicles using only a small amount of labeled data.Experimental results on real-world vehicle trajectory datasets demonstrate that the proposed method achieves a risk prediction accuracy of 90.76%,outperforming other baseline models in performance.
基金Project(2024YFB3410402)supported by the National Key R&D Program of ChinaProject(52075558)supported by the National Natural Science Foundation of China+2 种基金Project(2021RC3012)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProject(2023CXQD050)supported by the Central South University Innovation-Driven Research Program,ChinaProject(CX20230255)supported by the Fundamental Research Funds for the Central Universities,China。
文摘This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematical models of the tooth flanks for the face gear drives are established based on the gear skiving processes.Then,load tooth contact analysis(LTCA)model is established to calculate the contact performance data.Next,a two-stage optimization model is employed to determine the optimal parameters of the cutting edge with improved contact performances.The effectiveness of this method is validated through simulations and rolling tests.Compared with the traditional method,the proposed method can machine both the face gear and its mating pinion with a single cutter.Simulation results show that the proposed method avoids tooth surface edge contact,with the maximum tooth surface contact stress reduced by 31.7%,the contact ratio decreases by 21.5%,and the transmission error increases by 22.3%.Rolling tests verify the consistency of tooth surface contact patterns between simulations and experiments.The proposed method provides a reference for the cutting edge design of skiving cutters for face gear pairs.
基金supported by the Tianjin Manufacturing High Quality Development Special Foundation(No.20232185)the Roycom Foundation(No.70306901).
文摘Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00245084)by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(RS-2024-00415938,HRD Program for Industrial Innovation)and Soonchunhyang University.
文摘This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.
基金The National Key Research and Development Program of China,No.2023YFC3206601。
文摘Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.
文摘Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspects of the driving decisions(strategic decision,tactical decision and operation decision)to analyze the economy of vehicle energy.The analytic hierarchy process(AHP)is used to assign the weight of the internal evaluation indexes,so as to form a complete assessment for drivers'eco-driving behaviors.The research result can not only quantitatively describe the energy-saving effect of drivers'decisions,but also put forward targeted driving suggestions to optimize drivers'eco-driving behaviors.This assessment model helps to clarify the potential of eco-driving on energy economy of transportation in a hierarchical way,and provides a valuable theoretical basis for the further promotion and application of eco-driving education.
基金Under the auspices of National Natural Science Foundation of China(No.42571219)Key Project of Zhejiang Province Soft Science Research Plan(No.2023C25014)。
文摘Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.
基金supported by the National Natural Science Foundation of China(No.42001084)the Major Science and Technology Projects of Xinjiang Uygur Autonomous Region(Nos.2022A03009-2,2022A03009)the Third Xinjiang Scientific Expedition Program(No.2022xjkk1303)。
文摘Exploring hydroclimatic variability and its driving mechanisms during the Holocene is essential for comprehending both historical and prospective responses of water resources to climatic shifts in Arid Central Asia(ACA)region.However,debate persists regarding whether dryland lakes in this region exhibited aridification or humidification during the Holocene.Lopnur serves as the terminal lake of Tarim rivers during the Holocene,which offers an ideal natural laboratory to address the questions.In this study,a high-resolution chronological framework was established through precise radiocarbon dating.Multi-proxy analyses,including geochemical composition,grain size distributions,MS,LOI,and C/N ratios were conducted from a lacustrine profile in the core area of“Great ear”in the southern part of Lopnur catchment.These analyses enabled the reconstruction of hydrological dynamics and facilitated the disentanglement of independent signals linked to climate variability,runoff fluctuations,and lake-level changes.The results demonstrate that the MidHolocene(7800–4000 cal yr B.P.)was characterized by cold and humid conditions,resulting in elevated surface runoff and lake level.The Late Holocene(4000–1300 cal yr B.P.)experienced intensified aridification,characterized by reduced runoff and declining lake level.These evidences suggested a climatic regime of a distinctive alternation between“cold-wet”and“warm-dry”climatic regimes during the Mid-to-Late Holocene.Compared with the previous studies from adjacent regions,we speculate that the hydroclimatic evolution of Lopnur catchment possibly influenced by a complex interplay of large spatial scale forcings,including variations in annual insolation,greenhouse gas concentrations,and ice sheets,as well as the localized controls such as topographic features,vegetation cover,and cloud-radiative feedbacks.Our findings enhance the understanding of past climatic complexity and provide valuable insights for future water resource management strategies in drylands.
基金supported by the National Key R&D Program of China(No.2024YFF1306502)three Special Programs of the National Natural Science Foundation of China(Nos.42341101,42442045,42307220)the Basic Scientific Research Business Funds of Central Universities(Nos.300102263401,300102265501,300102264103)。
文摘As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and driving mechanisms of such landslides remains relatively limited.To address this knowledge gap,the present study integrated data analysis,field investigations,and remote sensing interpretation to construct a landslide database for the core area of the Qinling Mountains,and systematically analyzed the spatial patterns,development characteristics,and environmental driving factors of shallow landslides.The results reveal that shallow landslides are predominantly small-to-medium in scale,concentrated in regions with an altitude of 800–1000 m and a slope gradient of approximately 30°,with a distinct tendency to develop on sunny(southfacing)slopes.The occurrence frequency of these landslides exhibits a significant positive correlation with the soil moisture content of the weathered layer and the degree of groundwater enrichment in the study area.Specifically,these landslides are mainly developed in bedrock fissure water zones and karst fissure water zones with favorable water-bearing capacity,indicating that rainfall and surface hydrological processes are the key triggering factors for shallow landslides.Notably,vegetation exerts a mediating role in the"vegetation-hydrology-landslide"system:shallow landslides occur most frequently in areas with artificial or shrub-grass vegetation,peaking at a moderate coverage of 50%–60%.This peak suggests that vegetation within this range is ineffective at regulating soil moisture,while the interaction between specific vegetation types and hydrological enrichment further exacerbates landslide risk.By prioritizing the weights of vegetation and hydrological factors,we enhanced the information quantity model,which significantly improved its performance and increased the AUC value to 0.83.These findings confirm the pivotal roles of vegetation and hydrological factors,thereby providing a robust scientific basis for targeted landslide prevention and control in this region.
基金National Natural Science Foundation of China,No.42277488Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA26010301。
文摘Understanding the evolution and mechanisms of livestock industry agglomeration provides valuable policy insights for reconciling growing meat demand with constrained resource endowments. This study analyzes the spatial agglomeration of livestock industry at the county level across China from 2000 to 2022 using the localization quotient and Moran's I. An interpretable machine learning approach is employed to test hypotheses concerning the driving mechanisms underlying the spatial distribution of livestock industry. The results show that the agglomeration of China's livestock industry is intensifying, with the agro-pastoral transitional zone(APTZ) emerging as a prominent agglomeration area and distinct agglomeration patterns observed within the zone as well as in its eastern and western regions. Proximity to markets has become an increasingly important determinant of livestock industry agglomeration in China. This market-driven shift has heightened the demand for agricultural feed, prompting the livestock industry to relax its dependence on local natural resource endowments and gradually relocate eastward. Regionally, the agglomeration within the APTZ is shaped by the joint effects of natural and social factors. Natural factors dominate agglomeration dynamics in the western regions of the zone, whereas social factors are more influential in its eastern regions.
基金funded by the National Natural Science Foundation of China(42230720)the 2023 Annual Qinghai Province"Kunlun Talents-High-end Innovation and Entrepreneurship Talent"Program Project.
文摘The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronounced spatiotemporal heterogeneity and fragmented distribution patterns,rendering them highly sensitive to environmental change.This study integrated Sentinel-2 remote sensing imagery with the SedInConnect model to delineate wetland patch distributions and calculate the Index of Connectivity(IC)values across the basin.Based on IC values,we stratified field sampling sites into high-,moderate-,and lowconnectivity gradient groups to analyze the relationships among plant community characteristics,vegetation spatial patterns,and wetland connectivity in the Qaidam Basin.Partial Least Squares Path Modeling(PLSPM)was further employed to quantify the driving mechanisms underlying wetland vegetation characteristics.The results revealed that wetland connectivity across the basin was generally low,with IC values up to 1.32 and displaying a west-to-east decreasing gradient.The west and northwest were characterized by relatively continuous high-connectivity wetland networks,while fragmented and low-connectivity wetlands predominated in the east and southeast.Connectivity regulated wetland vegetation patterns primarily by affecting patch size,fragmentation,and internal adjacency.High-connectivity areas had higher class area(CA),largest patch index(LPI),and area-weighted mean patch size(AREA_AM)than low-connectivity areas.Connectivity had the strongest effect on vegetation coverage,which declined sharply from 87.577%in highconnectivity areas to 12.152%in low-connectivity areas.Meanwhile,species diversity showed a moderately negative response to connectivity changes,whereas species evenness remained relatively unaffected.PLS-PM explained 78.300%and 67.500%of the variance in vegetation community and vegetation pattern,respectively.Climate played a dominant role in shaping vegetation characteristics,with significant negative effects on both vegetation community and pattern.Topography influenced vegetation indirectly through climate,and connectivity was influenced by both drivers and exerted positive effects on vegetation community and pattern.This study reveals the multi-pathway driving mechanisms underlying vegetation pattern formation in alpine wetlands,providing a theoretical foundation and decision-support framework for the scientific conservation and adaptive management of wetlands in the Qaidam Basin.
基金National Natural Science Foundation of China under Grants No.62171047,U22B2001,62271065,62001051Beijing Natural Science Foundation under Grant L223027BUPT Excellent Ph.D Students Foundation under Grants CX2021114。
文摘This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee driving safety,which is always ignored in existing works.Therefore,we propose a vehicular image segmentation-oriented semantic communication system,termed VIS-SemCom,focusing on transmitting and recovering image semantic features of high-important objects to reduce transmission redundancy.First,we develop a semantic codec based on Swin Transformer architecture,which expands the perceptual field thus improving the segmentation accuracy.To highlight the important objects'accuracy,we propose a multi-scale semantic extraction method by assigning the number of Swin Transformer blocks for diverse resolution semantic features.Also,an importance-aware loss incorporating important levels is devised,and an online hard example mining(OHEM)strategy is proposed to handle small sample issues in the dataset.Finally,experimental results demonstrate that the proposed VIS-SemCom can achieve a significant mean intersection over union(mIoU)performance in the SNR regions,a reduction of transmitted data volume by about 60%at 60%mIoU,and improve the segmentation accuracy of important objects,compared to baseline image communication.
