Sine-wave drive and square-wave drive are two common motor control strategies.This study constructs a mathematical model capable of predicting the distribution of electromagnetic force waves in synchronous reluctance ...Sine-wave drive and square-wave drive are two common motor control strategies.This study constructs a mathematical model capable of predicting the distribution of electromagnetic force waves in synchronous reluctance motors(SynRMs)under these two drive methods,and comparatively analyzes the vibration phenomena induced by electromagnetic forces under different drive methods.It aims to provide an effective tool for predicting the distribution of electromagnetic force waves in SynRMs,while exploring the influence of drive modes on their vibration characteristics.The study focuses on a 4-pole,36-slot 5.5 kW SynRM.Based on the magnetomotive force(MMF)-permeance method,incorporating the special rotor structure and the characteristics of current harmonics under square-wave drive,an air-gap flux distribution function is established.Meanwhile,Maxwell’s stress tensor method is adopted to analyze how the air-gap flux density relates to electromagnetic excitation force waves.Subsequently,this analysis is applied to forecast the spatiotemporal distribution features of radial electromagnetic force waves.Finite element simulations are conducted to compute the modal and vibration responses of the SynRM,followed by a comparative analysis of the vibration characteristics under the two drive methods.Additionally,a 6-pole,36-slot SynRM is used for additional comparative verification.Ultimately,the effectiveness of the simulation results is verified through experiments.展开更多
Timely acquisition of chicken behavioral information is crucial for assessing chicken health status and production performance.Video-based behavior recognition has emerged as a primary technique for obtaining such inf...Timely acquisition of chicken behavioral information is crucial for assessing chicken health status and production performance.Video-based behavior recognition has emerged as a primary technique for obtaining such information due to its accuracy and robustness.Video-based models generally predict a single behavior from a single video segment of a fixed duration.However,during periods of high activity in poultry,behavior transition may occur within a video segment,and existing models often fail to capture such transitions effectively.This limitation highlights the insufficient temporal resolution of video-based behavior recognition models.This study presents a chicken behavior recognition and localization model,CBLFormer,which is based on spatiotemporal feature learning.The model was designed to recognize behaviors that occur before and after transitions in video segments and to localize the corresponding time interval for each behavior.An improved transformer block,the cascade encoder-decoder network(CEDNet),a transformer-based head,and weighted distance intersection over union(WDIoU)loss were integrated into CBLFormer to enhance the model's ability to distinguish between different behavior categories and locate behavior boundaries.For the training and testing of CBLFormer,a dataset was created by collecting videos from 320 chickens across different ages and rearing densities.The results showed that CBLFormer achieved a mAP@0.5:0.95 of 98.34%on the test set.The integration of CEDNet contributed the most to the performance improvement of CBLFormer.The visualization results confirmed that the model effectively captured the behavioral boundaries of chickens and correctly recognized behavior categories.The transfer learning results demonstrated that the model is applicable to chicken behavior recognition and localization tasks in real-world poultry farms.The proposed method handles cases where poultry behavior transitions occur within the video segment and improves the temporal resolution of video-based behavior recognition models.展开更多
Nickel-based superalloy(GH4169)is an ideal material for preparing turbine blades.Profile grinding of the fir-treeshaped turbine blade root can easily cause thermal damage to the workpiece specimen.This study aims to e...Nickel-based superalloy(GH4169)is an ideal material for preparing turbine blades.Profile grinding of the fir-treeshaped turbine blade root can easily cause thermal damage to the workpiece specimen.This study aims to enhance the suppression of alloy thermal damage by regulating the thickness of the oxide film on the cubic boron nitride(CBN)grinding wheel during the electrolytic in-process dressing(ELID)-assisted grinding process.A theoretical model for calculating the thickness of oxide film in ELID-assisted grinding was developed.Finite element simulation was conducted using the electrolytic film-forming process of the grinding wheel.The effects of electrical/nonelectrical parameters on the oxide film characteristics and grinding responses were addressed.The optimal matching scheme of process parameters was established.The results showed that the film layer of the grinding wheel at the blade root cam is more seriously damaged,and the workpiece surface is rougher.Further optimization of the electrode is demanded to achieve different dressing effects at various positions of the grinding wheel based on the workpiece profile.By reducing the interelectrode gap(h_(e)),increasing the power supply voltage(E_(o)),and controlling the electrolysis time(△t)at 10-15 min,the preferred film-forming efficiency and grinding quality can be achieved.By increasing the grinding wheel speed(V_(s))or decreasing the workpiece feed rate(V_(f))and grinding depth(a_(p)),the grinding thermal damage can be suppressed.A larger value of V_(f)or apcan be selected to acquire a compromise between grinding quality and film-forming efficiency after increasing the value of Vs.The optimal combination of electrical and nonelectrical parameters during this test is E_(o)=120 V,△t=15 min,h_(e)=0.1 mm,V_f=50 mm min^(-1),V_s=30 m s^(-1),and a_(p)=0.4 mm.展开更多
基金supported by the Science and Technology Project of State Grid Corporation of China Headquarters under Grant 5500-202416156A-1-1-ZN.
文摘Sine-wave drive and square-wave drive are two common motor control strategies.This study constructs a mathematical model capable of predicting the distribution of electromagnetic force waves in synchronous reluctance motors(SynRMs)under these two drive methods,and comparatively analyzes the vibration phenomena induced by electromagnetic forces under different drive methods.It aims to provide an effective tool for predicting the distribution of electromagnetic force waves in SynRMs,while exploring the influence of drive modes on their vibration characteristics.The study focuses on a 4-pole,36-slot 5.5 kW SynRM.Based on the magnetomotive force(MMF)-permeance method,incorporating the special rotor structure and the characteristics of current harmonics under square-wave drive,an air-gap flux distribution function is established.Meanwhile,Maxwell’s stress tensor method is adopted to analyze how the air-gap flux density relates to electromagnetic excitation force waves.Subsequently,this analysis is applied to forecast the spatiotemporal distribution features of radial electromagnetic force waves.Finite element simulations are conducted to compute the modal and vibration responses of the SynRM,followed by a comparative analysis of the vibration characteristics under the two drive methods.Additionally,a 6-pole,36-slot SynRM is used for additional comparative verification.Ultimately,the effectiveness of the simulation results is verified through experiments.
基金Supported by Scientific Research Fund of Zhejiang Provincial Education Department(Y202457020).
文摘Timely acquisition of chicken behavioral information is crucial for assessing chicken health status and production performance.Video-based behavior recognition has emerged as a primary technique for obtaining such information due to its accuracy and robustness.Video-based models generally predict a single behavior from a single video segment of a fixed duration.However,during periods of high activity in poultry,behavior transition may occur within a video segment,and existing models often fail to capture such transitions effectively.This limitation highlights the insufficient temporal resolution of video-based behavior recognition models.This study presents a chicken behavior recognition and localization model,CBLFormer,which is based on spatiotemporal feature learning.The model was designed to recognize behaviors that occur before and after transitions in video segments and to localize the corresponding time interval for each behavior.An improved transformer block,the cascade encoder-decoder network(CEDNet),a transformer-based head,and weighted distance intersection over union(WDIoU)loss were integrated into CBLFormer to enhance the model's ability to distinguish between different behavior categories and locate behavior boundaries.For the training and testing of CBLFormer,a dataset was created by collecting videos from 320 chickens across different ages and rearing densities.The results showed that CBLFormer achieved a mAP@0.5:0.95 of 98.34%on the test set.The integration of CEDNet contributed the most to the performance improvement of CBLFormer.The visualization results confirmed that the model effectively captured the behavioral boundaries of chickens and correctly recognized behavior categories.The transfer learning results demonstrated that the model is applicable to chicken behavior recognition and localization tasks in real-world poultry farms.The proposed method handles cases where poultry behavior transitions occur within the video segment and improves the temporal resolution of video-based behavior recognition models.
基金supported by the Shanghai“Explorer Program”Project(Grant No.24TS1414500)the 10th Sino-Hungarian Intergovernmental Scientific and Technological Cooperation Project(Grant No.2024-10-2)。
文摘Nickel-based superalloy(GH4169)is an ideal material for preparing turbine blades.Profile grinding of the fir-treeshaped turbine blade root can easily cause thermal damage to the workpiece specimen.This study aims to enhance the suppression of alloy thermal damage by regulating the thickness of the oxide film on the cubic boron nitride(CBN)grinding wheel during the electrolytic in-process dressing(ELID)-assisted grinding process.A theoretical model for calculating the thickness of oxide film in ELID-assisted grinding was developed.Finite element simulation was conducted using the electrolytic film-forming process of the grinding wheel.The effects of electrical/nonelectrical parameters on the oxide film characteristics and grinding responses were addressed.The optimal matching scheme of process parameters was established.The results showed that the film layer of the grinding wheel at the blade root cam is more seriously damaged,and the workpiece surface is rougher.Further optimization of the electrode is demanded to achieve different dressing effects at various positions of the grinding wheel based on the workpiece profile.By reducing the interelectrode gap(h_(e)),increasing the power supply voltage(E_(o)),and controlling the electrolysis time(△t)at 10-15 min,the preferred film-forming efficiency and grinding quality can be achieved.By increasing the grinding wheel speed(V_(s))or decreasing the workpiece feed rate(V_(f))and grinding depth(a_(p)),the grinding thermal damage can be suppressed.A larger value of V_(f)or apcan be selected to acquire a compromise between grinding quality and film-forming efficiency after increasing the value of Vs.The optimal combination of electrical and nonelectrical parameters during this test is E_(o)=120 V,△t=15 min,h_(e)=0.1 mm,V_f=50 mm min^(-1),V_s=30 m s^(-1),and a_(p)=0.4 mm.
