To study the occurrence mechanism of rock burst during mining the irregular working face,the study took irregular panel 7447 near fault tectonic as an engineering background.The spatial fracture characteristic of over...To study the occurrence mechanism of rock burst during mining the irregular working face,the study took irregular panel 7447 near fault tectonic as an engineering background.The spatial fracture characteristic of overlying strata was analyzed by Winkler elastic foundation beam theory.Furthermore,the influence law of panel width to suspended width and limit breaking span of key strata were also analyzed by thin plate theory.Through micro-seismic monitoring,theoretical analysis,numerical simulation and working resistance of support of field measurement,this study investigated the fracture characteristic of overlying strata and mechanism of rock burst in irregular working face.The results show that the fracture characteristic of overlying strata shows a spatial trapezoid structure,with the main roof being as an undersurface.The fracture form changes from vertical‘‘O-X"type to transverse‘‘O-X"type with the increase of trapezoidal height.From the narrow mining face to the wide mining face,the suspended width of key strata is greater than its limit breaking width,and a strong dynamic load is produced by the fracture of key strata.The numerical simulation and micro-seismic monitoring results show that the initial fracture position of key strata is close to tailgate 7447.Also there is a high static load caused by fault tectonic.The dynamic and static combined load induce rock burst.Accordingly,a cooperative control technology was proposed,which can weaken dynamic load by hard roof directional hydraulic fracture and enhance surrounding rock by supporting system.展开更多
Anxiety disorders have become one of the most severe psychiatric disorders,and the incidence is increasing every year.They impose an extraordinary personal and socioeconomic burden.Anxiety disorders are influenced by ...Anxiety disorders have become one of the most severe psychiatric disorders,and the incidence is increasing every year.They impose an extraordinary personal and socioeconomic burden.Anxiety disorders are influenced by multiple complex and interacting genetic,psychological,social,and environmental factors,which contribute to disruption or imbalance in homeostasis and eventually cause pathologic anxiety.The selection of a suitable animal model is important for the exploration of disease etiology and pathophysiology,and the development of new drugs.Therefore,a more comprehensive understanding of the advantages and limitations of existing animal models of anxiety disorders is helpful to further study the underlying pathological mechanisms of the disease.This review summarizes animal models and the pathogenesis of anxiety disorders,and discusses the current research status to provide insights for further study of anxiety disorders.展开更多
Harvesting represents the crucial stage in the cultivation process of Agaricus bisporus mushrooms.An important way for the production process of Agaricus bisporus to reduce costs and increase income is to ensure timel...Harvesting represents the crucial stage in the cultivation process of Agaricus bisporus mushrooms.An important way for the production process of Agaricus bisporus to reduce costs and increase income is to ensure timely harvest of Agaricus bisporus,reduce harvesting costs,and improve harvesting efficiency.There are many disadvantages in manual picking,such as high labor intensity,time-consuming work and high cost.In this study,a set of mushroom picking platform including climbing mechanism,picking robot,and control system was designed and developed.The picking robot consisted of a truss mechanism,an image acquisition device,a mushroom collection device,and a picking actuator.The profile picking actuator could realize the function of constant force clamping.An online size detection algorithm for Agaricus bisporus based on deep image processing was proposed.The algorithm included removal of abnormal noise points,background segmentation,coordinate conversion,and diameter detection.The precision picking system for Agaricus bisporus with coordinate compensation function controlled by Industrial Personal Computer was designed,and the visual control interface was developed based on Labview.Through the performance test,the reliability of machine vision recognition and the overall operating stability of the picking platform were verified.The test results showed that in the process of machine vision recognition,the recognition accuracy rate was higher than 92.50%,the missed detection rate was lower than 4.95%,the false detection rate was lower than 2.15%,and the diameter measurement error was less than 4.50%.The image processing algorithm had high recognition rate and small diameter measurement error,which could meet the requirements of picking operation.The picking platform’s picking success rate was higher than 95.45%,the picking damage rate was lower than 3.57%,and the picking output rate was higher than 87.09%.Compared with manual picking,the recognition accuracy rate of the picking platform was increased by 6.70%,the picking output rate was increased by 1.51%.The overall performance of the picking platform was stable and practical.展开更多
The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To addre...The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform consists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall construction was designed to be compressed and lightweight;Secondly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.Compared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest control research and the development of smart agriculture.展开更多
Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment.To precisely recognize these boundaries,a detection method for unmanned tractor...Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment.To precisely recognize these boundaries,a detection method for unmanned tractor plow areas based on RGB-Depth(RGB-D)cameras was proposed,and the feasibility of the detection method was analyzed.This method applied advanced computer vision technology to the field of agricultural automation.Adopting and improving the YOLOv5-seg object segmentation algorithm,first,the Convolutional Block Attention Module(CBAM)was integrated into Concentrated-Comprehensive Convolution Block(C3)to form C3CBAM,thereby enhancing the ability of the network to extract features from plow areas.The GhostConv module was also utilized to reduce parameter and computational complexity.Second,using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model,the mask image was processed to extract contour boundaries,align the contours with the depth map,and obtain the boundary distance information of the plowed area.Last,based on farmland information,the calculated average boundary distance was corrected,further improving the accuracy of the distance measurements.The experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99%for plowed areas and that the ranging accuracy improved with decreasing detection distance.The ranging error at 5.5 m was approximately 0.056 m,and the average detection time per frame is 29 ms,which can meet the real-time operational requirements.The results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units.展开更多
The chassis of rice transplanter tends to vibrate severely in the severe working environment,causing a severe effect on the operational performance and driving comfort.In order to avoid this situation,this paper const...The chassis of rice transplanter tends to vibrate severely in the severe working environment,causing a severe effect on the operational performance and driving comfort.In order to avoid this situation,this paper constructs a vibration evaluation system of the rice transplanter and carries out experimental analysis.According to the optimal acceleration sensor placement scheme,a test platform system was designed.Taking the high-speed transplanter chassis as the research object,this study carried out the experiments modal analysis and optimization on the chassis.The three-dimensional model of the transplanting machine chassis established by SolidWorks was imported into ANSYS Workbench for finite element modal simulation analysis.Comparing the two modal analyses,it is found that the results data of the two analysis methods were very close.After optimization,the length x1,the section width x2 and the thickness of the hollow beam x3 of the main load-bearing beam of the frame were as follows:x1=1641.5 mm,x2=26.7 mm,x3=5 mm,respectively.The maximum overshoot of the low-level system was reduced by 28.57%.It has been verified that the vibration of the whole machine has been effectively reduced.展开更多
In order to accelerate the development of sowing mechanization of virus-free potato minituber,a conical diversion virus-free potato minituber precision seed-metering Device was designed according to the structural cha...In order to accelerate the development of sowing mechanization of virus-free potato minituber,a conical diversion virus-free potato minituber precision seed-metering Device was designed according to the structural characteristics and agronomic requirements of virus-free potato minituber.The device is mainly composed of conical turntable,transmission shaft,outer baffle of seed-metering Device,baffle,seed outlet,seed cleaning device,type hole,etc.The working principle of conical diversion precision seed-metering device for virus-free potato minituber was expounded,and the stress analysis of virus-free potato minituber in each region was carried out.By EDEM discrete element simulation software,the structure of the type hole is optimized to determine the optimal structure of the type hole and according to the physical characteristics of virus-free potato minituber,the single factor experiments of the effects of length of type hole,cone disc speed and cone disc angle on seed filling performance were completed.The orthogonal regression test was carried out with the length of type hole,cone disc speed,and cone disc angle as the test objects,and the leakage rate and qualified rate as the response indexes.The regression models of leakage rate,replay rate,and qualified rate were established,and the parameters of the regression model were optimized.The optimal parameter combination is that the length of type hole is 33.61 mm,the cone disc speed is 6.35 r/min,and the cone disc angle is 26.59°.Bench test was carried out under the optimal conditions,the leakage rate was 3.80%,the replay rate was 0.80%,and the qualified rate was 95.40%,which was basically consistent with the prediction results of the regression model,and met the requirements of precision sowing of virus-free potato minituber.展开更多
基金supported by the Key Project of National Natural Science Foundation of China (No. 51634001)the National Natural Science Foundation of China (Nos. 51404269 and 51674253)+1 种基金the State Key Research Development Program of China (No. 2016YFC0801403)the Key Research Development Program of Jiangsu Province, China (No. BE2015040)
文摘To study the occurrence mechanism of rock burst during mining the irregular working face,the study took irregular panel 7447 near fault tectonic as an engineering background.The spatial fracture characteristic of overlying strata was analyzed by Winkler elastic foundation beam theory.Furthermore,the influence law of panel width to suspended width and limit breaking span of key strata were also analyzed by thin plate theory.Through micro-seismic monitoring,theoretical analysis,numerical simulation and working resistance of support of field measurement,this study investigated the fracture characteristic of overlying strata and mechanism of rock burst in irregular working face.The results show that the fracture characteristic of overlying strata shows a spatial trapezoid structure,with the main roof being as an undersurface.The fracture form changes from vertical‘‘O-X"type to transverse‘‘O-X"type with the increase of trapezoidal height.From the narrow mining face to the wide mining face,the suspended width of key strata is greater than its limit breaking width,and a strong dynamic load is produced by the fracture of key strata.The numerical simulation and micro-seismic monitoring results show that the initial fracture position of key strata is close to tailgate 7447.Also there is a high static load caused by fault tectonic.The dynamic and static combined load induce rock burst.Accordingly,a cooperative control technology was proposed,which can weaken dynamic load by hard roof directional hydraulic fracture and enhance surrounding rock by supporting system.
基金National Natural Science Foundation of ChinaGrant/Award Number:82104793 and 82104836+5 种基金Natural Science Foundation of Hunan ProvinceGrant/Award Number:2023JJ60482Openof TCM First-class Disciplines in HNUCMGrant/Award Number:2022ZYX18Science and Technology talent promotion Project of Hunan ProvinceGrant/Award Number:2023TJ-N22。
文摘Anxiety disorders have become one of the most severe psychiatric disorders,and the incidence is increasing every year.They impose an extraordinary personal and socioeconomic burden.Anxiety disorders are influenced by multiple complex and interacting genetic,psychological,social,and environmental factors,which contribute to disruption or imbalance in homeostasis and eventually cause pathologic anxiety.The selection of a suitable animal model is important for the exploration of disease etiology and pathophysiology,and the development of new drugs.Therefore,a more comprehensive understanding of the advantages and limitations of existing animal models of anxiety disorders is helpful to further study the underlying pathological mechanisms of the disease.This review summarizes animal models and the pathogenesis of anxiety disorders,and discusses the current research status to provide insights for further study of anxiety disorders.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFD2001100)the Major Science and Technology Programs of Henan Province(Grant No.221100110800)the Henan Provincial Major Science and Technology Special Project(Longmen Laboratory First-Class Project,Grant No.231100220200).
文摘Harvesting represents the crucial stage in the cultivation process of Agaricus bisporus mushrooms.An important way for the production process of Agaricus bisporus to reduce costs and increase income is to ensure timely harvest of Agaricus bisporus,reduce harvesting costs,and improve harvesting efficiency.There are many disadvantages in manual picking,such as high labor intensity,time-consuming work and high cost.In this study,a set of mushroom picking platform including climbing mechanism,picking robot,and control system was designed and developed.The picking robot consisted of a truss mechanism,an image acquisition device,a mushroom collection device,and a picking actuator.The profile picking actuator could realize the function of constant force clamping.An online size detection algorithm for Agaricus bisporus based on deep image processing was proposed.The algorithm included removal of abnormal noise points,background segmentation,coordinate conversion,and diameter detection.The precision picking system for Agaricus bisporus with coordinate compensation function controlled by Industrial Personal Computer was designed,and the visual control interface was developed based on Labview.Through the performance test,the reliability of machine vision recognition and the overall operating stability of the picking platform were verified.The test results showed that in the process of machine vision recognition,the recognition accuracy rate was higher than 92.50%,the missed detection rate was lower than 4.95%,the false detection rate was lower than 2.15%,and the diameter measurement error was less than 4.50%.The image processing algorithm had high recognition rate and small diameter measurement error,which could meet the requirements of picking operation.The picking platform’s picking success rate was higher than 95.45%,the picking damage rate was lower than 3.57%,and the picking output rate was higher than 87.09%.Compared with manual picking,the recognition accuracy rate of the picking platform was increased by 6.70%,the picking output rate was increased by 1.51%.The overall performance of the picking platform was stable and practical.
基金the National Key Research and Development Program of China Project(Grant No.2021YFD 2000700)the Foundation for University Youth Key Teacher of Henan Province(Grant No.2019GGJS075)the Natural Science Foundation of Henan Province(Grant No.202300410124).
文摘The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform consists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall construction was designed to be compressed and lightweight;Secondly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.Compared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest control research and the development of smart agriculture.
基金financially supported by the National Key Research and Development Program(NKRDP)projects(Grant No.2023YFD2001100)Major Science and Technology Programs in Henan Province(Grant No.221100110800)Major Science and Technology Special Project of Henan Province(Longmen Laboratory First-class Project)(Grant No.231100220200).
文摘Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment.To precisely recognize these boundaries,a detection method for unmanned tractor plow areas based on RGB-Depth(RGB-D)cameras was proposed,and the feasibility of the detection method was analyzed.This method applied advanced computer vision technology to the field of agricultural automation.Adopting and improving the YOLOv5-seg object segmentation algorithm,first,the Convolutional Block Attention Module(CBAM)was integrated into Concentrated-Comprehensive Convolution Block(C3)to form C3CBAM,thereby enhancing the ability of the network to extract features from plow areas.The GhostConv module was also utilized to reduce parameter and computational complexity.Second,using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model,the mask image was processed to extract contour boundaries,align the contours with the depth map,and obtain the boundary distance information of the plowed area.Last,based on farmland information,the calculated average boundary distance was corrected,further improving the accuracy of the distance measurements.The experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99%for plowed areas and that the ranging accuracy improved with decreasing detection distance.The ranging error at 5.5 m was approximately 0.056 m,and the average detection time per frame is 29 ms,which can meet the real-time operational requirements.The results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units.
基金This work was supported by the National Natural Science Foundation of China(Grant No.51875175)the Natural Science Foundation of Henan Province(Grant No.202300410124)+1 种基金Scientific and technological project in Henan Province(Grant No.212102110223)Special projects for industrial foundation reconstruction and high-quality development of manufacturing industry in MIIT.
文摘The chassis of rice transplanter tends to vibrate severely in the severe working environment,causing a severe effect on the operational performance and driving comfort.In order to avoid this situation,this paper constructs a vibration evaluation system of the rice transplanter and carries out experimental analysis.According to the optimal acceleration sensor placement scheme,a test platform system was designed.Taking the high-speed transplanter chassis as the research object,this study carried out the experiments modal analysis and optimization on the chassis.The three-dimensional model of the transplanting machine chassis established by SolidWorks was imported into ANSYS Workbench for finite element modal simulation analysis.Comparing the two modal analyses,it is found that the results data of the two analysis methods were very close.After optimization,the length x1,the section width x2 and the thickness of the hollow beam x3 of the main load-bearing beam of the frame were as follows:x1=1641.5 mm,x2=26.7 mm,x3=5 mm,respectively.The maximum overshoot of the low-level system was reduced by 28.57%.It has been verified that the vibration of the whole machine has been effectively reduced.
基金The work was financially supported by the Major Science and Technology Special Plan of Yunnan Province Subproject(Grant No.2018ZC001-302)the National Innovation Method Work Special Project(Grant No.2018IM030106)the Talent Support Plan in Henan Province(Grant No.ZYQNBJRC2021-04).
文摘In order to accelerate the development of sowing mechanization of virus-free potato minituber,a conical diversion virus-free potato minituber precision seed-metering Device was designed according to the structural characteristics and agronomic requirements of virus-free potato minituber.The device is mainly composed of conical turntable,transmission shaft,outer baffle of seed-metering Device,baffle,seed outlet,seed cleaning device,type hole,etc.The working principle of conical diversion precision seed-metering device for virus-free potato minituber was expounded,and the stress analysis of virus-free potato minituber in each region was carried out.By EDEM discrete element simulation software,the structure of the type hole is optimized to determine the optimal structure of the type hole and according to the physical characteristics of virus-free potato minituber,the single factor experiments of the effects of length of type hole,cone disc speed and cone disc angle on seed filling performance were completed.The orthogonal regression test was carried out with the length of type hole,cone disc speed,and cone disc angle as the test objects,and the leakage rate and qualified rate as the response indexes.The regression models of leakage rate,replay rate,and qualified rate were established,and the parameters of the regression model were optimized.The optimal parameter combination is that the length of type hole is 33.61 mm,the cone disc speed is 6.35 r/min,and the cone disc angle is 26.59°.Bench test was carried out under the optimal conditions,the leakage rate was 3.80%,the replay rate was 0.80%,and the qualified rate was 95.40%,which was basically consistent with the prediction results of the regression model,and met the requirements of precision sowing of virus-free potato minituber.
