In recent years,serious food safety accidents are of frequent occurrence. Although government has taken many practical and feasible measures to contain food safety accidents,new food safety accidents still emerge in l...In recent years,serious food safety accidents are of frequent occurrence. Although government has taken many practical and feasible measures to contain food safety accidents,new food safety accidents still emerge in large numbers. In this situation,food safety control is a long-term and arduous task to be performed jointly by many government departments. Finally,it presents corresponding countermeasures and recommendations on the basis of current situations of food safety in Hunan Province,problem causes,in combination with control measures related to food safety both at home and abroad.展开更多
Limonin, the main bioactive phytochemical constituent of limonoids with multi-functions, is enriched in citrus fruits and often found at a high concentration in citrus seeds. The present study was attempted to introdu...Limonin, the main bioactive phytochemical constituent of limonoids with multi-functions, is enriched in citrus fruits and often found at a high concentration in citrus seeds. The present study was attempted to introduce a new and efficient extraction method to isolate limonoids from pummelo seeds, and to evaluate the antioxidant property of the main constituent limonin in Hep G2 cells. Three key single factors were identified for the extraction of limonoids from pummelo seeds using the Box-Behnken experiment design of response surface methodology(RSM), and the optimized extraction parameters were treatment with 89.68 m L of anhydrous acetone for 4.62 h at 78.94C, while the yield of limonoids was 11.52 mg/g. The structure of isolated main constituent of the limonoids was further identified as limonin by Fourier transform infrared(FT-IR) spectrometer and nuclear magnetic resonance(NMR)spectrum. Moreover, the molecular data in Hep G2 cells revealed that limonin exerted its anti-oxidant property mainly by the activation of nuclear factor(erythroid-2)-like 2(Nrf2)/kelch-like ECHassociated protein 1(Keap1)-antioxidant response element(ARE) pathway in the form of transcriptional regulation of Nrf2 m RNA and posttranscriptional regulation of Nrf2/Keap1 system. These results demonstrate that pummelo seeds are an ideal source of limonoids, and limonin is proved to exert its anti-oxidant property by the activation of Nrf2/Keap1 pathway.展开更多
This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder(DCAE)for small negative samples.The proposed DCAE scheme combines the advantages of supervised learning and unsupe...This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder(DCAE)for small negative samples.The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning.In order to reduce the high cost of training Deep Neural Networks,this paper pre-trained the Convolutional Neural Networks(CNN)through open labelled datasets.Through transferring learning,the encoder part of the traditional Convolutional Auto-Encoder was replaced by the first three layers of the CNN,and a small number of defect samples were used to fine-tune the parameters.A threshold discrimination scheme was designed to evaluate the model detection,realising the self-explosion detection of insulator by judging the residual result and abnormal score.The experimental results show that compared with the existing insulator self-explosion detection schemes,the proposed scheme can reduce the model training time by up to 40%,and the recognition accuracy can reach 97%.Moreover,this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.展开更多
Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on ed...Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on edge computing and DL(deep learning).In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge,a lightweight SSD(Single Shot MultiBox Detector)target recognition network is designed at the edge,which adopts the MobileNets network to replace VGG16 network in the original model to reduce redundant computing.In the cloud,three detection algorithms(Faster-RCNN,Retinanet,YOLOv3)with obvious differences in detection performance are selected to obtain the coordinates and confidence of the insulator self-explosion area,and then the self-explosion fault detection of the overhead transmission line is realized by a novel multimodel fusion algorithm.The experimental results show that the proposed scheme can effectively reduce the amount of uploaded data,and the average recognition accuracy of the cloud is 95.75%.In addition,it only increases the power consumption of edge devices by about 25.6W/h in their working state.Compared with the existing online monitoring technology of insulator selfexplosion at home and abroad,the proposed scheme has the advantages of low transmission delay,low communication cost and high diagnostic accuracy,which provides a new idea for online monitoring research of power internet of things equipment.展开更多
文摘In recent years,serious food safety accidents are of frequent occurrence. Although government has taken many practical and feasible measures to contain food safety accidents,new food safety accidents still emerge in large numbers. In this situation,food safety control is a long-term and arduous task to be performed jointly by many government departments. Finally,it presents corresponding countermeasures and recommendations on the basis of current situations of food safety in Hunan Province,problem causes,in combination with control measures related to food safety both at home and abroad.
基金partially supported by Natural Science Foundation of China(31101268)Core Research Program 1515 of Hunan Agricultural University of China to Si Qin
文摘Limonin, the main bioactive phytochemical constituent of limonoids with multi-functions, is enriched in citrus fruits and often found at a high concentration in citrus seeds. The present study was attempted to introduce a new and efficient extraction method to isolate limonoids from pummelo seeds, and to evaluate the antioxidant property of the main constituent limonin in Hep G2 cells. Three key single factors were identified for the extraction of limonoids from pummelo seeds using the Box-Behnken experiment design of response surface methodology(RSM), and the optimized extraction parameters were treatment with 89.68 m L of anhydrous acetone for 4.62 h at 78.94C, while the yield of limonoids was 11.52 mg/g. The structure of isolated main constituent of the limonoids was further identified as limonin by Fourier transform infrared(FT-IR) spectrometer and nuclear magnetic resonance(NMR)spectrum. Moreover, the molecular data in Hep G2 cells revealed that limonin exerted its anti-oxidant property mainly by the activation of nuclear factor(erythroid-2)-like 2(Nrf2)/kelch-like ECHassociated protein 1(Keap1)-antioxidant response element(ARE) pathway in the form of transcriptional regulation of Nrf2 m RNA and posttranscriptional regulation of Nrf2/Keap1 system. These results demonstrate that pummelo seeds are an ideal source of limonoids, and limonin is proved to exert its anti-oxidant property by the activation of Nrf2/Keap1 pathway.
基金Outstanding Youth Fund Project of Jiangxi Natural Science Foundation,Grant/Award Number:20202ACBL214021National Natural Science Foundation of China,Grant/Award Number:52167008,51867010+1 种基金Science and Technology Project of Education Department of Jiangxi Province,Grant/Award Number:GJJ210650Key Research and Development Program of Jiangxi Province,Grant/Award Number:20202BBGL73098。
文摘This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder(DCAE)for small negative samples.The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning.In order to reduce the high cost of training Deep Neural Networks,this paper pre-trained the Convolutional Neural Networks(CNN)through open labelled datasets.Through transferring learning,the encoder part of the traditional Convolutional Auto-Encoder was replaced by the first three layers of the CNN,and a small number of defect samples were used to fine-tune the parameters.A threshold discrimination scheme was designed to evaluate the model detection,realising the self-explosion detection of insulator by judging the residual result and abnormal score.The experimental results show that compared with the existing insulator self-explosion detection schemes,the proposed scheme can reduce the model training time by up to 40%,and the recognition accuracy can reach 97%.Moreover,this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.
基金supported by the Natural Science Foundation of China(52167008)Outstanding Youth Fund Project of Jiangxi Natural Science Foundation(20202ACBL214021)+1 种基金Key Research and Development Plan of Jiangxi Province(20202BBGL73098)Science and Technology Project of Education Department of Jiangxi Province(GJJ210650)。
文摘Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on edge computing and DL(deep learning).In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge,a lightweight SSD(Single Shot MultiBox Detector)target recognition network is designed at the edge,which adopts the MobileNets network to replace VGG16 network in the original model to reduce redundant computing.In the cloud,three detection algorithms(Faster-RCNN,Retinanet,YOLOv3)with obvious differences in detection performance are selected to obtain the coordinates and confidence of the insulator self-explosion area,and then the self-explosion fault detection of the overhead transmission line is realized by a novel multimodel fusion algorithm.The experimental results show that the proposed scheme can effectively reduce the amount of uploaded data,and the average recognition accuracy of the cloud is 95.75%.In addition,it only increases the power consumption of edge devices by about 25.6W/h in their working state.Compared with the existing online monitoring technology of insulator selfexplosion at home and abroad,the proposed scheme has the advantages of low transmission delay,low communication cost and high diagnostic accuracy,which provides a new idea for online monitoring research of power internet of things equipment.
