Smart lighting system based on PLC (Power Line Communication) is lack of extensibility due to low data rate and non-standard communication protocol. 6LoWPAN is an IP-based communication standard for WSN (Wireless Sens...Smart lighting system based on PLC (Power Line Communication) is lack of extensibility due to low data rate and non-standard communication protocol. 6LoWPAN is an IP-based communication standard for WSN (Wireless Sensor Network) proposed by IETF. We upgraded the SEMS smart lighting system from PLC to 6LoWPAN, PLC nodes were replaced by 6LoWPAN nodes and centralized controllers were replaced by border routers. 6LoWPAN system testing was carried out on the street after the implementation. The results show that smart lighting system based on 6LoWPAN is better than PLC in transmission rate, signal coverage range, compatibility and extensibility.展开更多
Datasets are very important in image recognition research based on machine learning methods.In particular,advanced methods such as deep learning and transfer learning are more dependent on datasets used for training m...Datasets are very important in image recognition research based on machine learning methods.In particular,advanced methods such as deep learning and transfer learning are more dependent on datasets used for training models.The quality of datasets directly affects the final effect of these methods.In the research of crop disease image recognition,due to the complication of the agricultural environment and the variety of crops,datasets are scarce at present.Therefore,more and more researches adopt methods based on transfer learning,which can make up for the lack of data in the target domain with the help of other datasets.Among these methods,the selection of auxiliary domain datasets has great impact on the modeling effect of target domain.In order to clarify the impact of datasets on the research of crop disease image recognition,this study used different deep neural network frameworks to study and compare the effects of different datasets in crop disease image recognition based on transfer learning.The selected datasets include PlantVillage and Image Database for Agricultural Diseases and Pests Research(IDADP),which have been widely used in recent studies.And the selected deep neural network frameworks include ResNet50,InceptionV3,and EfficientNet.In the method of this study,the datasets are preprocessed first,such as data enhancement.After dividing the auxiliary domain and the target domain,the selected deep neural network frameworks are used to pre-train the model on the auxiliary domain dataset.Finally,the parameter-based transfer learning method was used to construct the corresponding crop disease recognition model in the target.In the experiments,multiple different datasets and different models were tested and compared.The results show that when the test set samples and training sample scenarios are consistent,the recognition accuracy of different network frameworks on multiple test sets is generally high.When the scenarios of test set samples and training samples are inconsistent,the recognition of various test sets by different network models cannot obtain ideal results.For the recognition of crop disease images that are collected from the actual cultivation environment,the use of IDADP dataset modeling is better,and it has more practical value in the actual application of crop disease image recognition.展开更多
Due to the tremendous volume of data generated by urban surveillance systems, big data oriented lowcomplexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automati...Due to the tremendous volume of data generated by urban surveillance systems, big data oriented lowcomplexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions.展开更多
文摘Smart lighting system based on PLC (Power Line Communication) is lack of extensibility due to low data rate and non-standard communication protocol. 6LoWPAN is an IP-based communication standard for WSN (Wireless Sensor Network) proposed by IETF. We upgraded the SEMS smart lighting system from PLC to 6LoWPAN, PLC nodes were replaced by 6LoWPAN nodes and centralized controllers were replaced by border routers. 6LoWPAN system testing was carried out on the street after the implementation. The results show that smart lighting system based on 6LoWPAN is better than PLC in transmission rate, signal coverage range, compatibility and extensibility.
基金supported by the National Natural Science Foundation of China(Grant No.31871521,No.32071901)“Machine learning dataset for agricultural image caption”in the National Basic Science Data Center(NO.NBSDC-DB-20).
文摘Datasets are very important in image recognition research based on machine learning methods.In particular,advanced methods such as deep learning and transfer learning are more dependent on datasets used for training models.The quality of datasets directly affects the final effect of these methods.In the research of crop disease image recognition,due to the complication of the agricultural environment and the variety of crops,datasets are scarce at present.Therefore,more and more researches adopt methods based on transfer learning,which can make up for the lack of data in the target domain with the help of other datasets.Among these methods,the selection of auxiliary domain datasets has great impact on the modeling effect of target domain.In order to clarify the impact of datasets on the research of crop disease image recognition,this study used different deep neural network frameworks to study and compare the effects of different datasets in crop disease image recognition based on transfer learning.The selected datasets include PlantVillage and Image Database for Agricultural Diseases and Pests Research(IDADP),which have been widely used in recent studies.And the selected deep neural network frameworks include ResNet50,InceptionV3,and EfficientNet.In the method of this study,the datasets are preprocessed first,such as data enhancement.After dividing the auxiliary domain and the target domain,the selected deep neural network frameworks are used to pre-train the model on the auxiliary domain dataset.Finally,the parameter-based transfer learning method was used to construct the corresponding crop disease recognition model in the target.In the experiments,multiple different datasets and different models were tested and compared.The results show that when the test set samples and training sample scenarios are consistent,the recognition accuracy of different network frameworks on multiple test sets is generally high.When the scenarios of test set samples and training samples are inconsistent,the recognition of various test sets by different network models cannot obtain ideal results.For the recognition of crop disease images that are collected from the actual cultivation environment,the use of IDADP dataset modeling is better,and it has more practical value in the actual application of crop disease image recognition.
基金supported by the projects under the grants Nos. 2016B050502001 and 2015A050502003
文摘Due to the tremendous volume of data generated by urban surveillance systems, big data oriented lowcomplexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions.
