Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space

In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.

A Novel Hybrid Tag Identification Protocol for Large-Scale RFID Systems

Radio frequency identification technology is one of the main technologies of Internet of Things(IoT).Through the transmission and reflection of wireless radio frequency signals,non-contact identification is realized,and multiple objects identification can be realized.However,when multiple tags communicate with a singleton reader simultaneously,collision will occur between the signals,which hinders the successful transmissions.To effectively avoid the tag collision problem and improve the reading performance of RFID systems,two advanced tag identification algorithms namely Adaptive M-ary tree slotted Aloha(AMTS)based on the characteristics of Aloha-based and Query tree-based algorithms are proposed.In AMTS,the reader firstly uses the framed slotted Aloha protocol to map the tag set to different time slots,and then identify the collided tags using binary search method based on collision factor or mapping table.Both performance analysis and extensive experimental results indicate that our proposed algorithms significantly outperforms most existing anti-collision approaches in tag dense RFID systems.

A Lightweight Model of VGG-U-Net for Remote Sensing Image Classification

Remote sensing image analysis is a basic and practical research hotspot in remote sensing science.Remote sensing images contain abundant ground object information and it can be used in urban planning,agricultural monitoring,ecological services,geological exploration and other aspects.In this paper,we propose a lightweight model combining vgg-16 and u-net network.By combining two convolutional neural networks,we classify scenes of remote sensing images.While ensuring the accuracy of the model,try to reduce the memory of themodel.According to the experimental results of this paper,we have improved the accuracy of the model to 98%.The memory size of the model is 3.4 MB.At the same time,The classification and convergence speed of the model are greatly improved.We simultaneously take the remote sensing scene image of 64×64 as input into the designed model.As the accuracy of the model is 97%,it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low accuracy.Therefore,the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.

Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition

In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of pattern recognition.The research and development of high-efficiency,highquality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective.This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network(CNN)model and proposes a recognitionmodel that combines amulti-scale convolution module with a neural network model based on Visual Geometry Group(VGG).The accuracy and loss of the training set and the test set are used to evaluate the performance of the model.The test accuracy of this model is 97.1%that has increased 5.87%over VGG.Furthermore,the memory requirement is 26.1M,only 1.6%of the VGG.Experiment results show that this model performs better in terms of accuracy,recognition speed and memory size.

Carbon emissions reduction potentiality for railroad transportation based on life cycle assessment

This study addresses the comparative carbon emissions of different transportation modes within a unified evaluation framework,focusing on their carbon footprints from inception to disposal.Specifically,the entire life cycle carbon emissions of High-Speed Rail(HSR),battery electric vehicles,conventional internal combustion engine vehicles,battery electric buses,and conventional internal combustion engine buses are analyzed.The life cycle is segmented into vehicle manufacturing,fuel or electricity production,operational,and dismantlingrecycling stages.This analysis is applied to the Beijing-Tianjin intercity transportation system to explore emission reduction strategies.Results indicate that HSR demonstrates significant carbon emission reduction,with an intensity of only 24%-32% compared to private vehicles and 47%-89% compared to buses.Notably,HSR travel for Beijing-Tianjin intercity emits only 24% of private vehicle emissions,demonstrating the emission reduction benefits of transportation structure optimization.Additionally,predictive modeling reveals the potential for carbon emission reduction through energy structure optimization,providing a guideline for the development of effective transportation management systems.

Sika Deer Behavior Recognition Based on Machine Vision

With the increasing intensive and large-scale development of the sika deer breeding industry,it is crucial to assess the health status of the sika deer by monitoring their behaviours.A machine vision-based method for the behaviour recognition of sika deer is proposed in this paper.Google Inception Net(GoogLeNet)is used to optimise the model in this paper.First,the number of layers and size of the model were reduced.Then,the 5×5 convolution was changed to two 3×3 convolutions,which reduced the parameters and increased the nonlinearity of the model.A 5×5 convolution kernel was used to replace the original convolution for extracting coarse-grained features and improving the model’s extraction ability.A multi-scale module was added to the model to enhance the multi-faceted feature extraction capability of the model.Simultaneously,the Squeeze-and-Excitation Networks(SE-Net)module was included to increase the channel’s attention and improve the model’s accuracy.The dataset’s images were rotated to reduce overfitting.For image rotation,the angle wasmultiplied by 30°to obtain the dataset enhanced by rotation operations of 30°,60°,90°,120°and 150°.The experimental results showed that the recognition rate of this model in the behaviour of sika deer was 98.92%.Therefore,the model presented in this paper can be applied to the behaviour recognition of sika deer.The results will play an essential role in promoting animal behaviour recognition technology and animal health monitoring management.

Sika Deer Facial Recognition Model Based on SE-ResNet

The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.

Flexible Strain Sensor Based on 3D Electrospun Carbonized Sponge

Flexible strain sensor has attracted much attention because of its potential application in human motion detection.In this work,the prepared strain sensor was obtained by encapsulating electrospun carbonized sponge(CS)with room temperature vulcanized silicone rubber(RTVS).In this paper,the formation mechanism of conductive sponge was studied.Based on the combination of carbonized sponge and RTVS,the strain sensing mechanism and piezoresistive properties are discussed.After research and testing,the CS/RTVS flexible strain sensor has excellent fast response speed and stability,and the maximum strain coefficient of the sensor is 136.27.In this study,the self-developed CS/RTVS sensor was used to monitor the movements of the wrist joint,arm elbow joint and fingers in real time.Research experiments show that CS/RTVS flexible strain sensor has good application prospects in the field of human motion monitoring.

Segmentation of Remote Sensing Images Based on U-Net Multi-Task Learning

In order to accurately segment architectural features in highresolution remote sensing images,a semantic segmentation method based on U-net network multi-task learning is proposed.First,a boundary distance map was generated based on the remote sensing image of the ground truth map of the building.The remote sensing image and its truth map were used as the input in the U-net network,followed by the addition of the building ground prediction layer at the end of the U-net network.Based on the ResNet network,a multi-task network with the boundary distance prediction layer was built.Experiments involving the ISPRS aerial remote sensing image building and feature annotation data set show that compared with the full convolutional network combined with the multi-layer perceptron method,the intersection ratio of VGG16 network,VGG16+boundary prediction,ResNet50 and the method in this paper were increased by 5.15%,6.946%,6.41%and 7.86%.The accuracy of the networks was increased to 94.71%,95.39%,95.30%and 96.10%respectively,which resulted in high-precision extraction of building features.