无结构网格二维潮流模型的开发及应用

基于无结构网格的半隐海洋环流模型——"类UnTrim(unstructured grid version of trim)模型"开发了一个二维潮流数学模型.模型在平面上采用无结构三角网格,并采用二维浅水方程作为控制方程.利用水槽试验对模型的潮流模拟和干湿判别进行了验证,验证结果显示模型是稳定可靠的.将模型用于长江口的水动力过程模拟,并利用实测水文资料对模型进行了率定和验证计算.结果表明,模拟结果与实际情况一致.

HARTIV:Human Activity Recognition Using Temporal Information in Videos

Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.

Development and Evaluation of Self-propelled Cabbage/Cauliflower Harvester

In the present study,self-propelled cabbage/cauliflower harvester was designed,developed and evaluated.The machine consisted of different components like engine,frame,shearing(cutting)unit and power transmission unit.The power transmission unit consisted of main clutch,shearing blade operating clutch,belt drive unit,chain and sprocket drive,universal joint and cutter blade assembly.The main working principle of harvester is based on shearing of crop stem against high-speed rotating blade.The power from the engine is transmitted by belt-pulley drive unit to transmission shaft on which chain and sprocket is mounted on one side and then power is transmitted to shearing blade coupling with the help of a stationary pulley and fixed socket.Average mean head diameter of the selected cabbage and cauliflower was 89.5±15.24 mm and 107.5±15.24 mm,respectively.Average mean stem(plant)diameter of the selected cabbage and cauliflower was 18±4.85 mm and 21.5±3.08 mm,respectively.The shearing force increased with increase in diameter of stem.The optimum performance of the machine was achieved when it was operated at 1.5 km/h forward speed and the shearing blade moving at speed of 147 rpm.The mean field capacity for developed prototype was observed as 0.063 ha/h and 0.053 in case of cabbage and cauliflower,respectively with field efficiency of 91.97 and 90.48%.The average head damage was negligible(0.15%)for both the crops.The average untrimmed percentage with developed harvester was 3.2 and 3.0%in case of cabbage and cauliflower crop,respectively.The developed machine helps to increase the field capacity in cabbage/cauliflower harvesting due to 7-times more capacity and 50%cheaper compared to traditional method of cabbage/cauliflower harvesting.At the operating condition of forward speed(1.5 km/h)and shearing blade speed(147 rpm),the machine could harvest 0.5 ha of cabbage and 0.42 ha of cauliflower farm per day of 8-h.This same task would have required between 15 labour per day if entirely done manually.