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research-article | 06-November-2020

Competitive fitness analysis using Convolutional Neural Network

through a filter that lets the larvae, but not the adults, though. To further improve Caenorhabditis fitness analysis, here we present (i) a fitness assay protocol involving separation of parental and offspring generations and (ii) an open-source model for image analysis based on a convolutional neural network (CNN). Machine learning models are very convenient for image analysis because they can be trained on a sample of pictures to analyze images of interest with a high output rate. The concept of

Joanna K. Palka, Krzysztof Fiok, Weronika Antoł, Zofia M. Prokop

Journal of Nematology, Volume 52 , 1–15

research-article | 25-August-2020

1/10th scale autonomous vehicle based on convolutional neural network

throttle angles given for the motors by the controller while driving. In the next stage, these data are trained in a CNN (convolutional neural network) model on the host PC. The generated machine learning model is then transferred to the raspberry pi. This stage is called the ‘autopilot mode or ‘test mode’ where the vehicle is tested to run without any human intervention or control on the same track. This time, the camera takes images, and the CNN model analyses these images in real-time and also

Avishkar Seth, Alice James*, Subhas C. Mukhopadhyay

International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–17

Article | 30-November-2018

Street View House Number Identification Based on Deep Learning

designed manually (such as SIFT, SURF, HOG, etc.), and the features of the artificial design are well interpreted. However, in the face of complex backgrounds, changing fonts and various deformations, it is rather troublesome and difficult to extract more general features[7]. The Convolutional Neural Network (CNN) is a multi-layered supervised learning neural network. Although the training process requires a large amount of data compared with the traditional method, the convolutional neural network can

Haoqi Yang, Hongge Yao

International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 3, 47–52

Article | 09-April-2018

Lidar Image Classification based on Convolutional Neural Networks

This paper presents a new method of recognition of lidar cloud point images based on convolutional neural network. This experiment uses 3D CAD ModelNet, and generates 3D point cloud data by simulating the scanning process of lidar. The data is divided into cells, and the distance is represented by gray values. Finally, the data is stored as grayscale images. Changing the number of cells dividing point cloud results in different experimental results. Experiments show that the proposed method has

Yang Wenhui, Yu Fan

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 158–162

Article | 07-May-2018

3D Target Recognition Based on Decision Layer Fusion

Ma Xing, Yu Fan, Yu Haige, Wei Yanxi, Yang Wenhui

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 1, 19–22

Article | 30-November-2020

Deep Periocular Recognition Method via Multi-Angle Data Augmentation

°, 150° and 180°, can be well recognized. C. MobileNet V2 test results MobileNetV2 is used as the recognition method of Convolutional Neural Network and InceptionV3 network. The eye peripheral data sets are all expanded eye peripheral images. A total of 1000 groups of eye peripheral samples, 42 samples for each group, 42, 000 training sets, 14, 000 verification sets and 14, 000 test sets. The experimental parameters of the MobileNetV2 lightweight network model are set as Table VII. The maximum

Bo Liu, Songze Lei, Yonggang Li, Aokui Shan, Baihua Dong

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 1, 11–17

Article | 02-April-2020

Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN

, the traditional survey situation is that the site construction surveyors scan the survey images generated by radar equipment one by one according to their expert knowledge. This traditional method has a large workload, a large human factor, and a certain rate of omission and error. In recent years, with the continuous improvement of GPU, the field of deep learning is booming. In 2006, Hinton[4] and other researcher proposed the concept of deep learning, using convolutional neural network (CNN) to

He Li, Yubian Wang

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 44–53

Article | 14-October-2020

Hierarchical Image Object Search Based on Deep Reinforcement Learning

that can obtain high reward values in the environment, and find the best strategy to solve the problem in such constant interaction. Based on this idea, this paper use reinforcement learning technology to simulate the human visual attention mechanism. The agent is taught to change the shape of the bounding box and focus only on a significant part of the image at a time, and then extract its features through the convolutional neural network. Finally, the object of image positioning and

Wei Zhang, Hongge Yao, Yuxing Tan

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 65–72

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