research-article | 06-November-2020
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,
Zofia M. Prokop
Journal of Nematology, Volume 52 , 1–15
research-article | 25-August-2020
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
Subhas C. Mukhopadhyay
International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–17
Article | 30-November-2018
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.
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
International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 3, 47–52
Article | 09-April-2018
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
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 158–162
Article | 07-May-2018
International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 1, 19–22
Article | 30-November-2020
°, 150° and 180°, can be well recognized.
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
International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 1, 11–17
Article | 02-April-2020
, 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 and other researcher proposed the concept of deep learning, using convolutional neural network (CNN) to
International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 44–53
Article | 14-October-2020
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
International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 65–72