scholarly journals Badminton Backcourt Stroke Route Planning Method Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yanping Ma
Keyword(s):  
Deep Learning ◽  
Adaptive Learning ◽  
Planning Process ◽  
Route Planning ◽  
Planning Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
Line Planning ◽  
Good Convergence ◽  
Good Learning

In order to improve the planning ability of the badminton backcourt stroke line, this study designs a badminton backcourt stroke line planning method based on deep learning. Firstly, the trajectory adaptive learning method of motion primitives is used to design the hitting line nodes and path space, so as to construct the shortest distributed grid structure model of the hitting line. Then, the constraint parameters of hitting route planning are analyzed, and then the hitting position and player posture are controlled according to node positioning and shortest path optimization deployment. Finally, the adaptive optimization of the route planning process is realized by combining the deep learning method. The simulation results show that this method has good learning control ability and good convergence performance and improves the reliability of badminton backcourt hitting line planning.

scholarly journals Adaptive deep learning for head and neck cancer detection using hyperspectral imaging

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ling Ma ◽  
Guolan Lu ◽  
Dongsheng Wang ◽  
Xulei Qin ◽  
Zhuo Georgia Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Cancer Detection ◽  
Adaptive Learning ◽  
Animal Experiments ◽  
Hyperspectral Images ◽  
Learning Method ◽  
Adaptive Weights ◽  
Benign Tissue ◽  
The Right

AbstractIt can be challenging to detect tumor margins during surgery for complete resection. The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model. Specifically, an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel. According to the output hypothesis of each pixel, the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights. The auto-encoder network is again trained based on these updated pixels. The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels, and thus can improve the detection performance. The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32% and a specificity of 91.31% in our animal experiments. This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection, especially, for the tumor whose margin is indistinct and irregular.

scholarly journals Comparing Deep and Shallow Neural Networks in Forecasting Call Center Arrivals

2021 ◽  
Author(s):  
Andrea Manno ◽  
Fabrizio Rossi ◽  
Stefano Smriglio ◽  
Luigi Cerone
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Call Center ◽  
Planning Process ◽  
Workforce Planning ◽  
Predictive Performance ◽  
Learning Method ◽  
Input Selection ◽  
Echo State Networks ◽  
Real World Datasets

Abstract Forecasting volumes of incoming calls is the first step of the workforce planning process in call centers and represents a prominent issue from both research and industry perspectives. We investigate the application of Neural Networks to predict incoming calls 24 hours ahead. In particular, a Deep Learning method known as Echo State Networks, is compared with a completely different shallow Neural Networks strategy, in which the lack of recurrent connections is compensated by a careful input selection. The comparison, carried out on three different real world datasets, reveals similar predictive performance, although the shallow approach seems to be more robust and less demanding in terms of time-to-predict.

PENERAPAN METODE COOPERATIVE LEARNING DALAM PEMBELAJARAN MENULIS TEKS IKLAN, SLOGAN, DAN POSTER UNTUK PENINGKATAN AKTIVITAS DAN KREATIVITAS SISWA KELAS VIII SMP PASUNDAN 2 BANDUNG

2021 ◽  
Vol 6 (2) ◽  
pp. 334-353
Author(s):  
Titin Nurhayatin ◽  
Adi Rustandi ◽  
Eggie Nugraha ◽  
Anne Kusmini
Keyword(s):  
High School ◽  
Cooperative Learning ◽  
Learning Outcomes ◽  
Junior High School ◽  
Lesson Study ◽  
Planning Process ◽  
Junior High ◽  
Learning Method ◽  
Research Subjects ◽  
Class Viii

  This research is motivated by learning conditions that are still not optimal, both process and learning outcomes. Therefore, we conducted a research to apply lesson study by using the Cooperative Learning method in Indonesian Language, in writing advertisement, slogans, and posters texts in class VIII of Pasundan 2 Junior High School Bandung. This research is expected to improve learning outcomes, activities and creativities. The research method used is an experimental method. The research subjects were students of Pasundan 2 Bandung Junior High School Class VIII C. This research was an implementation of plan, do and see. The validity of the data is done through triangulation techniques. Based on the results of the study it can be concluded that the application of lesson study using the Cooperative Learning method is carried out in accordance with the stages in the lesson study, namely plan, do and see. Lesson study makes the learning planning process more mature so that in the implementation of learning the model lecturer feels more prepared. Lesson Study can effectively improve the quality of learning, both processes and results. This can be seen from the test results which are tested on the average significant improvement. Likewise in the process, based on the observations made, the activity and creativity of students of Pasundan 2 Junior High School Bandung increased. Thus, it can be concluded that the implementation of lesson study with cooperative learning effectively increases the activity, creativity, and learning outcomes of students in Pasundan 2 Junior High School Bandung significantly.

Toward Greater Intelligence in Route Planning: A Graph-Aware Deep Learning Approach

2020 ◽  
Vol 14 (2) ◽  
pp. 1658-1669 ◽  
Author(s):  
Zirui Zhuang ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Haifeng Sun ◽  
Jianxin Liao
Keyword(s):  
Deep Learning ◽  
Route Planning ◽  
Learning Approach

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

A Deep Learning Method for Frame Selection in Videos for Structure from Motion Pipelines

2021 ◽  
Author(s):  
Francesco Banterle ◽  
Rui Gong ◽  
Massimiliano Corsini ◽  
Fabio Ganovelli ◽  
Luc Van Gool ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure From Motion ◽  
Learning Method ◽  
Frame Selection

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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