“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Thank you for downloading nonlinear programming analysis and methods. As you may know, people have search numerous times for their chosen novels like this nonlinear programming analysis and methods, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their laptop. nonlinear programming analysis and methods is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the nonlinear programming analysis and methods is universally compatible with any devices to read.

Nonlinear Programming Analysis And Methods

Objective assessment of an athlete’s performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific moveme...

/pdf/machine-and-deep-learning-for-sport-specific-movement-4yjzoe8cro.pdf

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Metagenomics has become an integral part of defining microbial diversity in various environments. Many ecosystems have characteristically low biomass and few cultured representatives. Linking potential metabolisms to phylogeny in environmental microorganisms is important for interpreting microbial community functions and the impacts these communities have on geochemical cycles. However, with metagenomic studies there is the computational hurdle of 'binning' contigs into phylogenetically related units or putative genomes. Binning methods have been implemented with varying approaches such as k-means clustering, Gaussian mixture models, hierarchical clustering, neural networks, and two-way clustering; however, many of these suffer from biases against low coverage/abundance organisms and closely related taxa/strains. We are introducing a new binning method, BinSanity, that utilizes the clustering algorithm affinity propagation (AP), to cluster assemblies using coverage with compositional based refinement (tetranucleotide frequency and percent GC content) to optimize bins containing multiple source organisms. This separation of composition and coverage based clustering reduces bias for closely related taxa. BinSanity was developed and tested on artificial metagenomes varying in size and complexity. Results indicate that BinSanity has a higher precision, recall, and Adjusted Rand Index compared to five commonly implemented methods. When tested on a previously published environmental metagenome, BinSanity generated high completion and low redundancy bins corresponding with the published metagenome-assembled genomes.

BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation

This publication was made by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation).

Image Inpainting: A Review

Multi-scale semantic image inpainting with residual learning and GAN

In recent years smart sport equipments have achieved great success in professional and amateur sports, as well as body sensory systems; now discovering interesting knowledge in the surge of data from those embedded sensors used in sports is necessary and the focus of our research. In this paper, we investigate golf swing data classification method based on deep convolutional neural network (deep CNN) fed with multi-sensor golf swing signals. Our smart golf club integrates two orthogonally affixed strain gage sensors, 3-axis accelerometer and 3-axis gyroscope, and collects real-world golf swing data from professional and amateur golf players. Furthermore we explore the performance of our well-trained CNN-based classifier and evaluate it on the real-world test set in terms of common indicators including accuracy, precision-recall, and F1-score. Experiments and corresponding results show that our CNN-based model can satisfy the requirement of accuracy of golf swing classification, and outperforms support vector machine (SVM) method.

Multi-sensor Golf Swing Classification Using Deep CNN.

Multi-sensor Golf Swing Classification Using Deep CNN

Human motion and gesture recognition receive much concern in sports field, such as physical education and fitness for all. Although plenty of mature applications appear in sports training using photography, video camera, or professional sensing devices, they are either expensive or inconvenient to carry. MEMS devices would be a wise choice for students and ordinary body builders as they are portable and have many built-in sensors. In fact, recognition of hand gestures is discussed in many studies using inertial sensors based on similarity matching. However, this kind of solution is not accurate enough for human movement recognition and cost much time. In this paper, we discuss motion recognition in sports training using features extracted from distance estimation of different kinds of sensors. To deal with the multivariate motion sequence, we propose a solution that applies Max-Correlation and Min-Redundancy strategy to select features extracted with interclass distance similarity estimation. With this method, we are able to screen out proper features that can distinguish motions in different classes effectively. According to the results of experiment in real world application in dance practice, our solution is quite effective with fair accuracy and low time cost.

https://journals.sagepub.com/doi/pdf/10.1155/2016/7483536

Sports Motion Recognition Using MCMR Features Based on Interclass Symbolic Distance

In recent years, smart sports equipment and body sensor systems have become popular in professional and amateur sports. One of a few remaining problems in real-time applications is the discovery of knowledge from the embedded sensors data. In sports training, such knowledge helps accelerated motor learning. The authors start with exploring the possibilities of the classification of golf swing performance with the 1-D convolutional neural network (CNN) in real-time. They thoroughly investigate multiple golf swing data classifiers based on CNNs fed with multi-sensor signals. The authors test the possibilities of real-time performance of CNN methods on the multi-length sequences. In addition, they thoroughly evaluate the performance of their well-trained CNN-based classifier on the aforementioned test set in terms of common indicators. Experiments and corresponding results show that the authors' models can satisfy the real-time requirement of the accuracy of the classification and outperform support vector machine (SVM).

Libin Jiao

Papers

Multi-scale semantic image inpainting with residual learning and GAN

Multi-sensor Golf Swing Classification Using Deep CNN.

Multi-sensor Golf Swing Classification Using Deep CNN

Sports Motion Recognition Using MCMR Features Based on Interclass Symbolic Distance

Towards Real-Time Multi-Sensor Golf Swing Classification Using Deep CNNs