2016 3rd International Conference on Green Technology and Sustainable Development (GTSD)最新文献_第4页

Numerical and Experimental Analyses of the Deep Drawing Camera Cover 深拉深相机罩的数值与实验分析

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.60

T. Do, S. Pham, Nhan Le

{"title":"Numerical and Experimental Analyses of the Deep Drawing Camera Cover","authors":"T. Do, S. Pham, Nhan Le","doi":"10.1109/GTSD.2016.60","DOIUrl":"https://doi.org/10.1109/GTSD.2016.60","url":null,"abstract":"The camera cover is a complex 3D-part with many corners and is difficult to form in deep drawing process. One of the most common defects is the tearing that occurs at corners due to the tensile stress concentration. The suitable determination of die and punch parameters leads to reduce the defects and improve the quality of drawn part. Both numerical and experimental analysis can be useful to analyze the deep drawing process for determining the optimal values of tool parameters. In this paper, the formability of camera cover in deep drawing process was considered relating to the geometrical parameters of the die edge radius (RD), the pocket radius on bottom of die (RD1) and the punch nose radius (RP). The results showed that the thickening occurred in the flange area, the stretching occurred in the side wall and the tearing could be occurred in the corners above the punch nose that related to stress concentration if the tool parameters were not optimized. Also, the numerical and experimental results were a good agreement. Both numerical and experimental results cleared that the quality of camera cover relating the thickness was improved when the die geometric parameters were determined suitably with values of RD = 8 mm, RD1 = 4 mm and RP = 2 mm.","PeriodicalId":340479,"journal":{"name":"2016 3rd International Conference on Green Technology and Sustainable Development (GTSD)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131518568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Depth-Based Hand Pose Segmentation with Hough Random Forest 基于深度的Hough随机森林手部姿态分割

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.45

Wei-jiun Tsai, Ju-Chin Chen, K. W. Lin

{"title":"Depth-Based Hand Pose Segmentation with Hough Random Forest","authors":"Wei-jiun Tsai, Ju-Chin Chen, K. W. Lin","doi":"10.1109/GTSD.2016.45","DOIUrl":"https://doi.org/10.1109/GTSD.2016.45","url":null,"abstract":"With the development of image processing and computer vision technology, using gesture to communicate with the machine will not only appear in scientific move or just a conceptual product. Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. With this, we can have a more convenient life. Therefore, our goal is using image processing algorithm to effectively recognize the correct gesture from camera and present a user-friendly human-machine interface. In recent years, gesture recognition has become a popular and important issue. It can be used for robot control, appliances control, gaming control, etc. We present an algorithm which is able to correctly calculate the accurate finger joint position and then evaluate the gesture. This algorithm is divided into two parts: hand position detection and gesture recognition. In gesture detection, we capture the depth image from depth camera to solve the problem caused by illumination and background. In gesture recognition, we use an object recognizing algorithm to make the difficult evaluating finger movement problem corresponds to an easier voting classify problem. Depth camera helps our classifier avoid the illumination problem from incorrectly recognizing object.","PeriodicalId":340479,"journal":{"name":"2016 3rd International Conference on Green Technology and Sustainable Development (GTSD)","volume":"56 27","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114050182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Study of Strain and Residual Stress Distribution in the Thickness Direction by Layer Removal Method and X-Ray Diffraction 用去层法和x射线衍射研究厚度方向上的应变和残余应力分布

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.43

Tran Minh Tien, C. L. Chi, Phoi Nguyen Vinh, Nguyen La Ly, Tuyen Luu Anh

引用次数: 3

Phase Quantitative Computation for Multi-Phase Materials by Means of X-Ray Diffraction 多相材料的x射线衍射相定量计算

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.44

C. L. Chi, Son Nguyen Hung, Nguyen La Ly, Tuyen Luu Anh

引用次数: 1

The Cactus Device - An Ultimate Solution for Irrigation 仙人掌装置——灌溉的终极解决方案

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.67

Duong Thi Cam Tu, Pham Bach Duong, Ngoc Thi Vuong, Phan Hoang Luan, N. H. Nghia, Pham Ngoc Dien, N. H. Quang, Cai Hoang Buu

引用次数: 1

System Development for Patient's Physiological Data Storage and Real-Time Vital Signs Monitoring 患者生理数据存储与实时生命体征监测系统开发

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.27

Chin-Dar Tseng, Jia-Hong Su, Tsair-Fwu Lee

引用次数: 0

A Novel Multi-Axis Compliant Prosthetic Ankle Foot to Support the Rehabilitation of Amputees 一种支持截肢者康复的新型多轴柔性踝关节足假肢

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.61

H. Pham, Minh-Nhat Le, Van-Trinh Mai

引用次数: 6

Video Super-Resolution via Convolution Neural Network 基于卷积神经网络的视频超分辨率

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.46

Tsung-Hsin Wei, Ju-Chin Chen

{"title":"Video Super-Resolution via Convolution Neural Network","authors":"Tsung-Hsin Wei, Ju-Chin Chen","doi":"10.1109/GTSD.2016.46","DOIUrl":"https://doi.org/10.1109/GTSD.2016.46","url":null,"abstract":"Nowadays, people might need super resolution to obtain high quality images. Super resolution algorithm enhances high frequency information (texture or edges) to improve the image quality. We can do more things with super resolution, such as road surveillance system. The image quality would be degraded by illumination, angle, distance, and other conditions, and it will result in failing to recognize license plate or human face. Interpolation is a traditional method for super resolution, but this method does not ignore high frequency information. Therefore, example-based super-resolution methods were proposed. Besides, deep learning has great performance in many applications. However, for super-resolution, the computational complexity via deep learning algorithm is high and it needs some time to generate a high-resolution image. The time might be acceptable for single image. But what if we have to enhance video, it will take a lot of time to rebuild. Therefore, our research aims to solve this problem. We present a faster super resolution for video based on deep learning. We find the different patches between frame and frame. Add these patches into neural net and rebuild high resolution image to lower the total compute time.","PeriodicalId":340479,"journal":{"name":"2016 3rd International Conference on Green Technology and Sustainable Development (GTSD)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126930996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fault Diagnosis for High Voltage Distribution Networks Using Pseudorandom Binary Sequence and Cross Correlation Technique 基于伪随机二值序列和互相关技术的高压配电网故障诊断

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.51

M. Cho, Hoang Thi Thom, J. Hsu

引用次数: 1

A Low Consuming Power Solar Heat Collection Tracker for Solar Thermal Applications 一种用于太阳能热应用的低功耗太阳能热收集跟踪器

2016 3rd International Conference on Green Technology and Sustainable Development (GTSD) Pub Date : 2016-11-01 DOI: 10.1109/GTSD.2016.21

Chao-Kai Yang, T. Cheng

引用次数: 0