2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)最新文献_第2页

Quality of Service Measurement and Prediction through AI Technology 基于人工智能技术的服务质量测量与预测

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9302008

Yung-Chang Lai, C. Kao, Jhih-Dao Jhan, Fei-Hua Kuo, C. Chang, Tai-Chueh Shih

{"title":"Quality of Service Measurement and Prediction through AI Technology","authors":"Yung-Chang Lai, C. Kao, Jhih-Dao Jhan, Fei-Hua Kuo, C. Chang, Tai-Chueh Shih","doi":"10.1109/ECICE50847.2020.9302008","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9302008","url":null,"abstract":"With the development of Information Technology (IT) and Software-Defined Networking (SDN), Communications Service Providers (CSPs) can collect much information from telecommunication circuits. However, some of the existing circuit measurement has not been used well. For CSPs, it is important to find more efficient ways of utilizing the circuit measurement, e.g., delay, jitter, packet loss, and speed-test results for customer satisfaction. One of the most popular ways is to use speed-test tools (such as Speedtest online) to measure the service rate of the application layer. However, it is difficult to justify whether the telecommunication circuit is normal or not. For example, when the speed-test result of a specific circuit is 90 Mbps, the physical line rate may be 100Mbps. To address the above issues, we first investigate the measurement and management mechanisms of the existing telecommunications networks, including the core components and protocols. In this paper, we leverage artificial intelligence (AI) technologies to predict whether customers complain or not. We evaluate the proposed AI model by using the real data from telecommunication circuits and analyze the key performance metrics.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126196574","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: 3

Analysis of Subsampled Image Size for Detection and Identification of Brake Pad Contours by Using Deep Learning 基于深度学习的下采样图像尺寸分析及其对刹车片轮廓的检测与识别

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9301977

Jyh-Wei Chen

引用次数: 2

Algorithm of Foveal Avascular Zone Detection with Denoising Mechanism Learning on Optical Coherence Tomography Angiography Images 基于去噪机制学习的光学相干断层血管成像中央凹无血管区检测算法

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9301965

Yih-Cherng Lee, Jian-Jiun Ding, L. Yeung, Tay-Wey Lee, Chia-Jung Chang, Yu-Tze Lin

引用次数: 0

Optimization of CFRP Drilling Process with Multi-Criteria Using TGRA 基于TGRA的CFRP多准则钻孔工艺优化

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9301991

Quang-Phuoc Tran, T. Le, Shyh-Chour Huang

{"title":"Optimization of CFRP Drilling Process with Multi-Criteria Using TGRA","authors":"Quang-Phuoc Tran, T. Le, Shyh-Chour Huang","doi":"10.1109/ECICE50847.2020.9301991","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301991","url":null,"abstract":"This study aims to prove the influence of wetness on the mechanical characteristic of carbon fiber–reinforced plastic (CFRP). There is no suitable method for using lubrication fluids in the composite industry. There is a significant impact on CFRP machining capabilities by the geometry of the cutting tool and cryogenic liquid cooling. This paper selects the optimal parametric combinations of the carbon fiber reinforced plastic drilling process by using Taguchi optimization technique base on grey relation analysis (GRA) technique (TGRA). The parameters of the drilling process such as the helical angle of the blade, the coolant gas, and the feed rate were found by observing multi-responses output such as surface roughness and diameter of the drill hole. The following parameters resulted in a larger value of grey relational grade (GRG) to reduce the surface roughness and the error deviation of the diameter dimension: the speed of the spindle motor was 6100 rpm, the feed rate was 1006 mm/min, the helical angle of the cutting blade was triple, the coolant gas was CO2-N2 mixture. Finally, an analysis of variance (ANOVA) is used to analyze the effect of each parameter process in the estimation of GRG. The results showed that the coolant gas had an outstanding effect on the surface roughness and error deviation of the diameter dimension.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132516533","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: 7

Design and Fabrication of Microprobe for Vertical Measurement using SEM SEM垂直测量微探头的设计与制造

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9301958

P. Gao, W. Rong, Tao Zou, Lefeng Wang, Lining Sun

引用次数: 0

Framework for Evaluation Index System of Carrying Water Resource 承载水资源评价指标体系框架

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9302020

Xiaofan Sun, Qilong Ren, W. Hsu

引用次数: 0

A Fast Method to Detect Collision for Five-axis Machining with GPU 基于GPU的五轴加工快速碰撞检测方法

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9301919

Cheng-Yan Siao, Jhe-Wei Lin, Rong-Guey Chang

{"title":"A Fast Method to Detect Collision for Five-axis Machining with GPU","authors":"Cheng-Yan Siao, Jhe-Wei Lin, Rong-Guey Chang","doi":"10.1109/ECICE50847.2020.9301919","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301919","url":null,"abstract":"The applications of five-axis machining have been used widely recently. Owing to the expensive cost of machine tools, how to detect the collision in real time has become a critical issue. Indeed, in order to ensure that G-codes will not result in the collision, the developers may use some tools before the stage to process the five-axis machine tool on off line. Moreover, to reduce long execution time on off line, we propose a parallel method to remedy it in this paper. The objective of the proposed approach aims at improving the performance to detect collision in parallel by utilizing the functions of a GPU (Graphics Processing Unit).We address the issue above by inducing six separating axis in plan and 11 separating axis in non-plan for two triangle meshes. Then we propose a parallel approach by implementing a CUDA ( Compute Unified Device Architecture ) program based on a GPU. Finally, with our domain knowledge and experiences, we attempt to optimize the proposed work with loop unrolling and prefetching techniques to improve performance.. The result shows that our work is very efficiently by using the two techniques.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123351689","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

Effect of Gas-assisted-foaming Mechanism and Vent-area Design on Bubble-microstructure and Shrinkage of Polystyrene Foam-Injection Molding 气助发泡机理及通气孔设计对聚苯乙烯泡沫注塑成型泡沫微观结构及收缩率的影响

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9302009

Po-Wei Huang, H. Peng, Y. Ke, Po-Chih Chen, Jun-Hao Mao

引用次数: 0

Effects of Initial Stress on SiGe Capacitive Pressure Sensor 初始应力对SiGe电容式压力传感器的影响

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9302024

Jen-Chieh Li, Shin-Jin Ho, Chung-Yang Sue

引用次数: 0

Privacy-Risk Detection in Microservices Composition Using Distributed Tracing 使用分布式跟踪的微服务组合中的隐私风险检测

2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2020-10-23 DOI: 10.1109/ECICE50847.2020.9301952

Deeksha Gorige, Eyhab Al-Masri, Sergey Kanzhelev, H. Fattah

{"title":"Privacy-Risk Detection in Microservices Composition Using Distributed Tracing","authors":"Deeksha Gorige, Eyhab Al-Masri, Sergey Kanzhelev, H. Fattah","doi":"10.1109/ECICE50847.2020.9301952","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301952","url":null,"abstract":"It is a common task when employing the microservices architecture to integrate a number of loosely coupled entities that communicate with each other resulting in service requests that disseminate through a number of service endpoints. As the number of service endpoints increases, identifying the path to which a service request passes through the network becomes a time consuming and challenging task. In addition, as part of service requests, personal data may be shared across a number of service providers without end-users’ knowledge. Hence, tracing service requests and the extent to which data is flowing from one service endpoint to another becomes inevitable. In this paper, we introduce a distributed tracing Privacy Risk Detection (dtPRD) framework for identifying potential privacy and security risks associated with the dissemination of data through the path a service request undergoes. Identifying any risks associated with data sharing across a service path or plan can help in classifying service endpoints that are vulnerable or have the potential of exposing data without the end user’s knowledge. Throughout the paper, we present experimental and validation results of our proposed approach which show the effectiveness and usefulness of the dtPRD framework.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122395981","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: 7