2018 2nd International Conference on Engineering Innovation (ICEI)最新文献

Criterion Evaluation of Accessible Packaging Design for Aging Society 面向老龄化社会的无障碍包装设计标准评价

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448612

Jeeranuch Buddeejeen, A. Kengpol

引用次数: 0

Influence of Anodizing Current on Structure and Color Intensity of Anodized Aluminium Alloys 阳极氧化电流对阳极氧化铝合金组织和颜色强度的影响

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448843

Phonnipha Soisrihaa, Phattaranis Akaratatreepongb, Thotsaphon Threrujirapapongc, Sirichai Leelachaod, Kattareeya Taweesupe, Patama Visuttipitukulf

引用次数: 1

An IoT-enabled Platform for Power Consumption Measurement of Low-Power Wireless Sensor Devices 基于物联网的低功耗无线传感器设备功耗测量平台

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448991

Kanitkorn Khanchuea, Rawat Siripokarpirom

引用次数: 4

Diabetic retinopathy fundus image classification using discrete wavelet transform 离散小波变换在糖尿病视网膜病变眼底图像分类中的应用

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448628

Mobeen Ur Rehman, Zeeshan Abbas, Sharzil Haris Khan, S. Ghani, Najam

引用次数: 13

Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network 基于分割卷积神经网络干扰的皮肤损伤分类

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448814

M. Rehman, Sharzil Haris Khan, S. Danish Rizvi, Zeeshan Abbas, Adil Zafar

{"title":"Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network","authors":"M. Rehman, Sharzil Haris Khan, S. Danish Rizvi, Zeeshan Abbas, Adil Zafar","doi":"10.1109/ICEI18.2018.8448814","DOIUrl":"https://doi.org/10.1109/ICEI18.2018.8448814","url":null,"abstract":"Classification of skin lesions plays a crucial role in diagnosing various, local and gene related, medical conditions in the field of dermoscopy. Estimation of these biomarkers are used to provide some insight, while detecting cancerous cells and classifying the lesion as either benign or malignant. This paper presents groundwork for detection of skin lesions with cancerous inclination by segmentation and subsequent application of Convolution Neural Network on dermoscopy images. Images included in ISIC-2016 were used as dataset. Images with skin lesions were segmented based on individual channel intensity thresholding. The resultant images were fed into CNN for feature extraction. The extracted features were then used for classification by an ANN classifier. Previously, several approaches have been used for subject diagnostic with varying degree of success. However, room is still available for exploring other techniques for improving proportion of successfully detected malignant lesions. As compared to a previous best of 97%, methodology presented in this paper yielded an accuracy of 98.32%.","PeriodicalId":333863,"journal":{"name":"2018 2nd International Conference on Engineering Innovation (ICEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132111886","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: 50

Strain Hardening Analysis of SUS 304 Stainless Steel Cup for Multi Stage Deep Drawing Using Finite Element Simulation Comparison with Experiment Result sus304不锈钢杯件多段拉深应变硬化有限元模拟与试验对比分析

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448624

Chutwin Amree, S. Chaijit

{"title":"Strain Hardening Analysis of SUS 304 Stainless Steel Cup for Multi Stage Deep Drawing Using Finite Element Simulation Comparison with Experiment Result","authors":"Chutwin Amree, S. Chaijit","doi":"10.1109/ICEI18.2018.8448624","DOIUrl":"https://doi.org/10.1109/ICEI18.2018.8448624","url":null,"abstract":"This work aims at the numerical investigation on the multi-stage deep drawing method of cup from SUS 304 stainless steel sheet thickness 0.8 mm. In order to reveal strain-hardening characteristics in each stage and reduce the stage forming. The elasto-plastic finite-element analysis was used for determining the stress, strain, and strain flow of multi-stage deep drawing cup of SUS304. The plastic flow behavior used in this work was obtained from the tensile test under uniaxial state. The effective strain and percentage in thinning distributions were examined from both of the finite-element analysis and experiments. Strain hardening exponent values of each stages are determined by using uniaxial tensile test. Form this study, the simulation results are good agreement with the experimental results. The increased tensile strength were reduced the strain quantities in every stage and a proper strain hardening plays an important rule to the formability. Especially, the increased strain hardening with respect to insufficient die movement or excessive pressure of bank holder force will affect the failure modes.","PeriodicalId":333863,"journal":{"name":"2018 2nd International Conference on Engineering Innovation (ICEI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114732016","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

Graphical User Interface Design Guideline for an Automatic Machine Using an Official Raspberry Pi Touch Screen 使用官方树莓派触摸屏的自动机器的图形用户界面设计指南

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448534

Teerawoot Tijai, Sansiri Tanachutiwat

{"title":"Graphical User Interface Design Guideline for an Automatic Machine Using an Official Raspberry Pi Touch Screen","authors":"Teerawoot Tijai, Sansiri Tanachutiwat","doi":"10.1109/ICEI18.2018.8448534","DOIUrl":"https://doi.org/10.1109/ICEI18.2018.8448534","url":null,"abstract":"In this article, we propose a graphical user interface design guideline for an automatic machine using an official Raspberry Pi touchscreen. Experiments have been performed to determine the appropriate button and text sizes in such a way that users can utilize the GUI with speed, accuracy, and usability. The experiments were conducted on five human subjects who are familiar with general electronic device touchscreens. The first experiment was to determine the minimum text size that users can accurately read within 0.5 seconds. The second experiment was to determine the minimum button size and space between two adjacent buttons. Based on the experimental results, a GUI guideline for RPI touchscreens of a specified size handling automation systems is proposed. The guideline suggests the optimal button size, text size, graphic display elements, shape, color, etc. At the end, a case-study GUI, designed with the help of the proposed guideline, has been developed and installed on a 7-inch RPI touchscreen manipulating an automatic thin-film slicing machine. Usability, functionality, and desirability of such a GUI are extraordinarily marvelous.","PeriodicalId":333863,"journal":{"name":"2018 2nd International Conference on Engineering Innovation (ICEI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115759466","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

Forecasting Self-Consumption Solar Power Capacity of Industry and Business Sector in Thailand: a System Dynamic Model 预测泰国工商业部门自用太阳能发电能力:一个系统动态模型

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448633

Chayaporn Maitreesorasunte, Jing Tang, Rujira Chaysiri, C. Jeenanunta, W. Pannakkong, Hashini Vitharana, P. Parthanadee

引用次数: 1

Comparing Filtering Techniques in Restaurant Recommendation System 餐厅推荐系统中过滤技术的比较

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448528

Nanthaphat Koetphrom, Panachai Charusangvittaya, D. Sutivong

{"title":"Comparing Filtering Techniques in Restaurant Recommendation System","authors":"Nanthaphat Koetphrom, Panachai Charusangvittaya, D. Sutivong","doi":"10.1109/ICEI18.2018.8448528","DOIUrl":"https://doi.org/10.1109/ICEI18.2018.8448528","url":null,"abstract":"This paper studies the key analytics of the restaurant recommendation system, namely predicting restaurant satisfaction rating based on customer and restaurant characteristics. As food industry grows and offers more variety of restaurants, customers generally have difficulty discovering a restaurant that suits or satisfies them. This paper aims to predict restaurant satisfaction rating based on three methodologies: content-based filtering, collaborative filtering, and hybrid filtering. For content-based filtering, this paper proposes using regression to create a prediction model from customer and restaurant characteristics. For collaborative filtering, our proposed model employs a combination of cluster analysis, similarity test, and weighted sum in order to analyze factors that influence the satisfaction rating. Cluster analysis helps to reduce the impact of sparsity in collaborative filtering. Subsequently, hybrid filtering is proposed to combine the results from the two techniques above to generate the final rating. Our results have shown that hybrid filtering outperforms content-based filtering using regression model and collaborative filtering using cluster-based technique.","PeriodicalId":333863,"journal":{"name":"2018 2nd International Conference on Engineering Innovation (ICEI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125817431","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

2018 2nd International Conference on Engineering Innovation (ICEI) Pub Date : 2018-07-01 DOI: 10.1109/ICEI18.2018.8448663

Jakkrit Thamprajamjit, P. Surin

引用次数: 1