2018 International Conference on Orange Technologies (ICOT)最新文献_第4页

Future prospects of artificial intelligence and how it drive to smart society 人工智能的未来前景以及它如何推动智能社会

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705920

Yi-Chun Lin, Yen-Ting Lin, Po-Sen Huang

引用次数: 1

Monitoring of Electrical Output Power-Based Internet of Things for Micro-Hydro Power Plant 基于物联网的微水电厂输出功率监测

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705786

S. Ginting, J. W. Simatupang, Iksan Bukhori, E. Kaburuan

引用次数: 10

Information System Audit in SaaS Start-Up Company using COBIT 4.1 Focus on Deliver and Support Domain 基于COBIT 4.1的SaaS初创公司信息系统审计——关注交付和支持领域

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705886

Boike Janus Anshory, Sfenrianto Sfenrianto, E. Kaburuan, E. Peranginangin, Qatrunnada Fadhila

引用次数: 1

Business Intelligence for Evaluating Loan Collection Performance at Bank 评估银行贷款催收绩效的商业智能

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705829

Karona Cahya Susena, Dita Madonna Simanjuntak, Parwito, Wardi Fadillah, Yulyardo, A. S. Girsang

引用次数: 3

Design of Chinese Depression Spoken Speech Corpus Based on Psychological Self-related Processing 基于心理自相关加工的汉语抑郁症口语语料库设计

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705832

Xiaoyong Lu, Haizhen An, Yanqin Li, Renjun Li, Chaochao Pan

引用次数: 3

Mobile Payment Services Adoption: The Role of Perceived Technology Risk 移动支付服务的采用:感知技术风险的作用

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705859

Trianggoro Wiradinata

引用次数: 15

Automatic Drug Pills Detection based on Convolution Neural Network 基于卷积神经网络的药品自动检测

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705849

Yang-Yen Ou, A. Tsai, Jhing-Fa Wang, Jiun Lin

{"title":"Automatic Drug Pills Detection based on Convolution Neural Network","authors":"Yang-Yen Ou, A. Tsai, Jhing-Fa Wang, Jiun Lin","doi":"10.1109/ICOT.2018.8705849","DOIUrl":"https://doi.org/10.1109/ICOT.2018.8705849","url":null,"abstract":"In this study, an automatic drug pills detection system is proposed for visual system. Two stages, detection and classification, are included in the automatic drug pills detection system. In detection stage, the drug-pill localization is provided for pills location, architecture of deep convolution neural network has been applied to extract feature and construct feature pyramid with stronger semantics. The regression and the classification models are improved to output the position of the pills; The second stage is the drug pills classification, which uses the drug pills position output by the pills localization stage, the deep convolutional neural network is use to classify the pill types. The proposed database contains 131 categories of drug pill. There are total 1,680 images with 3144 annotations for localization and over 470,000 images for classification. The experiment result contains a verification dataset, includes 400 pills images with 2825 annotations. Finally, the experiment result is shown that, the top-1 accuracy rate is 79.4%. Top-3 and Top-5 accuracy are 88.3% and 91.8%. The proposed system has achieved well experiment result.","PeriodicalId":402234,"journal":{"name":"2018 International Conference on Orange Technologies (ICOT)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762268","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: 11

Blood Flow Patterns in The Left Ventricle by Echodynamography Method 超声心动图法研究左心室血流模式

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705826

S. Oktamuliani, K. Hasegawa, Y. Saijo

{"title":"Blood Flow Patterns in The Left Ventricle by Echodynamography Method","authors":"S. Oktamuliani, K. Hasegawa, Y. Saijo","doi":"10.1109/ICOT.2018.8705826","DOIUrl":"https://doi.org/10.1109/ICOT.2018.8705826","url":null,"abstract":"The left ventricle has an important function in our body. The left ventricle receives oxygenated blood from the left atrium and brings through the aorta to the body. Therefore, estimation and visualization of blood flow velocity in the left ventricle are essential in clinical diagnosis. Color Doppler ultrasound is one of the instruments for left ventricle assessment that is used to detect blood flow velocity in the heart. However, color Doppler ultrasound shows the two-dimensional (2D) distribution of the one-dimensional (1D) velocity component of blood flow, forward or away along the transducer. The purpose of this study is to estimate and visualize the blood flow velocity in the left ventricle of the healthy volunteer and heart failure volunteers (mitral stenosis and mitral regurgitation) by Doppler ultrasound using echodynamography (EDG) method. The purpose of EDG is to estimate velocity component in a perpendicular direction to the ultrasound beam and to visualize the 2D velocity vectors of blood flow in the left ventricle. EDG composed the flow into the vortex (vortices laminar flow) and base flow (non-vortical laminar flows) components. The separation of flow is based on the continuity equation of fluid dynamics and hydrodynamics characteristics (stream function and flow function). The results show the blood flow velocity vectors are higher in the heart failure cases than the healthy case and indicated the vortex existence in the heart failure cases. In conclusion, the EDG method successfully estimated and visualized blood flow velocity in the left ventricle of the healthy and heart failure volunteers. Furthermore, the EDG method may contribute to the practical and clinical diagnosis of left ventricle assessment.","PeriodicalId":402234,"journal":{"name":"2018 International Conference on Orange Technologies (ICOT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129365600","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

A Comparison Study Between Deep Learning and Conventional Machine Learning on White Blood Cells Classification 深度学习与传统机器学习在白细胞分类上的比较研究

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705892

Made Satria Wibawa

引用次数: 10

DNN Regression Model and Microphone Array for Noise Reduction System on Real Field 实场降噪系统的DNN回归模型与麦克风阵列

2018 International Conference on Orange Technologies (ICOT) Pub Date : 2018-10-01 DOI: 10.1109/ICOT.2018.8705927

Sheng-Chieh Lee, Che-Wen Chen, Bo-Hao Su, Yi-Shiun Chen, Shih-Pang Tseng, Jhing-Fa Wang

{"title":"DNN Regression Model and Microphone Array for Noise Reduction System on Real Field","authors":"Sheng-Chieh Lee, Che-Wen Chen, Bo-Hao Su, Yi-Shiun Chen, Shih-Pang Tseng, Jhing-Fa Wang","doi":"10.1109/ICOT.2018.8705927","DOIUrl":"https://doi.org/10.1109/ICOT.2018.8705927","url":null,"abstract":"In this study, we proposed a regression model based on the deep neural network (DNN) and a microphone array for noise reduction system on a supermarket robot. The proposed speech enhancement model can be separated into two parts: the training phase and the enhancement phase. A large amount of data is used for DNN model to train the mapping function, and to separate the estimated features of clean speech signal according to the features of noisy speech signal. In addition, convolutional recurrent neural networks are added to enhance the output performance of the original DNN model. The main objective is to take advantage of convolutional networks in feature extraction and to use recurrent networks to have the ability to process temporal models. This proposed system is used to overcome the noise interference in real life. The system can achieve the better speech recognition rate and provide correct sentences for the backend dialogue system. The microphone array is used for collecting audio, which not only to reduce the noise at the front end, but also to enhance the collected audio distance of the system.","PeriodicalId":402234,"journal":{"name":"2018 International Conference on Orange Technologies (ICOT)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134575723","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