2021 International Conference on Platform Technology and Service (PlatCon)最新文献

{"title":"An Efficient Neural Network based on Early Compression of Sparse CT Slice Images","authors":"A-Seong Moon, Sanghyuck Lee, S. Cho, TaeGeon Lee, Hanyong Lee, Jae-Soung Lee","doi":"10.1109/PlatCon53246.2021.9680749","DOIUrl":"https://doi.org/10.1109/PlatCon53246.2021.9680749","url":null,"abstract":"Recently, research on diagnosing diseases through artificial intelligence has been conducted in various medical fields, including Thyroid-associated ophthalmopathy. We introduce a computationally efficient CNN architecture, which is optimized for CT images and designed especially for mobile devices with very limited computing power. The proposed architecture utilizes three operations, pointwise convolution, depth-wise separable convolution and channel shuffle, to reduce computation cost for handling a series of CT image slices for a patient. On CT images, the proposed model achieves ∼ 3.5 × actual speedup over ShuffleNet-v2 without degenerating prediction accuracy.","PeriodicalId":344742,"journal":{"name":"2021 International Conference on Platform Technology and Service (PlatCon)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127333296","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}

{"title":"Design and Implementation of Real-time Bio Signals Management System based on HL7 FHIR for Healthcare Services","authors":"Jaehak Yu, Soonhyun Kwon, Sejin Park, Jong-Arm Jun, C. Pyo","doi":"10.1109/PlatCon53246.2021.9680756","DOIUrl":"https://doi.org/10.1109/PlatCon53246.2021.9680756","url":null,"abstract":"Recently, attention has been focused on services that combine medical technology with ICT technologies such as artificial intelligence, big data, Internet of Things, and block chains. In addition, Research on healthcare services that can collect bio signal data through wearable sensors using IoT technology and monitor and manage health based on the collected data is increasing significantly. In particular, in a situation where the world is entering a rapidly aging society, health care services are being researched and developed in the direction of preventing diseases in advance and maintaining a healthy life. Healthcare services are bringing important changes in the pandemic era caused by covid-19. There is a need for a system capable of efficiently sharing and exchanging information of heterogeneous services to prevent emergencies and support optimal medical services. In this paper, we designed and developed a system that can collect, convert, and store bio-signals from various wearable sensors into international standard data to develop such healthcare services. HL7 (health level seven) FHIR (fast healthcare interoperability resources) applied mutandis in this paper is a standard protocol for data exchange between medical information systems of real-time collected bio signals. In this paper, we implement an interface module that converts bio signals such as EEG (electroencephalography), ECG (electrocardiogram), EMG (electromyography), and PPG (photoplethysmography) collected in real time from a wearable sensor into a message structure defined by HL7 FHIR. The interface module consists of a client part and a server part. The client part generates a variety of signal data from the healthcare service user and delivers the message to the server part. The server part is designed and implemented to parse the received message by segment field unit and transmit whether the message is abnormal or not to the client part. The system designed and implemented in this paper will be utilized as a technology that can mutually share and exchange medical information in a customized healthcare service that reflects the needs of various customers and a telemedicine system.","PeriodicalId":344742,"journal":{"name":"2021 International Conference on Platform Technology and Service (PlatCon)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124447907","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}

{"title":"CEMO : Cloud Edge Architecture Development for a Multi Object Tracking","authors":"Jihyun Seo, Jae-Geun Cha, Hyunhwa Choi, Sumin Jang, Daewon Kim, Sunwook Kim","doi":"10.1109/PlatCon53246.2021.9680750","DOIUrl":"https://doi.org/10.1109/PlatCon53246.2021.9680750","url":null,"abstract":"Due to increase of video surveillance situation, advance of autonomous driving technology, and development of artificial neural network, the multi-object tracking (MOT) has been attracted attention in the computer vision community. Moreover, the importance of multi-input processing and real-time analysis is increasing with the need for fast processing of many videos. Modern multi-object trackers use sequential processing to input continuous frames of video and derive tracking trajectories for all objects mainly on a single server. When performing deep learning with high computation on a single server, latency inevitably occurs. The latency is the main reason that the tracker cannot meet the real-time requirements. Removing the number of operations to reduce latency will immediately lead to poor performance of tracker. Cloud edge computing is the best way to meet the real-time distributed requirements because it can solve the data transmission delay problem of traditional cloud computing and effectively cooperate between edge devices. In this paper, we propose a new system structure called Cloud Edge Multi Object (CEMO) tracker for developing deep learning-based cloud-edge real-time video analysis applications. CEMO tracker is a container-based microservice structure that divides large application functions into small, independent units, and is a flexible architecture based on Kubernetes, a container orchestration platform. CEMO tracker, which can efficiently perform operations on simultaneous input, integrate the results and show them to users, is expected to solve multiple objects tracking problems efficiently through distributed cloud edge computing technology.","PeriodicalId":344742,"journal":{"name":"2021 International Conference on Platform Technology and Service (PlatCon)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125670415","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}