• Journal of Korean Society for Quality Management
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• v.43 no.4
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• pp.573-588
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• 2015

Purpose: For improving result of estimated remaining useful life in Prognostics and Health Management (PHM), a system which is able to consider a lot of environment and load data is required. Method: A load profile monitoring system was presented based on cloud computing for gathering and processing raw data which is included environment and load data. Result: Users can access results of load profile information on the Internet. The developed system provides information which consists of distribution of load data, basic statistics, etc. Conclusion: We developed the load profile monitoring system for considering much environment and load data. This system has advantages such as improving accessibility through smart device, reducing cost, and covering various conditions.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• Convergence Security Journal
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• v.23 no.1
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• pp.79-87
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• 2023

Precision Medicine, which utilizes personal health information, genetic information, clinical information, etc., is growing as the next-generation medical industry. In Korea, medical institutions and information communication companies have coll aborated to provide cloud-based Precision Medicine Hospital Information Systems (P-HIS) to about 90 primary medical ins titutions over the past five years, and plan to continue promoting and expanding it to primary and secondary medical insti tutions for the next four years. Precision medicine is directly related to human health and life, making information protecti on and healthcare information protection very important. Therefore, this paper analyzes the preliminary research on inform ation protection models that can be utilized in cloud-based Precision Medicine Hospital Information Systems and ultimately proposes research on ways to improve information protection in P-HIS.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.275-278
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• 2002

The recognition of the natural background radiation is important not only for radiological education but also for the promotion of people's scientific view about radiation. We made a "room" on the web showing natural background radiation as part of a VRM (Virtual Radiation Museum). The "room" shows the video images of the tracks of charged particles from natural background radiation, alpha and beta ray track from known sources using a Large Scale Diffusion Cloud Chamber. The purpose of this study is to make clear the origin of a kind of track (named A-track) which is thick and easy to recognize with the length less than several cm in the cloud chamber, and to make numerical explanation of its counting rate. The study was carried out using a Large Scale Diffusion Cloud Chamber (Phywe, Germany) installed in the Niigata Science Museum. The Model RNC (Pylon Electronics, Canada) was used as Rn-222 source. Ra-226 activity in RNC was 111.6 Bq calibrated with NIST protocol. Rn-222 gas was injected into the cloud chamber. Continuous video recording with use of Digital Handycam (SONY, Japan) was carried out for 360 min. after injection of Rn-222 gas. The number of alpha-ray track (alpha track) in the video images was analyzed. The growth and decay curve of the total activity of Rn-222 and its alpha emitting progeny were calculated and compared with the count of the alpha tracks. As a result the alpha tracks formed by Rn-222 injection resemble A-Tracks. The relationship between A-track in the cloud chamber and atmospheric Rn is discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3699-3719
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• 2017

Conjunctive keyword search encryption is an important technique for protecting sensitive personal health records that are outsourced to cloud servers. It has been extensively employed for cloud storage, which is a convenient storage option that saves bandwidth and economizes computing resources. However, the process of searching outsourced data may facilitate the leakage of sensitive personal information. Thus, an efficient data search approach with high security is critical. The multi-user search function is critical for personal health records (PHRs). To solve these problems, this paper proposes a novel multi-user conjunctive keyword search scheme (mNCKS) without a secure channel against keyword guessing attacks for personal health records, which is referred to as a secure channel-free mNCKS (SCF-mNCKS). The security of this scheme is demonstrated using the Decisional Bilinear Diffie-Hellman (DBDH) and Decision Linear (D-Linear) assumptions in the standard model. Comparisons are performed to demonstrate the security advantages of the SCF-mNCKS scheme and show that it has more functions than other schemes in the case of analogous efficiency.

• Smart Structures and Systems
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• v.21 no.5
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• pp.611-621
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• 2018

Compared with the highway bridges, the relatively higher requirement on the safety and comfort of vehicle makes the high-speed railway (HSR) bridges need to present enhanced dynamic performance. To this end, installing a health monitor system (HMS) on selected key HSR bridges has been widely applied. Typically, the HSR takes fully enclosed operation model and its skylight time is very short, which means that it is not easy to operate the acquisition devices and download data on site. However, current HMS usually involves manual operations, which makes it inconvenient to be used for the HSR. Hence, a HMS named DASP-MTS (Data Acquisition and Signal Processing - Monitoring Test System) that integrates the internet, cloud computing (CC) and virtual instrument (VI) techniques, is developed in this study. DASP-MTS can realize data acquisition and transmission automatically. Furthermore, the acquired data can be timely shared with experts from various locations to deal with the unexpected events. The system works in a Browser/Server frame so that users at any places can obtain real-time data and assess the health situation without installing any software. The developed integrated HMS has been applied to the Xijiang high-speed railway arch bridge. Preliminary analysis results are presented to demonstrate the efficacy of the DASP-MTS as applied to the HSR bridges. This study will provide a reference to design the HMS for other similar bridges.

• Proceedings of the KAIS Fall Conference
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• 2010.11b
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• pp.859-861
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• 2010

The construction companies are facing potential EHS(Environment, Health & Safety) of major accidents to cause casualties or a financial loss and increasing social responsibility. So, they have to voluntarily accomplish the EHS management system rather than passively with regard to EHS regulation. In this study, the integrated EHS management system is developed based on cloud computing, and construction companies are to materialize self-regulation EHS process of construction workplace and to standardize the total EHS business process using the developed system. The proposed system also provides risk analysis, education/control and continuous improvement for EHS tasks and users can easily access the system on the web at a low price through cloud computing service. Eventually, the integrated system contributes to the managerial improvement by minimizing economic and physical loss caused by construction accidents.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.27-39
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• 2018

In this paper, we describe the algorithms, EEG classification methods, and position data analysis methods using EEG and ADS1299 sensors. In addition, it is necessary to manage the amount of real-time data of location data and EEG data and to extract data efficiently. To do this, we explain the process of extracting important information from a vast amount of data through a cloud server. The electrical signals extracted from the brain are measured to determine the psychological state and health status, and the measured positions can be collected using the position sensor and triangulation method.

• Genomics & Informatics
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• v.16 no.4
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• pp.24.1-24.4
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• 2018

Taxonomic identification is fundamental to all microbiology studies. Particularly in metagenomics, which identifies the composition of microorganisms using thousands of sequences, its importance is even greater. Identification is inevitably affected by the choice of database. This study was conducted to evaluate the accuracy of three widely used 16S databases-Greengenes, Silva, and EzBioCloud-and to suggest basic guidelines for selecting reference databases. Using public mock community data, each database was used to assign taxonomy and to test its accuracy. We show that EzBioCloud performs well compared with other existing databases.

• Smart Structures and Systems
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• v.26 no.1
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• pp.11-21
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• 2020

The hinge joint is the key to the overall cooperative working performance of the assembled beam bridge, and it is also the weakest part during the service period. This paper proposes a method for monitoring and evaluating the lateral cooperative working performance of fabricated beam bridges based on dynamic strain correlation coefficient indicator. This method is suitable for monitoring and evaluation of hinge joints status between prefabricated girders and overall cooperative working performance of bridge, without interruption of traffic and easy implementation. The remote cloud monitoring and diagnosis system was designed and implemented on a real assembled beam bridge. The algorithms of data preprocessing, online indicator extraction and status diagnosis were given, and the corresponding software platform and scientific computing environment for cloud operation were developed. Through the analysis of real bridge monitoring data, the effectiveness and accuracy of the method are proved and it can be used in the health monitoring system of such bridges.