• Progress in Medical Physics
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• v.28 no.3
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• pp.129-133
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• 2017

The machine log files recorded by a scanning control unit in proton beam therapy system have been studied to be used as a quality assurance method of scanning beam deliveries. The accuracy of the data in the log files have been evaluated with a standard calibration beam scan pattern. The proton beam scan pattern has been delivered on a gafchromic film located at the isocenter plane of the proton beam treatment nozzle and found to agree within ${\pm}1.0mm$. The machine data accumulated for the scanning beam proton therapy of five different cases have been analyzed using a statistical method to estimate any systematic error in the data. The high-precision scanning beam log files in line scanning proton therapy system have been validated to be used for off-line scanning beam monitoring and thus as a patient-specific quality assurance method. The use of the machine log files for patient-specific quality assurance would simplify the quality assurance procedure with accurate scanning beam data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.19-27
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• 2022

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.119-124
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• 2004

Real data as web log file tend to be incomplete. But we have to find useful knowledge from these for optimal decision. In web log data, many useful things which are hyperlink information and web usages of connected users may be found. The size of web data is too huge to use for effective knowledge discovery. To make matters worse, they are very sparse. We overcome this sparse problem using Markov Chain Monte Carlo method as multiple imputations. This missing value imputation changes spare web data to complete. Our study may be a useful tool for discovering knowledge from data set with sparseness. The more sparseness of data in increased, the better performance of MCMC imputation is good. We verified our work by experiments using UCI machine learning repository data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.685-703
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• 2024

Cloud computing provides each consumer with a large-scale computing tool. Different Cyber Attacks can potentially target cloud computing systems, as most cloud computing systems offer services to many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If strong security is needed, then the service of stronger security using more rules or patterns is provided, since it needs much more computing resources. A new way of security system is introduced in this work in cloud environments to the VM on account of resources allocated to customers are ease. The main spike of Fog computing is part of the cloud server's work in the ongoing study tells the step-by-step cloud server to change the tremendous measurement of information because the endeavor apps are relocated to the cloud to keep the framework cost. The cloud server is devouring and changing a huge measure of information step by step to reduce complications. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and Fog layer based on the guard intensity and the key is provoked for ingress the file. The monitoring center sustains the Activity Log, Risk Table, and Health Records. Cloud computing and Fog computing were combined in this paper to review data movement and safe information about MDHC.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.24-27
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• 2001

Nowadays, increasing demand of the customized products has led to an increased usage of rapid prototyping in the product development. However, the acquisition price of a rapid prototyping equipment is still too high that not every body can afford to buy one. To offer a wide access to the users who need physical prototypes, a connection of the rapid prototyping equipment to the Internet is a viable step. It would allow a large group of customers all over the world to use the manufacturing capability of a service provider offering this kind of manufacturing service. To realize how such an e-manufacturing concept can look like, a LOM-type 3D printer developed at KIST has been used as test site and connected to the Internet. A possible user can log on to the server of the equipment and view his STL file and start the building operation from a remote place. To see whether the operation runs properly, a CCD camera is used to transmit the actual state of operation online. The result so far proves the feasibility of rapid prototyping on the Internet as well as an order-adaptive manufacturing system via web.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.499-510
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• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.

• Neonatal Medicine
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• v.18 no.2
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• pp.301-309
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• 2011

Purpose: Point-of-care tests (POCTs) have the potential to significantly influence management of neonates. The aim of this study was to assess the clinical usefulness of the POCT chemistry analyzer in a neonatal intensive care unit (NICU). Methods: Blood samples of neonates admitted to the NICU were tested using a POCT chemistry analyzer (Piccolo Xpress Chemistry Analyzer, Abaxis, Union City, CA, USA) and a central laboratory chemical analyzer (Chemistry analyzer 7600-110, Hitachi Ltd., Tokyo, Japan) from March to September, 2010. Correlation of 15 analytes between the POCT and the central laboratory machine was evaluated. For consistency of the POCT, three consecutive samplings were performed. Differences among the three tests were recorded. The causes of performance errors were checked through log files. Results: One hundred of 112 pairs of tests for accuracy performed in 54 neonates showed a high correlation between the two machines. Twelve performance errors occurred during the 112 tests. The most common error was insufficient sample error. Eighteen triplet tests performed in 18 patients for consistency revealed a difference range of 3-10%, which was considered to be acceptable. No error occurred during the 54 tests. Conclusion: The POCT is capable of analyzing multiple analytes with a minimal amount of whole blood in a short time. The few performance errors noted presently are likely preventable. This POCT is concluded to be suitable for use as a simple and rapid diagnostic method in the NICU with a minimal amount of blood collected in a less invasive manner.