• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.45-53
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• 2003

This paper addresses the issues related to the development of product model and applications fer nuclear power plants based on STEP and PLIB standards. The ISO standards which can be applied are; STEP(Standard for the Exchange of Product Model Data) AP(application protocol) 221/231, AP 230/225, AP 227, ISO 13584 PLIB, ISO 15926 RDL. The data models of the AP's and ISO 15926 RDL are reviewed and an application system is proposed to exchange and share the design data of the nuclear power plant.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.31-39
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• 2004

In this paper, we consider two components system which the lifetimes follow bivariate pareto model with bivariate random censored data. We assume that the censoring times are independent of the lifetimes of the two components. We develop large sample test for testing independence between two components. Also we present a simulation study which is the test based on asymptotic normal distribution in testing independence.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.629-636
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• 2007

A kernel type semi-supervised estimate is proposed. The proposed estimate is based on the penalized least squares loss and the principle of Gaussian Random Fields Model. As a result, we can estimate the label of new unlabeled data without re-computation of the algorithm that is different from the existing transductive semi-supervised learning. Also our estimate is viewed as a general form of Gaussian Random Fields Model. We give experimental evidence suggesting that our estimate is able to use unlabeled data effectively and yields good classification.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.41-47
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• 2008

Cluster based fuzzy model tree has certain drawbacks to decrease performance of testinB data when over-fitting of training data exists. To reduce the sensitivity of performance due to over-fitting problem, we proposed a modified cluster based fuzzy model tree with node information. To construct model tree, cluster centers are calculated by fuzzy clustering method using all input and output attributes in advance. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. In the prediction step, membership values are calculated by using fuzzy distance between input attributes and all centers that passing the nodes from root to leaf nodes. Finally, data prediction is performed by the weighted average method with the linear models and fuzzy membership values. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional cluster based fuzzy model tree.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.75-84
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• 2017

As there has been growing interests in PHR-based personalized health management project, various institutions recently explore safe methods of recording personal medical and health information. In particular, innovative medical solution can be realized when medical researchers and medical service institutes can generally get access to patient data. As EMR data is extremely sensitive, there has been no progress in clinical information exchange. Moreover, patients cannot get access to their own health data and exchange it with researchers or service institutions. It can be operated in terms of technology, yet policy environment are affected by state laws as well as Privacy and Security Policy. Blockchain technology-independent, in transaction, and under test-is introduced in the medical industry in order to settle these problems. In other words, medical organizations can grant preliminary approval on patient information exchange by using the safely encrypted and distributed Blockchain ledger and can be managed independently and completely by individuals. More apparently, medical researchers can gain access to information, thereby contributing to the scientific advance in rare diseases or minor groups in the world. In this paper, we focused on how to manage personal medical information and its protective use and proposes medical treatment exchange system for patients based on a permissioned Blockchain network for the safe PHR operation. Trusted Model for Sharing Medical Data (TMSMD), that is proposed model, is based on exchanging information as patients rely on hospitals as well as among hospitals. And introduce medical treatment exchange system for patients based on a permissioned Blockchain network. This system is a model that encrypts and records patients' medical information by using this permissioned Blockchain and further enhances the security due to its restricted counterfeit. This provides service to share medical information uploaded on the permissioned Blockchain to approved users through role-based access control. In addition, this paper presents methods with smart contracts if medical institutions request patient information complying with domestic laws by using the distributed Blockchain ledger and eventually granting preliminary approval for sharing information. This service will provide an independent information transaction and the Blockchain technology under test will be adopted in the medical industry.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.533-542
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• 2020

This study proposes the logical data model design of a spatial data model that complies with international standards for the waterworks of underground facilities. We conduct a preliminary study related to underground spatial data standards and data models, and review the status of the existing systems. Then, we defined the conceptual design direction of underground spatial data model based on the problems and issues. Next, we defined the terminology, classification, semantic relationships of waterworks. Next, for the conceptual design of the underground spatial data model, we defined the naming criteria for all data according to the waterworks classification. In addition, a logical model is drawn and described using UML (Unified Modeling Language) diagrams. Based on the results, it is expected that the accuracy related to underground facilities data will be improved.

• Journal of ILASS-Korea
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• v.27 no.3
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• pp.155-160
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• 2022

Recently, major developed countries have strengthened automobile fuel efficiency regulations and carbon dioxide emission allowance standards to curb climate change caused by global warming worldwide. Accordingly, research and manufacturing on electric vehicles that do not emit pollutants during actual driving on the road are being conducted. Several automobile companies are producing and testing electric vehicles to commercialize them, but it takes a lot of manpower and time to test and evaluate mass-produced electric vehicles with driving mileage of more than 300km on a per-charge. Therefore, in order to reduce this, a simulation model was developed in this study. This study used vehicle information and MCT speed profile of small electric vehicle as basic data. It was developed by applying Simulink, which models the system in a block diagram method using MATLAB software. Based on the vehicle dynamics, the simulation model consisted of major components of electric vehicles such as motor, battery, wheel/tire, brake, and acceleration. Through the development model, the amount of change in battery SOC and the mileage during driving were calculated. For verification, battery SOC data and vehicle speed data were compared and analyzed using CAN communication during the chassis dynamometer test. In addition, the reliability of the simulation model was confirmed through an analysis of the correlation between the result data and the data acquired through CAN communication.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.27-31
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• 2023

Purpose: Establishment of a real-time monitoring system for odor control in traditional markets in Gangwon-do and a system for linking prevention facilities. Research design, data and methodology: Build server and system logic based on data through real-time monitoring device (sensor-based). A temporary data generation program for deep learning is developed to develop a model for odor data. Results: A REST API was developed for using the model prediction service, and a test was performed to find an algorithm with high prediction probability and parameter values optimized for learning. In the deep learning algorithm for AI modeling development, Pandas was used for data analysis and processing, and TensorFlow V2 (keras) was used as the deep learning library. The activation function was swish, the performance of the model was optimized for Adam, the performance was measured with MSE, the model method was Functional API, and the model storage format was Sequential API (LSTM)/HDF5. Conclusions: The developed system has the potential to effectively monitor and manage odors in traditional markets. By utilizing real-time data, the system can provide timely alerts and facilitate preventive measures to control and mitigate odors. The AI modeling component enhances the system's predictive capabilities, allowing for proactive odor management.

• Journal of Navigation and Port Research
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• v.48 no.5
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• pp.368-375
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• 2024

This research develops a big data-based CS model for maritime traffic assessment, motivated by global shipping growth, the impact of COVID-19, changes in consumer culture, and Industry 4.0 expansion in maritime sectors. Maritime traffic, crucial for global trade, demands effective management for safety and efficiency. This study aims to quantitatively and objectively evaluate maritime traffic smoothness by analyzing ship operation data. The CS model focuses on unique maritime characteristics, leveraging big data to enhance traffic management solutions and safety. The research methodology includes analyzing domestic and international trends and data to reflect maritime spatiality and continuity. The model's efficacy is tested through case studies on major port routes, comparing it with existing models to suggest improvements. This new approach provides a framework for optimizing maritime traffic routes and supports autonomous, unmanned, and smart ship operations, setting a new paradigm for maritime traffic management.

• Journal of the Society of Disaster Information
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• v.5 no.1
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• pp.122-139
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• 2009

USN Middleware plays roles of broker between sensors and applications. It collects sensor data, decides the situation and sends the result to the applications. It is not good to decide the situation from one sensor data, because it may error data or reflect small part of all. In this paper, we propose the disaster management model based on the concept 'group' and 'semantic information' from the sensing data. Group is the primary unit to decide the situation. It consists of several sensors which were installed in the same place and had the same pre-defined condition to act. For example, all fire sensors in the room simultaneously trigger the ring when the same pre-defined temperature is recorded. Then, the all fire sensors are included to the same one sensor group. All operations of the intelligent USN middleware are based on the 'group' unit. Disaster information is the result of the interpretation of the sensing data. based on the 'group', the disaster meaning is processed.