• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.867-876
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• 2013

Metabolic syndrome has been known as a major factor of cardiovascular disease. Several metabolic disorders, particularly chronic disease is complex, and from individuals that appear in our country, the prevalence of the metabolic syndrome is increasing gradually. Therefore, this study, using a multi-factor dimensionality reduction method, checks the major single risk factor of metabolic syndrome and suggests a new diagnosis results of metabolic syndrome. Data of 3990 adults who responded to all the questionnaires of health interview are used from the database of the 5th Korea national health and nutrition examination survey conducted in 2010. As the result, the most dangerous single risk factor for metabolic syndrome was waist circumference and the most dangerous combination factors were waist circumference, triglyceride, and hypertension. This is the result of a new diagnosis of the metabolic syndrome. Especially, waist circumference, low HDL-cholesterol and hypertension were the most dangerous combination for male. In particular, the combination of waist circumference, triglyceride and diabetes was dangerous for obese people.

• Geotechnical Engineering
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• v.10 no.3
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• pp.79-96
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• 1994

A tunnel design expert system entitled NESTED is developed using the artificial neural network. The expert system includes three neural network computer models designed for the stability assessment of underground openings and the estimation of correlation between the RMR and Q systems. The expert system consists of the three models and the computerized rock mass classification programs that could be driven under the same user interface. As the structure of the neural network, a multi -layer neural network which adopts an or ror back-propagation learning algorithm is used. To set up its knowledge base from the prior case histories, an engineering database which can control the incomplete and erroneous information by learning process is developed. A series of experiments comparing the results of the neural network with the actual field observations have demonstrated the inferring capabilities of the neural network to identify the possible failure modes and the support timing. The neural network expert system thus complements the incomplete geological data and provides suitable support recommendations for preliminary design of tunnels in rock masses.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.856-859
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• 2013

Ships and marine structures(lighthouses and buoys for AtoN) have AIS(Automatic Identification System) for transmission messages which include navigational and environmental information. VTS Center and surrounding ships receive and apply the information to safety navigation. A main characteristic of AIS messages is open to general people, so many researches are in progress. In this paper, we design an information management system which considers marine vessel traffic and environmental information in korean littoral sea. The system gathers and processes the information, and stores the processed data to multi-stage database. Also the system visualizes the stored data to use analysis and statistics based on ENC.

• Computers and Concrete
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• v.22 no.2
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• Journal of Preventive Medicine and Public Health
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• v.40 no.4
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• pp.321-328
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• 2007

Objectives : The aim of this study was to evaluate the roles of cigarette smoking, alcohol consumption, tuberculosis, and their interactions in the risk of lung cancer in a Korean cohort. Methods : The study subjects comprised 13,150 males and females aged above 20 years old. During the follow up period from 1993 to 2002, 79 lung cancer cases were identified by the central cancer registry and the national death certificate database. Information on cigarette smoking, alcohol consumption and the history of physician-diagnosed tuberculosis was obtained by interview. Indirect chest X-ray findings were also evaluated to ascertain tuberculosis cases. Cox proportional hazard models were used to estimate relative risks (RR) and 95% confidence intervals (CI) after adjusting for age and gender. Results : Cigarette smoking was statistically significantly associated with an increased risk of lung cancer [for current smokers, RR = 2.33 (95% CI = 1.23 - 4.42) compared to non-smokers]. After further adjustment for cigarette smoking, both alcohol consumption and tuberculosis showed no statistically significant association with the risk of lung cancer [for current drinkers, RR = 0.80 (95% CI = 0.48 - 1.33) compared to non-drinkers] [for tuberculosis cases, RR = 1.17 (95% CI = 0.58 - 2.36) compared to non-cases]. There was no statistically significant interaction between cigarette smoking and alcohol consumption (p-interaction = 0.38), or cigarette smoking and tuberculosis (p-interaction = 0.74). Conclusions : Although cigarette smoking was confirmed as a risk factor of lung cancer in this cohort study, this study suggests that alcohol consumption and tuberculosis may not be associated with the risk of lung cancer.

• Journal of the Korean Society for Railway
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• v.11 no.4
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• pp.364-370
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• 2008

In this paper, we proposed new sensor network architecture with autonomous robots based on beacon mode and implemented real time monitoring system in real test-bed environment. The proposed scheme offers beacon based real-time scheduling for reliable association process with parent nodes and dynamically assigns network address by using NAA (Next Address Assignment) mechanism. For the large scale multi-sensor processing, our real-time monitoring system accomplished the intelligent database processing, which can generate not only the alert messages to the civilians but also process various sensing data such as fire, air, temperature and etc. Moreover, we also developed mobile robot which can support network mobility. Though the performance evaluation by using real test-bed system, we illustrate that our proposed system demonstrates promising performance for emergence monitoring systems.

• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.267-278
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• 2003

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, in order to predict tunnel-induced ground movements, Tunnel Behavior Prediction System (TBPS) was developed by using these artificial neural networks model, based on a Held instrumentation database (i.e. crown settlement, convergence, axial force of rock bolt, compressive and shear stress of shotcrete, stress of concrete lining etc.) obtained from 193 location data of 31 different tunnel sites where works are completed. The study and test of the network were performed by Back Propagation Algorithm which is known as a systematic technique for studying the multi-layer artificial neural network. The tunnel behaviors predicted by TBPS were compared with monitored data in the tunnel sites and numerical analysis results. This study showed that the values obtained from TBPS were within allowable limits. It is concluded that this system can effectively estimate the tunnel ground movements and can also be used f3r tunneling feasibility study, and basic and detailed design and construction of tunnel.

• Korean Journal of Construction Engineering and Management
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• v.10 no.5
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• pp.148-157
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• 2009

When an emergency situation happens in buildings, the top priority is to ensure the occupant from danger as soon as possible. Achieving that goal is a multifaceted and difficult task. However, current evacuation systems have many deficiencies in dealing with the emergency in multi-level structures. The shortage of abilities to continuously update database, predict the future situation and provide the information to users with contextual information is the limit in current systems. Thus, it is very crucial to introduce Evacuation Information System (EIS), which is able to respond quickly to the emergency, and transfer the information to both the administrator and the occupant. The main purpose of this paper is to build EIS on the basis of the indoor Geographical Information System (GIS). When the emergency happens, EIS gives the instruction to Emergency Response Model (ERM) at once. ERM carries out the order and calculates the optimal evacuation routes, then sends the result to EIS. At last, EIS transmits evacuation messages to the occupant who implements evacuation plan. This paper highlights the benefits of EIS in two aspects. One is that EIS can update the data continuously to support evacuation strategy-making. The other is that it can transmit evacuation messages to both the administrator and the occupant.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.543-548
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• 2014

Multiple-model based speech recognition framework (MMSR) has been known to be very successful in speech recognition. Since it uses multiple hidden Markov modes (HMMs) that corresponds to various noise types and signal-to-noise ratio (SNR) values, the selected acoustic model can have a close match with the test noisy speech. However, since the number of HMM sets is limited in practical use, the acoustic mismatch still remains as a problem. In this study, we experimentally determined the optimal SNR mapping between the test noisy speech and the HMM set to mitigate the mismatch between them. Improved performance was obtained by employing the SNR mapping instead of using the estimated SNR from the test noisy speech. When we applied the proposed method to the MMSR, the experimental results on the Aurora 2 database show that the relative word error rate reduction of 6.3% and 9.4% was achieved compared to a conventional MMSR and multi-condition training (MTR), respectively.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1113-1121
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• 2011

Recently, a variety of data mining techniques has been applied in various fields like healthcare, insurance, and internet shopping mall. Association rule mining is a popular and well researched method for discovering interesting relations among large set of data items. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are three primary quality measures for association rules; support and confidence and lift. In this paper we consider some similarity measures with negative co-occurrence frequencies which is widely used in cluster analysis or multi-dimensional analysis as association thresholds. The comparative studies with support, confidence and some similarity measures are shown by numerical example.