• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.46-51
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• 2004

his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier.

• Journal of Korea Water Resources Association
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• v.39 no.1 s.162
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• pp.35-46
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• 2006

In this study, we proposed a layout of the integrated decision support system in order to prevent the contamination and to manage risk in water supply networks for safe and smooth water supply. We evaluated the priority of risk factors to detect anomaly in water supply networks using PROMETHEE and ANP techniques, which are applied to various Multi-Criteria Decision Making area in Europe and America. To develop the model, we selected pH, residual chlorine concentration, discharge, hydraulic pressure, electrical conductivity, turbidity, block leakage and water temperature as the key data item. We also chose pipe corrosion, pipe burst and water pollution in pipe as the criteria and then we present the results of PROMETHEE and ANP analysis. The evaluation results of the priority of risk factors in water supply networks will provide basic data to establish a contingency plan for accidents so that we can establish the specific emergency response procedures.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.49-62
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• 2021

The objective of this study was to evaluate the flood control capacity of the agricultural reservoir based on state-of-the-art climate change scenario - SSP (Shared Socioeconomic Pathways). 18 agricultural reservoirs were selected as the study sites, and future rainfall data based on SSP scenario provided by CMIP6 (Coupled Model Intercomparison Project 6) was applied to analyze the impact of climate change. The frequency analysis module, the rainfall-runoff module, the reservoir operation module, and their linkage system were built and applied to simulate probable rainfall, maximum inflow, maximum outflow, and maximum water level of the reservoirs. And the maximum values were compared with the design values, such as design flood of reservoirs, design flood of direct downstream, and top of dam elevation, respectively. According to whether or not the maximum values exceed each design value, cases were divided into eight categories; I-O-H, I-O, I-H, I, O-H, O, H, X. Probable rainfall (200-yr frequency, 12-h duration) for observed data (1973~2020) was a maximum of 445.2 mm and increased to 619.1~1,359.7 mm in the future (2011~2100). For the present, 61.1% of the reservoirs corresponded to I-O, which means the reservoirs have sufficient capacity to discharge large inflow; however, there is a risk of overflowing downstream due to excessive outflow. For the future, six reservoirs (Idong, Baekgok, Yedang, Tapjung, Naju, Jangsung) were changed from I-O to I-O-H, which means inflow increases beyond the discharge capacity due to climate change, and there is a risk of collapse due to dam overflow.

• Journal of Preventive Medicine and Public Health
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• v.52 no.5
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• pp.308-315
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• 2019

Objectives: Estimating influenza-associated mortality is important since seasonal influenza affects persons of all ages, causing severe illness or death. This study aimed to estimate influenza-associated mortality, considering both periodic changes and age-specific mortality by influenza subtypes. Methods: Using the Microdata Integrated Service from Statistics Korea, we collected weekly mortality data including cause of death. Laboratory surveillance data of respiratory viruses from 2009 to 2016 were obtained from the Korea Centers for Disease Control and Prevention. After adjusting for the annual age-specific population size, we used a negative binomial regression model by age group and influenza subtype. Results: Overall, 1 859 890 deaths were observed and the average rate of influenza virus positivity was 14.7% (standard deviation [SD], 5.8), with the following subtype distribution: A(H1N1), 5.0% (SD, 5.8); A(H3N2), 4.4% (SD, 3.4); and B, 5.3% (SD, 3.7). As a result, among individuals under 65 years old, 6774 (0.51%) all-cause deaths, 2521 (3.05%) respiratory or circulatory deaths, and 1048 (18.23%) influenza or pneumonia deaths were estimated. Among those 65 years of age or older, 30 414 (2.27%) all-cause deaths, 16 411 (3.42%) respiratory or circulatory deaths, and 4906 (6.87%) influenza or pneumonia deaths were estimated. Influenza A(H3N2) virus was the major contributor to influenza-associated all-cause and respiratory or circulatory deaths in both age groups. However, influenza A(H1N1) virus-associated influenza or pneumonia deaths were more common in those under 65 years old. Conclusions: Influenza-associated mortality was substantial during this period, especially in the elderly. By subtype, influenza A(H3N2) virus made the largest contribution to influenza-associated mortality.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• The Journal of Korean Institute of Information Technology
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• v.18 no.5
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• pp.55-64
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• 2020

Estimating the state of charge (SOC) of a battery is essential for increasing the stability and reliability of a photovoltaic system. In this study, battery SOC estimation methods were proposed using artificial neural networks (ANNs) with gradient descent (GD), Levenberg-Marquardt (LM), and scaled conjugate gradient (SCG), and an adaptive neuro-fuzzy inference system (ANFIS). The charge start voltage and the integrated charge current were used as input data and the SOC was used as output data. Four models (ANN-GD, ANN-LM, ANN-SCG, and ANFIS) were implemented for battery SOC estimation and compared using MATLAB. The experimental results revealed that battery SOC estimation using the ANFIS model had both the highest accuracy and highest convergence speed.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.261-270
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• 2020

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify "topics" that are corresponding to "key" technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these "key" technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the "key" promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.308-313
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• 2021

In this study, tweets about the 2019 Koreas-US DMZ Summit were collected along with a time sequence and analyzed by a sequential topic modeling method, Dynamic Topic Modeling(DTM). In microblogging services such as Twitter, unstructured data that mixes news and an opinion about a single event occurs at the same time on a large scale, and information and reactions are produced in the same message format. Therefore, to grasp a topic trend, the contextual meaning can be found only by performing pattern analysis reflecting the characteristics of sequential data. As a result of calculating the DTM after obtaining the topic coherence score and evaluating the Latent Dirichlet Allocation(LDA), 30 topics related to news reports and opinions were derived, and the probability of occurrence of each topic and keywords were dynamically evolving. In conclusion, the study found that DTM is a suitable model for analyzing the trend of integrated topics in a specific event over time.

• Journal of Environmental Impact Assessment
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• v.31 no.5
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• pp.271-285
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• 2022

The AERMET, the AERMOD meteorological preprocessing program, mainly used for environmental impact assessment and Integrated Environmental Permit System (IEPS) in Korea, has not considered the land covers characterasitics, and used only the past meteorological data format CD-144. In this study, two results of AERMET application considering CD-144 format and ISHD format, being used internationally, were compared. Also, the atmospheric dispersion characteristics were analyzed with consideration of land cover. In the case of considered the CD-144 format, the actual wind speed was not taken into account in the weak wind (0.6~0.9m/s) and other wind speed due to the unit conversion problem. The predicted concentration considering land cover data was up to 387% larger depending on the topographic and emission conditions than without consideration of land cover. In conclusion, when using meteorological preprocessing program in AERMOD modelling, AERMET, with ISHD format, land cover characterasitics in the area should be considered.