• 한국노년학
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• v.29 no.4
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• pp.1413-1425
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• 2009

The objective of the work is to analyze the factors affecting on changes in filial piety responsibility expectation. For the analysis, this study focuses on the two factors-aging effect and cohort effect. This work analyzes the 4 wave Hallym Aging Panel Data with random intercept model. In the study cohort is divided by the criteria of birth year 1940. and the former cohort is called colony-war cohort and the latter cohort is called industrialization-democratization cohort. The results are in following. First, older cohort shows higher filial piety responsibility expectation score than younger cohort. Second, age shows no relationship with filial responsibility expectation score. Third, male and resident in rural area shows higher score. Forth income, year of schooling, and subjective health show negative relationship with responsibility score.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.321-324
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• 2008

Type-2 퍼지 집합은 Type-1 퍼지 집합에서는 다루기 어려운 언어적인 불확실성을 더욱 효과적으로 다룰 수 있다. TSK 퍼지 로직 시스템(TSK Fuzzy Logic Systems; TSK FLS)은 Mamdani 모델과 함께 가장 널리 사용되는 FLS이다. 본 연구의 Interval Type-2 TSK FLS 모델은 전반부에서 Type-2 퍼지 집합을 이용하고 후반부는 계수가 상수인 1차식을 사용한다. 전반부의 파라미터는 오류역전파 방법(Back-propagation)을 통한 학습으로 결정되고, 후반부 파라미터(계수)들은 Least squre method(LSM)를 사용하여 결정된 값을 사용하여 모델을 구축한다. 본 논문에서는 Type-1 TSK FLS과 Type-2 TSK FLS의 성능을 가스로 공정 데이터를 적용하여 비교 분석한다. 또한 랜덤 화이트 가우시안 노이즈를 추가한 테스트 데이터를 사용하여 노이즈에 대한 성능을 분석한다.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.667-680
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• 2021

We investigated changes in frost days and frost-free periods and to comparatively assess frost event prediction models developed using logistic regression (LR), random forest (RF), and long short-term memory (LSTM) networks. The meteorological variables for the model development were collected from the Suwon, Cheongju, and Gwangju stations for the period of 1973-2019 for spring (March - May) and fall (September - November). The developed models were then evaluated by Precision, Recall, and f-1 score and graphical evaluation methods such as AUC and reliability diagram. The results showed that significant decreases (significance level of 0.01) in the frequencies of frost days were at the three stations in both spring and fall. Overall, the evaluation metrics showed that the performance of RF was highest, while that of LSTM was lowest. Despite higher AUC values (above 0.9) were found at the three stations, reliability diagrams showed inconsistent reliability. A further study is suggested on the improvement of the predictability of both frost events and the first and last frost days by the frost event prediction models and reliability of the models. It would be beneficial to replicate this study at more stations in other regions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.1
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• pp.59-69
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• 2004

Password-based authenticated group key exchange protocols provide a group of user, communicating over a public(insecure) channel and holding a common human-memorable password, with a session key to be used to construct secure multicast sessions for data integrity and confidentiality. In this paper, we present a password-based authenticated group key exchange protocol and prove the security in the random oracle model and the ideal cipher model under the intractability of the decisional Diffie-Hellman(DH) problem and computational DH problem. The protocol is scalable, i.e. constant round and with O(1) exponentiations per user, and provides forward secrecy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.46-55
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• 2009

We study the problem of searching documents containing each of several keywords (conjunctive keyword search) over encrypted documents. A conjunctive keyword search protocol consists of three entities: a data supplier, a storage system such as database, and a user of storage system. A data supplier uploads encrypted documents on a storage system, and then a user of the storage system searches documents containing each of several keywords. Recently, many schemes on conjunctive keyword search have been suggested in various settings. However, the schemes require high computation cost for the data supplier or user storage. Moreover, up to now, their securities have been proved in the random oracle model. In this paper, we propose efficient conjunctive keyword search schemes over encrypted documents, for which security is proved without using random oracles. The storage of a user and the computational and communication costs of a data supplier in the proposed schemes are constant. The security of the scheme relies only on the hardness of the Decisional Bilinear Diffie-Hellman (DBDH) problem.

• Journal of the Korean Geosynthetics Society
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• v.19 no.1
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• pp.45-53
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• 2020

There is an increasing tendency to try to make predictive analysis using measurement data based on machine learning techniques in the railway industries. In this paper, it was predicted that Track quality index (TQI) using vehicle acceleration data based on the machine learning method. The XGB (XGBoost) was the most accurate with 85% in the all data sets. Unlike the SVM model with a single algorithm, the RF and XGB model with a ensemble system were considered to be good at the prediction performance. In the case of the Surface TQI, it is shown that the acceleration of the z axis is highly related to the vertical direction and is in good agreement with the previous studies. Therefore, it is appropriate to apply the model with the ensemble algorithm to predict the track quality index using the vehicle vibration acceleration data because the accuracy may vary depending on the applied model in the machine learning methods.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1811-1812
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• 2006

본 논문은 공간상에서 움직이는 다중신호원의 신호 도달 방향 추정과 신호원이 보낸 신호를 다중 센서 어레이를 이용하여 판별하는 문제를 다룬다. 일반적으로 정지하고 있는 신호원의 방향 추정은 어레이에서의 출력 방정식의 방향 벡터들이 시불변인 상수이기 때문에 잡음환경 하에서도 샘플링된 벡터(Snapshot) 수가 늘어날수록 훨씬 정확하게 방향 추정이 가능하지만 신호원이 움직이는 경우에는 신호원의 방향이 변하게 되어 결국 어레이 방정식의 방향 벡터들이 시변이므로 추정값의 분산(variance)이 커지게 되어 정확한 추정이 어렵게 된다. 이러한 경우에 대한 정량적인 분석 예는 드물어 실제 여러 가지 추정 기법들의 특성이 어떻게 나타나는지 가늠하기가 어렵다. 따라서 본 논문에서는 이러한 경우에 대한 시나리오 예를 설정한 후 이 시나리오에 따른 랜덤 가우시안 잡음 하에서의 수치 데이터 모델을 생성하여 수신기에서는 미지의 값인 이 데이터에 대해 기존의 DOA 추정 기법을 이용하여 추정을 수행하여 그 정량적인 결과들을 계산해 봄으로써 시변인 경우에서의 그 성능을 판단해 보기로 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.163-181
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• 2004

Group key agreement protocols are designed to solve the fundamental problem of securely establishing a session key among a group of parties communicating over a public channel. Although a number of protocols have been proposed to solve this problem over the years, they are not well suited for a high-delay wide area network; their communication overhead is significant in terms of the number of communication rounds or the number of exchanged messages, both of which are recognized as the dominant factors that slow down group key agreement over a networking environment with high communication latency. In this paper we present a communication-efficient group key agreement protocol and prove its security in the random oracle model under the factoring assumption. The proposed protocol provides perfect forward secrecy and requires only a constant number of communication rounds for my of group rekeying operations, while achieving optimal message complexity.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.