• 한국건축시공학회지
• /
• 제11권3호
• /
• pp.238-246
• /
• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• International Journal of Naval Architecture and Ocean Engineering
• /
• 제4권4호
• /
• pp.386-402
• /
• 2012

In this paper, a method to estimate ice loads as a function of the buttock angle of an icebreaker is presented with respect to polycrystalline freshwater ice. Ice model tests for different buttock angles and impact velocities are carried out to investigate ice pressure loads and tendencies of ice pressure loads in terms of failure modes. Experimental devices were fabricated with an idealized icebreaker bow shape, and medium-scale ice specimens were used. A dry-drop machine with a freefall system was used, and four pressure sensors were installed at the bottom to estimate ice pressure loads. An estimation equation was suggested on the basis of the test results. We analyzed the estimation equation for design ice loads of the International Association of Classification Societies (IACS) classification rules. We suggest an estimation equation considering the relation between ice load, buttock angle, and velocity by modifying the equations given in the IACS classification rules.

• 대한간호학회지
• /
• 제40권6호
• /
• pp.903-912
• /
• 2010

Purpose: This study was conducted to examine whether the level of classification for long-term care service under longterm care insurance reflects resource utilization level for residents in nursing homes. Methods: From 2 long-term care facilities, the researchers selected 95 participants and identified description and time of care services provided by nurses, certified caregivers, physical therapists and social workers during a 24-hr-period. Results: Resource utilization level was: 281.04 for level 1, 301.05 for level 2 and 270.87 for level 3. Resource utilization was not correlated with level. Differences in resource utilization within the same level were similar with the coefficient of variance, 22.7-27.1%. Physical function was the most influential factor on long-term care scores (r=.88, p<.001). The level for long-term care service did not reflect differences in resource utilization level of residents on long-term care insurance. Conclusion: The results of this study indicate that present grading for long-term care service needs to be reconsidered. Further study is needed to adjust the long-term care classification system to reflect the level of resource utilization for care recipients on the long-term care insurance.

• 한국산학기술학회논문지
• /
• 제9권5호
• /
• pp.1284-1288
• /
• 2008

최근에 국내외적으로 위치기반 서비스에 이용되는 GPS(Global Positioning System), 식별을 통한 유통 물류 서비스를 제공하는 RFID(Radio Frequency IDentification), 온도, 습도, 기압 등의 상황 정보 수집하는 센서 네트워크를 위한 미들웨어 및 인터페이스에 대한 요구가 증가하고 있다. 이에 본 논문에서는 RFID, GPS와 센서 네트워크에서 수집된 상황 데이터를 효과적으로 통합 처리하는 서비스 미들웨어를 구현하고 동작을 검증한다. 제시한 서비스 미들웨어는 임시저장, 중복 데이터 제거, 인증, 분류 및 데이터베이스 저장 등의 기능을 제공하며, RFID, GPS와 센서 네트워크의 인터페이스로부터 소켓 인터페이스를 이용하여 연결되고, 다양한 유비쿼터스 응용 서비스를 위해 데이터베이스를 이용하여 연동한다. 제안한 통합 서비스 미들웨어를 통해 기존의 RFID, GPS, 센서 네트워크 시스템에 따라 개발된 개별 미들웨어의 한계를 극복하고 복합적인 유비쿼터스 응용 시스템을 개발할 것으로 기대한다.

• Journal of information and communication convergence engineering
• /
• 제1권3호
• /
• pp.157-162
• /
• 2003

We propose a learning method for improving the performance of the backpropagation algorithm. The proposed method is using a fuzzy logic system for automatic tuning of the learning rate of each weight. Instead of choosing a fixed learning rate, the fuzzy logic system is used to dynamically adjust the learning rate. The inputs of fuzzy logic system are delta and delta bar, and the output of fuzzy logic system is the learning rate. In order to verify the effectiveness of the proposed method, we performed simulations on the XOR problem, character classification, and function approximation. The results show that the proposed method considerably improves the performance compared to the general backpropagation, the backpropagation with momentum, and the Jacobs'delta-bar-delta algorithm.

• 전자공학회논문지B
• /
• 제33B권2호
• /
• pp.118-130
• /
• 1996

Fuzzy inference is very useful in expressing ambiguous problems quantitatively and solving them. But like the most of the knowledge based inference systems. It has many difficulties in constructing rules and no learning capability is available. In this paper, we proposed a fuzzy inference system based on fuzy associative memory to solve such problems. The inference system proposed in this paper is mainly composed of learning phase and inference phase. In the learning phase, the system initializes it's basic structure by determining fuzzy membership functions, and constructs fuzzy rules in the form of weights using learning function of fuzzy associative memory. In the inference phase, the system conducts actual inference using the constructed fuzzy rules. We applied the fuzzy inference system proposed in this paper to a pattern classification problem and show the results in the experiment.

• 융합정보논문지
• /
• 제11권8호
• /
• pp.168-175
• /
• 2021

본 연구는 뇌성마비 아동의 대동작 기능과 먹기, 마시기 기능, 구강 운동 기능과의 상관관계를 확인해보고, 뇌성마비 아동의 대동작 기능의 심한 정도에 따른 연하 재활의 필요성을 확인하기 위해 시행되었다. 뇌성마비 진단을 받은 아동 61명을 대상으로 대동작 기능 분류 체계(GMFCS), 먹기와 마시기 기능분류 체계(EDACS), 구강 운동 기능 검사(OMAS)를 사용하여 대동작 기능, 먹고 마시기 기능, 구강 운동 기능 수준을 평가하고 각 기능 간 상관관계를 확인하였다. 본 연구의 결과는 대동작, 먹고 마시기 기능, 구강 운동 기능 사이에서 유의한 상관관계를 보였다. 즉, 대동작 기능 저하가 심할수록 먹고 마시기 기능과 구강 운동 기능 저하 역시 낮은 기능 수준을 보였다. 본 연구를 통하여 뇌성마비 아동의 섭식활동을 평가하고 치료함에 있어, 아동의 대동작 기능에 따른 먹고 마시기 기능, 구강 운동 기능에 대한 확인이 필요할 것으로 보인다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제8권12호
• /
• pp.483-490
• /
• 2019

네트워크 침입 탐지 시스템(NIDS)에서 분류의 기능은 상당히 중요하며 탐지 성능은 다양한 특징에 따라 달라진다. 최근 딥러닝에 대한 연구가 많이 이루어지고 있으나 네트워크 침입탐지 시스템에서는 많은 수의 트래픽과 고차원의 특징으로 인하여 속도가 느려지는 문제점이 있다. 따라서 딥러닝을 분류에 사용하는 것이 아니라 특징 추출을 위한 전처리 과정으로 사용하며 추출한 특징을 기반으로 분류하는 연구 방법을 제안한다. 딥러닝의 대표적인 비지도 학습인 Stacked AutoEncoder를 사용하여 특징을 추출하고 Random Forest 분류 알고리즘을 사용하여 분류한 결과 분류 성능과 탐지 속도의 향상을 확인하였다. IOT 환경에서 수집한 데이터를 이용하여 정상 및 공격트래픽을 멀티클래스로 분류하였을 때 99% 이상의 성능을 보였으며, AE-RF, Single-RF와 같은 다른 모델과 비교하였을 때도 성능 및 탐지속도가 우수한 것으로 나타났다.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2013년도 춘계 학술논문 발표대회
• /
• pp.256-257
• /
• 2013

Roof and exterior wall of general formal buildings are designed and constructed through design focused exterior wall system and drainage and waterproof roof system. However, there are no classification of exterior wall and roof in freeform buildings and they are integrated as a surface of freeform buildings. Therefore it is necessary to develop the dry roof construction method using double skin roof system satisfying the design and function of the envelope. In this study, we have an effort to develop construction method of double-skin roof system using metal panel and PV.

• 한국과학교육학회지
• /
• 제32권3호
• /
• pp.524-531
• /
• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.