• 한국터널지하공간학회 논문집
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• 제22권3호
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• pp.239-248
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• 2020

터널 굴착 시 정확한 암반 분류는 적합한 지보패턴을 설치하는 데 도움을 준다. 암반의 분류를 위해 주로 RMR (Rock Mass Ration)과 Q값을 산정하여 수행되며, 페이스 매핑(face mapping)을 바탕으로 산정된다. 점보드릴 및 프로브드릴의 기계 데이터을 활용하거나 딥러닝을 활용한 굴착면 사진 분석 등의 방법이 암반등급 분류를 예측하기 위해 사용되고 있으나, 분석 시 오랜 시간이 소요되거나, 굴착면 전방의 암반등급을 파악할 수 없다는 점에서 한계를 갖는다. 본 연구에서는 순환인공신경망(Recurrent neural network, RNN)을 활용하여 굴착면 전방의 Q값을 예측하는 방법을 개발하였고 페이스 매핑으로부터 획득한 Q값과 비교/검증하였다. 4,600여개의 굴착면 데이터 중 70%를 학습에 활용하였고, 나머지 30%는 검증에 사용하였다. 학습의 횟수와 학습에 활용한 이전굴착면의 개수를 변경하여 학습을 수행하였다. 예측된 Q값과 실제 Q값의 유사도는 RMSE (root mean square error)를 기준으로 비교하였다. 현재 굴착면과 바로 직전의 굴착면의 Q값을 활용하여 600회 학습하여 예측한 Q값의 RMSE값이 가장 작은 것을 확인하였다. 본 연구의 결과는 학습에 사용한 데이터 값 등이 변화하는 경우 변화할 수 있으나 터널에서의 이전 지반상태가 앞으로의 지반상태에 영향을 미치는 시스템을 이해하고, 이를 통해 터널 굴착면 전방의 Q값의 예측이 가능할 것으로 판단된다.

• 한국컴퓨터정보학회논문지
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• 제10권4호
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• pp.365-373
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• 2005

유비쿼터스 환경을 위한 웹 기반 교육 시스템으로서 다양한 온라인 학습에 대한 새로운 교수 모형이 요구되고 있다. 또한, 학습자의 요구에 맞는 코스웨어의 주문이 증가되고 있는 추세이며 그에 따라 웹 기반 교육시스템에 효율적이고 자동화된 교육 에이전트의 필요성이 인식되고 있다. 그러나 현재 연구되고 있는 많은 교육 시스템들은 학습자 성향에 맞는 코스를 적절히 서비스해 주지 못할 뿐 아니라 지속적인 피드백과 학습자가 코스를 학습함에 있어서 취약한 부분을 재학습 할 수 있도록 도와주는 서비스를 원활히 제공하지 못하고 있다. 본 논문에서는 취약성 분석 알고리즘을 이용한 학습자 중심의 유비쿼터스 환경팩터를 통한 코스 스케줄링 멀티 에이전트 시스템을 제안한다. 제안한 시스템은 먼저 학습자의 학습 평가 결과를 분석하고 학습자의 학습 성취도를 계산하며, 이 성취도를 에이전트의 스케줄에 적용하여 학습자에게 적합한 코스를 제공하고, 학습자는 이러한 코스에 따라 능력에 맞는 반복된 학습을 통하여 적극적인 완전학습을 수행하게 된다.

• 여성건강간호학회지
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• 제27권3호
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• pp.230-242
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• 2021

Purpose: The purpose of this study was to develop a maternal nursing competency reinforcement program for nursing students and assess the program's effectiveness in Korea. Methods: The maternal nursing competency reinforcement program was developed following the ADDIE model. This study employed an explanatory sequential mixed methods design that applied a non-blinded, randomized controlled trial with nursing students (28 experimental, 33 control) followed by open-ended interviews with a subset (n=7). Data were analyzed by both qualitative and quantitative analysis methods. Results: Repeated measures analysis of variance showed that significant differences according to group and time in maternal nursing performance; assessment of and intervention in postpartum uterine involution and vaginal discharge (F=24.04, p<.001), assessment of and intervention in amniotic membrane rupture (F=36.39, p<.001), assessment of and intervention in delivery process through vaginal examination (F=32.42, p<.001), and nursing care of patients undergoing induced labor (F=48.03, p<.001). Group and time improvements were also noted for problem-solving ability (F=9.73, p<.001) and emotional intelligence (F=4.32, p=.016). There were significant differences between groups in self-directed learning ability (F=13.09, p=.001), but not over time. The three main categories derived from content analysis include "learning with a colleague by simulation promotes self-reflection and learning," "improvement in maternal nursing knowledge and performance by learning various countermeasures," and "learning of emotionally supportive care, but being insufficient." Conclusion: The maternal nursing competency reinforcement program can be effectively utilized to improve maternal nursing performance, problem-solving ability, and emotional intelligence for nursing students.

• 한국수학교육학회지시리즈C:초등수학교육
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• 제8권1호
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• pp.33-50
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• 2004

This study is intended to investigate the effect on the development of multiplicative thinking and multiplicative ability by teaching repeated addition, rate, comparison, area-array, and combination problems. Two research questions are established: first, is there any difference of multiplicative thinking between the experimental group(the modeling of problem situation learning group) and the control group(the traditional learning group)\ulcorner Second, is there any difference of multiplicative ability between the experimental group and the control group\ulcorner The treatment process for the experimental group is based on modeling problem situations for nine lesson periods. In order to answer the research questions the chi-square analysis was used for the first research question and the t-test was used for the second one. The findings are summarized as follows: there is no significant difference of multiplicative thinking be1ween the experimental and the control group but there is significant difference of multiplicative ability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권7호
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• pp.3413-3430
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• 2017

With the fast increasing popularity of mobile services, ubiquitous mobile devices with enhanced sensing capabilities collect and share local information towards a common goal. The recent Mobile Crowd Sensing (MCS) paradigm enables a broad range of mobile applications and undoubtedly revolutionizes many sectors of our life. A critical challenge for the MCS paradigm is to induce mobile devices to be workers providing sensing services. In this study, we examine the problem of sensing task assignment to maximize the overall performance in MCS system while ensuring reciprocal advantages among mobile devices. Based on the overlapping coalition game model, we propose a novel workload determination scheme for each individual device. The proposed scheme can effectively decompose the complex optimization problem and obtains an effective solution using the interactive learning process. Finally, we have conducted extensive simulations, and the results demonstrate that the proposed scheme achieves a fair tradeoff solution between the MCS performance and the profit of individual devices.

• 한국IT서비스학회지
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• 제13권3호
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• pp.299-308
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• 2014

Due to indiscriminately received spam messages on information society, spam messages cause damages not only to person but also to our community. Nowadays a lot of spam filtering techniques, such as blocking characters, are studied actively. Most of these studies are content-based spam filtering technologies through machine learning.. Because of a spam message transmission techniques are being developed, spammers have to send spam messages using term spamming techniques. Spam messages tend to include number of nouns, using repeated words and inserting special characters between words in a sentence. In this paper, considering three features, SPSS statistical program were used in parameterization and we derive the equation. And then, based on this equation we measured the performance of classification of spam messages. The study compared with previous studies FP-rate in terms of further minimizing the cost of product was confirmed to show an excellent performance.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2006년도 추계학술대회 논문집
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• pp.1405-1410
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• 2006

Purpose of this research is that offers basic data for optimized design using neural network method to calculate consolidation settlement in study area. In this research, preformed the neural network method that analyzed the settlement characteristics of soft ground nearby study area. Thus, data base established on ground properties and consolidation settlement of neighboring area. In addition, designed the optimum neural network model for prediction of settlement through network learning and consolidation settlement prediction using consolidation settlement DB and ground properties DB. Optimized neural network model decided by repeated learning for various case of hidden layers. In this study, proposed that the optimized consolidation settlement calculation method using neural network and verified which is the optimized consolidation settlement calculation method using neural network.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.144-146
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• 2004

It is known that Global Positioning System(GPS) is the most efficient navigation system because it provides precise position information on the all areas of Earth regardless of metrology. Until now, the size of GPS receivers has become smaller and the performance of receivers has become higher. So receivers provide the position information of not only static system but also dynamic system. Usually, users make similar movement trajectory according to their life pattern and it is possible to build up efficient database by collecting only the repeated users' position. Because position information calculated by the receiver is erroneous about 10-30m within 5% error tolerance, the position information is oscillated even on the same area. In this paper, we propose the system that can estimate whether users are out of trajectory or in dangerous situation by soft-computing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1825-1844
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• 2019

Political campaigns circulate manipulative opinions in online communities to implant false beliefs and eventually win elections. Not only is this type of manipulation unfair, it also has long-lasting negative impacts on people's lives. Existing tools detect political manipulation based on a supervised classifier, which is accurate when trained with large labeled data. However, preparing this data becomes an excessive burden and must be repeated often to reflect changing manipulation tactics. We propose a practical detection system that requires moderate groundwork to achieve a sufficient level of accuracy. The proposed system groups opinions with similar properties into clusters, and then labels a few opinions from each cluster to build a classifier. It also models each opinion with features deduced from raw data with no additional processing. To validate the system, we collected over a million opinions during three nation-wide campaigns in South Korea. The system reduced groundwork from 200K to nearly 200 labeling tasks, and correctly identified over 90% of manipulative opinions. The system also effectively identified transitions in manipulative tactics over time. We suggest that online communities perform periodic audits using the proposed system to highlight manipulative opinions and emerging tactics.

• Journal of Information Processing Systems
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• 제18권5호
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• pp.701-710
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• 2022

With the development of artificial intelligence technology, various methods have been developed for recognizing objects in images using machine learning. Image segmentation is the most effective among these methods for recognizing objects within an image. Conventionally, image datasets of various classes are trained simultaneously. In situations where several classes require segmentation, all datasets have to be trained thoroughly. Such repeated training results in low training efficiency because most of the classes have already been trained. In addition, the number of classes that appear in the datasets affects training. Some classes appear in datasets in remarkably smaller numbers than others, and hence, the training errors will not be properly reflected when all the classes are trained simultaneously. Therefore, a new method that separates some classes from the dataset is proposed to improve efficiency during training. In addition, the accuracies of the conventional and proposed methods are compared.