• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.681-684
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• 2021

In this paper, we study image clustering without labeling using machine learning techniques. We proposed an unsupervised machine learning technique to design an image clustering model that automatically categorizes images into groups. Our experiment focused on inception convolutional neural networks (inception V3) with k-mean methods to cluster images. For this, we collect the public datasets containing Food-K5, Flowers, Handwritten Digit, Cats-dogs, and our dataset Rice Germination, and the owner dataset Palm print. Our experiment can expand into three-part; First, format all the images to un-label and move to whole datasets. Second, load dataset into the inception V3 extraction image features and transferred to the k-mean cluster group hold on six classes. Lastly, evaluate modeling accuracy using the confusion matrix base on precision, recall, F1 to analyze. In this our methods, we can get the results as 1) Handwritten Digit (precision = 1.000, recall = 1.000, F1 = 1.00), 2) Food-K5 (precision = 0.975, recall = 0.945, F1 = 0.96), 3) Palm print (precision = 1.000, recall = 0.999, F1 = 1.00), 4) Cats-dogs (precision = 0.997, recall = 0.475, F1 = 0.64), 5) Flowers (precision = 0.610, recall = 0.982, F1 = 0.75), and our dataset 6) Rice Germination (precision = 0.997, recall = 0.943, F1 = 0.97). Our experiment showed that modeling could get an accuracy rate of 0.8908; the outcomes state that the proposed model is strongest enough to differentiate the different images and classify them into clusters.

• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.98-98
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• 2023

ChatGPT(Chat과 Generative Pre-trained Transformer의 합성어)는 사용자와 주고받는 대화의 과정을 통해 질문에 답하도록 설계된 대형언어모델로, 지도학습과 강화학습을 모두 사용하여 세밀하게 조정된 인공지능 챗봇이다. ChatGPT는 주고받은 대화와 대화의 문맥을 기억할 수 있으며, 보고서나 실제로 작동하는 파이썬 코드를 비롯한 인간과 유사하게 상세하고 논리적인 글을 만들어 낼 수 있다고 알려져있다. 본 연구에서는 수자원시스템분야의 문제해결에 있어 ChatGPT의 적용가능성을 사례기반으로 확인하고, ChatGPT의 올바른 활용을 위해 필요한 사항에 대해 고찰하였다. 수자원시스템분야의 대표적인 연구주제인 상수관망시스템의 누수인지와 수리해석을 통한 문제해결에 ChatGPT를 활용하였다. 즉, 딥러닝 기반의 데이터분석을 활용한 누수인지와 오픈소스기반의 수리해석 모델을 활용한 관망시스템 적정 분석을 목표로 ChatGPT와 대화를 진행하고, ChatGPT에 의해 제안된 코드를 구동하여 결과를 분석하였다. ChatGPT가 제시한 코드의 구동결과를 사전에 연구자가 직접 구현한 코드구동 결과와 비교분석하였다. 분석결과 ChatGPT가 제시한 코드가 보다 더 간결할 수 있으며, 상대적으로 경쟁력 있는 결과를 도출하는 것을 확인하였다. 다만, 상대적으로 간결한 코드와 우수한 구동결과를 획득하기 위해서는 해당 도메인의 전문적 지식을 바탕으로 적절한 다수의 질문을 해야 하며, ChatGPT에 의해 작성된 코드의 의미를 명확히 해석하거나 비판적 분석을 하기 위해서는 전문가지식이 반드시 필요함을 알 수 있었다.

• Journal of Engineering Education Research
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• v.11 no.3
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• pp.12-23
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• 2008

The purpose of this study is to explore teaching and learning strategies through analyzing cases of foreign engineering education. With the analysis criteria composed of engineering education model, teaching and learning method, evaluation strategy, and technology supporting strategy, 10 foreign colleges of engineering in 5 countries were examined and analyzed. Teaching and learning strategies deduced from analysis state as follows. First of all, it need to develop engineering education models that reform should be made in systematic approach to teaching and learning, workplaces and laboratories, evaluation, technology support, etc. Secondly, the strategy for teaching and learning recommends supporting student directed learning, active learning participation, and collaboration learning by inductive learning strategies such as problem based learning, inquiry learning, project based learning, studio based learning, and blended learning. Thirdly, the evaluation strategy suggests that evaluation should be made to reflect students' learning and facilitate continuous learning based current learning results while it is necessary to build up a whole evaluation system. Finally, it is the educational technology approach for systematic engineering education that is required considering that many foreign colleges of engineering have reformed engineering education through technology supporting systems and are maximizing research and education in connection with other universities. This study is expected to contribute as preliminary data in developing further teaching and learning models and strategies for nurturing engineering students.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.400-400
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• 2023

급격한 기후변화로 인해 일사량, 지표면 온도 및 이산화탄소 농도가 꾸준히 상승함에 따라 수문 순환의 불균형을 초래함과 하천 및 호소 내 수질 또한 악화되고 있는 추세이다. 특히, 국내의 경우, 기후변화 및 인위적 요인에 의해 하천 및 호소에서의 수위 감소 및 수온 증가로 인해 부영양화가 증가되고 있고, 이로 인한 유해 녹조의 발생빈도를 높이는 결과를 초래한다. 현재 국내에서는 유인 수질 관측 및 자동 수질관측 시스템을 통해 주요 수질인자를 모니터링 하고 있으나 시·공간적인 변동성을 파악하는데 제한점이 있다. 이러한 한계점을 극복하기 위해 국·내외에서 광학위성을 이용한 수질인자 추정 알고리즘 개발과 관련된 연구들이 진행되고 있다. 이에 따라, 본 연구에서는 NASA에서 제공하는 Landsat-8 위성과 ESA에서 제공하는 Sentinel-2자료가 동화된 Harmonized Landsat Sentinel-2 위성자료를 활용한 클로로필-a (Chl-a)를 추정하고자 한다. 이를 위해, 본 연구에서는 1) 단순 회귀 분석, 2) Akaike information criteria (AIC) 기반 최적화 회귀 분석 및 3) Random forest (RF)를 활용하였다. 또한, HLS 위성 자료의 적용성을 평가하기 위해 미국 오하이오 주에 위치하고 있는 130여개의 중규모 및 대규모 호소에서 2000년부터 2021년까지 수집된 클로로필-a 관측치를 활용하였다. 두 가지 수질 추정 모형에 대한 정확도 검증에 앞서 오하이오 주 내에서의 클로로필-a의 시계열적 변동성에 대하여 분석하였다. 전반적으로, 2000년부터 2016년까지는 Chl-a가 꾸준히 증가하는 경향성을 나타내었으나, 그 이후로는 감소하는 추세를 나타내었다. 이를 기반으로, 각 방법론을 통해서 나온 Chl-a 추정치에 대해서 통계적 검증을 수행하였다. 결과, 단순 회귀 분석을 통해 추청된 Chl-a값의 결정계수는 0.34였지만, AIC 기반 모델과 RF모형을 사용한 결과 결정계수가 각각 0.82와 0.92로 향상된 것을 확인할 수 있었다. 이와 더불어, spatial 및 temporal window와 더불어 호소의 크기에 따른 정확도 분석 또한 수행하였다. 그 결과, temporal window 가 정확도에 가장 큰 영향을 미치는 것으로 나타났으며, 호소의 크기가 작을수록 정확도가 낮아지는 것을 확인 할 수 있었다. 본 연구의 결과를 토대로 추후 국내 호소에 대해 상기 모형들의 적용성 평가를 수행하여 효율적인 수질 모니터링 시스템 구축으로 이어질 수 있을 것으로 기대된다.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.109-117
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• 2012

The purpose of this study is to give the preliminary data and suggestion for introducing and spreading e-learning engineering education through analyzing learning behaviors, learning motivations, and satisfaction of e-learning engineering students. Especially, this comparative study analyzes each research domain according to majors and grades, thereby suggesting more specific and practical results. 2,745 students registered in 38 subjects of e-learning in 2 Universities were analyzed for this study. The study result shows that engineering students are attending around 2 e-learning subjects with a duration of about 30 minutes once a week. The main of learning motivation for e-learning was not easy test level and feasibility of acquiring credit but advantages of e-learning such as freedom of time and space, learning by repetition. The satisfaction scores of e-learning were lower compared to the aspects of system and contents Based on these results, first, an active spread of e-learning to engineering education is necessary because the demand from the engineering students is high enough and they have desirable learning behavior and learning motivation for it. Second, the characteristics of grades need to be taken into consideration on operation of e-learning. Third, a successful e-learning process needs more meticulous and active operation.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.49-57
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• 2021

With the advent of the era of the fourth industry, more and more scientific techniques are being used to solve decision-making problems. In particular, big data analysis technology is developing as it becomes easier to collect numerical data. Therefore, in this study, in order to overcome the limitations of qualitatively analyzing investment trends, the association of various products was analyzed using associated analysis techniques. For the experiment, two experimental periods were divided based on the COVID-19 economic crisis, and sales information from individuals, institutions, and foreign investors was collected, and related analysis algorithms were implemented through r software. As a result of the experiment, institutions and foreigners recently invested in the KOSPI and KOSDAQ markets and bought futures and products such as ETF. Individuals purchased ETN and ETF products together, which is presumed to be the result of the recent great interest in sector investment. In addition, after COVID-19, all investors tended to be passive in investing in high-risk products of futures and options. This paper is thought to be a useful reference for product sales and product design in the financial field.

• Journal of Engineering Education Research
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• v.24 no.1
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• pp.24-33
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• 2021

As online learning continues to be extended, many engineering colleges are engaged in online learning activities. One of the core competencies required of engineering students in a knowledge-convergence society is communication skills. Online discussion activities are frequently used in educational field to improve communication skills. Efforts are being made to provide visual dashboards in online discussion activity systems to more effectively support online discussion activities. However there is less qualitative studies on students' experience in discussion activities. The purpose of this study is to explore the experience of engineering students participating in discussion activities using online discussion systems and visual dashboards. We interviewed 15 students who participated in online discussion activities to achieve their research objectives about their experience in utilizing the online discussion system, their perception of visual dashboards, and their experience in discussion activities. As a result of the study, students' perception of the use of the online discussion activity system, the visual dashboard, and the perception of a sense of social presence were understood. To be more effective in providing tool support, such as discussion activity systems and visual dashboards in online discussion activities, instructors need to understand the nature of learners' online discussion activities.