• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.793-796
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• 2005

금형공장의 생산 일정관리에 있어서 실제 가공작업에 대한 실적데이터의 집계는 매우 중요하지만, 이러한 작업상황을 기록하는 것에 대해서 현장 작업자는 비협조적이고 반감을 갖고 있는 실정이다. 현장에서는 작업자의 개입없이 CNC 장비의 컨트롤러와의 직접적인 인터페이스를 통해서 신호를 추출하고 자동으로 작업상황을 파악할 수 있는 시스템을 구축하려고 시도하고 있지만, 컨트롤러 메이커마다 다르게 적용해야 하고 많은 비용을 요구한다. 이러한 이유로 본 연구에서는 저가의 PC 카메라를 장비에 설치하여 가공상황에 대한 동영상을 수집하고 영상처리 알고리즘을 적용하여 가공시간을 집계하는 방법을 제안한다. 제안된 방법은 CNC 컨트롤러에 독립적으로 운용되며 저렴하게 시스템을 구축할 수 있는 장점이 있다. 본 연구에서는 무인가공과 유인가공 상황에 시범적으로 적용 및 운영함으로써 시스템의 활용가능성을 살펴보았다.

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.461-468
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• 2008

<그림 1>과 <그림 2>, <그림 3>은 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수출입을 2007년부터 2008년까지 추정한 값이며, 자료는 2001년도 이후의 관세청 수출입 실적자료를 활용하였다. <그림 1>은 SAENGF는 제조업용 로봇 국내생산의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <그림 2>는 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수출을 2007년부터 2008년까지 추정한 값이며, EXPORTF는 제조업용 로봇 수출의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <그림 3>은 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수입을 2007년부터 2008년까지 추정한 값이며, IMPORTF는 제조업용 로봇 수출의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <표 1>은 국내 제조업용 로봇의 국내생산, 수출과 수입의 추정치이며, ARIMA모형을 사용하였으며, 자료는 2001년도 이후의 데이터로 관세청 수출입 실적자료를 활용하였다.

• Management & Information Systems Review
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• v.7
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• pp.125-140
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• 2001

본 고는 1990년대 10년간의 정부의 정보통신 경쟁도입 정책과 정보통신시장에서의 발전성과를 살펴본 후, 최근 정체상태에 빠져들고 있는 국내 정보통신산업의 현상황을 점검해 보고 향후의 지속적인 성장 발전을 위한 요인들을 중심으로 시장상황을 전망해 보기 위하여 작성되었다. 이를 위해 본 고에서는 우선 1990년대 10년간 정보통신시장에서의 발전성과와 시장구조 변화, 그리고 IMF 이후 3년간의 주요 발전지표를 살펴본 후, 현재까지 집계된 2001년 10월까지의 실적데이터를 이용하여 정체상태에 빠져들고 있는 국내 정보통신산업의 현상황을 점검해 보았다. 이와 더불어 인터넷, 이동통신, 디지털방송, 정보보호, 정보가전, 소프트웨어 등 향후 정보통신산업의 성장엔진으로 부각되고 있는 신기술산업들의 성장추세와 정보통신산업의 성장발전에 영향을 미치게 될 각종 영향요인들을 중심으로 향후의 단 중기적인 정보통신산업 성장전망을 제시해 보았다. 본 고는 향후의 정보통신산업을 전망하는데 있어 단순한 수치위주의 전망보다는 정보통신산업에 영향을 미치고 있는 요인들을 중심으로 향후의 성장전망을 점검해 보았다는 점에 의의가 있다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.459-460
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• 2007

전압은 무효전력과 밀접한 연관성을 가지기 때문에 전체 계통의 전압 운영은 통일된 기준이 아니라 지역별로 수립되어야 한다. 이러한 사실을 바탕으로 본 논문에서는 우리나라 계통의 상황을 고려하여 기 운전된 실적 데이터를 지역별로 구분하여 분석하고 그 결과를 이용하여 지역별 전압 운영 기준을 검토한다.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Korean Journal of Construction Engineering and Management
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• v.18 no.6
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• pp.26-37
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• 2017

Preliminary scheduling at the initial stage of planning phase is usually performed with limited information and details. Therefore, the reliability and accuracy of preliminary scheduling is affected by personal experiences and skills of the schedule planners, and it requires enormous managerial effort (or workload). Reusing of historical data of the similar projects is important for efficient preliminary scheduling. However, understanding the structure of historical data and applying them to a new project requires a great deal of experience and knowledge. In this context, this paper propose a framework and methodology for automated preliminary schedule generation based on historical database. The proposed methodology and framework enables to automatically generate CPM schedules for airport projects in the early planning stage in order to enhance the reliability and to reduce the workload by using structured knowledge and experience.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.394-399
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• 2016

This study examines how the patent results of the University Academic-Industrial Cooperation influence technology transfer. Statistical analysis was performed by using 2013 panel data from the Ministry of Education and Science Technology(MEST) National Research Foundation of Korea(NRF) and the results are as follows. The results show that the patent result factors that have a positive effect on the total number of technology transfers are domestic patent application numbers, foreign patent application numbers, future technology(6T) patent application numbers, science technology patent application numbers. The factors that have a positive effect on increasing royalty are the total number of technology transfers. Domestic patent application numbers, future technology(6T) patent application numbers and science technology patent application numbers have a positive effect on patent results. The results implicate that more research and development is needed for more patents to be applied, that the main focus should be on future technology(6T) and science technology fields, and that effort should be directed at planning negotiation strategies for the term of the contract. However, this study is the need to research, including primary research is so patent performance may be limited in having only been considered in future studies of human and material resources and operating system factors that may be presented to the essential elements of the Industry-Academic Cooperation Foundation this raises.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Korean Journal of Construction Engineering and Management
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• v.4 no.3 s.15
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• pp.137-145
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• 2003

The project control system based on the actual values of three objects shall be operated continuously in a timely manner. For collecting/tracking accurate actual performance data, a reasonable basis of measuring work performance and its related measuring methods are needed. Therefore, this research proposes a method of developing and operating the construction progress measurement system. The problem of the conventional method is the difficulty to construct control accounts and to define the basis of measuring the performance of each control account. Therefore, this research proposes the preferable, formal methodology that produces the progress value of the smallest work unit by surveying the installed quantities and estimates percent complete of groups of works or entire project by earned value concept. This research in connection with the hereafter research of the weight value of control accounts will contribute to apply in practice and to develop the scientific construction management technique in the construction industry. Further researches how to trend and forecast the project using the measured progress value are recommended for putting the prosed system of this research to practical use.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.23-30
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• 2022

Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM's performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.