• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.72-80
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• 2004

The objective of the research is to develop the automated vertical controllable pilot-type equipment for PHC piles. The motivation for the research is that inherent problems related to vertical control during pile driving. The paper explains the current vertical control methods and problems, design and manufacturing of the pilot-type automated equipment and its testing and discussions of the results.

• Journal of Biosystems Engineering
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• v.9 no.2
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• pp.97-113
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• 1984

날씨가 더운지방에 소규모(小規模) 목장(牧場)들이 먼거리를 두고 산재(散在)되어 있는 경우, 각목장(各牧場)에서 생산(生産)된 우유(牛乳)를 수집(收集)해서 처리공장(處理工場)까지 수송하는 데는 많은 어려움이 있다. 현대화(現代化)된 우유처리공장(牛乳處理工場)이나 저온저장시설이 충분(充分)하지 못한 아열대지방(亞熱帶地方)에 위치(位置)한 발전도상국에서는 우유(牛乳)를 장시간(長時間) 수집(收集)하여 먼거리를 수송하는 동안 많은 양(量)의 우유(牛乳)가 부패 손실(損失)되고 있다. 이 문제를 해결(解決)하기 위하여, 그곳 현지목장(現地牧場)에 강하게 쪼이는 태양열(太陽熱)을 이용(利用)하는 것은 대단히 바람직 하다. 에너지 절약이나, 우유(牛乳)의 부패손실(腐敗損失) 막기 위하여, 태양열(太陽熱)을 이용(利用)한 소규모우유처리기(小規模牛乳處理機)를 개발하여 현지목장(現地牧場)에서 직접(直接) 우유를 처리(處理)하여 인근주민에게 공급하는 것이 가장좋은 해결방법이라 판단하고, 그 실현가능성을 확인(確認)하기 위하여 실제로 태양열(太陽熱)우유처리기(處理機) 개발(開發)을 위한 그 모형(模型)의 회로구성(回路構成)을 고찰(考察)한 결과(結果) 다음과 같다. 1. 태양열(太陽熱) 우유처리기(牛乳處理機)의 모형(模型)은 다음 4개(個)의 회로(回路)로 구성(構成)되었다. 가. 우유(牛乳)순환 회로(回路) 나. 가열회로(加熱回路) 다. 예냉회로(豫冷回路) 라. 냉각회로(冷却回路) 2. 우유가열회로(牛乳加熱回路)는 태양열(太陽熱)을 이용(利用)하였으며 냉각회로(冷却回路)는 압축식 냉각기(冷却機)를 이용(利用)하였다. 3. 자동제어(自動制御)시스템을 장치(裝置)하여 태양강도변화(太陽强度變化)에 따른 우유처리량(牛乳處理量) 조절(調節)을 자동화(自動化)할 수 있도록 하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1467-1482
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• 2017

Recently, Interests on The Fourth Industrial Revolution has been increased. In the manufacturing sector, the introduction of Smart Factory, which automates and intelligent all stages of manufacturing based on Cyber Physical System (CPS) technology, is spreading. The complexity and uncertainty of smart factories are likely to cause unexpected problems, which can lead to manufacturing process interruptions, malfunctions, and leakage of important information to the enterprise. It is emphasized that there is a need to perform systematic management by analyzing the threats to the Smart Factory. Therefore, this paper systematically identifies the threats using the STRIDE threat modeling technique using the data flow diagram of the overall production process procedure of Smart Factory. Then, using the Attack Tree, we analyze the risks and ultimately derive a checklist. The checklist provides quantitative data that can be used for future safety verification and security guideline production of Smart Factory.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.115-124
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• 2019

This study is an empirical study of 'the effect of the key factors of Smart Factory on management performance through internal environmental factors in small and medium enterprises'. The purpose of the research is to verify that the implementation of a Smart Factory affects the performance of management and contribute to the continued development of the company, and to suggest the national policy of expanding the deployment of a Smart Factory. The procedures were surveyed by working-level officials of small and medium-sized manufacturing companies with a Smart Factory and statistically analyzed with the SPSS and SMART PLS. The results of the study showed that first, the environmental factors within the company had a positive effect on the key components of the Smart Factory. Second, the key factor in Smart Factory has had a positive impact on management performance. The above evidence shows that the key factors in smart factory considering the environmental factors of an enterprise affect its management performance, thus laying the theoretical foundation for the performance of smart factory construction. In the future, we will study how to build a Smart Factory.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.67-77
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• 2019

Many small and medium-sized enterprises are deploying Smart Factory without due consideration of the circumstances in which the entity is in or outside of the entity, such as environmental changes and the capabilities of its members. Therefore, utilization and effectiveness are also low after deployment. This study verifies 'the establishment of a Smart Factory through the environmental factors and absorption capabilities of small businesses' through empirical research. The survey was received by people working for small and medium-sized companies that have established Smart Factory. The results of the study showed that first, environmental factors within and outside the company had a positive effect on the absorption capacity within the company. Second, the absorption capacity within a company has had a positive effect on the deployment of a Smart Factory. Based on the above proof, it has been proved to be effective if the core areas of the Smart Factory are built on the basis of the company's internal and external environmental factors and absorption capabilities when constructing Smart Factory in small businesses. In the future, we will study the achievements of smart factory construction.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.281-288
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• 2022

AGVs are often used in industrial applications to transport heavy materials around a large industrial building, such as factories or warehouses. In particular, in fulfillment centers their usefulness is maximized for automation. To increase productivity in warehouses such as fulfillment centers, sophisticated path planning of AGVs is required. We propose a scheme that can be applied to QMIX, a popular cooperative MARL algorithm. The performance was measured with three metrics in several fulfillment center layouts, and the results are presented through comparison with the performance of the existing QMIX. Additionally, we visualize the transport paths of trained AGVs for a visible analysis of the behavior patterns of the AGVs as heat maps.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.329-335
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• 2023

Avocado, a superfood selected by Time magazine and one of the late ripening fruits, is one of the foods with a big difference between local prices and domestic distribution prices. If this sorting process of avocados is automated, it will be possible to lower prices by reducing labor costs in various fields. In this paper, we aim to create an optimal classification model by creating an avocado dataset through crawling and using a number of deep learning-based transfer learning models. Experiments were conducted by directly substituting a deep learning-based transfer learning model from a dataset separated from the produced dataset and fine-tuning the hyperparameters of the model. When an avocado image is input, the model classifies the ripeness of the avocado with an accuracy of over 99%, and proposes a dataset and algorithm that can reduce manpower and increase accuracy in avocado production and distribution households.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.81-95
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• 2001

Plant construction projects usually take much higher uncertainty and risks than the projects from other domains. This implies the importance of plant construction project management should be more emphasized than the other domain. Especially, the overall successes of the projects often depend on the performance of process planning and scheduling performed at the initial stage of the project. However, most plant construction projects suffer great difficulties in establishing proper process planning and scheduling timely because of unstructureness and dynamicity of environment of the project itself In this paper, we propose a knowledge-based process planning and scheduling approach in a plant construction domain to cope this problem. First, we modulize process planning knowledge and present the knowledge representation scheme. Second, we propose an inferencing mechanism to build a process planning for plant construction based on the represented process planning knowledge. Since our approach automate the initial process planning, which was usually done by manual way, it can improve the correctness and also completeness of the process plan and schedule by reducing the time to plan and allowing simulations on the various situation. We also design and implement this our approach as a real working system, and it is successfully applied to real plant construction cases from a leading construction company in Korea. Based on this success, we expect our approach can be easily applied to the projects of other areas, while contributing to enhancement in productivity and quality of project management.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.1
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• pp.124-132
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• 2002

In this paper, we present a vision algorithm and method for input image improvement and preprocessing of dented and raised characters on the sidewall of tires. we define optical condition between reflect coefficient and reflectance by the physical vector calculate. On the contrary this work will recognize the engraved characters using the computer vision technique. Tire input images have all most same grey levels between the characters and backgrounds. The reflectance is little from a tire surface. therefore, it's very difficult segment the characters from the background. Moreover, one side of the character string is raised and the other is dented. So, the captured images are varied with the angle of camera and illumination. For optimum Input images, the angle between camera and illumination was found out to be with in 90$^{\circ}$. In addition, We used complex filtering with low-pass and high-pass band filters to improve input images, for clear input images. Finally we define equation reflect coefficient and reflectance. By doing this, we obtained good images of tires for pattern recognition.