• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.469-472
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• 2003

음성 중심의 기존 2G CDMA/PCS의 성능 및 용량을 분석하기 위한 트래픽 모델링에서는 시간당 평균 호 발생률, 발생 간격의 분포, 호 유지시간(Holding Time), 그리고 최번시(Busy Hour)를 결정하는 것이 주요 과제였으며, 이를 이용한 트래픽 엔지니어링은 음성호의 Blocking 확률과 지연시간을 최소화 하기위한 충분한 호 자원 확보에 중심을 두었던 것이 사실이다. 그러나 CDMA2000 1X 및 1xEV-DO/DV와 같은 3G 고속 데이터 이동통신 시스템에서의 패킷 데이터 트래픽의 특성은 자기 유사성(Self-similarity)이라는 성질을 가진다는 것은 잘 알려진 사실이다. 이와같은 고속 데이터 이동통신 시스템에서 요구되는 효율적인 망의 설계 및 디멘져닝을 위해서는 무엇보다도 데이터 트래픽의 주요 특성인 버스트함과 자기유사성이 반영된 모델 분석이 요구된다. 이러한 관점에서 본 논문에서는 데이터 트래픽의 자기유사성 및 큐잉 지연을 고려한 유효대역폭 산출식을 유도하여 시뮬레이션 결과와 비교 분석하였다.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.255-257
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• 1998

이동 통신 서비스의 통화 품빌을 개선하기 위해서는 무선 기지국의 유지.보수가필요하다. 이를 위해서는 기지국 및 단말기의 CDMA 필드 데이터를 측정하여 분석하는 툴을 필요로한다. 본 논문에서는 측정된 CDMA 필드데이처를 벡터지도에 표시하고 분석에 필요한 여러 가지 정보들을 조회할 수 있는 기능과 측정 데이터의 통계 처리 기능을 가지는 분석툴을 개발한다. 이러한 시스템의 설계 및 개발을 위해서 객체 지향 방법론을 사용한다.이러한 분석 툴을 이용함으로써 최적화된 셀설계를 위한 무선기지국의 효율적인 유지.보수가 이루어 질 수 있다.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.245-250
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• 2023

Digital twin technology, which copies information from real space into virtual space, is being used in a variety of fields.Interest in digital twins is increasing, especially in advanced manufacturing fields such as Industry 4.0-based smart manufacturing. Operating a digital twin system generates a large amount of data, and the data generated has different characteristics depending on the technology field, so it is necessary to efficiently manage resources and use an optimized digital twin platform technology. Research on digital twin pipelines has continued, mainly in the advanced manufacturing field, but research on high-speed pipelines suitable for data in the plant field is still lacking. Therefore, in this paper, we propose a pipeline design method that is specialized for digital twin data in the plant field that is rapidly poured through Apache Kafka. The proposed model applies plant information on a Revit basis. and collect plant-specific data through Apache Kafka. Equipped with a lightweight CFD engine, it is possible to create a digital twin model that is more suitable for the plant field than existing digital twin technology for the manufacturing field.

• 한국기계연구소 소보
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• s.16
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• pp.111-116
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• 1986

엔지니어링 데이터로서 금속재료의 저주기 피로특성 규명은 중요한 의미를 가지며 이에 관한 연구는 1950년대 초부터 본격적으로 시작되었다고 볼 수 있다. 저주기 피로과정은 응력 반복수가 1-20,000회 정도에서 파괴가 일어나는 현상을 주 대상으로 하며 이의 이론적 설명을 위하여 Coffin 1) 과 Manson 2) 의 정리를 요약하여 인용하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2987-2992
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• 2015

In this paper, we propose the framework under a standardized task configuration to improve data accuracy and to provide unified system for the outfitting production design in shipyards. Due to the mismatching engineering data, the wrong designs or drawings were produced. With these wrong information, the production process can be broken and faced a big problem during production stage. In this study, we propose novel framework and its components which can offer better supporting for the design task and its process to improve productivity and efficiency with knowledge based engineering support system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.1
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• pp.93-103
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• 2005

SEDT(System Engineering Design Tool) has been developed for small satellite conceptual design with an aim to verifying the nanosatellite HAUSAT-2 design. The program can calculate the mass and power of whole satellite system having specific mission and estimate the system cost based on mission and user requirements. It is containing various analysis data of more than 200 small satellites. The database will provide the trend analysis results of the small satellites which will become important design factors. This tool has also been verified by applying more than 10 small satellite data through case studies.

• Journal of Korea Technology Innovation Society
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• v.9 no.2
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• pp.260-278
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• 2006

This paper provides an empirical evidence that the entry of a technology supplier depends on the markets for technology. In particular, using the chemical industry dataset, we test the hypothesis that the number of specialized engineering firms (SEFs) depends on the number of licensors in the market. Moreover, the number of plant investment, which is a market demand shifter is positively related with the entry of the SEFs, while the effect of the GDP pre capita does not have a significant effect on the entry of the SEFs.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.