• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.515-522
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• 2024

Energy consumption is steadily increasing, and buildings in particular account for more than 20% of the total energy consumption around the world. As an effort to cost-effectively manage the energy consumption of buildings, many research groups have recently focused on Smart Building Energy Management Systems (BEMS), which are deepening the research depth by applying artificial intelligence(AI). In this paper, we propose a reinforcement learning-based energy control method for smart energy buildings integrated with V2G station, which aims to reduce the total energy cost of the building. The results of performance evaluation based on the energy consumption data measured in the real-world building shows that the proposed method can gradually reduce the total energy costs of the building as the learning process progresses.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.45-46
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• 2018

본 논문에서는 시간에 엄격한(Time critical) 산업용 IoT(Industrial IoT) 환경의 무선 센서 네트워크 시스템 상의 효율적인 패킷 전달과 정확도(Accuracy) 향상을 위해 강화학습과 EDF 알고리즘을 혼합한 스케쥴링 기법을 제안한다. 이 방식은 다중 대기열(Multiple queue) 환경에서 각 대기열의 요구 정확도(Accuracy Requirement)를 기준으로 최대한 패킷 처리를 미룸으로써 효율적인 CPU자원 분배와 패킷 손실율(Packet Loss)을 조절한다. 제안하는 기법은 무선 센서 네트워크 상의 가변적이고 예측 불가능한 환경에 대한 사전지식이 없이도 요구하는 서비스의 질(Quality of service)를 만족할 수 있도록 한다. 또한 정확도를 요구조건으로 제시하여 마감시간이 중요시되는 작업에서도 효율을 최대화한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.45-46
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• 2019

본 논문에서는 Upper Confidence Bound (UCB)를 이용한 효율적인 패킷 스케줄링 기법을 제안한다. 기존 e-greedy 등 강화학습의 보상을 극대화 할 수 있는 행동을 선택하는 것과 다르게, 제안된 UCB를 이용한 강화학습 패킷 스케줄링 기법은 각 상태에서 행동을 선택한 횟수를 추가적으로 고려한다. 이는 보다 효율적인 강화학습의 탐구(Exploration)를 가능케 한다. 본 논문에서는 컴퓨터 시뮬레이션을 통하여 제안하는 UCB를 이용한 강화학습 패킷 스케줄링 기법이 기존의 e-greedy 및 softmax를 기반으로 한 패킷 스케줄링 기법에 비해 정확도 측면에서 향상된 정확도를 보인다.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.19-33
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• 2019

Recently, manufacturers have been struggling to efficiently use production equipment as their production methods become more sophisticated and complex. Typical factors hindering the efficiency of the manufacturing process include setup cost due to job change. Especially, in the process of using expensive production equipment such as semiconductor / LCD process, efficient use of equipment is very important. Balancing the tradeoff between meeting the deadline and minimizing setup cost incurred by changes of work type is crucial planning task. In this study, we developed a scheduling model to achieve the goal of minimizing the duedate and setup costs by using reinforcement learning in parallel machines with duedate and work preparation costs. The proposed model is a Deep Q-Network (DQN) scheduling model and is a reinforcement learning-based model. To validate the effectiveness of our proposed model, we compared it against the heuristic model and DNN(deep neural network) based model. It was confirmed that our proposed DQN method causes less due date violation and setup costs than the benchmark methods.

• The Inventors News
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• no.53
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• pp.1-16
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• 2006

'여성기업지원에 관한 법률' 개정 한 목소리/발행인 칼럼/'제4회 여성발명경진대회' 수준 높아졌다/심사착수 예정시기 직접 통지 서비스 실시/특허청.한국기계연구원, 업무협약체결/낙도어린이들에게 꿈과 희망 심어주는 초청 행사 가져/특허청 팀장 선발 방식 변화 통한 팀제 강화/디자인 권리화 지원사업 실시한다/'DMB 특허품과 지재권전략 세미나'/'2006 독일 국제발명품 전시회' 회원 4명 수상/고성능 하이브리드 보호복, 출원 증가/'이달의 기능 한국인' 박순복 씨 선정/모방상표, 더 이상 등록 받을 수 없다/국내제약업계, 유사브랜드 너무많아/'2006 여성 재활용 발명경진대회' 개최/순수 한방재료로 만든 헤어 클리닉 화제/발명자에게 편리한 특허제도 마련/차로 마시는 '허브 추출물'로 살충제 만들어/종이컵에도 웰빙 바람이 불고 있다/특허공보 통해 '나의 발명' 확인가능/지역특산품도 지리적 표시로 보호 받는다/한미약품,'비만치료제 특허권 분재' 연승/국내특허, 해외에서 신속하게 심사 처리/'스판덱스 특허소송'에서 일본업체 패소/아모레, 다국적 화장품회사 로레알에 승소/제7차 한국.유럽 특허청장 회담 개최/고부가가치 창출하는 단백질 의약품 개발 필요/한방 진료에도 변화의 새바람 분다/에너지 절감'기능성 유리' 출원 급증/역사 속의 발명품/하루 10분 발명교실/특허Q&A/세상을 밝히는 여성들의 발명 아이디어/'특허넷' 정부기관 최초 CMMI 레벨4인증 획득/'해외지재권 보호 가이드북' 제작배포하다/아이디어 착상 및 발명 기법/고정관념을 깨트려 블루오션을 장악하라/에반스의 증기제분기/50년 후엔 동물과도 대화할 수 있다/첩보용 도구 전달 '발명팀' 실제 존재/중소기업 위한'2006 특허유통 페스티벌' 개최/출원료.심사청구료 반환제도 도입, 시행/'지재권 e-러닝 콘텐츠' 전 세계특허청 교육 자료로 활용/대한변리사회, 미 특허법 세미나 개최/한국여성발명협회 회원사 발명품 가이드

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.157-164
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• 2020

In 2020, due to COVID-19, all universities in Korea were conducting non-face-to-face classes. The purpose of this study is to study what factors affect academic achievement under such non-face-to-face instruction, especially for engineering students where practical training is important. Validity of the statistical hypothesis defined in this study by applying a structural equation model using questionnaires about academic achievement for engineering students at University D for this study. In addition, I would like to suggest what factors should be considered in non-face-to-face classes, especially in engineering colleges. As a result of the study, it was found that students' Q&A, feedback and e-learning system had a direct influence on academic achievement. In addition, it was confirmed that they had an indirect influence on academic achievement through the parameters of theory class and practical class.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.571-578
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• 2021

This study compares Online SW-based maker education in terms of self-efficacy, learning motivation, and interest after applying differently according to blended learning strategies. First, a SW maker program for blended learning was developed and applied as a live seminar-type class including real-time interactive and a support-providing class consisting of online content and Q&A. As a result of comparing the differences between students according to the two strategies divided into pre- and post- survey, in the self-efficacy part, there was a significant difference in the positive efficacy and the overall part, and in the learning motivation part, the live seminar form was significantly higher in the confidence part. In the interest part, the support-providing form showed a significantly higher average in the instrumental interest and nervous part. In order to maintain the effect of maker activities like existing face-to-face situations in Online learning, it is necessary to increase sharing time between students, an integrated learning environment, and sufficient provision of exploration time and learning materials.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.13-18
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• 2022

In this paper, we proposed VW-FedAVG(Validation based Weighted FedAVG) which updates the global model by weighting according to performance verification from the models of each device participating in the training. The first method is designed to validate each local client model through validation dataset before updating the global model with a server side validation structure. The second is a client-side validation structure, which is designed in such a way that the validation data set is evenly distributed to each client and the global model is after validation. MNIST, CIFAR-10 is used, and the IID, Non-IID distribution for image classification obtained higher accuracy than previous studies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.6
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• pp.403-408
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• 2021

Currently, in the design process of civil structures such as bridges, it is common to make final products by repeating the process of redesigning, if the initial design is found to not meet the standards after a structural review. This iterative process extends the design time, and causes inefficient consumption of engineering manpower, which should be put into higher-level design, on simple repetitive mechanical work. This problem can be resolved by automating the design process, but the external analysis program used in the design process has been the biggest obstacle to such automation. In this study, we constructed an AI-based automation system for the bridge design process, including an interface that could control both a reinforcement learning algorithm, and an external analysis program, to replace the repetitive tasks in the current design process. The prototype of the system built in this study was developed for a 2-span RC Rahmen bridge, which is one of the simplest bridge systems. In the future, it is expected that the developed interface system can be utilized as a basic technology for linking the latest AI with other types of bridge designs.