• 대한임베디드공학회논문지
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• 제15권1호
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• pp.1-8
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• 2020

A derivation method for a quantized gradient for machine learning on an embedded system is proposed, in this paper. The proposed differentiation method induces the quantized gradient vector to an objective function and provides that the validation of the directional derivation. Moreover, mathematical analysis shows that the sequence yielded by the learning equation based on the proposed quantization converges to the optimal point of the quantized objective function when the quantized parameter is sufficiently large. The simulation result shows that the optimization solver based on the proposed quantized method represents sufficient performance in comparison to the conventional method based on the floating-point system.

• 한국산업융합학회 논문집
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• 제2권1호
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.

• 음성과학
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• 제6권
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• pp.83-101
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• 1999

The aim of this study is to provide a learning method of prosodic rhythm for Taegu North Kyungsang Korean speakers to learn French rhythm more effectively. The rhythmic properties of spoken French and Taegu North Kyungsang Korean dialect are different from each other. Therefore, we try to provide a basic rhythmic model of the two languages by dividing into three parts: syllable, rhythmic unit and accent, and intonation. To do so, we recorded French of Taegu Kyungsang Korean speakers, and then analysed and compared the rhythmic properties of Korean and French by spectrograph. We tried to find rhythmic mistakes in their French pronunciation, and then established a learning model to modify them. After training with the CSL Macro learning model, we observed the output result. However, although learners understand the method we have proposed, an effective method which is possible by repeating practice must be arranged to be actually used in direct verbal communications in a well-developed learning programme. Hence, this study may play an important role at the level of preparation in the setting of an effective rhythmic learning programme.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.637-638
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• 2013

디지털화된 새로운 패러다임(paradigm)은 교수 미디어에 있어서도 많은 변화를 가져오고 있다. 과거의 일방적 지식전달방식에서 탈피하여 피교육자 스스로 선택적으로 강의를 찾아다니면서 본인의 능력에 맞도록 수업을 할 수 있으며, 각종 e-Learning 컨텐츠의 활용은 물론 피드백이 가능한 커뮤니케이션을 요구하게 되었다. 본 연구에서는 디자인 기술 분야의 교과목에서 도입중인 e-Learning을 중심으로 고려되어야 할 사항 및 효율적인 학습방법과 그 활성화 방안의 한 가지를 제시하였다.

• 전자공학회논문지
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• 제49권9호
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• pp.370-378
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• 2012

본 연구에서는 다양한 모바일 기기에 운용 가능한 표준화와 고도화 기능을 충족시키기 위한 하이브리드 웹 기반의 스마트 러닝 시스템 구축 방안을 제안한다. 제안한 연구에서는 스마트 러닝 시스템 기능 정의를 바탕으로 표준 API 사양서 작성하고 이를 근거로 PC 기반의 e-러닝 시스템의 데이터를 API 연동 규격에 맞게 ASP 페이지를 구현한다. 그리고 PC 기반의 e-러닝 시스템과 모바일 기반의 스마트 러닝 시스템 간의 데이터 연동은 표준 API 방식 연동을 통한 Json/XML 형태의 데이터를 Https 프로토콜 기반으로 상호 연동을 수행하도록 한다. 제안한 시스템은 별도의 기반 시스템 및 지원 시스템 없이 즉시 데이터 교환이 가능한 구성을 가지며 PC 기반의 e-러닝 시스템의 수정 및 변화 없이 모바일 기반으로 학습 서비스가 가능함으로써 비용적인 측면에서 효과적인 시스템 구축이 가능함을 알 수 있다.

• Industrial Engineering and Management Systems
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• 제12권4호
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• pp.368-379
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• 2013

Efficiency in work training is a perennial issue due to high-diversity low-volume production, particularly for manufacturers producing office machines which are manually assembled by workers. To reduce the training cost, parts used in training are usually reused; a trainer disassembles a product assembled by a worker in training. This paper proposes a training method that employs disassembly usually performed by a trainer. This method assigns both assembly and disassembly to a worker in training, in contrast to the conventional method. The effectiveness of the proposed method is experimentally discussed in terms of learning assembly motions and work procedure at each learning stage, namely, "undergoing learning," "immediately after learning," and "seven days after learning." The effectiveness of the training method is confirmed. The method improves the stability of work procedure recollection immediately after training. Furthermore, at seven days after training, it improves retention of the assembly motions and work procedure, and also promotes and maintains memory related to product structure.

• 수산해양교육연구
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• 제28권4호
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• pp.1057-1067
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• 2016

National Competency Standards are the major administrative project to build a competence-based society. The manpower for the global society should be recognized by performance rather than educational records. Therefore, all colleges should first nurture NCS-type manpower based on field needs. This study comparatively analyzed the differences between the lecture style of four-year colleges and the outcomes of problem-solving and project-based learning method to prove why it is necessary to introduce the NCS program. Especially, It will review the constraints and measures of NCS introduction to overcome in a four-year university. Through this, it can be used as a means to help improve the field conformity of a four-year college curriculum by presenting the development and utilization of curriculum-based NCS in a four-year university. As a result, it was found that the overall satisfaction with the problem-solving and project-based learning method was above average. Many students were dissatisfied with the traditional teaching methods and the new project-based learning method was relatively effective in college education. Students' participation also improved. Based on the evaluation of learning performance, the new method was found more satisfactory than the old teaching method in terms of comprehension of professional knowledge in various fields, nurturing of logical thinking skills, acquisition of analytical skills, comprehensive thinking skills, creative problem recognition, and open-minded thinking skills.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.233-244
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• 2023

Although artificial intelligence and machine learning technologies have been used in various fields, problems with personal information protection have arisen based on centralized data collection and processing. Federated learning has been proposed to solve this problem. Federated learning is a process in which clients who own data in a distributed data environment learn a model using their own data and collectively create an artificial intelligence model by centrally collecting learning results. Unlike the centralized method, Federated learning has the advantage of not having to send the client's data to the central server. In this paper, we quantitatively present the performance improvement when federated learning is applied using the building change detection learning data. As a result, it has been confirmed that the performance when federated learning was applied was about 29% higher on average than the performance when it was not applied. As a future work, we plan to propose a method that can effectively reduce the number of federated learning rounds to improve the convergence time of federated learning.

• 부동산연구
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• 제27권3호
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• pp.71-86
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• 2017

본 연구의 목적은 딥 러닝 방법을 부동산가격지수 예측에 적용해보고, 기존의 시계열분석 방법과의 비교를 통해 부동산 시장 예측의 새로운 방법으로서 활용가능성을 확인하는 것이다. 딥 러닝(deep learning)방법인 DNN(Deep Neural Networks)모형 및 LSTM(Long Shot Term Memory networks)모형과 시계열분석 방법인 ARIMA(autoregressive integrated moving average)모형을 이용하여 여러 가지 부동산가격지수에 대한 예측을 시도하였다. 연구결과 첫째, 딥 러닝 방법의 예측력이 시계열분석 방법보다 우수한 것으로 나타났다. 둘째, 딥 러닝 방법 중에서는 DNN모형의 예측력이 LSTM모형의 예측력보다 우수하나 그 정도는 미미한 수준인 것으로 나타났다. 셋째, 딥 러닝 방법과 ARIMA모형은 부동산 가격지수(real estate price index) 중 아파트 실거래가격지수(housing sales price index)에 대한 예측력이 가장 부족한 것으로 나타났다. 향후 딥 러닝 방법을 활용함으로써 부동산 시장에 대한 예측의 정확성을 제고할 수 있을 것으로 기대된다.

• 전자공학회논문지CI
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• 제39권4호
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• pp.19-27
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• 2002

역전파 알고리즘의 성능 개선을 위해서 학습율을 자동 조정하는 방식을 제안하였다. 제안한 방식은 각각의 연결강도의 학습율을 퍼지 논리 시스템을 이용하여 자동 조정하는 방식으로 각각의 연결강도에 대해서 ${\Delta}$와 $\bar{{\Delta}}$를 구하여 퍼지 논리 시스템의 입력으로 사용하고, 학습율을 출력으로 사용하였다. 제안한 방식을 N-패리티 문제, 함수 근사, 숫자 패턴 분류에 대한 시뮬레이션 결과 일반적인 역전파 알고리즘, 모멘텀 방식, Jacobs의 delta-bar-delta 방식보다 성능이 개선됨을 확인하였다.