• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1205-1213
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• 2023

This study delves into methods for enhancing the power efficiency of air compressor systems, with the primary objective of significantly impacting industrial energy consumption and environmental preservation. The paper scrutinizes Shinhan Airro Co., Ltd.'s power efficiency optimization technology and employs machine learning ensemble models to simulate power efficiency optimization. The results indicate that Shinhan Airro's optimization system led to a notable 23.5% increase in power efficiency. Nonetheless, the study's simulations, utilizing machine learning ensemble techniques, reveal the potential for a further 51.3% increase in power efficiency. By continually exploring and advancing these methodologies, this research introduces a practical approach for identifying optimization points through data-driven simulations using machine learning ensembles.

• The Journal of Information Technology
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• v.8 no.1
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• pp.119-124
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• 2005

Learning procedure in the neural network is updating of weights between neurons. Unadequate initial learning coefficient causes excessive iterations of learning process or incorrect learning results and degrades learning efficiency. In this paper, adaptive learning algorithm is proposed to increase the efficient in the learning algorithms of Kohonens Self-Organization Neural networks. The algorithm updates the weights adaptively when learning procedure runs. To prove the efficiency the algorithm is experimented to clustering of the random weight. The result shows improved learning rate about 42~55% ; less iteration counts with correct answer.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.253-255
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• 2016

본 연구에서는 컴퓨터 프로그래밍 학습자들의 학습 성향을 조사하고 그들의 평소 학습 성향이 어떤 평가로 이어지는지에 대해 관찰한다. 또한 학습 성향과 이전 평가 결과가 다음 학기 프로그래밍 수업에 어느 정도 동기 부여가 되는지를 분석하여 학습자들의 성향과 능력에 맞는 방법으로 학습 동기를 부여할 수 있는 방법을 모색하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.293-294
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• 2015

본 논문에서는 컴퓨터 프로그래밍 수업에 피어 튜터링을 적용할 때 팀 구성 방법을 다르게 하여 참여 학습자들의 성적 변화를 비교해보았다. 그 결과 팀 구성시 친분보다는 수준을 고려할수록 성적 상승에 긍정적으로 작용했으며 특히 친분에 의한 팀 구성시 학습자들의 학습 수준 차이가 크면 상위 학습자 성적 하력이 더 큰 것으로 분석되었다.

• The Journal of Information Technology
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• v.7 no.3
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• pp.11-18
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• 2004

Learning procedure in the neural network is updating of weights between neurons. Unadequate initial learning coefficient causes excessive iterations of learning process or incorrect learning results and degrades learning efficiency. In this paper, adaptive learning algorithm is proposed to increase the efficient in the learning algorithms of Self-Organization Neural Networks. The algorithm updates the weights adaptively when learning procedure runs. To prove the efficiency the algorithm is experimented to classification of strokes which is the reference handwritten character. The result shows improved classification rate about 1.44~3.65% proposed method compare with Kohonan and Mao's algorithms, in this paper.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.135-141
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• 2016

This paper was conducted to verify the relationship of understanding of learning, service quality, efficiency of convergence e-business valuation. For this paper, it surveyed 179 students and the data were collected Nov. 2~30, 2015. This research analyzed all the samples through ANOVA, t-test utilized by SPSS 22.0 The results as follow. First, understanding of learning, service quality, efficiency had statistically significant difference according to gender. Second, understanding of learning, service quality, efficiency showed statistically significant difference according to grade. The results of this study reveal the fact that the general characteristics like gender and grade made difference to the understanding of learning, service quality, efficiency. Furthermore, this paper may need to include various variables of understanding of learning, service quality, efficiency and suggest research about several university.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.243-244
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• 2012

본 논문에서는 단계별 학습이 필요한 컴퓨터 프로그래밍 수업에 피어 튜터링 제도를 적용하여 정규 수업과 병행하여 운영하고 피어 튜터링에 참여한 학습자들의 참여 전과 후의 성적 변화를 비교해봄으로써 피어 튜터링 제도가 단계별 학습에 얼마나 효과적인지를 분석해본다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.417-418
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• 2012

본 논문에서는 학습자의 수준에 따라 맞춤형 학습이 필요한 컴퓨터 프로그래밍 수업에서 학습자들에게 다양한 형태의 맞춤형 학습 사례들을 적용해보고 각 사례에 참여한 학습자들의 프로그램 영역별 학습 효과를 분석하여 학습자들에게 효과적인 맞춤형 학습 유형이 어떤 것인지를 분석해보았다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.365-373
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• 2000

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications and merits, some features of DEA remain bothersome. DEA offers no guideline about to which direction relatively inefficient DMUs improve since a reference set of an inefficient DMU, several efficient DMUs, hardly provides a stepwise path for improving the efficiency of the inefficient DMU.In this paper, we aim to show that DEA can be used to evaluate the efficiency of life insurance companies while overcoming its limitation with the aids of machine learning methods.