• 스마트미디어저널
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• 제11권5호
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• pp.82-90
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• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• 한국항공운항학회지
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• 제26권4호
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• pp.101-109
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• 2018

In order to predict the future needs of the aircraft repair parts, each military group develops and applies various techniques to their characteristics. However, the aircraft and the equipped weapon systems are becoming increasingly advanced, and there is a problem in improving the hit rate by applying the existing demand prediction technique due to the change of the aircraft condition according to the long term operation of the aircraft. In this study, we propose a new prediction model based on the conventional time-series analysis technique to improve the prediction accuracy of aircraft repair parts by using machine learning model. And we show the most effective predictive method by demonstrating the change of hit rate based on actual data.

• 상하수도학회지
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• 제23권2호
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• pp.241-249
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• 2009

It is well known that the trend of water demand in large-size water supply systems has been suddenly changed, and many expansions of water supply facilities become unnecessary. To be cost-effective, thus, politicians as well as many professionals lay stress on the adaptive management of water supply facilities. Failure in adapting to the new trend of demand is sure to be the most critical reason of unnecessary expansions. Hence, we try to develop the model and modeling procedure that do not depend on the old data of demand, and provide engineers with the fast learning process. To forecast water demand of Seoul, the Bayesian parameter estimation was applied, which is a representative method for statistical pattern recognition. It results that we can get a useful time-series model after observing water demand during 6 years, although trend of water demand were suddenly changed.

• 한국인터넷방송통신학회논문지
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• 제21권3호
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• pp.7-14
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• 2021

자동화된 스마트 그리드의 등장은 이러한 문제에 대응을 위한 필수적인 장치가 되고 있으며 스마트 그리드 기반 사회로의 진전을 가져오고 있다. 스마트 그리드는 전기 공급 업체와 소비자 간의 양방향 통신을 가능하게 하는 새로운 패러다임이다. 스마트 그리드는 전력 그리드를 보다 안정적이고 신뢰할 수 있으며 효율적이고 안전하게 만들기 위한 엔지니어의 이니셔티브로 인해 등장했다. 스마트 그리드는 전력 소비자가 전력 사용에서 더 큰 역할을 할 수 있는 기회를 창출하고 전력을 현명하고 효율적으로 사용하도록 동기를 부여한다. 이에 본 연구에서는 기계 학습을 통한 전력 수요 관리에 중점을 둔다. 기계 학습을 사용한 수요 예측과 관련하여 현재 다양한 기계 학습 모델이 소개되어 적용되고 있는 데 이에 관한 체계적인 접근이 요구되고 있다. 특히 GP 학습 모델의 경우에 일반 소비 예측 및 데이터의 가시화와 관련해서 다른 학습 모델보다 장점이 있지만, 스마트 미터 데이터의 예측과 관련해서는 데이터 독립성에 강한 영향을 받는다.

• 에너지공학
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• 제28권4호
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• pp.82-93
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• 2019

본 연구에서는 기존 열수요 예측 시스템이 공휴일과 같은 특정 일자의 열수요 예측율이 저하되는 문제점을 개선하기 위해 새로운 모델을 제안한다. 제안된 모델은 사계절 혼합형 신경망 모델(Four Season Mixed Heat Demand Prediction Neural Network Model)로서 열수요 예측율 상승하였고, 특히 예측일 유형별(평일/주말/공휴일) 열수요 예측율이 크게 증가하였다. 제안된 모델은 다음과 같은 과정을 통해 선정되었다. 특정 계절에 예측일 유형별로 고른 오차를 갖는 모델을 선정하여 전체 예측 모델을 구성한다. 학습 시간의 단축과 과도학습을 방지하기 위해 구조적으로 단순화된 서로 다른 4개의 모델을 각각 학습한 후에 다양한 조합을 통해 최적의 예측 오차를 보여주는 모델을 선정하였다. 모델의 출력은 예측일의 24시간의 시간대별 열수요이며 총합은 일일 총열수요이다. 이 예측값을 통해 효율적인 열공급 계획을 수립 할 수 있으며, 목적에 따라 출력값을 선택하여 활용할 수 있다. 제안된 모델의 일일 열 총수요 예측의 경우, 전체 MAPE(Mean Absolute Percentage Error, 평균 절대 비율 오차)가 개별 모델의 5.3~6.1%에서 5.2%로 향상되었고, 공휴일 열수요예측은 4.9~7.9%에서 2.9%로 크게 개선되었다. 본 연구에서는 한국 지역난방공사에서 제공한 특정 아파트 단지의 34개월 분량의(2015년 1월~ 2017년10월) 시간단위 열수요 데이터를 활용하였다.

• 한국어교육
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• 제28권4호
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• pp.297-335
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• 2017

Recently, Chinese universities have been putting their priority on cultivating industry-academia linked talents, catching up with social change and industrial demand. Accordingly, vocational purpose education is being emphasized even in Korean language education. When facing active trade between Korea and China, the importance of business Korean language education will be magnified, and therefore, the demand for business Korean textbooks will grow accordingly. To strengthen the basis for the development of future business Korean textbooks, this study conducted a general analysis of a business Korean textbook for Chinese learners. Specifically, the textbook was examined by largely dividing it into external and internal structures. After dividing the internal structure into "purpose of compilation", "composition of the textbook", "learning contents", and "learning activity", the composition of the textbook was once again divided into "overall composition" and "unit composition", and the learning contents was further divided into "subject", "language content", and "supplementary knowledge." Furthermore, an analysis was conducted. The status and directions for future development of business Korean textbooks for Chinese learners are delineated and suggestions for improvement are provided. This study has its significant in that a general analysis was conducted on a business Korean textbook for Chinese learners, and is expected to be used as basic research material for the future development of business Korean textbooks.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.60-67
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• 2020

Recently, artificial intelligence related technologies including machine learning are being applied to various fields, and the demand is also increasing. In particular, with the development of AR, VR, and MR technologies related to image processing, the utilization of computer vision based on deep learning has increased. The algorithms for object recognition and detection based on deep learning required for image processing are diversified and advanced. Accordingly, problems that were difficult to solve with the existing methodology were solved more simply and easily by using deep learning. This paper introduces various deep learning-based object recognition and extraction algorithms used to detect and recognize various objects in an image and analyzes the technologies that attract attention.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• 공학교육연구
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• 제15권4호
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• pp.109-117
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• 2012

The purpose of this study is to give the preliminary data and suggestion for introducing and spreading e-learning engineering education through analyzing learning behaviors, learning motivations, and satisfaction of e-learning engineering students. Especially, this comparative study analyzes each research domain according to majors and grades, thereby suggesting more specific and practical results. 2,745 students registered in 38 subjects of e-learning in 2 Universities were analyzed for this study. The study result shows that engineering students are attending around 2 e-learning subjects with a duration of about 30 minutes once a week. The main of learning motivation for e-learning was not easy test level and feasibility of acquiring credit but advantages of e-learning such as freedom of time and space, learning by repetition. The satisfaction scores of e-learning were lower compared to the aspects of system and contents Based on these results, first, an active spread of e-learning to engineering education is necessary because the demand from the engineering students is high enough and they have desirable learning behavior and learning motivation for it. Second, the characteristics of grades need to be taken into consideration on operation of e-learning. Third, a successful e-learning process needs more meticulous and active operation.

• 한국수학교육학회지시리즈A:수학교육
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• 제60권1호
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• pp.61-76
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• 2021

본 연구는 수학 예비교사들이 과제의 인지적 노력 수준 변형에서 겪는 오류와 어려움을 분석하여, 수학 과제 변형과 관련한 수학 예비교사 교육에 유의미한 시사점을 제공하는 것을 목적으로 한다. 이를 위해 24명의 수학 예비교사들을 대상으로 수직이등분선의 성질에 대한 추론 과제를 높은 수준과 낮은 수준으로 변형하는 활동과 이에 대한 반성 및 수정 기회를 제공하였다. 변형 과제를 중심으로 예비교사들이 과제의 수준 변형에서 겪는 오류와 어려움을 분석한 결과, 과제 수준의 판단 관점에서 PNC와 PWC 과제의 구분에 제한된 이해를 보였으며, 과제의 외형적인 요소에 의존하는 간섭 현상을 확인하였다. 과제 수준의 변형 관점에서 예비교사들은 과제의 목표와 수직적 위계를 간과하거나 변형 유형의 편향성을 보였다. 한편 예비교사들은 반성 및 수정 활동을 통해 자신들의 변형 과제의 오류를 인식하고 개선할 수 있었으며, 도구의 범주를 Geogebra를 포함한 공학적 도구로 확장하는 모습을 보여주었다.