• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• 한국컴퓨터정보학회논문지
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• 제11권4호
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• pp.27-33
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• 2006

향후 도래하는 유비쿼터스 환경에서는 모든 전자기기가 유/무선 통신망으로 연결되어 사용자는 언제 어디서나 원하는 정보에 접근할 수 있을 것으로 본다. 특히, u-러닝(Ubiquitous-Learning)은 교육학적인 측면뿐 아니라 어디서나 접근할 수 있다는 유비쿼터스적인 측면을 통해 학습의 효과를 보다 향상시킬 수 있어야 한다. 현재까지 추진된 u-러닝의 연구로는, PDA를 이용하여 학습 컨텐츠를 야외에서 학습하거나 원격지 모바일 환경의 학생들을 교실 수업에 참여시키는 스마트교실 등을 들 수가 있는데, 기존의 연구들에서는 학습자의 상호작용이나 협동학습 등의 교육학적인 속성과 유비쿼터스의 환경적 속성을 만족시키지 못하고 있다. 따라서 본 연구에서는 대학 등을 대상으로 어디서나 교실 환경에 참여하는 유비쿼터스 환경에 적합하면서도 교육학적인 면을 만족시키는 모바일 교실 프레임워크를 제안하고자 한다.

• 디지털융복합연구
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• 제13권3호
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• pp.31-36
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• 2015

전자통신 및 스마트기기 사용의 확산과 더불어 스마트플랫폼 시대에 맞는 강의 및 새로운 서비스 구상이 필요하고, 다양한 콘텐츠 및 시스템 변화에 대비하여 교육 시스템을 설계, 구현할 필요성이 강조되고 있다. 국내 사이버 대학들도 스마트폰 기반의 모바일 캠퍼스 구축 등 융복합 기술을 통한 새로운 정보통신 환경에 부합하는 교육시스템과 네트워크 기반 구축에 빠르게 대처하고 있는 실정이다. 이에 본 연구의 목적은 K대학의 사례연구를 통해 클라우드 기반 시스템 설계와 구현, 활용효과를 제시함으로서 효과적인 도입 방법 및 개선점 등을 시사하는 것이다. K대학의 행아웃 학습시스템 도입사례를 통해 교수와 학생간 자유로운 의견 교환 및 정보공유를 통해 학생의 학업성취도 및 만족도가 향상되었고, 시간 및 운영비용 등의 절감을 통해 효율성이 향상되었음을 확인할 수 있었다. 또한, 이 연구 결과는 실시간 영상강의의 새로운 모델을 발굴하고 학습 환경 개선을 위한 클라우드 기반 서비스 플랫폼을 제시한다.

• 방송공학회논문지
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• 제24권7호
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• pp.1266-1275
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• 2019

With increasing criminal rates and number of CCTVs, much attention has been paid to intelligent surveillance system on the horizon. Object detection and tracking algorithms have been developed to reduce false alarms and accurately help security agents immediately response to undesirable changes in video clips such as crimes and accidents. Many studies have proposed a variety of algorithms to improve accuracy of detecting and tracking objects outside tunnels. The proposed methods might not work well in a tunnel because of low illuminance significantly susceptible to tail and warning lights of driving vehicles. The detection performance has rarely been tested against the tunnel environment. This study investigated a feasibility of object detection and tracking in an actual tunnel environment by utilizing YOLOv3 and Kernelized Correlation Filter. We tested 40 actual video clips to differentiate pedestrians and motorcycles to evaluate the performance of our algorithm. The experimental results showed significant difference in detection between pedestrians and motorcycles without false positive rates. Our findings are expected to provide a stepping stone of developing efficient detection algorithms suitable for tunnel environment and encouraging other researchers to glean reliable tracking data for smarter and safer City.

• 한국IT서비스학회지
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• 제20권6호
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• pp.107-123
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• 2021

Recently, there have been a number of researchers in the field of education who are actively exploring the educational applications of Blockchain technology, even though it is still in its infancy. Some researchers have been investigating its application in educational administration to issue academic credentials' or maintain student records with distributed ledger, which is the basis of Blockchain technology. Whereas, others have been examining its application in redesigning learning systems that are being used in various contexts, including online learning and lifelong education. In that vein, this paper aims to discuss a liberal arts education system which will be supported by Blockchain-based 'smart contracts'. At present, active efforts are being made to innovate liberal arts education in Korea, centered around government-funded university innovation projects and there have been reports of great achievements. However, if the Blockchain technology is applied to innovating the liberal arts education, we will innovate not only the liberal arts education but also university education as a whole. In this paper, there are suggestions on how to build a learner-centered educational environment where a liberal arts education system is supported by Blockchain-based smart contracts. First of all, the current innovation in liberal arts education and its limitations are discussed, followed by ways in which Blockchain-based smart contracts can reframe the liberal arts education system. Last but not least, the paper addresses implications of the Blockchain technology applications in liberal arts education, along with their future prospects.

• 산업경영시스템학회지
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• 제40권2호
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• pp.68-77
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• 2017

In order to implement the smart home environment, we need an intelligence service platform that learns the user's life style and behavioral patterns, and recommends appropriate services to the user. The intelligence service platform should embed a couple of effective and efficient data mining algorithms for learning from the data that is gathered from the smart home environment. In this study, we evaluate the suitability of data mining algorithms for smart home intelligent service platforms. In order to do this, we first develop an intelligent service scenario for smart home environment, which is utilized to derive functional and technical requirements for data mining algorithms that is equipped in the smart home intelligent service platform. We then evaluate the suitability of several data mining algorithms by employing the analytic hierarchy process technique. Applying the analytical hierarchy process technique, we first score the importance of functional and technical requirements through a hierarchical structure of pairwise comparisons made by experts, and then assess the suitability of data mining algorithms for each functional and technical requirements. There are several studies for smart home service and platforms, but most of the study have focused on a certain smart home service or a certain service platform implementation. In this study, we focus on the general requirements and suitability of data mining algorithms themselves that are equipped in smart home intelligent service platform. As a result, we provide a general guideline to choose appropriate data mining techniques when building a smart home intelligent service platform.

• 스마트미디어저널
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• 제12권10호
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• pp.9-18
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• 2023

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

• 농업과학연구
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• 제46권2호
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• 한국컴퓨터산업학회논문지
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• 제6권5호
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• pp.749-756
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• 2005

향후 도래하는 유비쿼터스 환경에서는 모든 전자기기가 유/무선 통신망으로 연결되어 사용자는 언제 어디서나 원하는 정보에 접근할 수 있다. 특히, u-러닝(Ubiquitous-Learning)은 교육학적인 측면 뿐 아니라 어디서나 접근할 수 있다는 유비쿼터스적인 측면을 통해 학습의 효과를 보다 향상시킬 수 있다. 현재까지 추진된 u-러닝의 연구사례로, 주어진 학습 컨텐츠를 PDA를 이용하여 야외에서 학습할 수 있도록 하며, 원격지 모바일 환경의 학생들을 교실 수업에 참여시키는 스마트교실 등이 있다. 그러나 기존의 연구들에서는 학습자의 상호작용이나 협동학습 등의 교육학적인 속성과 유비쿼터스의 환경적 속성을 만족시키지 못함에 따라 본 연구에서는 어디서나 교실 환경에 참여하는 유비쿼터스 환경에 적합하면서도 교육학적인 면을 만족시키는 모바일 교실 프레임워크를 제안하고자 한다.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.993-1001
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• 2021

This study aims to determine the factors influencing student performance using the teaching and learning process through e-learning based on the unified theory of acceptance and use technology (UTAUT). This study also sets out to propose additional variables to expand the UTAUT model to be more suitable to use in higher education. This research conducted a literature review, expert interviews, and a self-administered survey involving 200 students at tertiary institutions in Riau province, Indonesia. The questionnaire data were analyzed using SmartPLS 2. This study shows that UTAUT constructs, namely, social influence, facility conditions, and effort expectancy have a significant influence on student behavior and performance, while the performance expectancy variable shows no significant effect. The additional variables, including lecturer characteristics, external motivation, and organizational structure, directly affect student performance. However, concerning student behavior, motivation and environment are the only variables with a significant effect. The results of this study suggest the behavior deteminant such as lecturer characteristics, motivation and environment, and organizational structure improve student performance. This study investigates factors affecting the performance of university students through the learning employing e-learning by developing the UTAUT constructs to include the lecturer characteristics, motivation and environment, and organizational structure in improving student performance.