• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.39-44
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• 2023

With the development of e-learning teaching methods, the demand for blended learning, which combines face-to-face education and e-learning, is increasing, and it shows a learning effect that can replace the existing face-to-face class. Engineering subjects have various learning activities such as practice, so it is not easy to operate them with traditional blended learning. Therefore, a different teaching and learning design is required according to the learning activities required for the subject. In this paper, examples of teaching method design and operation for blended learning in engineering subjects were introduced, and their effects investigated and analyzed. Learning activities were subdivided into theoretical classes, practical classes, quizzes and Q&A, assignments and solutions, and teaching and learning methods such as online videos, LMS utilization, and face-to-face classes were applied according to learning activities. According to the results of the student satisfaction survey, blended learning showed higher satisfaction than pure online and face-to-face classes in engineering subjects, and showed differentiated satisfaction for each learning activity.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.15-16
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• 2018

본 논문에서는 다수의 센서 노드로 구성된 Internet of Things (IoT) 환경에서 새로운 환경에 대해 적응하는데 걸리는 시간을 줄이기 위한 새로운 스케줄링 기법을 제안한다. IoT 환경에서는 데이터 수집 및 전송 패턴이 사전에 정의되어 있지 않기 때문에 기존 정적인 Packet scheduling 기법으로는 한계가 있다. Q-learning은 네트워크 환경에 대한 사전지식 없이도 반복적 학습을 통해 Scheduling policy를 확립할 수 있다. 본 논문에서는 기존 Q-learning 스케줄링 기법을 기반으로 각 큐의 패킷 도착률에 대한 bound 값을 이용해 Q-table과 Reward table을 초기화 하는 새로운 Q-learning 스케줄링 기법을 제안한다. 시뮬레이션 결과 기존 기법에 비해 변화하는 패킷 도착률 및 서비스 요구조건에 적응하는데 걸리는 시간이 감소하였다.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.454-462
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• 2021

The purpose of this study was to identify learners with poor learning in basic cooking practice subjects for college students majoring in cooking, operate a basic practical ability improvement program, and apply an action learning teaching method. We tried to analyze the subjective perception of learners using the Q methodology. In addition, it was intended to improve the major competencies for the operation of practical programs related to cooking training, field training, and employment of learners. The survey was conducted from May 1st to 20th, 2020 for first-year students in C cuisine major. As a result of the analysis, a total of three types were derived. Type 1 (N=7): Self-directed learning effect type, Type 2 (N=8): Problem Solving Effect Types, Type 3 (N=6): Peer learning effect type, each unique feature type Was analyzed as. Through the progress of this study and the derivation of implications, it is expected that it will be useful data for the application of teaching and learning methods related to practical work and program operation in cooking-related departments.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.283-292
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• 2021

As universities have operated non-face-to-face semesters due to COVID-19, although instructors applying flipped learning to their classes also have changed it into non-face-to-face ways, there is still a lack of exploratory research on effectiveness of the new form of flipped learning. In this study, we explored the effectiveness of the non-face-to-face flipped learning by analyzing students' learning experiences throughout FGI and survey. By doing so, we sought to provide in-depth insights for successful implications of non-face-to-face flipped learning classes ultimately. The findings showed that many learners positively evaluated non-face-to-face flipped learning in terms of interactions, including quizzes, team activities, and interpersonal interactions (e.g., Q&A, feedback) with professors in non-face-to-face flipped learning classes. The result of the survey also showed significant differences in the pre-post test regarding learner's perceived learning achievement. Based on these findings, the implications were discussed.

• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.30-39
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• 2014

The purpose of this study is to suggest the feasible strategies to vitalize e-learning through investigating the characteristics of e-learning and the evaluations of distance learners on online courses in the domestic universities in Korea. First, in order to accomplish this, 10 Universities and 17 Cyber Universities were selected to explore their characteristics and main projects of e-learning for the administration level investigation. Secondly, content analysis of the bulletin board systems(BBS) and in-depth interviews on distance learners in Cyber Universities were conducted for the user level investigation. The results revealed that Universities in Korea were focused on establishing mobile or smart campuses, diversifying online educational contents, enhancing online interactive systems, and educating e-learning system and smart device utilization. However, distance learners reported that mobile e-learning lacked stability when taking online courses despite its convenience for purpose of academic administration. In addition, distance learners requested the social application workshops to improve on their learning experience as well as the interactions among peers. Therefore, it is important to focus more on how to establish the education-oriented e-learning environment rather than how to implement the administrative projects to animate e-learning in the domestic universities in Korea.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.557-559
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• 2022

최근 딥러닝의 기술발전으로 자연어 처리 분야에서 Q&A, 문장추천, 개체명 인식 등 다양한 연구가 진행 되고 있다. 딥러닝 기반 자연어 처리에서 좋은 성능을 보이는 트랜스포머 기반 BERT 모델의 성능향상에 대한 다양한 연구도 함께 진행되고 있다. 본 논문에서는 토픽모델인 잠재 디리클레 할당을 이용한 토픽별 지식그래프 분류와 입력문장의 토픽을 추론하는 방법으로 K-BERT 모델을 학습한다. 분류된 토픽 지식그래프와 추론된 토픽을 이용해 K-BERT 모델에서 대용량 지식그래프 사용의 효율적 방법을 제안한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.319-319
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• 2020

최근 데이터 과학의 획기적인 발전 덕분에 딥러닝 (Deep Learning) 알고리즘이 개발되어 다양한 분야에 널리 적용되고 있다. 본 연구에서는 인공신경망 중 하나인 LSTM(Long-Short Term Memory) 네트워크를 사용하여 댐 유입량을 예측하였다. 구체적인 내용으로, (1) LSTM에 필요한 입력 데이터를 효율적으로 사전 처리하는 방법, (2) LSTM의 하이퍼 매개변수를 결정하는 방법 및 (3) 다양한 손실 함수(Loss function)를 선택하고 그 영향을 평가하는 방법 등을 다루었다. 제안된 LSTM 모델은 강우량(R), 댐유입량(Q) 기온(T), 기저유량(BF) 등을 포함한 다양한 입력 변수들의 함수로 가정하였으며, CCF(Cross Correlations), ACF(Autocorrelations) 및 PACF(Partial Autocorrelations) 등의 기법을 사용하여 입력 변수를 결정하였다. 다양한 sequence length를 갖는 (즉 t, t-1, … t-n의 시간 지연을 갖는) 입력 변수를 적용하여 데이터 학습에 최적의 시퀀스 길이를 결정하였다. LSTM 네트워크 모델을 적용하여 2014년부터 2020년까지 한강 유역 9개의 댐 유입량을 추정하였다. 본 연구로부터 댐 유입량을 예측하는 것은 홍수 및 가뭄 통제를 위한 필수 요건들 중 하나이며 수자원 계획 및 관리에 도움이 될 것이다.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.1-7
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• 2022

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.