• Structural Engineering and Mechanics
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• v.85 no.1
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• pp.15-27
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• 2023

Using artificial intelligence and internet of things methods in engineering and industrial problems has become a widespread method in recent years. The low computational costs and high accuracy without the need to engage human resources in comparison to engineering demands are the main advantages of artificial intelligence. In the present paper, a deep neural network (DNN) with a specific method of optimization is utilize to predict fundamental natural frequency of a cylindrical structure. To provide data for training the DNN, a detailed numerical analysis is presented with the aid of functionally modified couple stress theory (FMCS) and first-order shear deformation theory (FSDT). The governing equations obtained using Hamilton's principle, are further solved engaging generalized differential quadrature method. The results of the numerical solution are utilized to train and test the DNN model. The results are validated at the first step and a comprehensive parametric results are presented thereafter. The results show the high accuracy of the DNN results and effects of different geometrical, modeling and material parameters in the natural frequencies of the structure.

• ETRI Journal
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• v.44 no.6
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• pp.955-965
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• 2022

A low Earth orbit (LEO) satellite constellation could be used to provide network coverage for the entire globe. This study considers multi-beam frequency reuse in LEO satellite systems. In such a system, the channel is time-varying due to the fast movement of the satellite. This study proposes an efficient power and bandwidth allocation method that employs two linear machine learning algorithms and take channel conditions and traffic demand (TD) as input. With the aid of a simple linear system, the proposed scheme allows for the optimum allocation of resources under dynamic channel and TD conditions. Additionally, efficient projection schemes are added to the proposed method so that the provided capacity is best approximated to TD when TD exceeds the maximum allowable system capacity. The simulation results show that the proposed method outperforms existing methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.151-157
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• 2023

RPL (IPv6 Routing Protocol for Low-power Lossy Network) is a standardized routing protocol for LLNs (Low power and Lossy Networks) by the IETF (Internet Engineering Task Force). RPL creates routes and builds a DODAG (Destination Oriented Directed Acyclic Graph) through OF (Objective Function) defining routing metrics and optimization objectives. RPL supports a leaf mode which does not allow any child nodes. In this paper, we propose INFRA-RPL which provides a dynamic leaf mode functionality to a leaf node with the mobility. The proposed protocol is implemented in the open-source IoT operating system, Contiki-NG and Cooja simulator, and its performance is evaluated. The evaluation results show that INFRA-RPL outperforms the existing protocols in the terms of PDR, latency, and control message overhead.

• Annual Conference of KIPS
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• 2009.11a
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• pp.501-502
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• 2009

본 연구는 mobile IP에서 smooth handoff의 지연 요소를 최대한 줄이기 위해 지역 관리 방식의 FA를 사용하여 성능을 향상시키는 방안에 대해 연구하였다. 본 연구에서는 전송지연, 처리율 등의 분석을 실시하였으며 모든 FA에 버퍼를 내장한 방식과 지역관리용 FA를 이용한 방식에 대하여도 각종 성능을 측정하여 기존의 방식에 비해 어느 정도 성능 향상이 있는지 확인하였다. 본 연구를 통해 지역관리 기능을 수행하는 GFA를 핸드오프 발생 간격이 25초 이상에서는 바인딩 시간 단축에 의한 효과가 있음을 확인할 수 있었다. 이와 같은 연구 결과를 실제 무선 인터넷망 구축에 활용한다면 무선 인터넷망 구축에 관한 관련 자료가 많지 않은 현 상황에서 FA와 MN의 용량과 성능을 결정하는데 보탬이 될 것으로 확신한다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.121-122
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• 2022

In this study, compared and analyzed various Activation Functions to present a methodology for developing a natural intelligence-based prediction system. As a result of the analysis, ELU was the best with RMSE: 62.87, R²: 0.96, and the error rate was 4%. However, it is considered desirable to construct a prediction system by combining each algorithm model for optimization.

• Advances in nano research
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• v.14 no.4
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• pp.329-334
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• 2023

Organic ferroelectric material found vast application in a verity of engineering and health technology fields. In the present study, we investigated the application of the deformable organic ferroelectric in motion measurement and improving performance in tennis players. Flexible ferroelectric material P(VDF-TrFE) could be used in wearable motion sensors in tennis player transferring velocity and acceleration data to collecting devises for analyzing the best pose and movements in tennis players to achieve best performances in terms of hitting ball and movement across the tennis court. In doing so, ferroelectric-based wearable sensors are used in four different locations on the player body to analyze the movement and also a sensor on the tennis ball to record the velocity and acceleration. In addition, poses of tennis players were analyzed to find out the best pose to achieve best acceleration and movement. The results indicated that organic ferroelectric-based sensors could be used effectively in sensing motion of tennis player which could be utilized in the optimization of posing and ball hitting in the real games.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1735-1754
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• 2022

The reliability and performance of WiFi need optimization because of the rising number of WiFi users day by day. A highlighted point is saving power while transmitting and receiving packets using WiFi devices. Wake-on-Wlan (WoW) is also implemented to improve energy consumption, but it also needs betterment. This paper will introduce universal ideas to transmit and receive packets using low-power technology like Bluetooth or BLE (Bluetooth low energy). While looking for power-saving ways in this research, WiFi connection and maintenance also take care using lesser power-consuming technology. Identifying different use-cases where low power technology can help save energy and maintain 802.11 connection is part of the research. In addition, the proposed method discuss energy saving with unicast and broadcast/multicast data. Calculation of power-saving and comparison with standalone WiFi usage clearly shows the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.182-183
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• 2022

In this study, compared and analyzed various loss function minimization techniques to present a methodology for developing a natural intelligence-based prediction system. As a result of the analysis, He Initialization was the best with RMSE: 3.78, R2: 0.94, and the error rate was 6%. However, it is considered desirable to construct a prediction system by combining each technique for optimization.

• Annual Conference of KIPS
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• 2022.05a
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• pp.610-613
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• 2022

센서 데이터의 중요성이 커지면서 센서 데이터 처리 연구의 수요가 증가하고 있다. 센서 데이터 기반의 딥러닝 모델 개발 시, 센서 데이터 단일 값에 의한 출력이 아닌 시계열적인 특성을 반영하여 연속적인 데이터 간의 연관성을 파악할 수 있는 슬라이딩 윈도우 기법을 통해 효율적으로 데이터를 분석하고 처리할 수 있다. 하지만, 기존의 방법들은 학습 성능(학습 시간 및 모델 성능)에 미치는 영향을 평가하는 기준 없이 입력 데이터의 윈도우 사이즈를 임의로 설정하여 데이터를 처리해 왔다. 따라서, 본 논문은 학습 시간과 모델 성능을 기준으로 센서 데이터의 윈도우 사이즈 최적화 기법을 제안한다. 제안한 방법은 전류를 이용하여 스위치와 다이오드 온도를 추정하는 가상 센서(virtual sensor) 실험 테스트베드에 적용하여, 학습 시간 중심으로는 5%의 윈도우 사이즈를, 모델 성능 중심으로는 R2 SCORE 의 값을 0.9295 로 갖는 8%의 윈도우 사이즈가 최적으로 도출되었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.858-859
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• 2022

본 논문에서는 프린지 패턴을 생성하는 딥러닝 기반의 WGAN-GP 네트워크의 최적화 방법을 제안한다. 기존의 복소 프린지 패턴 생성을 위한 GAN 모델은 생성의 정확도뿐만 아니라 학습의 안정성이 다소 부족하였다. 이에 따라 WGAN-GP 등의 업그레이드 된 방법을 사용하였지만, 네트워크 구조 및 파라미터에 따른 최적화가 필요하다. 보다 정확도 높은 정확도를 가진 프린지 패턴 생성을 위해 learning rate decay 사용하여 학습된 결과를 epoch 별 그래프로 최적화 전의 결과와 비교하고, 홀로그램과 복원 결과에 대한 PSNR 을 비교한다.