• Current Optics and Photonics
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• v.2 no.5
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• pp.443-447
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• 2018

We propose a diode-pumped cesium laser using frequency locking of a pump laser that can effectively increase the maximum output power of the cesium laser. We simultaneously monitored the absorption spectrum of cesium and the laser output power, and the frequency of pump laser was locked at the center of the $D_2$ absorption line of the cesium atom to obtain an effective gain enhancement. Using this scheme, we have achieved output power increase of ~0.1 W compared to when frequency locking was not applied. Furthermore, by optimizing the temperature of the cesium cell and the reflectivity of the output coupler, we successfully achieved an output power of 1.4 W using the pump power of 2.9 W, providing a slope efficiency of 61.5% and optical-to-optical efficiency of 49%.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.54-64
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• 2021

Electric vehicles use lithium-ion battery packs as the power supply, where the batteries are connected in series or parallel. The temperature control of each battery is essential to ensure a consistent overall temperature. This study focused on reducing ohmic heating caused by batteries to realize a uniform battery temperature. The battery spacing was optimized to improve air cooling, and the tilt angle between the batteries was varied to optimize the internal structure of the batterypack. Simulations were performed to evaluate the effects of these parameters, and the results showed that the optimal scheme effectively achieved a uniform battery temperature under a constant power discharge. These findings can contribute to future research on cooling methods for battery packs.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.443-444
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• 2019

효율적인 이미지 검색을 위해 고차원 데이터 색인에 대한 연구가 진행되고 있다. 거리 기반 색인 구조는 다차원 데이터를 색인하는데 자주 활용되는데, k-최근접 질의 처리에서 초기 탐색 범위를 전체 영역의 1%만으로 결정한다. 본 논문에서는 거리 기반 색인구조에서 k-최근접 질의를 효율적으로 처리하기 위해 데이터 분포 기반의 최적화 및 질의 처리 비용 기반 최적화 기법을 제안한다.

• Current Applied Physics
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• v.18 no.11
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• pp.1441-1446
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• 2018

We demonstrate an acousto-optic mode converter based on a tapered optical fiber to efficiently generate orbital angular momentum states of light. In our scheme an acoustic wave is deployed to the waist of tapered optical fiber where two degenerate $HE_{21}$ modes leading to +1 and -1 orbital angular momentum eigen-modes are resonantly excited. The excitation of $TM_{01}$ and $TE_{01}$ modes is suppressed by enlarging the intermodal index difference between near-degenerate spatial modes. Numerical calculation for optimization of the taper diameter is provided. The experimental characterization of generated states is performed by analyzing the output far-field pattern and the spatial interference fringes with a uniform reference beam.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.1-8
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• 2022

Supporting real-time flows with delay and throughput constraints is an important challenge for future wireless networks. In this paper, we develop an optimal scheduling scheme to optimally choose the packets to transmit. The optimal transmission strategy is based on an observable Markov decision process. The novelty of the work focuses on a priority-based probabilistic packet scheduling strategy for efficient packet transmission. This helps in providing guaranteed services to real time traffic in Heterogeneous Wireless Networks. The proposed scheduling mechanism is able to optimize the desired performance. The proposed scheduler improves the overall end-to-end delay, decreases the packet loss ratio, and reduces blocking probability even in the case of congested network.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.156-162
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• 2022

Image compression plays an important role in encoding and improving various forms of images in the digital era. Recent researches have focused on the principle of deep learning as one of the most exciting machine learning methods to show that it is good scheme to analyze, classify and compress images. Various neural networks are able to adapt for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks and convolution neural networks. In this review paper, we discussed how to apply the rule of deep learning to obtain better image compression with high accuracy, low loss-ness and high visibility of the image. For those results in performance, deep learning methods are required on justified manner with distinct analysis.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.113-117
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• 2021

The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

• ETRI Journal
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• v.44 no.6
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• pp.936-944
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• 2022

The mobile network allows redundant transmission via disjoint paths to support high-reliability communication (e.g., ultrareliable and low-latency communications [URLLC]). Although redundant transmission can improve communication reliability, it also increases network costs (e.g., traffic and control overhead). In this study, we propose a reliability-guaranteed multipath allocation algorithm (RG-MAA) that allocates appropriate paths by considering the path setup time and dynamicity of the reliability paths. We develop an optimization problem using a constrained Markov decision process (CMDP) to minimize network costs while ensuring the required communication reliability. The evaluation results show that RG-MAA can reduce network costs by up to 30% compared with the scheme that uses all possible paths while ensuring the required communication reliability.

• 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.