• 한국정보통신학회논문지
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• 제25권11호
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• pp.1545-1550
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• 2021

다위상 분해 기법 기반의 부밴드 구조에서 단순화한 부밴드 인접투사 알고리즘(Simplified SAP; SSAP)을 위한 최적 가변 스텝사이즈 조정 방법을 제안한다. 제안한 방법은 부밴드 적응필터의 계수 갱신 시점에서 평균자승편차(MSD)를 최소화하도록 유도된 최적값을 제시한다. 유색 입력 신호를 사용하는 SSAP 알고리즘에서 제안한 최적 스텝사이즈의 적용은 빠른 수렴속도와 작은 정상상태오차를 보장한다. AR(2) 신호와 실제 음성을 입력 신호로 사용하여 수행한 컴퓨터 모의실험의 결과는 제안한 최적 스텝사이즈의 유효성을 입증한다. 또한 모의실험 결과는 기존 여러 적응 알고리즘과 비교하여 제안한 알고리즘이 더 빠른 수렴속도와 양호한 정상상태오차를 가지고 있음을 보인다.

• ETRI Journal
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• 제32권2호
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• pp.273-280
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• 2010

The overall channel capacity of a multihop wireless path drops progressively over each hop due to the cascading effect of noise and interference. Hence, without optimal rate adaptation, the video quality is expected to degrade significantly at any client located at a far-edge of an ad-hoc network. To overcome this limitation, decoding and forwarding (DF), which fully decodes codewords at each intermediate node, can be employed to provide the best video quality. However, complexity and memory usage for DF are significantly high. Consequently, we propose syndrome-based partial decoding (SPD). In the SPD framework an intermediate node partially decodes a codeword and relays the packet along with its syndromes if the packet is corrupted. We demonstrate the efficacy of the proposed scheme by simulations using actual 802.11b wireless traces. The trace-driven simulations show that the proposed SPD framework, which reduces the overall processing requirements of intermediate nodes, provides reasonably high goodput when compared to simple forwarding and less complexity and memory requirements when compared to DF.

• Nuclear Engineering and Technology
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• 제53권6호
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• pp.1970-1978
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• 2021

The aim of this study was to optimize the target, moderator, and collimator (TMC) in a neutron beam generator for the accelerator-based BNCT (A-BNCT) system. The optimization employed the Monte Carlo Neutron and Photon (MCNP) simulation. The optimal geometry for the target was decided as the one with the highest neutron flux among nominates, which were called as angled, rib, and tube in this study. The moderator was optimized in terms of consisting material to produce appropriate neutron energy distribution for the treatment. The optimization of the collimator, which wrapped around the target, was carried out by deciding the material to effectively prevent the leakage radiations. As results, characteristic of the neutron beam from the optimized TMC was compared to the recommendation by the International Atomic Energy Agent (IAEA). The tube type target showed the highest neutron flux among nominates. The optimal material for the moderator and collimator were combination of Fluental (Al₂0₃+AlF₃) with ⁶⁰Ni filter and lead, respectively. The optimized TMC satisfied the IAEA recommendations such as the minimum production rate of epithermal neutrons from thermal neutrons: that was 2.5 times higher. The results can be used as source terms for shielding designs of treatment rooms.

• 웰빙융합연구
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• 제7권3호
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• pp.1-11
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• 2024

Purpose: The purpose of this study is to present an efficient emission reduction ratio of plastic to reduce carbon dioxide, the main cause of greenhouse gases. Research design, data and methodology: This study calculated the absolute value of carbon dioxide by setting an equation through the emission coefficient using the US EPA's WARM model. Results: In the recycling ratio of 70%, it was found that the energy recovery ratio was 15.6%, which was the energy recovery ratio without generating carbon dioxide. When carbon dioxide is generated by changing plastic waste emissions, optimal efficiency is achieved by reducing emissions by 10% to 30% of energy recovery ratio, 20% to 50% of energy recovery ratio, and 30% to 80% or more of energy recovery ratio. Conclusions: The recycling rate should be set at a minimum of 70%, so that a carbon dioxide-free energy recovery rate could be obtained during the recycling process, supporting an eco-friendly basis for environmental policies aimed at this rate. In addition, it was possible to suggest that it is essential to reduce emissions by at least 30% for eco-friendly recycling measures that can achieve both economic and environmental feasibility in the energy recovery process through incineration during recycling in Korea.

• 한국산업융합학회 논문집
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• 제26권6_2호
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• pp.1163-1169
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• 2023

In this study, the flow characteristics of the filter system were analyzed due to the effect of the density of the filter in the triple filter system. Flow analysis was performed as a flow passing through a porous medium. The flow characteristics of each filter system were analyzed by arranging filters with different densities in the forward flow flow and the reverse flow. The arrangement order of the triple filters was excellent in the case of forward fluid flow and in the case of higher density from the inside to the outside filter. In the reverse flow filter system, the performance of the system was the best in the case of reverse order filter arrangement. As a result of the analysis, Case II, which showed a pressure drop rate of 5.65% for forward flow, was the best in the reverse direction with a pressure drop rate of 14.25%. Considering reverse and forward flows, it was found that the optimal filter arrangement was most effective when the intermediate filter was the densest, and the inner or outer filter was less dense.

• 한국전기전자재료학회논문지
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• 제29권9호
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• pp.552-558
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• 2016

Goethite, ${\alpha}$-FeOOH have various applications such as absorbent, pigment and source for magnetic materials. Goethite particles were synthesized in a two step process, where $Fe(OH)_2$ were synthesized in nitrogen atmosphere using $FeSO_4$ as a raw material in the first process, and after that acicular goethite particles were obtained in an air oxidation process of $Fe(OH)_2$ in highly alkaline aqueous solution. Their phase and microstructure were investigated with XRD and FE-SEM. It was found that the morphology of goethite and the ratio of length-to-width (aspect ratio) of acicular goethite are dependent on the some factors such as R value ($OH^-/Fe^{2+}$), air flow rate and pH conditions. In particular, R value has the strongest influence on the synthesized goethite morphology. It is considered that the optimal value R is 4.5 because X-ray diffraction peaks of goethite have the highest intensity at that value. Morphology of goethite particles was controlled by air flow rates, showing that their size and aspect ratio are getting smaller and decrease, respectively as air flow rate increases. The largest goethite particle obtained is about 1,500 nm in length and 150 nm in diameter.

• 융합신호처리학회논문지
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• 제21권3호
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• pp.148-153
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• 2020

합성곱 신경망 모형에서 높은 정확도를 얻기 위해서는 최적의 하이퍼파라미터를 설정하는 작업이 필요하다. 하지만 높은 성능을 낼 수 있는 하이퍼파라미터 값이 정확히 알려진 바가 없으며, 자료마다 최적의 하이퍼파라미터 값이 달라질 수 있기 때문에 매번 실험을 통해서 찾아야만 한다. 또한, 하이퍼파라미터 값들의 범위가 넓고 조합 수가 많기 때문에 시간과 계산량을 줄이기 위해서는 최적값을 찾기 위한 실험 계획을 먼저 한 후에 탐색을 하는 것이 필요하다. 그러나 아직까지 합성곱 신경망 모형에서 하이퍼파라미터 최적화를 위하여 실험계획법을 이용한 연구 결과가 보고되지 않았다. 본 논문에서는 이미지 분류 문제에서 통계방법 중 하나인 실험계획법의 요인배치법을 이용하여 실험 계획을 하고 합성곱 신경망 분석을 한 후에, 높은 성능을 갖는 값을 중심으로 그리드 탐색을 하여 최적의 하이퍼파라미터를 찾는 방법을 제안한다. 실험 계획을 통하여 각 하이퍼파라미터들의 탐색 범위를 줄인 후에 그리드 탐색을 함으로써 효율적으로 연산량을 줄이고 정확도를 높힐 수 있음을 보였다. 또한 실험 결과에서 모형 성능에 가장 큰 영향을 주는 하이퍼파라미터가 학습률이라는 것을 확인할 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.543-548
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• 2007

We present a dynamic Bayesian network (DBN) model of a generalized class of nonstationary birth-death processes. The model includes birth and death rate parameters that are randomly selected from a known discrete set of values. We present an on-line algorithm to obtain optimal estimates of the parameters. We provide a simulation of real-time characterization of load traffic estimation using our DBN approach.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.230-235
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• 2022

This study is to select an optimal object detection algorithm for designing a self-checkout counter to improve the inconvenience of payment systems for products without existing barcodes. To this end, a performance comparison analysis of YOLO v2, Tiny YOLO v2, and the latest YOLO v5 among deep learning-based object detection algorithms was performed to derive results. In this paper, performance comparison was conducted by forming learning data as an example of 'donut' in a bakery store, and the performance result of YOLO v5 was the highest at 96.9% of mAP. Therefore, YOLO v5 was selected as the artificial intelligence object detection algorithm to be applied in this paper. As a result of performance analysis, when the optimal threshold was set for each donut, the precision and reproduction rate of all donuts exceeded 0.85, and the majority of donuts showed excellent recognition performance of 0.90 or more. We expect that the results of this paper will be helpful as the fundamental data for the development of an automatic payment system using AI self-service technology that is highly usable in the non-face-to-face era.

• International Journal of Internet, Broadcasting and Communication
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• 제12권3호
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• pp.125-130
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• 2020

At the end of 1997, the volatility of the exchange rate intensified as the nation's exchange rate system was converted into a free-floating exchange rate system. As a result, managing the exchange rate is becoming a very important task, and the need for forecasting the exchange rate is growing. The exchange rate prediction model using the existing exchange rate prediction method, statistical technique, cannot find a nonlinear pattern of the time series variable, and it is difficult to analyze the time series with the variability cluster phenomenon. And as the number of variables to be analyzed increases, the number of parameters to be estimated increases, and it is not easy to interpret the meaning of the estimated coefficients. Accordingly, the exchange rate prediction model using artificial neural network, rather than statistical technique, is presented. Using DNN, which is the basis of deep learning among artificial neural networks, and LSTM, a recurrent neural network model, the number of hidden layers, neurons, and activation function changes of each model found the optimal exchange rate prediction model. The study found that although there were model differences, LSTM models performed better than DNN models and performed best when the activation function was Tanh.