• 상하수도학회지
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• 제19권2호
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• pp.144-148
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• 2005

The industrial development of large scale deep bed filters has been a very important step in the process of drinking water production and more recently in the tertiary treatment of wastewater. The target of deep bed filtration is the retention is the retention of small particles generally smaller than 30 microns at relatively small concentration, generally less than 30 mg/l from natural water (surface water or aquifers) or secondary treated wastewater. The relation between the retention efficiency and the characteristics of the particles has been extensively studied experimentally and through different models of retention. During the last years the development of new technologies (fiber filter, membrane modules) lead to more intensive processes compared to conventional sand filtration. Fiber filters can combine intensification with a decrease in specific energy needed however they cannot be operated under gravity like sand filters. Membrane filters (UF or MF) are much more intensive and efficient than sand filters. The specific energy needed is not so high (about $0.1Kwh/M^3$) but is higher than sand or fiber filter. A Life Cycle Analysis (LCA) has to be made for a complete comparison between these technologies taking in account that the efficiency of particle retention obtained by membrane filters is unique.

• 국제물리치료학회지
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• 제10권4호
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• pp.1873-1878
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• 2019

Background: Walking is a complex activity. The main components of walking include balance, coordination, and symmetrical posture. The characteristics of walking patterns of stroke patients include slow walking, measured by gait cycle and walking speed. This is an important factor that reflects post-stroke quality of life and walking ability. Objective: This study aimed to examine the effect of deep lumbar muscle stabilization exercise on the spatiotemporal walking ability of stroke patients. Design: Quasi-experial study Methods: The experiment was conducted 5 times per week for 4 weeks, with 30 minutes per session, on 10 subjects in the experimental group who performed the deep lumbar muscle stabilization exercise and 10 subjects in the control group who performed a regular exercise. Variables that represent the spatiotemporal walking ability (step length, stride length, step rate, and walking speed) were measured using GAITRrite before and after the experiment and were analyzed. Results: There was a significant difference in the pre- and post-exercise spatiotemporal walking ability between the two groups (p<.05). Furthermore, there was a significant difference in the step rate and walking speed between the two groups (p<.05). Conclusions: Deep lumbar muscle stabilization exercise is effective in improving the walking ability of stroke patients. Therefore, its application will help improve the spatiotemporal walking ability of stroke patients.

• 한국전자통신학회논문지
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• 제18권6호
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• pp.1151-1156
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• 2023

본 논문은 초기 리튬이온 배터리의 충·방전 데이터를 활용하여 리튬이온 배터리의 잔존 수명을 예측할 수 있는 딥러닝 모델을 제시한다. PNP(Positive and Negative Perceptron) 모델을 사용하여 DMP(Deep learning Model using PNP model)를 구축하였으며, DMP의 성능을 증명하기 위해 LSTM 모델을 사용하여 DML(Deep learning Model using LSTM model)을 구성하였다. DMP와 DML의 리튬이온 배터리의 잔존 수명 예측 성능을 비교하며, 오차 측정 방법은 RMSE(Root Mean Square Error)와 RMSPE(Root Mean Square Percentage Error)이다. 시험 데이터로 오차를 측정한 결과 DMP와 DML의 RMSE 차이는 144.62[Cycle]이며, RMSPE 차이는 3.37[%]로 DMP의 오차가 낮게 측정되었다. 이를 통해 우리는 DMP의 성능이 높은 것으로 증명하였으며, 이는 리튬이온 배터리 분야에서 PNP 모델이 LSTM 모델보다 성능이 뛰어남을 나타내었다.

• 열처리공학회지
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• 제7권4호
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• pp.307-317
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• 1994

The surf ace microstructure modification by $N^+$ ion implantation into 7050Al alloy and its low cycle fatigue behavior were investigated. Ion implantation method is to physically implant accelerated ions to the surface of a substrate. High dose of nitrogen($5{\times}10^{17}ions/cm^2$) were implanted into 7050Al alloy using current density of accellerating voltage of 100KeV. The implanted layers were characterized by Electron Probe-Micro Analysis(EPMA), Auger Elecron Spectroscopy(AES), X-Ray Diffraction(XRD), X-Ray Photoelectron Spectroscopy(XPS), and Transmission Electron Microscopy(TEM). The experimental results were compared with computer simulation data. It was shown that AlN was formed to 4500 ${\AA}$ deep. The low cycle fatigue life of the $N^4$ion modified material was prolonged by about three times the unimplanted one. The improved low cycle fatigue life was attributed to the formation of AlN and the damaged region on the surface by $N^+$ ion implantation.

• Carbon letters
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• 제25권
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• pp.84-88
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• 2018

At present, an important research area is the search for materials that are compatible with CMOS technology and achieve a satisfactory response rate and modulation efficiency. A strong local field of graphene surface plasmon polariton (SPP) can increase the interaction between light and graphene, reduce device size, and facilitate the integration of materials with CMOS. In this study, we design a new modulator of SPP-based cycle branch graphene waveguide. The structure comprises a primary waveguide of graphene-$LiNbO_3$-graphene, and a secondary cycle branch waveguide is etched on the surface of $LiNbO_3$. Part of the incident light in the primary waveguide enters the secondary waveguide, thus leading to a phase difference with the primary waveguide as reflected at the end of the branch and interaction coupling to enhance output light intensity. Through feature analysis, we discover that the area of the secondary waveguide shows significant localized fields and SPPs. Moreover, the cycle branch graphene waveguide can realize gain compensation, reduce transmission loss, and increase transmission distance. Numerical simulations show that the minimum effective mode field area is about $0.0130{\lambda}^2$, the gain coefficient is about $700cm^{-1}$, and the quality factor can reach 150. The structure can realize the mode field limits of deep subwavelength and achieve a good comprehensive performance.

• 대한임베디드공학회논문지
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• 제17권4호
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• pp.217-228
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• 2022

Currently, there are increasing demands for applying deep neural networks (DNNs) in the embedded domain such as classification and object detection. The DNN processing in embedded domain often requires custom hardware such as NPU for acceleration due to the constraints in power, performance, and area. Processing DNN models requires a large amount of data, and its seamless transfer to NPU is crucial for performance. In this paper, we developed a cycle-accurate NPU simulator to evaluate diverse NPU microarchitectures. In addition, we propose a novel technique for reducing the number of memory accesses when processing convolutional layers in convolutional neural networks (CNNs) on the NPU. The main idea is to reuse data with memory interleaving, which recycles the overlapping data between previous and current input windows. Data memory interleaving makes it possible to quickly read consecutive data in unaligned locations. We implemented the proposed technique to the cycle-accurate NPU simulator and measured the performance with LeNet-5, VGGNet-16, and ResNet-50. The experiment shows up to 2.08x speedup in processing one convolutional layer, compared to the baseline.

• Journal of Advanced Marine Engineering and Technology
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• 제34권4호
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• pp.470-476
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• 2010

발전소 온배수 및 폐열을 이용한 1 MW급 폐쇄형 해양온도차발전 사이클에 대한 성능을 비교 분석하였다. 폐쇄형 해양온도차발전 사이클에 대한 열역학적 모델은 랭킨 사이클이고, 기화기 증발 열원으로 발전소 온배수를 이용하여 사이클 효율, 기화기 및 응축기 열량 등 사이클 성능을 비교 분석하였다. 발전소 온배수 온도가 증가함에 따라 기화기 내 증발 포화압력은 상승하게 되고 그로 인해 사이클 효율은 증가하였고, 총 출력 1 MW에 필요한 기화기 및 응축기 용량은 감소하였다. 따라서 발전소 온배수는 폐쇄형 해양온도차발전에서 주요한 열원으로 사용될 수 있음을 알 수 있었다. 또한, 주위 이용 가능한 폐열이 있을 때 기화기 출구 작동유체와 열교환시켜 터빈으로 유입되는 작동유체의 온도를 상승시킨다면 사이클 효율은 크게 증가할 것이다.

• 방사성폐기물학회지
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• 제10권1호
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• pp.55-62
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• 2012

고준위폐기물 처분과 관련하여, 최근 저장소 형태의 처분장 개념에 대한 대안으로 검토되고 있는 시추공 처분 개념에 대한 연구 현황을 정리하고 시추공 처분 개념의 국내 적용 가능성과 필요한 연구 항목에 대해 논의하였다. 현재 미국과 스웨덴을 중심으로 논의된 시추공 처분 개념은 심부시추공을 설치하여 지하 3 - 5km 구간에 고준위폐기물을 처분하는 것을 의미하며, 현재까지의 연구 결과에 의하면 이 처분 개념은 심부지하수의 층상구조, 작은 규모의 지표시설 등으로 인해 처분 및 비용 효율이 클 것으로 예상된다. 이에 반해 국내에는 축적된 심부 지질 자료가 없어 적용 가능성에 대한 논의할 여지가 없다. 이에 저장소 형태의 처분장 개념에 대한 대안으로 시추공 처분 개념을 검토하기 위해서는 향후 심지층 자료 확보, 공학적 방벽 연구, 수치모의모델 개발, 처분 기술 개발 등의 연구가 필요하다.

• 방사성폐기물학회지
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• 제18권spc호
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• pp.21-36
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• 2020

With respect to spent nuclear fuels, disposal containers and bentonite buffer blocks in deep geological disposal systems are the primary engineered barrier elements that are required to isolate radioactive toxicity for a long period of time and delay the leakage of radio nuclides such that they do not affect human and natural environments. Therefore, the thermal stability of the bentonite buffer and structural integrity of the disposal container are essential factors for maintaining the safety of a deep geological disposal system. The most important requirement in the design of such a system involves ensuring that the temperature of the buffer does not exceed 100℃ because of the decay heat emitted from high-level wastes loaded in the disposal container. In addition, the disposal containers should maintain structural integrity under loads, such as hydraulic pressure, at an underground depth of 500 m and swelling pressure of the bentonite buffer. In this study, we analyzed the thermal stability and structural integrity in a deep geological disposal environment of the improved deep geological disposal systems for domestic light-water and heavy-water reactor types of spent nuclear fuels, which were considered to be subject to direct disposal. The results of the thermal stability and structural integrity assessments indicated that the improved disposal systems for each type of spent nuclear fuel satisfied the temperature limit requirement (< 100℃) of the disposal system, and the disposal containers were observed to maintain their integrity with a safety ratio of 2.0 or higher in the environment of deep disposal.

• 한국측량학회지
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• 제40권3호
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• pp.177-185
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• 2022

본 연구에서는 딥러닝을 통해 고해상도 광학 위성영상에 동종센서로 촬영한 영상을 참조하여 폐색 영역을 복원하는 방법을 제안하였다. 패치 단위로 분할된 영상에서 원본 영상의 화소 분포를 최대한 유지하며 폐색 영역을 모의한 영상과 주변 영상의 자연스러운 연속성을 위해 ConvNeXt 블록을 적용한 CycleGAN (Cycle Generative Adversarial Network) 방법을 사용하여 실험을 진행하였고 이를 3개의 실험지역에 대해 분석하였다. 또한, 학습패치 크기를 512*512화소로 하는 경우와 2배 확장한 1024*1024화소 크기의 적용 결과도 비교하였다. 서로 특징이 다른 3개의 지역에 대하여 실험한 결과, ConvNeXt CycleGAN 방법론이 기존의 CycleGAN을 적용한 영상, Histogram matching 영상과 비교하여 개선된 R² 값을 보여줌을 확인하였다. 학습에 사용되는 패치 크기별 실험의 경우 1024*1024화소의 패치를 사용한 결과, 약 0.98의 R²값이 산출되었으며 영상밴드별 화소 분포를 비교한 결과에서도 큰 패치 크기로 학습한 모의 결과가 원본 영상과 더 유사한 히스토그램 분포를 나타내었다. 이를 통해, 기존의 CycleGAN을 적용한 영상 및 Histogram matching 영상보다 발전된 ConvNeXt CycleGAN을 사용할 때 원본영상과 유사한 모의 결과를 도출할 수 있었고, 성공적인 모의를 수행할 수 있음을 확인하였다.