• Journal of Korea Multimedia Society
• /
• v.14 no.9
• /
• pp.1165-1174
• /
• 2011

In data broadcasting systems, servers continuously disseminate data items through broadcast channels, and mobile client only needs to wait for the data of interest to present on a broadcast channel. However, because broadcast channels are shared by a large set of data items, the expected delay of receiving a desired data item may increase. This paper explores the issue of designing proper data allocation on multiple broadcast channels to minimize the average expected delay time of all data items, and proposes a new data allocation scheme named two level bin-packing(TLBP). This paper first introduces the theoretical lower-bound of the average expected delay, and determines the bin capacity based on this value. TLBP partitions all data items into a number of groups using bin-packing algorithm and allocates each group of data items on an individual channel. By employing bin-packing algorithm in two step, TLBP can reflect a variation of access probabilities among data items allocated on the same channel to the broadcast schedule, and thus enhance the performance. Simulation is performed to compare the performance of TLBP with three existing approaches. The simulation results show that TLBP outperforms others in terms of the average expected delay time at a reasonable execution overhead.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.637-639
• /
• 2021

인공지능 기반의 생활폐기물의 인식 및 선별에서, 선별 정확도의 저하는 인식 대상의 형태적 다양성과 학습데이터 부족 및 불균등성에 기인한다. 본 연구에서는 비전 인공지능 기반의 효과적인 폐기물 선별을 위한 인식 시스템 및 감독학습 기반의 인공지능 학습 기법을 제안한다. 생활폐기물 중 순환자원적 가치가 높은 CAN, PET, 그리고 이와 형상적으로 유사한 폐기물에 대해 본 연구에서 제안된 시스템에서 물체원형 및 훼손된 형태의 총 18 종 이미지 데이터를 대상으로, 감독학습기반의 인공지능 모델 제작에서 최적의 데이터 레이블링을 위한 분류체계를 제시한다.

• Progress in Medical Physics
• /
• v.22 no.3
• /
• pp.140-147
• /
• 2011

The purpose of this study was to estimate internal motion using molecular sieve for quantitative improvement of lung tumor and to localize lung tumor in the small animal PET image by evaluated data. Internal motion has been demonstrated in small animal lung region by molecular sieve contained radioactive substance. Molecular sieve for internal lung motion target was contained approximately 37 kBq Cu-64. The small animal PET images were obtained from Siemens Inveon scanner using external trigger system (BioVet). SD-Rat PET images were obtained at 60 min post injection of FDG 37 MBq/0.2 mL via tail vein for 20 min. Each line of response in the list-mode data was converted to sinogram gated frames (2~16 bin) by trigger signal obtained from BioVet. The sinogram data was reconstructed using OSEM 2D with 4 iterations. PET images were evaluated with count, SNR, FWHM from ROI drawn in the target region for quantitative tumor analysis. The size of molecular sieve motion target was $1.59{\times}2.50mm$. The reference motion target FWHM of vertical and horizontal was 2.91 mm and 1.43 mm, respectively. The vertical FWHM of static, 4 bin and 8 bin was 3.90 mm, 3.74 mm, and 3.16 mm, respectively. The horizontal FWHM of static, 4 bin and 8 bin was 2.21 mm, 2.06 mm, and 1.60 mm, respectively. Count of static, 4 bin, 8 bin, 12 bin and 16 bin was 4.10, 4.83, 5.59, 5.38, and 5.31, respectively. The SNR of static, 4 bin, 8 bin, 12 bin and 16 bin was 4.18, 4.05, 4.22, 3.89, and 3.58, respectively. The FWHM were improved in accordance with gate number increase. The count and SNR were not proportionately improve with gate number, but shown the highest value in specific bin number. We measured the optimal gate number what minimize the SNR loss and gain improved count when imaging lung tumor in small animal. The internal motion estimation provide localized tumor image and will be a useful method for organ motion prediction modeling without external motion monitoring system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.29 no.9
• /
• pp.463-471
• /
• 2017

According to the Korea industrial standard of air conditioning systems (KS C 9306), cooling and heating loads for buildings can be calculated by using maximum and minimum temperature in BIN data. Cooling and heating loads can be determined by building set temperature and ambient temperature. Cooling and heating system capacity of buildings can be normally designed according to determined heating and cooling loads. Cooling and heating system capacity can be reduced by updated BIN data, applying TAC (Technical Advisory Committee) values. In this study, updated BIN data have been analyzed using ambient temperature of 19 areas in Korea for the last 10 years (2005~2014) provided by KMA (Korea Meteorological Administration). Building cooling and heating loads have been calculated following TAC based BIN data. As a result, designed system capacity decreased depending on applying TAC. Those were reduced as 7.1% ($100m^2$ building), 8.7% ($1,000m^2$ building) in cooling capacity, 11.7% in heating capacity when TAC 2.5% applied. And also, it is expected system initial and operating cost by decreasing system capacity.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.1031-1033
• /
• 2020

최근 지구온난화에 따른 기온 및 강수량 증가 등으로 인해 모기 개체 수가 증가함에 따라 말라리아, 일본뇌염, 뎅기열 등 모기를 통해 전파되는 질병에 감염병의 위험률도 높아지고 있어 머신러닝기반 기후 데이터를 활용하여 모기 개체 수를 예측할 수 있는 모델을 제안하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.602-605
• /
• 2022

본 연구는 대량의 상권 데이터를 바탕으로 머신 러닝과 딥러닝 분석을 이용하여 최적의 상권 입지를 추천하는 시스템 개발을 목표로 한다. 자영업자들의 오프라인 창업에 있어 개개인의 매장 정보에 기반한 입지 조건 판단은 앞으로의 매출에 중요한 시작점이다. 따라서 상권 정보를 기반으로 미래 매출을 예측하여 최적의 상권 입지를 추천하는 기술이 필요하다. 이를 위해 기존에 선행된 다수의 회귀 기법과 더불어 강하게 편향된 데이터를 레이블링 하여 다중 분류 기법으로도 문제를 접근한다. 최종적으로 딥러닝 모델과 합성하여 더 높은 성능을 이끌어내고 이로부터 편향 데이터 처리 방법과 딥러닝 모델과의 앙상블 중요성에 대해 논의하고자 한다.

• Communications of the Korean Institute of Information Scientists and Engineers
• /
• v.31 no.10
• /
• pp.31-38
• /
• 2013

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.59 no.1
• /
• pp.36-44
• /
• 2017

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
• /
• v.17 no.1
• /
• pp.28-43
• /
• 1988

The purpose of this research is to produce bin weather data for Seoul from Standard Weather Data. The intended use of these data is for input to recently developed models for simplified energy calculations and for generating variable-base degree-day information. The data produced under this study include $3^{\circ}C$ bin data covering the full range of dry-bulb temperatures with mean coincident wet-bulb and daytime coincident solar radiation, wet-bulb bins down to freezing temperature, wind speed bins with prevailing directions, and heating and cooling degree hours to nine different temperature bases. All of these data are tabulated in six separate time periods and total daily categories for monthly and annual periods.

• Proceedings of KOSOMES biannual meeting
• /
• 2017.11a
• /
• pp.211-211
• /
• 2017