• Title/Summary/Keyword: module performance

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Analysis of Performance of Building Integrated PV System into Cold Facade (건물일체형 Cold Facade PV 시스템의 성능 분석)

  • Kim, Hyun-Il;Kang, Gi-Hwan;Park, Kyung-Eun;Yu, Gwon-Jong;Suh, Seung-Jik
    • Proceedings of the KIEE Conference
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    • 2008.07a
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    • pp.1104-1105
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    • 2008
  • This paper presents the assesment of experimented data and estimated data for electrical and thermal performance evaluation of building integrated photovoltaic(BIPV) system of cold facade type. BIPV module is used to estimate the dependence of module temperature on irradiance, ambient temperature and indoor temperature. The module temperature of no free ventilated facade PV system is higher than cold facade PV system about 13.4$^{\circ}C$. By the results on simulation, the reduction of electrical power loss is 9.57% into cold facade according to free ventilation. The annual averaged PR of BIPV system into cold facade is about 73.1%.

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A Novel IGBT inverter module for low-power drive applications (소용량 전동기 구동용 새로운 IGBT 인버터 모듈)

  • Kim M. K.;Jang K. Y.;Choo B. H.;Lee J. B.;Suh B. S.;Kim T. H.
    • Proceedings of the KIPE Conference
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    • 2002.07a
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    • pp.158-162
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    • 2002
  • This paper presents a novel 3-phase IGBT module called the SPM (Smart Power Module). This is a new design developed to provide a very compact, low cost, high performance and reliable motor drive system. Several distinct design concepts were used to achieve the highly integrated functionality in a new cost-effective small package. An overall description to the SPM is given and actual application issues such as electrical characteristics, circuit configurations, thermal performance and power ratings are discussed

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Conceptual Design of Production Planning and Scheduling Module to Improve Delivery Quality

  • Choi, Jung-Sang
    • Journal of the military operations research society of Korea
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    • v.29 no.1
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    • pp.112-119
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    • 2003
  • The procedure and implementation of an simulation based production planning and scheduling system. Production planning and scheduling is important problem in manufacturing field. It is so important for delivery quality as well as productivity, too. In this paper, heuristic production planning and scheduling module for improving delivery quality and productivity will be discussed. Beginning with total demand and initial work in process, the algorithms for production scheduling and planning can efficiently generate a feasible production resources and capacity schedule results in high resource utilization, minimum number of the late orders and reduced labor variability. The algorithm is executed to achieve the best on time delivery performance. The developed heuristic algorithms in the module will be expected to provide the better delivery performance and productivity.

A performance comparison of heat sink using FEM in the natural convection (자연대류에서 유한요소법을 이용한 히트싱크의 성능비교)

  • Lee, Min;Lee, Chun-Kyu
    • Design & Manufacturing
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    • v.12 no.1
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    • pp.31-35
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    • 2018
  • The peltier thermoelectric module are used to cool the heat generated by electronic equipment. In order to increase the efficiency of the peltier thermoelectric module, the heat must be released to the outside. A heat sink is used to discharge such heat to the outside. in this paper, two types of heat sinks with internal tunnels were designed. And the heating and cooling performance of the heat sink with internal tunnel structure was compared and analyzed through ANSYS. The heat sink of the A type had better heat transfer than the heat sink of the B type. Which is about 70% improved.

Applied Spherical Lens with Reflect Mirror for the CPV module (반사판 적용 구형렌즈를 갖는 집광형 태양전지모듈)

  • Lee, Kang-Yeon;Jeong, Byeong-Ho;Kim, Hyo-Jin
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.25 no.11
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    • pp.83-90
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    • 2011
  • There are two main types of concentrating optical systems in use today: refractive types that use Fresnel lenses, and reflective systems that use one or more mirrors. Regardless of the chosen optical system, the result is concentrated sunlight being aimed at the sensitive face of the cell, to produce more energy from less photovoltaic material. In this paper, for the achieve trackerless CPV system, CPV module included that the spherical lens with reflect mirror makes it possible to achieve high and stable power generation performance for the high concentration photovoltaic power generation system and cope with the needs for a variety of shapes and sizes in flexible manners and that the multiple cavity assemble method greatly reduces costs. Development of these high performance multi-junction CPV module promises to accelerate growth in photovoltaic power generation.

Performance Analysis of Cooling Module using Peltier Elements (펠티어 소자를 이용한 냉방모듈 성능해석)

  • Han, Cheolheui
    • Journal of Institute of Convergence Technology
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    • v.1 no.1
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    • pp.5-8
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    • 2011
  • Thermal analysis of a cooling module using Peltier elements are performed using a commercial software, CFD-ACE+. A standard k-e two-equation turbulent model is applied in order to represent the turbulent shear stress. Computed values are compared with the theoretical values for the validation. The effect of mass flow rates and transferred heat amounts on the temperature distributions inside the cooling system is analyzed. It was found that the increase in the mass flow rates causes the exit temperature rise. The increase in the absorbed heat amount diminished the overall temperature on the fin surfaces. In the present analysis, the material characteristics of the Peltier element itself are not considered. In the future, the effect of the turbulence models and material characteristics will be studied in detail.

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Predicting Defect-Prone Software Module Using GA-SVM (GA-SVM을 이용한 결함 경향이 있는 소프트웨어 모듈 예측)

  • Kim, Young-Ok;Kwon, Ki-Tae
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.1
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    • pp.1-6
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    • 2013
  • For predicting defect-prone module in software, SVM classifier showed good performance in a previous research. But there are disadvantages that SVM parameter should be chosen differently for every kernel, and algorithm should be performed iteratively for predict results of changed parameter. Therefore, we find these parameters using Genetic Algorithm and compare with result of classification by Backpropagation Algorithm. As a result, the performance of GA-SVM model is better.

DEVELOPMENT OF COMPUTER SOFTWARE FOR CALCULATION OF VOLUMETRIC ERROR MAP IN 3 AXIS CMMs

  • Park, H.;M.Burdekin;G.Peggs
    • Proceedings of the Korean Society for Technology of Plasticity Conference
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    • 1992.03a
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    • pp.131-158
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    • 1992
  • Verification, calibration, and compensation are becoming more essential elements for manufacture and maintenance of high performance CMMs. A computer module of volumetric error generation has been developed to calculate volumetric errors (random as well as systematic) from measured parametric errors, accepting most types of CMMs in current use. New transformation rules have been derived to transform all the parametric errors with respect to the origin of working volume considered, then incorporated, then incorporated into the module of error calculation. Two cases of practical CMMs are tested with the developed module, and showed good performance.

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A Three-scale Pedestrian Detection Method based on Refinement Module (Refinement Module 기반 Three-Scale 보행자 검출 기법)

  • Kyungmin Jung;Sooyong Park;Hyun Lee
    • IEMEK Journal of Embedded Systems and Applications
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    • v.18 no.5
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    • pp.259-265
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    • 2023
  • Pedestrian detection is used to effectively detect pedestrians in various situations based on deep learning. Pedestrian detection has difficulty detecting pedestrians due to problems such as camera performance, pedestrian description, height, and occlusion. Even in the same pedestrian, performance in detecting them can differ according to the height of the pedestrian. The height of general pedestrians encompasses various scales, such as those of infants, adolescents, and adults, so when the model is applied to one group, the extraction of data becomes inaccurate. Therefore, this study proposed a pedestrian detection method that fine-tunes the pedestrian area by Refining Layer and Feature Concatenation to consider various heights of pedestrians. Through this, the score and location value for the pedestrian area were finely adjusted. Experiments on four types of test data demonstrate that the proposed model achieves 2-5% higher average precision (AP) compared to Faster R-CNN and DRPN.

Improving Performance of Recommendation Systems Using Topic Modeling (사용자 관심 이슈 분석을 통한 추천시스템 성능 향상 방안)

  • Choi, Seongi;Hyun, Yoonjin;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.21 no.3
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    • pp.101-116
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    • 2015
  • Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.