• Title/Summary/Keyword: Estimation of Technology values

검색결과 518건 처리시간 0.026초

다양한 크기를 갖는 입자들의 유체 용기 내부에서의 침전에 대한 수치적 접근방법의 검증 (VALIDATION OF NUMERICAL APPROACH FOR THE SEDIMENT OF MULTI-SIZE PARTICLES IN A FLUID CONTAINER)

  • 지영무;최상민
    • 한국전산유체공학회지
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    • 제18권2호
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    • pp.93-98
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    • 2013
  • In this paper, we reported the verification of numerical simulation approach for sedimentation of the multi-size particles in a container. The comparison between experimentally measured values and numerically evaluated values on settle down process of fully mixed mixture is carried out. In an attempt to represent the natural particle size distribution, various diameters of single particles are simulated and the results are compared with the outcome of the multi-size computation. When the empirical formula for mean particle size estimation is adopted to define the sediment diameter, computation and comparison are conducted.

DVB-S2 ACM 시스템을 위한 효율적인 채널 예측 및 패킷 스케줄링 기법 (Efficient Channel Estimation and Packet Scheduling Scheme for DVB-S2 ACM Systems)

  • 강동배;박만규;장대익;오덕길
    • 한국위성정보통신학회논문지
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    • 제7권1호
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    • pp.65-74
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    • 2012
  • 다양한 유형의 사용자 패킷들이 다중화 되어 사용자 단말(RCST, Return Channel via Satellite Terminal)들에게 전달되는 위성 포워드링크에서 패킷들 간의 중요도에 따른 QoS 보장은 매우 중요하다. 특히 강우와 같은 기상상태에 따라 무선 채널 상태가 변화하는 위성망의 경우에는 가용 대역폭을 고려하면서 QoS가 보장 되도록 패킷을 처리해야 한다. DVB-S2에서는 강우감쇠에 대한 대책으로서 적응형 부호 및 변조(Adaptive Coding and Modulation, ACM) 기법을 사용하여 전송효율을 증가시킨다. 하지만 위성링크의 전송 지연시간이 크기 때문에, RCST들이 피드백 하는 리턴채널 데이터를 기반으로 전송모드를 결정하는 ACM 기법을 효율적으로 적용하기 위해서는 채널 예측 알고리즘이 필요하다. 이에 본 논문에서는 RCST가 피드백 하는 강우감쇠 값 데이터와 미리 저장된 참조데이터를 이용하여 위성 링크의 채널을 예측하는 알고리즘 및 QoS와 공평성을 동시에 보장하는 스케줄링 기법을 제안한다. 성능평가 결과 제안하는 알고리즘이 채널을 정확히 예측함은 물론 QoS를 보장하면서 동시에 각 RCST들 간의 사용 대역폭의 공평성을 지원함을 보였다.

Fluctuation of estimates in an EM procedure

  • Kim, Seong-Ho;Kim, Sung-Ho
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2003년도 춘계 학술발표회 논문집
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    • pp.157-162
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    • 2003
  • Estimates from an EM algorithm are somewhat sensitive to the initial values for the estimates, and it is more likely when the model becomes larger and more complicated. In this article, we examined how the estimates fluctuate during an EM procedure for a recursive model of categorical variables. It is found that the fluctuation takes place mostly during the first half of the procedure and that it can be subdued by applying the Bayesian method of estimation. Both simulation data and real data are used for illustration.

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The Study of the Stray Load Loss and Mechanical Loss of Three Phase Induction Motor considering Experimental Results

  • Kim, Dong-Jun;Choi, Jae-Hak;Chun, Yon-Do;Koo, Dae-Hyun;Han, Pil-Wan
    • Journal of Electrical Engineering and Technology
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    • 제9권1호
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    • pp.121-126
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    • 2014
  • The accurate determination of induction motor efficiency depends on the estimation of the five losses of stator and rotor copper loss, iron loss, mechanical loss and stray load loss. As the mechanical and stray load losses are not calculated by electro-magnetic analysis, the values of these two losses are very important in induction motor design. In this paper, the values of mechanical loss and stray load loss are proposed through investigating testing data from commercial products of three phase induction motors under 37kW. If the values of this paper are applied to motor design, the accuracy of design and analysis can be improved. The losses of motors are obtained by using load and no-load test results following IEC 60034-2-1 standard.

불완전 검정일 기록이 RRTDM을 이용한 홀스타인 젖소의 유전평가에 미치는 영향 (The Effect of the Incomplete Lactation Records for Genetic Evaluations with Random Regression Test-Day Models (RRTDM) in Holstein Cattle)

  • 조주현;조광현;이광전
    • Journal of Animal Science and Technology
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    • 제47권2호
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    • pp.147-158
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    • 2005
  • The purpose of this study was to find out the effects that daughters' incomplete lactation records affect sire's breeding values through genetic evaluation using RRTDM(random regression test-day model). First, we estimated genetic parameters and breeding values on sires having complete lactation records of daughter by RRTDM, second, we changed complete lactation records of specific sires into incomplete records by various methods. Third, the breeding values were compared between complete and incomplete records. Finally, this study aimed to find out the methods to minimize the estimation errors of young bulls' breeding values. Data used in this study were collected from the dairy herd improvement program, and a total of 97,562 records were composed of 10,929 first parity with both parents known, since 1999. Breeding values on the daughters from randomly chosen sires were calculated and compared with among 90 day, 150day, and 200 day's incomplete records. For milk yields, sire's ranks of breeding values used by complete lactation records were very different from sire's ranks of breeding values obtained by incomplete lactation records(Rank_90 cut, 150cut, 200 cut).The differences were also obtained between complete lactation records(per305_full) and incomplete lactation record (per_90 cut, 150cut, 200 cut) in breeding values regarding persistency. Especially, the differences between per_90 cut and per305_full were very large(from 1.8 kg to 145kg).

인공지능 접근방법에 의한 S/W 공수예측 (Software Effort Estimation Using Artificial Intelligence Approaches)

  • 전응섭
    • 한국IT서비스학회:학술대회논문집
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    • 한국IT서비스학회 2003년도 추계학술대회
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    • pp.616-623
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    • 2003
  • Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However if we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

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Theoretical Limits Analysis of Indoor Positioning System Using Visible Light and Image Sensor

  • Zhao, Xiang;Lin, Jiming
    • ETRI Journal
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    • 제38권3호
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    • pp.560-567
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    • 2016
  • To solve the problem of parameter optimization in image sensor-based visible light positioning systems, theoretical limits for both the location and the azimuth angle of the image sensor receiver (ISR) are calculated. In the case of a typical indoor scenario, maximum likelihood estimations for both the location and the azimuth angle of the ISR are first deduced. The Cramer-Rao Lower Bound (CRLB) is then derived, under the condition that the observation values of the image points are affected by white Gaussian noise. For typical parameters of LEDs and image sensors, simulation results show that accurate estimates for both the location and azimuth angle can be achieved, with positioning errors usually on the order of centimeters and azimuth angle errors being less than $1^{\circ}$. The estimation accuracy depends on the focal length of the lens and on the pixel size and frame rate of the ISR, as well as on the number of transmitters used.

안내 로봇을 향한 관람객의 행위 인식 기반 관심도 추정 (Estimating Interest Levels based on Visitor Behavior Recognition Towards a Guide Robot)

  • 이예준;김주현;정의정;김민규
    • 로봇학회논문지
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    • 제18권4호
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    • pp.463-471
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    • 2023
  • This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.

Battery State Estimation Algorithm for High-Capacity Lithium Secondary Battery for EVs Considering Temperature Change Characteristics

  • Park, Jinho;Lee, Byoungkuk;Jung, Do-Yang;Kim, Dong-Hee
    • Journal of Electrical Engineering and Technology
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    • 제13권5호
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    • pp.1927-1934
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    • 2018
  • In this paper, we studied the state of charge (SOC) estimation algorithm of a high-capacity lithium secondary battery for electric vehicles (EVs) considering temperature characteristics. Nonlinear characteristics of high-capacity lithium secondary batteries are represented by differential equations in the mathematical form and expressed by the state space equation through battery modeling to extract the characteristic parameters of the lithium secondary battery. Charging and discharging equipment were used to perform characteristic tests for the extraction of parameters of lithium secondary batteries at various temperatures. An extended Kalman filter (EKF) algorithm, a state observer, was used to estimate the state of the battery. The battery capacity and internal resistance of the high-capacity lithium secondary battery were investigated through battery modeling. The proposed modeling was applied to the battery pack for EVs to estimate the state of the battery. We confirmed the feasibility of the proposed study by comparing the estimated SOC values and the SOC values from the experiment. The proposed method using the EKF is expected to be highly applicable in estimating the state of the high-capacity rechargeable lithium battery pack for electric vehicles.

ESTIMATION OF THE POWER PEAKING FACTOR IN A NUCLEAR REACTOR USING SUPPORT VECTOR MACHINES AND UNCERTAINTY ANALYSIS

  • Bae, In-Ho;Na, Man-Gyun;Lee, Yoon-Joon;Park, Goon-Cherl
    • Nuclear Engineering and Technology
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    • 제41권9호
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    • pp.1181-1190
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    • 2009
  • Knowing more about the Local Power Density (LPD) at the hottest part of a nuclear reactor core can provide more important information than knowledge of the LPD at any other position. The LPD at the hottest part needs to be estimated accurately in order to prevent the fuel rod from melting in a nuclear reactor. Support Vector Machines (SVMs) have successfully been applied in classification and regression problems. Therefore, in this paper, the power peaking factor, which is defined as the highest LPD to the average power density in a reactor core, was estimated by SVMs which use numerous measured signals of the reactor coolant system. The SVM models were developed by using a training data set and validated by an independent test data set. The SVM models' uncertainty was analyzed by using 100 sampled training data sets and verification data sets. The prediction intervals were very small, which means that the predicted values were very accurate. The predicted values were then applied to the first fuel cycle of the Yonggwang Nuclear Power Plant Unit 3. The root mean squared error was approximately 0.15%, which is accurate enough for use in LPD monitoring and for core protection that uses LPD estimation.