• Title/Summary/Keyword: Weighted Average Model

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An Efficient Comparing and Updating Method of Rights Management Information for Integrated Public Domain Image Search Engine

  • Kim, Il-Hwan;Hong, Deok-Gi;Kim, Jae-Keun;Kim, Young-Mo;Kim, Seok-Yoon
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.1
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    • pp.57-65
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    • 2019
  • In this paper, we propose a Rights Management Information(RMI) expression systems for individual sites are integrated and the performance evaluation is performed to find out an efficient comparing and updating method of RMI through various image feature point search techniques. In addition, we proposed a weighted scoring model for both public domain sites and posts in order to use the most latest RMI based on reliable data. To solve problem that most public domain sites are exposed to copyright infringement by providing inconsistent RMI(Rights Management Information) expression system and non-up-to-date RMI information. The weighted scoring model proposed in this paper makes it possible to use the latest RMI for duplicated images that have been verified through the performance evaluation experiments of SIFT and CNN techniques and to improve the accuracy when applied to search engines. In addition, there is an advantage in providing users with accurate original public domain images and their RMI from the search engine even when some modified public domain images are searched by users.

Inference Models for Tidal Flat Elevation and Sediment Grain Size: A Preliminary Approach on Tidal Flat Macrobenthic Community

  • Yoo, Jae-Won;Hwang, In-Seo;Hong, Jae-Sang
    • Ocean Science Journal
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    • v.42 no.2
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    • pp.69-79
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    • 2007
  • A vertical transect with 4 km length was established for the macrofaunal survey on the Chokchon macrotidal flat in Kyeonggi Bay, Incheon, Korea, 1994. Tidal elevation (m) and sediment mean grain size $(\phi)$ were inversely predicted by the transfer functions from the faunal assemblages. Three methods: weighted average using optimum value (WA), tolerance weighted version of the weighted average (WAT) and maximum likelihood calibration (MLC) were employed. Estimates of tidal elevation and mean grain size obtained by using the three different methods showed positively corresponding trends with the observations. The estimates of MLC were found to have the minimum value of sum of squares due to errors (SSE). When applied to the previous data $(1990\sim1992)$, each of three inference models exhibited high predictive power. This result implied there are visible relationships between species composition and faunas' critical environmental factors. Although a potential significance of the two major abiotic factors was re-affirmed, a weak tendency of biological interaction was detected from faunal distribution patterns across the flat. In comparison to the spatial and temporal patterns of the estimates, it was suggested that sediment characteristics were the primary factors regulating the distribution of macrofaunal assemblages, rather than tidal elevation, and the species composition may be sensitively determined by minute changes in substratum properties on a tidal flat.

The Study of Prediction Model of Gas Accidents Using Time Series Analysis (시계열 분석을 이용한 가스사고 발생 예측 연구)

  • Lee, Su-Kyung;Hur, Young-Taeg;Shin, Dong-Il;Song, Dong-Woo;Kim, Ki-Sung
    • Journal of the Korean Institute of Gas
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    • v.18 no.1
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    • pp.8-16
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    • 2014
  • In this study, the number of gas accidents prediction model was suggested by analyzing the gas accidents occurred in Korea. In order to predict the number of gas accidents, simple moving average method (3, 4, 5 period), weighted average method and exponential smoothing method were applied. Study results of the sum of mean-square error acquired by the models of moving average method for 4 periods and weighted moving average method showed the highest value of 44.4 and 43 respectively. By developing the number of gas accidents prediction model, it could be actively utilized for gas accident prevention activities.

The model of the weighted proportion estimation for forecasting the number of population (인구추계를 위한 가중비례추정모형)

  • Yoon, Yong Hwa;Kim, Jong Tae
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.2
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    • pp.311-320
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    • 2013
  • The purpose of this paper is to suggest the methods of forecasting the numbers of students. The generalized weighted proportion estimation models are suggested and used for forecasting the numbers of student until 2029. The results of the Monte Carlo simulation show that the suggested method is powerful for the forecasting. In conclusion, the numbers of the third grade high-school students will be less than the numbers of college admission quota from 2019.

Two Techniques of Angle-of-Arrival Estimation for Low-Data-Rate UWB Wireless Positioning (저속 초광대역 방식의 무선 측위에 알맞은 신호 도착 방향 추정 기법 두 가지)

  • Lee, Yong-Up;Lim, Kyeong-Sun;Park, Joo-Hyeon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37 no.3A
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    • pp.163-171
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    • 2012
  • The signal model and weighted-average based estimation techniques are proposed to estimate the angle-of-arrival (AOA) parameters of multiple clusters for a low data rate ultrawide band (LR-UWB) based wireless positioning system. It is observed that the weighted-average based AOA estimation technique gives an optimal AOA estimate under few clusters condition, and the average based AOA estimation technique gives a correct AOA estimate under many clusters condition through computer simulation. Also, we can observe that the variance estimation error decreases as SNR increases, and the proposed techniques are superior to the conventional technique from the viewpoint of performance.

Data Pattern Estimation with Movement of the Center of Gravity

  • Ahn Tae-Chon;Jang Kyung-Won;Shin Dong-Du;Kang Hak-Soo;Yoon Yang-Woong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.3
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    • pp.210-216
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    • 2006
  • In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

SHORT-TERM WIND SPEED FORECAST BASED ON ARMA MODEL IN JEJU ISLAND (제주도에서의 ARMA 모델을 기반으로한 단기 풍속 예측)

  • Do, Duy Phuong N.;Lim, Jintaek;Lee, Yeonchan;Oh, Ungjin;Choi, Jaeseok
    • Proceedings of the KIEE Conference
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    • 2015.07a
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    • pp.329-330
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    • 2015
  • From the results of previous my paper [10] in 2015 year of economic and electrical power storage research conference in Naju, this paper describes an application of autoregressive and moving average (ARMA) model to forecast hourly average wind speed (HAWS) in Jeju island. The models are used to build up short-term forecast of hourly average wind speed by the weighted sum of previous wind speed values.

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Decentralized Moving Average Filtering with Uncertainties

  • Song, Il Young
    • Journal of Sensor Science and Technology
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    • v.25 no.6
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    • pp.418-422
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    • 2016
  • A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

Merging Radar Rainfalls of Single and Dual-polarization Radar to Improve the Accuracy of Quantitative Precipitation Estimation (정량적 강우강도 정확도 향상을 위한 단일편파와 이중편파레이더 강수량 합성)

  • Lee, Jae-Kyoung;Kim, Ji-Hyeon;Park, Hye-Sook;Suk, Mi-Kyung
    • Atmosphere
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    • v.24 no.3
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    • pp.365-378
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    • 2014
  • The limits of S-band dual-polarization radars in Korea are not reflected on the recent weather forecasts of Korea Meteorological Administration and furthermore, they are only utilized for rainfall estimations and hydrometeor classification researches. Therefore, this study applied four merging methods [SA (Simple Average), WA (Weighted Average), SSE (Sum of Squared Error), TV (Time-varying mergence)] to the QPE (Quantitative Precipitation Estimation) model [called RAR (Radar-AWS Rainfall) calculation system] using single-polarization radars and S-band dual-polarization radar in order to improve the accuracy of the rainfall estimation of the RAR calculation system. As a result, the merging results of the WA and SSE methods, which are assigned different weights due to the accuracy of the individual model, performed better than the popular merging method, the SA (Simple Average) method. In particular, the results of TVWA (Time-Varying WA) and TVSSE (Time-Varying SSE), which were weighted differently due to the time-varying model error and standard deviation, were superior to the WA and SSE. Among of all the merging methods, the accuracy of the TVWA merging results showed the best performance. Therefore, merging the rainfalls from the RAR calculation system and S-band dual-polarization radar using the merging method proposed by this study enables to improve the accuracy of the quantitative rainfall estimation of the RAR calculation system. Moreover, this study is worthy of the fundamental research on the active utilization of dual-polarization radar for weather forecasts.