• 제목/요약/키워드: hierarchical data

검색결과 3,012건 처리시간 0.035초

주거비용에 영향을 미치는 요소 분석: 시스템다이내믹스 계수추정을 위한 다층모형과 회귀모형의 비교 (Determinants of Housing Cost: Hierarchical Linear Model for Estimating Coefficients of a Hosing System Dynamics Model)

  • 강명구
    • 한국시스템다이내믹스연구
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    • 제8권2호
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    • pp.253-273
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    • 2007
  • To measure the effect of school zone on housing cost, Linear Regression Model is widely used, and school zone is known as a key determinant of housing cost in Korea. However, when the Hierarchical Linear Model (HLM) is applied with the same data, school effect on housing cost becomes statistically non-significant. It is because HLM effectively separates the effect of individual housing's attributes from the group effect. In sum, the housing cost of Kangnam, where good public schools are located, is apparently is higher than that of Kangbuk. However, the school effect on housing cost (Level 2) becomes non-significant when individual housing's attributes (Level 1) are controlled with HLM.

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목적지의 가족집합을 이용한 향상된 ZigBee 라우팅 알고리즘 (Enhanced Routing Algorithm for ZigBee using a Family Set of a Destination Node)

  • 신현재;안세영;조영준;안순신
    • 전기학회논문지
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    • 제59권12호
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    • pp.2329-2336
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    • 2010
  • Hierarchical tree routing is a inefficient routing method of transmitting data in a wireless sensor network. Zigbee routing which is made to improve inefficiency of the hierarchical tree routing only fulfills the tree routing when a destination node don't exists in neighbor nodes of a router. We suggest a TFSR algorithm that is improved more than the zigbee routing. The TFSR algorithm generates a family set included a parent node and child nodes and over of a destination node, and uses this information. According to simulation results, the TFSR algorithm reduce routing costs over 30 percent in comparison with the hierarchical tree routing and the zigbee routing.

개인의 자기조절 인지전략과 공교육 수업제도가 학업성취에 미치는 효과 : 위계적 선형모형의 적용 (Effects of Individual Self-Regulated Cognitive Strategies and Public Education on Academic Achievement : Application of the Hierarchical Linear Model)

  • 이주리
    • 아동학회지
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    • 제30권4호
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    • pp.87-97
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    • 2009
  • This study used Hierarchical Linear Modeling analysis to investigate the effects of individual self-regulated cognitive strategies and public education on middle school students' academic achievement. Participants were 6389 (boys 3287, girls 3102) middle school students from the 2005 data of the Korea Education Longitudinal Study. Results were as follows : (1) there were significant differences among different schools in middle school students' academic achievement, i.e. 20% of variance in English achievement and 15% of variance in mathematics achievement were explained by school differences. (2) Students' elaboration and meta-cognitive strategy influenced academic achievement positively. (3) Predictor variables by ability grouping, supplementary class, and/or self-learning class had no significant effects on students' academic achievement.

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계층적 군집분석방법을 활용한 건물 부하의 전력수요예측 (Load Forecasting using Hierarchical Clustering Method for Building)

  • 황혜미;이성희;박종배;박용기;손성용
    • 전기학회논문지
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    • 제64권1호
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    • pp.41-47
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    • 2015
  • In recent years, energy supply cases to take advantage of EMS(Energy Management System) are increasing according to high interest of energy efficiency. The important factor for essential and economical EMS operation is the supply and demand plan the hourly power demand of building load using the hierarchical clustering method of variety statistical techniques, and use the real historical data of target load. Also the estimated results of study are obtained the reliability through separate tests of validity.

계층적 피라미드 구조와 DCT 영역에서의 분류 벡터 양지기를 이용한 점진적 영상전송 (Progressive Image Transmission Using Hierarchical Pyramid Structure and Classified Vector Quantizer in DCT Domain)

  • 박섭형;이상욱
    • 대한전자공학회논문지
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    • 제26권8호
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    • pp.1227-1237
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    • 1989
  • In this paper, we propose a lossless progressive image transmission scheme using hierarchical pyramid structure and classified vector quantizer in DCT domain. By adopting DCT to the hierarchical pyramid signals, we can reduce the spatial redundance. Moreover, the DCT coefficients can be encoded efficiently by using classified vector quantizer in DCT domain. The classifier is simply based on the variance of a subblock. Also, the mirror set of training set of images can improve the robustness of codebooks. Progressive image transmission can be achieved through following processes: from top to bottom level of planes in a pyramid, and from high to low AC variance class in a plane. Some simulation results with real images show that the proposed coding scheme yields a good performance at below 0.3 bpp and an excellent result at 0.409 bpp. The proposed coding scheme is well suited for lossless progressive image transmission as well as image data compression.

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저전송률 동영상 압축을 위한 새로운 계층적 움직임 추정기의 VLSI 구조 (A New VLSI Architecture of a Hierarchical Motion Estimator for Low Bit-rate Video Coding)

  • 이재헌;나종범
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 1999년도 하계종합학술대회 논문집
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    • pp.601-604
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    • 1999
  • We propose a new hierarchical motion estimator architecture that supports the advanced prediction mode of recent low bit-rate video coders such as H.263 and MPEG-4. In the proposed VLSI architecture, a basic searching unit (BSU) is commonly utilized for all hierarchical levels to make a systematic and small sized motion estimator. Since the memory bank of the proposed architecture provides scheduled data flow for calculating 8$\times$8 block-based sum of absolute difference (SAD), both a macroblock-based motion vector (MV) and four block-based MVs are simultaneously obtained for each macroblock in the advanced prediction mode. The proposed motion estimator gives similar coding performance compared with full search block matching algorithm (FSBMA) while achieving small size and satisfying the advanced prediction mode.

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Sire Evaluation of Count Traits with a Poisson-Gamma Hierarchical Generalized Linear Model

  • Lee, C.;Lee, Y.
    • Asian-Australasian Journal of Animal Sciences
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    • 제11권6호
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    • pp.642-647
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    • 1998
  • A Poisson error model as a generalized linear mixed model (GLMM) has been suggested for genetic analysis of counted observations. One of the assumptions in this model is the normality for random effects. Since this assumption is not always appropriate, a more flexible model is needed. For count traits, a Poisson hierarchical generalized linear model (HGLM) that does not require the normality for random effects was proposed. In this paper, a Poisson-Gamma HGLM was examined along with corresponding analytical methods. While a difficulty arises with Poisson GLMM in making inferences to the expected values of observations, it can be avoided with the Poisson-Gamma HGLM. A numerical example with simulated embryo yield data is presented.

An Efficient Scheduling Method for Grid Systems Based on a Hierarchical Stochastic Petri Net

  • Shojafar, Mohammad;Pooranian, Zahra;Abawajy, Jemal H.;Meybodi, Mohammad Reza
    • Journal of Computing Science and Engineering
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    • 제7권1호
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    • pp.44-52
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    • 2013
  • This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.

A Multi-Chain Based Hierarchical Topology Control Algorithm for Wireless Sensor Networks

  • Tang, Hong;Wang, Hui-Zhu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권9호
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    • pp.3468-3495
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    • 2015
  • In this paper, we present a multi-chain based hierarchical topology control algorithm (MCHTC) for wireless sensor networks. In this algorithm, the topology control process using static clustering is divided into sensing layer that is composed by sensor nodes and multi-hop data forwarding layer that is composed by leader nodes. The communication cost and residual energy of nodes are considered to organize nodes into a chain in each cluster, and leader nodes form a tree topology. Leader nodes are elected based on the residual energy and distance between themselves and the base station. Analysis and simulation results show that MCHTC outperforms LEACH, PEGASIS and IEEPB in terms of network lifetime, energy consumption and network energy balance.

Text Categorization for Authorship based on the Features of Lingual Conceptual Expression

  • Zhang, Quan;Zhang, Yun-liang;Yuan, Yi
    • 한국언어정보학회:학술대회논문집
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    • 한국언어정보학회 2007년도 정기학술대회
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    • pp.515-521
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    • 2007
  • The text categorization is an important field for the automatic text information processing. Moreover, the authorship identification of a text can be treated as a special text categorization. This paper adopts the conceptual primitives' expression based on the Hierarchical Network of Concepts (HNC) theory, which can describe the words meaning in hierarchical symbols, in order to avoid the sparse data shortcoming that is aroused by the natural language surface features in text categorization. The KNN algorithm is used as computing classification element. Then, the experiment has been done on the Chinese text authorship identification. The experiment result gives out that the processing mode that is put forward in this paper achieves high correct rate, so it is feasible for the text authorship identification.

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