• Title/Summary/Keyword: Hierarchical Function

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Classification of Bodytype on Adult Male for the Apparel Sizing System (Part 4) -Bodytype of Lower Part of Trunk from the Photographic Data- (남성복의 치수규격을 위한 체형 분류(제4보) -사진 자료에 의한 하체부의 분류-)

  • 김구자
    • Journal of the Korean Society of Clothing and Textiles
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    • v.20 no.6
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    • pp.1062-1070
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    • 1996
  • Concept of the comfort and fitness has become a major concern in the basic function of the ready-made clothes. Until now, ready-made clothes were not made by on the basis of the bodytype, but by the body size only. This research was performed to classify and characterize the bodytypes of Korean adult males. Sample size was 1290 subjects and their age range was from 19 to 54 years old. 15 variables from the photographic data of 1112 subjects were applied to analyse the bodytype of th\ulcorner lower part of trunk. Data were analyzed by the multivariate method, especially factor and cluster analysis. The groups forming a cluster can be subdivided into 5 sets by crosstabulation extracted by the hierarchical cluster analysis. 5 bodytypes classified by the photographic sources could be combined with the anthropcmetric data and were demonstrated with 5 silhouette. Type 2 and 3 in the lower part of trunk were dominant and were composed of the majority of 56.8% of the subjects. Bodytypes of Korean males were influenced by the degree of posture erectness and of curvature of the front side of the body in waist and abdomen.

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Classification of Bodytype on Adult Male for the Apparel Sizing System (I) - Bodytype of Trunk from the Anthropometric Data - (남성복(男性服)의 치수규격을 위한 체형분류(I) - 직접계측자료에 의한 동체부의 분류 -)

  • Kim, Ku Ja;Lee, Soon Weon
    • Journal of the Korean Society of Clothing and Textiles
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    • v.17 no.2
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    • pp.281-289
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    • 1993
  • Concept of the comfort and fitness becomes a major concern in the basic function of the ready-made clothes. Accordingly a more sophiscated classification of the human morphological characteristics is strongly required for the effective clothing construction. This research was performed to classify and characterize Korean adult males anthropometrically. Sample size was 1290 subjects and their age range was from 19 to 54 years old. Sampling was carried out by the stratified sampling method. Data were collected by the direct anthropometric measurement. 75 variables in total were applied to classify the bodytypes. Data were analyzed by the multivariate method, especially factor and cluster analysis. The high factor loading items extracted by factor analysis were based to determine the variables of the cluster analysis for the similar bodytypes respectively. In the part of the trunk, 19 variables from the data were applied to classify the bodytypes of trunk by Ward's minimum variance method. The groups forming a cluster were subdivided into 5 sets by cross-tabulation extracted by the hierarchical culster analysis. Type 3 and 4 in trunk were composed of the majority of 55.6% of the subjects. The Korean adult males had relatively well-balanced bodytypes in trunk.

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Clustering properties and halo occupation of Lyman-break galaxies at z ~ 4

  • Park, Jaehong;Kim, Han-Seek;Wyithe, Stuart B.;Lacey, Cedric G.;Baugh, Carlton M.
    • The Bulletin of The Korean Astronomical Society
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    • v.40 no.1
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    • pp.59.3-60
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    • 2015
  • We investigate the clustering properties of Lyman-break galaxies (LBGs) at z ~ 4. Using the hierarchical galaxy formation model GALFORM, we predict the angular correlation function (ACF) of LBGs and compare this with the measured ACF from combined survey fields consisting of the Hubble eXtreme Deep Field (XDF) and CANDELS. We find that the predicted ACF is in a good agreement with the measured ACFs. However, when we divide the model LBGs into bright and faint subset, the predicted ACFs are less consistent with observations. We quantify the dependence of clustering on luminosity and show that the fraction of satellite LBGs is important for determining the amplitude of ACF at small scales. We find that central LBGs predominantly reside in ${\sim}10^{11}h^{-1}M_{solar}$ haloes and satellites reside in haloes of mass ${\sim}10^{12}-10^{13}h^{-1}M_{solar}$. The model predicts fewer bright satellite LBGs than is inferred from the observation. LBGs in the tails of the redshift distribution contribute significant additional clustering signal, especially on small scales. This spurious clustering may affect the interpretation of the halo occupation distribution, including the minimum halo mass and abundance of satellite LBGs.

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Study on Internet of Things Based Low-Power Wireless Sensor Network System for Wild Vegetation Communities Ecological Monitoring (야생식생군락 생태계 모니터링을 위한 사물인터넷 기반의 저전력 무선 센서네트워크 시스템에 관한 연구)

  • Kim, Nae-Soo;Lee, Kyeseon;Ryu, Jaehong
    • Journal of Information Technology Services
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    • v.14 no.1
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    • pp.159-173
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    • 2015
  • This paper presents a study on the Internet of Things based low-power wireless sensor networks for remote monitoring of wildlife ecosystem due to climate change. Especially, it is targeting the wild vegetation communities ecological monitoring. First, we performed a pre-test and analysis for selecting the appropriate frequency for the sensor network to collect and deliver information reliably in harsh propagation environment of the forest area, and selected for sensors for monitoring wild vegetation communities on the basis of considerations for selecting the best sensor. In addition, we have presented the platform concept and hierarchical function structures for effectively monitoring, analyzing and predicting of ecosystem changes, to apply the Internet of Things in the ecological monitoring area. Based on this, this paper presents the system architecture and design of the Internet of Things based low-power wireless sensor networks for monitoring the ecosystem of the wild vegetation communities. Finally, we constructed and operated the test-bed applied to real wild trees, using the developed prototype based on the design.

Work Domain Analysis Based on Abstraction Hierarchy: Modelling Concept and Principles for Its Application (추상화계층에 기반한 작업영역분석의 모델링 개념 및 적용 원칙)

  • Ham, Dong-Han
    • Journal of the Korea Safety Management & Science
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    • v.15 no.3
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    • pp.133-141
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    • 2013
  • As a work analysis technique, Work Domain Analysis (WDA) aims to identify the design knowledge structure of a work domain that human operators interact with through human-system interfaces. Abstraction hierarchy (AH) is a multi-level, hierarchical knowledge representation framework for modeling the functional structure of any kinds of systems. Thus, WDA based on AH aims to identify the functional knowledge structure of a work domain. AH has been used in a range of work domains and problems to model their functional knowledge structure and has proven its generality and usefulness. However, many of researchers and system designers have reported that it is never easy to understand the concepts underlying AH and use it effectively for WDA. This would be because WDA is a form of work analysis that is different from other types of work analysis techniques such as task analysis and AH has several unique characteristics that are differentiated from other types of function analysis techniques used in systems engineering. With this issue in mind, this paper introduces the concepts of WDA based on AH and offers a comprehensive list of references. Next, this paper proposes a set of principles for effectively applying AH for work domain analysis, which are developed based on the author's experiences, consultation with experts, and literature reviews.

A novel approach for the design of multi-class reentrant manufacturing systems

  • Yoo, Dong-Joon;Jung, Jae-Hak;Lee, In-Beum;Lee, Euy-Soo;Yi, Gyeong-beom
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.710-715
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    • 2004
  • The design problem of manufacturing system is addressed, adopting the closed queueing network model with multiple loops and re-entrant flows. The entire design problem is divided into two hierarchical sub-problems of (1) determining the station configuration and (2) optimizing the lot constitution; then they are tackled by neighbor search algorithm (NSA) and greedy mean value analysis (GMVA), respectively. Unlike the conventional MVA concerning multi-class closed queueing networks, the GMVA doesn't stick to a fixed lot proportion; rather it tries to find the optimal balance. The NSA, on the other hand, improves the object function value by altering the station configuration successively with its superior neighbor. The moderate time complexity, presented in big-${o}$ notation, enables us to apply the method even to the large-size practical cases, and the CPU time of an enlarged problem can be approximated by the same equation. The validity of our analytic approach is backed up by simulation studies with a widespread simulation package.

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Adaptive Importance Channel Selection for Perceptual Image Compression

  • He, Yifan;Li, Feng;Bai, Huihui;Zhao, Yao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.9
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    • pp.3823-3840
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    • 2020
  • Recently, auto-encoder has emerged as the most popular method in convolutional neural network (CNN) based image compression and has achieved impressive performance. In the traditional auto-encoder based image compression model, the encoder simply sends the features of last layer to the decoder, which cannot allocate bits over different spatial regions in an efficient way. Besides, these methods do not fully exploit the contextual information under different receptive fields for better reconstruction performance. In this paper, to solve these issues, a novel auto-encoder model is designed for image compression, which can effectively transmit the hierarchical features of the encoder to the decoder. Specifically, we first propose an adaptive bit-allocation strategy, which can adaptively select an importance channel. Then, we conduct the multiply operation on the generated importance mask and the features of the last layer in our proposed encoder to achieve efficient bit allocation. Moreover, we present an additional novel perceptual loss function for more accurate image details. Extensive experiments demonstrated that the proposed model can achieve significant superiority compared with JPEG and JPEG2000 both in both subjective and objective quality. Besides, our model shows better performance than the state-of-the-art convolutional neural network (CNN)-based image compression methods in terms of PSNR.

A Study on Real Time Pitch Alteration of Speech Signal (음성신호의 실시간 피치변경에 관한 연구)

  • 김종국;박형빈;배명진
    • The Journal of the Acoustical Society of Korea
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    • v.23 no.1
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    • pp.82-89
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    • 2004
  • This paper describes how to reduce the effect of an occupation threshold by that the transform of mixture components of HMM parameters is controlled in hierarchical tree structure to prevent from over-adaptation. To reduce correlations between data elements and to remove elements with less variance, we employ PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible, and decline the effect of over-adaptation. When we set lower occupation threshold and increase the number of transformation function, ordinary WLLR adaptation algorithm represents lower recognition rate than SI models, whereas the proposed MLLR adaptation algorithm represents the improvement of over 2% for the word recognition rate as compared to performance of SI models.

A Study on Simplification of Machine Learning Model (기계학습 모델의 간략화 방법에 대한 연구)

  • Lee, Gye-Sung;Kim, In-Kook
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.16 no.4
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    • pp.147-152
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    • 2016
  • One of major issues in machine learning that extracts and acquires knowledge implicit in data is to find an appropriate way of representing it. Knowledge can be represented by a number of structures such as networks, trees, lists, and rules. The differences among these exist not only in their structures but also in effectiveness of the models for their problem solving capability. In this paper, we propose partition utility as a criterion function for clustering that can lead to simplification of the model and thus avoid overfitting problem. In addition, a heuristic is proposed as a way to construct balanced hierarchical models.

A Novel Multi-view Face Detection Method Based on Improved Real Adaboost Algorithm

  • Xu, Wenkai;Lee, Eung-Joo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.11
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    • pp.2720-2736
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    • 2013
  • Multi-view face detection has become an active area for research in the last few years. In this paper, a novel multi-view human face detection algorithm based on improved real Adaboost is presented. Real Adaboost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. After that, we proved that the function of sample weight adjusting method and weak classifier training method is to guarantee the independence of weak classifiers. A coarse-to-fine hierarchical face detector combining the high efficiency of Haar feature with pose estimation phase based on our real Adaboost algorithm is proposed. This algorithm reduces training time cost greatly compared with classical real Adaboost algorithm. In addition, it speeds up strong classifier converging and reduces the number of weak classifiers. For frontal face detection, the experiments on MIT+CMU frontal face test set result a 96.4% correct rate with 528 false alarms; for multi-view face in real time test set result a 94.7 % correct rate. The experimental results verified the effectiveness of the proposed approach.