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검색결과 264건 처리시간 0.022초

Role of Landscape Architectural Space in Urban Culture;Perception of Mountains among Residents in Kohoku New Town in Japan

  • Furuya, Katsunori;Kumura, Yuko
    • 한국조경학회:학술대회논문집
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    • 한국조경학회 2007년도 Journal of Landscape Architecture in Asia Vol.3
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    • pp.94-98
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    • 2007
  • Mountains have been playing an important role in the Japanese culture. It is important from landscape planning perspectives to maintain mountains in the proximity of cities. In this thesis, the geographical perception of residents in Kohoku New Town has been studied. Geographical changes before and after the Kohoku New Town development were clarified first. Following these clarifications, interviews with residents who moved into the area before and after the development were conducted. In this investigation, the interviewees were asked about mountains, valleys, hills, and slopes; and the areas that they recognize on the map were then specified. From these results, the mountain area which residents recognize and the actual geographical features were compared. The geographical characteristics of the mountains that the residents recognize were revealed, and the perception of mountains was defined. Not only did geographical features and vegetation affect the perception of mountains, but also residents' personal experiences were important. As a result, new information for future geographical landscape planning has been obtained.

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적응 이진화를 이용한 지문인식 전처리에 관한 연구 (A Study on the Fingerprint Recognition Preprocessing using adaptive binary method)

  • 조성원;김재민
    • 한국지능시스템학회논문지
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    • 제12권3호
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    • pp.227-230
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    • 2002
  • 지문인식을 위한 중요한 전처리 과정중의 하나는 영상의 이진화 과정이다 이진화 과정은 그레이 레벨의 영상( gray scale input image)을 받아들여 이진의 영상(binary image)으로 만드는 것이다. 이진화 과정에 있어서의 어려운 점은 적절한 임계값(threshold value)을 찾는 것이다. 된 논문에서는 국부적인 융선과 골의 밝기의 특성에 따라 적절한 임계값을 선택하는 적응 이진화 방법을 제시한다. 실험을 통하여 게시된 방법은 기존의 방법과 비교하여 족은 성능을 보여주고 있음을 입증하였다.

다척도 지붕에지 검출방법을 이용한 지문영상의 전처리에 대한 연구 (A Study On Preprocessing of Fingerprint Image Using Multi-Scale Roof Edges)

  • 김수겸
    • Journal of Advanced Marine Engineering and Technology
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    • 제29권2호
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    • pp.217-224
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    • 2005
  • A new roof edge detection method based on multi level scales of wavelet function is proposed in this paper roof edge and its direction are obtained in this new methods at one time. Besides. scale characteristics of detecting roof edge is analyzed. And a few new methods on fingerprint image pre-processing are described. A method segmenting foreground/background of fingerprint images is proposed, in which Prior estimation of direction field is not required any more. A segmentation method based on multi-scale roof edges is implemented. and the valid scale range of the method is defined. too. And the method is used to segment ridges and valleys in fingerprint images simultaneously The exact direction fields made up of the direction of each point in ridges can be obtained when detecting ridges exactly based on the roof edge detector, in comparison with the traditional coarse estimation of direction fields. Obviously. it will establish a solid foundation for the sequent fingerprint identification.

SCALE MODEL EXPERIMENTS FOR ECHO PHENOMENA OF YINGYING PAGODA

  • Chen, Hsiao;Chen, Tong
    • 한국음향학회:학술대회논문집
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    • 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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    • pp.791-795
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    • 1994
  • In this paper, the echo phenomena of Yingying Pagoda(ancient Chinese architecture), which may be resulted from interferences of reflection and diffraction by the pagoda eaves when pulse sound source is at some suitable positions, are investigated by an 1:2 scale model. There are valleys in frequency spectrum due to the interferences. On the other hand, taking eaves as wedges approximately, numerical spectral estimates are obtained from the closed-form impulse solution for diffraction of pulse point-source radiation by an infinite rigid wedge. The results of the numerical computations are similar to those of the model experiments. The study is a helpful guide to reconstruction or maintenance of this kind of ancient buildings.

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특이점 추출을 통한 지형데이터의 빠른 삼각망 생성 (Fast Triangulation of Terrain Data through Unique Point Extraction)

  • 전경훈;구자영
    • 대한원격탐사학회지
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    • 제19권6호
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    • pp.457-464
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    • 2003
  • 불규칙 삼각망은 대표적으로 사용되는 지형 모델링 방법 가운데 하나이다. 이 방법은 적은 데이터 량으로 지형의 특징을 잘 표현할 수 있고, 렌더링 시간을 단축시킬 수 있다. 본 논문에서는 능선 검출 알고리즘을 이용하여 지형데이터로부터 능선과 계곡을 검출하고, 이를 불규칙 삼각망의 구성을 위한 정점들의 집합으로 사용함으로써 기존 방식과 거의 동등한 오차수준에서 삼각망의 구성시간을 현저하게 단축시키는 방법을 제안하고 있다.

Palmprint Verification Using Multi-scale Gradient Orientation Maps

  • Kim, Min-Ki
    • Journal of the Optical Society of Korea
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    • 제15권1호
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    • pp.15-21
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    • 2011
  • This paper proposes a new approach to palmprint verification based on the gradient, in which a palm image is considered to be a three-dimensional terrain. Principal lines and wrinkles make deep and shallow valleys on a palm landscape. Then the steepest slope direction in each local area is first computed using the Kirsch operator, after which an orientation map is created that represents the dominant slope direction of each pixel. In this study, three orientation maps were made with different scales to represent local and global gradient information. Next, feature matching based on pixel-unit comparison was performed. The experimental results showed that the proposed method is superior to several state-of-the-art methods. In addition, the verification could be greatly improved by fusing orientation maps with different scales.

Neural network heterogeneous autoregressive models for realized volatility

  • Kim, Jaiyool;Baek, Changryong
    • Communications for Statistical Applications and Methods
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    • 제25권6호
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    • pp.659-671
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    • 2018
  • In this study, we consider the extension of the heterogeneous autoregressive (HAR) model for realized volatility by incorporating a neural network (NN) structure. Since HAR is a linear model, we expect that adding a neural network term would explain the delicate nonlinearity of the realized volatility. Three neural network-based HAR models, namely HAR-NN, $HAR({\infty})-NN$, and HAR-AR(22)-NN are considered with performance measured by evaluating out-of-sample forecasting errors. The results of the study show that HAR-NN provides a slightly wider interval than traditional HAR as well as shows more peaks and valleys on the turning points. It implies that the HAR-NN model can capture sharper changes due to higher volatility than the traditional HAR model. The HAR-NN model for prediction interval is therefore recommended to account for higher volatility in the stock market. An empirical analysis on the multinational realized volatility of stock indexes shows that the HAR-NN that adds daily, weekly, and monthly volatility averages to the neural network model exhibits the best performance.

First Record of the Marsh Fly Genus Ditaeniella (Diptera: Sciomyzidae) from Korea

  • Son, Yeongjin;Suh, Sang Jae
    • Animal Systematics, Evolution and Diversity
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    • 제35권2호
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    • pp.73-75
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    • 2019
  • Members of this family Sciomyzidae are known as marsh flies and snail-killing flies because most of the known larvae are obligate predators of Mollusca, especially freshwater and terrestrial Gastropoda. Most species are found in the shallow ephemeral aquatic habitats with rich organic substrates, such as the hard-water streams, small ponds and lakes in mountain valleys. To date, a total of 8 marsh fly species in 4 genera have been known in Korea. During a taxonomic survey of the family Sciomyzidae in Korea, the authors have found the genus Ditaeniella Sack, 1939; thus, it was discovered for the first time in Korea. This genus can be distinguished by the other related taxa with hairs over much of the mesopleuri, hairs on the prosternum and one orbital seta. In addition, the nominate species, Ditaeniella grisescens Meigen, 1830 was also firstly recoded in the Korean fauna.

Variational autoencoder for prosody-based speaker recognition

  • Starlet Ben Alex;Leena Mary
    • ETRI Journal
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    • 제45권4호
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    • pp.678-689
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    • 2023
  • This paper describes a novel end-to-end deep generative model-based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker-specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable-like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency (F0), are then extracted at the syllable level and used to train/adapt a speaker-dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender-dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.

다중 피크의 영역 성장 기법에 의한 전기영동 젤의 영상 분석 ((Image Analysis of Electrophoresis Gels by using Region Growing with Multiple Peaks))

  • 김영원;전병환
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제30권5_6호
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    • pp.444-453
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    • 2003
  • 최근 생명공학(BT)에 대한 관심이 집중되면서, 새로운 생리활성 물질을 찾거나 유전자 정보를 분석하기 위한 목적으로 전기영동 젤의 영상 분석 기술에 대한 요구가 급증하고 있다. 이를 위해서는 젤 영상의 레인에서 각 밴드의 위치와 양을 정확히 측정해야 한다. 기존 연구에서는 주로 레인의 프로파일에서 피크를 탐색하는 접근방법을 사용하는데, 이 피크의 위치는 밴드에 있는 최대 자기 화소의 위치도 아니고 더욱이 밴드 무게중심의 위치도 아니기 때문에 밴드의 대표 위치로 인정하기 어렵다. 또한, 피크 추출을 쉽게 하기 위해 다양한 영상 향상 처리를 적용하기 때문에 밴드의 양을 측정하기에는 부적절한 경우가 많다. 본 논문에서는 영상의 상대적인 밝기를 변화시키지 않으면서 먼저 밴드의 영역을 추출한 후, 밴드 영역의 밝기 합으로 양을 구하고 이의 무게중심을 밴드 위치로 정하는 방식을 채택한다. 실제로, 먼저 젤 영상 히스토그램에 엔트로피기반 임계치를 설정하여 레인을 추출한 후, 밴드 영역 추출을 위해 서로 다른 세 가지 방법을 시도한다. 첫째, 추출된 레인을 이등분하는 중심선을 탐색하여 피크와 밸리를 찾고, 피크의 상하 밸리를 각 밴드의 최소 포함 박스영역으로 지정하는 방법(MER), 둘째, 앞의 방법에서와 같이 구한 피크를 영역 성장의 시드로 사용하여 이웃하는 밴드와의 중첩을 해결하면서 밴드 영역을 추출하는 방법(RG-1), 셋째, 이와 달리 레인을 삼등분하는 두 탐색선에서 피크를 찾고 동일한 밴드에 속하는 피크 쌍을 결정한 후 영역을 성장하는 방법(RG-2)을 제안한다. 이상의 세 방법을 비교하기 위해 밴드의 위치 및 양을 측정한 결과, 밴드 위치의 평균 오차는 레인의 길이를 단위 크기로 정규화 할 때, MER 방법이 6%, RG-1 방법이 3%, RG-2 방법이 1%로 나타났다. 또한, 밴드 양의 평균 오차는 레인 내 밴드들의 양의 합을 단위 크기로 정규화 할 때, MER 방법이 8%, RG-1 방법이 5%, RG-2 방법이 2%로 나타났다. 결과적으로, RG-2 방법이 밴드의 위치 및 양 추출에 있어서 정확도가 가장 높은 것으로 판명되었다.