• 한국산학기술학회논문지
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• 제11권12호
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• pp.5165-5176
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• 2010

본 연구는 최근 극심한 쇠퇴현상을 겪고 있는 천안역 앞 명동패션거리 일대를 대상으로 도심재생차원에서의 가로활성화 방안을 모색한다. 천안시내 기존도심 지역의 물리 환경적 측면, 역사 문화적 측면, 사회 경제적 측면에서 현황과 문제점들을 검토하고, 아울러 가로 방문자들을 대상으로 가로이용행태와 가로활성화와 관련한 의식을 조사하였다. 연구를 통해 제안된 해당 지역의 잠정적인 가로활성화 방안들을 요약하면 다음과 같다. 물리 환경적 측면에서는 기존도심 토지소유자들의 개발동기를 유발할 수 있도록 법적 인센티브 장치를 마련하는 것이 요구되며, '명동거리' 내에 휴식 및 녹지공간 조성, 기존건물을 활용한 문화예술 공간의 확충과 함께 지하공간을 활용한 공영주차장 조성 등이 제안된다. 사회 경제적 측면에서는 기존 가로별 업종 특성을 최대한 살리면서 획일적이지 않도록 '명동거리', '은행길', '옛시청길' 등 가로별로 독특한 테마를 설정하고 각각의 테마업종들이 예시된다. 역사 문화적 차원에서는 기존 도심이 갖고 있는 역사적 요소들을 적극적으로 발굴하여 역사탐방로 등의 프로그램을 개발하고 기존 패션거리와 연계하여 보행네트워크화 하는 방안이 제시된다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1371-1374
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• 2002

This paper compares four network con-figurations for using as the logical topology in multi- hop wavelength division multiplexing (WDM) networks. The regular network configurations studied in this paper axe ShuffleNet, de Bruijn graph, hypercube, and Man-hattan street network. Instead of using the weight mean hop distance of node placement problem for comparing optimum logical topology, we introduce a new objective function that includes h and the network cost. It can be seen that the network cost strongly depends on the logical topology selected for the implementation of the network. The objective of this paper is to find an optimum logical topology for WDM networks that gives low as well as low network cost.

• 한국환경생태학회:학술대회논문집
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• 한국환경생태학회 2009년 학술대회 논문집 2호
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• pp.87-91
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• 2009

• 한국주거학회논문집
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• 제26권3호
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• pp.55-64
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• 2015

This study is conducted to understand the social value of Nakwon building, which has been instilled in the architecture of the building, through an analysis of formative elements unique to the center. The architectural characteristics of Nakwon building have been formed over a long period of time in relation with its surroundings and social change. Before looking into the formation of the center, this study investigates the street network planning of downtown. The street network was planned to expand in a north-south direction in order to accommodate a future increase of traffic volume as a result of industrialization and a population increase in downtown. This was manifested in expanding Samilro Street which passes through the lower part of Nakwon building, as well as in forming the architecture of the center. That is, the center has formed a symbiotic relationship with its surrounding areas, sharing space with them. The interior of the center seems to have an independent form while keeping a relationship with its external format, but it is seeking change internally in response to external change. Interior space has been departmentalized over time since its initial establishment, and the internal traffic has also been subdivided accordingly. This is attributable to the fact that social change in the neighboring areas affected the formation of Nakwon building, and that in turn contributed to forming the unique characteristics of the building. Nakwon building is one of the deteriorated buildings in downtown Seoul. However, it was built out of social need to share space within the city, and it has been changed according to social need for the 'distribution of space within the building.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.855-870
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• 2020

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.515-518
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• 2019

Semantic segmentation has remained as a challenging problem in the field of computer vision. Given the immense power of Convolution Neural Network (CNN) models, many complex problems have been solved in computer vision. Semantic segmentation is the challenge of classifying several pixels of an image into one category. With the help of convolution neural networks, we have witnessed prolific results over the time. We propose a convolutional neural network model which uses Fully CNN with deconvolutional pixel layers. The goal is to create a hierarchy of features while the fully convolutional model does the primary learning and later deconvolutional model visually segments the target image. The proposed approach creates a direct link among the several adjacent pixels in the resulting feature maps. It also preserves the spatial features such as corners and edges in images and hence adding more accuracy to the resulting outputs. We test our algorithm on Karlsruhe Institute of Technology and Toyota Technologies Institute (KITTI) street view data set. Our method achieves an mIoU accuracy of 92.04 %.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• 대한교통학회지
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• 제10권2호
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• pp.25-42
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• 1992

The purpose of this paper is to construct a model to compute a progression adjustment factor on a signalized network. In a way to construct the model, a simulation method is introduced and the TRAF-NETSIM is used as a tool of simulation. The structure of the network chooses an urban arterial network so as to measure the effect of progression and compute average stopped delay on each link. A regression model is constructed by using the results of the simulation. The stepwise variable selection in the regression model in used. The findings of this paper are as follows: i)The secondary queue and platoon ratio are sensitive to the values of the progression adjustment factor ii) The continuous model can practically reflect on various situations in the real world. The platoon adjustment factor can be computed by this model and the data required for this model can be easily obtained in the field.

• 말소리와 음성과학
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• 제7권4호
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• pp.3-8
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• 2015

In this paper, we investigate an input dimension reduction method using continuous word vector in deep neural network language model. In the proposed method, continuous word vectors were generated by using Google's Word2Vec from a large training corpus to satisfy distributional hypothesis. 1-of-${\left|V\right|}$ coding discrete word vectors were replaced with their corresponding continuous word vectors. In our implementation, the input dimension was successfully reduced from 20,000 to 600 when a tri-gram language model is used with a vocabulary of 20,000 words. The total amount of time in training was reduced from 30 days to 14 days for Wall Street Journal training corpus (corpus length: 37M words).

• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.162-167
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• 2012

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.