• Journal of the Korean Home Economics Association
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• v.31 no.2
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• pp.205-212
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• 1993

In order to research the display of the apparel store, 164 women's clothing in Taegu were investigated, from September 1. to October 31.1991. The result was as follows; 1. The perpendicular style of the show window glasses is widely used regardless of the store types. 2. The show window story is most frequently found at street-level in Taegu brand stores, but there are a few multi-storied show windows in National bland stores. 3. The open style background is used in a great part of Taegu brands, but the semi-open style is widely used in most National blands. 4. The arrangement of simular colors is used in a great parts of National brands, but the arrangement of different colors is used in many Taegu brands. 5. The base light and spot light is used at a high rate regardless of the stores, but the mannequin is more often used in National brands and Taegu brands.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.10
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• pp.515-529
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• 2017

EnergyPlus, which is widely used in various fields, provides Simple Window Model, a window model that can be used practically. However, the results of building load using the model are different from those of the standard model. The main cause of the deviation by Simple Window Model was analyzed to be due to the assumption that all windows were considered as single layer. The purpose of this study is to propose a window model that improves the cause of deviation by Simple Window Model and can be easily calculated from the algebraic relations. The proposed window model solved the heat balance equation algebraically by using seven window characteristic coefficients. The coefficient relationships consisted of the heat transmission coefficient and solar heat gain coefficient as input parameters make practical use and calculation possible. As a result of comparing the deviation between each window model by implementing the dynamic analysis method, the proposed window model showed that the deviation of the total heating/cooling energy consumption was reduced to 1/3 compared to Simple Window Model for one year. Although the maximum energy consumption did not show any significant improvement, the indoor temperature evaluation showed significantly reduced deviation.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.138-146
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• 2003

A critical issue in area-based stereo matching lies in selecting a fixed rectangular window size. Previous stereo methods doesn't deal effectively with occluding boundary due to inevitable window-based problems, and so give inaccurate and noisy matching results in areas with steep disparity variations. In this paper, a variable window approach is presented to estimate accurate, detailed and smooth disparities for three-dimensional structure reconstruction. It makes the smoothing of depth discontinuity reduced by evaluating corresponding correlation values and intensity gradient-based similarity in the three-dimensional disparity space. In addition, it investigates maximum connected match candidate points and then devise the novel arbitrarily shaped variable window representative of a same disparity to treat with disparity variations of various structure shapes. We demonstrate the performance of the proposed variable window method with synthetic images, and show how our results improve on those of closely related techniques for accuracy, robustness, matching density and computing speed.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.1-6
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• 2019

In the field of industrial applications, the real-time performance of the target detection problem is very important. The most serious time consumption in the pedestrian detection process is the extraction phase of the candidate window. To accelerate the speed, in this paper, a fast extraction of pedestrian candidate window based on the BING (Binarized Normed Gradients) algorithm replaces the traditional sliding window scanning. The BING features are extracted with the positive and negative samples and input into the two-stage SVM (Support Vector Machine) classifier for training. The obtained BING template may include a pedestrian candidate window. The trained template is loaded during detection, and the extracted candidate windows are input into the classifier. The experimental results show that the proposed method can extract fewer candidate window and has a higher recall rate with more rapid speed than the traditional sliding window detection method, so the method improves the detection speed while maintaining the detection accuracy. In addition, the real-time requirement is satisfied.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.4
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• pp.49-54
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• 1995

This paper is an experimental study on the influence of the demonstration effect by color and light source in show window. This experiment used semantic differentical method in model show window of actual size. The important outcomes of this study are summarized below. 1. In the result by factor analysis, three factors are classified. They are diversity, emotion, lightness. 2. In the study on the influence by light source, incandescence lamp is the most effective light source in every items. 3. In the result of the study on the influence by color source, according to each factor bring out following result ; Green is the most effective in diversity factor and ligthness factor. Yellow is the most effective in emotion factor.

• Korea Journal of Hospital Management
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• v.15 no.1
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• pp.27-48
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• 2010

The purpose of this paper is to analyze the efficiency change and its determinants of the regional public hospitals. We utilize 34 regional public hospital's panel data for 6 years from 2003 to 2008. We use DEA(Data Envelopment Analysis)-CCR, BCC model, DEA/Window model, and DEA Profiling. The empirical results show the following findings. First, technical efficiency shows that approximately 3.6% of inefficiency exists on the regional public hospitals and it reveals that the cause for technical inefficiency is due to scale inefficiency. Second, DEA/Window results show that the stable dissimilarity by standard deviation, LDP of CCR. Third, the results of partial efficiency by DEA Profiling show that increase efficiency depends on the number of beds, doctors, and nurses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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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.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.538-547
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• 2022

Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.343-347
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• 2008

Wakeup schemes that turn off sensors' radio when communication is not necessary have great potential in energy saving. At the start of each beacon interval in the IEEE 802.11 power saving mode specified for DCF, each node periodically wakes up for duration called the ATIM Window. However, in the power saving mechanism specified in IEEE 802.11, all nodes use the same ATIM window size. Since the ATIM window size critically affects throughput and energy consumption, a fixed ATIM window does not perform well in all situations. This paper proposes an adaptive mechanism to dynamically choose an ATIM window size according to network condition. Simulation results show that the proposed scheme outperforms the IEEE 802.11 power saving mechanism in terms of the amount of power consumed and the packet delivery ratio.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.3
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• pp.163-172
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• 2003

A stereo matching has been an important tool for the reconstruction of 3D terrain. The current state of stereo matching technology has reached the level where a very elaborate DEM(Digital Elevation Map) can be obtained. However, there still exist many factors causing DEM error in stereo matching. This paper propose a new method to reduce the error caused by the lack of significant features in the correlation window The proposed algorithm keeps the correlation window as small as possible, as long as there is a significant feature in the window. Experimental results indicate that the proposed method increases the DEM accuracy by $72.65\%$ in the plain area and $41.96\%$ in the mountain area over the conventional scheme. Comparisons with Kanade's result show that the proposed method eliminates spike type of errors more efficiently than Kanade's adaptive window technique and produces reliable DEM.