• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4977-4996
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• 2016

The existence of excessively large and too filled network buffers, known as bufferbloat, has recently gained attention as a major performance problem for delay-sensitive applications. Researchers have made three types of suggestions to solve the bufferbloat problem. One is End to End (E2E) congestion control, second is deployment of Active Queue Management (AQM) techniques and third is the combination of above two. However, these solutions either seem impractical or could not obtain good bandwidth utilization. In this paper, we propose a Transmission Control Protocol（TCP）delayed window update mechanism which uses a congestion detection approach to predict the congestion level of networks. When detecting the network congestion is coming, a delayed window update control strategy is adopted to maintain good protocol performance. If the network is non-congested, the mechanism stops work and congestion window is updated based on the original protocol. The simulation experiments are conducted on both high bandwidth and long delay scenario and low bandwidth and short delay scenario. Experiment results show that TCP delayed window update mechanism can effectively improve the performance of the original protocol, decreasing packet losses and queuing delay while guaranteeing transmission efficiency of the whole network. In addition, it can perform good fairness and TCP friendliness.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.373-383
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• 2018

The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.75-80
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• 2008

Recently, Energy savings in buildings has received much attention in response to the increased needs for global warming and better comforts of the occupants in apartment housing. This study proposes the method which uses the sun control window film to reduce the cooling load and heating load improving the thermal performance of the building and it improves an energy efficiency. The film which used in actual measurement has a low shading coefficient and a solar energy transmission. so we measured the surface temperature of the triple Low-e glazing system used and inside temperature according to the change of outside temperature and solar energy to study thermal performance evaluation. As a result, it was helpful to use window film insulation to reduce inside temperature in summer and to keep room warm in winter.

• The Research Journal of the Costume Culture
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• v.25 no.5
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• pp.577-592
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• 2017

The purpose of this study was to propose various directions for effectively proposing window displays that satisfy changing consumer needs by investigating and analyzing the characteristics of unexpected expressions used in recent Christmas windows. Research was conducted by investigating unexpectedness in window displays through literature reviews and previous studies. To observe unexpectedness in Christmas windows, website-based case images of window displays from the six years between 2010 and 2015 were collected from the department stores of Bergdorf Goodman, Printemps, Selfridges, and Isetan. Unexpected expressions in department-store Christmas window displays could be categorized into four expressive methods of hybrid expressions, figurative expressions, amusing expressions, and exaggerative expressions. The results were as follows: First, hybrid expressions are interpreted by consumers as having new or diverse meanings that change the original external forms of subjects or objects. Second, Christmas colors are used in the window backgrounds' figurative expressions, but these windows do not use excessive expressions; furthermore, these windows personify people as animals and anthropomorphize animals as people, using subjects to depict other subjects to show unfamiliar images. Third, amusing expressions are used to decorate windows with unique and novel ideas that provide stimulation and amusement for customers and capture their attention through the composition of windows that entail childish props and elements that create funny. Fourth, exaggerative expressions deliberately stretch and expand subjects or objects in windows to elicit customers' curiosity and interest through emphasis.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.569-579
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• 2021

Purpose: The purpose of this study is the time series analysis for predicting the yield of crops applicable to each farm using environmental variables measured by smart farms cultivating tomato. In addition, it is intended to confirm the influence of environmental variables using a deep learning model that can be explained to some extent. Methods: A time series analysis was performed to predict production using environmental variables measured at 75 smart farms cultivating tomato in two periods. An LSTM-based encoder-decoder model was used for cases of several farms with similar length. In particular, Dual Attention Mechanism was applied to use environmental variables as exogenous variables and to confirm their influence. Results: As a result of the analysis, Dual Attention LSTM with a window size of 12 weeks showed the best predictive power. It was verified that the environmental variables has a similar effect on prediction through wieghtss extracted from the prediction model, and it was also verified that the previous time point has a greater effect than the time point close to the prediction point. Conclusion: It is expected that it will be possible to attempt various crops as a model that can be explained by supplementing the shortcomings of general deep learning model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.245-254
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• 2008

This paper proposes a unified multi-modal input framework to interface the recognition engines such as IBM ViaVoice and Microsoft handwriting-recognition system with general window applications, particularly, for pen-input displays. As soon as user pushes a hardware button attached to the pin-input display with one hand, the current window of focus such as a internet search window and a word processor is overlaid with a transparent window covering the whole desktop; upon which user inputs handwriting with the other hand, without losing the focus of attention on working context. As well as freeform handwriting on this transparent input overlay as a sketch pad, the user can dictate some words and draw diagrams to communicate with the system.

• Journal of IKEEE
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• v.27 no.3
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• pp.251-257
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• 2023

In this paper, we propose a dual-structured self-attention method that improves the lack of regional features of the vision transformer's self-attention. Vision Transformers, which are more computationally efficient than convolutional neural networks in object classification, object segmentation, and video image recognition, lack the ability to extract regional features relatively. To solve this problem, many studies are conducted based on Windows or Shift Windows, but these methods weaken the advantages of self-attention-based transformers by increasing computational complexity using multiple levels of encoders. This paper proposes a dual-structure self-attention using self-attention and neighborhood network to improve locality inductive bias compared to the existing method. The neighborhood network for extracting local context information provides a much simpler computational complexity than the window structure. CIFAR-10 and CIFAR-100 were used to compare the performance of the proposed dual-structure self-attention transformer and the existing transformer, and the experiment showed improvements of 0.63% and 1.57% in Top-1 accuracy, respectively.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.1
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• pp.54-61
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• 2004

The purpose of this study is to improve of energy performance in school buildings. Many building renovations have mainly focused on commercial buildings and houses, but school buildings have no attention in this field although there are many buildings that show degraded energy performance and there are many old fashioned buildings which need renovation. This study was carried out through the survey, field study, energy simulation and life cycle cost analysis. The results of this study can be summarized as follows: In model building, large amount of heat were lost at the building envelope, such as non-insulated skins, window-sills and window-frame joints. According to the simulation result, about 15％ of heating energy is saved by the insulating works compared to pre-renovation condition. Also, LCC analysis revealed to be more effective to select a exteria wall insulation such as a dryvit system.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.795-798
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• 2016

Transmission X-ray tubes based on carbon nanotube have attracted significant attention recently. In most of these tubes, tungsten is used as the target material. In this article, the well-known simulator Geant4 was used to obtain some of the tungsten target parameters. The optimal thickness for maximum production of usable X-rays when the target is exposed to electron beams of different energies was obtained. The linear variation of optimal thickness of the target for different electron energies was also obtained. The data obtained in this study can be used to design X-ray tubes. A beryllium window was considered for the X-ray tube. The X-ray energy spectra at the moment of production and after passing through the target and window for different electron energies in the 30-110 keV range were also obtained. The results obtained show that with a specific thickness, the target material itself can act as filter, which enables generation of X-rays with a limited energy.