• BMB Reports
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• v.37 no.6
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• pp.648-656
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• 2004

This paper deals with the development of a predictive probabilistic model, a composite dependency-reflecting model (CDRM), which was designed to detect core promoter regions and transcription start sites (TSS) in vertebrate genomic DNA sequences, an issue of some importance for genome annotation. The model actually represents a combination of first-, second-, third- and much higher order or long-range dependencies obtained using the expanded maximal dependency decomposition (EMDD) procedure, which iteratively decomposes data sets into subsets on the basis of dependency degree and patterns inherent in the target promoter region to be modeled. In addition, decomposed subsets are modeled by using a first-order Markov model, allowing the predictive model to reflect dependency between adjacent positions explicitly. In this way, the CDRM allows for potentially complex dependencies between positions in the core promoter region. Such complex dependencies may be closely related to the biological and structural contexts since promoter elements are present in various combinations separated by various distances in the sequence. Thus, CDRM may be appropriate for recognizing core promoter regions and TSSs in vertebrate genomic contig. To demonstrate the effectiveness of our algorithm, we tested it using standardized data and real core promoters, and compared it with some current representative promoter-finding algorithms. The developed algorithm showed better accuracy in terms of specificity and sensitivity than the promoter-finding ones used in performance comparison.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4429-4447
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• 2018

Local binary descriptors are well-suited for many real-time and/or large-scale computer vision applications, while their low computational complexity is usually accompanied by the limitation of performance. In this paper, we propose a new optimization framework, RLDB (Robust-LDB), to improve a typical region-based binary descriptor LDB (local difference binary) and maintain its computational simplicity. RLDB extends the multi-feature strategy of LDB and applies a more complete region-comparing configuration. A cascade bit selection method is utilized to select the more representative patterns from massive comparison pairs and an online learning strategy further optimizes descriptor for each specific patch separately. They both incorporate LDP (linear discriminant projections) principle to jointly guarantee the robustness and distinctiveness of the features from various scales. Experimental results demonstrate that this integrated learning framework significantly enhances LDB. The improved descriptor achieves a performance comparable to floating-point descriptors on many benchmarks and retains a high computing speed similar to most binary descriptors, which better satisfies the demands of applications.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.7 no.1
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• pp.42-51
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• 1995

In order to establish a theoretical basis for the analyses of transient behaviors in stratified thermal storage tanks, analytical approaches to an improved one-dimensional model are made. In the present model the storage tank is treated as a finite region with an adiabatic tank exit, whereas it has been considered as a simple semi-infinite region previously. Application of the Laplace transformation and the Inversion theorem to the governing equations makes it possible to obtain an exact infinite-series solution, which is convergent only at sufficiently large time. Accordingly a complementary solution which is available for short times, i.e., the time range of this study is sought by an approximate method. The approximate solution which is rigorously validated through the examination of neglected terms in the solution procedure agrees quite well with the exact one. Moreover, it is simpler to use and more convenient to interpret the physical meaning of the solution. Comparison of the present solution with the previous ones shows relatively large difference near the tank bottom, which results from the more realistic boundary condition adopted in the present model. Some representative results by the approximate solution including effects of the Peclet number on temperature distrbutions are illustrated to show the utility of this study. In consequence, it is expected that the present results based on the improved model replace the foregoing ones as a new theoretical reference for studies of thermal stratification fields.

• Corrosion Science and Technology
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• v.8 no.6
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• pp.238-242
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• 2009

Corrosion of metallic materials occurs by the reaction with corrosive environment. In general, corrosive environments are classified as atmospheric, marine, soil etc. and regardless of any corrosive environments, reduction of thickness and cracking and degradation are induced by corrosion. Among several corrosive environments, knowing the atmospheric corrosiveness of a region, city, or country is considered of ultimate importance for major industrialists and investors who require knowledge of the corrosive impact of the atmosphere on everyday materials such as carbon steel, weathering steel, zinc, copper, and aluminium. This is why the atmospheric corrosiveness map is needed. This paper dealt with corrosion properties between several waters in the region and carbon steel, weathering steel, galvanized steel, pure copper, and pure aluminium at the representative rural area of Korea - Andong.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.93-100
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• 2019

One of critical issues in dealing with 360-degree realistic contents is the performance degradation in searching and recognition process since they support up to 4K UHD quality and have all image angles including the front, back, left, right, top, and bottom parts of a screen. To solve this problem, in this paper, we propose an efficient search and comparison method for 360-degree realistic contents. The proposed method first corrects the distortion at the less distorted regions such as front, left and right parts of the image excluding severely distorted regions such as upper and lower parts, and then it extracts feature points at the corrected region and selects the representative images through sequence classification. When the query image is inputted, the search results are provided through feature points comparison. The experimental results of the proposed method shows that it can solve the problem of performance deterioration when 360-degree realistic contents are recognized comparing with traditional 2D contents.

• Smart Structures and Systems
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• v.32 no.1
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.17-24
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• 2024

Recently, retail technology has been developed by the rapid evolution of e-commerce and a representative example is ESL technology. In this study, we investigate ESL technology, market status and forecasts, and analyze the competitive structure between relational companies. Market analysis refers to data from market reports of Marketsandmarkets and Research, and internet media. In ESL, the display field is predicted to account for 43% of the total market in 2026, and is converting from LCD to electronic paper. The segmented type is becoming more advanced into the full-graphic type, and CAGR of 18.7% for 3-7 inches and 20.6% for 7-10 inches is predicted. The demand for ESL is greatest in North America and Europe, but CAGR is the highest in the Asia-Pacific region at 19.1%. Since ESL technology has a lot of overlap with semiconductor and display technology, the Asia-Pacific region is relatively advantageous, and this has led to rapid growth of domestic companies. However, it is expected that competition from European companies that are actually owned by Chinese companies will increase in the future, so continuous technological development and new market development are necessary.

• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.511-529
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• 2022

The forest vegetation of the Korean Peninsula is dominated by deciduous Fagaceae forests. The study aimed to comparethe correlation between species composition and environmental factors in Namsan, Odaesan, and Ulleungdo. A vegetation survey of 75 sites was conducted from May to August 2018. Seven vegetation types were classified. The inland representative vegetation was classified as a Quercus mongolica community, and the island representative vegetation was classified as a Fagus multinervis community. The Quercus mongolica community was subdivided into the Aria alnifolia group, representative of cities, and the Tilia amurensis group, representative of mountainous regions. Analysis of important values and indicator species to examine the succession trends according to regional types showed that urban and island forestswere maintained as Fagaceae communities, and that mountainous region foreststransitioned to broadleaf species, such as Tilia amurensis and Carpinus cordata. A CCA analysis of vegetation type and site environmental factors showed that altitude had the biggest effect on species composition at the same latitude. The study results should contribute to a better understanding of the Korean Peninsula forest ecosystem characteristics and provide basic data for establishing a systematic conservation and restoration plan.

• KIEAE Journal
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• v.17 no.2
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• pp.13-20
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• 2017

Meteorological data is one of the important factors in the calculation of building energy demand. The purposes of this study are to review the limitations of the typical meteorological data of ECO2 program and to create the new typical meteorological data and then analyze the building energy demands for additional regions which are not included in the existing 13 region in the ECO2 program. The extended typical meteorological data to a total of 33 regions were based on IWEC(International Weather for Energy Calculations) data files and were created in the form applicable to the building energy efficiency rating certification system. As a result of comparing the heating energy demands of a representative region with the surrounding regions in each of five regions in Korea, the variance of Cv(RMSE) ranged from 36% to 344% and MBE ranged from -32% to 190% for the whole regions. This suggests that the difference of heating energy demand may vary greatly depending on the region where the meteorological data is used and the meteorological data of more detailed regions is needed for reliable calculation of building energy demand.