• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.734-746
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• 2020

Purpose: Increase in the size and number of underpasses rises occurrence of disasters such as fire and flooding inundation in underpasses. In the occurrence of disasters, the underpasses are more vulnerable to expose of crucial disasters than the general roads due to they are built underground. Therefore, The purpose of this paper is to derive system improvement items to prevent and control disasters in underpasses. Method: A hierarchical model of disaster impact factors and alternatives was developed based on prior researches and expert advices on disaster analyses and impact factors in the underpasses. The developed model was employed for surveys of pairwise comparison, and rankings of improvement were determined by applying the AHP method. Result: With a consistency of the surveys, results of relative weights of evaluation criteria(traffic accidents, fire, flooding inundation) and alternatives(law, system/planning, maintenance/human factor/environment) shows that improvement of laws and system related to the fire disaster is a top priority to prevent and control disaster of the underpasses. Conclusion: From experts' point of view, strengthening laws and systems related to disater prevention facilities such as water spray facilities, external(ground) exit in relation to fire in underpasses showed that it is an alternative to prevent disasters and minimize damage to underpasses.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.1-22
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• 2018

The value chain has been utilized as a strategic tool to improve competitive advantage, mainly at the enterprise level and at the industrial level. However, in order to conduct value chain analysis at the enterprise level, the client companies of the parent company should be classified according to whether they belong to it's value chain. The establishment of a value chain for a single company can be performed smoothly by experts, but it takes a lot of cost and time to build one which consists of multiple companies. Thus, this study proposes a model that automatically classifies the companies that form a value chain based on actual transaction data. A total of 19 transaction attribute variables were extracted from the transaction data and processed into the form of input data for machine learning method. The proposed model was constructed using the Random Forest algorithm. The experiment was conducted on a automobile parts company. The experimental results demonstrate that the proposed model can classify the client companies of the parent company automatically with 92% of accuracy, 76% of F1-score and 94% of AUC. Also, the empirical study confirm that a few transaction attributes such as transaction concentration, transaction amount and total sales per customer are the main characteristics representing the companies that form a value chain.

• Ecology and Resilient Infrastructure
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• v.5 no.1
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• pp.35-44
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• 2018

Social, economic and environmental problems caused by rapid urbanization have been recently overcome by various civic participation projects. Local governance and resident - led partnership through field - based cooperative operating systems from urban regeneration to village projects are considered success factors. Among these, the village greening project which directly affects the residents and requires spontaneity requires the role and cooperation of the various participating actors due to the sharing of public space and private space. Social capital plays a key role in the sustainability and participation of the above - mentioned business as a relational capital centered on trust and participation, network and norms. Therefore, empirical research is needed. In this study, basic research was carried out to build a formation model of social capital in participation - type greening project expanding urban green space system to living area. We analyzed the elements of participation, the components of business progress, and the factors of social capital formation through literature review and in - depth interviews with participating experts. The purpose of this study is to provide basic data of social capital formation model for analyzing sustainability and activation strategies in the future.

• Resources Recycling
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• v.29 no.3
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• pp.11-23
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• 2020

This study aims to investigate breakage and liberation characteristics of iron ore from Shinyemi mine, Jeongseon by ball mill. Parameters of breakage functions for three grade samples of iron ore were obtained using single-sized-feed breakage test and back-calculation based on nonlinear programming. The results showed that with the increase in the grade of iron ore, the breakage rate factor decrease whereas the particle size sensitivity decreases. This results from retardation of microcrack-propagation by magnetite grain in the ore. Breakage distribution analysis showed that the breakage mechanism appear to be impact fracture dominant with the increase of grade owing to the stress distribution effect by magnetite grain. Degree of liberation (DOL) increased with the increase in grade and decrease in particle size, respectively. Using the breakage function and size-DOL relationship, a model that can predict time-dependent-DOL is established. When scale-up factors from operating condition are available, the model is expected to be capable of predicting size and DOL with time in actual mining process.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.662-669
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• 2015

There has been an increased interest in the mitigation of wax deposition because wax, which usually accumulates in subsea oil-production systems, interrupts stable oil production and significantly increases the cost. To guarantee a required oil flow by mitigating wax deposition, we need to obtain a reliable estimation of the wax deposition. In this research, we perform simulations to understand the major mechanisms that lead to wax deposition, namely molecular diffusion, shear stripping reduction, and aging. While the model variables (shear reduction multiplier, wax porosity, wax thermal conductivity, and molecular diffusion multiplier) can be measured experimentally, they have high uncertainty. We perform an analysis of these variables and the amount of water and gas in the multiphase flow to determine these effects on the behavior of wax deposition. Based on the results obtained during this study for a higher wax porosity and molecular diffusion multiplier, we were able to confirm the presence of thicker wax deposits. As the shear reduction multiplier decreased, the thickness of the wax deposits increased. As the amount of water increased, there was also an increase in the amount of wax deposits until 40% water cut and decreased. As the amount of gas increased, the amount of wax deposits increased because of the loss of the light hydrocarbon component in the liquid phase. The results of this study can be utilized to estimate the wax deposition behavior by comparing the experiment (or field) and simulation data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.5
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• pp.345-352
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• 2014

In this paper, the CAD data for the optimal shape design obtained by isogeometric shape optimization is directly used to fabricate the specimen by using 3D printer for the experimental validation. In a conventional finite element method, the geometric approximation inherent in the mesh leads to the accuracy issue in response analysis and design sensitivity analysis. Furthermore, in the finite element based shape optimization, subsequent communication with CAD description is required in the design optimization process, which results in the loss of optimal design information during the communication. Isogeometric analysis method employs the same NURBS basis functions and control points used in CAD systems, which enables to use exact geometrical properties like normal vector and curvature information in the response analysis and design sensitivity analysis procedure. Also, it vastly simplify the design modification of complex geometries without communicating with the CAD description of geometry during design optimization process. Therefore, the information of optimal design and material volume is exactly reflected to fabricate the specimen for experimental validation. Through the design optimization examples of elasticity problem, it is experimentally shown that the optimal design has higher stiffness than the initial design. Also, the experimental results match very well with the numerical results. Using a non-contact optical 3D deformation measuring system for strain distribution, it is shown that the stress concentration is significantly alleviated in the optimal design compared with the initial design.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.5 s.347
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• pp.54-63
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• 2006

This work proposes a 14b 100MS/s 0.18um CMOS ADC with optimized resolution, conversion speed, die area, and power dissipation to obtain the performance required in the fourth-generation mobile communication systems. The 3-stage pipeline ADC, whose optimized architecture is analyzed and verified with behavioral model simulations, employs a wide-band low-noise SHA to achieve a 14b level ENOB at the Nyquist input frequency, 3-D fully symmetric layout techniques to minimize capacitor mismatch in two MDACs, and a back-end 6b flash ADC based on open-loop offset sampling and interpolation to obtain 6b accuracy and small chip area at 100MS/s. The prototype ADC implemented in a 0.18um CMOS process shows the measured DNL and INL of maximum 1.03LSB and 5.47LSB, respectively. The ADC demonstrates a maximum SNDR and SFDR of 59dB and 72dB, respectively, and a power consumption of 145mW at 100MS/s and 1.8V. The occupied active die area is $3.4mm^2$.

• Korean Journal of Construction Engineering and Management
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• v.18 no.3
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• pp.63-73
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• 2017

In contemporary society, it is increasingly common to spend more time indoors. As such, there is a continually growing desire to build comfortable and safe indoor environments. Along with this trend, however, there are some serious indoor-environment challenges, such as the quality of indoor air and Sick House Syndrome. To address these concerns the government implements various systems to supervise and manage indoor environments. For example, green building certification is now compulsory for public buildings. There are three categories of green building certification related to indoor air in Korea: Health-Friendly Housing Construction Standards, Green Standard for Energy & Environmental Design(G-SEED), and Indoor Air Certification. The first two types of certification, Health-Friendly Housing Construction Standards and G-SEED, evaluate data in a drawing plan. In comparison, the Indoor Air Certification evaluates measured data. The certification using data from a drawing requires a considerable amount of time compared to other work. A 2D tool needs to be employed to measure the area manually. Thus, this study proposes an automatic assessment process using a Building Information Modeling(BIM) model based on 3D data. This process, using open source Industry Foundation Classes(IFC), exports data for the certification system, and extracts the data to create an Excel sheet for the certification. This is expected to improve the work process and reduce the workload associated with evaluating indoor air conditions.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.