• Journal of Internet of Things and Convergence
• /
• v.8 no.6
• /
• pp.55-63
• /
• 2022

In recent years, with the rapid development of large and medium-sized urban rail transit in China, the total operating mileage of high-speed railway and the total number of EMUs(Electric Multiple Units) are rising. The system complexity of high-speed EMU is constantly increasing, which puts forward higher requirements for the safety of equipment and the efficiency of maintenance.At present, the maintenance mode of high-speed EMU in China still adopts the post maintenance method based on planned maintenance and fault maintenance, which leads to insufficient or excessive maintenance, reduces the efficiency of equipment fault handling, and increases the maintenance cost. Based on the intelligent operation and maintenance technology of PHM(prognostics and health management). This thesis builds an integrated PHM platform of "vehicle system-communication system-ground system" by integrating multi-source heterogeneous data of different scenarios of high-speed EMU, and combines the equipment fault mechanism with artificial intelligence algorithms to build a fault prediction model for traction motors of high-speed EMU.Reliable fault prediction and accurate maintenance shall be carried out in advance to ensure safe and efficient operation of high-speed EMU.

• Journal of the Korea Society for Simulation
• /
• v.19 no.4
• /
• pp.291-299
• /
• 2010

As recent advances in science, technology and performance requirements of the weapons system are getting highly diversified and complex, the performance requirements also get stringent and strict. Moreover, the weapons system should be intimately connected with other systems such as watchdog system, command and control system, C4I system, etc. However, a tremendous amount of time, cost and risk being spent to acquire new weapons system, and not being diminished compared to the rapid pace of its development speed. Defense Modeling and Simulation(M&S) comes into the spotlight as an alternative to overcoming these difficulties as well as constraints. In this paper, we propose the development process of virtual test framework based on the synthetic environment as a tool to analyze the effectiveness of the weapons system of underwater engagement model. To prove the proposed concept, we develop the test-bed of virtual test using Delta3D simulation engine, which is open source S/W. We also design the High Level Architecture and Real-time Infrastructure(HLA/RTI) based Federation for the interoperation with heterogeneous simulators. The significance of the study entails (1)the rapid and easy development of simulation tools that are customized for the Korean Theater of War; (2)the federation of environmental entities and the moving equations of the combat entities to manifest a realistic simulation.

• Asia Marketing Journal
• /
• v.14 no.2
• /
• pp.157-176
• /
• 2012

Brand switching data frequently used in market structure analysis is adequate to analyze non- durable goods, because it can capture competition between specific two brands. But brand switching data sometimes can not be used to analyze goods like automobiles having long term duration because one of main assumptions that consumer preference toward brand attributes is not changed against time can be violated. Therefore a new type of data which can precisely capture competition among durable goods is needed. Another problem of using brand switching data collected from actual purchase behavior is short of explanation why consumers consider different set of brands. Considering above problems, main purpose of this study is to analyze market structure for durable goods with consideration set. The author uses exploratory approach and latent class clustering to identify market structure based on heterogeneous consideration set among consumers. Then the relationship between some factors and consideration set formation is analyzed. Some benefits and two demographic variables - age and income - are selected as factors based on consumer behavior theory. The author analyzed USA automotive market with top 11 brands using exploratory approach and latent class clustering. 2,500 respondents are randomly selected from the total sample and used for analysis. Six models concerning market structure are established to test. Model 1 means non-structured market and model 6 means market structure composed of six sub-markets. It is exploratory approach because any hypothetical market structure is not defined. The result showed that model 1 is insufficient to fit data. It implies that USA automotive market is a structured market. Model 3 with three market structures is significant and identified as the optimal market structure in USA automotive market. Three sub markets are named as USA brands, Asian Brands, and European Brands. And it implies that country of origin effect may exist in USA automotive market. Comparison between modal classification by derived market structures and probabilistic classification by research model was conducted to test how model 3 can correctly classify respondents. The model classify 97% of respondents exactly. The result of this study is different from those of previous research. Previous research used confirmatory approach. Car type and price were chosen as criteria for market structuring and car type-price structure was revealed as the optimal structure for USA automotive market. But this research used exploratory approach without hypothetical market structures. It is not concluded yet which approach is superior. For confirmatory approach, hypothetical market structures should be established exhaustively, because the optimal market structure is selected among hypothetical structures. On the other hand, exploratory approach has a potential problem that validity for derived optimal market structure is somewhat difficult to verify. There also exist market boundary difference between this research and previous research. While previous research analyzed seven car brands, this research analyzed eleven car brands. Both researches seemed to represent entire car market, because cumulative market shares for analyzed brands exceeds 50%. But market boundary difference might affect the different results. Though both researches showed different results, it is obvious that country of origin effect among brands should be considered as important criteria to analyze USA automotive market structure. This research tried to explain heterogeneity of consideration sets among consumers using benefits and two demographic factors, sex and income. Benefit works as a key variable for consumer decision process, and also works as an important criterion in market segmentation. Three factors - trust/safety, image/fun to drive, and economy - are identified among nine benefit related measure. Then the relationship between market structures and independent variables is analyzed using multinomial regression. Independent variables are three benefit factors and two demographic factors. The result showed that all independent variables can be used to explain why there exist different market structures in USA automotive market. For example, a male consumer who perceives all benefits important and has lower income tends to consider domestic brands more than European brands. And the result also showed benefits, sex, and income have an effect to consideration set formation. Though it is generally perceived that a consumer who has higher income is likely to purchase a high priced car, it is notable that American consumers perceived benefits of domestic brands much positive regardless of income. Male consumers especially showed higher loyalty for domestic brands. Managerial implications of this research are as follow. Though implication may be confined to the USA automotive market, the effect of sex on automotive buying behavior should be analyzed. The automotive market is traditionally conceived as male consumers oriented market. But the proportion of female consumers has grown over the years in the automotive market. It is natural outcome that Volvo and Hyundai motors recently developed new cars which are targeted for women market. Secondly, the model used in this research can be applied easier than that of previous researches. Exploratory approach has many advantages except difficulty to apply for practice, because it tends to accompany with complicated model and to require various types of data. The data needed for the model in this research are a few items such as purchased brands, consideration set, some benefits, and some demographic factors and easy to collect from consumers.

• Journal of Korea Spatial Information System Society
• /
• v.5 no.1 s.9
• /
• pp.49-63
• /
• 2003

Recently, two international standard organizations, ISO and OGC, have done the work of standardization for GIS. Current standardization work for providing interoperability among GIS DB focuses on the design of open interfaces. But, this work has not considered procedures and methods for designing GIS DB. Eventually, GIS DB has its own model. When we share the data by open interface among heterogeneous GIS DB, differences between models result in the loss of information. Our aim in this paper is to revise the design guidelines for geographic information databases in order to make consistent spatial data models, logical structures, and semantic structure of populated geographical databases. In details, we propose standard guidelines which convert ISO abstract schema into relation model, object-relation model, object-centered model, and geometry-centered model. Furthermore, we provide sample models for applying these guidelines in commercial GIS S/Ws. Building GIS DB based on design guidelines proposed in the paper has the following advantages: the interoperability among databases, the standardization of schema definitions, and the catalogue of GIS databases through.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.1009-1029
• /
• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Korean Society of Environmental Engineers
• /
• v.27 no.3
• /
• pp.253-261
• /
• 2005

Catalytic wet oxidation of ppm levels of trichloroethylene (TCE) in water has been conducted using $TiO_2$-supported cobalt oxides at a given temperature and weight hourly space velocity. 5% $CoO_x/TiO_2$ might be the most promising catalyst for the wet oxidation at $36^{\circ}C$ although it exhibited a transient behavior in time on-stream activity. Not only could the bare support be inactive for the wet decomposition reaction, but no TCE removal also occurred by the process of adsorption on $TiO_2$ surface. The catalytic activity was independent of all particle sizes used, thereby representing no mass transfer limitation in intraparticle diffusion. Characterization of the $CoO_x$ catalyst by acquiring XPS spectra of both fresh and used Co surfaces gave different surface spectral features of each $CoO_x$. Co $2p_{3/2}$ binding energy of Co species exposed predominantly onto the outermost surface of the fresh catalyst appeared at 781.3 eV, which is very similar to the chemical states of $CoTiO_x$ such as $Co_2TiO_4$ and $CoTiO_3$. The spent catalyst possessed a 780.3 eV main peak with a satellite structure at 795.8 eV. Based on XPS spectra of reference Co compound, the TCE-exposed Co surfaces could be assigned to be in the form of mainly $Co_3O_4$. XRD measurements indicated that the phase structure of Co species in 5% $CoO_x/TiO_2$ catalyst even before reaction is quite comparable to the diffraction lines of external $Co_3O_4$ standard. A model structure of $CoO_x$ present on titania surfaces would be $Co_3O_4$, encapsulated in thin-film $CoTiO_x$ species consisting of $Co_2TiO_4$ and $CoTiO_3$, which may be active for the decomposition of TCE in a flow of water.

• The Journal of the Petrological Society of Korea
• /
• v.9 no.4
• /
• pp.211-237
• /
• 2000

Major element zoning has been analyzed in garnet porphroblasts obtained from the Grt-St and Ky-Grt-St grade assemblages in Zones I on the northern flank of the Pelham Dome, north central Massachusetts. These porphyroblasts grew during multiple phases of deformation and meta-morphism revealed by the inclusion trail geometry plus the chemical zoning patterns within garnet porphyroblasts. Unusual zoning patterns, including zoning reversals and gradient changes in XMn, zlgzag patterns in Fe/(Fe +Mg) and staircase-shaped patterns in XCa, are coincident with textural truncations and other changes in microstructure within the garnet porphrublasts. Chemical variations in plagioclase, biotite, muscovite and staurolite combined with inclusion trail geometry and petrography reveal that the garnet zoning patterns are modified by combinations of the following. (1) Uni-and divariant reactions involving garnet consumption(Grt+ Chl+Ms=St+Bt+Qtz + $H_2$O) and production(St+Ms + Qtz= Bt+ Grt +A1$_2$$SiO_{5}$ + $H_2$O). (2) Deformation induced episudic ionit dissolution, preferential diffusion and re-distribution during foliation development. (3) P-T changes during growth of the porphyroblasts. The P-T paths combined with petrographic and inclusion trail morphology observations consist of two pattens; (1) heating/compression during NW-SE shortening; and (2) decompression with cooling during NNW-SSE shortening. Based on temperature-time(T-t) geochronological data and late-Paleozoic tectonic model, Alleghanian metamorphism, which is the result of heterogeneous shearing concentrated along the boundary between the Abalone Terrane(Pelham dome) and cover rocks(Bronson Hill Terrane), has produced Ky-St-Ms mineral assemblage during Pennsylvanian(290-300 Ma) in Shutesbury area. However, temperature of alleghanian metamorphism was not high enough to form garnet and staurolite in the Northfiled syncline area. Alleghanian metamorphism has affected only the matrix due to heterogeneous shearing in the study area.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.1-19
• /
• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.221-241
• /
• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• KDI Journal of Economic Policy
• /
• v.32 no.1
• /
• pp.131-169
• /
• 2010

I develop a matching model in which risk-averse workers face borrowing constraints and make a labor force participation decision as well as a job search decision. A sharp distinction between unemployment and out of the labor force is made: those who look for work for a certain period but find no job are classified as the unemployed and those who do not look for work are classified as those out of the labor force. In the model, the job search decision consists of two steps. First, each individual who is not working obtains information about employment opportunities. Second, each individual who decides to search has to take costly actions to find a job. Since individuals differ with respect to asset holdings, they have different reservation job-finding probabilities at which an individual is indifferent between searching and not searching. Individuals, who have large asset holdings and thereby are less likely to participate in the labor market, have high reservation job-finding probability, and they are less likely to search if they have less quality of information. In other words, if individuals with large asset holdings search for job, they must have very high quality of information and face very high actual job-finding probability. On the other hand, individuals with small asset holdings have low reservation job-finding probability and they are likely to search for less quality of information. They face very low actual job-finding probability and seem to remain unemployed for a long time. Therefore, differences in the quality of information explain heterogeneous job search decisions among individuals as well as higher job finding probability for those who reenter the labor market than for those who remain in the labor force. The effect of the extended maximum duration of unemployment insurance benefits on the aggregate labor market and the labor market flows is investigated. The benchmark benefit duration is set to three months. As maximum benefit duration is extended up to six months, the employment-population ratio decreases while the unemployment rate increases because individuals who are eligible for benefits have strong incentives to remain unemployed and decide to search even if they obtain less quality of information, which leads to low job-finding probability and then high unemployment rate. Then, the vacancy-unemployment ratio decreases and, in turn, the job-finding probability for both the unemployed and those out of the labor force decrease. Finally, the outflow from nonparticipation decreases with benefit duration because the equilibrium job-finding probability decreases. As the job-finding probability decreases, those who are out of the labor force are less likely to search for the same quality of information. I also consider the matching model with two states of employment and unemployment. Compared to the results of the two-state model, the simulated effects of changes in benefit duration on the aggregate labor market and the labor market flows are quite large and significant.