• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.21-35
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• 2022

With the support of local and central governments, various incentive policies for "green" cars have been established, and the number of electric vehicle users has been rapidly increasing in recent years. As a result, much attention is being given to establishing a user-centered charging infrastructure. A standard for the number of electric vehicle chargers to be supplied is being prepared based on building characteristics, but there is quite limited research on the appropriate ratio of slow and fast chargers based on the characteristics of each place. Therefore, this study derived an appropriate penetration ratio based on data about the distribution ratio of common slow chargers. These data were collected using a survey of actual electric vehicle users. Next, an analysis was done on how to categorize the needs of charging environments and to determine what criteria or characteristics to use for categorization. Based on the results of the survey analysis, three types of places were derived. Type-1 places require 10% of chargers to be slow chargers, Type-2 places require 40-60% of chargers to be slow chargers (i.e., around equal distribution of slow and fast chargers), and Type-3 places require more than 80% of chargers to be slow chargers. The required levels of slow chargers were classified by place type and by individual using latent class cluster analysis, which made it possible to categorize them into five clusters related to socioeconomic variables, vehicle characteristics, traffic, and charging behaviors. It was found that there was a high correlation between charging behavior, weekend travel behavior, gender, and income. The results and insights from this study could be used to establish charging infrastructure policies in the future and to prepare standards for supplying charging infrastructure according to changes in the electric vehicle market.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of the Korean Geographical Society
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• v.39 no.4
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• pp.643-664
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• 2004

This study aims to review what geographers have contributed to GIS industries and national needs. To-be-geographers and geographers are expected to meet the gap between what we have teamed in school and what we have to do after graduation. The characteristics of GIS industry in the 1990 are summarized with approximate evaluation of the contribution of geographers in each stage. Author introduced the requirement for the licenses of geomatics and geospatial engineering experts and the other licenses, which are important to get a job in GIS industry from 2003 to 2004. A set of questionnaire on the user's requirements was given to GIS people in private companies and public GIS research centers and analyzed. Author found that they put an emphasis on hands-on experiences and programming skills. no advantages or geography such as capability or integration and inter-disciplinary collaboration were not appreciated. The prospects for the GIS tend to be positive but the reflectance of the prospect was not accompanied by at the same degree of preference for geography. Most government strategies for the next ten years' GIS focus on new-growth leading industries. SWOT(strength, weakness, opportunity, threat) analysis of geography for GIS industry will give some directions such as telematics, regional marketing strategies with web-based GIS technology, location based service. That means intra-disciplinary study in geography will evoke the potentiality of GIS, compared with interdisciplinary studies.

• The Korean Journal of Nuclear Medicine
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• v.35 no.2
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• pp.89-99
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• 2001

Purpose: In this study, we developed a new software tool for the analysis of renal scintigraphy which can be modified more easily by a user who needs to study new clinical applications, and the appropriateness of the results from our program was studied. Materials and Methods: The analysis tool was programmed with IDL5.2 and designed for use on a personal computer running Windows. For testing the developed tool and studying the appropriateness of the calculated glomerular filtration rate (GFR), $^{99m}Tc$-DTPA was administered to 10 adults in normal condition. In order to study the appropriateness of the calculated mean transit time (MTT), $^{99m}Tc-DTPA\;and\;^{99m}Tc-MAG3$ were administered to 11 adults in normal condition and 22 kidneys were analyzed. All the images were acquired with ORBITOR. the Siemens gamma camera. Results: With the developed tool, we could show dynamic renal images and time activity curve (TAC) in each ROI and calculate clinical parameters of renal function. The results calculated by the developed tool were not different statistically from the results obtained by the Siemens application program (Tmax: p=0.68, Relative Renal Function: p:1.0, GFR: p=0.25) and the developed program proved reasonable. The MTT calculation tool proved to be reasonable by the evaluation of the influence of hydration status on MTT. Conclusion: We have obtained reasonable clinical parameters for the evaluation of renal function with the software tool developed in this study. The developed tool could prove more practical than conventional, commercial programs.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.1
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• pp.35-57
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• 2021

This paper is to grasp the current trends of national bibliographies through analyzing representative national bibliographies using literature review, analysis of national bibliographies' web pages and survey. First, in order to conform to the definition of a national bibliography as a record of a national publication, it attempts to include a variety of materials from print to electronic resources, but in reality it cannot contain all the materials, so there are exceptions. It is impossible to create a general selection guide for national bibliography coverage, and a plan that reflects the national characteristics and prepares a valid and comprehensive coverage based on analysis is needed. Second, cooperation with publishers and libraries is being made to efficiently generate national bibliography. For the efficiency of national bibliography generation, changes should be sought such as the standardization and consistency, the collection level metadata description for digital resources, and the creation of national bibliography using linked data. Third, national bibliography is published through the national bibliographic online search system, linked data search, MARC download using PDF, OAI-PMH, SRU, Z39.50, and mass download in RDF/XML format, and is integrated with the online public access catalog or also built separately. Above all, national bibliographies and online public access catalogs need to be built in a way of data reuse through an integrated library system. Fourth, as a differentiated function for national bibliography, various services such as user tagging and national bibliographic statistics are provided along with various browsing functions. In addition, services of analysis of national bibliographic big data, links to electronic publications, and mass download of linked data should be provided, and it is necessary to identify users' needs and provide open services that reflect them in order to develop differentiated services. Through the current trends and considerations of the national bibliographies analyzed in this study, it will be possible to explore changes in national and international national bibliography.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Land and Housing Review
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• v.8 no.1
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• pp.1-11
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• 2017

The government has been constructing and supplying public rental housing to ordinary people in order to stabilize housing since 1989. However, the public rental houses initially supplied to ordinary people are at high risk for safety accidents due to the deterioration of the facilities. Therefore, this study is aimed to propose a solution to solve the life safety hazards of the old rental housing complex as a follow-up study of Analysis of Accident Patterns and Hazard Factor for Life Safety in Rental Housing Complex. Types of life safety accidents that occur in public rental housing complexes are sliding, falling, crash, falling objects, breakage, fire accidents, traffic accidents and criminal accidents. The types of safety accidents that occur in rental housing complexes analyzed in this study are sliding, crashes, falling objects, and fire accidents. Although the incidence of safety accidents such as falling, breakage, traffic accidents and crime accidents in public rental housing complexes is low, these types are likely to cause safety accidents. The method of this study utilized interviews and seminar results, and it suggested ways to solve the life safety hazards in rental housing complexes. Interviews were conducted with residents and managers of rental housing complexes. Seminars were conducted twice with experts in construction, maintenance, asset management, housing welfare and safety. Through interviews and seminars, this study categorizes the life safety hazards that occur in rental housing complexes by types of accidents and suggests ways to resolve them as follows. (1) sliding ; use of flooring materials with high friction coefficient, installation of safety devices such as safety handles, implementation of maintenance, safety inspections and safety education, etc. (2) falling ; supplementation of safety facilities, Improvement of the design method of the falling parts, Safety education, etc. (3) crash ; increase the effective width of the elevator door, increase the effective width of the lamp, improve the lamp type (U type ${\rightarrow}$ I type), etc. (4) falling objects and breakage ; design of furniture considering the usability of residents, replacement of old facilities, enhancement of safety consciousness of residents, safety education, etc. (5) fire accidents ; installation of fire safety equipment, improvement by emergency evacuation, safety inspection and safety education, etc. (6) traffic accidents ; securing parking spaces, installing safety facilities, conducting safety education, etc. (7) criminal accidents; improvement of CCTV pixels, installation of street lights, removal of blind spots in the complex, securing of security, etc. The roles of suppliers, administrators and users of public rental housing proposed in this study are summarized as follows. Suppliers of rental housing should take into consideration the risk factors that may arise not only in the design and construction but also in the maintenance phase and should consider the possibility of easily repairing old facilities considering the life cycle of rental housing. Next, Administrators of rental housing should consider the safety of the users of the rental housing, conduct safety checks from time to time, and immediately remove any hazardous elements within the apartment complex. Finally, the users of the rental housing needs to form a sense of ownership of all the facilities in the rental housing complex, and efforts should be made not to cause safety accidents caused by the user's carelessness. The results of this study can provide the necessary information to enable residents of rental housing complexes to live a safe and comfortable residential life. It is also expected that this information will be used to reduce the incidence of safety accidents in rental housing complexes.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.6
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• pp.10-20
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• 2014

Today, most roof spaces are being designed as places for resting. The use of the roof spaces needs to be raised otherwise, budgeting or costs involved can be wasteful. A well-made plan is needed to increase the use of the roof spaces. The behavior of and preference for roof spaces could differ depending on building usage because the users of these roof spaces can be different. Therefore, this study selected 4 building types depending on usage: public buildings, educational and research buildings, medical buildings, and commercial buildings. Two buildings that created roof spaces per building type were selected. A survey was undertaken of the user experience of roof spaces on the buildings. The behavior and preference of roof spaces depending on building types were analyzed and the results are as follows. The behavior of using roof spaces regarding purpose, motivation, frequency, and average length of stay were different depending on the building types. In terms of purpose, over all four building types, taking a rest was the primary reason for using roof spaces. However, talking and smoking in public buildings, smoking, taking a walk or stretching, and viewing the exterior landscape in educational and research buildings, taking a walk or stretching and talking in medical buildings, taking care of children and talking in commercial buildings were also important reasons for using roof spaces. The preference of roof space components such as plants, paving materials, and facilities were different depending on the building types. In terms of plants, the users of public buildings preferred herbaceous plants and vegetables/aquatic plants more than the users of other building types. The users of medical buildings preferred vegetables/aquatic plants, and the users of commercial buildings preferred arbores, herbaceous plants, and vegetables/aquatic plants more than the users of other building types. This study provides empirical data for the behavior and the preference of roof spaces depending on building types. These findings could provide new insights into how to increase the use of roof spaces.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.903-916
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• 2021

An area's topography refers to the shape of the earth's surface, described by its elevation, slope, and aspect, among other features. The topographical conditions determine energy flowsthat move water and energy from higher to lower elevations, such as how much solar energy will be received and how much wind or rain will affect it. Another common factor, the topographic wetness index (TWI), is a calculation in digital elevation models of the tendency to accumulate water per slope and unit area, and is one of the most widely referenced hydrologic topographic factors, which helps explain the location of forest vegetation. Analyses of topographical factors can be calculated using a geographic information system (GIS) program based on digital elevation model (DEM) data. Recently, a large number of free open source software (FOSS) GIS programs are available and developed for researchers, industries, and governments. FOSS GIS programs provide opportunitiesfor flexible algorithms customized forspecific user needs. The majority of biodiversity in island areas exists at about 20% higher elevations than in land ecosystems, playing an important role in ecological processes and therefore of high ecological value. However, island areas are vulnerable to disturbances and damage, such as through climate change, environmental pollution, development, and human intervention, and lacks systematic investigation due to geographical limitations (e.g. remoteness; difficulty to access). More than 4,000 of Korea's islands are within a few hours of its coast, and 88% are uninhabited, with 52% of them forested. The forest ecosystems of islands have fewer encounters with human interaction than on land, and therefore most of the topographical conditions are formed naturally and affected more directly by weather conditions or the environment. Therefore, the analysis of forest topography in island areas can be done more precisely than on its land counterparts, and therefore has become a major focus of attention in Korea. This study is focused on calculating the performance of different topographical factors using FOSS GIS programs. The test area is the island forests in Korea's south and the DEM of the target area was processed with GRASS GIS and SAGA GIS. The final slopes and TWI maps were produced as comparisons of the differences between topographic factor calculations of each respective FOSS GIS program. Finally, the merits of each FOSS GIS program used to calculate the topographic factors is discussed.