• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.99-113
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• 1999

This paper describes a study which was undertaken in Ulsan City. It attempted to develop stepwise procedures that would aid Ulsan City in making decision of primary importance: what is the optimal site location for establishing a new Industrial Technology Research Park for public development\ulcorner The presented modeling procedures are an adaption of a number of exiting methods for the evaluation of industrial site potential. The procedures to determine the best site location can be divided into three phases : (i)defining the information necessary to compare potential sites, (ii) collecting the information for each site and (iii)evaluation potential sites utilizing the location model.

• Journal of the Korean housing association
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• v.13 no.3
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• pp.1-9
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• 2002

Desolation of the earth due to environmental pollution is rising as a world wide problem and concern. At this point we need to look into the problem and set up a direction for the future. In order to reveal the change of garbage problems in our county's civil life, a researcher analyzed 369 garbage related articles from the first edition of Dong A Ilbo up to 1990. The following is the result of data from garbage related articles. It is organized by age and era(10 years). 1) Number of articles by year roses in 1921 after first publication of garbage problem article. In the 1930s, the number of articles drastically increased in 1937. From then on, the number of articles declined until early 1970s but roses again from 1978. 2) Yearly change in articles was a mere 1.2 columns in between 1920 to 1960. In the 1970's, relative importance increased and over 5 columns were published. Articles rose in the 1980s with over 3.4 and 5 columns. 3) The contents of the articles can be classified into cleaning problems, collecting and transporting, expenses, and recycling. Garbage disposal problems continued until the 1970s. Regarding garbage collecting problems, form of collecting container and location was discussed. Laws were revised after garbage disposal areas were discussed in the 1920s. Expenses were levied from the 1930s and rising cost and double charge problems were subjected. Garbage recycling began in the 1920s and continued until 1900s.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.567-575
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• 2017

Location-based services play an important role in many applications such as disaster warning systems and recommendation systems. These applications often require not only location information (e.g., name, latitude, longitude, etc.) but also the impact of events (e.g., earthquake, typhoon, etc.) on locations. Recently, to provide the impact of an event on a location, how to calculate location trustiness by using multimodal information such as earthquake information and disaster sensor data is researched. In the previous approach, the linear decrement of impact value of an event is applied to obtain the location trustiness of a specific location. In this paper, we propose a new approach to enrich location trustiness, that is, the impact of an event on a location, by using social media information additionally. Firstly, we design a collecting system for earthquake information and social media data. Secondly, we present an approach of location trustiness calculation based on earthquake information. Finally, we propose a new approach to enrich location trustiness by augmenting the trustiness in spatially distributed manner based on social media.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.437-444
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• 2023

Location-based services play a vital role in our daily lives. While these services enhance user convenience, user's privacy is at risk because they lead to a rapid surge in collecting and utilizing location information for a user. In this paper, we design and implement an application that securely records and manages user location information. We enhance the privacy protection aspect concerning location information by providing some features. Utilizing Room DB, we store collected personal location information in the user's local database instead of the server of the location-based service provider. Furthermore, user can initiate and terminate recording at their discretion, thereby enhancing the protection of personal information related to location data. User's unease regarding their movement paths is reduced by empowering them to have control over their own location information.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.391-394
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• 2007

The field survey of a tsunami damage is carried out to collect and obtain real data. There data can be used to make the countermeasures against possible tsunami attackings. In the survey, many data collecting techniques such as interviewing with local people, taking pictures of the inundated field, recording the location by the GPS, measuring the level and distance of the maximum flood level can be used for practical and effective survey.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.902-905
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• 2008

With development of internet, location-based service (LBS) is getting more interest in wire and wireless network, and many studies are in progress to provide various services such as GIS and GPS technique related to this. Today positioning and information providing service is accomplished by mobile communication companies' chasing the cell or building unique GIS system, but there is a lot of building-cost that must be endured to define boundary of collecting precise location information from chasing Cell and provide unique location information service. In this paper, it desires to make the database of location information with using GPS, relatively precise satellite positioning system, to cut the cost of building location information service, and to present service models such as children's positioning service that is to reduce the cost of building GIS by using Web Open API and provide location information of discrete object in real-time.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1963-1969
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• 2009

This paper introduces method to estimate pollutant negative influence to polymer type insulator according to the international standard. To accomplish this goal, effective sample collecting method was surface was collected directly with the same dimension. Distilled Through this method pollute is easily and accurately collected. The second step is pollutant analysis. Several analyze item is selected such as quantity, conductivity, contact angle, Optical Microscope(OM), IR spectrometer(FT-IR), Equivalent Salt Deposit Density(ESDD), Thermal Analyzer(TA) and ICP-AES. The third step, best represent tunnel was selected considering location, length and natural surroundings. Also to consider the difference at inside the tunnel, several bracket insulators were selected along to the location. To make the result precise, above procedure was repeated several times at the same target. Finally relation among type of train, numbers of movement, surroundings, length will be considered in combination with the pollution. With this result pollute map for KORAIL could be accomplished and inspect period will be optimized case by case.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.17-22
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• 2011

A considerable number of construction disasters occur on pathways. Safety management is usually performed on construction sites to prevent accidents in activity areas. This means that the safety management level of hazards on pathways is relatively minimized. Many researchers have noted that hazard identification is fundamental to safety management. Thus, algorithms for helping safety managers to identify hazardous areas are developed using automated data collection technology. These algorithms primarily search for potential hazardous areas by comparing workers' location logs based on a real-time location system and optimal routes based on BIM. Potential hazardous areas are filtered by identified hazardous areas and activity areas. After that, safety managers are provided with information about potential hazardous areas and can establish proper safety countermeasures. This can help to improve safety on construction sites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1528-1533
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• 2020

Recently, indoor location recognition technology using Wi-Fi fingerprints has been applied and operated in various industrial fields and public services. Along with the interest in machine learning technology, location recognition technology based on machine learning using wireless signal data around a terminal is rapidly developing. At this time, in the process of collecting radio signal data required for machine learning, the accuracy of location recognition is lowered due to distorted or unsuitable data for learning. In addition, when location recognition is performed based on data collected at a specific location, a problem occurs in location recognition at surrounding locations that are not included in the learning. In this paper, we propose a learning data preprocessing technique to obtain an improved position recognition result through the preprocessing of the collected learning data.