• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.81-94
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• 2016

The Korean real-time traffic responsive control systems, Cycle Offset Split Model of Seoul (COSMOS), employs a single theoretical model to estimate the degree-of-saturation (DS) on approaches. However, the deployment of the system has been accomplished without practical consideration of its field performance. This paper delivers a diagnosis study performed to find the relationships yet known on the DS values against the operational conditions unproved in theory but ordinarily observed in field practice. Based on the analysis of the historical log data (476,505 cycles) obtained from the COSMOS server, it was found; (1) full coverage of lane detections should perform better than the sample coverage of detection in ordinary conditions, (2) the sample coverage of detection perform better than the other case with an exclusive bus lane, (3) detection in which a shared lane is involved provide poor estimation of DS, (4) poor DS estimation when a detection lane is adjacent to a shared lane, and (5) the DS values obtained during a day can hardly be stable all time. The findings suggest traffic engineers a progressive direction to move forward for the next real-time traffic control systems.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.236-242
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• 2019

In this study, we developed the system that can collect and store environmental disaster data into the database and use it for environmental disaster management by converting structured and unstructured documents such as images into electronic documents. In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. Environmental disaster information is one of important elements of disaster cycle. Environment disaster information management refers to the act of managing and processing electronic data about disaster cycle. However, these information are mainly managed in the structured and unstructured form of reports. It is necessary to manage unstructured data for disaster information. In this paper, the intelligent generation approach is used to convert handout into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored into the disaster database system. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle. The expected effect of this research will be able to apply it to smart environmental disaster management and decision making by combining artificial intelligence technologies and historical big data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1655-1665
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• 2014

Dynamic displacements of structures shows general behavior of structures. Generally, It is used to estimate structure condition and trustworthy physical quantity directly. Especially, measuring vertical displacement which is affected by moving load is very important part to find or identify a problem of bridge in advance. However directly measuring vertical displacement of the bridge is difficult because of test conditions and restriction of measuring equipment. In this study, Artificial Neural Network (ANN) is used to suggest estimation method of bridge displacement to overcome constrain conditions, restriction and so on. Horizontal strain and vertical displacement which are measured by appling random moving load on the bridge are applied for learning and verification of ANN. Measured horizontal strain is used to learn ANN to estimate vertical displacement of the bridge. Numerical analysis is used to acquire learning data for axis strain and vertical displacement for applying ANN. Moving load scenario which is made by vehicle type and vehicle distance time using Pearson Type III distribution is applied to analysis modeling to reflect real traffic situation. Estimated vertical displacement in respect of horizontal strain according to learning result using ANN is compared with vertical displacement of experiment and it presents vertical displacement of experiment well.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.27-40
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• 2006

The traffic information is provided, which based on the volume of traffic, speed, occupancy collected through the currently operating Vehicle Detector System(VDS). In addition to the trend in utilization fold of traffic information is increasing gradually with the applied various fields and users. Missing data in Vehicle detector data means series of data transmitted to controller without specific property. The missing data does not have a data property, so excluded at the whole data Process Hence, increasing ratio of missing data in VDS data inflicts unreliable representation of actual traffic situation. This study presented the imputation process due out which applied the methodologies that utilized adjacent stations reference and historical data utilize about missing data. Applied imputation process methodologies to VDS data or SeoHaeAn/Kyongbu Expressway, currently operation VDS, after processes at missing data ratio of an option. Imputation process held presented to per lane-30seconds-period, and morning/afternoon/daily time scope ranges classified, and analyzed an error of imputed data preparing for actual data. The analysis results, an low error occurred relatively in the results of the imputation process way that utilized a historical data compare with adjacent stations reference methods.

• Economic and Environmental Geology
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• v.49 no.3
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• pp.181-191
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• 2016

We estimate the eruption history and magmatic eruptive volumes of each rock units to evaluate the volcanic eruption scale and volcanic hazard of the Ulleung Island. Especially, Maljandeung Tuff represents about 19~5.6 ka B.P. from $^{14}C$ dating, and Albong Trachyandesite, about 0.005 Ma from K-Ar dating in recent age dating data. These ages reveal evidences of volcanic activities within the last 10,000 years, indicating that the Ulleung Island can classify as an active volcano with possibility of volcanic eruption near future. Accumulated DRE-corrected eruptive volume is calculated at $40.80km^3$, within only the island. The calculated volumes of each units are $3.71km^3$ in Sataegam Tuff, and $0.10km^3$ in Maljandeung Tuff but $12.39km^3$ in accounting the distal and medial part extended into southwestern Japan. Volcanic explosivity indices range 1 to 6, estimating from the volumes of each pyroclastic deposits. The colossal explosivity indices are 5 in Sataegam Tuff, and 6 in Maljandeung Tuff in accounting the distal and medial part. Therefore, it is necessary for appropriate researches regarding possibility of volcanic eruption of the island, and establishment system of the evaluation and preparation for volcanic hazard based on the researches is required.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.21-29
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• 2018

In the era of the 4-th industrial revolution, the concept of smart factory is emerging. There are efforts to predict the occurrences of facility errors which have negative effects on the utilization and productivity by using data analysis. Data composed of the situation of a facility error and the type of the error, called the facility error log, is required for the prediction. However, in many manufacturing companies, the types of facility error are not precisely defined and categorized. The worker who operates the facilities writes the type of facility error in the form with unstructured text based on his or her empirical judgement. That makes it impossible to analyze data. Therefore, this paper proposes a framework for constructing a phrase network to support the identification and classification of facility error types by using facility error logs written by operators. Specifically, phrase indicating the types are extracted from text data by using dictionary which classifies terms by their usage. Then, a phrase network is constructed by calculating the similarity between the extracted phrase. The performance of the proposed method was evaluated by using real-world facility error logs. It is expected that the proposed method will contribute to the accurate identification of error types and to the prediction of facility errors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1166-1174
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• 2015

There were the attempts to prevent the spread of harmful materials of the agri-food through the record tracking of the products with the bar code, the partial information tracking of the agri-food storage and the delivery vehicle, or the control of the temperature by intuition. However, there were many problems in the attempts because of the insufficient information, the information distortion and the independent information network of each distribution company. As a result, it is difficult to prevent the spread over the life-cycle of the agri-food using the attempts. To solve the problems, we propose the mechanism mainly to do context awareness, predict, and track the harmful materials of agri-food using big data processing.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.358-361
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• 2004

The objective of establishing IETM (Interactive Electronic Technical Manual) is not the just making into on-1 me system of technical manuals such as specifications. It is to produce construction standard documents in an electronic manual system to turn technical contents such as construction methods in technical manuals into multimedia information. Also, it is to maximize real-time information delivery by using visual data of construction sites. IETM enables to check not only design standards in the design but also applied situation of a construction method based on proper design standards and historical construction data on a real time basis. This study suggests a modeling method of IETM applicable to design and construction phase. The study includes a framework and a scenario for developing design IETM for construction project.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1751-1758
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• 2018

An integrated medical information system that integrates systems consisting of different environments centered on hospital information systems should be provided as a system that prioritizes the improvement of the quality of medical services, customer satisfaction, and patient safety. The RBAC-based medical information system is granted the access right according to task type, role, and rules. Through this, it is possible to use SMS channel, medical reservation and cancellation, customized statistics, and CRM / EMR interworking service using multi-channel to enable communication service without help of counselor and reduce the default rate of reservation patient, Operational improvement services can be extended to medical staff, patients and their families, as well as expanding to important decisions for patients.