• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.133-139
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• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.215-222
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• 2022

The purpose of air traffic flow management is to balance demand and capacity in the national airspace, and its performance relies on an accurate capacity prediction of the airport or airspace. This paper developed a regression model that predicts the number of aircraft actually departing and arriving in a terminal maneuvering area. The regression model is based on a boosting ensemble learning algorithm that learns past aircraft operational data such as time, weather, scheduled demand, and unfulfilled demand at a specific airport in the terminal maneuvering area. The developed model was tested using historical departure and arrival flight data at Incheon International Airport, and the coefficient of determination is greater than 0.95. Also, the capacity of the terminal maneuvering area of interest is implicitly predicted by using the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1708-1734
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• 2022

In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value.

• International Journal of Highway Engineering
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• v.17 no.4
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• pp.99-105
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• 2015

PURPOSES: This study aims to establish the priority of introducing anti-icing spray system for regions of the National Highways in South Korea. Using this study, a logical plan for instituting such an anti-icing spray system can be established for the National Highways. METHODS : The Analytical Hierarchy Process (AHP) was employed to prioritize the implementation of an anti-icing spray system on Korean highways. For this purpose, an existing scoring table developed by the Ministry of Land, Infrastructure Transport Affair was slightly modified in order to reflect recent trends in winter maintenance. A survey was conducted to gather the preferences regarding the developed hierarchy of road experts and agencies. Finally, the final score was produced by integrating the scoring results with estimated weights for each evaluation criterion. RESULTS: In general, Honam and the metropolitan areas have relatively high priority while other areas such as Chungcheong, Young Nam, and Gang Won appear to be uniform in importance in terms of establishing an anti-icing spray system. This result may indicate that historical weather data and traffic volumes are significant factors in deciding in winter maintenance polices CONCLUSIONS : In this study, useful insights are suggested regarding winter maintenance by simultaneously performing rapid snow removal and proactive treatment. Issues of resource allocation may be potential research items in the field transportation engineering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• Journal of the Korean Society of Marine Environment & Safety
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• v.5 no.2
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• pp.1-14
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• 1999

To find characteristics and areas of greater risk of oil spill at the coastal waters in Korea, some of risk factors were analyzed with historical data of oil spill and marine traffic. As a result, it is characterized that frequency of oil spill is increasing year by year and greatest percentage of spill source is fishing boat. It is proposed that the ports of Ulsan, Yeosu, Incheon and Pusan will be designated as primary area of risk as they have a higher risk of oil spills and its response authority is required to maintain appropriate regional response capability for prompt and effective response to a future spill incident. In addition, the regional response equipments at Ulsan are examined under a assumption of a medium size spill and it is found that the use of chemical dispersant can be an alternative when mechanical containment and recovery is not feasible in this area, and the existing response equipments may be appropriate to address that size of spill. However, the response authority is required to maintain more numbers of stronger boom for unsheltered waters and more quantity of concentrate dispersant to disperse all spilled oils on the water, furthermore the response authority should be prepared for a possible future catastrophic spill with sufficient equipments.

• Electronics and Telecommunications Trends
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• v.35 no.2
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• pp.17-27
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• 2020

Predicting where and when a crime may occur in an area of interest is one of many strategies of predictive policing. Multidimensional analysis, including CCTV, can overcome the limitations of hotspot prediction, especially of violent crimes. In order to identify the precursors of a crime, it is necessary to analyze dynamic data such as attributes and activities of people, social information, environmental information, traffic flows, and weather. These parameters can be recognized by CCTV. In addition, it provides accurate analysis of the circumstances of a crime in a dynamic situation, calculates the risk, and predicts the probability of a crime occurring in the near future. Additionally, it provides ways to gather historical criminal datasets, including sensitive personal information.

• ETRI Journal
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• v.44 no.2
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• pp.220-231
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• 2022

Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet-of-Things applications. Traffic congestion and environmental concerns can be alleviated by sharing mobility, which reduces the number of vehicles on the road network. The taxi-parcel sharing problem has been considered as an efficient planning model for people and goods flows. In this paper, we enhance the functionality of a current people-parcel taxi sharing model. The adapted model analyzes the historical request data and predicts the current service demands. We then propose two novel online routing algorithms that construct optimal routes in real-time. The objectives are to maximize (as far as possible) both the parcel delivery requests and ride requests while minimizing the idle time and travel distance of the taxis. The proposed online routing algorithms are evaluated on instances adapted from real Cabspotting datasets. After implementing our routing algorithms, the total idle travel distance per day was 9.64% to 12.76% lower than that of the existing taxi-parcel sharing method. Our online routing algorithms can be incorporated into an efficient smart shared taxi system.

• 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.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.