• 산업경영시스템학회지
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• 제13권21호
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• pp.111-117
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• 1990

This paper treats the problem to schedule for train with low transit priority so as to maximizing the number that can be sent during given time without interfering with the fixed schedule for train with high transit priority in a track. We transform the this problem into Time-Expanded Network without traverse time through application of Ford-Fulkerson Model, develop a TENET GENerator(TENETGEN) and obtain the data of TENET using developed TENETGEN. Finally, we seek the optimal solution to these data with Dinic's Maximal-Flow Algorithm and examine the availability of our procedures in personal computer.

• Journal of Korea Trade
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• 제25권8호
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• pp.57-74
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• 2021

Purpose - This study aims to analyze the characteristics of network construction by Norwegian Air and AirAsia X, which are recognized as leading airlines in the long-haul LCC market. Based on this analysis, this study intends to provide implications for networking strategies for Korean LCCs that seek to enter the long-haul market when the aviation market stabilizes again upon the end of the COVID-19 pandemic. Design/methodology - To conduct the network analysis on long-haul low-cost airlines, the Official Airline Guide (OAG) Schedule Analyzer was used to extract long-haul data of Norwegian Air and AirAsia X. To analyze the trend of the long-haul route network, we obtained the data from 3 separate years between 2011 and 2019. The network was analyzed using UCINET 6.0 in order to examine the network structure of long-haul low-cost airlines and the growth trend of each stage. Findings - Analyzing the network of long-haul routes by visualizing the network structure of low-cost carriers showed the following results. In its early years, Norwegian Air's long-haul route network, centering on regional airports in Spain and Sweden, connected European regions, the Middle East, and Africa. As time passed, however, the network expanded and became steadily strong as the airline connected airports in other European countries to North America and Asia. In addition, in 2011, AirAsia X showed links to parts of Europe, such as London and Paris, the Middle East and India, and Australia and Northeast Asia, centering on the Kuala Lumpur Airport. Although the routes in Europe were suspended, the network continued to expand while concentrating on routes of less than approximately 7,000 km. It was found that instead of giving up on ultra-long-haul routes such as Europe, the network was further expanded in Northeast Asia, such as the routes in Korea and Japan centering on China. Originality/value - Until the COVID-19 pandemic broke out, Norwegian Air actively expanded long-haul routes, resulting in the number of long-haul routes quintupling since 2011. The unfortunate circumstance, wherein the world aviation market was rendered stagnant due to the outbreak of COVID-19, hit Norwegian Air harder than any other low-cost carriers. However, in the case of AirAsia X, it was found that it did not suffer as much damage as Norwegian Air because it initially withdrew from unprofitable routes over 7,000 km and grew by gradually increasing profitable destinations over shorter distances. When the COVID-19 pandemic ends and the aviation market stabilizes, low-cost carriers around the world, including Korea, that enter the long-haul route market will need to employ strategies to analyze the marketability of potential routes and to launch the routes that yield the highest profits without being bound by distance. For stable growth, it is necessary to take a conservative stance; first, by reviewing the business feasibility of the operating a small number of highly profitable routes, and second, by gradually expanding these routes.

• Earthquakes and Structures
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• 제26권2호
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• pp.87-96
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• 2024

This paper proposes a logistic multinomial regression approach to model the spatial cross-correlation of damage probabilities among different damage states in an expanded transportation network. Utilizing Bayesian theory and the multinomial logistic model, we analyze the damage states and probabilities of bridges while incorporating damage correlation. This correlation is considered both between bridges in a network and within each bridge's damage states. The correlation model of damage probabilities is applied to the seismic assessment of a portion of Tehran's transportation network, encompassing 26 bridges. Additionally, we introduce extra daily traffic time (EDTT) as an operational parameter of the transportation network and employ the shortest path algorithm to determine the path between two nodes. Our results demonstrate that incorporating the correlation of damage probabilities reduces the travel time of the selected network. The average decrease in travel time for the correlated case compared to the uncorrelated case, using two selected EDTT models, is 53% and 71%, respectively.

• Journal of Information Science Theory and Practice
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• 제10권spc호
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• pp.12-24
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• 2022

This study attempted to investigate changes in collaboration structure for each stage of national Research and Development (R&D) in the artificial intelligence (AI) field through analysis of a co-author network for papers written under national R&D projects. For this, author information was extracted from national R&D outcomes in AI from 2014 to 2019. For such R&D outcomes, NTIS (National Science & Technology Information Service) information from the KISTI (Korea Institute of Science and Technology Information) was utilized. In research collaboration in AI, power function structure, in which research efforts are led by some influential researchers, is found. In other words, less than 30 percent is linked to the largest cluster, and a segmented network pattern in which small groups are primarily developed is observed. This means a large research group with high connectivity and a small group are connected with each other, and a sporadic link is found. However, the largest cluster grew larger and denser over time, which means that as research became more intensified, new researchers joined a mainstream network, expanding a scope of collaboration. Such research intensification has expanded the scale of a collaborative researcher group and increased the number of large studies. Instead of maintaining conventional collaborative relationships, in addition, the number of new researchers has risen, forming new relationships over time.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 Korea-Russia Joint Conference on Geometics
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• pp.59-63
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• 2004

As the applied fields of GIS are expanded to the transportation, developing internet-based applications for transportation information is getting attention increasingly. Most applications developed so far are primarily focused on guidance systems for owner-driven cars. Although some recent ones are devoted to public transportation systems, they show limitations in dealing with the following aspects: (i) people may change transportation means not only within the same type but also among different modes such as between buses and subways, and (ii) the system should take into account the time taken in transfer from one mode to the other. This study suggest the framework for developing a public transportation guidance system that generates optimized paths in the transportation network of mixed means including buses, subways and other modes. For this study, the Genetic Algorithms are used to find the best routes that take into account transfer time and other service-time constraints.

• 한국IT서비스학회지
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• 제19권1호
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• pp.1-16
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• 2020

Suncheon City Regeneration Project is known as the concept of cultural residents. Through the previous projects, the residents' capabilities have been improved, and the projects have been carried out according to their strategies. For this reason, participants in urban regeneration projects are important. The purpose of this study is to actually identify the 'rescue center' and 'direct relationship' with the analysis utilizing the characteristics of social networks NetMiner solution of the participants, who led the project, Suncheon. Surveys and interviews were conducted for participants, and the characteristics of social networks were analyzed in time series to quantify and visualize the results. As a result of the analysis, social networks were changed among the participants before and after the urban regeneration project. Initially, loose networks were denser over time, and initially networks formed only around participants were expanded over time. Network analysis has revealed that the system is strengthening with urban regeneration projects in the form of public and public-private cooperation. This highlights the need for a city-centered urban regeneration strategy centered on people and shows that a dense network of participants can be a success factor.

• 상하수도학회지
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• 제30권5호
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• pp.491-500
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• 2016

Pressure monitoring is expected to be expanded in a water distribution system according to accelerated development of smart water network management technologies caused by appearances of affordable digital infrastructures like computing, storage and bandwidth. However, the placement of pressure sensors has been determined by engineer's technical decisions since there is no well-defined criteria for deciding a suitable location of pressure sensor. This study presents a placement method of pressure sensors based on the consideration of allowable error in calibrating water network analysis modeling. The proposed method is to find a minimum set of pressure sensors for achieving a reliable management of water transmissions main and increasing the efficiency of their real-time operation. In the case study in Y area's transmission main, the proposed method shows equally distributed pressure sensors in terms of hydraulics. It is expected that the proposed method can be used to manage transmission mains stably and construct a robust real-time network analysis system as a minimal criteria.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.401-413
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• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• 한국컴퓨터정보학회논문지
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• 제22권11호
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• pp.97-104
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• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2000년도 춘계학술대회
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• pp.55-60
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• 2000

In recent dates, Internet advertising effects are expanded by the steep increment of the Internet users and the extension of the advertising market will be accelerated through Internet. This paper indicates the importance of Internet advertising and suggests the solution of a network advertising service. The system Is divided into an Advertise Server, an Advertiser and a Web Publisher. This study proposes both the collection and the analysis of traffic data in real time. Also, the banner advertising frames are smoothed for the impression using the solution for the banner exchange engine and are developed using various impression methods, that is, Fixed/variable Banner, Scheduling Banner, Multi-Impression Banner, and Frame Remote Control to increase the advertising effect. And then to increase the advertising effect web publisher, which is specialized in lottery site, is constructed using the network advertising service and various advertising technology.