• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.38-50
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• 2023

Recent advances in artificial intelligence (AI) technology have enabled the integration of AI technology into image sensors, such as Closed-Circuit Television (CCTV), to detect specific traffic incidents. However, most incident detection methods have been carried out using fixed equipment. Therefore, there have been limitations to incident detection for all roadways. Nevertheless, the development of mobile image collection and analysis technology, such as image sensors and edge-computing, is spreading. The purpose of this study is to estimate the reducing effect of the incident detection time according to the introduction level of mobile image collection and analysis equipment (or connected vehicles). To carry out this purpose, we utilized data on the number of incidents collected by the Suwon branch of the Gyeongbu expressway in 2021. The analysis results showed that if the market penetration rate (MPR) of connected vehicles is 4% or higher for two-lane expressway and 3% or higher for three-lane expressways, the incident detection time was less than one minute. Furthermore, if the MPR is 0.4% or higher for two-lane expressways and 0.2% or higher for three-lane expressways, the incident detection time decreased compared to the average incident detection time announced by the Korea Expressway Corporation for both two-lane and three-lane expressways.

• Spatial Information Research
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• v.2 no.2
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• pp.147-163
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• 1994

This paper presents three methods of generating the transportation network data out of the topological geographic database in the hope that the conversion of the geographic database file containing the topology to the conventional node-link type trans¬portation network file may facilitate the integration between transportation planning mod¬els and GIS by alleviating the inherent problems of both computing environments. One way of the proposed conversion method is to use the conversion software that allows the bi-directional conversion between the UTPS (Urban Transportation Planning System) type transportation planning model and GIS. The other two methods of data structure conversion approach directly transform the GIS's user-level topology into the transportation network data topology, and have been introduced with codes programmed with FORTRAN and AML (Arc Macro Language) of ARC/INFO. If used successfully, any approach would not only improve the efficiency of transportation planning process and the associated decision-making activities in it, but enhance the productivity of trans¬portation planning agencies.

• Journal of Ocean Engineering and Technology
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• v.33 no.5
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• pp.427-434
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• 2019

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.5
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• pp.325-330
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• 2014

The era of convergence is being applied to all areas of Information and Communication Technology (ICT). The convergence of broadcasting service and communication service almost occurs on smart devices including smartphone. The smart hybrid Digital Multimedia Broadcasting (DMB) is a typical example of the convergence of broadcasting and wireless communication service. The hybrid mobile broadcasting service can support seamless video, 3D, high quality, and additional data services based on network connection between the broadcasting and wireless network. The gateway and terminal (including apps on the smartphone) take the role of the main components on the hybrid service. This paper presents the service concept, main components structure, the implementation of gateway and terminals, and field test to the urban areas for the mobile hybrid system.

• Interaction and multiscale mechanics
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• v.1 no.1
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• pp.103-121
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• 2008

This paper presents an optimization-based method for computing a minimal bounding ellipsoid that contains the set of static responses of an uncertain braced frame. Based on a non-stochastic modeling of uncertainty, we assume that the parameters both of brace stiffnesses and external forces are uncertain but bounded. A brace member represents the sum of the stiffness of the actual brace and the contributions of some non-structural elements, and hence we assume that the axial stiffness of each brace is uncertain. By using the $\mathcal{S}$-lemma, we formulate a semidefinite programming (SDP) problem which provides an outer approximation of the minimal bounding ellipsoid. The minimum bounding ellipsoids are computed for a braced frame under several uncertain circumstances.

• International Journal of Automotive Technology
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• v.3 no.3
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• pp.89-94
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• 2002

This paper presents an architecture for distributed control systems and its underlying methodological framework. Ideas and concepts of distributed systems, artificial intelligence, and soft computing are merged into a unique architecture to provide cooperation, flexibility, and adaptability required by knowledge processing in intelligent control systems. The distinguished features of the architecture include a local problem solving capability to handle the specific requirements of each part of the system, an evolutionary case-based mechanism to improve performance and optimize controls, the use of linguistic variables as means for information aggregation, and fuzzy set theory to provide local control. A distributed traffic control system application is discussed to provide the details of the architecture, and to emphasize its usefulness. The performance of the distributed control system is compared with conventional control approaches under a variety of traffic situations.

• Structural Engineering and Mechanics
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• v.50 no.1
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• pp.37-52
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• 2014

The accurate determination of cable tension is important to the monitoring of the condition of a cable-stayed bridge. When applying a vibration-based formula to identify the tension of a real cable under sag, stiffness and boundary conditions, the resulting error must not be overlooked. In this work, by resolving the implicit frequency function of a real cable under the above conditions numerically, indirect methods of determining the cable force and a method to calculate the corresponding cable mode frequency are investigated. The error in the tension is studied by numerical simulation, and an empirical error correction formula is presented by fitting the relationship between the cable force error and cable parameters ${\lambda}^2$ and ${\xi}$. A case study on two real cables of the Shanghai Changjiang Bridge shows that employing the method proposed in this paper can increase the accuracy of the determined cable force and reduce the computing time relative to the time required for the finite element model.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.399-414
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• 2013

The smart sensor technology has opened new horizons for assessing and monitoring structural health of civil infrastructure. Smart sensor's unique features such as onboard computation, wireless communication, and cost effectiveness can enable a dense network of sensors that is essential for accurate assessment of structural health in large-scale civil structures. While most research efforts to date have been focused on realizing wireless smart sensor networks (WSSN) on bridge structures, relatively less attention is paid to applying this technology to buildings. This paper presents a decentralized damage detection using the WSSN for building structures. An existing flexibility-based damage detection method is extended to be used in the decentralized computing environment offered by the WSSN and implemented on MEMSIC's Imote2 smart sensor platform. Numerical simulation and laboratory experiment are conducted to validate the WSSN for decentralized damage detection of building structures.

• Nutritional Sciences
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• v.2 no.2
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• pp.113-118
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• 1999

Critical evaluation of energy intake data from dietary studies is difficult but important. To investigate the underreporting of total energy intake, we analyzed the one-day dietary intake data collected by 24-hour recall method from 550 elderly Koreans aged 60 years or older. Underreporting was addressed by computing the ratio of energy intake (EI) to estimated basal metabolic rate (BMRest). EI : BMRest ratio was found to be 1.38 for, men and 1.33 for women, with about 14% of men and women classified as underreporters. Underreporting of energy intake was highest in men and women who were overweight, had lower family income, or no school education. For men, the most significant variables to predict the ratio of energy intake to estimated basal metabolic. rate (EI : BMRest) were weight status, members of household, alcohol consumption and age, while income and education level were most significant for women.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.26-34
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• 2024

Most human emotions are conveyed through facial expressions, which represent the predominant source of emotional data. This research investigates the impact of crowds on human emotions by analysing facial expressions. It examines how crowd behaviour, face recognition technology, and deep learning algorithms contribute to understanding the emotional change according to different level of crowd. The study identifies common emotions expressed during congestion, differences between crowded and less crowded areas, changes in facial expressions over time. The findings can inform urban planning and crowd event management by providing insights for developing coping mechanisms for affected individuals. However, limitations and challenges in using reliable facial expression analysis are also discussed, including age and context-related differences.