• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.141-155
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• 2017

As the use of SNS becomes more active, many people are posting their thoughts about specific events in their SNS in the form of text. As a result, SNS is used in various fields such as finance and distribution to conduct service satisfaction surveys and consumer monitoring. However, in the transportation area, there are not enough cases to utilize unstructured data analysis such as emotional analysis. In this study, we developed an emotional analysis methodology that can be used in transportation by using highway VOC data, which is atypical data collected by Korea Expressway Corporation. The developed methodology consists of morpheme analysis, emotional dictionary construction, and emotional discrimination of the collected unstructured data. The developed methodology was verified using highway related tweet data. As a result of the analysis, it can be guessed that many information and information about the construction and the accident were related to the highway during the analysis period. Also, it seems that users complain about the delay caused by construction and accident.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.583-589
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• 2002

The Internet is absolutely contributed to information telecommunication revolution nowadays. Realizing local network at the various type of buildings such as a company and a university, ethernet is used for subnet and FDDI, ATM are used for backbone mainly in order to get internet services. Processing TCP/IP protocol suite and analyzing the protocol exactly is essential to detecting the problem occurring in the network and developing communication equipment. This paper presents implementation of ethernet LAN protocol analyser which monitors and simulates ICP/IP protocol suite carrying the Internet and non-Internet protocol such as Netware and NetBIOS. MS window98 and visual C are used for development environment and application program operates on the NDIS firmware. The performance analysis on the proposed system is carried out as monitoring and simulating the traffic over LAN of a university. In the result of monitoring the system, the processing time of a packet captured over the LAN is about 1.5ms. In case of simulating the system, the processing time to be taken carrying out TCP connection and disconnection once is packet is about 8.6ms. The performance analysis of monitoring and simulation results satisfies with 10 Mbps ethernet LAN environment.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Smart Structures and Systems
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• v.15 no.3
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• pp.665-682
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• 2015

Structural identification or St-Id is 'the parametric correlation of structural response characteristics predicted by a mathematical model with analogous characteristics derived from experimental measurements'. This paper describes a St-Id exercise on Humber Bridge that adopted a novel two-stage approach to first calibrate and then validate a mathematical model. This model was then used to predict effects of wind and temperature loads on global static deformation that would be practically impossible to observe. The first stage of the process was an ambient vibration survey in 2008 that used operational modal analysis to estimate a set of modes classified as vertical, torsional or lateral. In the more recent second stage a finite element model (FEM) was developed with an appropriate level of refinement to provide a corresponding set of modal properties. A series of manual adjustments to modal parameters such as cable tension and bearing stiffness resulted in a FEM that produced excellent correspondence for vertical and torsional modes, along with correspondence for the lower frequency lateral modes. In the third stage traffic, wind and temperature data along with deformation measurements from a sparse structural health monitoring system installed in 2011 were compared with equivalent predictions from the partially validated FEM. The match of static response between FEM and SHM data proved good enough for the FEM to be used to predict the un-measurable global deformed shape of the bridge due to vehicle and temperature effects but the FEM had limited capability to reproduce static effects of wind. In addition the FEM was used to show internal forces due to a heavy vehicle to to estimate the worst-case bearing movements under extreme combinations of wind, traffic and temperature loads. The paper shows that in this case, but with limitations, such a two-stage FEM calibration/validation process can be an effective tool for performance prognosis.

• Journal of Navigation and Port Research
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• v.42 no.1
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• pp.25-30
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• 2018

This study was carried out to analyze the risk attitude based on situation awareness of the Vessel Traffic Service Operator (VTSO) on the risk of collision between vessels during the monitoring of vessel traffic through the use of the VTS system. In general, when two vessels are in the close quarters situation, we analyzed the degree of risk of collision from the subjective viewpoint of the VTSOs through an administered survey. Chiefly, we analyzed the risk attitudes of each VTSO in the close quarters situation, by comparing it with the calculated value by the CoRi, which is the ship collision risk model from the VTSO's viewpoint. As a result, it was confirmed that more than 40% of the total VTSO was noted as being in a weak risk aversion type of category. Through a review of the results of analyzing the risk attitude of VTSO according to gender, age, VTS career, VTS center position, accident experience, and boarded career, it was found that there was a significant difference in the VTS career, VTS center position and accident experience. In addition, a regression model that is able to further explain the risk attitude of VTSO was derived as a factor that confirmed the significant difference and applied to CoRi to predict the collision risk according to the individual VTSO to be used as a fundamental information gathering tool for providing more accurate and safe VTS service at sea.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.2
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• pp.170-178
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• 2011

In this paper, a research was performed to develop a wireless measurement system for bridge monitoring using MEMS sensor and bluetooth wireless communication module. First, in order to prove the suitability of MEMS sensor for the bridge measurement, its ranges of measuring acceleration and of frequency response were experimented. Also, the quality of wireless communication was tested by an experiment on long-distance communication for the knowledge of maximum communication distance, and also by an experiment on the data transmit-receive capability both inside and outside of a steel box bridge. Later, placing the wireless acceleration sensor system that had been developed in our lab on a bridge in public service, we acquired vibration data from the bridge under traffic load and analyzed its dynamic characteristics in realtime. For the analysis of the data, NExT & ERA algorithm were employed. The result of analysis was compared to the FE analysis of the same bridge, and the comparison made it possible to evaluate the performance of wireless acceleration sensor system. As a result, it was proven that the wireless acceleration sensor system developed with the use of MEMS sensor and bluetooth wireless communication module could be effectively applied to the measurement of structure whose vibration feature was low frequency like a bridge.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.29-39
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• 2006

With the increasing popularity of the wireless network, the vulnerability issue on IEEE 802.1x Wireless Local Area Network (WLAN) are more serious than we expected. Security issues range from mis-configured wireless Access Point(AP) such as session hijacking to Denial of Service(DoS) attack. We propose a new system based on intrusion detection or prevention mechanism to protect the wireless network against these attacks. The proposed system has a security solution on AP that includes an intrusion detection and protection system(IDS/IPS) as an embedded module. In this paper, we suggest integrated wireless IDS/IPS module on AP with wireless traffic monitoring, analysis and packet filtering module against malicious wireless attacks. We also present that the system provides both enhanced security and performance such as on the university wireless campus network.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• Journal of Korea Port Economic Association
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• v.30 no.3
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• pp.15-37
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• 2014

Sustainability is a broad concept involving economic, social and environmental issues in operational and managerial processes. To assist ports to implant sustainability practices into their operations, this paper conceptualizes the structure of sustainability practice in port operations, based on interviews undertaken at Busan port in early 2013. Results revealed that, as a strategic practice to improve their internal business processes, sustainability practices necessitate the simultaneous pursuit of container traffic growth, low environmental impacts and corporate responsible image making, operational efficiency, efficiency of the use of the port area and sustainable growth. Through thematic analysis, the relevant practices were clustered into four sub-dimensions incorporating environmental technologies, continual monitoring and upgrading, internal process improvement, and cooperation and communication. Further, reporting the relevant issues such as barriers and challenges in carrying out sustainability practices, the findings provide useful insights for strategic agenda to assist ports to incorporate sustainability practices in their operations.

• Smart Structures and Systems
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• v.30 no.6
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.