• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.12
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• pp.1397-1404
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• 2014

Grab cranes are used for multi-purpose when the sand and soil are deposited into harbor wharf or the undersea construction is performed. Among the components of crane grab, the wire drum and disc brake pad are key expendables and have disadvantages that lot of heat is generated and very expensive when replacing them. In this study, the thermal image analysis for the disc brake, which works with wire drum of the crane is suggested. The suggested system performs the pad thermal diagnosis through the thermal image using the characteristics that the disc and pad surface temperatures are distributed abnormally before the brake failure and the disc pad damage. Therefore, the damage by the failure can be prevented by discovering the abnormality of the machine parts before failure and the life cycle of the pad and the cost can be extended and saved by operating the crane performing constant checkup for the overload.

• Smart Structures and Systems
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• v.10 no.3
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• pp.229-251
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• 2012

Guided waves have shown a great potential for structural health monitoring (SHM) applications. In contrast to traditional non-destructive testing (NDT) methodologies, a key element of SHM approaches is the high process of automation. The monitoring system should decide autonomously whether the host structure is intact or not. A basic requirement for the realization of such a system is that the sensors are permanently installed on the host structure. Thus, baseline measurements become available that can be used for diagnostic purposes, i.e., damage detection, localization, etc. This paper contributes to guided wave-based inspection in anisotropic materials for SHM purposes. Therefore, computational strategies are described for both, the solution of the complex equations for wave propagation analysis in composite materials based on exact elasticity theory and the popular global matrix method, as well as the underlying equations of two active damage localization algorithms for anisotropic structures. The result of the global matrix method is an angular and frequency dependent wave velocity characteristic that is used subsequently in the localization procedures. Numerical simulations and experimental investigations through time-delay measurements are carried out in order to validate the proposed theoretical model. An exemplary case study including the calculation of dispersion curves and damage localization is conducted on an exemplary unidirectional composite structure where the ultrasonic signals processed in the localization step are simulated with the spectral element method. The proposed study demonstrates the capabilities of the proposed algorithms for accurate damage localization in anisotropic structures.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1054-1064
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• 2017

With the development of cloud computing, internet technology and Internet of Things(IoT), most of applications are being smarter and changing from native application to SaaS (Software as a Service) application. New versatile SaaS applications are being released through various app portals (e.g. appstore, googleplay, T-Store, and so on). However, a user has a difficulty in searching, choosing an suitable application to him. It is also hard for him to know what functions of each SaaS application are useful. He wants to be recommended something inter-operated SaaS service according to his personality and his situation. Therefore, this paper presents a way of making mash-up of SaaS applications in order to provide the most convenient inter-operated SaaS service to user. This paper also presents SaaS Application Mash-up Framework (SAMF), complex context and rule matrix. The proposed SAMF is a main system that totally manage SaaS application mash-up service. Complex context and rule matrix are key components in order to recommend what SaaS applications are needed and how those SaaS applications are inter-operated. The SAMF collects complex contexts (User Description, Status Description, SaaS Service Description) in order to choose which SaaS applications are useful, analyze what functions to use, how to mash-up.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.223-232
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• 2016

This study focuses on the new teaching assistance robot platform and the cloud-based education service model to support the server. In the client area we would like to use the teacher assistant robot in elementary school classrooms to utilize the language education service platform. Emerging IoT technology will be adopted to provide a comfortable classroom environment and various media interfaces. Extensive precedent review and case study have been conducted to identify basic requirements of proposed service platform. Embedded system and technology for image recognition, speech recognition, autonomous movement, display, touch screen, IR sensor, GPS, and temperature-humidity sensor were extensively investigated to complete the service. Key findings of this paper are optimized service platform with cloud server system and possibilities of potential smart classroom with intelligent robot by adopting IoT and BIM technology.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.159-167
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• 2015

The nature of port logistics requires global visibility and traceability. However, the traditional RFID technology still applied cannot meet these demands. IP-USN and M2M in port logistics have faced challenges of the prerequisite of network composition and immense communications at the base where a grand number of containers are installed. To resolve the issue, this study suggests an IP-RFID-based smart port logistics service platform. The IP-RFID-based port logistics service system resolved the communication problem by separating the RFID tag and AP, which allows internet connection, from the tag. The tags connected with thermo-sensors, humidity sensors, pressure sensors, GPS, etc. are attached to the containers insuring global visibility and traceability, key factors in logistics, by obtaining desired real-time information regardless of time and location.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.793-801
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• 2022

Most Bluetooth devices are commonly used in various ways these days, but they can be often lost due to small-size devices. However, most Bluetooth protocol do not provide authentication functions to legitimate owners, and thus someone who obtains the lost Bluetooth device can easily connect to their smart devices to use it. In this paper, we propose NBAS can authenticates legitimate owners using NFT on lossy Bluetooth devices.NBAS generates a digital wallet on the blockchain using the decentralized network Ethereum blockchain and facilitating the MAC address of the Bluetooth device in the digital wallet. The owner of the wallet uses a private key to certify the Bluetooth device using NFT. The initial pairing time of NBAS was 10.25 sec, but the reconnection time was 0.007 sec similar to the conventional method, and the pairing rejection time for unapproved users was 1.58 sec on average. Therefore, the proposed NBAS effectively shows the device authentication over the conventional Bluetooth.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.170-178
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• 2022

A high-performing Earned Value Management System (EVMS) can influence project success and help stakeholders meet project objectives. Although EVMS processes are well-supported by technical guidelines and standards, project managers often face challenges related to the project culture, team, resources, and business practices that make up the project environment within which an EVMS is being used. A comprehensive literature review revealed a lack of a data-driven and consistent assessment frameworks that can gauge the environment surrounding EVMS implementation. This paper will discuss the EVMS environment of construction and environmental projects, and examine its impact on cost performance. The authors used a multi-method approach to identify 27 environment factors that make up the EVMS environment, assessing them on 18 construction and environmental projects worth over $2 billion of total cost. Research methods employed include: (1) a literature review of more than 300 references; (2) a survey of 294 respondents; and (3) remote research charrettes with more than 60 participating expert practitioners. Culture (one of the identified environment categories) was found to be relatively more important in terms of its impact on the EVMS environment, followed by people, practices, and resources. These exploratory results show statistically significant differences in cost performance between completed projects with either a good or poor environment, for the sample projects. Key environment factors are outlined, and guidance is provided to practitioners around how to set up an effective EVMS environment in a construction or environmental project to inform decision-making and support achieving the project cost objectives successfully.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.124-132
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• 2022

Recently, due to COVID-19, online remote classes and non-face-to-face exams have made it difficult to manage class attitudes and exam cheating. Therefore, there is a need for a system that automatically recognizes and detects the behavior of students online. Action recognition, which recognizes human action, is one of the most studied technologies in computer vision. In order to develop such a technology, data including human arm movement information and information about surrounding objects, which can be key information in online classes and exams, are needed. It is difficult to apply the existing dataset to this system because it is classified into various fields or consists of daily life action. In this paper, we propose a dataset that can classify attitudes in real-time online tests and classes. In addition, it shows whether the proposed dataset is correctly constructed through comparison with the existing action recognition dataset.

• Smart Structures and Systems
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• v.31 no.3
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• pp.283-299
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• 2023

Structural stability of floating platforms has long since been a crucial issue in the field of marine engineering. Excessive motions would not only deteriorate the operating conditions but also seriously impact the safety, service life, and production efficiency. In recent decades, several control devices have been proposed to reduce unwanted motions, and an attractive one is the tuned heave plate (THP). However, the THP system may reduce or even lose its effectiveness when it is mistuned due to the shift of dominant wave frequency. In the present study, a novel adaptive tuned heave plate (ATHP) is proposed based on inerter by adjusting its inertance, which allows to overcome the limitation of the conventional THP and realize adaptations to the dominant wave frequencies in real time. Specifically, the analytical model of a representative semisubmersible platform (SSP) equipped with an ATHP is created, and the equations of motion are formulated accordingly. Two optimization strategies (i.e., J₁ and J₂ optimizations) are developed to determine the optimum design parameters of ATHP. The control effectiveness of the optimized ATHP is then examined in the frequency domain by comparing to those without control and controlled by the conventional THP. Moreover, parametric analyses are systematically performed to evaluate the influences of the pre-specified frequency ratio, damping ratio, heave plate sizes, peak periods and wave heights on the performance of ATHP. Furthermore, a Simulink model is also developed to examine the control performance of ATHP in the time domain. It is demonstrated that the proposed ATHP could adaptively adjust the optimum inertance-to-mass ratio by tracking the dominant wave frequencies in real time, and the proposed system shows better control performance than the conventional THP.