• 전기의세계
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• v.44 no.8
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• pp.34-38
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• 1995

• 전기의세계
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• v.44 no.9
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• pp.43-47
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• 1995

• Journal of Engineering Education Research
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• v.15 no.5
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• pp.69-77
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• 2012

GNTECH-ICEE, which this study seeks to investigate and evaluate, demonstrates a new system of training innovative engineers. An essential component of this operation is a Small Engineering Group (SEG) that links professors, students and industrial experts together, to study and apply different techniques in determining the processes and products that relate industrial sectors needs. As an education program, SEG also provides a right direction for educating students, and generates industry-university link based human resources. Through these efforts, GNTECH-ICEE has effectively trained creative, professional, and practical engineers, by operating a variety of programs for meeting industrial needs and enhancing engineering education. SEG has many merits that have influenced its success so far, but the program also faces some challenges. The merits include; strong group bondage, practical ability incubation, and efficient administrative support. In terms of demerits, it is evident that sufficient theoretical education and local small-middle size enterprises (SMEs)' sustainable participation cannot be warranted. Thus, we propose that initiative strategies have been helpful to maximize GNTECH-ICEE's goal of making students into multi-player engineer, but continuously financial and administrative strategies be put into place in order to guarantee SMEs' long-term devotion to the program, and to help create a sustainable network between students and the companies involved.

• Journal of Engineering Education Research
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• v.17 no.2
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• pp.25-34
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• 2014

The purpose of this study is to examine examples of college engineering students who utilize a creative workshop that are in line with non-curricular activity support both at a domestic and foreign learning environment. It also seeks the improvements of a non-curricular teaching plan utilizing Tongmyong University's Creative Engineering Center. To achieve the intended goal, it has carried out survey satisfaction levels targeting students who visited the Creative Engineering Center and has suggested the way for sustainable operations of a Creative workshop at Tongmyong university's Creative Engineering Center to perform the development of student projects, the securement of infrastructure and the development of equipment training program in conjunction with the University Specialization.

• Journal of Engineering Education Research
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• v.19 no.2
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• pp.70-75
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• 2016

This paper deals with a result of case study for the development of CEA(Course-Embedded Assessment) design tool for engineering education accreditation implementing programs. Many programs have been devoting efforts to apply CEA to their engineering education. In order to effectively apply the CEA to educational program, it is required to develop the standardized form or scheme for CEA application. As a preliminary approach, we propose the design tool and the result of a case study for CEA application in engineering education.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.990-996
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• 2015

This paper proposes an enhanced scheme adding a sign detector at the front of the correlator in STDR (sequence time domain reflectometry) system. We have executed simulations to show the improvement of detection performance in two fault types and various fault locations. Consequently, it can be shown that the proposed scheme improves the detection performance of the location of far-fault without increasing the computational complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.715-722
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• 2016

In this paper, by applying the linear least squared error algorithm, we derive an estimation technique for three dimensional target location when a number of radars are used in detecting a target. The proposed technique is then enhanced by combining GPS information and by assigning variable weights to information sources. The enhanced performance of proposed techniques is confirmed via simulation. It is also observed from simulation results that the performance is robust to the uncertainty of information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.11
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• pp.637-644
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• 2014

This paper investigates the detection performance of fault location using STDR(sequence time domain reflectometry) and SSTDR(spread spectrum time domain reflectometry) with various length and types of sequences, and then, proposes an improved detection technique by eliminating the injected signal in SSTDR. The detection error rates are compared and analyzed in power line channel model with various fault locations, fault types, and spreading sequences such as m-sequence, binary Barker sequence, and 4-phase Frank sequence. It is shown that the proposed technique is able to improve the detection performance obviously when the reflected signal is weak or the fault location is extremely close.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.447-454
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• 2024

Construction site monitoring is pivotal for overseeing project progress to ensure that projects are completed as planned, within budget, and in compliance with applicable laws and safety standards. Additionally, it seeks to improve operational efficiency for better project execution. To achieve this, many researchers have utilized computer vision technologies to conduct automatic site monitoring and analyze the operational status of equipment. However, most existing studies estimate real-world 3D information (e.g., object tracking, work status analysis) based only on 2D pixel-based information of images. This approach presents a substantial challenge in the dynamic environments of construction sites, necessitating the manual recalibration of analytical rules and thresholds based on the specific placement and the field of view of cameras. To address these challenges, this study introduces a novel method for 3D visualization and status analysis of construction site objects using 3D reconstruction technology. This method enables the analysis of equipment's operational status by acquiring 3D spatial information of equipment from single-camera images, utilizing the Sam-Track model for object segmentation and the One-2-3-45 model for 3D reconstruction. The framework consists of three main processes: (i) single image-based 3D reconstruction, (ii) 3D visualization, and (iii) work status analysis. Experimental results from a construction site video demonstrated the method's feasibility and satisfactory performance, achieving high accuracy in status analysis for excavators (93.33%) and dump trucks (98.33%). This research provides a more consistent method for analyzing working status, making it suitable for practical field applications and offering new directions for research in vision-based 3D information analysis. Future studies will apply this method to longer videos and diverse construction sites, comparing its performance with existing 2D pixel-based methods.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.455-462
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• 2024

In the construction industry, known for its dynamic and hazardous environments, there exists a crucial demand for effective safety incident prevention. Traditional approaches to monitoring on-site safety, despite their importance, suffer from being laborious and heavily reliant on subjective, paper-based reports, which results in inefficiencies and fragmented data. Additionally, the incorporation of computer vision technologies for automated safety monitoring encounters a significant obstacle due to the lack of suitable training datasets. This challenge is due to the rare availability of safety accident images or videos and concerns over security and privacy violations. Consequently, this paper explores an innovative method to address the shortage of safety-related datasets in the construction sector by employing generative artificial intelligence (AI), specifically focusing on the Stable Diffusion model. Utilizing real-world construction accident scenarios, this method aims to generate photorealistic images to enrich training datasets for safety surveillance applications using computer vision. By systematically generating accident prompts, employing static prompts in empirical experiments, and compiling datasets with Stable Diffusion, this research bypasses the constraints of conventional data collection techniques in construction safety. The diversity and realism of the produced images hold considerable promise for tasks such as object detection and action recognition, thus improving safety measures. This study proposes future avenues for broadening scenario coverage, refining the prompt generation process, and merging artificial datasets with machine learning models for superior safety monitoring.