• IE interfaces
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• v.18 no.3
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• pp.247-252
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• 2005

In the automated production environment in the present days, the minimization of manual operation becomes a very important factor in increasing the efficiency of the production system. The exit burr produced through the milling operation on the edge of workpiece usually requires manual deburring process to enhance the level of precision of the resulting product. So far, researchers have developed various methods to understand the formation of exit burr in cutting process. One method to analytically identify the formation of exit burr was to use the geometrical information of CAD and CAM data used in automated machining. This method, in turn, generated the information resulting from the analysis such as burr type, cutting region, and exit angle. Up to now, the geometrical data were restricted to the single feature and single path. In this paper, a method to deal with the complicated geometric features such as line segment, arc, hole, and spline will be presented and validated using the field data. This method also deals with the complex workpiece shape which is a combination of multiple features. As for the cutting path, multiple tool path is analyzed in order to simulate the real cutting process. All this analysis is combined into a Windows based software and real data are used to validate the program in the conclusion.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Journal of Power Electronics
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• v.10 no.6
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• pp.604-610
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• 2010

Experiments in electrical engineering should mirror the key components of successful research and development: Understand the basic theory needed, test the resulting concepts by simulation and verify these, finally, in the experiment. For optimal learning outcome continuous monitoring of the progress of each individual student is necessary, immediately repeating those subjects which have not been learned successfully. Classically, this is the task of the teacher. In case of remote-controlled experiments this monitoring process and the repetition of subjects should be automated for optimal learning outcome. This paper describes a remote-controlled experiment combining theory, simulation and the experiment itself with an automated monitoring process. Only the evaluation of the experimental results and their comparison to the simulation results has to be checked by a teacher. This paper describes the details of the educational structure for a remote-controlled experiment introducing active filtering of harmonics. For better understanding the content of the learning material (theory and simulation) as well as the results of the experiment and the underlying booking system are shortly presented.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.329-330
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• 2020

본 논문에서는 Azure 클라우드 플랫폼의 가상서버 호스팅을 이용해 데이터 수집 환경을 구축하고, Azure에서 제공하는 자동화된 기계학습(Automated Machine Learning, AutoML)을 기반으로 데이터 분석 방법에 관한 연구를 수행했다. 가상 서버 호스팅 환경에 LAMP(Linux, Apache, MySQL, PHP)를 설치하여 데이터 수집환경을 구축했으며, 수집된 데이터를 Azure AutoML에 적용하여 자동화된 기계학습을 수행했다. Azure AutoML은 소모적이고 반복적인 기계학습 모델 개발을 자동화하는 프로세스로써 기계학습 솔루션 구현하는데 시간과 자원(Resource)를 절약할 수 있다. 특히, AutoML은 수집된 데이터를 분류와 회귀 및 예측하는데 있어서 학습점수(Training Score)를 기반으로 보유한 데이터에 가장 적합한 기계학습 모델의 순위를 제공한다. 이는 데이터 분석에 필요한 기계학습 모델을 개발하는데 있어서 개발 초기 단계부터 코드를 설계하지 않아도 되며, 전체 기계학습 시스템을 개발 및 구현하기 전에 모델의 구성과 시스템을 설계해볼 수 있기 때문에 매우 효율적으로 활용될 수 있다. 본 논문에서는 NPU(Neural Processing Unit) 학습에 필요한 데이터 수집 환경에 관한 연구를 수행했으며, Azure AutoML을 기반으로 데이터 분류와 회귀 등 가장 효율적인 알고리즘 선정에 관한 연구를 수행했다.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1188-1194
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• 2024

High-energy manufacturing processes, including laser welding, are actively being adopted not only in precision machinery industries but also in the shipbuilding and construction sectors. Laser welding, in particular, is gaining prominence in the industry due to its faster welding speed and reduced distortion compared to conventional arc welding methods. Integration of automated welding systems is anticipated to address challenges in shipbuilding and construction industries, which are currently facing a shortage of skilled labor. For successful implementation of automated welding systems, it is essential to predict and design for the post-welding effects, such as residual deformation and stresses. However, in the case of high-energy manufacturing like laser welding, the welding bead morphology differs from that of arc welding, and the heat load conditions applied during simulation are distinct. To facilitate accurate simulation predictions, the development of a suitable heat source for predicting welding bead morphology in high-energy manufacturing processes is crucial. The Block-dumping method is proposed for rapid simulation and on-site application, with the shape of the welding bead being imperative for its effectiveness. In this study, data on the welding bead morphology of Nickel-based steel was obtained. Using Deep Learning techniques, we successfully predicted the bead morphology and confirmed minimal discrepancies when compared to actual results. This outcome allows for the simulation of welding under untested conditions, offering practical applicability in the field. Additionally, we present a heat source model (heat load condition) to ensure a highly accurate interpretation of the results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.233-233
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• 1996

• Korean Journal of Construction Engineering and Management
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• v.11 no.6
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• pp.14-23
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• 2010

A considerable number of construction disasters occurs on pathway. A safety management in construction sites is usually performed to prevent accidents in activity areas. This means that safety management level of hazards on pathway is relatively minified. Many researchers have introduced that a hazard identification is fundamental of safety management. Thus, algorithms for helping safety managers' hazardous area identification is developed using automated data collection technology. These algorithms primarily search potential hazardous area by comparing workers' location logs based on real-time locating system and optimal routes based on BIM. And potential hazardous areas is filtered by identified hazardous areas and activity areas. After that, safety managers are provided with information about potential hazardous areas and can establish proper safety countermeasures. This can help improving safety in construction sites.

• Safety and Health at Work
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• v.10 no.4
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• pp.461-469
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• 2019

Background: Collisions involving workers and mobile machines continue to be a major concern in underground coal mines. Over the last 30 years, these collisions have resulted in numerous injuries and fatalities. Recently, the Mine Safety and Health Administration (MSHA) proposed a rule that would require mines to equip mobile machines with proximity detection systems (PDSs) (systems designed for automated collision avoidance). Even though this regulation has not been enacted, some mines have installed PDSs on their scoops and hauling machines. However, early implementation of PDSs has introduced a variety of safety concerns. Past findings show that workers' trust can affect technology integration and influence unsafe use of automated technologies. Methods: Using a mixed-methods approach, the present study explores the effect that factors such as mine of employment, age, experience, and system type have on workers' trust in PDSs for mobile machines. The study also explores how workers are trained on PDSs and how this training influences trust. Results: The study resulted in three major findings. First, the mine of employment had a significant influence on workers' trust in mobile PDSs. Second, hands-on and classroom training was the most common types of training. Finally, over 70% of workers are trained on the system by the mine compared with 36% trained by the system manufacturer. Conclusion: The influence of workers' mine of employment on trust in PDSs may indicate that practitioners and researchers may need to give the organizational and physical characteristics of each mine careful consideration to ensure safe integration of automated systems.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.367-372
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• 2010

A power plant's heat exchangers work under poor conditions, such as high temperature, high pressure, corrosion, mechanical stress and vibration. Especially, partial joint-weld of heat exchanger's header to stub-tube is the place where incomplete penetration flaws can easily occur. But, it is hard to evaluate the safety of the structure by conventional nondestructive testing techniques. So it is necessary to test integrity of the weld inside and to develop testing technique and equipment that can detect the flaws at the weld point in order to enhance reliability of the test result. In this study, we developed a suitable automated ultrasonic testing system that can inspect the partial joint-weld of header to stub-tube of power plant. Finally, we showed the efficiency of the automated ultrasonic-testing-system from the application.

• Journal of Platform Technology
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• v.11 no.3
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• pp.45-55
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• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.