• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.945-961
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• 2019

Tongue diagnosis is one of the most important diagnostic methods in Traditional Chinese Medicine (TCM). Tongue image segmentation aims to extract the image object (i.e., tongue body), which plays a key role in the process of manufacturing an automated tongue diagnosis system. It is still challenging, because there exists the personal diversity in tongue appearances such as size, shape, and color. This paper proposes an innovative segmentation method that uses image thresholding, gray projection and active contour model (ACM). Specifically, an initial object region is first extracted by performing image thresholding in HSI (i.e., Hue Saturation Intensity) color space, and subsequent morphological operations. Then, a gray projection technique is used to determine the upper bound of the tongue body root for refining the initial object region. Finally, the contour of the refined object region is smoothed by ACM. Experimental results on a dataset composed of 100 color tongue images showed that the proposed method obtained more accurate segmentation results than other available state-of-the-art methods.

• Elastomers and Composites
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• 제56권1호
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• pp.20-31
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• 2021

There is an increasing demand for the rolling resistance reduction in truck bus radial (TBR) tires in the tire industry. In TBR tires, natural rubber is used as a base polymer to prevent wear and satisfy required physical properties (cut and chip). A binary filler system (silica and carbon black) is used to balance the durability of the tire and rolling resistance performance. In this study, natural rubber (NR) compounds applied with a binary filler system were manufactured at different cure temperatures for vulcanizate structure analysis. The vulcanizate structures were categorized into carbon black bound rubber, silica silane rubber network, and chemical crosslink density by sulfur. Regardless of the cure temperature, the cross-link density per unit content of carbon black had a greater effect on the properties than silica due to affinity with NR. The relationship analysis between the mechanical, viscoelastic properties with vulcanizate structure could be a guideline for manufacturing practical TBR compounds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2345-2358
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• 2022

This paper presents a method to generate stained-glass animation from video inputs. The method initially segments an input video volume into several regions considered as fragments of glass by mean-shift segmentation. However, the segmentation predominantly results in over-segmentation, causing several tiny segments in a highly textured area. In practice, assembling significantly tiny or large glass fragments is avoided to ensure architectural stability in stained glass manufacturing. Therefore, we use low-frequency components in the segmentation to prevent over-segmentation and subdivide segmented regions that are oversized. The subdividing must be coherent between adjacent frames to prevent temporal artefacts, such as flickering and the shower door effect. To temporally subdivide regions coherently, we obtain a panoramic image from the segmented regions in input frames, subdivide it using a weighted Voronoi diagram, and thereafter project the subdivided regions onto the input frames. To render stained glass fragment for each coherent region, we determine the optimal match glass fragment for the region from a dataset consisting of real stained-glass fragment images and transfer its color and texture to the region. Finally, applying lead came at the boundary of the regions in each frame yields temporally coherent stained-glass animation.

• 실천공학교육논문지
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• 제13권3호
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• pp.483-490
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• 2021

본 논문에서는 스마트팩토리 기반의 CPPS(Cyber Physical Production System) 및 VR(Virtual Reality) 기술을 활용한 스마트팩토리 통합 기술 교육 플랫폼 개발과 플랫폼을 활용한 교육 방법들을 제안하였다. 3D 디지털 트윈과 연동이 가능하며 BOP(Bill of Process) 기반의 제조 공정을 통합하는 방법을 학습할 수 있도록 플랫폼을 개발하였다. 또한 디지털 트윈은 OPC-UA 서버를 통해 메카니컬 시스템과 디지털 트윈 뿐만 아니라 가상 현실까지 연계하여 통합 스마트팩토리 기반의 교육 플랫폼을 구축하였다. 이러한 플랫폼을 기반으로 스마트팩토리 통합 플랫폼은 BOP 기반 디지털 트윈 시뮬레이션, OPC-UA 통합, MES 시스템, SCADA 시스템, VR 연동으로 스마트팩토리 통합 플랫폼의 개별 요소들을 가지도록 제안하였다.

• Smart Structures and Systems
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• 제29권3호
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• pp.433-444
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• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.

• 산업경영시스템학회지
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• 제45권3호
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• pp.204-213
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• 2022

Most of the prior studies on the servitization of manufacturing companies have been actively studied, focusing on the performance and cases of servitization at the single company level. According to the results, most of the servitization at the single company level has been expanded based on the relevance of the company's core products. However, the form of companies that form a large axis of the Korean economy is a large-scale business group, and these business groups incorporate service affiliates for various purposes, so they show different characteristics from that of a single corporate. In addition, since the purpose of forming a business group is different for each business group, the service relevance between affiliates within the business group is different. Therefore, this study aims to examine the effect of service relevance between affiliates within a business group on the management performance of each business group. To this end, an empirical analysis will be conducted using panel data for 10 years from 2011 to 2020 for a total of 98 affiliates listed on KOSPI and KOSDAQ of 9 domestic business groups. Based on these results, the direction for improving management performance and establishing future servitization strategies for large business groups in Korea will be expected to be made.

• 한국해양공학회지
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• 제37권5호
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• pp.190-197
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• 2023

In the shipbuilding and marine industry, digital transformation activities are promoted primarily by large shipyards. However, bottlenecks are observed across value chains, and digital transformation effects are reducing because of the cost and technical challenges encountered by supplies. In this study, we proposed a win-win cooperation model for large, small, and medium-sized companies using digital transformation based on the characteristics of the shipbuilding and marine industry through case studies. We investigated the digital transformation progress in German and Korean small and medium-sized enterprises (SMEs). In addition, we identified information-sharing methods and management challenges encountered in enterprise resource planning and manufacturing execution systems in the collaboration process of pipes, panels, blocks, etc. of SMEs that are suppliers of a Korean shipyard, and clarified communication by building a platform based on a common format between shipyards and suppliers. Further, we proposed a standard model of a digital transformation system for enhancing the collaboration between large companies and suppliers and proposed a basic plan including strategies to efficiently and effectively build a digital transformation system based on the standard model.

• 한국안전학회지
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• 제38권5호
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• pp.43-50
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• 2023

With the advent of industrialization, consumers and end-users demand more reliable products. Meeting these demands requires a comprehensive approach, involving tasks such as market information collection, planning, reliable raw material procurement, accurate reliability design, and prediction, including various reliability tests. Moreover, this encompasses aspects like reliability management during manufacturing, operational maintenance, and systematic failure information collection, interpretation, and feedback. Improving product reliability requires prioritizing it from the initial development stage. Failure mode and effect analysis (FMEA) is a widely used method to increase product reliability. In this study, we reanalyzed using the FMEA method and proposed an improved method. Domestic railways lack an accurate measurement method or system for maintenance, so maintenance decisions rely on the opinions of experienced personnel, based on their experience with past faults. However, the current selection method is flawed as it relies on human experience and memory capacity, which are limited and ineffective. Therefore, in this study, we further specify qualitative contents to systematically accumulate failure modes based on the Failure Modes Table and create a standardized form based on the Master FMEA form to newly systematize it.

• 산업경영시스템학회지
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• 제47권1호
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• pp.9-19
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• 2024

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.

• Design & Manufacturing
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• 제15권2호
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• pp.23-29
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• 2021

The specific strength of magnesium alloy is four times that of iron and 1.5 times that of aluminum. For this reason, its use is increasing in the transportation industry which is promoting weight reduction. At room temperature, magnesium alloy has low formability due to Hexagonal closed packed (HCP) structure with relatively little slip plane. However, as the molding temperature increases, the formability of the magnesium alloy is greatly improved due to the activation of other additional slip systems, and the flow stress and elongation vary greatly depending on the temperature. In addition, magnesium alloys exhibit asymmetrical behavior, which is different from tensile and compression behavior. In this study, a jig was developed that can measure the plane deformation behavior on the surface of a material in tensile and compression tests of magnesium alloys in warm temperature. A jig was designed to prevent buckling occurring in the compression test by applying a certain pressure to apply it to the tensile and compression tests. And the tensile and compressive behavior of magnesium at each temperature was investigated with the developed jig and DIC equipment. In each experiment, the strain rate condition was set to a quasi-static strain rate of 0.01/s. The transformation temperature is room temperature, 100℃. 150℃, 200℃, 250℃. As a result of the experiment, the flow stress tended to decrease as the temperature increased. The maximum stress decreased by 60% at 250 degrees compared to room temperature. Particularly, work softening occurred above 150 degrees, which is the recrystallization temperature of the magnesium alloy. The elongation also tended to increase as the deformation temperature increased and increased by 60% at 250 degrees compared to room temperature. In the compression experiment, it was confirmed that the maximum stress decreased as the temperature increased.