• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Annual Conference of KIPS
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• 2022.11a
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• pp.501-503
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• 2022

초해상이란 해상도가 낮은 영상을 해상도가 높은 영상으로 합성하는 기술이다. 딥러닝은 영상의 해상도를 높이는 초해상 기술에도 응용되며 실현은 2아4년에 발표된 SRCNN(Super Resolution Convolutional Neural Network) 모델로부터 시작됐다. 이후 오토인코더 (Autoencoders) 구조로는 SRCAE(Super Resolution Convolutional Autoencoders), 합성된 영상을 실제 영상과 통계적으로 구분되지 않도록 강제하는 GAN (Generative Adversarial Networks) 구조로는 SRGAN(Super Resolution Generative Adversarial Networks) 모델이 발표됐다. 모두 SRCNN의 성능을 웃도는 모델들이나 그중 가장 높은 성능을 끌어내는 SRGAN 조차 아직 완벽한 성능을 내진 못한다. 본 논문에서는 SRGAN의 성능을 개선하기 위해 사전 훈련된 특징 추출기(Pre-trained Feature Extractor) VGG(Visual Geometry Group)-19 모델을 변경하고, 기존 모델과 성능을 비교한다. 실험 결과, VGG-19 모델보다 윤곽이 뚜렷하고, 실제 영상과 더 가까운 영상을 합성할 수 있는 모델을 발견할 수 있을 것으로 기대된다.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.80-88
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• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.399-408
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• 2020

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.299-310
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• 2023

According to Jacques Lacan, human desire stems from a primitive lack, This lack acts as a driving force, propelling humans to continually seek pleasure and satisfaction. Given its intricacies, the interplay between the subject's and the overall human desire is intricate, making complete realization of desire unattainable. Through Jacques Lacan's theory of desire, the researcher aims to explore the balance achieved as the subject in the movie navigates ideals, frustrations, and growth. This paper focuses on the analysis of the movie 'Parasite' using Amedeo Giorgi's phenomenological method. By doing so, the researcher seeks to uncover the essence of human desire, offering insights and a theoretical framework for cinematic creation.

• Journal of Korean Medicine Rehabilitation
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• v.33 no.4
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• pp.145-156
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• 2023

Objectives The purpose of this review is to analyze trends of domestic research in Korean medicine treatment for frozen shoulder. Methods Among the clinical papers published from January 1, 2000 to August 2023, papers that treated frozen shoulders with Korean medicine treatment were searched through five domestic databases (Oriental Medicine Advanced Searching Integrated System, KMbase, Research Information Sharing Service, Science ON, Korean studies Information Service System). Results Finally, 12 studies were included. Three studies were clinical trials and 9 were observational studies. The most commonly used treatment was acupuncture. Range of movement (range of motion) and visual analogue scale were frequently used as measurement methods. Studies with significant effects were 66.6% in controlled trials and 100% in studies without control group. Conclusions In this study, literature on Korean medicine treatment for frozen shoulder was reviewed. As a result of the review, Korean medicine treatment for frozen shoulder showed significant results. However, there have been several limitations and suggest that more research and higher levels of research on Korean medicine treatment of frozen shoulder need to be conducted.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.333-339
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• 2023

In three-dimensional(3D) modeling, texturing plays a crucial role as a visual element, imparting detail and realism to models. In contrast to traditional texturing methods, the current trend involves utilizing AI tools such as Leonardo.Ai and Meshy to create textures for 3D models in a more efficient and precise manner. This paper focuses on 3D texturing, conducting a comprehensive comparative study of AI tools, specifically Leonardo.Ai and Meshy. By delving into the performance, functional differences, and respective application scopes of these two tools in the generation of 3D textures, we highlight potential applications and development trends within the realm of 3D texturing. The efficient use of AI tools in texture creation also has the potential to drive innovation and enhancement in the field of 3D modeling. In conclusion, this research aims to provide a comprehensive perspective for researchers, practitioners, and enthusiasts in related fields, fostering further innovation and development in this domain.

• Journal of Korean Neurosurgical Society
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• v.67 no.2
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• pp.202-208
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• 2024

Objective : Failed back surgery syndrome (FBSS) is a common long-term complication following spine surgeries characterized by chronic persistent pain; different strategies of management were employed to deal with it. This clinical trial aims to compare the efficacy of Pregabalin and Gabapentin in the management of this condition. Methods : A double-blind, randomized, comparative study (clinical trial registry NCT05324761 on 11th April 2022) with two parallel arms with Pregabalin and Gabapentin were used in arms one and two, respectively. Visual analog scale was used for basal and endpoint assessment of pain. T-test and analysis of covariance were used to deal with different variables. A pairwise test was used to compare pairs of means. Results : Of 66 patients referred to the trial, 64 were eligible, with 60 patients completing the 30 days trial. Both pregabalin and gabapentin effectively reduce pain, with significant p-values of 0.001 for each group. However, the pregabalin group was superior to gabapentin in pain reduction (p=0.001). Gender was an insignificant factor (p=0.574 and p=0.445 for the pregabalin and gabapentin groups, respectively, with a non-significant reduction (p=0.393) for both groups in total. Location of stenosis before surgery and type of surgery performed show non-significant effect on pain reduction for both groups. Conclusion : Both pregabalin and gabapentin effectively and safely relieve neuropathic pain associated with FBSS; pregabalin was significantly more effective irrespective of the patients' gender.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.80-85
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• 2024

The concept of single-modal digital art originated in the 20th century and has evolved through three key stages. Over time, digital art has transformed into multi-modal interaction, representing a new era in art forms. Based on multi-modal theory, this paper aims to explore the characteristics of interactive digital art in innovative art forms and its impact on user experience. Through an analysis of practical application of multi-modal interactive digital art, this study summarises the impact of creative models of digital art on the physical and mental aspects of user experience. In creating audio-visual-based art, multi-modal digital art should seamlessly incorporate sensory elements and leverage computer image processing technology. Focusing on user perception, emotional expression, and cultural communication, it strives to establish an immersive environment with user experience at its core. Future research, particularly with emerging technologies like Artificial Intelligence(AR) and Virtual Reality(VR), should not merely prioritize technology but aim for meaningful interaction. Through multi-modal interaction, digital art is poised to continually innovate, offering new possibilities and expanding the realm of interactive digital art.