• Steel and Composite Structures
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• 제29권6호
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• 산업융합연구
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• 제13권1호
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• pp.41-47
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• 2015

The purpose of this study was to investigate how environment-friendly activities through nature affects attitude of children. 5-6 years old children were targeted for study from March to December, 2014. Rapid changes of modern society made increase of female workers and their participation rates in economic activity which results children to play more time with artificial toys and media. There are 3 stages to investigate effects. $1^{st}$ stage is to know about woods by visiting woods and experience environment-friendly activity. $2^{nd}$ stage is to experience woods with 5 senses not only in real woods but also in classroom. Lastly $3^{rd}$ stage is to make art work with natural object and make woods in classroom. Changes of hildren's attitude and view toward to the nature were recorded and analyzed by anecdotes perpetual inventory and environment-friendly attitude examination. By analysis of infant gives you the opportunity to encounter nature, often in conjunction with ongoing enjoys nature-friendly program in the classroom to play with toys, rather than a complete natural objects gradually formalized when presenting a natural, concentrated than the previous game this time is enhanced and creativity through nature through the promotion doeeojim and attitude to nature is also eco-friendly activities byeonhwadoem program showed that the impact on the attitude of the infant.

• 한국게임학회 논문지
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• 제19권2호
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• pp.23-32
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• 2019

VR 콘텐츠의 대중화가 더딘 것은 시각적인 새로운 경험 즉, '흥미' 이상의 '효용'을 만들어내지 못했기 때문이다. 가상현실 콘텐츠의 효용은 기능적 현실감에서 출발하며 그것을 증진시키기 위해서 사실적인 인터랙션이 요구된다. 본 연구는 구체적으로 네트워크 플레이, 캐릭터 인공지능, 햅틱 구현의 3가지 방법을 제시하고 있다. 가설을 확인하기 위하여 기획에서부터 콘텐츠 제작, 플레이 테스트, 기술 검증까지 야구를 소재로 한 VR 콘텐츠 제작의 전 단계를 수행하였다. 최종 결과물에 대한 사용자 및 평가 기관의 테스트를 통하여 사실적인 시각 효과와 플레이 연출, 진동에 의한 타격감까지 콘텐츠의 현실감을 높이는 데 기여한 것으로 평가되었다.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• 한국멀티미디어학회논문지
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• 제25권10호
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• pp.1535-1545
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• 2022

Sign language has completely different meaning depending on the direction of the hand or the change of facial expression even with the same gesture. In this respect, it is crucial to capture the spatial-temporal structure information of each movement. However, sign language translation studies based on Sign2Gloss2Text only convey comprehensive spatial-temporal information about the entire sign language movement. Consequently, detailed information (facial expression, gestures, and etc.) of each movement that is important for sign language translation is not emphasized. Accordingly, in this paper, we propose Spatial-temporal Keypoints Centered Sign2Gloss2Text Translation, named STKC-Sign2 Gloss2Text, to supplement the sequential and semantic information of keypoints which are the core of recognizing and translating sign language. STKC-Sign2Gloss2Text consists of two steps, Spatial Keypoints Embedding, which extracts 121 major keypoints from each image, and Temporal Keypoints Embedding, which emphasizes sequential information using Bi-GRU for extracted keypoints of sign language. The proposed model outperformed all Bilingual Evaluation Understudy(BLEU) scores in Development(DEV) and Testing(TEST) than Sign2Gloss2Text as the baseline, and in particular, it proved the effectiveness of the proposed methodology by achieving 23.19, an improvement of 1.87 based on TEST BLEU-4.

• 한국공간구조학회논문집
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• 제21권4호
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• pp.73-80
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• 2021

A mid-story isolation system was proposed for seismic response reduction of high-rise buildings and presented good control performance. Control performance of a mid-story isolation system was enhanced by introducing semi-active control devices into isolation systems. Seismic response reduction capacity of a semi-active mid-story isolation system mainly depends on effect of control algorithm. AI(Artificial Intelligence)-based control algorithm was developed for control of a semi-active mid-story isolation system in this study. For this research, an practical structure of Shiodome Sumitomo building in Japan which has a mid-story isolation system was used as an example structure. An MR (magnetorheological) damper was used to make a semi-active mid-story isolation system in example model. In numerical simulation, seismic response prediction model was generated by one of supervised learning model, i.e. an RNN (Recurrent Neural Network). Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm The numerical simulation results presented that the DQN algorithm can effectively control a semi-active mid-story isolation system resulting in successful reduction of seismic responses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• Journal of Information Technology Applications and Management
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• 제30권6호
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• pp.1-15
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• 2023

With the advent of the Fourth Industrial Revolution, propelled by digital technology, we are transitioning into an era of hyperconnectivity, where everything and objects are becoming interconnected. A smart supply chain refers to a supply chain system where various sensors and RFID tags are attached to objects such as machinery and products used in the manufacturing and transportation of goods. These sensors and tags collect and analyze process data related to the products, providing meaningful information for operational use and decision-making in the supply chain. Before the spread of COVID-19, the fundamental principles of supply chain management were centered around 'cost minimization' and 'high efficiency.' A smart supply chain overcomes the linear delayed action-reaction processes of traditional supply chains by adopting real-time data for better decision-making based on information, providing greater transparency, and enabling enhanced collaboration across the entire supply chain. Therefore, in this study, a hierarchical model for building a smart supply chain was constructed to systematically derive the importance of key factors that should be strategically considered in the construction of a smart supply chain, based on the major factors identified in previous research. We applied AHP (Analytical Hierarchy Process) techniques to identify urgent improvement areas in smart SCM initiatives. The analysis results showed that the external supply chain integration is the most urgent area to be improved in smart SCM initiatives.

• 문화기술의 융합
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• 제9권5호
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• pp.697-706
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• 2023

본 연구는 현악사중주의 역사를 살펴보고, 현대에 제시된 융합 공연을 분석하여 미래의 청중이 수용할만한 새로운 공연의 패러다임을 제안하는 것을 목표로 한다. 연구의 과정에서는 과거와 현대의 현악사중주가 어떻게 발전했는지를 면밀하게 살펴보고, 그 과정에서 나타난 청중의 변화에 관하여 분석한다. 더불어 현대 현악사중주의 기술 융합 공연 사례로부터 새로운 청중의 수요에 따른 오늘날의 클래식 공연산업이 어떠한 변화를 맞을 수 있을지를 모색한다. 연구의 결과로 현대의 현악사중주는 미디어와 AI 기술의 융합을 통한 새롭고 독창적인 방향의 공연이 필요하다는 결론을 내렸다.

• 반도체디스플레이기술학회지
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• 제22권4호
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• pp.53-58
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• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.