• 한국산업융합학회 논문집
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• 제22권5호
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• pp.467-473
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• 2019

In this study, approached to improve durability of the multi-functional nano-pattern fabricated on the curved lens surface using nanoimprint lithography (NIL) was proposed, and the effects of the proposed methods on functionality after wear test were examined. To improve the mechanical property of ultraviolet(UV)-curable resin, UV-NIL was conducted at the elevated temperature around $60^{\circ}C$. In addition, micro/nano hierarchical structures was fabricated on the lens surface with a durable film mold. Analysis on the worn surfaces of nano-hole pattern and hierarchical structures and measurements on the static water contact angle and critical water volume for roll-off indicated that the UV curing process with elevated temperature is effective to maintain wettability by increasing hardness of resin. Also, it was found that the micro-scale pattern is effective to protect nano-pattern from damage during wear test.

• Structural Engineering and Mechanics
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• 제72권2호
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• pp.275-291
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• 2019

In a super-large underground with "large span and high side wall", it is buried in mountains with uneven lithology, complicated geostress field and developed geological structure. These surrounding rocks are more susceptible to stability issues during the construction period. This paper takes the left bank of Baihetan hydropower station (span is 34m) as a case study example, wherein the deformation mechanism of surrounding rock appears prominent. Through analysis of geological, geophysical, construction and monitoring data, the deformation characteristics and factors are concluded. The failure mechanism, spatial distribution characteristics, and evolution mechanism are also discussed, where rock mechanics theory, $FLAC^{3D}$ numerical simulation, rock creep theory, and the theory of center point are combined. In general, huge underground cavern stability issues has arisen with respect to huge-scale and adverse geological conditions since settling these issues will have milestone significance based on the evolutionary pattern of the surrounding rock and the correlation analyses, the rational structure of the factors, and the method of nonlinear regression modeling with regard to the construction and development of hydropower engineering projects among the worldwide.

• 무역학회지
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• 제44권4호
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• pp.205-220
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• 2019

This article recapitulates the recent changes in trade laws, which may be accentuated due to the intriguing emergence of fortified protectionism and Mega FTAs. It points out the need to formulate not only the corporate strategy for enhancing the product differentiation and architectural capabilities but also the public policy, which comprises the industrial adjustment policy to cope with possible negative impulses caused by the digital trade and foreign direct investment. It is imperative for Korea to facilitate the alignment between corporate strategy and industrial adjustment policy as an effective means of enhancing industrial structure by nurturing those linkage effects between relevant forward and backward industries. Given the drastically volatile trade norms of multi-track trade policies, it may be a pivotal momentum for Korea to pursue a paradigm shift of its trade policy with a prime objective of achieving such an alignment between corporate strategy and industrial adjustment policy, which affords increased value-added and the further development of product or generic technology instead of resorting to the misuses and abuses of economies of scale and production technology for the maximization of export amount.

• Steel and Composite Structures
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• 제42권3호
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• pp.299-313
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• 2022

Steel-concrete-steel composite structures with bidirectional webs (SCSBWs) are used in large-scale projects and exhibit good mechanical performances and constructional efficiency. The shear behaviors of SCSBW deep beam members in key joints or in locations subjected to concentrated forces are of concern in design. To address this issue, experimental program is investigated to examine the deep-beam shear behaviors of SCSBWs, in which the cracking process and force transfer mechanism are revealed. Compared with the previously proposed truss model, it is found that a strut-and-tie model is more suitable for describing the shear mechanism of SCSBW deep beams with a short span and sparse transverse webs. According to the experimental analyses, a new model is proposed to predict the shear capacities of SCSBW deep beams. This model uses strut-and-tie concept and introduces web shear and dowel action to consider the coupled multi mechanisms. A stress decomposition method is used to distinguish the contributions of different shear-transferring paths. Based on case studies, a simplified model is further developed, and the explicit solution is derived for design efficiency. The proposed models are verified using experimental data, which are proven to have good accuracy and efficiency and to be suitable for practical application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3364-3382
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• 2023

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC, mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.15-29
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• 2024

A fetoscope is an optical endoscope, which is often applied in fetoscopic laser photocoagulation to treat twin-to-twin transfusion syndrome. In an operation, the clinician needs to observe the abnormal placental vessels through the endoscope, so as to guide the operation. However, low-quality imaging and narrow field of view of the fetoscope increase the difficulty of the operation. Introducing an accurate placental vessel segmentation of fetoscopic images can assist the fetoscopic laser photocoagulation and help identify the abnormal vessels. This study proposes a method to solve the above problems. A novel encoder-decoder network with a dual-path structure is proposed to segment the placental vessels in fetoscopic images. In particular, we introduce a channel attention mechanism and a continuous convolution structure to obtain multi-scale features with their weights. Moreover, a switching connection is inserted between the corresponding blocks of the two paths to strengthen their relationship. According to the results of a set of blood vessel segmentation experiments conducted on a public fetoscopic image dataset, our method has achieved higher scores than the current mainstream segmentation methods, raising the dice similarity coefficient, intersection over union, and pixel accuracy by 5.80%, 8.39% and 0.62%, respectively.

• Design & Manufacturing
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• 제11권1호
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• pp.39-44
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• 2017

The domestic mold industry is composed of 6,560 small and medium sized mold companies as of 2015. The structure of mold industry centered on less than 10 people in the past has been improved in the direction of increasing number of medium and large scale companies with more than 20 competitors with global competitiveness and has maintained its position as the world's second largest mold exporter with global competitiveness. Nevertheless, the manpower structure and corporate competitiveness structure of the mold industry is very high, with the proportion of production manpower reaching 70% and shortage rate of 10% or more in order to respond to the orders of customers. However, the development base for new employees with technological skills required by the industrial field is poor, and the inflow of young people is very limited due to factors such as the avoidance of small and medium enterprises and production jobs. It is expected that the labor shortage of mold enterprises will be further increased in the future. In the mold industry, due to the characteristics of small quantity multi-product production corresponding to the demand of the consumer, many production processes are individually and independently carried out, resulting in low labor productivity, and the structural time required for the worker to increase the working time Due to limitations, the working hours per week of the employees are about 50 hours. The implementation of the working time reduction bill, which is recently promoted by the government, is a crisis factor. In order to cultivate the mold industry, it is necessary to expand the base of molds to meet the intensification of global competition, the convergence of technologies to actively respond to the restructuring of the industrial structure, and the response to the new industry, It is necessary to improve labor productivity through policies such as development and dissemination of system, and to secure price, delivery and quality competitiveness in global market.

• 대한원격탐사학회지
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• 제35권6_3호
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• pp.1273-1283
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• 2019

이 논문에서는 작물 분류를 목적으로 합성곱 신경망 구조에 다중 규모의 입력 영상으로부터 추출가능한 다양한 공간특징을 가중 결합하는 모델을 제안하였다. 제안 모델은 합성곱 계층에서 서로 다른 크기의 입력패치를 이용하여 공간특징을 추출한 후, squeeze-and-excitation block을 통해 추출한 공간특징의 중요도에 따라 가중치를 부여한다. 제안 모델의 장점은 분류에 유용한 특징들을 추출하고 특징의 상대적 중요도를 분류에 이용하는데 있다. 제안 모델의 분류 성능을 평가하기 위해 미국 일리노이 주에서 수집한 다중시기 Landsat-8 OLI 영상을 이용한 작물 분류 사례연구를 수행하였다. 유용한 패치 크기 결정을 위해 먼저 단일 패치 모델에서 패치 크기가 작물 분류에 미치는 영향을 분석하였다. 그 후에 단일 패치 모델과 특징의 중요도를 고려하지 않는 다중 패치 모델과 분류 성능을 비교하였다. 비교 실험 결과, 제안 모델은 연구지역에서 재배하는 작물의 공간 특징을 고려함으로써 오분류 양상을 완화시켜 비교 모델들에 비해 가장 우수한 분류 정확도를 나타냈다. 분류에 유용한 공간특징의 상대적 중요도를 고려하는 제안 모델은 작물뿐만 아니라 서로 다른 공간특성을 보이는 객체 분류에도 유용하게 적용될 수 있을 것으로 기대된다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.