• Structural Engineering and Mechanics
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• 제71권6호
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• pp.627-641
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• 2019

The paper focuses on bending analysis of the functionally graded (FG) plates with arbitrary shapes and boundary conditions. The material property of FG plates is modelled by using the power law distribution. Based on the first order shear deformation plate theory (FSDT), the governing equations as well as boundary conditions are formulated and obtained by using the principle of virtual work. The coupled Boundary Element-Radial Basis Function (BE-RBF) method is established to solve the complex FG plates. The proposed methodology is developed by applying the concept of the analog equation method (AEM). According to the AEM, the original governing differential equations are replaced by three Poisson equations with fictitious sources under the same boundary conditions. Then, the fictitious sources are established by the application of a technique based on the boundary element method and approximated by using the radial basis functions. The solution of the actual problem is attained from the known integral representations of the potential problem. Therefore, the kernels of the boundary integral equations are conveniently evaluated and readily determined, so that the complex FG plates can be easily computed. The reliability of the proposed method is evaluated by comparing the present results with those from analytical solutions. The effects of the power index, the length to thickness ratio and the modulus ratio on the bending responses are investigated. Finally, many interesting features and results obtained from the analysis of the FG plates with arbitrary shapes and boundary conditions are demonstrated.

• Wind and Structures
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• 제28권1호
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• pp.31-47
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• 2019

The accurate prediction of snow distributions under the wind action on roofs plays an important role in designing structures in civil engineering in regions with heavy snowfall. Affected by some factors such as building shapes, sizes and layouts, the snow drifting on roofs shows more three-dimensional characteristics. Thus, the research on three-dimensional snow distribution is needed. Firstly, four groups of stepped flat roofs are designed, of which the width-height ratio is 3, 4, 5 and 6. Silica sand with average radius of 0.1 mm is used to model the snow particles and then the wind tunnel test of snow drifting on stepped flat roofs is carried out. 3D scanning is used to obtain the snow distribution after the test is finished and the mean mass transport rate is calculated. Next, the wind velocity and duration is determined for numerical simulations based on similarity criteria. The adaptive-mesh method based on radial basis function (RBF) interpolation is used to simulate the dynamic change of snow phase boundary on lower roofs and then a time-marching analysis of steady snow drifting is conducted. The overall trend of numerical results are generally consistent with the wind tunnel tests and field measurements, which validate the accuracy of the numerical simulation. The combination between the wind tunnel test and CFD simulation for three-dimensional typical roofs can provide certain reference to the prediction of the distribution of snow loads on typical roofs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.213-221
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• 2022

Intelligent Character Recognition System for Account Payable (ICRS AP) Automation represents the process of capturing text from scanned invoices and extracting the key fields from invoices and storing the captured fields into properly structured document format. ICRS plays a very critical role in invoice data streamlining, we are interested in data like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. As companies attempt to cut costs and upgrade their processes, accounts payable (A/P) is an example of a paper-intensive procedure. Invoice processing is a possible candidate for digitization. Most of the companies dealing with an enormous number of invoices, these manual invoice matching procedures start to show their limitations. Receiving a paper invoice and matching it to a purchase order (PO) and general ledger (GL) code can be difficult for businesses. Lack of automation leads to more serious company issues such as accruals for financial close, excessive labor costs, and a lack of insight into corporate expenditures. The proposed system offers tighter control on their invoice processing to make a better and more appropriate decision. AP automation solutions provide tighter controls, quicker clearances, smart payments, and real-time access to transactional data, allowing financial managers to make better and wiser decisions for the bottom line of their organizations. An Intelligent Character Recognition System for AP Automation is a process of extricating fields like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. based on their x-axis and y-axis position coordinates.

• 상하수도학회지
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• 제37권5호
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• pp.281-288
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• 2023

This study was performed to evaluate the pollutants removal characteristics of two types of RBFs(Riverbank filtration, Riverbed filtration) intake facilities installed in Nakdong River and in Hwang River respectively. The capacity of each RBF is 45,000 m³/d for riverbank filtration intake facility and 3,500 m³/d for riverbed filtration intake facility. According to data collected in the riverbank filtration site, removal rate of each pollutant was about BOD(Biochemical Oxygen Demand) 52%, TOC(Total Organic Carbon) 57%, SS(Suspended Solids) 44%, Total coliforms 99% correspondingly. Furthermore, Microcystins(-LR,-YR,-RR) were not found in riverbank filtered water compared to surface water in Nakdong River. DOC(Dissolved Organic Carbon) and Humics which are precursors of disinfection byproduct were also reported to be removed about 59% for DOC, 65% for Humics. Based on data analysis in riverbed filtration site in Hwang River, removal rate of each contaminant reaches to BOD 33.3%, TOC 38.5%, SS 38.9%, DOC 22.2%, UV254 21.2%, Total coliforms 73.8% respectively. Additionally, microplastics were also inspected that there was no obvious removal rate in riverbed filtered water compared to surface water in Hwang River.

• 정보처리학회논문지B
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• 제14B권4호
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• pp.311-320
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• 2007

본 논문에서는 강건한 얼굴 포즈 추정과 실시간 표정제어가 가능한 비전 기반 3차원 얼굴 모델의 자동 표정 생성 방법 및 시스템을 제안한다. 기존의 비전 기반 3차원 얼굴 애니메이션에 관한 연구는 얼굴의 움직임을 나타내는 모션 추정을 반영하지 못하고 얼굴 표정 생성에 초점을 맞추고 있다. 그러나, 얼굴 포즈를 정확히 추정하여 반영하는 작업은 현실감 있는 얼굴 애니메이션을 위해서 중요한 이슈로 인식되고 있다. 본 연구 에서는 얼굴 포즈추정과 얼굴 표정제어가 동시에 가능한 통합 애니메이션 시스템을 제안 하였다. 제안된 얼굴 모델의 표정 생성 시스템은 크게 얼굴 검출, 얼굴 모션 추정, 표정 제어로 구성되어 있다. 얼굴 검출은 비모수적 HT 컬러 모델과 템플릿 매칭을 통해 수행된다. 검출된 얼굴 영역으로부터 얼굴 모션 추정과 얼굴 표정 제어를 수행한다. 얼굴 모션 추정을 위하여 3차원 실린더 모델을 검출된 얼굴 영역에 투영하고 광류(optical flow) 알고리즘을 이용하여 얼굴의 모션을 추정하며 추정된 결과를 3차원 얼굴 모델에 적용한다. 얼굴 모델의 표정을 생성하기 위해 특징점 기반의 얼굴 모델 표정 생성 방법을 적용한다. 얼굴의 구조적 정보와 템플릿 매칭을 이용하여 주요 얼굴 특징점을 검출하며 광류 알고리즘에 의하여 특징점을 추적한다. 추적된 특징점의 위치는 얼굴의 모션 정보와 표정 정보의 조합으로 이루어져있기 때문에 기하학적 변환을 이용하여 얼굴의 방향이 정면이었을 경우의 특징점의 변위인 애니메이션 매개변수(parameters)를 계산한다. 결국 얼굴 표정 복제는 두 개의 정합과정을 통해 수행된다. 애니메이션 매개변수 3차원 얼굴 모델의 주요 특징점(제어점)의 이동은 획득된 애니메이션 매개변수를 적용하여 수행하며, 정점 주위의 부가적 정점의 위치는 RBF(Radial Basis Function) 보간법을 통해 변형한다. 실험결과 본 논문에서 제안된 비전기반 애니메이션 시스템은 비디오 영상으로부터 강건한 얼굴 포즈 추정과 얼굴의 표정변화를 잘 반영하여 현실감 있는 애니메이션을 생성함을 입증할 수 있었다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제14권1호
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• pp.1-10
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• 2009

지하수 유동 수치 모사 프로그램 (FEFLOW 5.1)을 이용하여 수평 방사형 집수정 취수에 따른 강변여과 지역의 지하수 유동을 분석하였다. 양수량, 대수층 두께, 취수정과 하천 사이의 이격거리, 하천 바닥의 투수 능력(Conductance)등의 조건 변화에 따른 집수정 인접 대수층의 수위강하가 계산되었다. 이들 조건 변화에 따라 지하수위 강하는 뚜렷한 변화를 보여주었다. 민감도 분석 결과, 대수층의 두께와 취수정과 하천 사이의 이격거리가 하천 바닥의 수리 전도에 비해 지하수위 강하에 더 민감하게 영향을 주는 것으로 평가되었다. 이러한 결과는 수평 방사형 집수정을 통한 강변여과 취수 가능지역을 선정하고 그 개발량을 추정할 때, 충적 대수층의 두께와 분포 특성이 중요한 요소임을 시사한다. 또한 FEFLOW의 1차원 선형 불연속 특징 요소를 활용한 수치 모사는 효과적으로 수평 방사형 집수정의 정량 평가와 강변여과 현장의 개발 가능량 추정을 할 수 있는 도구임이 밝혀졌다.

• Maxillofacial Plastic and Reconstructive Surgery
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• 제41권
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• pp.47.1-47.10
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• 2019

Background: Hyaluronic acid (HA) has been applied as a primary biomaterial for temporary soft tissue augmentation and as a carrier for cells and the delivery of growth factors to promote tissue regeneration. Although HA derivatives are the most versatile soft tissue fillers on the market, they are resorbed early, within 3 to 12 months. To overcome their short duration, they can be combined with cells or growth factors. The purpose of this study was to investigate the stimulating effects of human fibroblasts and basic fibroblast growth factors (bFGF) on collagen synthesis during soft tissue augmentation by HA hydrogels and to compare these with the effects of a commercial HA derivative (Restylane^®). Methods: The hydrogel group included four conditions. The first condition consisted of hydrogel (H) alone as a negative control, and the other three conditions were bFGF-containing hydrogel (HB), human fibroblast-containing hydrogel (HF), and human fibroblast/bFGF-containing hydrogel (HBF). In the Restylane^® group (HGF), the hydrogel was replaced with Restylane^® (R, RB, RF, RBF). The gels were implanted subdermally into the back of each nude mouse at four separate sites. Twelve nude mice were used for the hydrogel (n = 6) and Restylane^® groups (n = 6). The specimens were harvested 8 weeks after implantation and assessed histomorphometrically, and collagen synthesis was evaluated by RT-PCR. Results: The hydrogel group showed good biocompatibility with the surrounding tissues and stimulated the formation of a fibrous matrix. HBF and HF showed significantly higher soft tissue synthesis compared to H (p < 0.05), and human collagen type I was well expressed in HB, HF, and HBF; HBF showed the strongest expression. The Restylane^® filler was surrounded by a fibrous capsule without any soft tissue infiltration from the neighboring tissue, and collagen synthesis within the Restylane^® filler could not be observed, even though no inflammatory reactions were observed. Conclusion: This study revealed that HA-based hydrogel alone or hydrogel combined with fibroblasts and/or bFGF can be effectively used for soft tissue augmentation.

• 대한조선학회논문집
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• 제55권1호
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• pp.66-74
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• 2018

In order to evaluate the precision degree of the blocks on the dock, the shipyards recently started to use the point cloud approaches using the 3D scanners. However, they hesitate to use it due to the limited time, cost, and elaborative effects for the post-works. Although it is somewhat traditional instead, they have still used the electro-optical wave devices which have a characteristic of having less dense point set (usually 1 point per meter) around the contact section of two blocks. This paper tried to expand the usage of point sets. Our approach can estimate the rework time to weld between the Pre-Erected(PE) Block and Erected(ER) block as well as the precision of block construction. In detail, two algorithms were applied to increase the efficiency of estimation process. The first one is K-mean clustering algorithm which is used to separate only the related contact point set from others not related with welding sections. The second one is the Concave hull algorithm which also separates the inner point of the contact section used for the delayed outfitting and stiffeners section, and constructs the concave outline of contact section as the primary objects to estimate the rework time of welding. The main purpose of this paper is that the rework cost for welding is able to be obtained easily and precisely with the defective point set. The point set on the blocks' outline are challenging to get the approximated mathematical curves, owing to the lots of orthogonal parts and lack of number of point. To solve this problems we compared the Radial based function-Multi-Layer(RBF-ML) and Akima interpolation method. Collecting the proposed methods, the paper suggested the noble point matching method for minimizing the rework time of block-welding on the dock, differently the previous approach which had paid the attention of only the degree of accuracy.

• 농촌계획
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• 제30권1호
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.