• 한국의학물리학회지:의학물리
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• 제32권4호
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• pp.92-98
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• 2021

Purpose: This study automatically discriminates homogeneous and structure edge regions on computed tomography (CT) images, and it evaluates the noise level and edge preservation ratio (EPR) according to the different types of iterative reconstruction (IR). Methods: The dataset consisted of CT scans of 10 patients reconstructed with filtered back projection (FBP), statistical IR (iDose⁴), and iterative model-based reconstruction (IMR). Using the 10th and 85th percentiles of the structure coherence feature, homogeneous and structure edge regions were localized. The noise level was estimated using the averages of the standard deviations for five regions of interests (ROIs), and the EPR was calculated as the ratio of standard deviations between homogeneous and structural edge regions on subtraction CT between the FBP and IR. Results: The noise levels were 20.86±1.77 Hounsfield unit (HU), 13.50±1.14 HU, and 7.70±0.46 HU for FBP, iDose⁴, and IMR, respectively, which indicates that iDose⁴ and IMR could achieve noise reductions of approximately 35.17% and 62.97%, respectively. The EPR had values of 1.14±0.48 and 1.22±0.51 for iDose⁴ and IMR, respectively. Conclusions: The iDose⁴ and IMR algorithms can effectively reduce noise levels while maintaining the anatomical structure. This study suggested automated evaluation measurements of noise levels and EPRs, which are important aspects in CT image quality with patients' cases of FBP, iDose⁴, and IMR. We expect that the inclusion of other important image quality indices with a greater number of patients' cases will enable the establishment of integrated platforms for monitoring both CT image quality and radiation dose.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.35-42
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• 2021

In the article, the authors investigate the economic security system in the conditions of the powers transformation. It is substantiated that economic security acts as a certain system that includes components and at the same time acts as a subsystem of the highest order. It is determined that the economic security system of regions acting as a system has its subsystems, which include: production, financial, environmental, innovation, investment and social subsystems. The parameters of the economic security system include relative economic independence, economic stability and self-development of economic systems, and it is proved that an important feature of economic security in addition to its systemic nature is multi-vector. It is substantiated that the monitoring of ensuring the economic security system of the development of economic systems of different levels in the conditions of the powers transformation should contain the analysis of social, economic and ecological development of regions; spheres of possible dangers of the development of regional economic systems; the nature of the threats; the degree of the possibility of threats; time perspective of economic development threats; possible consequences of losses for economic entities; the impact of threats to the object of the economic entities' activity; possible asymmetry of economic development of regional economic entities. Possible threats as a consequence of the powers transformation have been identified. A PEST analysis of the impact of factors of different nature on economic security and the development of regional economic systems in the powers transformation is carried out. A recurrent ratio is proposed for the economic security system in the conditions of the powers transformation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• 디지털정책학회지
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• 제1권2호
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• pp.53-59
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• 2022

본 논문에서는 단일 영상을 기반으로 초해상도를 생성하는 과정에서 계층 구조를 거쳐 추출된 보조 경계 특징을 이용한 방법을 제안하였다. 초해상도의 품질을 향상시키기 위해서는 영상 내 경계 영역을 선명하게 표현하면서도 각 영역의 형태를 명확하게 구분하여야 한다. 제안하는 방법은 초해상도 과정에서 품질을 결정하는 중요한 요인인 경계 영역을 입력 영상의 구조적 형태를 유지하면서 개선된 초해상도 결과를 생성하기 위해 딥러닝 기반의 초해상도 방법에서 영상의 경계 영역 정보를 보조적으로 활용하는 구조를 사용하였다. 딥러닝 기반의 초해상도를 수행하기 위한 그룹 컨볼루션 구조에 더해 보조 경계 추출을 위한 고주파 대역의 정보를 기반으로 별도의 계층적 구조의 경계 누적 추출 과정을 수행하여 이를 보조 특징으로써 활용하는 방법을 제안하였다. 실험 결과 기존 초해상도 대비 PSNR과 SSIM에서 약 1%의 성능 향상을 보였다.

• 한국재료학회지
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• 제32권8호
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• 한국표면공학회지
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• 제56권1호
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• pp.104-114
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• 2023

Three dimensional (3D) porous structures consisting of Cu@CoO core-shell-type nano-dendrites were synthesized and tested as the anode materials in lithium secondary batteries. For this purpose, first, the 3D porous films comprising Cu@Co core-shell-type nano-dendrites with various thicknesses were fabricated through the electrochemical co-deposition of Cu and Co. Then the Co shells were selectively anodized to form Co hydroxides, which was finally dehydrated to get Cu@CoO nanodendrites. The resulting electrodes exhibited very high reversible specific capacity almost 1.4~2.4 times the theoretical capacity of commercial graphite, and excellent capacity retention (~90%@50^th cycle) as compared with those of the existing transition metal oxides. From the analysis of the cumulative irreversible capacity and morphology change during charge/discharge cycling, it proved that the excellent capacity retention was attributed to the unique structural feature of our core-shell structure where only the thin CoO shell participates in the lithium storage. In addition, our electrodes showed a superb rate performance (70.5%@10.8 C-rate), most likely due to the open porous structure of 3D films, large surface area thanks to the dendritic structure, and fast electron transport through Cu core network.

• Structural Engineering and Mechanics
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• 제86권3호
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• pp.291-309
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• 2023

This paper addresses the finite element modeling of functionally graded porous (FGP) beams for free vibration and buckling behaviour cases. The formulated finite element is based on simple and efficient higher order shear deformation theory. The key feature of this formulation is that it deals with Euler-Bernoulli beam theory with only three unknowns without requiring any shear correction factor. In fact, the presented two-noded beam element has three degrees of freedom per node, and the discrete model guarantees the interelement continuity by using both C⁰ and C¹ continuities for the displacement field and its first derivative shape functions, respectively. The weak form of the governing equations is obtained from the Hamilton principle of FGP beams to generate the elementary stiffness, geometric, and mass matrices. By deploying the isoparametric coordinate system, the derived elementary matrices are computed using the Gauss quadrature rule. To overcome the shear-locking phenomenon, the reduced integration technique is used for the shear strain energy. Furthermore, the effect of porosity distribution patterns on the free vibration and buckling behaviours of porous functionally graded beams in various parameters is investigated. The obtained results extend and improve those predicted previously by alternative existing theories, in which significant parameters such as material distribution, geometrical configuration, boundary conditions, and porosity distributions are considered and discussed in detailed numerical comparisons. Determining the impacts of these parameters on natural frequencies and critical buckling loads play an essential role in the manufacturing process of such materials and their related mechanical modeling in aerospace, nuclear, civil, and other structures.

• 융합신호처리학회논문지
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• 제23권4호
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• pp.249-255
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• 2022

포트홀은 아스팔트 포장도로의 구조적 결함을 나타내는 중요한 단서임과 동시에 많은 인명 피해와 재산 피해를 일으킨다. 따라서 정확한 포트홀 탐지는 도로 표면의 유지보수에 있어서 중요한 과제이다. 포트홀 탐지를 위해 많은 머신러닝 기술이 도입되고 있으며 딥러닝 모델의 효율성을 높이기 위해 데이터 전처리가 필요하다. 본 논문에서는 포트홀 데이터셋에서 중요한 질감과 형태를 강조하는 전처리 방법을 제안한다. 제안된 전처리 방법은 Intensity transformation을 사용해 도로의 불필요한 요소를 줄이고 포트홀의 질감과 형태를 부각한다. 또한 Superpixel, Sobel edge detection을 사용해 포트홀의 특징을 검출한다. 제안된 전처리 방법과 기존의 전처리 방법의 성능 비교를 통해 포트홀 검출에서 제안된 전처리 방법이 기존 방법보다 더 효과적인 방법이라는 것을 보여준다.

• 한국건축시공학회지
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• 제23권2호
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• pp.197-207
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• 2023

콘크리트는 압축력에 잘 저항하고 내구성이 우수하여 널리 사용되는 재료이다. 하지만 구조물은 시공 단계에서 주변 환경, 사용되는 재료의 특성에 따라 완공된 후 표면의 균열, 구조물의 침하 등 다양한 하자가 발생하거나 시간이 지남에 따라 콘크리트 구조물 표면에 결함이 발생한다. 그대로 방치하면 구조물에 심각한 손상을 초래하기 때문에 안전 점검을 통해 검사해야 한다. 하지만 전문 검사원들이 직접 조사하기에 비용이 높고 육안으로 판단하는 외관 검사법을 사용한다. 고층 건물일수록 상세한 검사가 힘들다. 본 연구는 노후화로 인해 콘크리트 표면에 발생하는 결함 중 균열을 탐지하는 딥러닝 기반 시맨틱 세그먼테이션 모형과 해당 모형의 특징 추출과 일반화 성능을 높이기 위한 이미지 어그멘테이션 기법을 개발하였다. 이를 위해 공개 데이터셋과 자체 데이터셋을 결합하여 시맨틱 세그먼테이션용 데이터셋을 구축하고 대표적인 딥러닝 기반 시맨틱 세그먼테이션 모형들을 비교실험하였다. 콘크리트 내벽을 중점으로 학습한 모형의 균열 추출 성능은 81.4%이며, 개발한 이미지 어그멘테이션을 적용한 결과 3%의 성능향상을 확인하였다. 향후 고층 건물과 같이 접근성이 어려운 지점을 드론을 통해 콘크리트 외벽에서 균열을 검출할 수 있는 시스템을 개발함으로써 실질적으로 활용할 수 있기를 기대한다.

• Steel and Composite Structures
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• 제46권3호
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• pp.319-334
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• 2023

Localizing damages is an essential task to monitor the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Backpropagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions in thin, flat, and folded structures having 15⁰, 30⁰, 45⁰, and 60⁰ folding angle have been modeled and subjected to free vibration analysis by employing the Classical Plate Theory with Finite Element Method. A Four-nodded quadrilateral element having six degrees of freedom has been considered to represent those structures mathematically. The first ten natural frequencies have been obtained regarding healthy and cracked structures. To localize the crack, the ratios of the frequencies of the cracked flat and folded structures to those of healthy ones have been taken into account. Those ratios have been given to BPANN as the input variables, while the crack locations have been considered as the output variables. A total of 500 crack locations have been regarded within the dataset obtained from the results of the free vibration analysis. To build the best intelligent model, a feature search has been conducted for BAPNN regarding activation function, the number of hidden layers, and the number of hidden neurons. Regarding the analysis results, it is concluded that the BPANN is able to localize the cracks with an average accuracy of 95.12%.