Earticle

본 연구에서는 female adult mesh (FASH) 팬텀으로부터 획득한 CT 영상에 FNLM 알고리즘을 적용하여 유사도를 분석하고자 한다. 먼저, GATE (geant4 application for tomographic emission) 프로그램을 이용하여 FASH의 전신 CT 스캔 데이터를 모델링한 뒤 그 데이터로부터 MATLAB 프로그램을 통해 442 × 256 사이즈의 CT axial 영상을 획득하였다. 그리고 획득한 원본 이미지에 표준편차 0.002를 가지는 가우시안 노이즈를 부가함으로서 Noisy 이미지를 획득한 뒤 그 이미지에 fast non-local means (FNLM) 알고리즘을 가중치 조절 인자 h를 0.05로 적용하였다. 본 연구에서는 유사도를 평가하기 위하여 평균 제곱근 편차 (Root Mean Square Error, RMSE)와 최대 신호 대 잡음비 (Peak Signal-to-noise ratio, PSNR)를 사용하였다. 원본이미지와 FNLM 알고리즘이 적용된 영상과의 유사도를 평가 한 결과, 원본이미지와 노이즈 이미지의 유사도에 비하여 RMSE 수치는 1.45배 향상되었으며, PSNR 수치는 1.04배 향상되었다. 결론적으로 FNLM 알고리즘은 영상을 복원하는데 효과가 있음이 증명되었다.

In this study, we analyzed the similarity about image applied fast non-local means (FNLM) algorithm to CT images obtained from the female adult mesh (FASH) phantom. First, full-body CT scan data of the FASH was modeled using the genant4 application for tomographic emission (GATE) program, and from that data, a 442 × 256 CT axial image was obtained through the MATLAB program. Subsequently, a noisy image was obtained by adding Gaussian noise with a standard deviation of 0.002 to the acquired original image and the FNLM algorithm which has 0.05 weight was applied to the image. In this study, root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) were used to evaluate the similarity. As a result of evaluating the similarity between the original image and the image with FNLM algorithm, the RMSE level was 1.45 times and PSNR level was 1.04 times higher than the noisy image. In conclusion, we demonstrated that the FNLM algorithm can restore efficiently the noisy images.

Wireless mesh networks (WMNs) are wireless networks that are composed of mesh routers and mesh clients. Mesh routers have minimal mobility and form the backbone of WMNs. IEEE 802.11s-based WMNs have a default routing protocol, namely, a hybrid wireless mesh protocol (HWMP). In tree-based proactive mode, HWMP is completely centralized and constrained by the root node, which causes a bottleneck at the root node. In reactive mode, HWMP always initiates path discovery message broadcasting, which uses unnecessary power resources. In this paper, we propose a new routing protocol for WMNs based on HWMP. This protocol, which we refer to as the decentralized hybrid wireless mesh protocol (DHWMP), provides a different root for different transmissions. We also perform simulations to compare the performance of the proposed scheme with existing routing protocols, including HWMP, ad hoc on-demand distance vector (AODV) routing, and optimized link state routing (OLSR).

This paper presents a novel approach for non-iterative surface smoothing with feature preservation on arbitrary meshes. Laplacian operator is performed in a global way over the mesh. The surface smoothing is formulated as a quadratic optimization problem, which is easily solved by a sparse linear system. The cost function to be optimized penalizes deviations from the global Laplacian operator while maintaining the overall shape of the original mesh. The features of the original mesh can be preserved by adding feature constraints and barycenter constraints in the system. Our approach is simple and fast, and does not cause surface shrinkage and distortion. Many experimental results are presented to show the applicability and flexibility of the approach.

In this paper, we describe the application procedure of the adaptive mesh refinement (AMR) for the weighted essentially non-oscillatory schemes (WENO), and observe the effects of the derived algorithm when problems have piecewise smooth solutions containing discontinuities. We find numerically that the dissipation of the WENO scheme can be lessened by the implementation of AMR while the accuracy is maintained. We deduce from the experiments that the AMR-implemented WENO scheme captures shocks more efficiently than the WENO method using uniform grids.

In this paper, we present a framework for non-rigid alignment between a canonical 3D mesh of a non-rigid object and a single RGB-D image in a category-agnostic manner. Conventional optimization-based methods typically rely solely on geometric features, making them highly sensitive to ambiguities caused by occlusions and partial observations. To address these limitations, we propose the Neural Descriptor Field (NDF), which leverages semantic and geometric representations of pre-trained foundation models to construct a continuous 3D descriptor field via instance-specific optimization. During registration, point-wise features extracted from the input RGB-D image are compared with those from the NDF to establish robust correspondences, which are then used within a scoring-based mechanism to estimate a deformation-tolerant coarse pose. This coarse alignment is further refined through a test-time non-rigid optimization that minimizes features and geometric inconsistencies. The proposed two-stage framework ensures accurate registration even under substantial pose variations and shape deformations. To evaluate our approach under challenging conditions not covered by existing benchmarks, we construct a new synthetic dataset with diverse deformations and occlusions. We demonstrate that our method achieves superior accuracy and robustness compared to state-of-the-art techniques.

Underground structures are built into concrete like a architecture and public works. Concrete has watertightness as such, But buildings are leaking from the cracks. Recently, construction method do a lot of close construction in the downtown area. So architects are using the method for the outer layer of a two-layer wall to save spaces. They have been using Top-down method and waterproof agent method and others to outer layer of a two-layer wall method. But, There are many leakage in underground structure from lack on requirements performance of materials. Therefore, I hope to test the outside Waterproof Method of Ground using Wire-mesh and Non-hardening Viscosity Waterproof. In addition to, I wish to apply as outside waterproof method for the section for the outer layer of a two-layer wall in underground structure.

Reverse engineering technology refers to the process that creates a CAD model of an existing part using measuring devices. Recently, non-contact scanning devices have become more accurate and the speed of data acquisition has increased drastically. However, they generate thousands of points per second and various types of point data. Therefore. it becomes a important to handle the huge amount and various types of point data to generate a surface model efficiently. This paper proposes a new triangular mesh generation method using 3D grids. The geometric information of a part can be obtained from point cloud data by estimating normal values of the points. In our research, the non-uniform 3D grids are generated first for feature based data reduction based on the geometric information. Then, triangulation is performed with the reduced point data. The grid structure is efficiently used not only for neighbor point search that can speed up the mesh generation process but also for getting surface connectivity information to result in same topology surface with the point data. Through this integrated approach, it is possible to create surface models from scanned point data efficiently.

It has been applied the self adhesion waterproofing sheet which is developed from exist asphalt waterproofing sheet by heat and torch in domestic construction field. However, the problem of waterproofing have constantly happened due to air pocket condition and defect of joint part in waterproofing construction. Therefore, in this study, we would like to analyze the field application as testing in side of materials and construction method of self adhesion butyl rubber sheet and study of the materials performance.