Projects Lists Relating to Post-disaster Reconstruction

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Post-Disaster Reconstruction of Alishan Tsou Tribe in Different Place and Sustainable Development

After the Morakot disaster in 2009, the affected indigenous tribes suffered from the government’s use of permanent housing as a single reconstruction option, which forced the victims to leave their original land for a new life in a different reconstruction mode. The purpose of this study is to explore the ways adopted by tribal residents to maintain their own culture and tribal life in the process of disaster, post disaster reconstruction and post disaster adjustment. Veoveoana Village in Taiwan is an indigenous tribe that relocated after a disaster and was reconstructed and developed through tourism development. This study performed the research by participant observation and in-depth interviews on Veoveoana Village. The analytical results showed that: 1) development of the tourism industry can result in cultural reconstruction of the post-disaster tribe and maintain the people’s incomes;2) although the government constantly assists with the rehabilitation of tribal industry by various policies, the implementation cannot effectively continue and the outcome is insignificant;3) due to the gap between permanent prefabricated housing and original tribal cultural features, the residential rate is not high. According to the research findings, it is suggested that, in the process of post-disaster reconstruction, the government and private non-profit organizations should respect the intention of the majority of the indigenous people. In addition, it should cultivate professional manpower for the subsidized projects.

Variational regularization method of solving the Cauchy problem for Laplace's equation: Innovation of the Grad–Shafranov(GS) reconstruction

The simplified linear model of Grad-Shafranov （GS） reconstruction can be reformulated into an inverse boundary value problem of Laplace＇s equation. Therefore, in this paper we focus on the method of solving the inverse boundary value problem of Laplace＇s equation. In the first place, the variational regularization method is used to deal with the ill- posedness of the Cauchy problem for Laplace＇s equation. Then, the ＇L-Curve＇ principle is suggested to be adopted in choosing the optimal regularization parameter. Finally, a numerical experiment is implemented with a section of Neumann and Dirichlet boundary conditions with observation errors. The results well converge to the exact solution of the problem, which proves the efficiency and robustness of the proposed method. When the order of observation error δ is 10-1, the order of the approximate result error can reach 10-3.

Reconstruction method of differentiated backprojection-projection onto convex sets in the interior problem and design of bone-nail model

This work focuses on the application of the reconstruction method of differentiated backprojection （DBP）-projection onto convex sets （POCS） in the interior problem.First,we present the definition of the interior problem and real truncated Hilbert transform,and then outline the implementation steps of DBP-POCS.After that,we introduce the middle-part known condition for region of interest （ROI） accurate reconstruction and the unique condition of the interior problem,and verify the uniqueness and stability of the interior problem accurate reconstruction through numerical experiments,and then compare the results for the interior problem in reconstruction images using filtered backprojection （FBP）.In addition,the authors also design the application models of ROI reconstruction and make an initial attempt to the application of DBP-POCS method in the interior problem.

An algorithm for computed tomography image reconstruction from limited-view projections

With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.

A Novel Method for the Reconstruction of Road Profiles from Measured Vehicle Responses Based on the Kalman Filter Method

The estimation of the disturbance input acting on a vehicle from its given responses is an inverse problem.To overcome some of the issues related to ill-posed inverse problems,this work proposes a method of reconstructing the road roughness based on the Kalman filter method.A half-car model that considers both the vehicle and equipment is established,and the joint input-state estimation method is used to identify the road profile.The capabilities of this methodology in the presence of noise are numerically demonstrated.Moreover,to reduce the influence of the driving speed on the estimation results,a method of choosing the calculation frequency is proposed.A road vibration test is conducted to benchmark the proposed method.

Application of Reconstruction and Optimization Algorithms in Optical Tomography

Optical tomography is a non-invasive technique that uses visible or near infrared radiation to analyze biological tissues. Researchers take immense attention towards advancement in optical tomography because of its low cost and an advantage of providing anatomical information. Based on the information of optical characteristics, forward and inverse problem of tomography are solved. In this research, finite element method is employed for forward problem and gradient-based optimization algorithm is developed for inverse problem of optical tomography. It is found from simulations that information about imaging is processed more distinctly and in less computational time. Normal and abnormal conditions in imaging are readily distinguished.?Simulations are carried out in Matlab. Different scenarios are developed and are simulated to validate the performance of reconstruction and optimization algorithms in optical tomography.

Source reconstruction for bioluminescence tomography via L_(1/2)regularization

Bioluminescence tomography(BLT)is an important noninvasive optical molecular imaging modality in preclinical research.To improve the image quality,reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem.The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm.In this paper,we present a reconstruction method based on L_(1/2) regularization to enhance sparsity of BLT solution and solve the nonconvex L_(1/2) norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights.To assess the performance of the proposed reconstruction algorithm,simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms,including the weighted interior-point,L1 homotopy,and the Stagewise Orthogonal Matching Pursuit algorithm.Simulation results show that the proposed method yield stable reconstruction results under different noise levels.Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy,multiple-source resolving and image quality.

Implicit Shape Reconstruction of Unorganized Points Using PDE-Based Deformable 3D Manifolds

In this work we consider the problem of shape reconstruction from an unorganized data set which has many important applications in medical imaging, scientific computing, reverse engineering and geometric modelling. The reconstructed surface is obtained by continuously deforming an initial surface following the Partial Differential Equation (PDE)-based diffusion model derived by a minimal volume-like variational formulation. The evolution is driven both by the distance from the data set and by the curvature analytically computed by it. The distance function is computed by implicit local interpolants defined in terms of radial basis functions. Space discretization of the PDE model is obtained by finite co-volume schemes and semi-implicit approach is used in time/scale. The use of a level set method for the numerical computation of the surface reconstruction allows us to handle complex geometry and even changing topology,without the need of user-interaction. Numerical examples demonstrate the ability of the proposed method to produce high quality reconstructions. Moreover, we show the effectiveness of the new approach to solve hole filling problems and Boolean operations between different data sets.

The Matrix Transform for Reconstruction on Finite-Element-Based Photoacoustic Tomography

Numerical Finite-element method (FEM) based algorithms have been widely applied for the reconstruction of the photoacoustic image. As compared with the traditional analytic methods, the FEM based methods can be easily used to deal with problems with irregularly shaped imaging domain. However, the FEM based algorithms are usually computationally intensive because repeated manipulations of matrices with larger size are needed during the reconstruction process. To tackle such a problem, a novel method is proposed for reducing the size of the matrix to be inversed during the reconstruction process and hence speed up the inverse reconstruction without any sacrifice of the reconstruction accuracy.

A Compton scattering image reconstruction algorithm based on total variation minimization

Compton scattering imaging is a novel radiation imaging method using scattered photons.Its main characteristics are detectors that do not have to be on the opposite side of the source,so avoiding the rotation process.The reconstruction problem of Compton scattering imaging is the inverse problem to solve electron densities from nonlinear equations,which is ill-posed.This means the solution exhibits instability and sensitivity to noise or erroneous measurements.Using the theory for reconstruction of sparse images,a reconstruction algorithm based on total variation minimization is proposed.The reconstruction problem is described as an optimization problem with nonlinear data-consistency constraint.The simulated results show that the proposed algorithm could reduce reconstruction error and improve image quality,especially when there are not enough measurements.

Reconstruction of the Sturm-Liouville operator with discontinuities from a particular set of eigenvalues

Sturm-Liouville operators on a finite interval with discontinuities are considered. We give a uniqueness theorem for determining the potential and the parameters in boundary and under discontinuous conditions from a particular set of eigenvalues, and provide corresponding reconstruction algorithm, which can be applicable to McLaughlin-Rundell＇s uniqueness theorem （see J. Math. Phys. 28, 1987）.

Electrical Impedance Tomography Image Reconstruction Using Iterative Lavrentiev and L-Curve-Based Regularization Algorithm

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution inside a medium from measurements made on its surface. The impedance distribution reconstruction in EIT is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods are often used in solving inverse problems. However, for EIT image reconstruction, the generalized Tikhonov regularization methods may lose the boundary information due to its smoothing operation. In this paper, we propose an iterative Lavrentiev regularization and L-curve-based algorithm to reconstruct EIT images. The regularization parameter should be carefully chosen, but it is often heuristically selected in the conventional regularization-based reconstruction algorithms. So, an L-curve-based optimization algorithm is used for selecting the Lavrentiev regularization parameter. Numerical analysis and simulation results are performed to illustrate EIT image reconstruction. It is shown that choosing the appropriate regularization parameter plays an important role in reconstructing EIT images.

HYBRID REGULARIZED CONE-BEAM RECONSTRUCTION FOR AXIALLY SYMMETRIC OBJECT TOMOGRAPHY

In this paper,we consider 3 D tomographic reconstruction for axially symmetric objects from a single radiograph formed by cone-beam X-rays.All contemporary density reconstruction methods in high-energy X-ray radiography are based on the assumption that the cone beam can be treated as fan beams located at parallel planes perpendicular to the symmetric axis,so that the density of the whole object can be recovered layer by layer.Considering the relationship between different layers,we undertake the cone-beam global reconstruction to solve the ambiguity effect at the material interfaces of the reconstruction results.In view of the anisotropy of classical discrete total variations,a new discretization of total variation which yields sharp edges and has better isotropy is introduced in our reconstruction model.Furthermore,considering that the object density consists of continually changing parts and jumps,a high-order regularization term is introduced.The final hybrid regularization model is solved using the alternating proximal gradient method,which was recently applied in image processing.Density reconstruction results are presented for simulated radiographs,which shows that the proposed method has led to an improvement in terms of the preservation of edge location.

Reconstruction of Temperature Field in 3-D, Absorbing, Emitting, and Anisotropically Scattering Medium

The soft measurement technology of flame temperature field is an efficient method to learn the combustion status in furnace. Generally, it reconstructs the temperature field in furnace through the image of flame, which is a process to solve radiative inverse problem. In this paper, the flame of pulverized coal is considered as 3-D, absorbing, emitting, and anisotropically scattering non-gray medium. Through the study on inverse problem of radiative heat transfer, the temperature field in this kind of medium has been reconstructed. The mechanism of 3-D radiative heat transfer in a rectangular media, which is 2 m×3 m× 5 m and full of CO2, N2 and carbon particles, is studied with Monte Carlo method. The 3-D temperature field in this rectangular space is reconstructed and the influence of particles density profile is discussed.

结合竞争交互策略和淘汰重组机制的异构多蚁群算法

针对传统的蚁群算法在解决旅行商问题时(traveling salesman problem,TSP)存在着收敛速度慢、容易陷入局部最优等问题,提出了一种结合竞争交互策略和淘汰重组机制的异构多蚁群算法。建立一个异构多种群系统,算法采用竞争交互策略,根据不同时期各种群的汉明距离来自适应的调节交互周期;并利用竞争系数来差异化匹配交互对象,经过匹配后的交互对象之间通过最优解和信息素矩阵进行交互,通过该机制实现了算法收敛速度和多样性的平衡。算法采用了淘汰重组机制,会定期对寻优能力差的种群进行淘汰与重组,以加快算法的求解精度。采用多组不同规模的TSP算例进行仿真实验,结果表明,该算法在提高求解精度和收敛速度方面表现更优。

PBDB数据库在古地理重建中的重要应用进展

大数据时代的信息技术革命带来了科学研究的数字化变革,作为地球科学领域开展数字化科研重要里程碑的古生物数据库Paleobiology Database(PBDB),为古地理研究提供了重要的数据支撑。通过调研PBDB数据库平台内容及其在古地理重建中的应用实例,总结PBDB数据库目前在古地理重建中存在的问题,展望其未来的发展方向。结果表明:(1)古生物数据库包含大量的可溯源数据,是古生物领域研究的重要信息来源和成果交流平台,其中PBDB数据库具有可视化特点,能提供多种数据检索和下载方式,是古地理重建工作的首选古生物数据库;(2)PBDB数据库的分类学和采集记录数据,在约束古地理位置、修订古气候带划分方案、定量古高程古水深、校正古海岸线等古地理重建领域具有明显优势;(3)古生物数据库偏向于古生物研究领域,且古生物记录的历史可能存在偏差和不完整性,其在古地理重建中起辅助约束作用,而非绝对途径;(4)如何实现古生物数据库中数据的自动分类、筛选,如何精确约束古板块位置的相对经度,如何提升古气候、古高程、古水深等研究的精确性,是应用PBDB数据库进行古地理重建的重要发展方向。

脑磁源定位重建算法研究进展

脑磁图作为一种非侵入性大脑功能神经成像方法,以其高时间分辨率和无创性等特点得到临床的广泛关注。脑磁源定位研究的核心问题是利用脑外磁场数据(脑磁图)来推断脑内电流源的分布,并进一步确定神经源的位置和活动模式,这一逆问题的求解过程存在不唯一性和不适定性的难题。脑磁源定位重建方法分为分布源模型和偶极子定位。综述脑磁图和磁源成像的发展过程和研究进展,分布源模型包括最小范数估计法、低分辨率脑电磁断层扫描、欠定系统局域解法、贝叶斯推断估计法、波束成形器和稀疏源成像等;偶极子定位包括最大熵方法、最小二乘范数、多信号分类算法、神经网络和遗传算法等;论述各类算法的特色、局限性及其存在的问题;随着技术的不断进步,融合多种脑功能技术的多模态重建方法有望成为神经功能诊断领域的主导性检测技术。

基于改进最小方差波束成形的心磁信号的源重建

由于冠心病患者的心磁图在信噪比较低时,最小方差波束成形(MV)方法的源重建效果不理想,提出一种基于改进最小方差波束成形(IMV)方法,在MV方法的基础上,使用导联矩阵和磁场信号二阶特征矩阵组成约束矩阵,构造新的空间滤波器的权矩阵,从而降低输出噪声功率增益。利用点扩散函数理论比较IMV方法和MV方法的空间谱估计的单源分辨率;利用3个电流偶极子产生36通道磁场仿真数据,采用IMV方法和MV方法在低信噪比下对仿真数据进行源重建;最后对3名冠心病患者的36通道心磁图的R波峰和T波峰时刻数据进行源重建。结果表明,IMV方法的单源分辨率更高,对仿真数据及冠心病患者心磁数据的重建精度更好。

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.