Accelerated iterative solvers for the solution of electromagnetic scattering and wave propagation propagation problems

The aim of this work is to contribute to the development of accelerated iterative methods for the solution of electromagnetic scattering and wave propagation problems. In spite of recent advances in computer science, there are great demands for efficient and accurate techniques for the analysis of electromagnetic problems. This is due to the increase of the electrical size of electromagnetic problems and a large amount of design and analytical work dependent on simulation tools. This dissertation concentrates on the use of iterative techniques, which are expedited by appropriate acceleration methods, to accurately solve electromagnetic problems. There are four main contributions attributed to this dissertation. The first two contributions focus on the development of stationary iterative methods while the other two focus on the use of Krylov iterative methods. The contributions are summarised as follows: • The modified multilevel fast multipole method is proposed to accelerate the performance of stationary iterative solvers. The proposed method is combined with the buffered block forward backward method and the overlapping domain decomposition method for the solution of perfectly conducting three dimensional scattering problems. The proposed method is more efficient than the standard multilevel fast multipole method when applied to stationary iterative solvers. • The modified improvement step is proposed to improve the convergence rate of stationary iterative solvers. The proposed method is applied for the solution of random rough surface scattering problems. Simulation results suggest that the proposed algorithm requires significantly fewer iterations to achieve a desired accuracy as compared to the conventional improvement step. • The comparison between the volume integral equation and the surface integral equation is presented for the solution of two dimensional indoor wave propagation problems. The linear systems resulting from the discretisation of the integral equations are solved using Krylov iterative solvers. Both approaches are expedited by appropriate acceleration techniques, the fast Fourier transform for the volumetric approach and the fast far field approximation for the surface approach. The volumetric approach demonstrates a better convergence rate than the surface approach. • A novel algorithm is proposed to compute wideband results of three dimensional forward scattering problems. The proposed algorithm is a combination of Krylov iterative solvers, the fast Fourier transform and the asymptotic waveform evaluation technique. The proposed method is more efficient to compute the wideband results than the conventional method which separately computes the results at individual frequency points

Foreign bank penetration in Vietnam following Vietnam’s accession to the WTO: matching expectations with reality

Vietnam continuously liberalizes the financial market as a requirement for its accession to the World Trade Organization in 2007. This paper discusses the foreign investors’ expectation and their experience when penetrating into Vietnam’s market. The role of the foreign entrants is also assessed. By synthesizing and analyzing relevant research and reports, several important insights are discovered. Firstly, the presence of foreign investors and banks improves market competition, efficiency, and stability. Wholly and partly foreign-owned banks provide the spillover effects in management quality, in the introduction of world standard banking products and services, and in the application of information technology. Secondly, by looking into the foreign owned banks, it is found that the banks’ foreign investors are not likely to play an influential role in managing the banks they invested in. The motive of the investors to control the invested companies leads to their decision of holdings withdrawing

Gene Family Abundance Visualization based on Feature Selection Combined Deep Learning to Improve Disease Diagnosis

Advancements in machine learning in general and in deep learning in particular have achieved great success in numerous fields. For personalized medicine approaches, frameworks derived from learning algorithms play an important role in supporting scientists to investigate and explore novel data sources such as metagenomic data to develop and examine methodologies to improve human healthcare. Some challenges when processing this data type include its very high dimensionality and the complexity of diseases. Metagenomic data that include gene families often have millions of features. This leads to a further increase of complexity in processing and requires a huge amount of time for computation. In this study, we propose a method combining feature selection using perceptron weight-based filters and synthetic image generation to leverage deep-learning advancements in order to predict various diseases based on gene family abundance data. An experiment was conducted using gene family datasets of five diseases, i.e. liver cirrhosis, obesity, inflammatory bowel diseases, type 2 diabetes, and colorectal cancer. The proposed method provides not only visualization for gene family abundance data but also achieved a promising performance level

Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning

Typical computer-based parameter optimization and uncertainty quantification of the additive manufacturing process usually requires significant computational cost for performing high-fidelity heat transfer finite element (FE) models with different process settings. This work develops a simple surrogate model using a feedforward neural network (FFNN) for a fast and accurate prediction of the temperature evolutions and the melting pool sizes in a metal bulk sample (3D horizontal layers) manufactured by the DED process. Our surrogate model is trained using high-fidelity data obtained from the FE model, which was validated by experiments. The temperature evolutions and the melting pool sizes predicted by the FFNN model exhibit accuracy of 99% and 98%, respectively, compared with the FE model for unseen process settings in the studied range. Moreover, to evaluate the importance of the input features and explain the achieved accuracy of the FFNN model, a sensitivity analysis (SA) is carried out using the SHapley Additive exPlanation (SHAP) method. The SA shows that the most critical enriched features impacting the predictive capability of the FFNN model are the vertical distance from the laser head position to the material point and the laser head position.VINIF.2020.DA15 EDPOMP projec

Uncertainty Quantification in the Directed Energy Deposition Process Using Deep Learning-Based Probabilistic Approach

peer reviewedThis study quantifies the effects of uncertainty raised from process parameters, material properties, and boundary conditions in the directed energy deposition (DED) process of M4 High-Speed Steel using deep learning (DL)-based probabilistic approach. A DL-based surrogate model is first constructed using the data obtained from a finite element (FE) model, which was validated against experiment. Then, sources of uncertainty are characterized by the probabilistic method and are propagated by the Monte-Carlo (MC) method. Lastly, the sensitivity analysis (SA) using the variance-based method is performed to identify the parameters inducing the most uncertainty to the melting pool depth. Using the DL-based surrogate model instead of solely FE model significantly reduces the computational time in the MC simulation. The results indicate that all sources of uncertainty contribute to a substantial variation on the final printed product quality. Moreover, we find that the laser power, the convection, the scanning speed, and the thermal conductivity contribute the most uncertainties on the melting pool depth based on the SA results. These findings can be used as insights for the process parameter optimization of the DED process.EDPOM

Mangrove restoration in Vietnamese Mekong Delta during 2015-2020: Achievements and challenges

Mangrove forest in the Mekong Delta plays important roles in protecting coasts from soil erosion and strong waves, supplying seafood, and accumulating carbon. Despite these benefits, mangroves have been and continue to be severely damaged by the impacts of natural and socioeconomic activities. In recent years, large areas of mangrove forest have been restored through planting and other various management actions. In this study, we analyzed high-resolution WorldView-2 images to quantify changes in the mangrove forest in seven coastal provinces (Tien Giang, Ben Tre, Tra Vinh, Soc Trang, Bac Lieu, Ca Mau, and Kien Giang) of the Mekong Delta from 2015 to 2020. Our study is one of the first to analyze mangrove forest change at the commune scale, the smallest official administrative unit in Vietnam, to determine the area of restored mangroves. The potentials and challenges in future mangrove restoration were also assessed by analyzing satellite imagery and field survey data. In the study area, mangrove forest area increased by 11,184 ha (approximately 2,237 ha per year) from 79,593 ha in 2015 to 90,777 ha in 2020. A total area of 16,138 ha (approximately 20.3%) was lost due to mangrove conversion to other land uses, aquaculture activities and coastal erosion, etc., while 27,322 ha (approximately 34.1%) was restored or newly planted during state- and NGO-funded mangrove restoration projects and programs. These results confirmed that mangrove restoration projects and programs have played a significant role in maintaining and increasing mangrove forest cover in Mekong Delta. The results can also assist managers and decision makers in mangrove restoration evaluation, and suggest analyzing WorldView-2 images to monitor mangrove restoration over time in Vietnam

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573