Tree Defect Segmentation using Geometric Features and CNN

International audienceEstimating the quality of standing trees or roundwood after felling is a crucial step in forest production trading. The ongoing revolution in the forest sector resulting from the use of 3D sensors can also contribute to this step. Among them the terrestrial lidar scanning is a reference descriptive method offering the possibility to segment defects. In this paper, we propose a new reproducible method allowing to automatically segment the defects. It is based on the construction of a relief map inspired from a previous strategy and combining with a convolutional neural network to improve the resulting segmentation quality. The proposed method outperforms the previous results and the source code is publicly available with an online demonstration allowing to test the defect detection without any software installation

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Wind Turbine Wake Characterization with Remote Sensing and Computational Fluid Dynamics

Because of the dense arrays at most wind farms, the region of disturbed flow downstream of an individual turbine leads to reduced power production and increased structural loading for its leeward counterparts. Currently, wind farm wake modeling, and hence turbine layout optimization, suffer from an unacceptable degree of uncertainty, largely because of a lack of adequate experimental data for model verification. Accordingly, wake measurements were taken in two separate experiments, (1) using the ground-based High Resolution Doppler Lidar (HRDL) developed by the National Oceanic and Atmospheric Administration (NOAA) in the Turbine Wake and Inflow Characterization Study (TWICS) at the National Renewable Energy Laboratory (NREL), and (2) using nacelle-based long-range lidar at a wind farm in the western United States. The vantage point from the nacelle is favorable in that scans can more consistently transect the central part of the wake. The work presented here outlines a set of quantitative procedures for determining critical parameters from these extensive datasets—such as the velocity deficit, the size of the wake boundary, and the location of the wake centerline—and the results are categorized by ambient wind speed, turbulence, and atmospheric stability. Despite specific reference to lidar, the methodology is general and can be applied to extract wake characteristics from other remote sensor datasets, as well as output from numerical simulations. In an effort to help advance computational fluid dynamics (CFD) models of wind turbine wake dynamics, experimental results are compared to a large eddy simulation (LES) of a turbine operating in the stable boundary layer using the actuator disk parameterization in the Weather Research and Forecasting (WRF) Model. With the wake characteristics described above as metrics for model verification, the simulations show good agreement with the observations. Moreover, new features—namely rotor tilt and drag from the nacelle and tower—are added to the existing actuator disk framework in WRF. The inclusion of rotor tilt causes the vertical location of the wake center to shift upward, as confirmed by experimental measurements. Continued improvement to the actuator disk model in WRF will help lead to optimized turbine siting and controls at wind farms

腹部CT像上の複数オブジェクトのセグメンテーションのための統計的手法に関する研究

Computer aided diagnosis (CAD) is the use of a computer-generated output as an auxiliary tool for the assistance of efficient interpretation and accurate diagnosis. Medical image segmentation has an essential role in CAD in clinical applications. Generally, the task of medical image segmentation involves multiple objects, such as organs or diffused tumor regions. Moreover, it is very unfavorable to segment these regions from abdominal Computed Tomography (CT) images because of the overlap in intensity and variability in position and shape of soft tissues. In this thesis, a progressive segmentation framework is proposed to extract liver and tumor regions from CT images more efficiently, which includes the steps of multiple organs coarse segmentation, fine segmentation, and liver tumors segmentation. Benefit from the previous knowledge of the shape and its deformation, the Statistical shape model (SSM) method is firstly utilized to segment multiple organs regions robustly. In the process of building an SSM, the correspondence of landmarks is crucial to the quality of the model. To generate a more representative prototype of organ surface, a k-mean clustering method is proposed. The quality of the SSMs, which is measured by generalization ability, specificity, and compactness, was improved. We furtherly extend the shapes correspondence to multiple objects. A non-rigid iterative closest point surface registration process is proposed to seek more properly corresponded landmarks across the multi-organ surfaces. The accuracy of surface registration was improved as well as the model quality. Moreover, to localize the abdominal organs simultaneously, we proposed a random forest regressor cooperating intensity features to predict the position of multiple organs in the CT image. The regions of the organs are substantially restrained using the trained shape models. The accuracy of coarse segmentation using SSMs was increased by the initial information of organ positions.Consequently, a pixel-wise segmentation using the classification of supervoxels is applied for the fine segmentation of multiple organs. The intensity and spatial features are extracted from each supervoxels and classified by a trained random forest. The boundary of the supervoxels is closer to the real organs than the previous coarse segmentation. Finally, we developed a hybrid framework for liver tumor segmentation in multiphase images. To deal with these issues of distinguishing and delineating tumor regions and peripheral tissues, this task is accomplished in two steps: a cascade region-based convolutional neural network (R-CNN) with a refined head is trained to locate the bounding boxes that contain tumors, and a phase-sensitive noise filtering is introduced to refine the following segmentation of tumor regions conducted by a level-set-based framework. The results of tumor detection show the adjacent tumors are successfully separated by the improved cascaded R-CNN. The accuracy of tumor segmentation is also improved by our proposed method. 26 cases of multi-phase CT images were used to validate our proposed method for the segmentation of liver tumors. The average precision and recall rates for tumor detection are 76.8% and 84.4%, respectively. The intersection over union, true positive rate, and false positive rate for tumor segmentation are 72.7%, 76.2%, and 4.75%, respectively.九州工業大学博士学位論文 学位記番号: 工博甲第546号 学位授与年月日: 令和4年3月25日1 Introduction|2 Literature Review|3 Statistical Shape Model Building|4 Multi-organ Segmentation|5 Liver Tumors Segmentation|6 Summary and Outlook九州工業大学令和3年

Comprehensive Analytical Modeling of Laser Powder-Bed/Fed Additive Manufacturing Processes and an Associated Magnetic Focusing Module

State-of-the-art metal additive manufacturing (AM), mainly laser powder-fed AM (LPF-AM) and laser powder-bed AM (LPB-AM), has been used to produce high-quality, complex-shaped, and end-user metallic parts. To achieve desirable dimensional, microstructural and mechanical features of as-built components through fast process optimization or feedback-control-based adaptive processing adjustment, high fidelity and calculation-efficient processing model is urgently needed. The thesis research has been motivated by the need for time-efficient process models of both LPF-AM and LPB-AM. To this end, comprehensive accelerated models for these processes have been built and experimentally verified. The comprehensive process model of LPF-AM was built by an innovative analytical approach. Firstly, a mathematical module that couples laser heat flux and powder mass flow was developed, while considering the attenuated laser intensity distribution and the heated powder spatial distribution. Correspondingly, a powder catchment module was built in terms of a three-dimensional (3D) melt pool shape and powder stream spatial distribution. Integrating these physical modules into the thermal modeling, a coupled heat and mass comprehensive model of the LPF-AM process was achieved. Experimental depositions of Inconel 625 proves the model’s high accuracy in predicting as-built deposits’ geometry (a maximum error of ~6.2% for clad width, ~7.8% for clad height) and powder catchment efficiency (maximum error of less than ~6.8%). It was found that the predicted real-time melt pool peak temperatures match well with the experimental results in Stainless Steel (SS) 316L deposition. The calculated micro-hardness has a maximum prediction error of ~16.2% compared with the measured results. The predicted microstructural evolutions show reasonable agreement with the experimental observations for both SS 316L and Inconel 625 depositions. Moreover, sensitivity analysis shows that the powder feed rate has the largest positive effect on the clad height. The time-efficient process model of LPB-AM was achieved by a novel analytical approach that couples the critical physics of the process, while considering the volume shrinkage and the melting regime. The proposed model can perform a time-efficient prediction of the localized-transient thermal field, melt pool temperature distribution, and multi-track overlapping dimension. The powder bed was treated as a homogeneous medium with effective thermophysical properties derived from the randomly packed rain model. In addition, different melting regimes of the LPB-AM process were considered in the built model. A 3D heat source model with variant penetration depths, together with the varying melting regimes, was utilized to solve the transient thermal field. Moreover, the density and top surface roughness of the final parts were empirically modeled using response surface regression under a Box-Behnken design. Subsequently, the mechanical properties of the part and the in-situ build rates were simultaneously optimized by combining the built analytical models and empirical models with employing a multi-objective genetic algorithm. Experimental results with SS 17-4PH show that the predicted melt pool dimensions have a high degree of accuracy under steady melting regimes, with a maximum of ~14% error for the width prediction and ~15% error for the depth calculation. Furthermore, an optimized parameter solution set was provided based on the built 3D Pareto fronts. The built models’ calculation time for the localized-transient characteristics for LPF-AM and LPB-AM are ~4 ms and ~1.2 ms, respectively. These findings confirm the great potential of the present research to be used for fast process optimization and in-situ process control. In addition, a new magnetic concentration approach designed with various configurations was explored. This approach is designed to focus the diverging metal particles in the gas-powder stream of LPF-AM, thereby improving powder catchment and deposition accuracy. It was shown that the proposed permanent-magnet-based configurations may not be suitable for concentrating submillimeter-sized particles. However, an additional development, a doublet-electromagnet-quadrupoles-based configuration with high frequency, may be capable of concentrating the non-ferrous metallic particles (e.g., aluminum particle) with a radius of r_p≥150 μm

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives