Search results for: dimensional accuracy (DA)

Authors: Ping Li, Bing Feng, Junpu Zhao, Xudong Xie, Dangpeng Xu, Kuixing Zheng, Qihua Zhu, Xiaofeng Wei

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In laser driven inertial confinement fusion (ICF), the control of the temporal shape of the laser pulse is a key point to ensure an optimal interaction of laser-target. One of the main difficulties in controlling the temporal shape is the foot part control accuracy of high contrast pulse. Based on the analysis of pulse perturbation in the process of amplification and frequency conversion in high power lasers, an approach of beam spatio-temporal multiplexing is proposed to improve the control precision of high contrast pulse. In the approach, the foot and peak part of high contrast pulse are controlled independently, which propagate separately in the near field, and combine together in the far field to form the required pulse shape. For high contrast pulse, the beam area ratio of the two parts is optimized, and then beam fluence and intensity of the foot part are increased, which brings great convenience to the control of pulse. Meanwhile, the near field distribution of the two parts is also carefully designed to make sure their F-numbers are the same, which is another important parameter for laser-target interaction. The integrated calculation results show that for a pulse with a contrast of up to 500, the deviation of foot part can be improved from 20% to 5% by using beam spatio-temporal multiplexing approach with beam area ratio of 1/20, which is almost the same as that of peak part. The research results are expected to bring a breakthrough in power balance of high power laser facility.

Keywords: inertial confinement fusion, laser pulse control, beam spatio-temporal multiplexing, power balance

Procedia PDF Downloads 145

Authors: E. Keramaris

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In this study, laboratory experiments in open channel flows over a sand bed were conducted. A porous bed (sand bed) with porosity of ε=0.70 and porous thickness of s΄=3 cm was tested. Vertical distributions of velocity were evaluated by using a two-dimensional (2D) Particle Image Velocimetry (PIV). Velocity profiles are measured above the impermeable bed and above the sand bed for the same different total water heights (h= 6, 8, 10 and 12 cm) and for the same slope S=1.5. Measurements of mean velocity indicate the effects of the bed material used (sand bed) on the flow characteristics (Velocity distribution and Reynolds number) in comparison with those above the impermeable bed.

Keywords: particle image velocimetry, sand bed, velocity distribution, Reynolds number

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Authors: Min Jindong, Wang Mingxia

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With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

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Authors: Zuhier Altawallbeh

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In this paper, an explicit homotopic function is constructed to compute the Hochschild homology of a finite dimensional free k-module V. Because the polynomial algebra is of course fundamental in the computation of the Hochschild homology HH and the cyclic homology CH of commutative algebras, we concentrate our work to compute HH of the polynomial algebra.by providing certain homotopic function.

Keywords: hochschild homology, homotopic function, free and projective modules, free resolution, exterior algebra, symmetric algebra

Procedia PDF Downloads 402

Authors: Mohamed Belhorma, Aboubakar S. Bouchikhi, Belkacem Bounab

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This paper addresses modeling a Double A-Arm suspension, a three-dimensional nonlinear model has been developed using the multibody systems formalism. Dynamical study of the different components responses was done, particularly for the wheel assembly. To validate those results, the system was constructed and simulated by RecurDyn, a professional multibody dynamics simulation software. The model has been used as the Objectif function in an optimization algorithm for ride quality improvement.

Keywords: double A-Arm suspension, multibody systems, ride quality optimization, dynamic simulation

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Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

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Originated from graphite, graphene is a two-dimensional (2D) material that promises to revolutionize technology in many different areas, such as energy, telecommunications, civil construction, aviation, textile, and medicine. This is possible because its structure, formed by carbon bonds, provides desirable optical, thermal, and mechanical characteristics that are interesting to multiple areas of the market. Thus, several research and development centers are studying different manufacturing methods and material applications of graphene, which are often compromised by the scarcity of more agile and accurate methodologies to characterize the material – that is to determine its composition, shape, size, and the number of layers and crystals. To engage in this search, this study proposes a computational methodology that applies deep learning to identify graphene oxide crystals in order to characterize samples by crystal sizes. To achieve this, a fully convolutional neural network called U-net has been trained to segment SEM graphene oxide images. The segmentation generated by the U-net is fine-tuned with a standard deviation technique by classes, which allows crystals to be distinguished with different labels through an object delimitation algorithm. As a next step, the characteristics of the position, area, perimeter, and lateral measures of each detected crystal are extracted from the images. This information generates a database with the dimensions of the crystals that compose the samples. Finally, graphs are automatically created showing the frequency distributions by area size and perimeter of the crystals. This methodological process resulted in a high capacity of segmentation of graphene oxide crystals, presenting accuracy and F-score equal to 95% and 94%, respectively, over the test set. Such performance demonstrates a high generalization capacity of the method in crystal segmentation, since its performance considers significant changes in image extraction quality. The measurement of non-overlapping crystals presented an average error of 6% for the different measurement metrics, thus suggesting that the model provides a high-performance measurement for non-overlapping segmentations. For overlapping crystals, however, a limitation of the model was identified. To overcome this limitation, it is important to ensure that the samples to be analyzed are properly prepared. This will minimize crystal overlap in the SEM image acquisition and guarantee a lower error in the measurements without greater efforts for data handling. All in all, the method developed is a time optimizer with a high measurement value, considering that it is capable of measuring hundreds of graphene oxide crystals in seconds, saving weeks of manual work.

Keywords: characterization, graphene oxide, nanomaterials, U-net, deep learning

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Authors: Kevin Brochard, Herve Le Sourne, Guillaume Barras

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The design of a submarine’s hull is crucial for its operability and crew’s safety, but also complex. Indeed, engineers need to balance lightness, acoustic discretion and resistance to both immersion pressure and environmental attacks. Submarine explosions represent a first-rate threat for the integrity of the hull, whose behavior needs to be properly analyzed. The presented work is focused on the development of a simplified analytical method to study the structural response of a deeply immersed cylinder submitted to an underwater explosion. This method aims to provide engineers a quick estimation of the resulting damage, allowing them to simulate a large number of explosion scenarios. The present research relies on the so-called plastic string on plastic foundation model. A two-dimensional boundary value problem for a cylindrical shell is converted to an equivalent one-dimensional problem of a plastic string resting on a non-linear plastic foundation. For this purpose, equivalence parameters are defined and evaluated by making assumptions on the shape of the displacement and velocity field in the cross-sectional plane of the cylinder. Closed-form solutions for the deformation and velocity profile of the shell are obtained for explosive loading, and compare well with numerical and experimental results. However, the plastic-string model has not yet been adapted for a cylinder in immersion subjected to an explosive loading. In fact, the effects of fluid-structure interaction have to be taken into account. Moreover, when an underwater explosion occurs, several pressure waves are emitted by the gas bubble pulsations, called secondary waves. The corresponding loads, which may produce significant damages to the cylinder, must also be accounted for. The analytical developments carried out to solve the above problem of a shock wave impacting a cylinder, considering fluid-structure interaction will be presented for an unstiffened cylinder. The resulting deformations are compared to experimental and numerical results for different shock factors and different standoff distances.

Keywords: immersed cylinder, rigid plastic material, shock loading, underwater explosion

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Authors: Kenan Morani, Esra Kaya Ayana

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This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

Procedia PDF Downloads 127

Authors: Akiyoshi Wakatani

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This paper describes the Message Passing Interface (MPI) implementation of ADETRAN language, and its evaluation on SX-ACE supercomputers. ADETRAN language includes pdo statement that specifies the data distribution and parallel computations and pass statement that specifies the redistribution of arrays. Two methods for implementation of pass statement are discussed and the performance evaluation using Splitting-Up CG method is presented. The effectiveness of the parallelization is evaluated and the advantage of one dimensional distribution is empirically confirmed by using the results of experiments.

Keywords: iterative methods, array redistribution, translator, distributed memory

Procedia PDF Downloads 265

Authors: Yasar Samet Gokceoglu, Ayse Nur Incesu, Furkan Okatar, Berk Nimetoglu, Serkan Bayram, Turgut Akgul

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Ankle syndesmosis injuries pose a significant challenge in orthopedic practice due to their potential for prolonged recovery and chronic ankle dysfunction. Accurate diagnosis and management of these injuries are essential for achieving optimal patient outcomes. The use of radiological methods, such as X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a vital role in the accurate diagnosis of syndesmosis injuries in the context of ankle fractures. Treatment options for ankle syndesmosis injuries vary, with surgical interventions such as screw fixation and suture-button implantation being commonly employed. The choice of treatment is influenced by the severity of the injury and the presence of associated fractures. Additionally, the mechanism of injury, such as pure syndesmosis injury or specific fracture types, can impact the stability and management of syndesmosis injuries. Ankle fractures with syndesmosis injury present a complex clinical scenario, requiring accurate diagnosis, appropriate reduction, and tailored management strategies. The interplay between the mechanism of injury, associated fractures, and treatment modalities significantly influences the outcomes of these challenging injuries. The long-term outcomes and patient satisfaction following ankle fractures with syndesmosis injury are crucial considerations in the field of orthopedics. Patient-reported outcome measures, such as the Foot and Ankle Outcome Score (FAOS), provide essential information about functional recovery and quality of life after these injuries. When diagnosing syndesmosis injuries, standard measurements, such as the medial clear space, tibiofibular overlap, tibiofibular clear space, anterior tibiofibular ratio (ATFR), and the anterior-posterior tibiofibular ratio (APTF), are assessed through radiographs and computed tomography (CT) scans. These parameters are critical in evaluating the presence and severity of syndesmosis injuries, enabling clinicians to choose the most appropriate treatment approach. Despite advancements in diagnostic imaging, challenges remain in accurately diagnosing and treating ankle syndesmosis injuries. Traditional diagnostic parameters, while beneficial, may not capture the full extent of the injury or provide sufficient information to guide therapeutic decisions. This gap highlights the need for exploring additional diagnostic parameters that could enhance the accuracy of syndesmosis injury diagnoses and inform treatment strategies more effectively. The primary goal of this research is to evaluate the usefulness of traditional radiographic measurements in comparison to new CT-based measurements for diagnosing ankle syndesmosis injuries. Specifically, this study aims to assess the accuracy of conventional parameters, including medial clear space, tibiofibular overlap, tibiofibular clear space, ATFR, and APTF, in contrast with the recently proposed CT-based measurements such as the delta and gamma angles. Moreover, the study intends to explore the relationship between these diagnostic parameters and functional outcomes, as measured by the Foot and Ankle Outcome Score (FAOS). Establishing a correlation between specific diagnostic measurements and FAOS scores will enable us to identify the most reliable predictors of functional recovery following syndesmosis injuries. This comparative analysis will provide valuable insights into the accuracy and dependability of CT-based measurements in diagnosing ankle syndesmosis injuries and their potential impact on predicting patient outcomes. The results of this study could greatly influence clinical practices by refining diagnostic criteria and optimizing treatment planning for patients with ankle syndesmosis injuries.

Keywords: ankle syndesmosis injury, diagnostic accuracy, computed tomography, radiographic measurements, Tibiofibular syndesmosis distance

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: Sirilak Areerachakul, Nat Ployong, Supayothin Na Songkla

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This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: artificial neural network, classification, students, e-learning

Procedia PDF Downloads 417

Authors: Yumei Han, Ling Li, Naiqing Song, Xiaoping Yang, Yuping Han

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Educational think tank is agreeably regarded as significant part of a nation’s soft power to promote the scientific and democratic level of educational policy making, and it plays critical role of bridging educational research in higher institutions and educational policy making. This study explores the concept, functions and significance of educational think tank in China, and conceptualizes a three dimensional framework to analyze the approaches of transforming research-based higher institutions into effective educational think tanks to serve educational policy making in the nation wide. Since 2014, the Ministry of Education P.R. China has been promoting the strategy of developing new type of educational think tanks in higher institutions, and such a strategy has been put into the agenda for the 13th Five Year Plan for National Education Development released in 2017.In such context, increasing scholars conduct studies to put forth strategies of promoting the development and transformation of new educational think tanks to serve educational policy making process. Based on literature synthesis, policy text analysis, and analysis of theories about policy making process and relationship between educational research and policy-making, this study constructed a three dimensional conceptual framework to address the following questions: (a) what are the new features of educational think tanks in the new era comparing traditional think tanks, (b) what are the functional objectives of the new educational think tanks, (c) what are the organizational patterns and mechanism of the new educational think tanks, (d) in what approaches traditional research-based higher institutions can be developed or transformed into think tanks to effectively serve the educational policy making process. The authors adopted case study approach on five influential education policy study centers affiliated with top higher institutions in China and applied the three dimensional conceptual framework to analyze their functional objectives, organizational patterns as well as their academic pathways that researchers use to contribute to the development of think tanks to serve education policy making process.Data was mainly collected through interviews with center administrators, leading researchers and academic leaders in the institutions. Findings show that: (a) higher institution based think tanks mainly function for multi-level objectives, providing evidence, theoretical foundations, strategies, or evaluation feedbacks for critical problem solving or policy-making on the national, provincial, and city/county level; (b) higher institution based think tanks organize various types of research programs for different time spans to serve different phases of policy planning, decision making, and policy implementation; (c) in order to transform research-based higher institutions into educational think tanks, the institutions must promote paradigm shift that promotes issue-oriented field studies, large data mining and analysis, empirical studies, and trans-disciplinary research collaborations; and (d) the five cases showed distinguished features in their way of constructing think tanks, and yet they also exposed obstacles and challenges such as independency of the think tanks, the discourse shift from academic papers to consultancy report for policy makers, weakness in empirical research methods, lack of experience in trans-disciplinary collaboration. The authors finally put forth implications for think tank construction in China and abroad.

Keywords: education policy-making, educational research, educational think tank, higher institution

Procedia PDF Downloads 156

Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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Authors: Daohong Yang, Xiang Zhang, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) is a technology that obtains information in the environment for self-positioning and mapping. It is widely used in computer vision, robotics and other fields. Many visual SLAM systems, such as OBSLAM3, employ a constant-speed motion model that provides the initial pose of the current frame to improve the speed and accuracy of feature matching. However, in actual situations, the constant velocity motion model is often difficult to be satisfied, which may lead to a large deviation between the obtained initial pose and the real value, and may lead to errors in nonlinear optimization results. Therefore, this paper proposed a motion model based on acceleration, which can be applied on most SLAM systems. In order to better describe the acceleration of the camera pose, we decoupled the pose transformation matrix, and calculated the rotation matrix and the translation vector respectively, where the rotation matrix is represented by rotation vector. We assume that, in a short period of time, the changes of rotating angular velocity and translation vector remain the same. Based on this assumption, the initial pose of the current frame is estimated. In addition, the error of constant velocity model was analyzed theoretically. Finally, we applied our proposed approach to the ORBSLAM3 system and evaluated two sets of sequences on the TUM dataset. The results showed that our proposed method had a more accurate initial pose estimation and the accuracy of ORBSLAM3 system is improved by 6.61% and 6.46% respectively on the two test sequences.

Keywords: error estimation, constant acceleration motion model, pose estimation, visual SLAM

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Asif Arshid, Ying Huang, Denver Tolliver

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Finite Element (FE) method coupled with ever-increasing computational powers has substantially advanced the reliability of deterministic three dimensional structural analyses of a structure with uniform material properties. However, railways trackbed is made up of diverse group of materials including steel, wood, rock and soil, while each material has its own varying levels of heterogeneity and imperfections. It is observed that the application of probabilistic methods for trackbed structural analysis while incorporating the material and geometric variabilities is deeply underworked. The authors developed and validated a 3-dimensional FE based numerical trackbed model and in this study, they investigated the influence of variability in Young modulus and thicknesses of granular layers (Ballast and Subgrade) on the reliability index (-index) of the subgrade layer. The influence of these factors is accounted for by changing their Coefficients of Variance (COV) while keeping their means constant. These variations are formulated using Gaussian Normal distribution. Two failure mechanisms in subgrade namely Progressive Shear Failure and Excessive Plastic Deformation are examined. Preliminary results of risk-based probabilistic analysis for Progressive Shear Failure revealed that the variations in Ballast depth are the most influential factor for vertical stress at the top of subgrade surface. Whereas, in case of Excessive Plastic Deformations in subgrade layer, the variations in its own depth and Young modulus proved to be most important while ballast properties remained almost indifferent. For both these failure moods, it is also observed that the reliability index for subgrade failure increases with the increase in COV of ballast depth and subgrade Young modulus. The findings of this work is of particular significance in studying the combined effect of construction imperfections and variations in ground conditions on the structural performance of railroad trackbed and evaluating the associated risk involved. In addition, it also provides an additional tool to supplement the deterministic analysis procedures and decision making for railroad maintenance.

Keywords: finite element analysis, numerical modeling, probabilistic methods, risk and reliability analysis, subgrade

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Authors: M. Seguini, D. Nedjar

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An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.

Keywords: finite element method, geometric nonlinearity, material nonlinearity, soil-structure interaction, spatial variability

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Authors: S. Hellam, Y. Oulahrir, F. El Mounchid, A. Sadiq, S. Mbarki

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In this paper, we propose a method for three-dimensional (3-D)-model indexing based on defining a new descriptor, which we call new descriptor using spherical harmonics. The purpose of the method is to minimize, the processing time on the database of objects models and the searching time of similar objects to request object. Firstly we start by defining the new descriptor using a new division of 3-D object in a sphere. Then we define a new distance which will be used in the search for similar objects in the database.

Keywords: 3D indexation, spherical harmonic, similarity of 3D objects, measurement similarity

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Authors: Romulo D. C. Santos, Silvio M. A. Gama, Ramiro G. R. Camacho

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In this present paper, the thermos-fluid dynamics considering the mixed convection (natural and forced convections) and the principles of turbulence flow around complex geometries have been studied. In these applications, it was necessary to analyze the influence between the flow field and the heated immersed body with constant temperature on its surface. This paper presents a study about the Newtonian incompressible two-dimensional fluid around isothermal geometry using the immersed boundary method (IBM) with the virtual physical model (VPM). The numerical code proposed for all simulations satisfy the calculation of temperature considering Dirichlet boundary conditions. Important dimensionless numbers such as Strouhal number is calculated using the Fast Fourier Transform (FFT), Nusselt number, drag and lift coefficients, velocity and pressure. Streamlines and isothermal lines are presented for each simulation showing the flow dynamics and patterns. The Navier-Stokes and energy equations for mixed convection were discretized using the finite difference method for space and a second order Adams-Bashforth and Runge-Kuta 4th order methods for time considering the fractional step method to couple the calculation of pressure, velocity, and temperature. This work used for simulation of turbulence, the Smagorinsky, and Spalart-Allmaras models. The first model is based on the local equilibrium hypothesis for small scales and hypothesis of Boussinesq, such that the energy is injected into spectrum of the turbulence, being equal to the energy dissipated by the convective effects. The Spalart-Allmaras model, use only one transport equation for turbulent viscosity. The results were compared with numerical data, validating the effect of heat-transfer together with turbulence models. The IBM/VPM is a powerful tool to simulate flow around complex geometries. The results showed a good numerical convergence in relation the references adopted.

Keywords: immersed boundary method, mixed convection, turbulence methods, virtual physical model

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Mancho Manev

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The concept of almost paracontact Riemannian manifolds (abbr., apcR manifolds) was introduced by I. Sato in 1976 as an analogue of almost contact Riemannian manifolds. The notion of an apcR manifold of type (p,q) was defined by S. Sasaki in 1980, where p and q are respectively the numbers of the multiplicity of the structure eigenvalues 1 and -1. It also has a simple eigenvalue of 0. In our work, we consider (2n+1)-dimensional apcR manifolds of type (n,n), i.e., the paracontact distribution of the studied manifold can be considered as a 2n-dimensional almost paracomplex Riemannian distribution with almost paracomplex structure and structure group O(n) × O(n). The aim of the present study is to introduce a new class of apcR manifolds. Such a manifold is obtained using the construction of a certain Riemannian cone over it, and the resulting manifold is a paraholomorphic paracomplex Riemannian manifold (abbr., phpcR manifold). We call it a para-Sasaki-like Riemannian manifold (abbr., pSlR manifold) and give some explicit examples. We study the structure of pSlR spaces and find that the paracontact form η is closed and each pSlR manifold locally can be considered as a certain product of the real line with a phpcR manifold, which is locally a Riemannian product of two equidimensional Riemannian spaces. We also obtain that the curvature of the pSlR manifolds is completely determined by the curvature of the underlying local phpcR manifold. Moreover, the ξ-directed Ricci curvature is equal to -2n, while in the Sasaki case, it is 2n. Accordingly, the pSlR manifolds can be interpreted as the counterpart of the Sasaki manifolds; the skew-symmetric part of ∇η vanishes, while in the Sasaki case, the symmetric part vanishes. We define a hyperbolic extension of a (complete) phpcR manifold that resembles a certain warped product, and we indicate that it is a (complete) pSlR manifold. In addition, we consider the hyperbolic extension of a phpcR manifold and prove that if the initial manifold is a complete Einstein manifold with negative scalar curvature, then the resulting manifold is a complete Einstein pSlR manifold with negative scalar curvature. In this way, we produce new examples of a complete Einstein Riemannian manifold with negative scalar curvature. Finally, we define and study para contact conformal/homothetic deformations by deriving a subclass that preserves the para-Sasaki-like condition. We then find that if we apply a paracontact homothetic deformation of a pSlR space, we obtain that the Ricci tensor is invariant.

Keywords: almost paracontact Riemannian manifolds, Einstein manifolds, holomorphic product manifold, warped product manifold

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Authors: Mohammad Ali Al Qatawneh, Bespalchuk Pavel Ivanovich

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Computer navigation has been introduced in total knee arthroplasty to improve the accuracy of the procedure. Computer navigation improves the accuracy of bone resection in the coronal and sagittal planes. It was also noted that it normalizes the rotational alignment of the femoral component and fully assesses and balances the deformation of soft tissues in the coronal plane. The work is devoted to the advantages of using computer navigation technology in total knee arthroplasty in 62 patients (11 men and 51 women) suffering from gonarthrosis, aged 51 to 83 years, operated using a computer navigation system, followed up to 3 years from the moment of surgery. During the examination, the deformity variant was determined, and radiometric parameters of the knee joints were measured using the Knee Society Score (KSS), Functional Knee Society Score (FKSS), and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) scales. Also, functional stress tests were performed to assess the stability of the knee joint in the frontal plane and functional indicators of the range of motion. After surgery, improvement was observed in all scales; firstly, the WOMAC values decreased by 5.90 times, and the median value to 11 points (p < 0.001), secondly KSS increased by 3.91 times and reached 86 points (p < 0.001), and the third one is that FKSS data increased by 2.08 times and reached 94 points (p < 0.001). After TKA, the axis deviation of the lower limbs of more than 3 degrees was observed in 4 patients at 6.5% and frontal instability of the knee joint just in 2 cases at 3.2%., The lower incidence of sagittal instability of the knee joint after the operation was 9.6%. The range of motion increased by 1.25 times; the volume of movement averaged 125 degrees (p < 0.001). Computer navigation increases the accuracy of the spatial orientation of the endoprosthesis components in all planes, reduces the variability of the axis of the lower limbs within ± 3 °, allows you to achieve the best results of surgical interventions, and can be used to solve most basic tasks, allowing you to achieve excellent and good outcomes of operations in 100% of cases according to the WOMAC scale. With diaphyseal deformities of the femur and/or tibia, as well as with obstruction of their medullary canal, the use of computer navigation is the method of choice. The use of computer navigation prevents the occurrence of flexion contracture and hyperextension of the knee joint during the distal sawing of the femur. Using the navigation system achieves high-precision implantation for the endoprosthesis; in addition, it achieves an adequate balance of the ligaments, which contributes to the stability of the joint, reduces pain, and allows for the achievement of a good functional result of the treatment.

Keywords: knee joint, arthroplasty, computer navigation, advantages

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Authors: Chih Jou Hsiao, Chung Ming Lo, Li Chun Hsieh

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Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists.

Keywords: brain tumor, computer-aided diagnosis, gliomas, magnetic resonance imaging

Procedia PDF Downloads 255

Authors: Vincent Schlageter, Maroua Mestiri, Florian Denzinger, Hugo Raetzo, Michel Demierre

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Precise landslide monitoring with differential global navigation satellite system (GNSS) is well known, but technical or economic reasons limit its application by geotechnical companies. This study demonstrates the reliability and the usefulness of Geomon (Infrasurvey Sàrl, Switzerland), a stand-alone and cost-effective rover network. The system permits deploying up to 15 rovers, plus one reference station for differential GNSS. A dedicated radio communication links all the modules to a base station, where an embedded computer automatically provides all the relative positions (L1 phase, open-source RTKLib software) and populates an Internet server. Each measure also contains information from an internal inclinometer, battery level, and position quality indices. Contrary to standard GNSS survey systems, which suffer from a limited number of beacons that must be placed in areas with good GSM signal, Geomon offers greater flexibility and permits a real overview of the whole landslide with good spatial resolution. Each module is powered with solar panels, ensuring autonomous long-term recordings. In this study, we have tested the system on several sites in the Swiss mountains, setting up to 7 rovers per site, for an 18 month-long survey. The aim was to assess the robustness and the accuracy of the system in different environmental conditions. In one case, we ran forced blind tests (vertical movements of a given amplitude) and compared various session parameters (duration from 10 to 90 minutes). Then the other cases were a survey of real landslides sites using fixed optimized parameters. Sub centimetric-accuracy with few outliers was obtained using the best parameters (session duration of 60 minutes, baseline 1 km or less), with the noise level on the horizontal component half that of the vertical one. The performance (percent of aborting solutions, outliers) was reduced with sessions shorter than 30 minutes. The environment also had a strong influence on the percent of aborting solutions (ambiguity search problem), due to multiple reflections or satellites obstructed by trees and mountains. The length of the baseline (distance reference-rover, single baseline processing) reduced the accuracy above 1 km but had no significant effect below this limit. In critical weather conditions, the system’s robustness was limited: snow, avalanche, and frost-covered some rovers, including the antenna and vertically oriented solar panels, leading to data interruption; and strong wind damaged a reference station. The possibility of changing the sessions’ parameters remotely was very useful. In conclusion, the rover network tested provided the foreseen sub-centimetric-accuracy while providing a dense spatial resolution landslide survey. The ease of implementation and the fully automatic long-term survey were timesaving. Performance strongly depends on surrounding conditions, but short pre-measures should allow moving a rover to a better final placement. The system offers a promising hazard mitigation technique. Improvements could include data post-processing for alerts and automatic modification of the duration and numbers of sessions based on battery level and rover displacement velocity.

Keywords: GNSS, GSM, landslide, long-term, network, solar, spatial resolution, sub-centimeter.

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Authors: Hongbo Zhang, Xinlu Tang, Jiangwei Li, Chi Yan

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Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot.

Keywords: ADAS, home zone parking pilot, object detection, visual SLAM

Procedia PDF Downloads 65

Authors: Maryam Kamali Nezhad

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The Robert-Bourassa development (LG-2), the first to be built on the Grande Rivière, comprises two sets of eight turbines- generator units each, the East and West powerhouses. Each powerhouse has two tailrace tunnels with an average length of about 1178 m. The LG-2A powerhouse houses 6 turbine-generator units. The water is discharged through two tailrace tunnels with a length of about 1330 m. The objective of this work, at RB (LG-2), is; 1) to establish a new maximum transient level in the surge chamber, 2) to define the new maximum equipment flow rate for the future turbine-generator units, 3) to ensure safe access to various intervention locations in the surge chamber. The transient levels under normal operating conditions at the RB plant were determined in 2001 by the Hydraulics Unit of HQE using the "Chamber" software. It is a one-dimensional mass oscillation calculation software; it is used to determine the variation of the water level in the equilibrium chamber located downstream of a power plant during the load shedding of the power plant units; it can also be used in the case of an equilibrium stack upstream of a power plant. The RB (LG-2) plant study is based on the theoretical nominal geometry of the chamber and the tailrace tunnels and the flow-level relationship at the outlet of the galleries established during design. The software is used in such a way that the results have an acceptable margin of safety, especially with respect to the maximum transient level (e.g., resumption of flow at an inopportune time), to take into account the turbulent and three-dimensional aspects of the actual flow in the chamber. Note that the transient levels depend on the water levels in the river and in the steady-state equilibrium chambers. These data are established in the HQP CRP database and updated from time to time. The maximum transient levels in the RB-East and RB-West powerhouses surge chamber were revised based on the latest update (set 4) of in-river rating curves and steady-state surge chamber water levels. The results of the revision were also used to update the technical advice on the operating conditions for the aforementioned surge chamber access while considering revisions to the calculated water levels.

Keywords: generating station, surge chamber, maximum transient level, hydroelectric power station, turbine-generator, reservoir

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Authors: Devinder Singh, Arvind Kumar, Rajneesh Kumar

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The general solution of the equations for a homogeneous isotropic microstretch thermo elastic medium with mass diffusion for two dimensional problems is obtained due to normal and tangential forces. The integral transform technique is used to obtain the components of displacements, microrotation, stress and mass concentration, temperature change and mass concentration. A particular case of interest is deduced from the present investigation.

Keywords: normal force, tangential force, microstretch, thermoelastic, the integral transform technique, deforming force, microstress force, boundary value problem

Procedia PDF Downloads 615

Authors: Farnaz Farhangi

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Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

Procedia PDF Downloads 334