scholarly journals Positional quality assessment based on linear elements

2021 ◽  
pp. 11-31
Author(s):  
Antonio Tomás Mozas-Calvache
Keyword(s):  
Quality Assessment ◽  
Spatial Databases ◽  
State Of The Art ◽  
Quality Of Data ◽  
Positional Accuracy ◽  
Current State ◽  
Linear Elements ◽  
Spatial Quality

This study describes the current state of the art related to the assessment of the positional accuracy of spatial databases based on lines. The use of this type of element of spatial databases has increased in recent years because of the current possibilities of acquisition and sharing data of routes, roads, etc. Nowadays, users are also contributors and this supposes that the spatial quality of data acquired and shared by non-experts must be assessed as is the case with data produced by institutions and enterprises. In this context, several methods based on lines have been developed up to this time for several purposes. This study reviews these methods, analyzing their characteristics, measures, etc. In addition, more than 30 applications of these methods are also summarized. These applications include control of generalized and digitized lines, control of spatial databases, analysis of the displacement of lines between dates, etc

scholarly journals Smoothing additive manufactured parts using ns-pulsed laser radiation

2021 ◽  
Author(s):  
Florian Kuisat ◽  
Fernando Lasagni ◽  
Andrés Fabián Lasagni
Keyword(s):  
Surface Roughness ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Industrial Applications ◽  
Laser Source ◽  
Pulsed Laser Radiation ◽  
Current State ◽  
The Impact

AbstractIt is well known that the surface topography of a part can affect its mechanical performance, which is typical in additive manufacturing. In this context, we report about the surface modification of additive manufactured components made of Titanium 64 (Ti64) and Scalmalloy®, using a pulsed laser, with the aim of reducing their surface roughness. In our experiments, a nanosecond-pulsed infrared laser source with variable pulse durations between 8 and 200 ns was applied. The impact of varying a large number of parameters on the surface quality of the smoothed areas was investigated. The results demonstrated a reduction of surface roughness Sa by more than 80% for Titanium 64 and by 65% for Scalmalloy® samples. This allows to extend the applicability of additive manufactured components beyond the current state of the art and break new ground for the application in various industrial applications such as in aerospace.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

Modular Prosthetic Design for Transtibial Child Amputees

2017 ◽  
Author(s):  
Brendan M. Hickey ◽  
Samuel T. Woo ◽  
Sally F. Shady
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Lower Limb ◽  
State Of The Art ◽  
Insurance Coverage ◽  
Review Article ◽  
Prosthetic Design ◽  
Current State ◽  
Limb Deficiencies

Lower limb deficiencies and below knee amputations are the most common form of deficiency that may arise from disease or trauma, and returning a patient close to a normal quality-of-life requires prosthetics, which can be quite challenging. Children present even further difficulty to prosthetists and physicians than adults. Although the underlying prosthetic principles for adults are the same for children, additional considerations must be made for practicality, such as downsizing while maintaining its degree of complexity, and frequent appointments to account for the rapid growth of an adolescent. This review article will evaluate the current state-of-the-art in the field of transtibial-amputee prosthetics, review the insurance coverage a typical family would face, and suggest potential improvements to children’s biomimetic prostheses that aid in reducing the frequency of health care provider intervention.

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

scholarly journals Crowd-assessing quality in uncertain data linking datasets

2020 ◽  
Vol 35 ◽  
Author(s):  
Daniel Faria ◽  
Alfio Ferrara ◽  
Ernesto Jiménez-ruiz ◽  
Stefano Montanelli ◽  
Catia Pesquita
Keyword(s):  
State Of The Art ◽  
Uncertain Data ◽  
Reference Alignment ◽  
Quality Of Data ◽  
Real Experiment ◽  
Before And After

Abstract The quality of a dataset used for evaluating data linking methods, techniques, and tools depends on the availability of a set of mappings, called reference alignment, that is known to be correct. In particular, it is crucial that mappings effectively represent relations between pairs of entities that are indeed similar due to the fact that they denote the same object. Since the reliability of mappings is decisive in order to perform a fair evaluation of automatic linking methods and tools, we call this property of mappings as mapping fairness. In this article, we propose a crowd-based approach, called Crowd Quality (CQ), for assessing the quality of data linking datasets by measuring the fairness of the mappings in the reference alignment. Moreover, we present a real experiment, where we evaluate two state-of-the-art data linking tools before and after the refinement of the reference alignment based on the CQ approach, in order to present the benefits deriving from the crowd assessment of mapping fairness.

scholarly journals A review on the use of additive manufacturing to produce lower limb orthoses

2019 ◽  
Vol 5 (2) ◽  
pp. 85-94 ◽  
Author(s):  
Mohammed S. Alqahtani ◽  
Abdulsalam Al-Tamimi ◽  
Henrique Almeida ◽  
Glen Cooper ◽  
Paulo Bartolo
Keyword(s):  
Additive Manufacturing ◽  
Lower Limb ◽  
State Of The Art ◽  
Production Costs ◽  
Current State ◽  
Research Challenges ◽  
Fixation Devices ◽  
Alignment Correction ◽  
And Performance

Abstract Orthoses (exoskeletons and fracture fixation devices) enhance users’ ability to function and improve their quality of life by supporting alignment correction, restoring mobility, providing protection, immobilisation and stabilisation. Ideally, these devices should be personalised to each patient to improve comfort and performance. Production costs have been one of the main constraints for the production of personalised orthoses. However, customisation and personalisation of orthoses are now possible through the use of additive manufacturing. This paper presents the current state of the art of additive manufacturing for the fabrication of orthoses, providing several examples, and discusses key research challenges to be addressed to further develop this field.

scholarly journals Biomechanical behaviours of the bone–implant interface: a review

2019 ◽  
Vol 16 (156) ◽  
pp. 20190259 ◽  
Author(s):  
Xing Gao ◽  
Manon Fraulob ◽  
Guillaume Haïat
Keyword(s):  
Bone Tissue ◽  
Medical Devices ◽  
State Of The Art ◽  
Biomechanical Properties ◽  
Review Article ◽  
Implant Stability ◽  
Bone Implant ◽  
Current State ◽  
Mechanical Methods

In recent decades, cementless implants have been widely used in clinical practice to replace missing organs, to replace damaged or missing bone tissue or to restore joint functionality. However, there remain risks of failure which may have dramatic consequences. The success of an implant depends on its stability, which is determined by the biomechanical properties of the bone–implant interface (BII). The aim of this review article is to provide more insight on the current state of the art concerning the evolution of the biomechanical properties of the BII as a function of the implant's environment. The main characteristics of the BII and the determinants of implant stability are first introduced. Then, the different mechanical methods that have been employed to derive the macroscopic properties of the BII will be described. The experimental multi-modality approaches used to determine the microscopic biomechanical properties of periprosthetic newly formed bone tissue are also reviewed. Eventually, the influence of the implant's properties, in terms of both surface properties and biomaterials, is investigated. A better understanding of the phenomena occurring at the BII will lead to (i) medical devices that help surgeons to determine an implant's stability and (ii) an improvement in the quality of implants.

scholarly journals DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6457
Author(s):  
Hayat Ullah ◽  
Muhammad Irfan ◽  
Kyungjin Han ◽  
Jong Weon Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Reference Image ◽  
Primary Concern ◽  
Panoramic Images ◽  
Stitching Errors

Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

scholarly journals The Virtual Observatory Data, Standards and Tools a technical - user point of view

2010 ◽  
Vol 6 (S277) ◽  
pp. 224-229
Author(s):  
Christian Surace ◽  
Keyword(s):  
State Of The Art ◽  
Point Of View ◽  
Virtual Observatory ◽  
Quality Of Data ◽  
Data Standards ◽  
Data Standardization ◽  
Observatory Data ◽  
The World ◽  
Set Up

AbstractIn 2002 the International Virtual Observatory Alliance (IVOA) has been created in order to gather efforts on data standardization and dissemination. Since then, the virtual Observatory allowed to spread validated data all over the world and to use data from everywhere from earth. From the standards definitions to development of tools, developers have set up a technical infrastructure used by astronomers to easily search for data and make science with all available products, more tools and more confidence on the quality of data. The goal of this review is to present the state of the art of the VO data, standards and tools. This review focuses on basic astronomer's questions : what kind of data are accessible, how to deal with these data and how to use them.

A sustainable quality assessment model for the information delivery in E-learning systems

2018 ◽  
Vol 46 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Shahid Farid ◽  
Rodina Ahmad ◽  
Mujahid Alam ◽  
Atif Akbar ◽  
Victor Chang
Keyword(s):  
Quality Assessment ◽  
State Of The Art ◽  
Statistical Tests ◽  
Information Delivery ◽  
Learning Systems ◽  
Learning System ◽  
Assessment Model ◽  
Content Type ◽  
E Learning

Purpose The purpose of this study is to propose a sustainable quality assessment approach (model) for the e-learning systems keeping software perspective under consideration. E-learning is becoming mainstream due to its accessibility, state-of-the-art learning, training ease and cost effectiveness. However, the poor quality of e-learning systems is one of the major causes of several failures reported. Moreover, this arena lacks well-defined quality assessment measures. Hence, it is quite difficult to measure the overall quality of an e-learning system effectively. Design/methodology/approach A pragmatic mixed-model philosophy was adopted for this study. A systematic literature review was performed to identify existing e-learning quality models and frameworks. Semi-structured interviews were conducted with e-learning experts following empirical investigations to identify the crucial quality characteristics of e-learning systems. Various statistical tests like principal component analysis, logistic regression, chi-square and analysis of means were applied to analyze the empirical data. These led to an adequate set of quality indicators that can be used by higher education institutions to assure the quality of e-learning systems. Findings A sustainable quality assessment model for the information delivery in e-learning systems in software perspective has been proposed by exploring the state-of-the-art quality assessment/evaluation models and frameworks proposed for the e-learning systems. The proposed model can be used to assess and improve the process of information discovery and delivery of e-learning. Originality/value The results obtained led to conclude that very limited attention is given to the quality of e-learning tools despite the importance of quality and its effect on e-learning system adoption and promotion. Moreover, the identified models and frameworks do not adequately address quality of e-learning systems from a software perspective.

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