Damage classification of concrete structures. The state of the art report of RILEM Technical Committee 104-DCC activity

1991 ◽  
Vol 24 (4) ◽  
pp. 253-259 ◽  
Author(s):  
T. Javor
Keyword(s):  
State Of The Art ◽  
Concrete Structures ◽  
Technical Committee ◽  
The State ◽  
Rilem Technical Committee ◽  
Damage Classification

Hydrogel-Based Bioinks for Three-Dimensional Bioprinting: Patent Analysis

2021 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Ahmed Fatimi
Keyword(s):  
Tissue Engineering ◽  
Detailed Analysis ◽  
State Of The Art ◽  
Three Dimensional ◽  
Patent Analysis ◽  
The State ◽  
Driving Factors ◽  
3D Bioprinting ◽  
Preparation Methods

There are a variety of hydrogel-based bioinks commonly used in three-dimensional bioprinting. In this study, in the form of patent analysis, the state of the art has been reviewed by introducing what has been patented in relation to hydrogel-based bioinks. Furthermore, a detailed analysis of the patentability of the used hydrogels, their preparation methods and their formulations, as well as the 3D bioprinting process using hydrogels, have been provided by determining publication years, jurisdictions, inventors, applicants, owners, and classifications. The classification of patents reveals that most inventions intended for hydrogels used as materials for prostheses or for coating prostheses are characterized by their function or properties Knowledge clusters and expert driving factors show that biomaterials, tissue engineering, and biofabrication research is concentrated in the most patents.

scholarly journals State-of-the-art on monolingual lexicography for Croatia (Croatian)

2019 ◽  
Vol 7 (1) ◽  
pp. 65-77
Author(s):  
Kristina Štrkalj Despot ◽  
Lana Hudeček ◽  
Tomislav Stojanov ◽  
Nikola Ljubešić
Keyword(s):  
State Of The Art ◽  
The State ◽  
Writing Systems ◽  
Insight Into

In this minireview, the state of the art of the Croatian monolingual lexicography is presented. A brief overview and classification of all existing lexicographic resources is provided in the firts part of the minireview, followed by somewhat more detailed insight into the existing Croatian monolingual dictionaries and monolingual lexicographic projects, orthography dictionaries, and dictionary writing systems used.

scholarly journals Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1459 ◽  
Author(s):  
Tamás Czimmermann ◽  
Gastone Ciuti ◽  
Mario Milazzo ◽  
Marcello Chiurazzi ◽  
Stefano Roccella ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Visual Processing ◽  
State Of The Art ◽  
Industrial Applications ◽  
The State ◽  
Shape Error ◽  
Supervised Classifiers ◽  
Processing Techniques

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

On the transmodality of signs and their interpretants: Evidence from Peirce’s MS 599, Reason’s Rules

Semiotica ◽  
2019 ◽  
Vol 2019 (228) ◽  
pp. 223-235
Author(s):  
Winfried Nöth
Keyword(s):  
Media Studies ◽  
State Of The Art ◽  
The State ◽  
Close Reading ◽  
Communicative Process ◽  
External Sign ◽  
The One ◽  
The Way

AbstractThe paper begins with a survey of the state of the art in multimodal research, an international trend in applied semiotics, linguistics, and media studies, and goes on to compare its approach to verbal and nonverbal signs to Charles S. Peirce’s approach to signs and their classification. The author introduces the concept of transmodality to characterize the way in which Peirce’s classification of signs reflects the modes of multimodality research and argues that Peirce’s classification of the signs takes modes and modalities in two different respects into consideration, (1) from the perspective of the sign and (2) from the one of its interpretant. While current research in multimodality has its focus on the (external) sign in a communicative process, Peirce considers additionally the multimodality of the interpretants, i.e., the mental icons and indexical scenarios evoked in the interpreters’ minds. The paper illustrates and comments on the Peircean method of studying the multi and transmodality of signs in an analysis of Peirce’s close reading of Luke 19:30 in MS 599, Reason’s Rules, of c. 1902. As a sign, this text is “monomodal” insofar as it consists of printed words only. The study shows in which respects the interpretants of this text evince trans and multimodality.

scholarly journals A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 84 ◽  
Author(s):  
Atif Mehmood ◽  
Muazzam Maqsood ◽  
Muzaffar Bashir ◽  
Yang Shuyuan
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Early Stage ◽  
The State ◽  
Test Accuracy ◽  
Learning Approaches

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

scholarly journals Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

2020 ◽  
Vol 10 (14) ◽  
pp. 4791 ◽  
Author(s):  
Pedro Narváez ◽  
Steven Gutierrez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Automatic Classification ◽  
The State ◽  
Heart Sounds ◽  
Empirical Wavelet Transform ◽  
Art Methods

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

scholarly journals Modified Deep Neural Networks for Dog Breeds Identification

2018 ◽  
Author(s):  
Aydin Ayanzadeh ◽  
Sahand Vahidnia
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Data Sets ◽  
Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

scholarly journals Classification of emotions from the recognition of facial expressions applied to the prevention of secondary Alexithymia: an analysis of the state of the art

2020 ◽  
pp. 14-17
Author(s):  
Christian PADILLA-NAVARRO ◽  
Carlos ZARATE-TREJO ◽  
Georges KHALAF ◽  
Pascal FALLAVOLLITA
Keyword(s):  
Artificial Intelligence ◽  
Facial Expressions ◽  
State Of The Art ◽  
The State ◽  
Research Projects ◽  
Emotional Facial Expressions ◽  
Toronto Alexithymia Scale ◽  
Recognition Of Facial Expressions

Alexithymia is a condition that partially or completely deprives you of the ability to identify and describe emotions, and to show affective connotations in the actions of an individual. This problem has been taken to different research projects that seek to study its characteristics, forms of prevention, and implications, and that try to determine a measurement for the experience of an individual with this construct as well as the responses they provide to certain stimuli. Other studies that were reviewed aimed to find a connection between the responses of subjects diagnosed with alexithymia when facing a dynamic of emotional facial expressions to recognize and their assigned grade based on the Toronto Alexithymia Scale (TAS), a metric frequently used to evaluate the presence or absence of alexithymia in an individual. In this work, a review of the different articles that study this connection, as well as articles that describe the state of the art of the implementation of artificial intelligence algorithms applied to the treatment or prevention of secondary alexithymia is presented.

scholarly journals Optical fiber sensors based on sol–gel materials: design, fabrication and application in concrete structures

2021 ◽  
Author(s):  
Rita B. Figueira ◽  
José M. de Almeida ◽  
Bárbara Ferreira ◽  
Luís Coelho ◽  
Carlos J. R. Silva
Keyword(s):  
Structural Health Monitoring ◽  
Optical Fiber ◽  
Health Monitoring ◽  
State Of The Art ◽  
Sol Gel ◽  
Concrete Structures ◽  
Materials Design ◽  
Optical Fiber Sensors ◽  
The State ◽  
Fiber Sensors

This review provides an overview of the state-of-the-art of OFS based on sol–gel materials for diverse applications with particular emphasis on OFS for structural health monitoring of concrete structures.

scholarly journals Investigating Deep Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

2020 ◽  
Author(s):  
Alisson Hayasi da Costa ◽  
Renato Augusto C. dos Santos ◽  
Ricardo Cerri
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Feedforward Neural Networks ◽  
The State ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Large Application

AbstractPIWI-Interacting RNAs (piRNAs) form an important class of non-coding RNAs that play a key role in the genome integrity through the silencing of transposable elements. However, despite their importance and the large application of deep learning in computational biology for classification tasks, there are few studies of deep learning and neural networks for piRNAs prediction. Therefore, this paper presents an investigation on deep feedforward networks models for classification of transposon-derived piRNAs. We analyze and compare the results of the neural networks in different hyperparameters choices, such as number of layers, activation functions and optimizers, clarifying the advantages and disadvantages of each configuration. From this analysis, we propose a model for human piRNAs classification and compare our method with the state-of-the-art deep neural network for piRNA prediction in the literature and also traditional machine learning algorithms, such as Support Vector Machines and Random Forests, showing that our model has achieved a great performance with an F-measure value of 0.872, outperforming the state-of-the-art method in the literature.

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