scholarly journals The State of the Art Methods of Characterization and their Application to the Research and Development of Cement and Concrete

1994 ◽  
Vol 32 (12) ◽  
pp. 16-29
Author(s):  
Hiroshi Uchikawa
Keyword(s):  
Research And Development ◽  
State Of The Art ◽  
The State ◽  
Art Methods ◽  
Cement And Concrete

The State of the Art in Management Education Applicable to Aerospace Activities

1967 ◽  
Vol 71 (677) ◽  
pp. 342-343
Author(s):  
F. H. East
Keyword(s):  
Research And Development ◽  
Management Education ◽  
State Of The Art ◽  
Development Project ◽  
The State ◽  
The Other ◽  
The Past ◽  
Defence Budget ◽  
The One ◽  
Almost All

The Aviation Group of the Ministry of Technology (formerly the Ministry of Aviation) is responsible for spending a large part of the country's defence budget, both in research and development on the one hand and production or procurement on the other. In addition, it has responsibilities in many non-defence fields, mainly, but not exclusively, in aerospace.Few developments have been carried out entirely within the Ministry's own Establishments; almost all have required continuous co-operation between the Ministry and Industry. In the past the methods of management and collaboration and the relative responsibilities of the Ministry and Industry have varied with time, with the type of equipment to be developed, with the size of the development project and so on. But over the past ten years there has been a growing awareness of the need to put some system into the complex business of translating a requirement into a specification and a specification into a product within reasonable bounds of time and cost.

scholarly journals Smoothing vs. sharpening of colour images: Together or separated

2017 ◽  
Vol 2 (1) ◽  
pp. 299-316 ◽  
Author(s):  
Cristina Pérez-Benito ◽  
Samuel Morillas ◽  
Cristina Jordán ◽  
J. Alberto Conejero
Keyword(s):  
Image Denoising ◽  
Gaussian Distribution ◽  
State Of The Art ◽  
Visual Quality ◽  
The State ◽  
Art Methods ◽  
Efficiency And Effectiveness

AbstractIt is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.Both operations, smoothing noise and sharpening, have an opposite nature. Therefore, there are few approaches that simultaneously respond to both goals. We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

Towards Zero Defect Manufacturing paradigm: A review of the state-of-the-art methods and open challenges

2022 ◽  
Vol 134 ◽  
pp. 103548
Author(s):  
Bianca Caiazzo ◽  
Mario Di Nardo ◽  
Teresa Murino ◽  
Alberto Petrillo ◽  
Gianluca Piccirillo ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Art Methods

State-of-the-Art in Micro- and Meso-Scale Heat Exchangers

1999 ◽  
Author(s):  
Reza Shekarriz ◽  
Charles J. Call
Keyword(s):  
Heat Exchanger ◽  
Research And Development ◽  
Heat Exchangers ◽  
State Of The Art ◽  
The State ◽  
Manufacturing Processes ◽  
Review Of The Literature ◽  
Design Constraints ◽  
Recent Advances ◽  
Meso Scale

Abstract A review of the literature and the state-of-the-art in research and development of miniature heat exchangers is presented in this paper. The authors provide a discussion of what makes the micro- and meso-scales important, highlight the design constraints and challenges that surface when miniaturizing a heat exchanger, and outline and discuss the outstanding practical and scientific issues in this area. Finally, the most recent advances in manufacturing processes and application of these miniature heat exchangers are covered in this article.

scholarly journals Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3603
Author(s):  
Dasol Jeong ◽  
Hasil Park ◽  
Joongchol Shin ◽  
Donggoo Kang ◽  
Joonki Paik
Keyword(s):  
Large Scale ◽  
Body Shape ◽  
State Of The Art ◽  
The State ◽  
Whole Body ◽  
Distinctive Features ◽  
Common Features ◽  
Siamese Network ◽  
Art Methods ◽  
Triplet Loss

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.

scholarly journals MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval

2019 ◽  
Vol 33 ◽  
pp. 8513-8520 ◽  
Author(s):  
Jianwen Jiang ◽  
Di Bao ◽  
Ziqiang Chen ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Shape Representation ◽  
The State ◽  
Shape Retrieval ◽  
3D Shape Retrieval ◽  
3D Shape ◽  
Loop Level ◽  
Art Methods

3D shape retrieval has attracted much attention and become a hot topic in computer vision field recently.With the development of deep learning, 3D shape retrieval has also made great progress and many view-based methods have been introduced in recent years. However, how to represent 3D shapes better is still a challenging problem. At the same time, the intrinsic hierarchical associations among views still have not been well utilized. In order to tackle these problems, in this paper, we propose a multi-loop-view convolutional neural network (MLVCNN) framework for 3D shape retrieval. In this method, multiple groups of views are extracted from different loop directions first. Given these multiple loop views, the proposed MLVCNN framework introduces a hierarchical view-loop-shape architecture, i.e., the view level, the loop level, and the shape level, to conduct 3D shape representation from different scales. In the view-level, a convolutional neural network is first trained to extract view features. Then, the proposed Loop Normalization and LSTM are utilized for each loop of view to generate the loop-level features, which considering the intrinsic associations of the different views in the same loop. Finally, all the loop-level descriptors are combined into a shape-level descriptor for 3D shape representation, which is used for 3D shape retrieval. Our proposed method has been evaluated on the public 3D shape benchmark, i.e., ModelNet40. Experiments and comparisons with the state-of-the-art methods show that the proposed MLVCNN method can achieve significant performance improvement on 3D shape retrieval tasks. Our MLVCNN outperforms the state-of-the-art methods by the mAP of 4.84% in 3D shape retrieval task. We have also evaluated the performance of the proposed method on the 3D shape classification task where MLVCNN also achieves superior performance compared with recent methods.

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 Relational Prototypical Network for Weakly Supervised Temporal Action Localization

2020 ◽  
Vol 34 (07) ◽  
pp. 11053-11060
Author(s):  
Linjiang Huang ◽  
Yan Huang ◽  
Wanli Ouyang ◽  
Liang Wang
Keyword(s):  
State Of The Art ◽  
Previous Method ◽  
The State ◽  
Localization Method ◽  
Action Localization ◽  
Art Methods ◽  
Prototype Learning ◽  
Weakly Supervised ◽  
Temporal Action

In this paper, we propose a weakly supervised temporal action localization method on untrimmed videos based on prototypical networks. We observe two challenges posed by weakly supervision, namely action-background separation and action relation construction. Unlike the previous method, we propose to achieve action-background separation only by the original videos. To achieve this, a clustering loss is adopted to separate actions from backgrounds and learn intra-compact features, which helps in detecting complete action instances. Besides, a similarity weighting module is devised to further separate actions from backgrounds. To effectively identify actions, we propose to construct relations among actions for prototype learning. A GCN-based prototype embedding module is introduced to generate relational prototypes. Experiments on THUMOS14 and ActivityNet1.2 datasets show that our method outperforms the state-of-the-art methods.

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