scholarly journals System for Automated Infrared Image Processing Based on Neural Network Technologies

TEM Journal ◽  
2020 ◽  
pp. 1443-1454
Author(s):  
Sergey S. Kananadze
Keyword(s):  
Neural Network ◽  
Infrared Image ◽  
Digital Processing ◽  
Solid Domestic Waste ◽  
Infrared Image Processing ◽  
Object Based ◽  
Gradual Process ◽  
Data Volume ◽  
Network Technologies ◽  
Non Destructive

The article describes the main problems that non-destructive control experts encounter in processing and analyzing infrared (IR) images obtained using thermographic cameras: noisiness, information redundancy, excessive data volume, etc. A gradual process of automated digital processing and analysis of images on the basis of a software system is considered, which allows not only overcoming the drawbacks of the raw data described above, but also drawing a conclusion about the state of the object based on the obtained image independently. The ability to make expert decision is provided by a neural network component in a system on the basis of Kohonen network used for cluster image analysis. There are the examples of system operation applied to IR images of the lining of solid domestic waste (SDW) incinerator. The use of such systems allows reducing workload of an expert in processing large volumes of data and reducing the probability of errors due to such effect as vision "blurring".

Study of Circuits Fault Detection Based on Infrared Technology

2013 ◽  
Vol 385-386 ◽  
pp. 618-621 ◽  
Author(s):  
Xin Qiang Liu ◽  
Jian Guo Jiang
Keyword(s):  
Fault Detection ◽  
Influence Factors ◽  
Infrared Image ◽  
System Structure ◽  
Circuit Board ◽  
Infrared Image Processing ◽  
Infrared Technology ◽  
Diagnosis Method ◽  
Detection Instrument ◽  
Non Destructive

The circuit board fault detection based on infrared technology is a new non-contact and non-destructive method. During studying the system structure of circuit board fault infrared detection, the author analyzes the key influence factors of infrared information extraction, gives a new fault diagnosis method of infrared image processing and pattern recognition, then points out the problems in the practical application of fault detection instrument, provides the reference for the further study of the technology.

scholarly journals Development and Application of Infrared Thermography Non-Destructive Testing Techniques

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3851
Author(s):  
Zhi Qu ◽  
Peng Jiang ◽  
Weixu Zhang
Keyword(s):  
Nondestructive Testing ◽  
Infrared Thermography ◽  
Infrared Image ◽  
Development Trend ◽  
Thermal Excitation ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Infrared Image Processing ◽  
Testing Techniques ◽  
Non Destructive

Effective testing of defects in various materials is an important guarantee to ensure its safety performance. Compared with traditional non-destructive testing (NDT) methods, infrared thermography is a new NDT technique which has developed rapidly in recent years. Its core technologies include thermal excitation and infrared image processing. In this paper, several main infrared thermography nondestructive testing techniques are reviewed. Through the analysis and comparison of the detection principle, technical characteristics and data processing methods of these testing methods, the development of the infrared thermography nondestructive testing technique is presented. Moreover, the application and development trend are summarized.

Software platform for automated preparation and analytical processing of heterogeneous datasets

2020 ◽  
pp. 15-20 ◽  
Author(s):  
E. D. Avedyan ◽  
I. V. Voronkov
Keyword(s):  
Data Processing ◽  
Analytical Data ◽  
Volume Growth ◽  
Software Platform ◽  
External Data ◽  
Heterogeneous Datasets ◽  
Data Volume ◽  
Processing Resources ◽  
Modular Platform ◽  
Network Technologies

Summary: the article proposes new software platform for automating the processes of preprocessing and marking up datasets with the aim of further solving analytical problems such as image classification and processing textual and parametric information using neural network technologies. The software platform uses modern technologies and combines a large number of methods in the form of a modular platform, which can be supplemented as the tasks of analytical data processing become more complicated. The need to develop such a software platform is dictated primarily by the fact that, given the current level of data volume growth, the actual transition to deep data analytics remains unattainable without such software platforms, since confidentiality, access to information and the use of external data processing resources are required.

Enhancing the use of Object Based Contents Addressable Storage

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Simab Hasan Rizvi
Keyword(s):  
File System ◽  
Application Performance ◽  
Shared Storage ◽  
Data Intensive ◽  
Network Bandwidth ◽  
Storage Performance ◽  
Efficient Management ◽  
Object Based ◽  
Data Volume ◽  
Potential Benefits

In Today's age of Tetra Scale computing, the application has become more data intensive than ever. The increased data volume from applications, in now tackling larger and larger problems, and has fuelled the need for efficient management of this data. In this paper, a technique called Content Addressable Storage or CAS, for managing large volume of data is evaluated. This evaluation focuses on the benefits and demerits of using CAS it focuses, i) improved application performance via lockless and lightweight synchronization ofaccess to shared storage data, ii) improved cache performance, iii) increase in storage capacity and, iv) increase network bandwidth. The presented design of a CAS-Based file store significantly improves the storage performance that provides lightweight lock less user defined consistency semantics. As a result, this file system shows a 28% increase in read bandwidth and 13% increase in write bandwidth, over a popular file system in common use. In this paper the potential benefits of using CAS for a virtual machine are estimated. The study also explains mobility application for active use and public deployment.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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