scholarly journals Intello Labs: Non-Destructive Digital Commodity Grading

2021 ◽  
Vol 3 (1) ◽  
pp. 51-57
Author(s):  
Arunava Ghosh ◽  
Tuhin Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Solution Model ◽  
Farm Productivity ◽  
Non Destructive

This case illustrates Intello Labs, a leading agri-tech company, operating out of Gurgaon. As an agri-tech company, Intello Labs is trying to create an artificial intelligence (AI)-based solution model for its clients. The case dives deep into the issues of degrading farm productivity being faced by Kerala Cardamom Processing and Marketing Company (KCPMC), a client of Intello Labs. The case stands out as a means for understanding the application of the AI-based solutions being offered by Intello Labs to solve the degrading farm productivity issue of KCPMC. It addresses the concerns of the current agricultural-based businesses which mostly depend on manual commodity grading.

scholarly journals A NEW APPROACH TO ASSESSING THE OPERATIONAL SAFETY OF COMPOSITE MATERIALS AND PARTS OF COMPLEX STRUCTURES BASED ON ARTIFICIAL INTELLIGENCE METHODS AS A PART OF NEURAL NETWORKS AND DEEP RESULTS OF MULTI-CRITERIA COMPLEX NON-DESTRUCTIVE TESTING

2020 ◽  
pp. 18-27
Author(s):  
D. A. Akimov ◽  
A. D. Kleymenov ◽  
S. O. Kozelskaya ◽  
O. N. Budadin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Complex Structures ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
New Approach ◽  
Heterogeneous Information ◽  
Operational Safety ◽  
Non Destructive

The article proposes a new approach to assessing the operational safety of materials and parts of complex structures based on artificial intelligence methods based on artificial neural networks and multi-criteria complex non-destructive testing, and special mathematical and algorithmic support for systems for evaluating operational safety and predicting residual life under external influences. A method of morphological analysis of the procedures for using measurement tools for heterogeneous information with different a priori information, both about the type of characteristics and the distribution of errors in the input and output signals, has been developed. The classification of problems of measuring parameters for the integration of heterogeneous information is proposed. A macromodel of error is obtained that can be used for research purposes to minimize errors in the developed equipment or for the purpose of correcting errors during operation. A classification of methods for measuring heterogeneous information from the standpoint of probability distribution theory is proposed. Experimental testing of developed algorithms tailored aggregation of information non-destructive testing and adaptation to poorly formalized parameters, which confirmed the effectiveness of the developed methods and algorithms for assessment of structures and resource forecasting their operational reliability was carried out.

scholarly journals Quality Inspection of Food and Agricultural Products using Artificial Intelligence

2021 ◽  
Author(s):  
Maimunah Mohd Ali ◽  
Norhashila Hashim ◽  
Samsuzana Abd Aziz ◽  
Ola Lasekan
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Food Quality ◽  
High Performance ◽  
Agricultural Products ◽  
Special Focus ◽  
Quality Inspection ◽  
Big Data Technologies ◽  
Artificial Neural Network Ann ◽  
Non Destructive

A rising awareness for quality inspection of food and agricultural products has generated a growing effort to develop rapid and non-destructive techniques. Quality detection of food and agricultural products has prime importance in various stages of processing due to the laborious processes and the inability of the system to measure the whole of the food production. The detection of food quality has previously depended on various destructive techniques that require sample destruction and a large amount of postharvest losses. Artificial Intelligence (AI) has emerged with big data technologies and high-performance computation to create new opportunities in the multidisciplinary agri-food domain. This review presents the key concepts of AI comprising an expert system, artificial neural network (ANN), and fuzzy logic. A special focus is laid on the strength of AI applications in determining food quality for producing high and optimum yields. It was demonstrated that ANN provides the best result for modelling and effective in real-time monitoring techniques. The future use of AI for assessing quality inspection is promising which could lead to a real-time as well as rapid evaluation of various food and agricultural products.

Making Sense of Non-Destructive Evaluation Data with an Artificial Intelligence Approach

2006 ◽  
Vol 321-323 ◽  
pp. 294-297
Author(s):  
Minh Nguyen ◽  
Xiao Ming Wang ◽  
Greg Foliente
Keyword(s):  
Artificial Intelligence ◽  
Artificial Intelligence Technique ◽  
Engineering Structures ◽  
Making Sense ◽  
Material Condition ◽  
Non Destructive Evaluation ◽  
Potential Applications ◽  
Non Destructive ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

This paper presents the feasibility of an artificial intelligence technique for processing and interpretation of non-destructive evaluation (NDE) data from assessments of engineering structures. The technique used is a learning and reasoning approach with belief network. With this technique, causal factors and consequent indicators in the data structure in relation with structure/material condition can be modelled, and their causal relationship can be established using the NDE data as the learning resource. Fundamentals of the technique are briefly presented, and then the potential applications of the technique to NDE data are demonstrated in two case studies.

scholarly journals Applying Artificial Intelligence to Improve On-Site Non-Destructive Concrete Compressive Strength Tests

Crystals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1157
Author(s):  
Tu Quynh Loan Ngo ◽  
Yu-Ren Wang ◽  
Dai-Lun Chiang
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Prediction Models ◽  
Concrete Strength ◽  
Pulse Velocity ◽  
Statistical Regression ◽  
Concrete Compressive Strength ◽  
Major Drawback ◽  
Non Destructive

In the construction industry, non–destructive testing (NDT) methods are often used in the field to inspect the compressive strength of concrete. NDT methods do not cause damage to the existing structure and are relatively economical. Two popular NDT methods are the rebound hammer (RH) test and the ultrasonic pulse velocity (UPV) test. One major drawback of the RH test and UPV test is that the concrete compressive strength estimations are not very accurate when comparing them to the results obtained from the destructive tests. To improve concrete strength estimation, the researchers applied artificial intelligence prediction models to explore the relationships between the input values (results from the two NDT tests) and the output values (concrete strength). In-situ NDT data from a total of 98 samples were collected in collaboration with a material testing laboratory and the Professional Civil Engineer Association. In-situ NDT data were used to develop and validate the prediction models (both traditional statistical models and AI models). The analysis results showed that AI prediction models provide more accurate estimations when compared to statistical regression models. The research results show significant improvement when AI techniques (ANNs, SVM and ANFIS) are applied to estimate concrete compressive strength in RH and UPV tests.

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