Artificial intelligence (AI) is directly related to Data Science – the science about data that aims to extract value from a big mass of information. This value may reside in, for example, enhanced forecasting capabilities, discovering patterns, better decision-making. Basically, AI concerns with information processing algorithms and methodologies. Artificial intelligence operates on huge amounts of data, analyzes it, and uses gathered insight to develop solutions.Read More
We know that efficient supply chains can be a crucial part of differentiating an organization from its competitors. But how can a company truly leverage the advances in artificial intelligence and neural networks we’ve seen in just the last decade?
Particularly for Fast Moving Consumer Goods (FMCG), supply chain trends are emerging that must not be overlooked if a company wishes to become or remain a market leader. Supply chain agility, optimization, sustainability, and ethical considerations challenge established players and give disruptive newcomers opportunities to capture significant market share. This article covers the emerging role of AI in supply chain management. We discuss how it could help companies to more effectively analyze and improve the efficiency of their operations. There is an evolving role advanced technologies play in the FMCG industry. They have potential to dramatically impact supply chain optimization and supply chain productivity.Read More
Cracks on the surface are a major defect in concrete structures. Early crack detection allows preventing possible damage. There are various approaches to solving this problem. It can be manual inspection or automatic detection methods. But nowadays automatic detection methods include not only laser testing and radiographic testing. Progress in neural networks and computer vision allows us to use image processing for concrete surface crack detection.
In this article, we will share our approach to solving the problem mentioned above.
The feature of input of user data taken from printed documents for automated enterprise solutions is currently in great demand.
Such documents include:
- Official standard state documents, such as passport, personal insurance policy number (SNILS), driver’s license, birth certificate, etc.
- Printed documents used in the company document flow made according to company templates.
Our company focuses on development of software based on machine learning, computer vision, image processing, and optical character recognition. In this article, we describe our experience in development of a textual template recognition system which includes an Android mobile app and a template control server.
Many companies that produce really healthy beverages often need to control the quality of their products at one of the final stages.
Today we are going to discuss not the issue of checking the quality of the drink itself, but the method to control the level of liquid in the bottle and the position of the pasted label with the help of web cameras and Computer Vision.
Greetings, dear readers! In this article, I’m going to share how I counted drops. Yes, you read that right. DROPS
It all began when our team started studying machine learning, or to be more precise, we studied Python and OpenCV. During the practice task, I had a chance to implement the algorithm that would count the falling drops in the given video or in real time. And now I’ll tell you how it all went.
Flow charts are widely used in gas industry. They record parameters of a gas flow (like pressure) on a paper disc. This data is then used to calculate gas volume passed through a pipe.
The chart recorder contains two or three pens driven by sensors. These pens plot traces on a rotating paper disk. The disk performs one revolution per day or week, rarely per month. The disk is replaced after one revolution.
Currently disks are processed manually. The operator uses the vector graphics editor to convert the scanned bitmap to contours and then to tabular data. This approach relies heavily on the operator’s attention and experience. There is a high probability of mistakes. After all, such job is really boring!
We have developed an algorithm that performs this job automatically. Given a bitmap with a scanned chart, it produces tabular representation of chart data.