Sunday, June 26, 2022

SCADA

 


What is SCADA

SCADA stands for “Supervisory Control and Data Acquisition”. SCADA is a type of process control system architecture that uses computers, networked data communications and graphical Human Machine Interfaces (HMIs) to enable a high-level process supervisory management and control.

SCADA systems communicate with other devices such as programmable logic controllers (PLCs) and PID controllers to interact with industrial process plant and equipment.

SCADA systems can be run virtually, which allows the operator to keep a track of the entire process from his place or control room. Time can be saved by using SCADA efficiently. One such excellent example is, SCADA systems are used extensively in the Oil and Gas sector. Large pipelines will be used to transfer oil and chemicals inside the manufacturing unit.

 

SCADA History

  • Earlier to the birth of SCADA systems, manufacturing floors and industrial plants relied on the manual control and monitor using push buttons and analog equipment. As the size of the industries and manufacturing units grew in size, they started using relays and timers, that provided supervisory control to a certain extent.
  • Unfortunately, relays and timers were able to solve problems only with minimal automation functionality, and reconfiguring the system was difficult. So, a more efficient and fully automated system was required by all industries.
  • Computers were developed for industrial control purposes in the early 1950s. Slowly, the telemetry concept was introduced for virtual communication and transmission of data.
  • Around the year 1970, the term SCADA was coined along with the evolution of Microprocessors and PLC concepts.
  • This helped for the development of a fully automated system, that can be used remotely in Industry. As years rolled by, in the early 2000s, distributed SCADA systems were developed.
  • Modern SCADA systems came into existence that allowed us to control and monitor real-time data anywhere in the world.
  • The real-time interaction boomed up the business and took the growth of industries to greater heights. Even if the operator did not have much knowledge of software development, he was able to manage the modern SCADA systems.

 

Functions of SCADA Systems

In a nutshell, we can tell the SCADA system is a collection of hardware and software components that allows the manufacturing units to perform specific functions. Some of the important functions include 

1.     To monitor and gather data in real-time

2.     To interact with field devices and control stations via Human Machine Interface (HMI)

3.     To record systems events into a log file

4.     To control manufacturing process virtually

5.     Information Storage and Reports

The Future Of SCADA

Today, both public and private sector organizations are under greater pressure to provide increasing quality of service under ever-tightening budgets. Also, governmental regulations require stricter monitoring, greater energy efficiencies and detailed reporting. In this challenging environment, the organization’s supervisory control and data acquisition (SCADA) system offers new and exciting means to wring additional benefits out of a proven workhorse.

To understand how such a level of future SCADA sophistication is possible requires only a simple analysis of our progress to date. Since the introduction of computerized control systems in the 1960s, five generations of SCADA evolution can be clearly defined.

Made-to-order SCADA systems were developed For special-purpose applications such as NASA’s Johnson Space Center launch system in Houston. In the 1960s, Houston had a problem: The Space Center had a large number of essential variables to monitor, and there existed no off-the-shelf technology capable of providing the functionality required.

Legacy SCADA systems are those that continue to be used despite relatively poor performance and a lack of compatibility with other systems. Often, replacing hardware components is an expensive, unpalatable option for the customer. Proprietary SCADA systems tie the customer to one specific control device manufacturer, creating a difficult negotiating position for the customer during future purchases.
For these systems, the increased flexibility of new computer hardware and SCADA software offers an opportunity. While most SCADA software products support industry-standard control protocols, products such as VTS also support protocols for proprietary and legacy control products. These disparate systems can now be integrated into the same centralized management model while allowing customers the time to develop a plan for migration away from legacy and proprietary SCADA.


Uploading: 348304 of 348304 bytes uploaded.

Industry Trends

 

Review of SCADA evolution illuminates three important trends:

  • The incremental cost to apply SCADA to additional assets is decreasing
  • The amount of data being gathered is increasing.
  • Basic logic and control is becoming decoupled from the operator, reducing the relative importance of the HMI component of SCADA software.

For example, a SCADA application installed at Air Products and Chemicals of Allentown, PA uses Trihedral’s Visual Tag System (VTS) software to accumulate nationwide realtime data from 6000 tanks. Product usage history is used to forecast refill date ranges for each tank. The information is communicated to the organization’s SAP enterprise resource planning system. It is then summarized to schedule delivery routing to cut down product waste and make best use of the company’s fleet of $1 million cryogenic gas delivery trucks. The reduced cost of monitoring these widely dispersed assets made the application feasible, but at this time the company is still using human-based decision making to control daily functions.

Such geographically widespread asset management is not limited to million-dollar assets. As the cost of SCADA continues to decline on a per-asset basis, low cost assets such as vending machine inventories, department store inventories, restaurant grease traps, and many other items that could not be monitored feasibly in the past will become increasingly easier to incorporate into the SCADA model. One can only imagine a future in which telemetry devices as small as a grain of sand are used as locating beacons on personal jewellery.

Conclusion

Costs for basic SCADA components are expected to continue to decline in the future.This trend will support SCADA use in organizations with assets of lower individual value, leading to larger, more dispersed SCADA systems.Simultaneously, larger organizations will take advantage of the growing number of value-priced, wide-area communications options to interconnect geographically dispersed SCADA and business systems. SCADA software developers must understand how to leverage new technological advances in communications without excluding legacy systems.
Low-level SCADA integration will be simplified. The size and complexity of SCADA will increase at an accelerating rate, requiring the creation of tools and integration methods that provide fast, error-free replication for common SCADA tasks. Successful cooperation between vendors will be essential in providing the maximum benefit to the customer.
Finally, the SCADA system will function more and more as a large control loop, able to operate autonomously at increasingly higher levels, based on fewer inputs from operational personnel. Optimization methodologies will be applied in a myriad of situations, allowing organizations to develop larger SCADA systems without incurring unreasonable operational costs or significant staffing increases. As such, the value of the pure HMI component of SCADA software, as we know it today, will decline relative to the many other emerging benefits SCADA systems will offer.


References

  • NASA (Unknown date). Johnson Space Center Mission Control, USA Launch System Specifications, Retrieved October 8, 2009, from http://www.aerospace-technology.com/ projects/johnsoon/
  • NERC (various dates), CIP Standards, Retrieved October 8, 2009, from http://www .nerc.com/page.php?cid=2|20
  • Water Sector Coordinating Council Cyber Security Working Group (March, 2008), Roadmap to Secure Control Systems in the Water Sector, Retrieved October 8, 2009, from http://www.awwa.org/files/GovtPublic Affairs/PDF/WaterSecurityRoadmap031908. pdf
  • FDA (August, 2008), Guidance for Industry Part 11, Electronic Records; Electronic Signatures – Scope and Application, Retrieved October 11, 2009, from http://www.fda.gov/ downloads/RegulatoryInformation/Guid ances/ucm125125.pdf

Friday, June 17, 2022

                               


The process of employing computers to understand and analyze images is known as computer vision (both photos and videos). While these algorithms have existed in various forms since the 1960s, recent advances in Machine Learning, as well as advances in data storage, computing capabilities, and affordable high-quality input devices, have resulted in significant improvements in how well our software can explore this type of content.

 

What is computer vision?

Computer vision is the process of understanding digital images and videos using computers. It aims to automate operations that can be accomplished with human eyesight. This includes techniques for capturing, processing, analyzing, and comprehending digital images, as well as data extraction from the actual world to generate information. It also features subdomains like object identification, video tracking, and motion estimation, making it useful in fields like medicine, navigation, and object modeling.

To put it another way, computer vision uses a device with a camera to take photographs or videos, then analyses them. Computer vision attempts to understand the information in digital photos and videos. Additionally, to solve a variety of problems, extract something helpful and relevant from these photographs and videos. How computer vision works

 

How does computer vision work?

 



 Computer vision is an interdisciplinary scientific discipline that studies how computers can understand the visual world as images and videos. It aims to duplicate and automate operations that the human visual system can perform from an engineering standpoint. Artificial Intelligence (AI) is used in computer vision to teach computers how to interpret and understand the visual environment. Machines can effectively recognize and classify objects using digital photos from cameras and movies, as well as deep learning models. For more than half a century, computer scientists have attempted to give computer vision, resulting in the field of computer vision. The goal is to enable computers to deduce a high-level understanding of the visual world from images. Images and videos in digital format If you've ever used a digital camera or smartphone, you already know that computers can capture images and videos with greater fidelity and better than humans.



Where is Computer Vision used?

 

1. Healthcare

In healthcare, computer vision is widely used. The analysis of pictures, scans, and photographs is important in medical diagnosis. Computer vision technologies promise not only to simplify but also to prevent incorrect diagnoses and lower treatment costs by analyzing ultrasound images, MRIs, and CT scans, which are all part of modern medicine's regular repertoire. Computer vision isn't meant to take the position of medical experts; rather, it's meant to make their jobs easier and to help them make better decisions. Image segmentation helps diagnosis by detecting key areas on 2D or 3D scans and colorizing them to make black-and-white images easier to examine.

 



The COVID-19 pandemic is using this technology. Doctors can use image segmentation to discover COVID-19 and analyze and quantify the infection and disease's progression. On CT scans of the lungs, the trained image recognition system detects suspicious spots. It assesses their size and volume in order to track the sickness of affected people. Computer vision not only makes it easier for doctors to diagnose and monitor a new disease during treatment, but it also creates vital data for researchers to study the disease and its progression. Researchers gain from the collected data and created images as well, as it allows them to devote more time to experiments and tests rather than data collecting. 

    


2. Automotive Industry

Self-driving cars are one of the artificial intelligence use cases that have gotten the most attention in recent years. Autonomous vehicles are equipped with powerful cameras that film their surroundings across a large area for this purpose. An image recognition algorithm monitors the resulting footage in real-time, which requires the algorithm's ability to search for and identify significant things not just in static photos but also in a continuous flow of images.



Top Tools used for Computer Vision

 

There are currently a number of online programs that give Computer Vision algorithms as well as a platform for executing or creating new ones. These tools also provide a platform for integrating computer vision with a variety of other software and technologies. So, let's have a look at some computer vision tools right now!

1. OpenCV

OpenCV (Open-Source Computer Vision Library) is an open-source computer vision library with a variety of computer vision and machine learning functionalities.

OpenCV, which was first released in 2000 by Intel, contains a number of computer vision algorithms that can perform a variety of tasks such as facial detection and recognition, object identification, monitoring moving objects, tracking camera movements, tracking eye movements, extracting 3D models of objects, creating an augmented reality overlay with scenery, recognizing similar images in an image database, and so on. OpenCV includes interfaces for C++, Python, Java, MATLAB, and other programming languages, and it runs on Windows, Android, Mac OS, Linux, and other platforms.

2. MATLAB

In 1984, MathWorks released MATLAB, a numerical computing environment. It includes the Computer Vision Toolbox, which comprises a number of computer vision algorithms and routines. Object detection, object tracking, feature detection, feature matching, 3-D camera calibration, 3D reconstruction, and so on are examples of these techniques. Machine learning methods like YOLO v2, ACF, Faster R-CNN, and others can be used to develop and train custom object detectors in MATLAB. These algorithms can also be executed on multicore CPUs and graphics processing units (GPUs) to make them significantly faster. Code generation in C and C++ is supported by MATLAB toolbox methods.

 3. GPU Image

GPU Image is a framework or rather, an iOS library that allows you to apply GPU-accelerated effects and filters to images, live-motion videos, and movies. It is built on OpenGL ES 2.0. Running custom filters on a GPU calls for a lot of code to set up and maintain. GPU Image cuts down on all of that boilerplate and gets the job done for you.

Computer Vision as a Service

1. Microsoft Azure

Microsoft API allows you to analyze images, read the text in them, and analyze video in near-real-time. You can also flag adult content, generate thumbnails of images and recognize handwriting.



2. Google Cloud and Mobile Vision APIs

 Google Cloud Vision API enables developers to perform image processing by encapsulating powerful machine learning models in a simple REST API that can be called in an application. Also, its Optical Character Recognition (OCR) functionality enables you to detect text in your images.The Mobile Vision API lets you get


References

https://home-webflow.glair.ai/post/the-uses-case-of-computer-vision

https://hub.packtpub.com/top-10-computer-vision-tools/

https://www.analyticsvidhya.com/blog/2021/06/everything-happening-in-computer-vision-that-you-should-know/

 https://fullscale.io/blog/machine-learning-computer-vision/


 Blog by,

Gajanan Jadhav

 

SCADA

  What is SCADA SCADA stands for “Supervisory Control and Data Acquisition”. SCADA is a type of process control system architecture that u...