Automation Technologies in IT Industries

Home Articles Automation Technologies in IT Industries

Introduction

Automation refers to the technology by which any process or task is done with the minimum human effort in order to increase productivity and decrease the cost of operation. Through automation technologies in IT industries, certain instructions are employed which replace the manual IT processes carried out by IT professionals. These IT automation instructions can perform single actions as well as autonomous IT deployment which are carried out based-on-user behavior and other event triggers.  

The automation in IT industries is emerging day by day, with the continuation of new and advanced automation machines being invented. The robots in IT industries are responsible for handling most of the complex processes, completing them relatively faster than the manual processes. Moreover, automation technologies embedded with advanced robotics and learning modules complete the tasks with more precision and less error than humans. Thus, these advanced automation technologies are surely introduced as a favorable factor in terms of optimized cost, reduced effort, and increased production rate.

Examples of automation technologies in IT industries

There are numerous technologies being introduced in IT industries since the birth of automation, which are almost impossible to keep a record of. But, below given are the four most advanced automation technologies which are used in the IT industries:

  • Machine Vision

Automakers are looking for safer and reliable automation which can be beneficial for them in order to increase cost optimization. This need of automakers is fulfilled by Machine Vision (MV) technology which helps them by providing an automated machine inspection technology (Gaska, Chen & Summerville, 2016). The Machine Vision (MV) uses an image processing technique which includes conventional imaging, hyperspectral imaging, infrared imaging, line scan imaging, 3D surface imaging, and X-ray imaging. Moreover, smart cameras or smart sensors are used along with camera link interfaces which help them to record or capture the desired image. These captured images are then analyzed by the specialized software which generally uses FINITE Element Analysis Principle. These Machine Vision technologies help the industries save money which further helps them to gain a competitive advantage.

  • Collaborative Robots

Collaborative robots, also known as Cobots, are the category of robots which perform their task without the intervention of any human being. Apart from this, if any human being tries to enter into the working space of the Collaborative robots, they use specialized machine learning to pause all the running operations. These collaborative robots can help the IT industries perform multiple tasks at once in minimum time. As per ISO 10218, there are four types of Collaborative robots: safety Monitored Stop, Hand Guiding, Speed & Separation Monitoring, and Power & Force Limiting robots (Haifang Wang, Yu Rong, Shengtao Liu & Jinhua Cui, 2010). Moreover, these collaborative robots are also designed to be implemented in car building which may help the industries gain an innovative future in terms of the race for speed and manufacturing productivity rate.

  • Artificial Intelligence for Autonomous Cars

Artificial intelligence system refers to any system that can adapt its environment and take appropriate action in order to maximize the chance of achieving the desired goal. The statement proves to be true for the on research of the driverless and autonomous cars. The artificial intelligence in autonomous cars is been employed by creating and storing a map of the surroundings at initial stages using smart sensors such as radar, sonar, lasers, etc. Further, these inputs are processes and the most plausible trajectories which are plotted (Markwalter, 2017). Then the instructions are sent to the actuators of the car who are responsible for acceleration, braking, and steering. Moreover, certain applications embedded such as coded driving protocols, obstacle avoidance algorithms, predictive modeling, smart object discrimination, etc.  help the car to follow traffic rules and navigate past obstacles.

  • Cognitive computing in IoT connected cars

Cognitive Computing (CC) refers to the technology which is based on applications of artificial intelligence and signal processing. These applications are employed with the help of technologies such as machine learning, human language processing, speech and object processing, human-computer interaction and dialogue and narrative generation. Moreover, Cognitive Computing is used in building autonomous IoT connected cars using internet to communicate with each other (Winkler, Hametner, Östreicher & Biffl, 2010). These connected cars communicate with each other while recognizing driving patterns in order to link them with the emotional response of respective human drivers during driving scenarios such as applying brakes or following traffic rules. Cognitive Computing help the IT industries build safe, non-intervening traffic and effective automatic cars.

References

  • Gaska, T., Chen, Y., & Summerville, D. (2016). Leveraging driverless car investment in next-generation integrated modular avionics (IMA). 2016 IEEE/AIAA 35Th Digital Avionics Systems Conference (DASC). doi: 10.1109/dasc.2016.7778073
  • Haifang Wang, Yu Rong, Shengtao Liu, & Jinhua Cui. (2010). Fieldbus technology and rolling process automation. 2010 International Conference On Computer Design And Applications. doi: 10.1109/iccda.2010.5541484
  • Markwalter, B. (2017). The Path to Driverless Cars [CTA Insights]. IEEE Consumer Electronics Magazine, 6(2), 125-126. doi: 10.1109/mce.2016.2640625
  • Winkler, D., Hametner, R., Östreicher, T., & Biffl, S. (2010). A framework for automated testing of automation systems. 2010 IEEE 15Th Conference On Emerging Technologies & Factory Automation (ETFA 2010). doi: 10.1109/etfa.2010.5641264

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