Environmental monitoring should be considered a foundational element of any data center management strategy.

Industrial edge computing processes data locally within industrial environments, enabling real-time analytics and machine control. It’s used in predictive maintenance, robotics, and quality assurance by reducing latency and enhancing automation.

Gaining real-time insights into use of edge devices can help avoid downtime, reduce costs and optimize industrial performance. With today’s flexible edge devices, users can create tailored machine monitoring solutions that fit a wide variety of specific business needs.

A hybrid cloud computing and edge architecture enables critical, real-time decisions to happen at the edge and strategic, data-intensive analysis in the cloud. Expect to see more edge architectures and cloud computing in manufacturing processes, driving further innovation and efficiency improvements.

Rather than expecting a new AI model to help with critical processes, it should first be implemented in non-critical areas or an advisory capacity. For example, AI might first be used to analyze historical data and provide insights without the ability to directly affect operations.

Smart manufacturing is continuing to deploy diverse technologies to improve the performance of automation. This special report on next generation machine control looks at how AI, PLC virtualization, new controllers and software tools will provide solutions for real-time control, data connectivity and secure operations.

Users can implement automation applications more easily and cost-effectively with new AI tools. The rapid development of these AI tools, combined with a significant acceleration of hardware capabilities and integrated Ethernet interfaces, is opening up both new applications and segments.

Out of the box solutions move data easily and securely from operations to IT. IT specialists don’t always grasp the needs and challenges of their counterparts in OT. To align their needs and objectives with IT, they need surer footing to navigate today’s cybersecurity requirements and quality solutions to bring…

The main application areas for Convolutional Neural Networks (CNN) are pattern recognition and classification of objects contained in input data. This first part of a three part series in this issue explore how CNNs are a type of artificial neural network used in deep learning.

This three part series of articles explains convolutional neural networks (CNNs) and their significance in machine learning within AI systems. CNNs are powerful tools for extracting features from complex data. Part 2 puts the focus on the training process for neural networks.