Sustainability in Industry 5.0: Theory and Applications

Scientific programming in Industry 5.0 requires data pre-processing, classification, prediction, cluster analysis, mining multimedia, and advanced machine learning techniques. The convergence of intelligent systems and 5G wireless systems will solve industrial problems, such as autonomous robots and self-driving cars. Smart things, collaborative autonomous fleets, and platforms for integrating applications across domains are important topics. The Internet of robotic things, cloud robotics, and cognitive architecture are also discussed. Image compression and advanced machine learning techniques are essential. Smart manufacturing, the industrial Internet of things, and supply chain management are key aspects of Industry 5.0. The text is primarily written for graduate students and academic researchers in industrial engineering, manufacturing engineering, electrical engineering, production engineering, and mechanical engineering.

Format: Hardback
Length: 254 pages
Publication date: 19 February 2024
Publisher: Taylor & Francis Ltd

Scientific programming in Industry 5.0 requires a comprehensive approach that encompasses data pre-processing, classification, prediction, cluster analysis, mining multimedia, and advanced machine learning techniques. This multifaceted approach is essential to address the complex challenges posed by the industrial landscape of the future.

The convergence of intelligent systems and 5G wireless systems will play a pivotal role in solving industrial problems such as autonomous robots and self-driving cars. By leveraging the power of these technologies, industries can achieve greater efficiency, productivity, and safety. For instance, autonomous robots can be used to perform repetitive tasks with high precision and accuracy, reducing the need for human labor and improving the overall quality of production. Self-driving cars, on the other hand, can reduce traffic congestion, improve transportation safety, and increase transportation efficiency.

One of the key methods of smart things in collaborative autonomous fleets and platforms is the integration of applications across different business and industry domains. This allows for the seamless sharing of data and resources, enabling organizations to optimize their operations and achieve better outcomes. For example, a manufacturing company can integrate its supply chain management system with its customer relationship management system to improve inventory management, reduce waste, and enhance customer satisfaction.

Another important topic that is discussed in the text is the Internet of robotic things (IoT). The IoT refers to the network of physical devices, such as sensors, actuators, and robots, that are connected to the internet and can communicate with each other. This technology has the potential to revolutionize the industrial landscape by enabling the automation of processes, the real-time monitoring of equipment, and the improvement of decision-making. For instance, a manufacturing plant can use IoT sensors to monitor the temperature and pressure of its equipment, allowing for the early detection of potential problems and the implementation of preventive maintenance measures.

Cloud robotics is another important topic that is discussed in the text. Cloud robotics refers to the use of cloud computing and cloud storage to enable the remote control and management of robots. This technology has the potential to reduce the cost of robotics, increase the flexibility of robotics systems, and enable the deployment of robotics systems in remote and inaccessible locations. For instance, a mining company can use cloud robotics to remotely control its mining equipment, reducing the need for human labor and improving the safety of its workers.

Cognitive architecture for cyber-physical robotics is another important topic that is discussed in the text. Cognitive architecture refers to the design of intelligent systems that can perceive, interpret, and act in their environment. This technology has the potential to enable the development of more intelligent and autonomous robots that can interact with humans and other robots in a more natural and intuitive way. For instance, a robot that can recognize and interpret human facial expressions can be used in customer service or healthcare settings to provide more personalized and effective assistance.

Image compression and advanced machine learning techniques are also important topics that are discussed in the text. Image compression is a technique that is used to reduce the size of digital images without losing any information. This technique is essential in many applications, such as medical imaging, video streaming, and web browsing. Advanced machine learning techniques, such as deep learning and neural networks, are used to analyze and interpret large amounts of data, enabling the development of more accurate and efficient machine learning models.

In conclusion, scientific programming in Industry 5.0 requires a comprehensive approach that encompasses data pre-processing, classification, prediction, cluster analysis, mining multimedia, and advanced machine learning techniques. The convergence of intelligent systems and 5G wireless systems, the Internet of robotic things, cloud robotics, cognitive architecture for cyber-physical robotics, image compression, and advanced machine learning techniques are all important topics that are discussed in the text. By leveraging these technologies, industries can achieve greater efficiency, productivity, and safety, and can develop more intelligent and autonomous systems that can interact with humans and other robots in a more natural and intuitive way. The text is primarily written for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, electrical engineering, production engineering, and mechanical engineering.

Weight: 650g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032582016