Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Azizi, Aydin

doi:10.1155/2020/8564140

Cited by 39 publications

(17 citation statements)

References 20 publications

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“…In the field of robotic learning, which is currently developing rapidly [ 45 ], the application prospects of metalearning are also very broad. Especially in terms of robot operation skills, metalearning, as a “learning to learn” method, has made good progress [ 46 ].…”

Section: Specific Analysis Of the Application Of Metalearning In The Field Of Artificial Intelligencementioning

confidence: 99%

Review on the Application of Metalearning in Artificial Intelligence

Zhang

Wang

et al. 2021

Computational Intelligence and Neuroscience

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In recent years, artificial intelligence supported by big data has gradually become more dependent on deep reinforcement learning. However, the application of deep reinforcement learning in artificial intelligence is limited by prior knowledge and model selection, which further affects the efficiency and accuracy of prediction, and also fails to realize the learning ability of autonomous learning and prediction. Metalearning came into being because of this. Through learning the information metaknowledge, the ability to autonomously judge and select the appropriate model can be formed, and the parameters can be adjusted independently to achieve further optimization. It is a novel method to solve big data problems in the current neural network model, and it adapts to the development trend of artificial intelligence. This article first briefly introduces the research process and basic theory of metalearning and discusses the differences between metalearning and machine learning and the research direction of metalearning in big data. Then, four typical applications of metalearning in the field of artificial intelligence are summarized: few-shot learning, robot learning, unsupervised learning, and intelligent medicine. Then, the challenges and solutions of metalearning are analyzed. Finally, a systematic summary of the full text is made, and the future development prospect of this field is assessed.

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Section: Specific Analysis Of the Application Of Metalearning In The Field Of Artificial Intelligencementioning

confidence: 99%

Review on the Application of Metalearning in Artificial Intelligence

Zhang

Wang

et al. 2021

Computational Intelligence and Neuroscience

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“…One of the major utilizations of AI in the field of mobile robots is to develop a better-quality control system for the autonomous navigation of mobile robots. The genetic algorithm with an artificial neural network is an evolutionary technique for the estimation of optimal parameters for the gimbal joints of the mobile robot to optimize the robotic models [64]. The algorithm for the navigation of mobile robots is based upon the velocity during the methodology of obstacle avoidance and the algorithm for guidance-based tracking.…”

Section: Autonomous Navigation Of a Mobile Robotmentioning

confidence: 99%

A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives

Raj

Kos

2022

Applied Sciences

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Intelligent mobile robots that can move independently were laid out in the real world around 100 years ago during the second world war after advancements in computer science. Since then, mobile robot research has transformed robotics and information engineering. For example, robots were crucial in military applications, especially in teleoperations, when they emerged during the second world war era. Furthermore, after the implementation of artificial intelligence (AI) in robotics, they became autonomous or more intelligent. Currently, mobile robots have been implemented in many applications like defense, security, freight, pattern recognition, medical treatment, mail delivery, infrastructure inspection and developments, passenger travel, and many more because they are more intelligent nowadays with artificial intelligence technology. To study the developments of mobile robots, we have studied an extensive literature survey of the last 50 years. In this article, we discuss a complete century of mobile robotics research, major sensors used in robotics, some major applications of mobile robots, and their impact on our lives and in applied engineering.

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“…Manufacturing and Industry 4.0 Practices I4.0 technologies are expected to influence the sustainability practices in manufacturing organization (Azizi & Hakl, 2020). Sustainability at present can be considered as the core business strategy for the future of industries which in the report of United Nations Sustainability 2030 agenda which includes low impact industrialization, smart manufacturing, smart cities, responsible production and energy efficient buildings (Bag et al, 2018;Habib & Chimsom, 2019;Beier et al, 2020).…”

Section: Relationship Between Sustainablementioning

confidence: 99%

A Framework to Overcome Blockchain Enabled Sustainable Manufacturing Issues through Circular Economy and Industry 4.0 Measures

Jamwal

Agrawal

Sharma

2022

Int. j. math. eng. manag. sci.

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Adoption of Industry 4.0 (I4.0) and sustainable manufacturing practices plays an important role for manufacturing industries to sustain their globe market reputation. However, the adoption of I4.0 technologies in manufacturing practices is more concern for manufacturing industries. Volatile customer demands, changing manufacturing trends and market scenario has forced manufacturing organization to revisit their production system and incorporate sustainability practices. Blockchain enabled practices helps to maintain transparency and traceability in supply chain as well as manufacturing activities. But lack of framework related to blockchain enabled sustainable manufacturing has become a serious concern for policymakers and practitioners. Similarly, due to changing market scenario it is important to identify the solutions to overcome the blockchain enabled sustainable manufacturing issues through circular economy and I4.0 solution measures. The present study identifies the 28 critical challenges and 22 I4.0 and circular economy based solution measures to overcome the issues of blockchain enabled sustainable manufacturing. Further, a case study in electronics manufacturing industries is used to test the developed framework using hybrid multi-criteria decision making approach. The case study findings that organizational and managerial challenges are critical to blockchain enabled sustainable manufacturing adoption. In the end of study, we have proposed an integrated framework for blockchain enabled sustainable manufacturing practices for energy, waste, resource, quality and life cycle management. It is expected that present study will be helpful for the researchers, practitioners and policymakers to understand the complications in the adoption of blockchain enabled sustainable manufacturing practices.

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Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Cited by 39 publications

References 20 publications

Review on the Application of Metalearning in Artificial Intelligence

Review on the Application of Metalearning in Artificial Intelligence

A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives

A Framework to Overcome Blockchain Enabled Sustainable Manufacturing Issues through Circular Economy and Industry 4.0 Measures

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