Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

Chen, Michael Z. Q.; Zhang, David

doi:10.3390/pr9040664

Cited by 16 publications

(8 citation statements)

References 27 publications

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“…Thus, we applied this tool to our study of incomplete information games cases (PGG) rather than simple game dynamics cases (zero-sum games). The operating principle of this model is expected to gain momentum gradually for generalizability to real-world cases, and the algorithm optimization process undertaken is anticipated to be extended in terms of the definitions of game, payoff, and so on [13].…”

Section: Performance (Model Including Results)mentioning

confidence: 99%

Network Characteristic Control of Social Dilemmas in a Public Good Game: Numerical Simulation of Agent-Based Nonlinear Dynamics

Park

2022

Processes

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This paper proposes a possible mechanism for obtaining sizeable behavioral structures by simulating a network–agent dynamic on an evolutionary public good game with available social .learning. The model considers a population with a fixed number of players. In each round, the chosen players may contribute part of their value to a common pool. Then, each player may imitate the strategy of another player based on relative payoffs (whoever has the lower payoff adopts the strategy of the other player) and change his or her strategy using different exploratory variables. Relative payoffs are subject to incentives, including participation costs, but may also be subject to mutation, whose rate is sensitized by the network characteristics (social ties). The process discussed in this report is interesting and relevant across a broad range of disciplines that use game theory, including cultural evolutionary dynamics.

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Section: Performance (Model Including Results)mentioning

confidence: 99%

Network Characteristic Control of Social Dilemmas in a Public Good Game: Numerical Simulation of Agent-Based Nonlinear Dynamics

Park

2022

Processes

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“…The development and use of monitoring, optimization, and control technologies have recently been aided by artificial intelligence, data-driven technologies, cyber-physical systems, cloud computing, and cognitive computing [82]. Nonlinear algorithms, for instance, can be used to monitor dynamic systems and provide observer gains [83].…”

Section: Challenges and Prospects Of Ai Applied To Microcantilever Se...mentioning

confidence: 99%

Prospects and Challenges of AI and Neural Network Algorithms in MEMS Microcantilever Biosensors

Wang

Shi

et al. 2022

Processes

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This paper focuses on the use of AI in various MEMS (Micro-Electro-Mechanical System) biosensor types. Al increases the potential of Micro-Electro-Mechanical System biosensors and opens up new opportunities for automation, consumer electronics, industrial manufacturing, defense, medical equipment, etc. Micro-Electro-Mechanical System microcantilever biosensors are currently making their way into our daily lives and playing a significant role in the advancement of social technology. Micro-Electro-Mechanical System biosensors with microcantilever structures have a number of benefits over conventional biosensors, including small size, high sensitivity, mass production, simple arraying, integration, etc. These advantages have made them one of the development avenues for high-sensitivity sensors. The next generation of sensors will exhibit an intelligent development trajectory and aid people in interacting with other objects in a variety of scenario applications as a result of the active development of artificial intelligence (AI) and neural networks. As a result, this paper examines the fundamentals of the neural algorithm and goes into great detail on the fundamentals and uses of the principal component analysis approach. A neural algorithm application in Micro-Electro-Mechanical System microcantilever biosensors is anticipated through the associated application of the principal com-ponent analysis approach. Our investigation has more scientific study value, because there are currently no favorable reports on the market regarding the use of AI with Micro-Electro-Mechanical System microcantilever sensors. Focusing on AI and neural networks, this paper introduces Micro-Electro-Mechanical System biosensors using artificial intelligence, which greatly promotes the development of next-generation intelligent sensing systems, and the potential applications and prospects of neural networks in the field of microcantilever biosensors.

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“…Zheng et al [53] constructed a three-stage supply chain model using a system of differential equations to reveal the interplay among producers, distributors and end customers. Gao et al [54] outlined emerging research and application directions in condition measurement, optimization and advanced control of complex industrial processes. Berkowitz et al [55] and Baack et al [56] proposed the dynamic advertising model, and Chen et al [57] applied the delayed dynamic advertising model to the advertising cooperation among the supply chain members.…”

Section: Introductionmentioning

confidence: 99%

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

2023

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Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

Abstract: Complex industrial automation systems and processes, such as chemical processes, manufacturing systems, wireless network systems, power and energy systems, smart grids and so forth, have greatly contributed to our daily life [...]

Cited by 16 publications

References 27 publications

Network Characteristic Control of Social Dilemmas in a Public Good Game: Numerical Simulation of Agent-Based Nonlinear Dynamics

Network Characteristic Control of Social Dilemmas in a Public Good Game: Numerical Simulation of Agent-Based Nonlinear Dynamics

Prospects and Challenges of AI and Neural Network Algorithms in MEMS Microcantilever Biosensors

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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