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The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems.
Min Zhao; Zhenbo Ning; Baicun Wang; Chen Peng; Xingyu Li; Sihan Huang. Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model. Processes 2021, 9, 1080 .
AMA StyleMin Zhao, Zhenbo Ning, Baicun Wang, Chen Peng, Xingyu Li, Sihan Huang. Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model. Processes. 2021; 9 (6):1080.
Chicago/Turabian StyleMin Zhao; Zhenbo Ning; Baicun Wang; Chen Peng; Xingyu Li; Sihan Huang. 2021. "Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model." Processes 9, no. 6: 1080.
As an energy-intensive production system, aluminum profile production brings enormous pressure on the environment. The billet molding process is the key stage in aluminum profile production, which accounts for 30%~50% of the total energy consumption. In this study, quantitative analysis and weighted assessment of the overall environmental impact of the selections were conducted based on the life cycle assessment (LCA) method, and the ReCiPe model was used. Analysis of each sub-process of the base scenario showed that the raw material input, energy consumption, and material waste were the main influential factors on the total environmental impacts of the billet molding process. For each sub-process, a variety of machines and operational strategies can be selected according to product types. The overall environmental impact of the scenarios using five models of furnaces powered by common energy types and the operation strategies matching with aluminum billet types were evaluated and selected, considering the characteristics and constraints in actual production. A case study showed that the life cycle environmental impacts of optimized equipment selection and operation could differ by up to 16.39%. Feasible guidance towards more environmental-friendly and cleaner aluminum profile production was also provided to determine the optimal planning strategies.
Yiming Wang; Yanan Wang; Chen Peng; Tao Peng. Environmental Impact Minimization via Production Planning for Aluminum Billet Molding Process. Procedia CIRP 2021, 98, 169 -174.
AMA StyleYiming Wang, Yanan Wang, Chen Peng, Tao Peng. Environmental Impact Minimization via Production Planning for Aluminum Billet Molding Process. Procedia CIRP. 2021; 98 ():169-174.
Chicago/Turabian StyleYiming Wang; Yanan Wang; Chen Peng; Tao Peng. 2021. "Environmental Impact Minimization via Production Planning for Aluminum Billet Molding Process." Procedia CIRP 98, no. : 169-174.
To meet the increasingly diversified demand of customers, more mixed-flow shops are employed. The flexibility of mixed-flow shops increases the difficulty of scheduling. In this paper, a mixed-flow shop scheduling approach (MFSS) is proposed to minimise the energy consumption and tardiness fine (TF) of production with a special focus on non-processing energy (NPE) reduction. The proposed approach consists of two parts: firstly, a mathematic model is developed to describe how NPE and TF can be determined with a specific schedule; then, a multi-objective evolutionary algorithm with multi-chromosomes (MCEAs) is developed to obtain the optimal solutions considering the NPE-TF trade-offs. A deterministic search method with boundary (DSB) and a non-dominated sorting genetic algorithm (NSGA) are employed to validate the developed MCEA. Finally, a case study on an extrusion die mixed-flow shop is performed to demonstrate the proposed approach in industrial practice. Compared with three traditional scheduling approaches, the better performance of the MFSS in terms of computational time and solution quality could be demonstrated.
Chen Peng; Tao Peng; Yi Zhang; Renzhong Tang; Luoke Hu. Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop. Energies 2018, 11, 3382 .
AMA StyleChen Peng, Tao Peng, Yi Zhang, Renzhong Tang, Luoke Hu. Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop. Energies. 2018; 11 (12):3382.
Chicago/Turabian StyleChen Peng; Tao Peng; Yi Zhang; Renzhong Tang; Luoke Hu. 2018. "Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop." Energies 11, no. 12: 3382.