Zobrazeno 1 - 10
of 181
pro vyhledávání: '"Wenyin Gong"'
Publikováno v:
Complex System Modeling and Simulation, Vol 4, Iss 1, Pp 82-108 (2024)
This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy
Externí odkaz:
https://doaj.org/article/54de39c29f624d18b0db61b0b2b04198
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 3, Pp 3459-3471 (2024)
Abstract This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the maximum finish time and total energy consumption. To accomplish this, we i
Externí odkaz:
https://doaj.org/article/022d8aec135f4b838aece21425623b53
Publikováno v:
IET Control Theory & Applications, Vol 17, Iss 15, Pp 2017-2031 (2023)
Abstract Solving many‐objective optimization problems (MaOPs) by means of evolutionary algorithms obtains considerable attention in the community of evolutionary computation. However, it is a difficult task to effectively handle MaOPs with both reg
Externí odkaz:
https://doaj.org/article/fd78c5f75d2644a586f8218ecd7362a0
Publikováno v:
Complex System Modeling and Simulation, Vol 3, Iss 1, Pp 1-11 (2023)
A chip mounter is the core equipment in the production line of the surface-mount technology, which is responsible for finishing the mount operation. It is the most complex and time-consuming stage in the production process. Therefore, it is of great
Externí odkaz:
https://doaj.org/article/f6440686bf41457aba8f95dd41c5c889
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 5, Pp 4805-4816 (2023)
Abstract Distributed manufacturing is the mainstream model to accelerate production. However, the heterogeneous production environment makes engineer hard to find the optimal scheduling. This work investigates the energy-efficient distributed heterog
Externí odkaz:
https://doaj.org/article/9474a877ac6143d3a1bb6b595e852355
Publikováno v:
Egyptian Informatics Journal, Vol 24, Iss 3, Pp 100383- (2023)
This work aims to deal with the distributed heterogeneous unrelated parallel machine scheduling problem (DHUPMSP) with minimizing total tardiness (TDD) and makespan. To solve this complex combinatorial optimization problem, this work proposed a knowl
Externí odkaz:
https://doaj.org/article/2340729f44154f16ad63a39afc803068
Publikováno v:
Applied Sciences, Vol 13, Iss 18, p 10120 (2023)
In the era of deep learning, representational text-matching algorithms based on BERT and its variant models have become mainstream and are limited by the sentence vectors generated by the BERT model, and the SimCSE algorithm proposed in 2021 has impr
Externí odkaz:
https://doaj.org/article/41cc90c2f4934222a4191ca765ff63a7
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4459 (2023)
The main task of remote sensing image target detection is to locate and classify the targets of interest in remote sensing images, which plays an important role in intelligence investigation, disaster relief, industrial application, and other fields.
Externí odkaz:
https://doaj.org/article/fd0cffc0a4a546588d194f8b0e6ecd11
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 676-686 (2022)
Subspace clustering methods have become a powerful tool to cluster hyperspectral imaging (HSI) data as they ensure theoretical guarantees and empirical success. However, existing methods explore subspace representation in the Euclidean space, and thu
Externí odkaz:
https://doaj.org/article/8a9195eae4d84a8a877978585f1d00ab
Publikováno v:
Mathematical Biosciences and Engineering, Vol 19, Iss 2, Pp 1128-1153 (2022)
The accuracy of unknown parameters determines the accuracy of photovoltaic (PV) models that occupy an important position in the PV power generation system. Due to the complexity of the equation equivalent of PV models, estimating the parameters of th
Externí odkaz:
https://doaj.org/article/ba92fed3e4164766a6e5fff9ef91ce39