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2022 ◽  
Vol 2146 (1) ◽  
pp. 012008
Author(s):  
Su Dai ◽  
Yunlong Ma ◽  
Kai Wang ◽  
Bowen Liu ◽  
Changming Liu

Abstract In recent years, it is still a difficult problem to calculate the line loss of distribution system. The main reason is that China’s power system data is not perfect, the components are huge, the management function does not exist, the network structure is directly connected. Therefore, reducing the loss rate of power system has become an urgent problem for energy companies to solve. Developing effective methods for line loss reduction evidence and line loss assessment requires careful calculation and analysis of line loss, which ultimately contributes to and guides line loss management.


Author(s):  
Chuliang Wu ◽  
Hongsheng Zhang ◽  
Nanlin Fan ◽  
Guoqiang Liu ◽  
Xu Wang ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liying Jin ◽  
Wensheng Wang ◽  
HouYong Shu ◽  
Xuemei Ma ◽  
Chenxing Liang ◽  
...  

In view of the traditional maintainability allocation method for a certain shooter seat for maintainability allocation did not consider the lifecycle expense problem, the improved NSGA-II algorithm (iNSGA-II, for short) is adopted to establish a multiobjective comprehensive trade-off model for a certain shooter seat product lifecycle maintenance-related expenses and mean time to repair (MTTR, for short) and construct multiobjective optimization problem. The experimental results show that the Pareto optimal solution effectively solves the limitation of the traditional maintainability allocation method and then provides a basis for a certain shooter seat to obtain a reasonable maintainability allocation scheme. The superiority of the iNSGA-II algorithm to optimize the maintainability allocation of a certain shooter seat was verified by comparing it with the traditional maintainability allocation method.


2021 ◽  
Vol 15 (3) ◽  
pp. 291-305
Author(s):  
Rickman Roedavan ◽  
Bambang Pudjoatmodjo ◽  
Yahdi Siradj ◽  
Sazilah Salam ◽  
BQ Desy Hardianti

Serious games or applied games are digital games applied in serious fields such as education, advertising, health, business, and the military. Currently, serious game development is mostly based on the Game Development Life Cycle (GDLC) approach. A serious game is a game product with unique characteristics that require a particular approach to its development. This paper proposes a serious game development model adapted from the Game-Based Learning Foundation. This paper’s main contribution is to enhance knowledge in the game development field and game-related application research. The proposed model was validated using the relativism approach and it was used to develop several game prototypes for universities, national companies, and the military.


2021 ◽  
Author(s):  
jianxun chen ◽  
Qingcai Wu ◽  
Ganlong Wang ◽  
Pu Miao ◽  
Yi Li ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Rongshen Lai ◽  
Bo Gao ◽  
Wenguang Lin

Aiming at the no-wait flow shop scheduling problem with the goal of minimizing the maximum makespan, a discrete wolf pack algorithm has been proposed. First, the methods for solving the no-wait flow shop scheduling problem and the application research of the wolf pack algorithm were summarized, and it was pointed out that there was lack of research on the application of the wolf pack algorithm to solve the no-wait flow shop scheduling problem. According to the analysis of characteristics of the no-wait flow shop scheduling problem, the individual wolf was coded by a decimal integer; wolf searching behavior was realized through the exchange of different code bits in the individual wolf, and the continuous code segment of the head wolf was randomly selected to replace the corresponding code of the fierce wolf, by which the behaviors of wolves raiding and sieging were realized, and the population was updated according to the rule of “survival of the strong.” In particular, to fully explore the potential optimal solution in the solution space, loop operations were added to the wandering, summoning, and siege processes. Finally, based on a comparison with the leapfrog algorithm and the genetic algorithm, the effectiveness of the algorithm was verified.


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