The main motivation for the current study is to investigate the weight advantage of the aluminum material on pin-jointed (truss) structures. For this purpose, optimization algorithms that find the most suitable struct...
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The main motivation for the current study is to investigate the weight advantage of the aluminum material on pin-jointed (truss) structures. For this purpose, optimization algorithms that find the most suitable structural designs with aluminum and steel materials have been developed. The criterion for finding optimum designs is to minimize the weight of the structure and met the design constraints. The constraint functions are derived according to the "American Institute of Steel Structures - Allowable Stress Design" (AISC-ASD) and "Aluminum Association - Allowable Stress Design" (AA-ASD) design codes. To increase the reliability of the obtained results, three robust methods named the Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), and brainstorming Optimization (BSO) algorithms are utilized for the proposed optimization problem. Four real-scale pin-jointed structures are used to research the weight advantage of aluminum structures. Three of these structures are real-scale truss structures that were previously optimized as made of steel elements by other metaheuristics. The last pin-jointed structure is the real case project which is modeled and optimized for the first time in this study. According to the obtained results, significant optimal design differences are determined between the construction of the tackled pin-jointed structures with aluminum or steel structural elements. Considering other advantages such as transportation and maintenance, using aluminum material in pin-jointed structures is recommended.
Mobility pattern recognition is a complex task in vehicle ad hoc networks (VANET) because the driving state of each vehicle is different. An intelligent transportation system on VANET is used for traffic control and a...
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Mobility pattern recognition is a complex task in vehicle ad hoc networks (VANET) because the driving state of each vehicle is different. An intelligent transportation system on VANET is used for traffic control and accident prevention. For this reason, human driver behaviour is first analysed to identify mobility patterns. A novel driver behaviour prediction model using a Siamese deep learning architecture is proposed to achieve the goal. Here, an image-based behaviour prediction model is performed to achieve the highly accurate driving state of the driver. A warning message is forwarded to the neighbouring vehicles based on the driver's behaviour. Due to the dynamic properties of real-time vehicle mobility, a faster data transmission model is achieved using the ad hoc on-demand distance vector routing protocol. To achieve faster data transmission and nullified retransmission, here a weighted location-based routing model is framed. The optimization problem in the location-aided routing protocol is solved using the vector algorithm's weighted mean. As a result, the proposed method improved the throughput of ASHLOSR to 8.1% and AODV to 7.6%. For the safest driving of road user, an image-based neighbouring driver behaviour prediction model is highlighted and proposed in this research. Human driver behaviour is first analysed to identify mobility patterns. A novel driver behaviour prediction model using a Siamese deep learning architecture is proposed to achieve the goal. A warning or alert message of abnormal driver behaviour is successfully send to the vehicles through weighted location-aided routing protocol. The protocol has improved the VANET metrics by selecting optimal path of congestion free and shortest distance, which reduces retransmission rate. The optimization uses congestion-free route selection, so the proposed method increased the throughput to 8.1% from ASHLOSR and 7.6% from traditional ***
In view of the uncertainty and volatility of wind power generation and the inability to provide stable and continuous power, this paper proposes a hydrogen storage wind-gas complementary power generation system, using...
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In view of the uncertainty and volatility of wind power generation and the inability to provide stable and continuous power, this paper proposes a hydrogen storage wind-gas complementary power generation system, using Matlab/Simulink to simulate and model wind generators and gas turbines. Considering the economy and power supply reliability of the wind-gas complementary power generation system, and taking the economic and environmental cost of the system as the objective function, the capacity optimization model of the wind-gas complementary power generation system is established. The brain storming algorithm (BSO) is used to solve the optimization problem, and the BSO algorithm is used to optimize the BP neural network, which improves the accuracy of the BP neural network for load forecasting. Finally, a simulation is carried out with load data in a certain area, and the simulation verification verifies that BSO-BP can improve the accuracy of load forecasting and reduce the error of load forecasting. Multi-objective optimization of system economic cost and environmental cost through BSO algorithm can make the system cost reach the most reasonable level. Through the analysis of the calculation examples, it is verified that gas-fired power generation can effectively alleviate the volatility of wind power generation, showing the role and advantages of energy complementary power generation. Therefore, the wind-gas complementary system can effectively increase energy utilization and reduce wind curtailment.
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