One of the primary objectives of truss structure design optimization is to minimize the total weight by determining the optimal sizes of the truss members while ensuring structural stability and integrity against exte...
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One of the primary objectives of truss structure design optimization is to minimize the total weight by determining the optimal sizes of the truss members while ensuring structural stability and integrity against external loads. Trusses consist of pin joints connected by straight members, analogous to vertices and edges in a mathematical graph. This characteristic motivates the idea of representing truss joints and members as graph vertices and edges. In this study, a Graph Neural Network (GNN) is employed to exploit the benefits of graph representation and develop a GNN-based surrogate model integrated with a particleswarmoptimization (PSO) algorithm to approximate nodal displacements of trusses during the design optimization process. This approach enables the determination of the optimal cross-sectional areas of the truss members with fewer finite element model (FEM) analyses. The validity and effectiveness of the GNN-based optimization technique are assessed by comparing its results with those of a conventional FEM-based design optimization of three truss structures: a 10-bar planar truss, a 72-bar space truss, and a 200-bar planar truss. The results demonstrate the superiority of the GNN-based optimization, which can achieve the optimal solutions without violating constraints and at a faster rate, particularly for complex truss structures like the 200-bar planar truss problem.
Based on whether the hybrid energy storage system with hydrogen storage can well adapt to the problem of high permeability operation of renewable energy, this paper designs the hybrid energy storage configuration meth...
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ISBN:
(纸本)9781665435543
Based on whether the hybrid energy storage system with hydrogen storage can well adapt to the problem of high permeability operation of renewable energy, this paper designs the hybrid energy storage configuration method with electric hydrogen coupling. Firstly, the structure of hybrid energy storage multi-energy complementary power generation system is established, followed by the objective function, which combines the cost, expected energy not supplied, and energy surplus rate of the energy storage system. Then, the particle swarm optimization algorithm is used to solve the optimization problem. Finally, by comparing with the traditional single battery energy storage system, it is concluded that the hybrid energy storage technology can well adapt to the operation of high permeability, not only can well reduce the rate of renewable energy abandonment, but also can effectively reduce the expected energy not supplied and cost.
This paper discusses the multi-objective optimization problem of how demand side resources respond flexibly to power grid dispatching based on market environment. Demand side resources dominated by large industrial us...
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ISBN:
(数字)9781510651890
ISBN:
(纸本)9781510651890;9781510651883
This paper discusses the multi-objective optimization problem of how demand side resources respond flexibly to power grid dispatching based on market environment. Demand side resources dominated by large industrial users have great scheduling potential and response enthusiasm, especially flexible loads dominated by energy storage, adjustable loads and interruptible loads. Therefore, based on the analysis of the characteristics of flexible load participating in market aggregation, aiming at the minimum power abandonment of new energy and the lowest power consumption cost of users, comprehensively considering the power constraints of unit output and electrical equipment, the particle swarm optimization algorithm is used to verify and solve the example. Finally, the optimization goal of peak shaving and valley filling is realized to a certain extent, but there is still room for improvement.
Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversifi...
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Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particleswarmoptimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particleswarmoptimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.
Obtaining accurate point estimates and reliable interval prediction results for rainfall and runoff series is important to aid in water resource decision-making and planning management in a changing environment. In th...
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Obtaining accurate point estimates and reliable interval prediction results for rainfall and runoff series is important to aid in water resource decision-making and planning management in a changing environment. In this paper, we propose a two-stage hybrid model (P-CVEE-LDBNN). The model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods for data preprocessing and a Bayesian neural network (LDBNN) based on Langevin dynamics as a prediction model. Then, a new dataset partitioning method based on a particle swarm optimization algorithm, which is different from the traditional dataset partitioning method, is applied. A one-stage hybrid model (T-CEELDBNN) that integrates the CEEMDAN method and the traditional method of partitioning datasets, a one-stage hybrid model (P-CEE-LDBNN) that integrates the CEEMDAN method and the new method of partitioning datasets, and the two-stage hybrid P-CVEE-LDBNN model were compared. These models were applied to monthly runoff and monthly precipitation series from seven hydrological and meteorological stations in the Yellow River basin. The mean absolute error (MAE), absolute root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R) were used to evaluate the predictive ability of the models. The containment ratio (CR), the average bandwidth (IW), and the average asymmetry degree (S) were used to evaluate the interval prediction results of the models. The results show that (1) the subset obtained with the new method of data partitioning considering statistical properties is more favorable for model prediction. (2) The second decomposition approach based on VMD is a reliable method that can significantly improve the prediction accuracy of the final model. (3) Compared with traditional neural networks that can only obtain deterministic point prediction results, Bayesian methods can provide intervals with prediction resu
Osteoporosis is a skeletal disorder characterized by low bone mass, which compromises its resistance and increases the risk of fractures, and is a widespread problem worldwide. Currently, the gold standard for assessi...
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ISBN:
(纸本)9780738112091
Osteoporosis is a skeletal disorder characterized by low bone mass, which compromises its resistance and increases the risk of fractures, and is a widespread problem worldwide. Currently, the gold standard for assessing fracture risk is the measurement of the areal bone mineral density with Dual-Energy X-ray Absorptiometry. Several ultrasound techniques have been presented as alternatives. It has been shown that the estimation of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission, are associated with non-traumatic fractures in postmenopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. However, this model-based inverse approach tends to fail for the patients associated with poor guided mode information. A recent study has shown the potential of an automatic classification tool, Support Vector Machine, to analyze guided wave spectrum images independently of any waveguide model. The aim of this study is to explore how the classification accuracy varies with the number of features. optimization was done using the particle swarm optimization algorithm, while adjustment was made considering age, body mass index, and cortisone intake. The results show that adjusting the data and optimizing the parameters improved classification. Moreover, the number of features was reduced from 32 to 15, with 73.5% accuracy comparable to the gold standard.
Producing oil and extracting geothermal energy from the same reservoir is one type of the multigeneration systems that are garnering global attention. The aim of this research is to use machine learning methodologies ...
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For a class of linear uncertain flight control systems, in terms of the strip region, the problem of actuator continuous gain fault and reliable control is studied. At the same time, in order to solve the difficulty o...
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ISBN:
(纸本)9789881563804
For a class of linear uncertain flight control systems, in terms of the strip region, the problem of actuator continuous gain fault and reliable control is studied. At the same time, in order to solve the difficulty of selecting parameters of support vector machine, a parameter optimizationalgorithm based on improved particleswarmoptimization (MPSO) is proposed. Compared with grid search method, particleswarmoptimization and genetic algorithm, this method has the advantages of fast convergence, short time consuming and high accuracy. For the problem that the pole information is difficult to obtain, the algorithm theory of pole observer is given to realize the real-time observation of unknown system. Finally, the effectiveness of reliable controller design is further proved by numerical simulation.
In Multi-specialty hospitals, the quantity of accumulation of Bio-Medical Waste (BMW) is enormous when compared to clinics. The safe and timely disposal of BMW is very essential to avoid harmful effects to humans and ...
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In Multi-specialty hospitals, the quantity of accumulation of Bio-Medical Waste (BMW) is enormous when compared to clinics. The safe and timely disposal of BMW is very essential to avoid harmful effects to humans and environment. In this article, the inbound logistics involved in the collection of Bio-Medical Waste at a Private Multi-Specialty Hospital located in Coimbatore which contains 59 wards has been improved to avoid time delay. An optimized vehicle routing model has been framed for a set of 6 dedicated vehicles with the objective to minimize the time taken during the collection of BMW. For this purpose a mathematical model is generated and solved using particle swarm optimization algorithm (PSO). The results infer that, by following the optimized vehicle routes, the time delay is totally eliminated and in addition the time taken for collecting the BMW is reduced by 42%, i.e. from 6 h to 3 h 46 min. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research-2019.
Aiming at the problems of large positioning error and low positioning effect at the boundary of traditional RFID virtual tag positioning algorithm, this paper compares the influence of four different interpolation met...
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ISBN:
(纸本)9781665422604
Aiming at the problems of large positioning error and low positioning effect at the boundary of traditional RFID virtual tag positioning algorithm, this paper compares the influence of four different interpolation methods of virtual reference tag signal strength on positioning accuracy, and proposes a RFID virtual tag positioning algorithm based on Monte Carlo. The algorithm uses dynamic particles to replace the traditional static reference tag;introduces particle swarm optimization algorithm to update the Monte Carlo sample particleswarm, and gives different weights to the sampling particles based on the signal strength difference between the sampling particles and the undetermined tags, and finally completes the localization of the unknown tags through Monte Carlo resampling. The simulation results show that the algorithm can effectively improve the accuracy and stability of RFID positioning system compared with the traditional virtual tag positioning algorithm.
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