The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic...
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The emerging technology of Structural Health Monitoring (SHM) paved the way for spotting and continuous tracking of structural damage. One of the major defects in historical structures is cracking, which represents an...
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The emerging technology of Structural Health Monitoring (SHM) paved the way for spotting and continuous tracking of structural damage. One of the major defects in historical structures is cracking, which represents an indicator of potential structural deterioration according to its severity. This paper presents a novel crack severity recognition system using a hybrid filter-wrapper with multi-objective optimization feature selection method. The proposed approach comprises two main components, namely, (1) feature extraction based on hand-crafted feature engineering and CNN-based deep feature learning and (2) feature selection using hybrid filter-wrapper with a multi-objective improved salp swarm optimization. The proposed approach is trained and validated by utilizing 10 representative UCI datasets and 4 datasets of crack images. The obtained experimental results show that the proposed system enhances the performance of crack severity recognition with approximate to 37% and approximate to 31% increase in recognition average accuracy and F-measure, respectively. Also, a reduction rate of approximate to 67% is achieved in the extracted feature set with all the tested datasets compared to the conventional classification approaches using the whole set of features. Moreover, the proposed approach outperforms other approaches with classical feature selection methods in terms of feature reduction rate and computational time. It is noticed that using VGG16 learned features outperforms using the fused hand-crafted features by 17.7%, 15.9%, and 23.5% for fine, moderate, and severe crack recognition, respectively. The significance of this paper is to investigate and highlight the impact of applying multi-feature dimensionality reduction through adopting hybrid filter-wrapper with multi-objective optimization methods for feature selection considering the case study of crack severity recognition for SHM.
In this paper, a new hybrid solution to the Optimal Power Flow (OPF) problem is proposed. In order to achieve this goal, a new hybrid salp Swarm algorithm (HSSA) is proposed to find the optimal frontier of OPF problem...
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ISBN:
(纸本)9781728122205
In this paper, a new hybrid solution to the Optimal Power Flow (OPF) problem is proposed. In order to achieve this goal, a new hybrid salp Swarm algorithm (HSSA) is proposed to find the optimal frontier of OPF problem. The proposed hybrid algorithm combines the advantages of the salp swarm algorithm (SSA) and particle swarm optimization (PSO) algorithm. The proposed HSSA provides more efficient solutions even for conflict constraints. This method is applied on five objective functions called power generation cost, environmental pollution emissions, active power loss, voltage deviation and voltage stability. The tests and results of the proposed HSSA have been applied to IEEE 30 bus test system to demonstrate the high performance compared with other optimization methods in the literature. Single and bi-objectives studied cases are tested to prove the capability of the proposed HSSA compared with the original SSA and PSO as well as the existing methods in the literature.
The fast-paced proliferation of Distributed Energy Resources (DERs), including the aggregated capacity of electric vehicles and renewable generation sources on the day-ahead market clearance mechanism, has deteriorate...
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ISBN:
(纸本)9781665486057;9781665486040
The fast-paced proliferation of Distributed Energy Resources (DERs), including the aggregated capacity of electric vehicles and renewable generation sources on the day-ahead market clearance mechanism, has deteriorated the complexity of grid operation, especially at the distribution level. This study delves into a novel framework to perceive the insight into the interactions between the Distribution System Operator (DSO) and the Parking Lots (PLs), aiming to determine the power flow effectively and to estimate the charging and discharging profiles. Hence, a bi-level optimization model associated with the operational condition of DSO and parking lots aiming to minimize the costs subject to the prevailing technical and economic constraints is proposed. The suggested bi-level model seeks the equilibrium point regarding the mathematical constraints and optimality conditions by employing the salp meta-heuristic algorithm. The results imply that the correlation between parking lots and DSO goals is the decisive factor in optimal scheduling and equilibrium point.
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