Electric power transmission reliability depends critically on the crimping quality of overhead transmission lines. Poor crimping connections can lead to serious accidents, including wire breakage and dislodgement. Cri...
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Electric power transmission reliability depends critically on the crimping quality of overhead transmission lines. Poor crimping connections can lead to serious accidents, including wire breakage and dislodgement. Crimping pitch is a key factor for crimping quality, which is used to determine over-crimping or under-crimping. Currently, the indentation pitch is manually checked after operation, while the automatic method has not yet been widely studied. This paper proposes an automated crimp pitch measurement system for overhead transmission lines based on image processing with a composite algorithmic approach. First, the indentation enhancement algorithm and indentation segmentation algorithm are used on the captured image to accurately identify the indentation region. Subsequently, the Random Sample Consensus (RANSAC) algorithm utilizes identified edge points to perform robust indentation curve fitting. Finally, the actual crimp pitch is obtained according to the crimp pitch calculation formula. The test results show that the method's measurement accuracy of spacing reaches 3 mm, the maximum relative error is 3.64%, and the standard deviation of repeated measurements is 1.184 mm. The method proposed in this paper can be used for the measurement of crimp pitch, which is characterized by accuracy and efficiency. This approach is particularly suitable for quality control inspections of crimping connections in overhead transmission line systems.
This paper aims to study the application of composite algorithm in the direction planning of industrial economic development. This paper aims to explore the application of composite algorithm in the direction planning...
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Traveling Salesman Problem (TSP) is an NP-hard optimization problem that can be solved by a heuristic composite algorithm. A composite algorithm is a heuristic optimization model that combine tour construction algorit...
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ISBN:
(纸本)9781450376532
Traveling Salesman Problem (TSP) is an NP-hard optimization problem that can be solved by a heuristic composite algorithm. A composite algorithm is a heuristic optimization model that combine tour construction algorithm and tour improvement algorithm. Clarke Wright Savings heuristic is one of the best methods that produce a good initial solution, and local search is known to be a successful operator to make an improvement solution. This paper will present a composite algorithm as a preliminary model based on Clarke wright savings and local search K-opt to solve TSP. The experimental result shows that the proposed algorithm can solve a large problem instance of Traveling Salesman Problem up to 85.900 points, with competitive results, small variations of computing time for 30 problem instances, and relatively short computing time.
The paper deals with algorithmic issues of the process of separating periods in the pulsed signal of the radial artery. The periodization algorithm is a set of various procedures for processing an impulse signal that ...
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ISBN:
(纸本)9781538607985
The paper deals with algorithmic issues of the process of separating periods in the pulsed signal of the radial artery. The periodization algorithm is a set of various procedures for processing an impulse signal that can confidently allocate periods in a signal. It precedes the rest of the analysis stages. The procedures used in the algorithm are described. The expediency of their application is discussed. A block diagram of the developed algorithm for pulsed signal periodization is given. The verification of the developed algorithm was carried out on a representative sample. Several variants of the composite algorithm were implemented. As a result, an algorithm was chosen that combined the statistical algorithm and the corrective procedure - a configurable amplitude sieve. The analysis of the results of the developed algorithm has confirmed the acceptable efficiency of the periodization process for very diverse pulse signals.
Empirical evidence indicates that the strength of plastic flow toward the slope surface, resulting from the compression of a soft interlayer between upper and lower hard rock layers, increases with the thickness of th...
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Empirical evidence indicates that the strength of plastic flow toward the slope surface, resulting from the compression of a soft interlayer between upper and lower hard rock layers, increases with the thickness of the soft interlayer. Consequently, this study introduces the concept of the deformation viscosity effect in slopes containing soft interlayers, elucidating its underlying mechanism based on the presence of a constraint reaction force at the interface between soft and hard rock layers. Through finite element analysis of the Guiyang bank slope of the Huayudong Bridge, the objective existence of the viscosity effect is demonstrated, and its evolutionary behavior is examined. Additionally, the sensitivity of five key geometric and mechanical parameters of the soft interlayer on slope stability is quantitatively assessed using a composite algorithm that integrates range analysis, Grey Relational Analysis (GRA), and Analytic Hierarchy Process (AHP). The findings reveal that the sensitivity of factors affecting the stability of the Huayudong Bridge Guiyang bank slope is ranked as follows: inclination angle > cohesion > internal friction angle > thickness > weight. The analytical results provide valuable insights for the effective prevention and control of slopes with soft interlayers.
The effect of rivet length and sheet thickness on the cross-sectional formation and tensile-shear performance of self-piercing riveted joints in AA5754 aluminum alloy was examined through experimental investigation. T...
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The effect of rivet length and sheet thickness on the cross-sectional formation and tensile-shear performance of self-piercing riveted joints in AA5754 aluminum alloy was examined through experimental investigation. The influence degree of joining parameters on the forming quality was analyzed. It was revealed that rivet length and sheet thickness are pivotal factors influencing the tensile-shear strength of the joint, culminating in the identification of four optimal riveting process schemes:L(C)h(1A)h(2A),L(A)h(1A)h(2B),L(A)h(1A)h(2B) and L(B)h(1A)h(2B). A simulation model for self-piercing riveting was established, employing the GISSMO failure model and the modified Mohr-Coulomb (MMC) failure criteria to predict the damage and fracture of the aluminum alloy. A plethora of high-quality datasets depicting the cross-sections of the joints were derived from simulation analysis. Subsequently, the structure and hyperparameter determination method of traditional neural network prediction models were elucidated. By amalgamating the Aquila Optimization (AO) algorithm with the African Vultures Optimization algorithm (AVOA), a hybrid optimization algorithm model known as MIC_AOAVOA was developed. This model effectively harnesses the strengths of various algorithms to augment search efficiency and optimization capabilities. Strategies for population initialization and adaptive weight adjustments were incorporated to enhance the algorithm's convergence velocity and the quality of solutions. The cauchy opposition-based learning (COBL) and fitness-distance balance (FDB) strategy further refined the composite algorithm, bolstering its global search capabilities and population diversity. Comparative analyses were performed with single algorithm models and traditional BP neural network models, with an in-depth examination of the MIC_AOAVOA_BP model's prediction outcomes. Comprehensive evaluations utilizing error statistics and composite evaluation indicators demonstrated that the
A new method for optimizing a set of numerical parameters is presented. This method does not depend on prior knowledge of the goal function and of its derivatives. Besides, it is robust and does not require any other ...
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A new method for optimizing a set of numerical parameters is presented. This method does not depend on prior knowledge of the goal function and of its derivatives. Besides, it is robust and does not require any other method to achieve result near the global extreme (maximum/minimum) of the goal function. However, as the computational time spent by our method increases with the number of numerical parameters and with the size of each parameter to be optimized, in this work, it is applied together with the IGCHF method to construct accurate uncontracted basis sets to describe the ground states of some atoms. In these cases, the parameters to be optimized are the exponents of the Gaussian functions and the minimum total HF energy criterion is guaranteed by the variational principle. It is verified that the results calculated with the proposed new method are better than those obtained with the Monte Carlo and particle swarm methods, although the computational times spent in some cases by it are larger than those of the other two methods.
In this paper, a new family of controllably dissipative composite algorithms is developed to obtain reliable numerical response of structural dynamic problems. The proposed algorithm is a self-starting, unconditionall...
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In this paper, a new family of controllably dissipative composite algorithms is developed to obtain reliable numerical response of structural dynamic problems. The proposed algorithm is a self-starting, unconditionally stable and second-order accurate three sub-step composite algorithm. The new method includes two optimal sub-families of algorithms, both of which can control numerical dissipations in the high-frequency range by an intuitive way, and their numerical dissipations can range from the non-dissipative case to the asymptotic annihilating case. Besides, they actually involve only one free parameter and always share the identical effective stiffness matrices inside three sub-step to save the computational cost, which does not hold in some existing sub-step algorithms. Some numerical examples are given to show the superiority of the new algorithm with respect to controllable numerical dissipations and the ability of capturing the free-play nonlinearity.
The environment for practical applications of an energy storage system (ESS) in a microgrid system is very harsh, and therefore actual operating conditions become complex and changeable. In addition, the signal of the...
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The environment for practical applications of an energy storage system (ESS) in a microgrid system is very harsh, and therefore actual operating conditions become complex and changeable. In addition, the signal of the ESS sampling process contains a great deal of system and measurement noise, the sampled current fluctuates significantly, and also has high frequency. In this case, under such conditions, it is difficult to accurately estimate the state of charge (SOC) of the batteries in the ESS by common estimation methods. Therefore, this study proposes a compound SOC estimation method based on wavelet transform. This algorithm is very suitable for microgrid systems with large current, frequent fluctuating conditions, and high noise interference. The experimental results and engineering data show that the relative error of the method is 0.5%, which is much lower than the extend Kalman filter (EKF) based on wavelet transform.
In order to effectively improve the prediction accuracy, short-term wind speed combination prediction model based on ARIMA-GARCH and Elman is proposed. It adopts ARIMA model to linearly predict short-term wind speed a...
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ISBN:
(数字)9781728160672
ISBN:
(纸本)9781728160689
In order to effectively improve the prediction accuracy, short-term wind speed combination prediction model based on ARIMA-GARCH and Elman is proposed. It adopts ARIMA model to linearly predict short-term wind speed and GARCH to improve the non-stationary series heteroscedasticity problems. What's more, Elman neural network is used to nonlinearly predict short-term wind speed. Finally, the minimum weighting method of absolute error is used to determine the optimal linear weight of single model so as to realize short-term wind speed prediction. Case analysis shows that, compared with other prediction models, the combination model not only has significantly improved short-term wind speed prediction accuracy, but provides a certain reference to short-term wind speed prediction in relevant fields.
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