This paper proposes a knowledge model for root cause analysis (RCA) of complex systems based on fuzzy cognitive maps (FCMs) and particle swarm optimization algorithm (PSO). The process knowledge and experience of tech...
详细信息
作者：
Wang, LijuanGuo, NiWang, WeiZuo, HongchaoLanzhou Univ
Coll Atmospher Sci Lanzhou 730000 Gansu Peoples R China China Meteorol Adm
Key Lab Drought Climate Change & Disaster Reduct Key Lab Drought Climate Change & Disaster Reduct Inst Arid Meteorol Lanzhou 730020 Gansu Peoples R China
FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). I...
详细信息
FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particleswarmoptimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.
To improve the convergence and distribution of a multi-objective optimizationalgorithm, a hybrid multi-objective optimizationalgorithm, based on the quantum particleswarmoptimization (QPSO) algorithm and adaptive ...
详细信息
To improve the convergence and distribution of a multi-objective optimizationalgorithm, a hybrid multi-objective optimizationalgorithm, based on the quantum particleswarmoptimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.
particleswarmoptimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search a...
详细信息
ISBN:
(数字)9789811033223
ISBN:
(纸本)9789811033223;9789811033216
particleswarmoptimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search area with the help of two accelerating parameters. The proposed algorithm is tested over 12 benchmark test functions and compared with basic PSO and two other algorithms known as Gravitational search algorithm (GSA) and Biogeography based optimization (BBO). The result reveals that ABF-PSO will be a competitive variant of PSO.
The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitan...
详细信息
The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particleswarmoptimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality.
Providing electricity for the residential buildings which devote a high portion of energy consumption is very crucial all over the world. Not only electrical loads but also heating and cooling loads are required to be...
详细信息
Providing electricity for the residential buildings which devote a high portion of energy consumption is very crucial all over the world. Not only electrical loads but also heating and cooling loads are required to be supplied in a building. The aim of this paper is to supply cooling, heating, and electrical loads in a building by separating generation system and cogeneration system. In this regard, the optimal problem was developed to minimize the total costs using particle swarm optimization algorithm. As a numerical study, a high-rise building with 72 units located in Kerman is analyzed for eight different *** results show that in order to supply cooling, heating, and power, a cogeneration system consisting of a 195 kW micro gas turbine as prime mover, a 281 kW single-effect absorption chiller, a 439 kW air cooling compaction chiller, an 187 kW auxiliary boiler to compensate the heat load, and a 52.8 kW photovoltaic to generate employed electrical loads, is the best optimal system to supply the base building loads. In this optimized system, the annual electricity sales revenue is 93,251$, the annual cost of buying power is 7001$, the cost of buying fuel as annual consumption is 15,852.4$, and the annual production of carbon dioxide emissions in the building is 229.78 tons. The return on investment period for the aforementioned project is also estimated 4.98 years.
During the detecting process of geological exploration data, there is a variety of noise interferences, resulting in inaccurate data detection. When the current detection algorithm is used to detect high frequency But...
详细信息
During the detecting process of geological exploration data, there is a variety of noise interferences, resulting in inaccurate data detection. When the current detection algorithm is used to detect high frequency Butter data, detection efficiency is low. 'lb this end, a detection algorithm of high frequency clutter data based on optimized BP neural network particleswarmalgorithm is proposed. First, improved wavelet threshold denoising algorithm is utilized for clutter data denoising. Then, an optimized BP neural network particleswarmalgorithm is employed to detect the clutter data. Experimental results show that the proposed algorithm improves the accuracy of data detection.
In this paper, we propose to use the particleswarmoptimization (PSO) algorithm to improve the Multi-Scale Line Detection (MSLD) method for the retinal blood vessel segmentation problem. The PSO algorithm is applied ...
详细信息
ISBN:
(纸本)9783319598765;9783319598758
In this paper, we propose to use the particleswarmoptimization (PSO) algorithm to improve the Multi-Scale Line Detection (MSLD) method for the retinal blood vessel segmentation problem. The PSO algorithm is applied to find the best arrangement of scales in the basic line detector method. The segmentation performance was validated using a public high-resolution fundus images database containing healthy subjects. The optimized MSLD method demonstrates fast convergence to the optimal solution reducing the execution time by approximately 35%. For the same level of specificity, the proposed approach improves the sensitivity rate by 3.1% compared to the original MSLD method. The proposed method will allow to reduce the amount of missing vessels segments that might lead to false positives of red lesions detection in CAD systems used for diabetic retinopathy diagnosis.
A simple step-stress accelerated life testing plan with two stress variables is considered, when the failure times in each level of stress follow the lognormal distribution. The lognormal distribution is commonly used...
详细信息
A simple step-stress accelerated life testing plan with two stress variables is considered, when the failure times in each level of stress follow the lognormal distribution. The lognormal distribution is commonly used to model certain types of data that arise in several fields of engineering such as, for example, different types of lifetime data or coefficients of wear and friction. The problem of choosing the optimal times to change the stress level is investigated by minimizing the asymptotic variance of the reliability estimate and maximizing the determinant of Fisher information matrix. In this paper, we obtain the optimal bivariate step-stress accelerated life test using both the criteria. Due to the nonlinearity and complexity of problem, the particle swarm optimization algorithm is developed to calculate the optimal hold times. In this method, the research speed is very fast and the optimization ability is more. To illustrate the effect of the initial estimates on the optimal values, sensitivity analysis is performed. Finally, numerical studies are discussed to illustrate the proposed criterion. Simulation results show that the proposed optimum plan is robust.
Bike-sharing system has been launched in many cities, due to the essential merits. Along with the convenience brought by the rapid development of bike-sharing system, several severe problems also arise. The most serio...
详细信息
Bike-sharing system has been launched in many cities, due to the essential merits. Along with the convenience brought by the rapid development of bike-sharing system, several severe problems also arise. The most serious problem is the uneven distribution of bicycles. Thus the VRP model for bike-sharing inventory rebalancing and vehicle routing is formulated. Additionally, an improved particleswarmoptimization (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Five maintenance trucks are designated to execute all delivery tasks required by 25 spots. Capacities of all of the maintenance trucks are almost fully utilized. It is of considerable significance for bike-sharing enterprises to make optimal bike schedule.
暂无评论