Measurement of lumber moisture content based on PCA and GS-SVM

Zhang, Jiawei; Song, Wenlong; Bin, Jiang; Li, Mingbao

doi:10.1007/s11676-017-0448-x

Cited by 27 publications

(15 citation statements)

References 38 publications

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“…In this study, different types of data combinations above were experimentally simulated. The characteristic indexes of the first and second principal components were the horizontal and vertical axis, respectively [39]. The experimental results are shown in Figs.…”

Section: B Simulation and Resultsmentioning

confidence: 99%

Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

et al. 2020

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As a popular clustering algorithms, fuzzy c-means (FCM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and the local minimum problem of the fuzzy c-means new algorithm is proposed based on the simulated annealing (SA) algorithm and the genetic algorithm (GA). The combined algorithm utilizes the simulated annealing algorithm due to its local search abilities. Thereby, problems associated with the genetic algorithm, such as its tendency to prematurely select optimal values, can be overcome, and genetic algorithm can be applied in fuzzy clustering analysis. Moreover, the new algorithm can solve other problems associated with the fuzzy clustering algorithm, which include initial clustering center value sensitivity and convergence to a local minimum. Furthermore, the simulation results can be used as classification criteria for identifying several types of bearing faults. Compare with the dimensionless indexes, it shows that the mutual dimensionless indexes are more suitable for clustering algorithms. Finally, the experimental results show that the method adopted in this paper can improve the accuracy of clustering and accurately classify the bearing faults of rotating machinery. INDEX TERMS Rotating machinery, Mutual dimensionless indexes, Fuzzy c-means clustering algorithm, Genetic algorithm, Simulated annealing algorithm.

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Section: B Simulation and Resultsmentioning

confidence: 99%

Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

et al. 2020

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“…The Calinski criterion can be calculated using (2). The larger it is, the better the clustering effect.…”

Section: A K-means Clustering Methodsmentioning

confidence: 99%

“…Given the difficulty in collecting location data and changing the site of a facility once it has been determined, we carefully planned the research process [1], [2]. This study first obtained POI (point of interest) data from the API (Application Programming Interface) of Autonavi (one of the most famous map companies in China) and then collected and calculated the location data using the powerful spatial analysis and processing function of GIS (geographic information system) software.…”

Section: The Subject and Scope Of Internet Diagnosis And Treatment Inmentioning

confidence: 99%

Study on the Location of Private Clinics Based on K-Means Clustering Method and an Integrated Evaluation Model

Wang

Shao

et al. 2020

IEEE Access

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Appropriate location is an important prerequisite for the long-term survival and development of private medical institutions. However, in both theory and practice, the issue of location decision-making for private clinics has not been fully studied. We therefore aimed to provide a feasible scheme for the location of new private clinics. This paper combines the k-means clustering method and an integrated 2DULVs (twodimensional uncertain language variables)-TOPSIS (technique for order preference by similarity to ideal solution)-DSCCR (Dempster-Shafer conjunctive combination rule) model to screen and evaluate all of the areas in the target region for an Internet medical company to set up offline clinics. We first created geographic grids using GIS and collected point of interest (POI) data. We then used the k-means clustering method to obtain 10 suitable grids as alternatives. Last, we established an evaluation index system and used the proposed model to rank them. The results show that grids 178, 179 and 202 are more suitable for the company to establish offline clinics in the expansion of business. The results of this study are also consistent with those of the other three fusion methods. This paper provides a beneficial attempt for private clinics to make location decisions and can be extended to the strategic decision-making of other industries or other issues.

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“…Parameters to be optimized include the penalty parameter C and kernel function parameter γ for the Radial Basis Function (RBF) [25]. Various heuristic optimization algorithms have been proposed in recent years to optimize SVM parameters for improved classification and prediction accuracy, including the Grid Search (GS) algorithm [26], [27], Genetic Algorithm (GA) [28], [29], Particle Swarm Optimization (PSO) algorithm [30], [31], Fruit Fly Optimization Algorithm (FOA) [32], [33], Ant Colony Optimization (ACO) algorithm [34], and Gravitational Search Algorithm (GSA) [35], [36].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems

Xue

Bai

et al. 2019

IEEE Access

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The increasingly serious haze problem in China has brought about a growing public awareness of air quality. Precise air quality index (AQI) forecasts play an important role in both controlling air pollution and promoting the sustainable development of human society. However, the randomness, non-stationarity, and irregularity of the AQI series make its classifications very difficult. This paper introduces a time-varying inertia weighting (TVIW) strategy based on a combination of gravitation search algorithm (GSA) and particle swarm optimization (PSO) called the TVIW-PSO-GSA. The TVIW-PSO-GSA is utilized to optimize the penalty parameter C and kernel function parameter γ of a support vector machine (SVM) to create a hybrid TVIW-PSO-GSA-SVM algorithm. Twenty-three benchmark functions, five UCI datasets, and an AQI hierarchical classification example are tested to find that the convergence speed and performance of the TVI-PSO-GSA exceed those of other algorithms, and the TVIW-PSO-GSA-SVM algorithm also achieves higher classification accuracy and efficiency than the PSO-GSA-SVM, GSA-SVM, GA-SVM, or PSO-SVM, which indicates that the TVIW-PSO-GSA-SVM reliably and accurately classifies AQI and UCI datasets.INDEX TERMS Intelligent optimization algorithm, GSA, PSO, time-varying inertia weighting strategy, SVM.

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Measurement of lumber moisture content based on PCA and GS-SVM

Cited by 27 publications

References 38 publications

Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

Study on the Location of Private Clinics Based on K-Means Clustering Method and an Integrated Evaluation Model

A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems

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