Soft Computing in Remote Sensing Applications

Kumar, Anil; Krishnan, Rahul; Chakravarthi, Bharatesh; Deekshatalu, B. L.

doi:10.1007/s40010-017-0431-0

Cited by 6 publications

(3 citation statements)

References 109 publications

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“…Soft computing achieves low-cost solutions and robustness by tolerating uncertainties, inaccuracies, and incomplete truth values [27]. To efficiently complete daily duties, it imitates the molecular mechanisms of intelligent systems seen in nature (human perception, brain structure, evolution, immunity, etc.)…”

Section: Prediction Problems In Semiconductor Schedulingmentioning

confidence: 99%

PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems

Yu,

Jiang,

Zhang

et al. 2023

Applied Sciences

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There are currently many studies on data-driven optimization scheduling, but only a few studies have combined “closed-loop optimization” with “performance-driven”. Therefore, this research proposed a PSO-SVM-based (particle swarm optimization optimized support vector machine) scheduling method that reconciles the composite dispatching rules (CDR), performance-driving ideology, and feedback mechanism ideology. Firstly, the composite dispatching rules coalesce flexible equipment maintenance, multiple process constraints, and dynamic dispatching. Secondly, the performance-driving ideology is carried out through two learning models based on the PSO-SVM algorithm, based on targeted optimizing performances. Thirdly, the feedback mechanism ideology makes the scheduling method realize closed-loop optimizations adaptively. Finally, the superiority of the proposed scheduling method is validated in a semiconductor manufacturing system in China. Compared with CDR, the proposed scheduling method combines MOV, PR, and EU, respectively EU_ O, EU_ P, PCSR and ODR increased by 7.85%, 5.11%, 8.76%, 8.14%, 6.60%, and 7.33%, indicating the superiority of this method.

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Section: Prediction Problems In Semiconductor Schedulingmentioning

confidence: 99%

PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems

Yu,

Jiang,

Zhang

et al. 2023

Applied Sciences

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show abstract

“…(3) and (4) respectively, when D ik = ∥ x k − v i ∥ for all i and k > 1 , X contains at least c distinct data points and min J MPCM (U, V) is optimized, Eq. 3and (4).…”

Section: Fuzzy Based Modified Possibilistic C-means (Mpcm)mentioning

confidence: 99%

“…For information extraction, remote sensing images are classified into different classes using hard and soft classification techniques [4,5]. Hard classification techniques classify images into crisp classes and are applied for classifying homogenous pixels (i.e., pixels having only one class).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

2021

Self Cite

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This study presents a fuzzy approach, for detection of transitioned building footprints in urban area using medium resolution datasets. Multi-temporal remote sensing data sets from Landsat-8 Operational Land Imager and Sentinel-2A were used for generation of temporal indices database. The database was generated using class-based sensor independent-normalized difference vegetation index approach, with an aim to reduce spectral dimensionality of each image and maintain temporal dimensionality. The temporal indices database was subsequently used as input in Modified Possibilistic c-means classifier for transitioned building footprints extraction. The identified transitioned building locations were validated using ground samples as well as from Google images at four different test sites. For accuracy assessment, F-measure was calculated and its value was 0.75 or higher for all training and testing sites. Thus, using proposed fuzzy approach, transitioned building footprints were accurately identified compared to traditional techniques.

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A comparative study of the bivariate, multivariate and machine-learning-based statistical models for landslide susceptibility mapping in a seismic-prone region in China

et al. 2021

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Soft Computing in Remote Sensing Applications

Cited by 6 publications

References 109 publications

PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems

PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems

Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

A comparative study of the bivariate, multivariate and machine-learning-based statistical models for landslide susceptibility mapping in a seismic-prone region in China

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