Mineral deposit grade assessment using a hybrid model of kriging and generalized regression neural network

Singh, Rahul Kumar; Ray, Dipankar; Sarkar, B. C.

doi:10.1007/s00521-022-06951-w

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…In the task of image segmentation, the model based on the full convolutional network [20] has become the mainstream method to solve the problem of semantic segmentation. Among them, a series of models [21] constantly refresh the optimal results of semantic segmentation problems. e proposed GCN model points out the necessity of large convolution kernels and large receptive fields in semantic segmentation tasks [22].…”

Section: Related Workmentioning

confidence: 99%

Visual Information Computing and Processing Model Based on Artificial Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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This paper analyzes the parallel and serial information processing structure of visual system and proposes a visual information processing model with three layers: visual receptor layer, visual information conduction and relay layer, and information processing layer of visual information computing and processing area. Based on the analysis, abstraction, and simplification of the biological prototype of each layer in the visual system, a framework model of an artificial neural system corresponding to the visual system is proposed. An artificial neural network model is proposed to simulate the mechanism of visual attention. A network model is formed by introducing the saliency mask map as additional information on the benchmark network, and the selective enhancement operation is performed on the extracted features in different regions according to the mask map. The experimental results show that the visual computing processing network model can effectively improve the classification performance of the network when the appropriate saliency mask is used. The visual information computing and processing model network can work effectively for different data sets and different structures of the benchmark network, which is a universal network model. The complexity of visual information computing and processing model network is very small, and the improvement of network performance is not at the cost of increasing model complexity, but in the way of improving network efficiency. The performance of artificial neural network visual information computation and processing model is directly related to the performance of saliency map used as mask map.

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Section: Related Workmentioning

confidence: 99%

Visual Information Computing and Processing Model Based on Artificial Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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“…George Matheron further provided a detailed theoretical framework, initially proposed by Krige, on linear estimators for interpolation through the theory of regionalized variables published in [2]. Geostatistics techniques have been widely employed in various applications, including mining engineering [3][4][5][6], environmental sciences [7][8][9][10], and meteorology [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation

Supajaidee,

Chutsagulprom,

Moonchai

2024

Algorithms

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Ordinary kriging (OK) is a popular interpolation method for its ability to simultaneously minimize error variance and deliver statistically optimal and unbiased predictions. In this work, the adaptive moving window kriging with K-means clustering (AMWKK) technique is developed to improve the estimation obtained from the moving window kriging based on the K-means clustering proposed by Abedini et al. This technique specifically addresses the challenge of selecting appropriate windows for target points located near the borders, which can potentially be the source of errors. The AMWKK algorithm introduces a dynamic clustering approach within the moving window kriging, where each target site sequentially serves as a cluster centroid. The OK is then applied within the cluster encompassing the target point, ensuring localized and adaptive interpolation. The proposed method is compared with ordinary kriging and other moving window kriging variant approaches to estimate Thailand’s mean annual pressure and humidity in 2018. The results indicate superior estimation capabilities of the AMWKK approach in terms of distinct quantitative performance statistics. The advantage of using the AMWKK method for spatial interpolation can be attributed to the fact that it facilitates the automatic tuning of the window size at any estimation point. The algorithm is particularly effective when observations in the same cluster as target points are sparse.

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Geostatistics in Exploration and Mining

Sarkar

2023

Springer Proceedings in Earth and Environmental Sciences

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Mineral deposit grade assessment using a hybrid model of kriging and generalized regression neural network

Cited by 3 publications

References 35 publications

Visual Information Computing and Processing Model Based on Artificial Neural Network

Visual Information Computing and Processing Model Based on Artificial Neural Network

An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation

Geostatistics in Exploration and Mining

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