Lithology identification using an optimized KNN clustering method based on entropy-weighed cosine distance in Mesozoic strata of Gaoqing field, Jiyang depression

Wang, Xidong; Yang, Shenglai; Yang, Zhao; Ya, Wang

doi:10.1016/j.petrol.2018.03.034

Cited by 82 publications

(18 citation statements)

References 8 publications

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“…The cosine distance method was used to calculate the similarity between the test set and the training set in each local block, and the recognition was accumulated at the final [13]. The facial recognition of monogenic binary coding after fusion was supposed as .…”

Section: Methodsmentioning

confidence: 99%

Facial Expression Recognition Based on Local Features and Monogenic Binary Coding

Chen¹

2019

IJCAI

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Fast developing facial expression reciognition is one of the significant recognition technologies for biological features with high applied value. In this study, a monogenic binary coding algorithm was considered to illustrate a good matching with local features through the analysis of monogenic signal theory and monogenic binary algorithm. Then, the results of the facial expression recognition simulation experiment of monogenic based classical facial expression database, the Japanese Female Facial Expression (JAFFE) Database, and the results of traditional Local Binary Patterns-Sparse Representation-based Classification (LBP-SRC) residual fusion method were compared to illustrate the efficiency of the monogenic binary coding algorithm in the aspect of facial recognition and to provide a basis for the application of the monogenic signal theory in facial expression. Povzetek: Opisana je nova varianta algoritma za hitro prepoznavanje izrazov na obrazu .

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Section: Methodsmentioning

confidence: 99%

Facial Expression Recognition Based on Local Features and Monogenic Binary Coding

Chen¹

2019

IJCAI

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“…One type is the KNN algorithm for improving distance. The method of cosine KNN combined with entropy proposed by Wang et al [27] in 2018 is an example of this type of improvement. The second type of improvement method is based on the feature-weighted KNN algorithm, as the original feature weighting algorithm was not sufficiently accurate [28].…”

Section: Nearest Neighbor (Nn)mentioning

confidence: 99%

Performance Evaluation of Machine Learning in Wireless Connected Robotics Swarms

et al. 2020

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The transmission and negotiation of the robot swarms and the technical support provided by the communication technology are inseparable, and modulation recognition plays an important role in this transmission. Due to the diversity of current classifiers, choosing the correct classifier to improve the classification effect has become a key issue. To this end, this paper explores the performance of different classifiers in modulation recognition, selects six classifier families, and compares a total of 77 different single classifiers on the modulation dataset, which is implemented on the following three platforms: Weka, Python and MATLAB. The results show that the strong classifiers formed by the combination of weak classifiers is very effective, and Boosting, Bagging, and Random Forest are the three best classifier families. In addition, it was found that as the signal-to-noise ratio (SNR) increases, the overall performance of the classifier families gradually improves, but the ranking of the families performance remains consistent.

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“…W (1) represents the weight matrix between the input layer and the hidden layer. W (2) is the weight matrix between the hidden layer and the output layer. b (1) and b (2) are the bias vectors of the hidden and output layers, respectively.…”

Section: Sparse Autoencodermentioning

confidence: 99%

“…In the last few decades, a variety of land cover classification algorithms have been proposed [2]. They can be broadly categorized into supervised approaches, semi-supervised approaches, and unsupervised approaches according to whether manual annotations are utilized.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

et al. 2018

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Lithology classification is a crucial step in the prospecting process, and polarimetric synthetic aperture radar (Pol-SAR) imagery has been extensively used for it. However, despite significant improvements in both information content of Pol-SAR imagery and advanced classification approaches, lithology classification using Pol-SAR data may not provide satisfactory classification accuracy due to high similarity of certain classes. In this paper, a novel Pol-SAR lithology classification method based on a stacked sparse autoencoder (SSAE) is proposed. By using superpixel segmentation, new features can be extracted from dual-frequency Pol-SAR data, which can increase the class separability of the input data. Then, these features and the coherency matrices are incorporated into SSAE to classify the lithology. The classification performance is evaluated on an SIR-C dataset acquired over Xinjiang, China. The experimental result shows that this method is effective for lithology classification and can improve the overall accuracy up to 98.90%.

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Lithology identification using an optimized KNN clustering method based on entropy-weighed cosine distance in Mesozoic strata of Gaoqing field, Jiyang depression

Cited by 82 publications

References 8 publications

Facial Expression Recognition Based on Local Features and Monogenic Binary Coding

Facial Expression Recognition Based on Local Features and Monogenic Binary Coding

Performance Evaluation of Machine Learning in Wireless Connected Robotics Swarms

Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

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