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Full-Text Articles in Engineering
Discussion On Identification Standard Of Coal-Measure Graphite, Cao Daiyong, Wang Lu, Zhu Wenqing, Wu Guoqiang, Wei Yingchun, Ning Shuzheng, Wang Guixiang, Xiao Jincheng, Xu Xiang, Liu Kang
Discussion On Identification Standard Of Coal-Measure Graphite, Cao Daiyong, Wang Lu, Zhu Wenqing, Wu Guoqiang, Wei Yingchun, Ning Shuzheng, Wang Guixiang, Xiao Jincheng, Xu Xiang, Liu Kang
Coal Geology & Exploration
The identification and classification of coal-measure graphite are the precondition and basic work of mineral geological exploration, as well as resource exploitation and utilization. The preparation of identification standards of coal-measure graphite should follow the principles of science, systematicness, applicability and operability. Coal graphitization and coalification is a continuous progression plus jump process, and the characteristics of coal-measure graphite, including the step evolution of macromolecular structure and the heterogeneity of material composition, leads to the complexity of ore identification. In order to establish the scientific and practical identification standards, and from the study of metallogenic mechanism and the demand of …
Analysis Of Patch And Sample Size Effects For 2d-3d Cnn Models Using Multiplatform Dataset: Hyperspectral Image Classification Of Rosis And Jilin-1 Gp01 Imagery, Taşkin Kavzoğlu, Eli̇f Özlem Yilmaz
Analysis Of Patch And Sample Size Effects For 2d-3d Cnn Models Using Multiplatform Dataset: Hyperspectral Image Classification Of Rosis And Jilin-1 Gp01 Imagery, Taşkin Kavzoğlu, Eli̇f Özlem Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
Modern hyperspectral sensors provide a huge volume of data at spectral and spatial domains with high redundancy, which requires robust methods for analysis. In this study, 2D and 3D CNN models were applied to hyperspectral image datasets (ROSIS and Jilin-1 GP01) using varying patch and sample sizes to determine their combined impacts on the performance of deep learning models. Differences in classification performances in relation to particle and sample sizes were statistically analysed using McNemar?s test. According to the findings, raising the patch and sample size enhances the performance of the 2D/3D CNN model and produces more accurate results in …
Classification Of Special Vehicles On The Basis Of Commodity Nomenklature Of Foreign Economic Activity, K M. Karimkulov, U R. Khamroev, G R. Khamroev
Classification Of Special Vehicles On The Basis Of Commodity Nomenklature Of Foreign Economic Activity, K M. Karimkulov, U R. Khamroev, G R. Khamroev
Technical science and innovation
This article classifies the types of special vehicles as a commodity due to various technical changes in the technical and specific parameters of special vehicles, insufficient accuracy of attachment and parameters of additional vehicles. detected.As a result of the growing demand for special vehicles, the attachment of additional parts (exterior and interior design changes) by manufacturing plants has led to the classification of this vehicle into another category.As a result of the analysis, we can see in the analysis that from January 1, 2019 to 2022, a total of 11 others (others) CN FEA on 8704 commodity groups imported 8364 …
Classification And Phenological Staging Of Crops From In Situ Image Sequences By Deep Learning, Uluğ Bayazit, Deni̇z Turgay Altilar, Ni̇lgün Güler Bayazit
Classification And Phenological Staging Of Crops From In Situ Image Sequences By Deep Learning, Uluğ Bayazit, Deni̇z Turgay Altilar, Ni̇lgün Güler Bayazit
Turkish Journal of Electrical Engineering and Computer Sciences
Accurate knowledge of crop type information is not only valuable for verifying the declaration of farmers to obtain subsidy or insurance for the grown crop, but also for generating crop type maps that serve a variety of purposes in land monitoring and policy. On the other hand, accurate knowledge of crop phenological stage can help farm personnel apply fertilization and irrigation regimes on a timely basis. Although deep learning based networks have been applied in the past to classify the type and predict the phenological stage of crops from in situ images of fields, more advanced deep learning based networks, …
Identification Of Intrusive Massifs In The Nurata Mineralized Zones Based On Satellite Images, Samariddin Rabbimkulov, Abdulla Almordonov, Akmal Asadov, Abdimutal Tangirov
Identification Of Intrusive Massifs In The Nurata Mineralized Zones Based On Satellite Images, Samariddin Rabbimkulov, Abdulla Almordonov, Akmal Asadov, Abdimutal Tangirov
Technical science and innovation
The article presents the results of processing space images using PCA, ISODATA, K-Means and similar methods, as well as analysis based on GIS technologies. As a result of automatic and visual interpretation of the obtained images, intrusive complexes, linear and ring structures scattered throughout the Nurata region were identified. In particular, the result obtained by X-ray diffraction analysis proved that the composition of the 2R4G1B and 2R4G3B channels is one of the most effective methods for separating the intrusive massifs distributed in the region. Given that isolated intrusive massifs, linear and ring structures are directly related to gold mining zones …
The Analysis And Optimization Of Cnn Hyperparameters With Fuzzy Tree Modelfor Image Classification, Kübra Uyar, Şaki̇r Taşdemi̇r, İlker Ali̇ Özkan
The Analysis And Optimization Of Cnn Hyperparameters With Fuzzy Tree Modelfor Image Classification, Kübra Uyar, Şaki̇r Taşdemi̇r, İlker Ali̇ Özkan
Turkish Journal of Electrical Engineering and Computer Sciences
The meaningful performance of convolutional neural network (CNN) has enabled the solution of various state-of-the-art problems. Although CNNs achieve satisfactory results in computer-vision problems, they still have some difficulties. As the designed CNN models are deepened to achieve much better accuracy, computational cost and complexity increase. It is significant to train CNNs with suitable topology and training hyperparameters that include initial learning rate, minibatch size, epoch number, filter size, number of filters, etc. because the initialization of hyperparameters affects classification results. On the other hand, it is not possible to make a definite inference for the hyperparameter initialization and there …
Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed
Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed
Turkish Journal of Electrical Engineering and Computer Sciences
Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and double density dual tree DWT (discrete wavelet transform) coefficients are derived from the segmented region. The derived features are reduced further shortened …