scholarly journals Conductivity Classification of Non-Magnetic Tilting Metals by Eddy Current Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2608 ◽  
Author(s):  
Yue Du ◽  
Zhijie Zhang ◽  
Wuliang Yin ◽  
Shuang Zhu ◽  
Ziqi Chen ◽  
...  
Keyword(s):  
Eddy Current ◽  
Convenient Method ◽  
Mutual Inductance ◽  
Classification Algorithm ◽  
Phase Compensation ◽  
Classification Rate ◽  
Industrial Economy ◽  
Waste Classification ◽  
Resource Reuse

Metallic waste classification benefits the environment, resource reuse and industrial economy. This paper provides a fast, non-contact and convenient method based on eddy current to classify metals. The characteristic phase to characterize different conductivity is introduced and extracted from mutual inductance in the form of amplitude and phase. This characteristic phase could offer great separation for non-tilting metals. Although it is hard to classify tilting metals by only using the characteristic phase, we propose the technique of phase compensation utilizing photoelectric sensors to obtain the rectified phase corresponding to the non-tilting situation. Finally, we construct a classification algorithm involving phase compensation. By conducting a test, a 95 % classification rate is achieved.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

Design and first result of combined Langmuir-magnetic probe on J-TEXT tokamak

2021 ◽  
Author(s):  
Qinghu Yang ◽  
Zhipeng Chen ◽  
Zhigang Hao ◽  
Yangming Zhao ◽  
Xin Xu ◽  
...  
Keyword(s):  
Eddy Current ◽  
Magnetic Surface ◽  
Mutual Inductance ◽  
Magnetic Probe ◽  
Shielding Effect ◽  
Magnetic Fluctuations ◽  
Electromagnetic Mode ◽  
Pneumatic Device ◽  
Magnetic Fluctuation ◽  
Electromagnetic Signals

Abstract In order to measure boundary electrostatic and magnetic fluctuations simultaneously, a set of combined Langmuir-magnetic probe (CLMP) has been designed and built on Joint-Texas Experimental Tokamak (J-TEXT). The probe consists of 8 graphite probe pins and a 3D magnetic probe, driven by a mechanical pneumatic device. By means of simulation, the shielding effect of the graphite sleeve on the magnetic fluctuation signal is explored, and the influence of the eddy current was reduced by cutting the graphite sleeve. In the experiment, it has been verified that the mutual inductance of electromagnetic signals can be ignored. And a 70~90kHz electromagnetic mode is observed around the last closed magnetic surface (LCFS). The establishment of CLMP provides data for the exploration of the coupling of electrostatic and magnetic fluctuations.

Classification method of marine target motion pattern based on spatial-temporal trajectories

2021 ◽  
pp. 1-15
Author(s):  
Baichen Jiang ◽  
Wei Zhou ◽  
Jian Guan ◽  
Jialong Jin
Keyword(s):  
Pattern Classification ◽  
Training Sample ◽  
Classification Algorithm ◽  
Classification Model ◽  
Motion Pattern ◽  
Motion Patterns ◽  
Lp Norm ◽  
Sparse Representation Classification ◽  
Route Safety

Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-lp norm minimization. The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses lp norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.

Study, design and manufacture eddy current probes for industry applications

2021 ◽  
Vol 6 (2) ◽  
pp. 52-64
Author(s):  
Phuc Nguyen ◽  
Van Thuy Nguyen ◽  
Binh Duong Vuong ◽  
Minh Duc Do ◽  
Dinh Truong Trinh ◽  
...  
Keyword(s):  
Study Design ◽  
Eddy Current ◽  
Analytical Methods ◽  
Mode Of Operation ◽  
Industry Applications ◽  
Design And Manufacture

This study is based on the studying, designing and manufacturing of eddy current probes for industry applications. The main tasks of this study include: + Describes the overview and classification of eddy current probes (which can be classified into three categories based on the mode of operation: absolute eddy current probe, differential eddy current probe and reflect eddy current probe). + Describes the three methods of probe designing and manufacturing (including experimental, analytical and numerical designs). + Describes the designing and manufacturing of eddy current probes for industry applications, which based on experimental and analytical methods. Based on this study, we have successfully manufactured some current probes (including absolute eddy current probe, differential eddy current probe and reflect eddy current probe) for surface and tube inspections.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals A High Performace of Local Binary Pattern on Classify Javanese Character Classification

2018 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Ajib Susanto ◽  
Daurat Sinaga ◽  
Christy Atika Sari ◽  
Eko Hari Rachmawanto ◽  
De Rosal Ignatius Moses Setiadi
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Characteristic Extraction ◽  
Research Objects ◽  
Character Classification

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

A Study on Classification Algorithm of Rectangle Curved Hull Plates for Plate Fabrication

2016 ◽  
Vol 32 (03) ◽  
pp. 166-173
Author(s):  
ChanSuk Kim ◽  
Jong Gye Shin ◽  
Eungkon Kim ◽  
YangRyul Choi
Keyword(s):  
Classification Algorithm ◽  
Classification Methods ◽  
Boundary Curvature

Since a ship's hull consists of various curved plates, different fabrication methods are applied for efficient fabrication works of curved hull plates. Currently, the classification methods largely rely on division resolution and thus lead to insufficient reliability. This article proposes four standard shapes for fabrication by calculating boundary curvature of each curved plate so that same curvature areas could be acquired. Some examples are carried out for the classification of curved hull plates.

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