DCSCF Detection Method of MMC-DC-Grid Based on Statistical Feature and DBN-SOFTMAX

2021 ◽  
Author(s):  
Yuyu Zheng ◽  
Meiqin Mao ◽  
Liuchen Chang
Keyword(s):  
Detection Method ◽  
Statistical Feature ◽  
Grid Based

scholarly journals Islanding detection method of multi‐port photovoltaic DC micro grid based on harmonic impedance measurement

2019 ◽  
Vol 13 (14) ◽  
pp. 2604-2611 ◽  
Author(s):  
Jia Ke ◽  
Zhu Zhengxuan ◽  
Zhao Qijuan ◽  
Yang Zhe ◽  
Bi Tianshu
Keyword(s):  
Detection Method ◽  
Impedance Measurement ◽  
Islanding Detection ◽  
Micro Grid ◽  
Grid Based

scholarly journals Sistem Deteksi Cacat Kayu dengan Metode Deteksi Tepi SUSAN dan Ekstraksi Ciri Statistik

2018 ◽  
Vol 6 (1) ◽  
pp. 140
Author(s):  
PUTU DEBBY WANANDA ◽  
LEDYA NOVAMIZANTI ◽  
RATRI DWI ATMAJA
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Human Error ◽  
Detection Method ◽  
Second Order ◽  
Wood Products ◽  
Basic Material ◽  
Second Moment ◽  
Statistical Feature ◽  
Edge Detection Method

ABSTRAKKayu menjadi suatu bahan dasar untuk menghasilkan berbagai macam jenis produk olahan kayu. Untuk menghasilkan produk olahan kayu dengan kualitas tinggi, dengan ketahanan produk yang kuat, dan umur dari produk olahan kayu tersebut dapat bertahan lama maka diperlukan bahan dasar kayu yang berkualitas dalam artian tanpa cacat sebagai bahan dasarnya. Pada penelitian ini telah dirancang sebuah sistem pendeteksian kayu untuk mengklasifikasikan kayu normal (tanpa cacat) dan kayu rusak dengan metode deteksi tepi SUSAN dan ekstraksi ciri statistik orde kedua, dengan tingkat akurasi sebesar 90,67% dan waktu komputasi 2,5 detik. Sehingga mengurangi adanya human error dan efisiensi waktu dalam pensortiran. Parameter nilai threshold (t) = 0,1 pada metode deteksi tepi SUSAN, dan ciri angular second moment (ASM), correlation, variance, dan inverse different moment (IDM) pada metode ekstraksi ciri statistik orde kedua, memberikan hasil optimal dalam sistem ini.Kata kunci: cacat kayu, deteksi tepi SUSAN, ekstraksi ciri statistikABSTRACTWood becomes a basic material to produce various types of wood processing products. To produce high quality processed wood products, with robust product durability, and long life of the processed wood products can last a long time it takes quality wood base material in the sense without flaw as the basic material. In this research, we have designed a wood detection system to classify normal wood (without defects) and damaged wood with SUSAN edge detection method and second order statistic extraction with accuracy of 90.67% and computation time 2.5 seconds. Thus reducing human error and time efficiency in sorting. The threshold value parameter (t) = 0.1 on the SUSAN edge detection method, and angular second moment (ASM), correlation, variance, and inverse different moment (IDM) characteristics in second order statistical feature extraction methods, gives optimal results in this system.Keywords: wood defect, SUSAN edge detector, statistical feature extraction

scholarly journals Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method

2019 ◽  
Vol 9 (18) ◽  
pp. 3915
Author(s):  
Zhenyu Zhang ◽  
Hsi-Hsien Wei ◽  
Sang Guk Yum ◽  
Jieh-Haur Chen
Keyword(s):  
Data Collection ◽  
Object Detection ◽  
Detection Method ◽  
Gray Level ◽  
Structural Elements ◽  
Structural Element ◽  
Shape Information ◽  
Statistical Feature ◽  
Feature Based ◽  
Gray Level Histogram

Automatic object-detection technique can improve the efficiency of building data collection for semi-empirical methods to assess the seismic vulnerability of buildings at a regional scale. However, current structural element detection methods rely on color, texture and/or shape information of the object to be detected and are less flexible and reliable to detect columns or walls with unknown surface materials or deformed shapes in images. To overcome these limitations, this paper presents an innovative gray-level histogram (GLH) statistical feature-based object-detection method for automatically identifying structural elements, including columns and walls, in an image. This method starts with converting an RGB image (i.e. the image colors being a mix of red, green and blue light) into a grayscale image, followed by detecting vertical boundary lines using the Prewitt operator and the Hough transform. The detected lines divide the image into several sub-regions. Then, three GLH statistical parameters (variance, skewness, and kurtosis) of each sub-region are calculated. Finally, a column or a wall in a sub-region is recognized if these features of the sub-region satisfy the predefined criteria. This method was validated by testing the detection precision and recall for column and wall images. The results indicated the high accuracy of the proposed method in detecting structural elements with various surface treatments or deflected shapes. The proposed structural element detection method can be extended to detecting more structural characteristics and retrieving structural deficiencies from digital images in the future, promoting the automation in building data collection.

Solid-phase immunoelectron microscopy for rapid diagnosis of enteroviruses

1984 ◽  
Vol 42 ◽  
pp. 226-227 ◽  
Author(s):  
K. Pegg-Feige ◽  
F. W. Doane
Keyword(s):  
Electron Microscopy ◽  
Cell Culture ◽  
Immunoelectron Microscopy ◽  
Detection Method ◽  
Solid Phase ◽  
Protein A ◽  
Plant Viruses ◽  
Rapid Diagnosis ◽  
Virus Diagnosis ◽  
Antiviral Antibody

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.

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