A Method of Accuracy Reducing for Vector Data Based on Random Function

2012 ◽  
Author(s):  
Cui Hanchuan ◽  
Xu Hui ◽  
Zhu Changqing
Keyword(s):  
Random Function ◽  
Vector Data

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

دراسة الخصائص الجيومورفولوجية لطيات عين زالة ورافان وبطمة باستخدام معطيات التحسس النائي

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
ريان غازي ذنون ◽  
صهيب حميد الخفاجي
Keyword(s):  
Vector Data

استخدمت معطيات التحسس النائي في دراسة جيومورفولوجية لطيات عين زالة ورافان وبطمة شمالي العراق. بغية تحديد الاشكال الارضية وتمثيلها بهيأة خارطة جيومورفولوجية ، تم اعتماد المنهج الاستقرائي الذي يتضمن عمليتين مترابطتين هما: الملاحظة الجيولوجية وعلاقتها بالأشكال الظاهرة وذلك لتحديد السمات الجيومورفولوجية المميزة لمنطقة الدراسة، وقد استندت عملية الاستقراء هذه على نتائح التفسير البصري  للمرئية الفضائية المستخدمة في الدراسة الحالية. تم تحليل وتصنيف الاشكال الارضية في منطقة الدراسة حسب منشأها التكويني باستخدام مرئية فضائية للقمر الاصطناعي (Sentitial-2) والتي تتصف بقدرة تمييز مكانية قدرها (10) امتار. اذ تم ادخال المرئية الملونة في برنامج (ArcGis 10.3) لكي يتم تحديد الوحدات الجيومورفولوجية ورسمها من خلال اسلوب التمثيل الاتجاهي للبيانات (Vector Data). اسفرت النتائج عن تحديد عدد من الوحدات الجيومورفولوجية مثلت على خارطة معدة لهذا الغرض، وقد تم توثيق هذه الوحدات حقليا. لقد شهدت منطقة الدراسة تغيرات جوهرية في أنماط الغطاء الأرضي وتحويرا لبعض من الاشكال الجيومورفولوجية الظاهرة في المنطقة خلال الفترات الماضية الممتدة منذ الثمانينات من القرن الماضي وحتى يومنا هذ نتيجة بناء سد الموصل لذا فقد تم مراقبة التغييرات السابقة بالاستعانة بالمرئيات الفضائية المتعاقبة زمنيا والمعالجة رقميا بطريقة التصنيف الموجه وقد اسفرت النتائج عن تحديد الوضع الجيومورفولوجي للغطاء الارضي الحالي والتغيرات الحاصلة فيه بعد انشاء السد

Comment on the Security of an Order-Preserving Encryption Scheme Using Pseudo-Random Function

2016 ◽  
Vol E99.B (9) ◽  
pp. 2108-2111
Author(s):  
Minkyu KIM ◽  
Je HONG PARK ◽  
Dongyoung ROH
Keyword(s):  
Random Function ◽  
Encryption Scheme

Efficient Construction of Order-Preserving Encryption Using Pseudo Random Function

2015 ◽  
Vol E98.B (7) ◽  
pp. 1276-1283 ◽  
Author(s):  
Nam-Su JHO ◽  
Ku-Young CHANG ◽  
Do-Won HONG
Keyword(s):  
Random Function ◽  
Efficient Construction

Efficient Steganography Algorithm Based On DCT And Entropy Thresholding Technique

2018 ◽  
Vol 8 (1) ◽  
pp. 45
Author(s):  
Satvir Singh
Keyword(s):  
Relative Entropy ◽  
Experimental Work ◽  
Random Function ◽  
Signal To Noise Ratio ◽  
Binary Sequence ◽  
Threshold Value ◽  
Absolute Difference ◽  
Secret Message ◽  
Signal To Noise ◽  
Confidential Information

Steganography is the special art of hidding important and confidential information in appropriate multimedia carrier. It also restrict the detection of  hidden messages. In this paper we proposes steganographic method based on dct and entropy thresholding technique. The steganographic algorithm uses random function in order to select block of the image where the elements of the binary sequence of a secret message will be inserted. Insertion takes place at the lower frequency  AC coefficients of the  block. Before we insert the secret  message. Image under goes dc transformations after insertion of the secret message we apply inverse dc transformations. Secret message will only be inserted into a particular block if  entropy value of that particular block is greater then threshold value of the entropy and if block is selected by the random function. In  Experimental work we calculated the peak signal to noise ratio(PSNR), Absolute difference , Relative entropy. Proposed algorithm give high value of PSNR  and low value of Absolute difference which clearly indicate level of distortion in image due to insertion of secret message is reduced. Also value of  relative entropy is close to zero which clearly indicate proposed algorithm is sufficiently secure. 

Vector data contrast based on probabilistic theory and compound criteria

2010 ◽  
Vol 30 (10) ◽  
pp. 2602-2604
Author(s):  
Shun-ping ZHOU ◽  
Huai-ying LIU
Keyword(s):  
Vector Data ◽  
Probabilistic Theory

Improved algorithm for vector data compression based on dynamic programming

2008 ◽  
Vol 28 (1) ◽  
pp. 168-170 ◽  
Author(s):  
Fei-xiang CHEN ◽  
Zhi-wu ZHOU ◽  
Jian-bing ZHANG
Keyword(s):  
Dynamic Programming ◽  
Data Compression ◽  
Vector Data ◽  
Improved Algorithm

Rendering Vector Data over 3D Terrain with View-Dependent Perspective Texture-Mapping

2010 ◽  
Vol 22 (5) ◽  
pp. 753-761 ◽  
Author(s):  
Hong Chen ◽  
Xiaoan Tang ◽  
Yaohua Xie ◽  
Maoyin Sun
Keyword(s):  
Texture Mapping ◽  
Vector Data ◽  
3D Terrain

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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