Curve Representation Using Principal Component Analysis for Shape Optimization of Path Generating Mechanisms

2005 ◽  
Author(s):  
Nassim Khaled ◽  
Ahmad Smaili
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Simulated Annealing ◽  
Shape Optimization ◽  
Principal Component ◽  
Component Analysis ◽  
Path Generation ◽  
Starting Point ◽  
Curve Representation ◽  
Generation Mechanisms

The focus of this paper is on the synthesis of path generation mechanisms based on shape optimization. The principle component analysis (PCA) technique used in image processing is employed to represent the desired coupler curve of the mechanism and simulated annealing is used as the optimization tool. PCA representation is invariant under rotation, translation, scaling, and starting point. Once a shape-optimized mechanism is found, it is translated, rotated, and scaled to its final form. An illustrative example is introduced to demonstrate the proposed method.

scholarly journals A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

2019 ◽  
Vol 9 (22) ◽  
pp. 4733
Author(s):  
Cuiping Shao ◽  
Huiyun Li ◽  
Zheng Wang ◽  
Jiayan Fang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Error Resilience ◽  
Component Analysis ◽  
Cmos Technology ◽  
Error Correcting Codes ◽  
Memory Storage ◽  
Protection Scheme ◽  
Bit Flip

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

A Shape Parameterization Method Using Principal Component Analysis in Applications to Parametric Shape Optimization

2014 ◽  
Vol 136 (12) ◽  
Author(s):  
Kazuo Yonekura ◽  
Osamu Watanabe
Keyword(s):  
Principal Component Analysis ◽  
Shape Optimization ◽  
Parametric Optimization ◽  
Design Space ◽  
Optimization Algorithms ◽  
Principal Component ◽  
Component Analysis ◽  
Parameterization Method ◽  
Shape Parameterization ◽  
Set Of Points

This paper proposes a shape parameterization method using a principal component analysis (PCA) for shape optimization. The proposed method is used as a preprocessing tool of parametric optimization algorithms, such as genetic algorithms (GAs) or response surface methods (RSMs). When these parametric optimization algorithms are used, the number of parameters should be small while the design space represented by the parameters should be able to represent a variety of shapes. In order to define the parameters, PCA is applied to shapes. In many industrial fields, a large amount of data of shapes and their performance is accumulated. By applying PCA to these shapes included in a database, important features of the shapes are extracted. A design space is defined by basis vectors which are generated from the extracted features. The number of dimensions of the design space is decreased without omitting important features. In this paper, each shape is discretized by a set of points and PCA is applied to it. A shape discretization method is also proposed and numerical examples are provided.

scholarly journals IDENTIFIKASI TIGA KELOMPOK IKAN BELIDA (Chitala lopis) DI SUNGAI TULANG BAWANG, KAMPAR, DAN KAPUAS DENGAN PENDEKATAN BIOMETRIK

2017 ◽  
Vol 13 (2) ◽  
pp. 87
Author(s):  
Mas Tri Djoko Sunarno
Keyword(s):  
Principal Component Analysis ◽  
Sampling Site ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Station ◽  
Type Specimens ◽  
Purposive Sampling ◽  
Starting Point ◽  
Mouth Width ◽  
Peduncle Length

Aktivitas penangkapan lebih (over fishing), penggunaan alat tangkap yang tidak ramah lingkungan, dan perubahan kondisi lingkungan perairan menyebabkan kelestarian ikan belida (Chitala lopis) menjadi terancam. Untuk itu, diperlukan upaya konservasi yang tepat untuk melestarikan ikan ini. Tahap awal adalah melalui penelitian morfologi. Tujuan penelitian adalah mengidentifikasi keragaman jenis ikan belida di Sungai Tulang Bawang (Lampung), Kampar (Riau), dan Kapuas (Kalimantan Barat) melalui variasi bentuk tubuh dan karakter morfologi pembeda. Penentuan lokasi pengambilan contoh dilakukan secara purposive sampling. Untuk setiap spesies pengambilan contoh per lokasi berkisar antara 10 sampai dengan 30 spesimen. Contoh ditandai (tagging) dituliskan kode spesimen dan lokasi kemudian diawetkan dengan direndam larutan alkohol 75%. Pengukuran spesimen dengan digital kaliper di sisi tubuh sebelah kiri, pada 28 karakter morfologi. Data yang diperoleh distandarisasi, disajikan dalam % SL dan % HL yang merupakan subyek principal component analysis menggunakan Statistik 6.0. Tahap ke-2, menggunakan analysis diskriminan untuk mengisolasi ke tipe spesimen tadi menjadi kelompok yang terpisah, melihat karakter morfologi dominan (factor score coefficient) akhirnya hanya 1 karakter yang paling dominan. Terdapat 3 kelompok ikan belida yang memperlihatkan penampilan morfologi yang berbeda, dari ke-3 lokasi yang diamati. Pembeda ke-3 kelompok ikan belida di 3 sungai tersebut adalah peduncle length (tinggi punguk) (% HL) dan mouth width (lebar mulut) (% SL). Over fishing activities, implementation of unfriendly environmental gears and altered aquatic environment condition have endangered the feather fish (Chitala lopis). Therefore appropriate conservation efforts will be needed and research on morphology variance can be the starting point. The objective of research is to indentify the diversity of feather fish in Tulang Bawang (Lampung), Kampar (Riau), and Kapuas (Kalimantan Barat) rivers through body shape variations and it’s main morphology characters. Sampling station were chosen based on purposive sampling. Whatever possible, the number of samples range between 10 to 30 in every station sampling. Samples were tagged with specimen code and location, and then preserve using alcohol 75%. Measurements were made manually using dial calipers correct to tenth milimetre. Measurements were made on the left side of body, 28 point to point measurements. These characters were standardized, perform in % SL and % HL subject to principal component analysis using Statistica 6.0. Futher analysis using discriminant analysis to isolate 3 type specimens, find out the dominant characters, and finally see the most dominance characters. There are 3 groups of feather fish’s that performed different morphology characters from the sampling site, where as peduncle length (% HL) and mouth width (% SL) were the dominance characters.

Principal component analysis in medical image processing: a study

2015 ◽  
Vol 1 (1) ◽  
pp. 65 ◽  
Author(s):  
Dibyadeep Nandi ◽  
Amira S. Ashour ◽  
Sourav Samanta ◽  
Sayan Chakraborty ◽  
Mohammed A.M. Salem ◽  
...  
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Medical Image ◽  
Medical Image Processing ◽  
Principal Component ◽  
Component Analysis

Character Pattern Recognition Algorithm Based on Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5313-5318
Author(s):  
Feng Xian Tang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
License Plate ◽  
Specific Work ◽  
Advantages And Disadvantages

With the help of the study on mathematical theory and its progress and the development of the computer techniques, digital image processing technology has more and more been applied in each field. The pattern recognition judges unknown things by substituting machine for human eyes, which has a high application value. Thus, it becomes the major branch in image processing fields. The character recognition technology has developed rapidly because of its broad application prospect. Until now, it has been applied successfully in OCR and vehicle license plate recognition. However, it has certain difficulty for the pattern recognition to meet the specific requirements related to specific work scenes. This essay discusses several Eigen value selecting approaches and analyzes the advantages and disadvantages of each. For the template matching methods with penalty factors, in design, character recognition algorithm based on the principal component analysis is realized where scattering matrix between classes is as produced matrix.

Sign in / Sign up

Export Citation Format

Share Document