scholarly journals Sistem Identifikasi Relief pada Situs Bersejarah Menggunakan Perangkat Mobile Android (Studi Kasus Candi Borobudur)

2014 ◽  
Vol 10 (1) ◽  
pp. 133
Author(s):  
Rajif Agung Yunmar ◽  
Agus Harjoko
Keyword(s):  
Feature Extraction ◽  
Royal Society ◽  
Extraction Method ◽  
Nearest Neighbor ◽  
Life Story ◽  
Computation Time ◽  
Feature Extraction Method ◽  
Average Computation Time ◽  
The Temple ◽  
Image Object

Abstrak            Bangsa yang besar adalah bangsa yang menghargai sejarah dan asal usulnya. Relief yang terdapat pada candi Borobudur menggambarkan banyak cerita, termasuk sejarah dan asal usul bangsa ini. Mulai dari cerita kehidupan kalangan kerajaan, kehidupan masyarakat, dan adat istiadat pada saat bangunan candi tersebut dibuat, dsb.            Penelitian ini merancang sebuah perangkat lunak mobile Android untuk identifikasi citra relief Candi Borobudur sehingga dapat membantu wisatawan dalam menerjemahkan pesan dan informasi yang terkandung didalamnya. Metode ekstraksi ciri yang digunakan adalah Speeded-Up Robust Feature (SURF) dan hierarchical k-means tree nearest-neighbor untuk identifikasinya.            Pengujian identifikasi citra relief dilakukan dengan berbagai macam variasi, yaitu sudut, jarak, rotasi, intensitas cahaya dan keutuhan citra masukan untuk melihat pengaruhnya terhadap hasil pengenalan citra relief tersebut. Metode identifikasi yang diajukan memberikan hasil pengenalan sebesar 93.30% dengan rata-rata waktu komputasi 59.55 detik.  Abstract            The great nation built from people who can respects they history and origins. Reliefs at Borobudur temple contained many stories, including the history and origins of this nation. Starting from the life story of the royal, society, and customs at the time of the building of the temple was made, and so on.            This study develops mobile Android software for identification of Borobudur Temple relief image object so that it can help travelers in translating the story and the information contained therein. Feature extraction method used is speeded-Up Robust Feature (SURF) and hierarchical k-means tree nearest-neighbor for identification.            Identification testing of relief images is done by different variations, ie angle, distance, rotation, intensity of the light and wholeness of image input to see the effect on the relief image recognition results. The proposed identification method gives recognition results of 93.30% and the average computation time for 59.55 seconds.

scholarly journals An ANFIS Approach for Real Power Transfer Allocation

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Hussain Shareef ◽  
Saifulnizam Abd Khalid ◽  
Mohd Wazir Mustafa ◽  
Azhar Khairuddin
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Computation Time ◽  
Test System ◽  
Load Flow ◽  
Equivalent System ◽  
Power Transfer ◽  
Real Power ◽  
Feature Extraction Method ◽  
Flow Information

This paper proposes an adaptive neurofuzzy interface system (ANFIS) approach to identify the real power transfer between generators. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to train the designed ANFIS. It also incorporated an enhanced feature extraction method called principle component analysis (PCA) to reduce the input features to the ANFIS. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the ANFIS output compared to that of the MNE method. The ANFIS output provides promising results in terms of accuracy and computation time. Furthermore, it can be concluded that the ANFIS with enhanced feature extraction method reduces the time taken to train the ANFIS without affecting the accuracy of the results.

Ear Recognition Based on Supervised Learning Using Gabor Filters

2010 ◽  
Vol 29-32 ◽  
pp. 1127-1132
Author(s):  
Yong Liang Xiao ◽  
Shao Ping Zhu
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Extraction Method ◽  
Nearest Neighbor ◽  
Gabor Filters ◽  
Experimental Results ◽  
Feature Extraction Method ◽  
Nearest Neighbor Classifier ◽  
Ear Recognition ◽  
Neighbor Classifier

In this paper, we introduce a new ear recognition approach including a feature extraction method and the recognition framework. Firstly, we use a supervised kernel neighborhood preserving projection to extract discriminately ear feature, then we use generalized locally nearest neighbor classifier to recognize ear. Experimental results on USTB ear database show the effectiveness of our method.

scholarly journals Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 22
Author(s):  
Yuxing Li ◽  
Peiyuan Gao ◽  
Bingzhao Tang ◽  
Yingmin Yi ◽  
Jianjun Zhang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Noise Signal ◽  
Permutation Entropy ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Single Feature

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.

A Feature Extraction Method of Computer Viruses Based on Artificial Immune and Code Relevance

2011 ◽  
Vol 34 (2) ◽  
pp. 204-215 ◽  
Author(s):  
Wei WANG ◽  
Peng-Tao ZHANG ◽  
Ying TAN ◽  
Xin-Gui HE
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Artificial Immune ◽  
Computer Viruses ◽  
Feature Extraction Method

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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