scholarly journals A New Method to Evaluate the Appearance of Cotton Yarn Using Image Processing and Fuzzy Inference System Supported with Graphical User Interface

2018 ◽  
Vol 08 (05) ◽  
Author(s):  
Ghandi Ghazi Ahmad ◽  
Hiyam Khaddam ◽  
Maan Horani
2005 ◽  
Vol 475-479 ◽  
pp. 2107-2110 ◽  
Author(s):  
Fan Li ◽  
Jian Qin Mao ◽  
Hai Shan Ding ◽  
Wen Bo Zhang ◽  
Hui Bin Xu ◽  
...  

In this paper, a new method which combines the least square method with Tree-Structured fuzzy inference system is presented to approximate the Preisach distribution function. Firstly, by devising the input sequence and measure the output, discrete Preisach measure can be identified by the use of the least squares method. Then, the Preisach function can be obtained with Tree-Structured fuzzy inference system without any special smoothing means. So, this new method is not sensitive to noise, and is a universal approximator of the Preisach function. It collect the merit and overcome the deficiency of the existing methods.


Author(s):  
Shi Liu ◽  
Liangsheng Qu

The field balancing of flexible rotors is one of the key techniques to reduce vibration of large rotating machinery. Although in recent decades the balancing theory has been thoroughly studied and various balancing techniques have been well developed, the present balancing methods are still remain for further improvements in accuracy and efficiency. Firstly, most balancing methods need large numbers of trial runs to obtain the vibration responses of trial weights in different correcting planes. Secondly, the vibration response in each measured section is always taken from a single sensor, and thus are lack of comprehensive vibration information of rotor. In fact, the movement of rotor is a complex spatial motion, which can’t be objectively and reliably described just with a single sensor in each bearing section. In order to overcome above shortcomings of traditional balancing methods, this paper presents a new field balancing method for flexible rotors, which is based on adaptive neuro-fuzzy inference system (ANFIS). The new method successfully applies the information fusion, ANFIS and computer simulation together. It integrates and fully utilizes the information supplied from all proximity sensors by holospectrum for enhancing the balancing efficiency and accuracy. A fuzzy model is established to simulate the mapping relationship between vibration responses and balancing weights by using the ANFIS. The inputs into ANFIS are the amplitudes and phases of integrated vibration responses, while the outputs are the mass and azimuth of balancing weights. A fuzzy set with three membership functions (MFs) is used to describe the magnitude of vibration amplitudes or of balancing weights. Another fuzzy set with five MFs is used to describe the quadrant of vibration phases or of balancing weights. Based on the historical balancing data, a combination of least-square and back-propagation gradient descent methods is then used for training ANFIS membership function and node-parameters to model input (vibration response)/output (balancing weight) data. The simulation study shows that the ANFIS can obtain satisfactory balancing result after a single trial run. At the same time, with the help of computer simulation, different correction schemes can be compared and rapidly simulated to direct balancing operation. Finally, the effectiveness of the new method was validated by the experiments on balancing rig and in the field balancing practice of several 300MW turbo-generator units.


2021 ◽  
Vol 8 (1) ◽  
pp. 114
Author(s):  
Rizky Prabowo ◽  
Zuliana Nurfadlilah ◽  
Favorisen Rosyking Lumbanraja ◽  
Didik Kurniawan

<p><em>The automotive industry in Indonesia has significant increase in the past decade. A famous car company opened a manufacturing branch to increase its production capacity in Indonesia. An increase in sales is directly proportional to an increase in service to customers. Damage on electrical system is the majority of modern car. Unfortunately, car users have minimal knowledge of car electricity. This article describes the technique of detecting the level of damage to a car's electrical system using the Adaptive Neuro-Fuzzy Inference System (Anfis) concept. As a case study in designing the system in question is the electrical system on the Toyota Avanza. Formation of a fuzzy inference system which is used for the system formation process through a GUI-based interface design (Graphic User Interface). The output of the system is a fuzzy analysis based on the membership function of the Gaussian, Triangular and Trapezoid methods to obtain an analysis of the level of damage to the electrical system on a Toyota Avanza. From the results of the system test for starter system, firewire system and lighting system,  it is concluded that the analysis of the level of damage to the electrical system on the car using Anfis based on the Gaussian membership function model is more accurate(reach 85%) in predicting the level of damage to the analyzed electrical system.</em></p><p><em><strong>Keywords</strong></em><em>: Anfis, Electrical System, Fuzzy Inference System, Toyota Avanza</em> </p><p><em>Industri otomotif di Indonesia mengalami peningkatan signifikan dalam kurun waktu satu dekade belakangan ini. Perusahaan mobil terkenal membuka pabrik manufaktur untuk meningkatkan kapasitas produksinya di Indonesia. Peningkatan penjualan berbanding lurus dengan peningkatan layanan kepada pelanggan. Kerusakan sistem kelistrikan merupakan kerusakan yang mayoritas dialami pengguna kendaraan mobil terbaru masa kini. Sayangnya, pengguna kendaraan mobil memiliki pengetahuan yang kurang tentang kelistrikan. Artikel ini mendeskripsikan tentang teknik mendeteksi tingkat kerusakan sistem kelistrikan mobil dengan menggunakan konsep Adaptive Neuro-Fuzzy Inference System (ANFIS). Sebagai studi kasus dalam mendesain sistem yang dimaksud adalah sistem kelistrikan pada Mobil Toyota Avanza. Pembentukan fuzzy inference system yang kemudian digunakan untuk proses pembentukan sistem melalui desain interface berbasis GUI (Graphic User Interface). Keluaran dari sistem yang dibuat adalah analisa fuzzy berdasarkan fungsi keanggotaan metode Gaussian, Triangular dan Trapezoid untuk mendapatkan analisa tingkat kerusakan sistem kelistrikan pada mobil Toyota Avanza. Dari hasil uji sistem yang dilakukan pada sistem starter, sistem pengapian dan sistem penerangan diperoleh kesimpulan analisis tingkat kerusakan sistem kelistrikan pada mobil dengan menggunakan Anfis berdasarkan model membership function Gaussian adalah lebih akurat (mencapai 85%) dalam menduga tingkat kerusakan sistem kelistrikan yang dianalisa.</em></p><p><em><strong>Kata kunci</strong></em><em>: Anfis; Fuzzy Inference System; Sistem Kelistrikan; Toyota Avanza</em></p>


Author(s):  
Agustinus Eko Setiawan

Kerentanan adalah keadaan atau kondisi yang dapat mengurangi kemampuan masyarakat untuk mempersiapkan diri menghadapi bahaya atau ancaman bencana. Tujuan dari mengetahui kerentanan adalah untuk mengurangi kemungkinan dampak yang merugikan yang diakibatkan oleh bencana. Rumusan masalah dalam penelitian ini adalah bagaimana membandingkan metode fuzzy  mamdani dan fuzzy sugeno untuk mendeteksi daerah rentan banjir di Kecamatan Pringsewu. Dengan dibangunnya prototipe untuk menentukan daerah rentan banjir pada Kecamatan Pringsewu, ini dapat dijadikan upaya untuk mengurangi resiko banjir baik melalui pembangunan fisik maupun penyadaran dan peningkatan kemampuan menghadapi bencana. Penelitian ini menghasilkan simpulan yaitu aman, rentan dan banjir. Untuk perhitungan dimulai dengan menentukan himpunan fuzzy masing-masing variabel, pembentukan aturan fuzzy (implikasi), komposisi aturan menggunakan fungsi MAX, penegasan (defuzzifikasi). Sementara dalam prototipe dimulai menggunakan Graphic User Interface, kemudian dilakukan melengkapi kode pada sofware Matlab R2013a agar desain deteksi kerentanan dapat berfungsi. Setelah prototipe deteksi kerentanan banjir berhasil dibuat, data monografi Kecamatan Pringsewu dapat diinputkan kedalam prototipe. Selanjutnya akan diproses menggunakan metode Fuzzy Inference System yang telah dimasukan kedalam prototipe, kemudian hasil akan muncul dan hasil akhir metode Mamdani memiliki nilai akurasi 70% dan metode Sugeno memiliki nilai akurasi 48,33%. Sehingga dari hasil pengujian tersebut, menunjukan bahwa metode Mamdani lebih baik akurasinya dibandingkan dengan metode Sugeno.


Author(s):  
Phuc Q. Le ◽  
◽  
Abdullah M. Iliyasu ◽  
Jesus A. Garcia Sanchez ◽  
Fangyan Dong ◽  
...  

A 3D feature space is proposed to represent visual complexity of images based on Structure, Noise, and Diversity (SND) features that are extracted from the images. By representing images using the proposed feature space, the human classification of visual complexity of images as being simple, medium, or complex can be implied from the structure of the space. The structure of the SND space as determined by a clustering algorithm and a fuzzy inference system are then used to assign visual complexity labels and values to the images respectively. Experiments on Corel 1000A dataset, Web-crawled, and Caltech 256 object category dataset with 1000, 9907, and 30607 images respectively using MATLAB demonstrate the capability of the 3D feature space to effectively represent the visual complexity. The proposal provides a richer understanding about the visual complexity of images which has applications in evaluations to determine the capacity and feasibility of the images to tolerate image processing tasks such as watermarking and compression.


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