Wahyu Caesarendra
Dosen Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

Published : 6 Documents
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SUMMARY OF THE RECENT DEVELOPED TECHNIQUES FOR MACHINE HEALTH PROGNOSTICS

ROTASI VOLUME 16, NOMOR 1, JANUARI 2014
Publisher : ROTASI

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Abstract

This paper reviews relatively new developed techniques for machine health prognostics system. The prognostics assessment of machines is an important consideration for determining the remaining useful life (RUL) of machine components and prediction of future state of machines. The developed system has employed several approaches of machine health prognostics strategy such as data-driven, physical-based, and probability-based methods. The method of solution implemented artificial intelligence techniques including support vector machine (SVM), relevance vector machine (RVM), Dempster-Shafer theory, decision tree, particle filter, and autoregressive moving average/ generalized autoregressive conditional heteroscedasticity (ARMA/GARCH). Case studies of machine health prognostics are also presented to show the plausibility of the developed systems. Finally, this paper summarizes the research finding and directions of machine health prognostics system.

PERANCANGAN STRUKTUR FRAME QUADROTOR

ROTASI Vol 17, No 3 (2015): VOLUME 17, NOMOR 3, JULI 2015
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

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Abstract

Perancangan frame dari pesawat tanpa awak (Unmanned Aerial Vehicle) khususnya yang memiliki 4 buah rotor (quadrotor) adalah salah satu hal yang penting untuk menunjang fungsi quadrotor sebagai wahana terbang. Dengan desain frame yang kuat dan kokoh diharapkan quadrotor tidak mudah hancur ketika jatuh, sehingga komponenen elektronika seperti sensor dan mikrokontroller tidak hancur/rusak. Quadrotor dapat mengudara karena adanya gaya angkat yang diberikan oleh 4 rotor yang biasanya dipasang secara menyilang. Selain bisa dikendalikan dari jarak jauh, quadrotor memiliki fungsi penting yaitu dapat digunakan untuk membawa muatan/beban. Makalah ini membahas hasil studi dalam merancang frame quadrotor untuk mencari maksimum stress, getaran pribadi quadrotor beserta stress analisisnya pada kondisi quadorotor landing dan take-off.

STUDI KLASIFIKASI TUJUH GERAKAN TANGAN SINYAL ELECTROMYOGRAPHY (EMG) MENGGUNAKAN METODE PATTERN RECOGNITION

JURNAL TEKNIK MESIN Vol 4, No 3 (2016): VOLUME 4, NOMOR 3, JULI 2016
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

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Abstract

Pada studi ini, sinyal EMG diproses menggunakan 16 features extraction domain – waktu untuk mengklasifikasikan gerakan tangan seperti tripod, power, precision closed, finger point, mouse, hand open, dan hand close. 16 fitur dari masing – masing sinyal EMG dari gerakan tangan tersebut direduksi menggunkan principal component analysis (PCA) untuk mendapatkan satu set fitur baru yang memberikan informasi yang lebih kompek. Pattern recognition dari fitur baru tersebut diklasifikasikan menggunkan support vector machine (SVM). Pattern recognition digunakan pada masing – masing subjek dan menghasilkan persentase training  dan testing. Berdasarkan SVM training dan testing yang dihasilkan, sinyal EMG dari gerakan tangan sukses diklasifikasikan dan akurasi dari klasifikasi mencapai 80% - 86%.

A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection

Journal of Energy, Mechanical, Material and Manufacturing Engineering Vol 2, No 1 (2017)
Publisher : University of Muhammadiyah Malang

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Abstract

The progress of todays technology is growing very quickly. This becomes the motivation for the community to be able to continue and provide innovations. One technology to be developed is the application of brain signals or called with electroencephalograph (EEG). EEG is a non-invasive measurement method that represents electrical signals from brain activity obtained by placement of multiple electrodes on the scalp in the area of the brain, thus obtaining information on electrical brain signals to be processed and analyzed. Lie is an act of covering up something so that only the person who is lying knows the truth of the statement. The hidden information from lying subjects will elicit an EEG-P300 signal response using Independent Component Analysis (ICA) in different shapes of amplitude that tends to be larger around 300 ms after stimulation. The method used in the experiment is to invite subject in a card game so that the process can be done naturally and the subject can well stimulated. After the trials there are several results almost all subjects have the same frequency on the frequency of 24-27 Hz. This is a classification of beta waves that have a frequency of 13-30 Hz where the beta wave is closely related to active thinking and attention, focusing on the outside world or solving concrete problems.

Classification Method of Hand Gestures Based on Support Vector Machine

Computer Engineering and Applications Journal Vol 7 No 3 (2018)
Publisher : Universitas Sriwijaya

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Abstract

This paper presents the EMG signal classification based on PCA and SVM method. The data is acquired from the 5 subjects and each subject perform 7 hand gestures includes the tripod, power, precision closed, finger point, mouse, hand open, and hand close. Each gesture is repeated 10 times (5 data as training data and the 5 remaining data as testing data). Each of training and testing data are processed using 16 features extraction in time–domain and reduced using principal component analysis (PCA) to obtain new set of features. Features classification using support vector machine classify new set of features from each subject result 85% - 89% percentage of training classification. Training data classification is tested using testing data of EMG signals and giving accuracy reach 80% - 86%.

Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study

Journal of Energy, Mechanical, Material and Manufacturing Engineering Vol 3, No 2 (2018)
Publisher : University of Muhammadiyah Malang

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Abstract

Parkinson disease (PD) detection using pattern recognition method has been presented in literatures. This paper present multi-class PD detection utilizing voice and electromyography (EMG) features of Indonesian subjects. The multi-class classification consists of healthy control, possible stage, probable stage and definite stage. These stages are based on Hughes scale used in Indonesia for PD. Voice signals were recorded from 15 people with Parkinson (PWP) and 8 healthy control subjects. Voice and EMG data acquistion were conducted in dr Kariadi General Hospital Semarang, Central Java, Indonesia. Twenty two features are used for voice signal feature extraction and twelve features are emploed for EMG signal. Artificial Neural Network is used as classification method. The results of voice classification show that accuracy for testing step of 94.4%. For EMG classification, the accuracy of testing of 71%.