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All Journal Rotasi Journal of Energy, Mechanical, Material and Manufacturing Engineering Journal of Robotics and Control (JRC)

Wahyu Caesarendra

Dosen Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

Author-ID : 245211

Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

Published : 6 Documents Claim Missing Document

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Articles

A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection Caesarendra, Wahyu
Journal of Energy, Mechanical, Material and Manufacturing Engineering Vol 2, No 1 (2017)
Publisher : University of Muhammadiyah Malang

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.

Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study Putri, Farika; Caesarendra, Wahyu; Pamanasari, Elta Diah; Ariyanto, Mochammad; Setiawan, Joga D
Journal of Energy, Mechanical, Material and Manufacturing Engineering Vol 3, No 2 (2018)
Publisher : University of Muhammadiyah Malang

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%.

PERANCANGAN STRUKTUR FRAME QUADROTOR Setiawan, Joga Dharma; Caesarendra, Wahyu; Ariyanto, Mochammad
ROTASI Vol 17, No 3 (2015): VOLUME 17, NOMOR 3, JULI 2015
Publisher : Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

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.

SUMMARY OF THE RECENT DEVELOPED TECHNIQUES FOR MACHINE HEALTH PROGNOSTICS Widodo, Achmad; Caesarendra, Wahyu
ROTASI Vol 16, No 1 (2014): VOLUME 16, NOMOR 1, JANUARI 2014
Publisher : Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

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.

Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot Umam, Faikul; Fuad, Muhammad; Suwarno, Iswanto; Ma'arif, Alfian; Caesarendra, Wahyu
Journal of Robotics and Control (JRC) Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i2.17977

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.

Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi Triwiyanto, Triwiyanto; Caesarendra, Wahyu; Abdullayev, Vugar; Ahmed, Abdussalam Ali; Herianto, Herianto
Journal of Robotics and Control (JRC) Vol 4, No 1 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i1.17364

Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well.

Co-Authors Abdullayev, Vugar Achmad Widodo Ahmed, Abdussalam Ali Alfian Ma’arif Faikul Umam Herianto Herianto Joga Dharma Setiawan Mochammad Ariyanto Muhammad Fuad Pamanasari, Elta Diah Putri, Farika Setiawan, Joga D Suwarno, Iswanto

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