Joga Dharma Setiawan

Combined Probability Approach and Indirect Data-Driven Method for Bearing Degradation Prognostics

This study proposes an application of relevance vector machine (RVM), logistic regression (LR), and autoregressive moving average/generalized autoregressive conditional heteroscedasticity (ARMA/GARCH) models to assess failure degradation based on run-to-failure bearing simulating data. Failure degradation is calculated by using an LR model, and then regarded as the target vectors of the failure probability for training the RVM model. A multi-step-ahead method-based ARMA/GARCH is used to predict censored data, and its prediction performance is compared with one of Dempster-Shafer regression (DSR) method. Furthermore, RVM is selected as an intelligent system, and trained by run-to-failure bearing data and the target vectors of failure probability obtained from the LR model. After training, RVM is employed to predict the failure probability of individual units of bearing samples. In addition, statistical process control is used to analyze the variance of the failure probability. The result shows the novelty of the proposed method, which can be considered as a valid machine degradation prognostic model.

Modeling, simulation and validation of 14 DOF full vehicle model

An accurate full vehicle model is required in representing the behavior of the vehicle in order to design vehicle control system such as yaw control, anti roll control, automated highway system etc. There are many vehicle models built for the study of the vehicle dynamics specifically for the ride and handling behavior. This paper describes the vehicle model development of the vehicle model to study the behavior of the vehicle. The derivation of a 14 DOF vehicle model consisting of ride, handling and tire model is presented. The Magic tire formula was used as tire model. All the assumptions made for the 14 DOF vehicle model are stated. This 14 DOF vehicle model will be then validated using instrumented experimental vehicle for two steering inputs namely step steer and double lane change. The deviation of the outputs specifically the yaw rate, lateral acceleration and roll angle of the vehicle body and also the slip angle at each of the tire from the 14 DOF model simulation from the experimental results is discussed.

Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor

In this study, the EMG signals are processed using 16 time-domain features extraction to classify the finger movement such as thumb, index, middle, ring, and little. The pattern recognition of 16 extracted features are classified using artificial neural network (ANN) with two layer feed forward network. The network utilizes a log-sigmoid transfer function in hidden layer and a hyperbolic tangent sigmoid transfer function in the output layer. The ANN uses 10 neurons in hidden layer and 5 neurons in output layer. The training of ANN pattern recognition uses Levenberg-Marquardt training algorithm and the performance utilizes mean square error (MSE). At about 22 epochs the MSE of training, test, and validation get stabilized and MSE is very low. There is no miss classification during training process. Based on the resulted overall confusion matrix, the accuracy of thumb, middle, ring, and little is 100%. The confusion of index is 16.7%. The overall confusion matrix shows that the error is 3.3% and overall performance is 96.7%.

Virtual Reality Simulation of Fire Fighting Robot Dynamic and Motion

This paper presents one approach in designing a Fire Fighting Robot which has been contested annually in a robotic student competition in many countries following the rules initiated at the Trinity College. The approach makes use of computer simulation and animation in a virtual reality environment. In the simulation, the amount of time, starting from home until the flame is destroyed, can be confirmed. The efficacy of algorithms and parameter values employed can be easily evaluated. Rather than spending time building the real robot in a trial and error fashion, now students can explore more variation of algorithm, parameter and sensor-actuator configuration in the early stage of design. Besides providing additional excitement during learning process and enhancing students understanding to the engineering aspects of the design, this approach could become a useful tool to increase the chance of winning the contest.

Structural Damage Detection Using Randomized Trained Neural Networks

A computational method on damage detection problems in structures was developed using neural networks. The problem considered in this work consists of estimating the existence, location and extent of stiffness reduction in structure which is indicated by the changes of the structural static parameters such as deflection and strain. The neural network was trained to recognize the behaviour of static parameter of the undamaged structure as well as of the structure with various possible damage extent and location which were modeled as random states. The proposed techniques were applied to detect damage in a cantilever beam. The structure was analyzed using finite-element-method (FEM) and the damage identification was conducted by a back-propagation neural network using the change of the structural strain and displacement. The results showed that using proposed method the strain is more efficient for identification of damage than the displacement.

Pattern recognition methods for multi stage classification of parkinson's disease utilizing voice features

A number of papers has presented a pattern recognition method for Parkinsons Disease (PD) detection. However, the literatures only able to classify subjects as either healthy of suffering from PD. This paper presents a pattern recognition method for multi stage classification of PD utilizing voice features. 22 features are obtained from University of California-Irvine (UCI) data repository. These features are extracted using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). It is found that PCA is better than LDA in terms of extracting significant features. Some classifiers such as Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbor (KNN) and Adaptive Resonance Theory-Kohonen Neural Network (ART-KNN) are then used and compared. These methods are applied in multi stage classification. The classification results show that SVM has better testing accuracy than the other methods.

Electromyography (EMG) signal recognition using combined discrete wavelet transform based on Artificial Neural Network (ANN)

Rapid disability patients increasing over time and need a solution in the future. Hand amputation is one form of disability that common in Indonesian society. A possible solution would be necessary at the moment is the development of prosthetic hand that has the ability as a human hand. The development of neuroscience has now reached the stage of the bodys ability to use the signal as an input signal to operate a system. One of the applications of the science development is the use of electromyography (EMG) signals as an input to the control system to operate the prosthetic hand. This study is divided into two stages: a preliminary study and further research. Initial research focus in the process of EMG signal pattern recognition and advanced research focus in the development of a prototype prosthetic hand that is integrated with the controller system. Preliminary research indicates that the results of pattern recognition EMG signal using wavelet transform and Artificial Neural Network (ANN) classification has an accuracy rate of about 77.5 %. Based on these results, it can be concluded that the study results could be used as a signal input to program control of the prosthetic hand that will be developed in phase two.

A pattern recognition method for stage classification of Parkinson's disease utilizing voice features

This paper presents a pattern recognition method for multi-class classification of Parkinsons disease based on PCA, LDA and SVM. 22 voice features which are extracted and reduced using PCA and LDA. SVM is then used during the classification step. The classification accuracy between single features and PCA and LDA features are presented and the results show that the PCA features have greater accuracy than LDA features and the single features.

Structural analysis for in-service gas pipeline lowering using numerical method

Vibration analysis is a crucial process in ship design to guarantee crew’s comfortability and to save operating costs by predicting problematical resonance in advance. This study focuses on idealizing fluid mass in tanks by positioning point mass element and quad elements inside the tanks. Also, the added mass effect induced sea water surrounding a ship was included in the present study. The natural frequency obtained from free and forced ship vibration analysis were compared and assessed in the manner of ISO regulation. Finally, it was concluded that the quad element was more adequate to idealize fluid entities in tanks since it was difficult in arranging point mass elements at each grid points properly.Vibration analysis is a crucial process in ship design to guarantee crew’s comfortability and to save operating costs by predicting problematical resonance in advance. This study focuses on idealizing fluid mass in tanks by positioning point mass element and quad elements inside the tanks. Also, the added mass effect induced sea water surrounding a ship was included in the present study. The natural frequency obtained from free and forced ship vibration analysis were compared and assessed in the manner of ISO regulation. Finally, it was concluded that the quad element was more adequate to idealize fluid entities in tanks since it was difficult in arranging point mass elements at each grid points properly.

Development of 6 DOF Supernumerary Robotic Fingers Integrated with 3D Animation

In this study, numerical investigation of ship resistance and ship motions at the traditional Indonesian fishing vessel is presented. The Computational Fluid Dynamic (CFD) code is used to calculate three dimensional, incompressible, and RANS equations. Different types of the fishing vessel hull form were performed. In this research, the data were collected chosen in north and south coast of Java island. The models were drawing in three dimensional, required for performing the analysis, were developed using Rhinoceros. The present study, the open-source computational fluid dynamics library, OpenFOAM was used to predict the resistance with the interFOAM solver. For the motion analysis, using strip theory in the Maxsurf motions. The probability of deck wetness analysis and ship motion were performed for comparing the models. Both analyses of ship Response Amplitude Operators (RAOs) are performed at three types of sea state (slight, moderate, and rough water). The comparisons of the hull form design will be evaluated to get the best performance based on the sea state condition of the java island.