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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Azura Che Soh
Universiti Putra Malaysia

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Development of fall detection and activity recognition using threshold based method and neural network Sai Siong Jun; Hafiz Rashidi Ramli; Azura Che Soh; Noor Ain Kamsani; Raja Kamil Raja Ahmad; Siti Anom Ahmad; Asnor Juraiza Ishak
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1338-1347

Abstract

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.
Improvement of LMS adaptive noise canceller using uniform Poly-phase digital filter bank Alaa Hadi Mohammad; Azura Che Soh; Noor Faezah Ismail; Ribhan Zafira Abdul Rahman; Mohd Amran Mohd Radzi
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1258-1265

Abstract

This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.
Artificial neural network model and fuzzy logic control of dissolved oxygen in a bioreactor Nor Hana Mamat; Samsul Bahari Mohd Noor; Laxshan A/L Ramar; Azura Che Soh; Farah Saleena Taip; Ahmad Hazri Ab. Rashid
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1289-1297

Abstract

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network model gives better accuracy than MLP neural network. The model is further used in fuzzy logic controller design to simulate control of dissolved oxygen by manipulation of aeration rate.  Simulation result shows that the fuzzy logic controller can control the dissolved oxygen based on the given profile.
TRIGA PUSPATI reactor: model analysis and accuracy Nor Arymaswati Abdullah; Azura Che Soh; Samsul Bahari Mohd Noor; Ribhan Zafira Abd. Rahman; Julia Abd Karim
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp788-797

Abstract

There are many challenging issues with research reactor, such as time variation and uncertainty. Since its first criticality in 1982, the biggest changes in TRIGA PUSPATI Reactor system is the replacement of instrumentation and control console system from analogue to digital in 2013. Apart from providing methods of controlling the power reactor via the control rod movement, the Instrumentation and Control Console System also provides monitoring and display for all reactor parameters to protect the reactor from undue influences or abnormal circumstances. Meanwhile, the simulation model of the TRIGA PUSPATI Reactor system has been developed in the Simulink-MATLAB. The simulation model development is based on the research reactor mathematical representatives and the real plant parameters of TRIGA PUSPATI Reactor. However, the performance of this simulation model needs to be evaluated. Since there is no report or paper work found on the performance of the simulation model to represent the real system of RTP, the present study aims to carry out an analysis for more rigorous understanding of the TRIGA PUSPATI Reactor model simulation through validation and verification methods. After analysing the result, it was found that the simulation model has a good representation of a real plant.
Co-Authors Ahmad Hazri Ab. Rashid Alaa Hadi Mohammad Asnor Juraiza Ishak Farah Saleena Taip Hafiz Rashidi Ramli Julia Abd Karim Laxshan A/L Ramar Mohd Amran Mohd Radzi Noor Ain Kamsani Noor Faezah Ismail Nor Arymaswati Abdullah Nor Hana Mamat Raja Kamil Raja Ahmad Ribhan Zafira Abd. Rahman Ribhan Zafira Abdul Rahman Sai Siong Jun Samsul Bahari Mohd Noor Siti Anom Ahmad