Computational intelligence based power system security assessment and improvement under multi- contingencies conditions / Nor Rul Hasma Abdullah

This thesis presents new techniques for voltage stability assessment and improvement in power system under multi-contingencies. A line-based voltage stability index termed as Static Voltage Stability Index (SVSI) was used to evaluate the voltage stability condition on a line. The value of SVSI was computed to identify the most sensitive line and corresponding weak bus in the system

Observation of the Effects of Playing Games with the Human Brain Waves

The purpose of this paper is to observe the human brain waves when a person playing video games. The game proposed is Counter Strike (CS) 1.6. There are 30 samples of human brain wave will be collected. The EEG signal will be recorded before playing a game and after playing a game. The threshold value is used to filter the data collected to acquire clean brain waves. Then, extraction of sub-band Alpha and Beta is done by Band-pass filter. Power Spectral Density (PSD) is performed in analysing the brain waves to acquire peak amplitude of the Alpha and Beta sub-band frequencies. The pattern of Alpha and Beta is carried out by using the histogram to observe the relationship between games and mind state of humanity. It is observed that the Beta-band increase and Alpha-band decrease after the samples playing game

Five-level single source voltage converter controlled using selective harmonic elimination

The paper presents a 5-level cascaded H-bridge voltage source inverter. The converter topology composed of two-cascaded H-bridge modules connected in parallel and powered by a single DC source. The benefit of this topology in comparison with the conventional H-Bridge configuration is that it uses single input DC source instead of two to achieve the same output steps/levels. Selective Harmonic Elimination (SHE) is the modulation technique employed. The generated non-linear transcendental equations are solved using an optimised Genetic algorithms to find the switching angles. This property makes the topology and the control function suitable for three phase applications where triplen harmonics are said to cancel out at the line-to-line voltages. This concept of SHE modulation extends the value of the filter cut-off frequency, which translates to smaller sized filter, compact cooling system and reduced system weight. This advantage makes the topology attractive to automotive and renewable energy applications. The topology was simulated using PSIM software

Selective harmonic elimination using MFO for a reduced switch multi-level inverter topology

This research article proposed a new modified single-phase multi-level inverter topology with an optimal switching control strategy to reduce the inverter output harmonic distortion. The topology is configured to operate in asymmetric mode to generate eleven levels of output voltage steps. Additionally, a selective harmonic elimination technique has been deployed to minimize the switching loss and EMI. The Moth Flame Optimization (MFO) algorithm is deployed to compute the optimal switching angles. The proposed MLI topology is simulated in PSIM software using the optimized switching angles. The inverter performance parameters such as the total harmonic distortion (THD), harmonic amplitudes, switching, and conduction losses, were also analyzed and reported. The topology total harmonic distortion is 2.4%, hence satisfying the IEEE 519 standard

Correlation of Objective Assessment of Facial Paralysis with House-Brackmann Score

This article illustrated a brief review of some objective methods in assessing facial nerve function for facial nerve paralysis which were correlated with House-Brackmann Grading System (HBGS). A rigorous search of online databases such as Springer, Elsevier and IEEE was conducted from June, 2015 to November, 2016 to discover and analyze the previous works in facial nerve assessment methods for facial paralysis. Several domains such as facial grading system and methods used to evaluate the facial nerve function were extracted for further analysis. Different keywords were used to acquire the studies based on the desire criteria. A total of 8 articles were identified and were analyzed for inclusion in this search. In conclusion, this review has presented an initial overview for further improvements in objective facial nerve assessment which has to be correlated with subjective assessment to make it more reliable and useful in clinical practice.

Elbow Flexion and Extension Rehabilitation Exercise System Using Marker-less Kinect-based Method

This paper presents the elbow flexion and extension rehabilitation exercise system using marker-less Kinect-based method. The proposed exercise system is developed for the upper limb rehabilitation application that utilizes a low cost depth sensor. In this study, the Kinect skeleton tracking method is used to detect and track the joints of upper limb and then measure the angle of the elbow joint. The users perform the exercise in front of the Kinect sensor and the computer monitor. At the same time, they can see the results that displayed on the screen in real-time. The measurement of elbow joint angles are recorded automatically and has been compared to the reference values for the analysis and validation. These reference values are obtained from the normal range of motion (ROM) of the elbow. The results show the average flexion angle of the elbow joint that achieved by the normal user is 139.1° for the right hand and 139.2° for the left hand. Meanwhile, the average extension angle is 1.72° for the right hand and 2.0° for the left. These measurements are almost similar to the standard range of motion (ROM) reference values. The skeleton tracking works well and able to follow the movement of the upper arm and forearm in real-time

K-NN Classification of Brain Dominance

The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty-one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%

Brain Dominance Using Brainwave Signal

The study of brain dominance in human-computer interaction has increased in recent years in an attempt to address the need of users especially who cannot read or write. The objective of this paper is to determine the brain dominance from brainwave signal that are measured using Emotive device and to analyse the pattern of brain dominance brainwave signal by using signal processing. The result of Power Spectral Density (PSD) and Energy Spectral Density (ESD) from brainwave will be validated with Hermann Brain Dominance Instrumentation (HBDI) questionnaire. The result shows that most sample are left brain dominance. The result also shows that Beta and Delta indicate the left-brain dominance whereas Beta is indicates rightbrain dominance

Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels

Nowadays, many studies have been conducted to assess driver fatigue, as it has become one of the leading causes of traffic crashes. However, with the use of advanced features and machine learning approaches, EEG signals may be processed in an effective way, allowing fatigue to be detected promptly and efficiently. An optimal channel selection approach and a competent classification algorithm might be viewed as a critical aspect of efficient fatigue detection by the driver. In the present framework, a new channel selection algorithm based on correlation coefficients and an ensemble classifier based on random subspace k-nearest neighbour (k-NN) has been presented to enhance the classification performance of EEG data for driver fatigue detection. Moreover, power spectral density (PSD) was used to extract the feature, confirming the presented method's robustness. Additionally, to make the fatigue detection system faster, we conducted the experiment in three different time windows, including 0.5s, 0.75s, and 1s. It was found that the proposed method attained classification accuracy of 99.99% in a 0.5 second time window to identify driver fatigue by means of EEG. The outstanding performance of the presented framework can be used effectively in EEG-based driver fatigue detection

K-NN classification of brain dominance

The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%