Universiti Teknologi Petronas

About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study

Abstract: This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions. The fusion of brain signals acquired by fNIRS-EEG was performed at feature-level using CCA by maximizing the inter-subject covariance across modalities. The CCA result discovered the associations across the modalities and estimated the components responsible for these associations. The experiment results showed that mental stress experienced by this cohort of subjects is subregion specific and localized to the right ventrolateral PFC subregion. These suggest the right ventrolateral PFC as a suitable candidate region to extract biomarkers as performance indicators of neurofeedback training in stress coping.

Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization

Abstract: A multi-objective planning approach for electrical distribution systems using particle swarm optimization is presented in this paper. In this planning, the number of feeders and their routes, number and locations of sectionalizing switches, and number and locations of tie-lines of a distribution system are optimized. The multiple objectives to determine optimal values for these planning variables are: (i) minimization of total installation and operational cost and (ii) maximization of network reliability. The planning optimization is performed in two steps. In the first step, the distribution network structure, i.e., number of feeders, their routes, and number and locations of sectionalizing switches are determined. In the second step, the optimum number and locations of tie-lines are determined. Both the objectives are minimized simultaneously to obtain a set of non-dominated solutions in the first step of optimization. The solution strategy used for the first step optimization is the Strength Pareto Evolutionary Algorithm-2 (SPEA2) based multi-objective particle swarm optimization (SPEA2–MOPSO). In the second step, the solutions/networks obtained from the previous step are further optimized by placement of tie-lines. SPEA2-based binary MOPSO (SPEA2–BMOPSO) is used in the second step of optimization. The proposed planning algorithm is tested and evaluated on different practical distribution systems.

Trends of Syngas as a Fuel in Internal Combustion Engines

Abstract: Syngas from biomass and solid waste is a carbon-neutral fuel believed to be a promising fuel for future engines. It was widely used for spark-ignition engines in the WWII era before being replaced with gasoline. In this paper, the technological development, success, and challenges for application of syngas in power generating plants, the trends of engine technologies, and the potential of this fuel in the current engine technology are highlighted. Products of gasification vary with the variation of input parameters. Therefore, three different syngases selected from the two major gasification product categories are used as case studies. Their fuel properties are compared to those of CNG and hydrogen and the effects on the performance and emissions are studied. Syngases have very low stoichiometric air-fuel ratio; as a result they are not suitable for stoichiometric application. Besides, syngases have higher laminar flame speed as compared to CNG. Therefore, stratification under lean operation should be use...

Stock market prediction using machine learning techniques

Abstract: The main objective of this research is to predict the market performance of Karachi Stock Exchange (KSE) on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. The attributes used in the model includes Oil rates, Gold & Silver rates, Interest rate, Foreign Exchange (FEX) rate, NEWS and social media feed. The old statistical techniques including Simple Moving Average (SMA) and Autoregressive Integrated Moving Average (ARIMA) are also used as input. The machine learning techniques including Single Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM) are compared. All these attributes are studied separately also. The algorithm MLP performed best as compared to other techniques. The oil rate attribute was found to be most relevant to market performance. The results suggest that performance of KSE-100 index can be predicted with machine learning techniques.