Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Adsorption. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

Effects of financial development indicators on energy consumption and CO2 emission of European, East Asian and Oceania countries

Abstract: The financial crisis is considered a major economic issue and energy consumption and pollution are believed to be one of the most important environmental concerns in the new millennium. The review on investigation of the nexus among energy consumption, GDP growth, financial development and CO2 emission has shown no definitive conclusion. Apart from that, the effects of financial development on energy consumption are not yet understood properly. Thus, this article investigates the effects of financial indicator shocks i.e. credit market and stock market shocks on energy consumption and carbon dioxide (CO2 emissions) and vice versa. Panel Vector Auto Regression (PVAR) models were employed to investigate the relationships in 13 European and 12 East Asia and Oceania countries from 1989 to 2011. Findings emphasize the important role of CO2 emission and energy consumption on explaining each other׳s deviation, with only the degree of effect differing. Although energy consumption and CO2 emission shocks on financial indicators such as private sector credit is not very pronounced in both groups of countries, but the strength of energy consumption shock on stock return rate in European countries is greater than East Asian and Oceania countries. Conversely shocks to stock return rate influence energy consumption especially in long horizon in case of East Asia and Oceania countries.

Application of self-tuning fuzzy pid controller on industrial hydraulic actuator using system identification approach

Abstract: In this paper, Self Tuning Fuzzy PID controller is developed to improve the performance of the electro-hydraulic actuator. The controller is designed based on the mathematical model of the system which is estimated by using System Identification technique. The model is performed in a linear discrete model to obtain a discrete transfer function for the system. Model estimation procedures are done by using System Identification Toolbox in Matlab. Data for model estimation is taken from experimental works. Fuzzy logic is used to tune each parameter of PID controller. Through simulation in Matlab by selecting appropriate fuzzy rules are designed to tune the parameters Kp, Ki, and Kd of the PID controller, the performance of the hydraulic system has improved significantly compare to conventional PID controller.

Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion

Abstract: Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this paper, we present a deep-learning-based method to classify a user's opinion expressed in reviews (called RNSA). To the best of our knowledge, a deep learning-based method in which a unified feature set which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The RNSA employs the Recurrent Neural Network (RNN) which is composed by Long Short-Term Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about the word are vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity; sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review datasets. We obtained encouraging result. Experimental results show that (1) feature vectors in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c) word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2) our method that learns from this unified feature set can obtain significant performance than one that learns from a feature subset; (3) our neural model yields superior performance improvements in comparison with other well-known approaches in the literature.