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Publication

2D LiDAR based reinforcement learning for Multi-Target path planning in unknown environment

( 2023)

Nasr Abdalmanan

;

Kamarulzaman Kamarudin

;

Muhammad Aizat Abu Bakar

;

Mohd Hafiz Fazalul Rahiman

;

Ammar Zakaria

;

Syed Muhammad Mamduh Syed Zakaria

;

Latifah Munirah Kamarudin

Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments. Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets. To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor. We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map. The environment contained static obstacles and a single mobile robot. Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets. The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL. In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality. The VFH-QL method was then evaluated using a benchmark dataset. The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.

2 20

Publication

A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration

( 2011-08)

Ammar Zakaria

;

Ali Yeon Md Shakaff

;

Masnan, Maz Jamilah

;

Norazian Subari

;

Nazifah Ahmad Fikri

;

Abdul Hamid Adom

;

Mohd Noor Ahmad

;

Mahmad Nor Jaafar

;

Latifah Munirah Kamarudin

;

Abdul Hallis Abdul Aziz

;

Abu Hassan Abdullah

;

Supri A. Ghani

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

1 66

Publication

A five-level inverter using SEPIC converter for motor drive application

(AIP Publishing Ltd., 2023)

Muhamad Akmal Aazmi

;

Muhammad Izuan Fahmi Romli

;

A. Z. Iman

The research in inverter is one of the fast-evolving technology in the present era and many researchers start to replace the conventional transformer with different types of circuits. A major problem with the conventional transformers resulting in entire system in expensive range and voluminous. This paper will demonstrate a new technique to replace the conventional transformer concept by using dual DC/DC SEPIC Converter and modified H-bridge inverter to form a five-level inverter with multirange voltage selection which depending on the duty-cycle employed for SEPIC converter. The input DC voltage of the proposed inverter can be stepped-down or stepped-up the five-level output voltage waveform which make it suitable to drive the AC motor drive. A simulation using PowerSim with various duty-cycle values, are used to evaluate the suggested inverter.

4 1

Publication

A heuristic ranking approach on capacity benefit margin determination using pareto-based evolutionary programming technique

( 2015)

Muhammad Murtadha Othman

;

Nurulazmi Abd. Rahman

;

Ismail Musirin

;

Mahmud Fotuhi-Firuzabad

;

Abbas Rajabi-Ghahnavieh

This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.

4 10

Publication

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

( 2019)

Abdul Syafiq Abdull Sukor

;

Ammar Zakaria

;

Norasmadi Abdul Rahim

;

Latifah Munirah Kamarudin

;

Rossi Setchi

;

Hiromitsu Nishizaki

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.

19 16

Publication

A LabVIEW-based real-time GUI for switched controlled energy harvesting circuit for low voltage application

(Taylor and Francis, 2018)

MD Adnan Shahrukh Khan

;

R. K. Rajkumar

;

C. V. Aravind

;

Y. W. Wong

;

Muhammad Izuan Fahmi Romli

This paper develops a universal Real-Time Graphical User Interface for the first time for low-voltage energy Harvesting. The proposed GUI is built in LabVIEW with NIUSB 621 DAQ that synchronized the data to perform real-time analysis through the use of power electronics. A hybrid Vertical Axis Wind Turbine adapted to a 200 W Permanent Magnet Synchronous Generator is used for incorporating the supercapacitor-based battery charging energy harvesting system. The GUI displays the real-time energy harvesting output readings both graphically and digitally along with wind speed and angular velocity of the turbine. The model is built in such a way so that it could be used as a universal GUI for both wind and solar energy harvesting with minimal adjustment.

5 1

Publication

A simple duty cycle control technique to minimize torque ripple in open-end winding induction motor

(Institute of Advanced Engineering and Science (IAES), 2023)

Muhammad Zaid Aihsan

;

Auzani Jidin

;

Azrita Alias

;

Tole Sutikno

Modern electric vehicles (EVs) that drive an induction motor (IM) fed by a traction inverter are fast gaining popularity due to their simple configuration and robustness. The direct torque control (DTC) technique is one of the best control methods to drive the IM, especially in open-end winding configurations, as it offers more voltage vectors. However, the existence of hysteresis controllers and improper switching technique causes larger torque ripples that leads to variable switching frequency. The study will be focused on the open-end winding induction motor where the direct current (DC) power is fed from both sides of the stator windings using the dual inverter configuration. To minimize the torque ripples, a simple switching technique using the duty cycle control method is proposed by injecting a high-frequency square wave into the default inverter switching status to form the new pattern of voltage vectors. The effectiveness of the proposed technique is tested through MATLAB/Simulink software and validated experimentally with a lab-scale setup using a dSPACE controller. The findings show that the proposed method reduces torque ripple by over 50% while keeping the DTC's simple structure.

Publication

A systematic review of phacoemulsification cataract surgery in virtual reality simulators

( 2013-01-27)

Lam Chee Kiang

;

Kenneth Sundaraj

;

Mohd Nazri Sulaiman

The aim of this study was to review the capability of virtual reality simulators in the application of phacoemulsification cataract surgery training. Our review included the scientific publications on cataract surgery simulators that had been developed by different groups of researchers along with commercialized surgical training products, such as EYESI® and PhacoVision®. The review covers the simulation of the main cataract surgery procedures, i.e., corneal incision, capsulorrhexis, phacosculpting, and intraocular lens implantation in various virtual reality surgery simulators. Haptics realism and visual realism of the procedures are the main elements in imitating the actual surgical environment. The involvement of ophthalmology in research on virtual reality since the early 1990s has made a great impact on the development of surgical simulators. Most of the latest cataract surgery training systems are able to offer high fidelity in visual feedback and haptics feedback, but visual realism, such as the rotational movements of an eyeball with response to the force applied by surgical instruments, is still lacking in some of them. The assessment of the surgical tasks carried out on the simulators showed a significant difference in the performance before and after the training.

Publication

Accelerated thermal aging of Kraft papers impregnated with dielectric liquids

(Institute of Advanced Engineering and Science (IAES), 2023)

Imran Sutan Chairul

;

Norazhar Abu Bakar

;

Sharin Ab Ghani

;

Mohd Shahril Ahmad Khiar

;

Nor Hidayah Rahim

;

Syahrun Nizam Md Arshad @ Hashim

Accelerated thermal aging was conducted on Kraft papers impregnated with mineral insulating oil (MO) and palm insulating oil (PO), and the effect of aging time on the oils and Kraft papers was observed. Each sample consisted of insulating oil, dried Kraft paper, and weighed metal catalysts (copper, iron, zinc, and aluminum) in a bottle. Prior to aging, the bottles were left for 24 h at room temperature for impregnation to take place. The thermal aging experiments were carried out at 130 °C for 250, 500, and 750 h. The properties of the MO and PO (moisture content, acidity, and ultraviolet-visible absorption spectra) and the properties of the Kraft papers (tensile strength and colour) were determined. Results showed that the aged PO had higher moisture content compared with the aged MO. However, the Kraft papers impregnated with PO had better tensile strength after 750 h of aging, which may be attributed to the affinity of PO to moisture. This slows down the hydrolytic degradation mechanism. In terms of colour, the Kraft papers were darker than their original colour as the tensile strength decreased. To conclude, the Kraft paper impregnated with PO had higher tensile strength compared with those impregnated with MO.

Publication

An alternative approaches to predict flashover voltage on polluted outdoor insulators using artificial intelligence techniques

( 2020-04-01)

Salem, Ali. Ahmed. Ali

;

Rahman, Rahisman Abd

;

Kamarudin M.S.

;

Othman, Nordiana Azlin

;

Jamail, Nor. Akmal. Mohd.

;

Haziah Abdul Hamid

;

Ishak, Mohd. Taufiq

This paper presents an alternative approach for predicting critical voltage of pollution flashover by using Artificial Intelligence (AI) technique. Data from experimental works combined with the theoretical results from well-known theoretical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining critical voltage of flashover. Series of laboratory testing and measurement are carried for 1:1, 1:5 and 1:10 ratios of top to bottom surface salt deposit density on cup and pin insulators. Insulators variables such as height H, diameter D, form factor F, creepage distance L, equivalent salt deposit density (ESDD) and flashover voltage correction are identified and used to train the AI network. Comparative studies have evidently shown that the proposed (AI) technique gives the satisfactory results compared to the analytical model and test data with the Coefficient of determination R-Square value of more than 97%.

1 18

Publication

An emotion assessment of stroke patients by using bispectrum features of EEG Signals

( 2020)

Choong Wen Yean

;

Wan Khairunizam Wan Ahmad

;

Wan Azani Wan Mustafa

;

Murugappan Murugappan

;

Yuvaraj Rajamanickam

;

Abdul Hamid Adom

;

Mohammad Iqbal Omar

;

Bong Siao Zheng

;

Ahmad Kadri Junoh

;

Zuradzman Mohamad Razlan

;

Shahriman Abu Bakar

Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.

Publication

Analysis of space charge formation in LDPE in the presence of crosslinking byproducts

( 2012-02)

Nuriziani Hussin

;

George Chen

Cross-linking byproducts are suspected to be the main contributing factor in space charge formation observed in XLPE. To investigate the mechanism behind this phenomenon, low density polyethylene was soaked into three main crosslinking byproducts, acetophenone, α- methylstyrene and cumyl alcohol, and space charge measurements were performed using the Pulse Electroacoustic technique (PEA). It has been found that soaking LDPE in cumyl alcohol introduces more charges into the system, with homocharges and heterocharges accumulating within the sample compared to the additive free sample. In contrast, α- methylstyrene and acetophenone reduce the amount of accumulated charges. In terms of charge decay, all three byproducts enhance the decay process in the insulator. Further investigations were conducted in conditions where two byproducts are present in a sample. The results shows that acetophenone is a dominant byproduct in determining the charge density patter built up during the charging process, whilst the rate of charge decay is observed to be high in the presence of α-methylstyrene in the sample.

Publication

Analysis of the feasibility of adding a grid-connected hybrid photovoltaic system to reduce electrical load

(Institute of Advanced Engineering and Science (IAES), 2023)

Moranain Mungkin

;

Habib Satria

;

Dina Maizana

;

Muzamir Isa

;

Syafii Syafii

;

Muhammad Yonggi Puriza

The power generation system with hybrid system grid connected (HSGC) technology is an energy-saving technology that is able to compensate for electricity loads in an energy-efficient manner in today's technological advances. Electrical transient analyzer power (ETAP) simulation software is implemented so that the modeling will have an impact on the development of hybrid systems. Testing the reliability of the system is simulated at the load of school buildings, laboratories, mosques, and kindergarten schools. parameters obtained by evaluating the ratio of the voltage drop before and after the addition of photovoltaic. The value of the voltage drops decreases with the integration of hybrid photovoltaic. The school building panels experienced a voltage drop of 0.15%, reduced after the addition of photovoltaics and wind turbines. Then on the Laboratory panel, a voltage drops of 0.05% was obtained, on the mosque panel the voltage drop reached 0.11%, and on the kindergarten building panel the voltage drop reached 0.09% after the addition of photovoltaic hybrid. From this comparison it can be said that PV and wind turbines can affect the voltage drop and reduce the consumption of electricity loads from the grid caused by adding power from the hybrid photovoltaic hybrid to each load.

1 1

Publication

Analysis of wheeze sounds during tidal breathing according to severity levels in asthma patients

(Taylor & Francis, 2019)

Fizza Ghulam Nabi

;

Kenneth Sundaraj

;

Lam Chee Kiang

;

Rajkumar Palaniappan

Objective: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. Method: Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies F25, F50, F75, F90 and F99, mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. Results: All features generally showed statistical significance in most of the datasets for all severity levels [χ2 = 6.021–71.65, p < 0.05, η2 = 0.01–0.52]. Of the seven investigated features, only AP showed statistical significance in all the datasets. F25, F75, F90 and F99 exhibited statistical significance in at least six datasets [χ2 = 4.852–65.63, p < 0.05, η2 = 0.01–0.52], and F25, F50 and MF showed statistical significance with a large η2 in all trachea-related datasets [χ2 = 13.54–55.32, p < 0.05, η2 = 0.13–0.33]. Conclusion: The results obtained for the time-frequency features revealed that (1) the asthma severity levels of patients can be identified through a set of selected features with tidal breathing, (2) tracheal wheeze sounds are more sensitive and specific predictors of severity levels and (3) inspiratory and expiratory wheeze sounds are almost equally informative.

Publication

Artificial neural network application in an implemented lightning locating system

(Elsevier, 2020)

Kamyar Mehranzamir

;

Zulkurnain Abdul-Malek

;

Hadi Nabipour Afrouzi

;

Saeed Vahabi Mashak

;

Wooi Chin Leong

;

Roozbeh Zarei

Time difference of arrival (TDOA) technique is one of many bases to determine lightning strike location employed in a lightning locating system (LLS). In this technique, at least four measurement sensors are required to correctly locate a lightning strike. Usage of fewer number of sensors will result in non-unique solutions to the generated hyperbolas, and hence wrong lightning strike point. This research aims to correctly determine the strike point even if only three measuring sensors are utilized. An artificial neural network (ANN) based algorithm was developed for a 400 km2 coverage area in Southern Malaysia using time of arrival data collected at the three measuring stations over a certain period. The Levenberg–Marquardt algorithm is demonstrated to correctly identify the lightning strike coordinates with an average error of 350 m. The algorithm has helped the three-station TDOA-based LLS to successfully locate the lightning strike point with a remarkable accuracy comparable to that of commercial systems.

2 10

Publication

Auditory evoked potential response and hearing loss: a review

( 2015)

M. P Paulraj

;

Kamalraj Subramaniam

;

Sazali Bin Yaccob

;

Abdul Hamid Adom

;

C. R Hema

Hypoacusis is the most prevalent sensory disability in the world and consequently, it can lead to impede speech in human beings. One best approach to tackle this issue is to conduct early and effective hearing screening test using Electroencephalogram (EEG). EEG based hearing threshold level determination is most suitable for persons who lack verbal communication and behavioral response to sound stimulation. Auditory evoked potential (AEP) is a type of EEG signal emanated from the brain scalp by an acoustical stimulus. The goal of this review is to assess the current state of knowledge in estimating the hearing threshold levels based on AEP response. AEP response reflects the auditory ability level of an individual. An intelligent hearing perception level system enables to examine and determine the functional integrity of the auditory system. Systematic evaluation of EEG based hearing perception level system predicting the hearing loss in newborns, infants and multiple handicaps will be a priority of interest for future research.

3 10

Publication

Cervical cancer detection techniques: a chronological review

( 2023-05-17)

Wan Azani Wan Mustafa

;

Shahrina Ismail

;

Fahirah Syaliza Mokhtar

;

Hiam Alquran

;

Yazan Al-Issa

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.

2 9

Publication

Classification of Agarwood oil using an Electronic Nose

( 2010)

Wahyu Hidayat

;

Ali Yeon Md Shakaff

;

Mohd Noor Ahmad

;

Abdul Hamid Adom

Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.

1 18

Publication

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

( 2013-05-16)

Jerritta Selvaraj

;

Murugappan Murugappan

;

Wan Khairunizam Wan Ahmad

;

Sazali Yaacob

Background: Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.Methods: Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.Results: Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.Conclusions: The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.

Publication

Comparative analysis of conventional and modern high-rise hotels in Penang based on hourly simulation of cooling load performance using DesignBuilder

(Semarak Ilmu Publishing, 2023)

Muhammad Hafeez Abdul Nasir

;

Ahmad Sanusi Hassan

;

Aimi Salihah Abdul Nasir

;

Mohd Suhaimi Mohd-Danuri

;

Mohd Nasrun Mohd Nawi

;

Rafikullah Deraman

The study examines the energy efficiency performance of hotel façades in relation to the annual cooling load simulation. In achieving the objective, two case studies of high-rise city hotels are selected within the locality of Penang, Malaysia. The case studies are selected based on the year of construction coupled with the architectural styles encompassing conventional and modern design of hotel facades. In traditional hotel facades, passive design elements, including proper window and wall materials selection alongside window-to-wall ratio (WWR), are less significant. Comparatively, elements of passive design in modern hotel facades are notable. In assessing the thermal performance of the hotel façade, a case study of the conventional and modern high-rise city hotels in Penang are selected to undergo hourly cooling load simulation in the hotel guestroom using the DesignBuilder simulation program in establishing the hotel’s energy efficiency performance. The findings revealed the average annual cooling energy of the conventional and modern high-rise city hotel guestrooms is 553 kWh/m2 and 538 kWh/m2, respectively. The study concludes the elements of passive design, including WWR, window material selection, and external wall colour are crucial in determining energy-efficient hotel operations.

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