The generation revenue and demand payment assessment for pool based market model in Malaysia electricity supply industry

The objective of this paper is to address the economic benefits in term of generation revenue and demand payment for the pool based market model in Malaysia electricity supply industry (MESI). In pool market model, there are issues on the benefit of the generators such as too high system marginal price (SMP) during peak demand and no revenue during low demand. Therefore, conceptual study for two bus test system in MESI involving four generators around Peninsular Malaysia is conducted to perform the economic analysis in term of generation revenue and demand assessment considering existing single buyer model and pool based market model, i.e., pool model, spot market model and the proposed model, in order to identify which market model is superior. As a result, the proposed model managed to decrease the demand payment as it is proportional to generation revenue, even though the generation revenue is at intermediate value and succeed to increase the low and medium generator’s revenue

Dosimetric Assessment of Routine X-Ray Examination at Selected Health Clinics in Perak Using Commercialized Optically-Stimulated Luminescence Dosimeter (OSLD)

This study aims to compare entrance surface dose (ESD) values measured with nanoDot Al2O3:C optically-stimulated luminescence dosimeter (OSLD) and guidance level set under the second national dose survey which utilized old-version LiF:Mg,Ti thermoluminescence dosimeter (TLD). In this study, we conducted a dosimetric assessment for posteroanterior chest X-ray (PA-CXR) examinations performed at various community clinics in Perak, Malaysia. These clinics were selected as they were excluded from the first and second national dose survey conducted in Malaysia in 1993-1995 and 2005-2009, respectively. The ESD is obtained by mounting the OSLD on the surface of polymethyl methacrylate (PMMA) slabs. The PMMA slabs were then exposed to X-ray based on the current practice of respective clinics. The results show that the 3rd quartile of ESDs ranged from 0.180 mGy to 0.229 mGy which is less than the recommended guidance level of the second national dose survey by 77 %. ESD measured using OSLD was found to be lower than the guidance values recommended from the second national dose survey. The finding showed a good competency of the radiographer to optimize radiological practice specifically in routine X-ray examination

Estimation of Photovoltaic Module Parameters based on Total Error Minimization of I-V Characteristic

Mathematical Modelling of photovoltaic (PV) modules is important for simulation and performance analysis of PV system. Therefore, an accurate parameters estimation is necessary. Single-diode and two-diode model are widely used to model the PV system. However, it required to determine several parameters such as series and shunt resistances that not provided in datasheet.  The main goal of PV modelling technique is to obtain the accurate parameters to ensure the I-V characteristic is closed to the manufacturer datasheet. Previously, the maximum power error of calculated and datasheet value are considered as objective to be minimized for both models. This paper proposes the PV parameter estimation model based minimizing the total error of open circuit voltage (VOC), short circuit current (ISC) and maximum power (PMAX) where all these parameters are provided by the manufacturer. The performance of single-diode and two-diode models are tested on different type of PV modules using MATLAB. It found that the two-diode model obtained accurate parameters with smaller error compared to single-diode model. However, the simulation time is slightly higher than single-diode model due extra calculation required

An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis

To further improve the accuracy of classifier for cancer diagnosis, a hybrid model called GRA-SVM which comprises Support Vector Machine classifier and filter feature selection Grey Relational Analysis is proposed and tested against Wisconsin Breast Cancer Dataset (WBCD) and BUPA Disorder Dataset. The performance of GRA-SVM is compared to SVM’s in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The experimental results reveal that GRA-SVM improves the SVM accuracy of about 0.48 by using only two features for the WBCD dataset. For BUPA dataset, GRA-SVM improves the SVM accuracy of about 0.97 by using four features. Besides improving the accuracy performance, GRA-SVM also produces a ranking scheme that provides information about the priority of each feature. Therefore, based on the benefits gained, GRA-SVM is recommended as a new approach to obtain a better and more accurate result for cancer diagnosis

Kinetic Model and Simulation Analysis for Propane Dehydrogenation in an Industrial Moving Bed Reactor

A kinetic model for propane dehydrogenation in an industrial moving bed reactor is developed based on the reported reaction scheme. The kinetic parameters and activity constant are fine tuned with several sets of balanced plant data. Plant data at different operating conditions is applied to validate the model and the results show a good agreement between the model predictions and plant observations in terms of the amount of main product, propylene produced. The simulation analysis of key variables such as inlet temperature of each reactor (T ) and hydrogen to total hydrocarbon ratio (H2/THC) affecting process performance is performed to identify the operating condition to maximize the production of propylene. Within the range of operating conditions applied in the present studies, the operating condition to maximize the propylene production at the same weighted average inlet temperature (WAIT) is ΔT = -2, ΔT inrx1 = +1, ΔT inrx2 inrx = +1 , ΔT inrx4 inrx3 = +2 and ΔH2/THC= -0.02. Under this condition, the surplus propylene produced is 7.07 tons/day as compared with base case

Audio deformation based data augmentation for convolution neural network in vibration analysis

Audio deformations in audio processing have proved ability in preserve semantic meaning for audio signal. Convolution Neural Network (CNN) is among deep learning model that requires huge dataset during training for excellence performance Thus, data augmentation (DA) method is used to overcome the problem of limited dataset number for vibration analysis. Several signal processing phases including segmentation and image converting need to be performed before the vibration signal can be used as input for CNN. In this research, audio-deformation based DA is proposed in generating the additional vibration signal dataset. The proses is start by encoding the raw vibration signal to audio signal format to enable the audio deformation process performing, then decoding back into new vibration signal. Speed and amplify transformation are selected for audio deformation process. The new vibration data set of bearing fault detection problem are used for training CNN to validate the proposed approach. The results obtained from 13 experiments setting have shown that the proposed DA able to increase the accuracy of training for CNN until 13% compared with the previous DA method

Secured tracking and tracing system based on blockchain technology

Tracking and tracing management is a system which require recording of product's related information associated with product movement, shipping, transition between location until the product reach its final destination. In this management, traceability is a critical element to be satisfied by the business processes. Tracking and tracing of product is important for many purposes from the time product start its order process, prepared, shipping, movement from one delivery stakeholder to other delivery stakeholder until the product reach its destination. This is where we found the effectiveness of technology that is called the blockchain that could increase the safety of all tracking management processes. The blockchain technology since it emerges has contributed to many wide ranges of applications from various fields where safety and trust are critical in the field business process. Through this research, we are willing to present the contribution which can be offered by blockchain that obviously can increase the safety such like other tracking technology such as the use of QR-code, RFID, man-to-man delivery and few others

A review on sensing-based strategies of interior lighting control system and their performance in commercial buildings

Artificial lighting consumed significant amount of electrical energy in commercial buildings. Therefore, intelligent control strategies are widely implemented to reduce the lighting energy consumption. This paper presents comprehensive review of the current sensing-based strategies (i.e. occupancy, daylight and mixed), sensors placement methods (i.e. occupancy and light) and factors affecting the performance of the lighting control strategies. Based on literature survey, the sensors placement methods can be categorized into three approaches: Fixed, mathematical equation and optimization. The state-of-the-art of these approaches are discusses in details. It found that, the optimization-based approach capable to find the optimal sensor placement (numbers and positions) effectively. Moreover, the mixed strategy can be produced the highest energy savings up to 95% compared with other strategies. The occupancy pattern and building characteristics are the main factors to contribute higher energy savings of sensing-based strategies in commercial buildings

Automated positioning dual-axis solar tracking system with precision elevation and azimuth angle control

This paper presents a study on an automated positioning open-loop dual-axis solar tracking system. The solar tracker was designed and fabricated using standard cylindrical aluminium hollow and Polyuthrene (PE). The control system of the solar tracker was governed by Micro Controller Unit (MCU) with auxiliary devices which includes encoder and Global Positioning System (GPS). The sun path trajectory algorithm utilizing the astronomical equation and GPS information was also embedded in the system. The power generation performance of the dual-axis solar tracking system was compared with the fixed-tilted Photovoltaic (PV) system. It is found that the solar tracker is able to position itself automatically based on sun path trajectory algorithm with an accuracy of ±0.5°. The embedded Proportional Integral Derivative (PID) positioning system improves the tracking of elevation and azimuth angles with minimum energy consumption. It is reveals that the proposed solar tracker is able generate 26.9% and 12.8% higher power than fixed-tilted PV system on a clear and heavy overcast conditions respectively. Overall, the open-loop dual-axis solar tracker can be deployed automatically at any location on the earth with minimal configurations and is suitable for mobile solar tracking system