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30,468 Article Results

GENCO Optimal Bidding Strategy and Profit based Unit Commitment using Evolutionary Particle Swarm Optimization Illustrating the Effect of GENCO Market Power

10.11591/ijece.v8i4.pp1997-2013
Adline Bikeri , Christopher Muriithi , Peter Kihato
In deregulated electricity markets, generation companies (GENCOs) make unit commitment (UC) decisions based on a profit maximization objective in what is termed profit based unit commitment (PBUC). PBUC is done for the GENCOs demand which is a summation of its bilateral demand and allocations from the spot energy market. While the bilateral demand is known, allocations from the spot energy market depend on the GENCOs bidding strategy. A GENCO thus requires an optimal bidding strategy (OBS) which when combined with a PBUC approach would maximize operating profits. In this paper, a solution of the combined OBS-PBUC problem is presented. An evolutionary particle swarm optimization (EPSO) algorithm is implemented for solving the optimization problem. Simulation results carried out for a test power system with GENCOs of differing market strengths show that the optimal bidding strategy depends on the GENCOs market power. Larger GENCOs with significant market power would typically bid higher to raise market clearing prices while smaller GENCOs would typically bid lower to capture a larger portion of the spot market demand. It is also illustrated that the proposed EPSO algorithm has a better performance in terms of solution quality than the classical PSO algorithm.
Volume: 8
Issue: 4
Page: 1997-2013
Publish at: 2018-08-01

Dual Band to Wideband Pentagon-shaped Patch Antenna with Frequency Reconfigurability using EBGs

10.11591/ijece.v8i4.pp2557-2563
Raimi Dewan , M. K. A. Rahim , M. R. Hamid , M. F. M. Yusoff , H. A. Majid , B. A. F. Esmail
A dual band to wideband reconfigurable pentagon-shaped antenna with EBG unit cell is proposed. A minimal number of two EBG unit cell is deployed to realize frequency reconfigurable mechanism.  By varying the state of the EBG the antenna is capable to change its dual band operation to wideband alternately. There are three cases that have been analysed, first case is the EBG incorporated antenna with ideal and second is with the active EBG. Subsequently, the third cases is the fabricated ideal EBG incorporated antenna. The dual band operation is at 1.8 GHz and 5.2 GHz while the wide band from 1.6 GHz to 2.37 GHz (770 MHz). The proposed reconfigurable antenna is suitable to be implemented for LTE (1.6 GHz), Wi-Fi (5.2 GHz), WiMAX (2.3 GHz) and cognitive radio application.
Volume: 8
Issue: 4
Page: 2557-2563
Publish at: 2018-08-01

System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model

10.11591/ijece.v8i4.pp2327-2337
Heni Kusdarwati , Samingun Handoyo
This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.
Volume: 8
Issue: 4
Page: 2327-2337
Publish at: 2018-08-01

Angular Symmetric Axis Constellation Model for Off-line Odia Handwritten Characters Recognition

10.11591/ijict.v7i2.pp96-104
Pyari Mohan Jena , Soumya Ranjan Nayak
Optical character recognition is one of the emerging research topics in the field of image processing, and it has extensive area of application in pattern recognition. Odia handwritten script is the most research concern area because it has eldest and most likable language in the state of odisha, India. Odia character is a usually handwritten, which was generally occupied by scanner into machine readable form. In this regard several recognition technique have been evolved for variance kind of languages but writing pattern of odia character is just like as curve appearance; Hence it is more difficult for recognition. In this article we have presented the novel approach for Odia character recognition based on the different angle based symmetric axis feature extraction technique which gives high accuracy of recognition pattern. This empirical model generates a unique angle based boundary points on every skeletonised character images. These points are interconnected with each other in order to extract row and column symmetry axis. We extracted feature matrix having mean distance of row, mean angle of row, mean distance of column and mean angle of column from centre of the image to midpoint of the symmetric axis respectively. The system uses a 10 fold validation to the random forest (RF) classifier and SVM for feature matrix. We have considered the standard database on 200 images having each of 47 Odia character and 10 Odia numeric for simulation. As we have noted outcome of simulation of SVM and RF yields 96.3% and 98.2% accuracy rate on NIT Rourkela Odia character database and 88.9% and 93.6% from ISI Kolkata Odia numerical database.
Volume: 7
Issue: 2
Page: 96-104
Publish at: 2018-08-01

Removal of Fixed-valued Impulse Noise based on Probability of Existence of the Image Pixel

10.11591/ijece.v8i4.pp2106-2114
Ali Awad
This paper proposes a new approach for restoring images distorted by fixed-valued impulse noise. The detection process is based on finding the probability of existence of the image pixel. Extensive investigations indicate that the probability of existence of a pixel in an original image is bounded and has a maximum limit. The tested pixel is judged as original if it has probability of existence less than the threshold boundary. In many tested images, the proposed method indicates that the noisy pixels are detected efficiently. Moreover, this method is very fast, easy to implement and has an outstanding performance when compared with other well-known methods. Therefore, if the proposed filter is added as a preliminary stage to many filters, the final results will be improved.
Volume: 8
Issue: 4
Page: 2106-2114
Publish at: 2018-08-01

Evaluation of the Medical Image Compression using Wavelet Packet Transform and SPIHT Coding

10.11591/ijece.v8i4.pp2139-2147
Ismahane Benyahia , Mohammed Beladgham , Abdesselam Bassou
Wavelet transforms and wavelet packets are widely imposed in the analysis and resolution of problems related to science and technical engineering. Decomposition wavelet packet allows several frequency bands according to various levels of resolutions. We apply this transform (PWT) coupled with the SPIHT coder to reduce the limitations of conventional wavelet filter bank. The results obtained using the applied algorithm, are very satisfactory and encouraging compared to many of the best coders cited in the literature and show a visual and numerical superiority over conventional methods. These the promising results are confirmed by visual evaluation parameters (PSNR, MSSIM and VIF).
Volume: 8
Issue: 4
Page: 2139-2147
Publish at: 2018-08-01

Exploiting 2-Dimensional Source Correlation in Channel Decoding with Parameter Estimation

10.11591/ijece.v8i4.pp2633-2642
Muhammad Izzat Amir Mohd Nor , Mohd Azri Mohd Izhar , Norulhusna Ahmad , Hazilah Md. Kaidi
Traditionally, it is assumed that source coding is perfect and therefore, the redundancy of the source encoded bit-stream is zero. However, in reality, this is not the case as the existing source encoders are imperfect and yield residual redundancy at the output. The residual redundancy can be exploited by using Joint Source Channel Coding (JSCC) with Markov chain as the source. In several studies, the statistical knowledge of the sources has been assumed to be perfectly available at the receiver. Although the result was better in terms of the BER performance, practically, the source correlation knowledge were not always available at the receiver and thus, this could affect the reliability of the outcome. The source correlation on all rows and columns of the 2D sources were well exploited by using a modified Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm in the decoder. A parameter estimation technique was used jointly with the decoder to estimate the source correlation knowledge. Hence, this research aims to investigate the parameter estimation for 2D JSCC system which reflects a practical scenario where the source correlation knowledge are not always available. We compare the performance of the proposed joint decoding and estimation technique with the ideal 2D JSCC system with perfect knowledge of the source correlation knowledge. Simulation results reveal that our proposed coding scheme performs very close to the ideal 2D JSCC system.
Volume: 8
Issue: 4
Page: 2633-2642
Publish at: 2018-08-01

Optimization of Modified Sliding Mode Controller for an Electro-hydraulic Actuator System with Mismatched Disturbance

10.11591/ijece.v8i4.pp2148-2156
Siti Marhainis Othman , M. F. Rahmat , S. M. Rozali , Zulfatman Has , A. F. Z. Abidin
This paper presents the design of the modified sliding mode controller (MSMC) for the purpose of tracking the nonlinear system with mismatched disturbance. Provided that the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA), and particle swarm optimization (PSO) techniques are used to optimize these parameters in order to achieve a predefined system’s performance. In respect of system’s performance, it is evaluated based on the tracking error present between reference inputs transferred to the system and the system output. This is followed by verification of the efficiency of the designed controller in simulation environment under various values, with and without the inclusion of external disturbance. It can be seen from the simulation results that the MSMC with PSO exhibits a better performance in comparison to the performance of the similar controller with GSA in terms of output response and tracking error.
Volume: 8
Issue: 4
Page: 2148-2156
Publish at: 2018-08-01

Evaluation of a Multiple Regression Model for Noisy and Missing Data

10.11591/ijece.v8i4.pp2220-2229
Chanintorn Jittawiriyanukoon
The standard data collection problems may involve noiseless data while on the other hand large organizations commonly experience noisy and missing data, probably concerning data collected from individuals. As noisy and missing data will be significantly worrisome for occasions of the vast data collection then the investigation of different filtering techniques for big data environment would be remarkable. A multiple regression model where big data is employed for experimenting will be presented. Approximation for datasets with noisy and missing data is also proposed. The statistical root mean squared error (RMSE) associated with correlation coefficient (COEF) will be analyzed to prove the accuracy of estimators. Finally, results predicted by massive online analysis (MOA) will be compared to those real data collected from the following different time. These theoretical predictions with noisy and missing data estimation by simulation, revealing consistency with the real data are illustrated. Deletion mechanism (DEL) outperforms with the lowest average percentage of error.
Volume: 8
Issue: 4
Page: 2220-2229
Publish at: 2018-08-01

Evolution of Precision Agriculture Computing towards Sustainable Oil Palm Industry

10.11591/ijeecs.v11.i2.pp725-732
K.C. Goh , S.Y. Sim , H.H. Goh , K. Bilal , T.H. Sam , T.Y. Teoh , J. S. Tey
Precision technology elements have not been implemented yet into the sustainable oil palm industry because the knowledge and technology gap. To resolve the gaps, promote sustainability and integrate the technologies, Oil Palm Management System (OPAMS) was introduced. The precision technologies in OPAMS comprises of Geographical Information System (GIS), Global Positioning System (GPS), remote sensing and yield monitoring. A phase by phase System Development Life Cycle (SDLC) methodology was used to generate the said system with feedbacks from oil palm planters as the inputs for OPAMS’s key features. OPAMS ultimately aims to increase the awareness of the industry on the benefits of utilizing technology to improve plantation performances, increase business and environmental sustainability.
Volume: 11
Issue: 2
Page: 725-732
Publish at: 2018-08-01

Study and Dimensioning of the Tanks Dedicated to a Compressed Air Storage System (CAES)

10.11591/ijece.v8i4.pp2029-2037
Ilham Rais , H. Mahmoudi
The fundamental idea of storage is to transfer available energy During periods of low demand, using only a fraction of the fuel that would be consumed by the standard production machine (gas turbine, thermal engine, etc.). The main role of energy storage is therefore to introduce an energy degree of freedom to decouple Consumers and the producer by supplying or Delivering the difference between these two powers. In this paper is this paper presents a brief study and dimensioning of compressed air storage tanks to a hybrid system wind-PV . adopts the CAES system as a storage agent. Starting with the technical criteria on which the choice of reservoirs is based and the mechanical constraints that must be taken into consideration for dimensioning of the reservoirs
Volume: 8
Issue: 4
Page: 2029-2037
Publish at: 2018-08-01

Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval

10.11591/ijece.v8i4.pp2261-2271
Pushpalatha R. , K. Meenakshi Sundaram
Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm.  This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-art-works.
Volume: 8
Issue: 4
Page: 2261-2271
Publish at: 2018-08-01

Web based Water Turbidity Monitoring and Automated Filtration System: IoT Application in Water Management

10.11591/ijece.v8i4.pp2503-2511
S. Noorjannah Ibrahim , A. L. Asnawi , N. Abdul Malik , N. F. Mohd Azmin , A. Z. Jusoh , F. N. Mohd Isa
Water supplied to residential areas is prone to contaminants due to pipe residues and silt, and therefore resulted in cloudiness, unfavorable taste, and odor in water. Turbidity, a measure of water cloudiness, is one of the important factors for assessing water quality. This paper proposes a low-cost turbidity system based on a light detection unit to measure the cloudiness in water. The automated system uses Intel Galileo 2 as the microprocessor and a server for a web-based monitoring system. The turbidity detection unit consists of a Light Dependent Resistor (LDR) and a Light Emitting Diode (LED) inside a polyvinyl chloride (PVC) pipe. Turbidity readings were recorded for two different positionings; 90° and 180° between the detector (LDR) and the incident light (LED). Once the turbidity level reached a threshold level, the system will trigger the filtration process to clean the water. The voltage output captured from the designed system versus total suspended solid (TSS) in sample water is graphed and analyzed in two different conditions; in total darkness and in the present of ambient light. This paper also discusses and compares the results from the above-mentioned conditions when the system is submerged in still and flowing water. It was found that the trends of the plotted graph decline when the total suspended solid increased for both 90° and 180° detector turbidimeter in all conditions which imitate the trends of a commercial turbidimeter. By taking the consideration of the above findings, the design can be recommended for a low-cost real-time web-based monitoring system of the water quality in an IOT environment.
Volume: 8
Issue: 4
Page: 2503-2511
Publish at: 2018-08-01

Instruction Set Extension of a Low-End Reconfigurable Microcontroller in Bit-Sorting Implementation

10.11591/ijece.v8i4.pp2595-2601
Sani Irwan Md Salim , Yewguan Soo , Sharatul Izah Samsudin
The microcontroller-based system is currently having a tremendous boost with the revelation of platforms such as the Internet of Things. Low-end families of microcontroller architecture are still in demand albeit less technologically advanced due to its better I/O better application and control. However, there is clearly a lack of computational capability of the low-end architecture that will affect the pre-processing stage of the received data. The purpose of this research is to combine the best feature of an 8-bit microcontroller architecture together with the computationally complex operations without incurring extra resources. The modules’ integration is implemented using instruction set architecture (ISA) extension technique and is developed on the Field Programmable Gate Array (FPGA). Extensive simulations were performed with the and a comprehensive methodology is proposed. It was found that the ISA extension from 12-bit to 16-bit has produced a faster execution time with fewer resource utilization when implementing the bit-sorting algorithm. The overall development process used in this research is flexible enough for further investigation either by extending its module to more complex algorithms or evaluating other designs of its components.
Volume: 8
Issue: 4
Page: 2595-2601
Publish at: 2018-08-01

Conceptual Sentiment Analysis Model

10.11591/ijece.v8i4.pp2358-2366
Kranti Vithal Ghag , Ketan Shah
Bag-of-words approach is popularly used for Sentiment analysis. It maps the terms in the reviews to term-document vectors and thus disrupts the syntactic structure of sentences in the reviews. Association among the terms or the semantic structure of sentences is also not preserved. This research work focuses on classifying the sentiments by considering the syntactic and semantic structure of the sentences in the review. To improve accuracy, sentiment classifiers based on relative frequency, average frequency and term frequency inverse document frequency were proposed. To handle terms with apostrophe, preprocessing techniques were extended. To focus on opinionated contents, subjectivity extraction was performed at phrase level. Experiments were performed on Pang & Lees, Kaggle’s and UCI’s dataset. Classifiers were also evaluated on the UCI’s Product and Restaurant dataset. Sentiment Classification accuracy improved from 67.9% for a comparable term weighing technique, DeltaTFIDF, up to 77.2% for proposed classifiers. Inception of the proposed concept based approach, subjectivity extraction and extensions to preprocessing techniques, improved the accuracy to 93.9%.
Volume: 8
Issue: 4
Page: 2358-2366
Publish at: 2018-08-01
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