Search results for: forecast accuracy unemployment rate.

Authors: T. Mahalakshmi, Aswathi B. L., Achuthsankar S. Nair

Abstract:

Transcription factors are a group of proteins that helps for interpreting the genetic information in DNA. Protein-protein interactions play a major role in the execution of key biological functions of a cell. These interactions are represented in the form of a graph with nodes and edges. Studies have showed that some nodes have high degree of connectivity and such nodes, known as hub nodes, are the inevitable parts of the network. In the present paper a method is proposed to identify hub transcription factor proteins using sequence information. On a complete data set of transcription factor proteins available from the APID database, the proposed method showed an accuracy of 77%, sensitivity of 79% and specificity of 76%.

Keywords: Transcription Factor Proteins, Hub Proteins, Shannon Index, Transfer Free Energy to Surface (TFES).

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Authors: Ayad A. Abdulkafi, Shahir F. Nawaf, Mohammed K. Hussein, Ibrahim K. Sileh, Fouad A. Abdulkafi

Abstract:

Visible light communication (VLC) is a new approach of optical wireless communication proposed to support the congested radio frequency (RF) spectrum. VLC systems are combined with orthogonal frequency division multiplexing (OFDM) to achieve high rate transmission and high spectral efficiency. In this paper, we investigate the Pilot-Assisted Channel Estimation for DC biased Optical OFDM (PACE-DCO-OFDM) systems to reduce the effects of the distortion on the transmitted signal. Least-square (LS) and linear minimum mean-squared error (LMMSE) estimators are implemented in MATLAB/Simulink to enhance the bit-error-rate (BER) of PACE-DCO-OFDM. Results show that DCO-OFDM system based on PACE scheme has achieved better BER performance compared to conventional system without pilot assisted channel estimation. Simulation results show that the proposed PACE-DCO-OFDM based on LMMSE algorithm can more accurately estimate the channel and achieves better BER performance when compared to the LS based PACE-DCO-OFDM and the traditional system without PACE. For the same signal to noise ratio (SNR) of 25 dB, the achieved BER is about 5×10^-4 for LMMSE-PACE and 4.2×10^-3 with LS-PACE while it is about 2×10^-1 for system without PACE scheme.

Keywords: Channel estimation, OFDM, pilot-assist, VLC.

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Authors: Ersin Gose, Kenan Buyukatak, Onur Osman, Osman N. Ucan

Abstract:

To compress, improve bit error performance and also enhance 2D images, a new scheme, called Iterative Cellular-Turbo System (IC-TS) is introduced. In IC-TS, the original image is partitioned into 2N quantization levels, where N is denoted as bit planes. Then each of the N-bit-plane is coded by Turbo encoder and transmitted over Additive White Gaussian Noise (AWGN) channel. At the receiver side, bit-planes are re-assembled taking into consideration of neighborhood relationship of pixels in 2-D images. Each of the noisy bit-plane values of the image is evaluated iteratively using IC-TS structure, which is composed of equalization block; Iterative Cellular Image Processing Algorithm (ICIPA) and Turbo decoder. In IC-TS, there is an iterative feedback link between ICIPA and Turbo decoder. ICIPA uses mean and standard deviation of estimated values of each pixel neighborhood. It has extra-ordinary satisfactory results of both Bit Error Rate (BER) and image enhancement performance for less than -1 dB Signal-to-Noise Ratio (SNR) values, compared to traditional turbo coding scheme and 2-D filtering, applied separately. Also, compression can be achieved by using IC-TS systems. In compression, less memory storage is used and data rate is increased up to N-1 times by simply choosing any number of bit slices, sacrificing resolution. Hence, it is concluded that IC-TS system will be a compromising approach in 2-D image transmission, recovery of noisy signals and image compression.

Keywords: Iterative Cellular Image Processing Algorithm (ICIPA), Turbo Coding, Iterative Cellular Turbo System (IC-TS), Image Compression.

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Authors: Benalia Nadia, Bensiali Nadia, Mekki Mounira

Abstract:

In this paper, we propose an easily computable proximity index for predicting voltage collapse of a load bus using only measured values of the bus voltage and power; Using these measurements a polynomial of fourth order is obtained by using LES estimation algorithms. The sum of the absolute values of the polynomial coefficient gives an idea of the critical bus. We demonstrate the applicability of our proposed method on 6 bus test system. The results obtained verify its applicability, as well as its accuracy and the simplicity. From this indicator, it is allowed to predict the voltage instability or the proximity of a collapse. Results obtained by the PV curve are compared with corresponding values by QV curves and are observed to be in close agreement.

Keywords: least square method, Voltage Collapse, Voltage Stability, PV curve

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Authors: Dipankar Dhabak, Soumya Pandit

Abstract:

This paper presents an adaptive technique for generation of data required for construction of artificial neural network-based performance model of nano-scale CMOS inverter circuit. The training data are generated from the samples through SPICE simulation. The proposed algorithm has been compared to standard progressive sampling algorithms like arithmetic sampling and geometric sampling. The advantages of the present approach over the others have been demonstrated. The ANN predicted results have been compared with actual SPICE results. A very good accuracy has been obtained.

Keywords: CMOS Inverter, Nano-scale, Adaptive Sampling, ArtificialNeural Network

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Authors: M. Talebzadegan, S. Bina, I. Riazi

Abstract:

The purpose of this paper is to present an active solar energy system to supply heating demands of the teaching staff dormitory of the Islamic Azad University of Ramhormoz. The design takes into account the solar radiations and climate data of Ramhormoz town and is based on the daily warm water consumption for health demands of 450 residents of the dormitory, which is equal to 27000 lit of 50-C° water, and building heating requirements with an area of 3500 m² well-protected by heatproof materials. First, heating demands of the building were calculated, then a hybrid system made up of solar and fossil energies was developed and finally, the design was economically evaluated. Since there is only roof space for using 110 flat solar water heaters, the calculations were made to hybridize solar water heating system with heat pumping system in which solar energy contributes 67% of the heat generated. According to calculations, the net present value “N.P.V.” of revenue stream exceeds “N.P.V.” of cash paid off in this project over three years, which makes economically quite promising. The return of investment and payback period of the project is 4 years. Also, the internal rate of return (IRR) of the project was 25%, which exceeds bank rate of interest in Iran and emphasizes the desirability of the project.

Keywords: Solar energy, heat demand, renewable, pollution.

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Authors: Guezouli Larbi, Belkacem Soundes

Abstract:

The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered as a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: Text detection, CNN, PZM, deep learning.

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Authors: Muhammad Ali, Awais Adnan

Abstract:

Network on a chip (NoC) has been proposed as a viable solution to counter the inefficiency of buses in the current VLSI on-chip interconnects. However, as the silicon chip accommodates more transistors, the probability of transient faults is increasing, making fault tolerance a key concern in scaling chips. In packet based communication on a chip, transient failures can corrupt the data packet and hence, undermine the accuracy of data communication. In this paper, we present a comparative analysis of transient fault tolerant techniques including end-to-end, node-by-node, and stochastic communication based on flooding principle.

Keywords: NoC, fault-tolerance, transient faults.

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Authors: Nahla Barakat, Joachim Diederich

Abstract:

Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule-extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity.

Keywords: Data mining, hybrid rule-extraction algorithms, medical diagnosis, SVMs

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Authors: Mohammed Z. Khedher, Gheith A. Abandah, Ahmed M. Al-Khawaldeh

Abstract:

Recognition of characters greatly depends upon the features used. Several features of the handwritten Arabic characters are selected and discussed. An off-line recognition system based on the selected features was built. The system was trained and tested with realistic samples of handwritten Arabic characters. Evaluation of the importance and accuracy of the selected features is made. The recognition based on the selected features give average accuracies of 88% and 70% for the numbers and letters, respectively. Further improvements are achieved by using feature weights based on insights gained from the accuracies of individual features.

Keywords: Arabic handwritten characters, Feature extraction, Off-line recognition, Optical character recognition.

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Authors: Hsien-Chou Liao, Yu-Shiang Wang

Abstract:

Surveillance system is widely used in the traffic monitoring. The deployment of cameras is moving toward a ubiquitous camera (UbiCam) environment. In our previous study, a novel service, called GPS-VT, was firstly proposed by incorporating global positioning system (GPS) and visual tracking techniques for the UbiCam environment. The first prototype is called GODTA (GPS-based Moving Object Detection and Tracking Approach). For a moving person carried GPS-enabled mobile device, he can be tracking when he enters the field-of-view (FOV) of a camera according to his real-time GPS coordinate. In this paper, GPS-VT service is applied to the tracking of vehicles. The moving speed of a vehicle is much faster than a person. It means that the time passing through the FOV is much shorter than that of a person. Besides, the update interval of GPS coordinate is once per second, it is asynchronous with the frame rate of the real-time image. The above asynchronous is worsen by the network transmission delay. These factors are the main challenging to fulfill GPS-VT service on a vehicle.In order to overcome the influence of the above factors, a back-propagation neural network (BPNN) is used to predict the possible lane before the vehicle enters the FOV of a camera. Then, a template matching technique is used for the visual tracking of a target vehicle. The experimental result shows that the target vehicle can be located and tracking successfully. The success location rate of the implemented prototype is higher than that of the previous GODTA.

Keywords: visual surveillance, visual tracking, globalpositioning system, intelligent transportation system

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Authors: Satyasen Panda, Urmila Bhanja

Abstract:

This paper presents a SAC-OCDMA code with zero cross correlation property to minimize the Multiple Access Interface (MAI) as New Zero Cross Correlation code (NZCC), which is found to be more scalable compared to the other existing SAC-OCDMA codes. This NZCC code is constructed using address segment and data segment. In this work, the proposed NZCC code is implemented in an optical system using the Opti-System software for the spectral amplitude coded optical code-division multiple-access (SAC-OCDMA) scheme. The main contribution of the proposed NZCC code is the zero cross correlation, which reduces both the MAI and PIIN noises. The proposed NZCC code reveals properties of minimum cross-correlation, flexibility in selecting the code parameters and supports a large number of users, combined with high data rate and longer fiber length. Simulation results reveal that the optical code division multiple access system based on the proposed NZCC code accommodates maximum number of simultaneous users with higher data rate transmission, lower Bit Error Rates (BER) and longer travelling distance without any signal quality degradation, as compared to the former existing SAC-OCDMA codes.

Keywords: Cross Correlation, Optical Code Division Multiple Access, Spectral Amplitude Coding Optical Code Division Multiple Access, Multiple Access Interference, Phase Induced Intensity Noise, New Zero Cross Correlation code.

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Authors: B. Kubrak J. Wissink H. Herlina

Abstract:

Two-dimensional Direct Numerical Simulation (DNS) of high Schmidt number mass transfer in a convective flow environment (Rayleigh-B'enard) is carried out and results are compared to experimental data. A fourth-order accurate WENO-scheme has been used for scalar transport in order to aim for a high accuracy in areas of high concentration gradients. It was found that the typical spatial distance between downward plumes of cold high concentration water and the eddy size are in good agreement with experiments using a combined PIV-LIF technique for simultaneous and spatially synoptic measurements of 2D velocity and concentration fields.

Keywords: Air-Water Interface, DNS, Gas Transfer, LIF.

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Authors: Khosrow Maleknejad, Asyieh Ebrahimzadeh

Abstract:

In this paper, the numerical solution of optimal control problem (OCP) for systems governed by Volterra integro-differential (VID) equation is considered. The method is developed by means of the Legendre wavelet approximation and collocation method. The properties of Legendre wavelet together with Gaussian integration method are utilized to reduce the problem to the solution of nonlinear programming one. Some numerical examples are given to confirm the accuracy and ease of implementation of the method.

Keywords: Collocation method, Legendre wavelet, optimal control, Volterra integro-differential equation.

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Authors: Thomas Kampke

Abstract:

Equations with differentials relating to the inverse of an unknown function rather than to the unknown function itself are solved exactly for some special cases and numerically for the general case. Invertibility combined with differentiability over connected domains forces solutions always to be monotone. Numerical function inversion is key to all solution algorithms which either are of a forward type or a fixed point type considering whole approximate solution functions in each iteration. The given considerations are restricted to ordinary differential equations with inverted functions (ODEIs) of first order. Forward type computations, if applicable, admit consistency of order one and, under an additional accuracy condition, convergence of order one.

Keywords: Euler method, fixed points, golden section, multi-step procedures, Runge Kutta methods.

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Authors: Varun Gupta, Ankit Pandey

Abstract:

Crude oil market is an immensely complex and dynamic environment and thus the task of predicting changes in such an environment becomes challenging with regards to its accuracy. A number of approaches have been adopted to take on that challenge and machine learning has been at the core in many of them. There are plenty of examples of algorithms based on machine learning yielding satisfactory results for such type of prediction. In this paper, we have tried to predict crude oil prices using Long Short-Term Memory (LSTM) based recurrent neural networks. We have tried to experiment with different types of models using different epochs, lookbacks and other tuning methods. The results obtained are promising and presented a reasonably accurate prediction for the price of crude oil in near future.

Keywords: Crude oil price prediction, deep learning, LSTM, recurrent neural networks.

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Authors: Yongxian Jin, Jingzhou Huang

Abstract:

In an open real-time system environment, the coexistence of different kinds of real-time and non real-time applications makes the system scheduling mechanism face new requirements and challenges. One two-level scheduling scheme of the open real-time systems is introduced, and points out that hard and soft real-time applications are scheduled non-distinctively as the same type real-time applications, the Quality of Service (QoS) cannot be guaranteed. It has two flaws: The first, it can not differentiate scheduling priorities of hard and soft real-time applications, that is to say, it neglects characteristic differences between hard real-time applications and soft ones, so it does not suit a more complex real-time environment. The second, the worst case execution time of soft real-time applications cannot be predicted exactly, so it is not worth while to cost much spending in order to assure all soft real-time applications not to miss their deadlines, and doing that may cause resource wasting. In order to solve this problem, a novel two-level real-time scheduling mechanism (including scheduling profile and scheduling algorithm) which adds the process of dealing with soft real-time applications is proposed. Finally, we verify real-time scheduling mechanism from two aspects of theory and experiment. The results indicate that our scheduling mechanism can achieve the following objectives. (1) It can reflect the difference of priority when scheduling hard and soft real-time applications. (2) It can ensure schedulability of hard real-time applications, that is, their rate of missing deadline is 0. (3) The overall rate of missing deadline of soft real-time applications can be less than 1. (4) The deadline of a non-real-time application is not set, whereas the scheduling algorithm that server 0 S uses can avoid the “starvation" of jobs and increase QOS. By doing that, our scheduling mechanism is more compatible with different types of applications and it will be applied more widely.

Keywords: Hard real-time, two-level scheduling profile, open real-time system, non-distinctive schedule, soft real-time

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Authors: Gil-Eon Jeong, Sung-Kie Youn, K. C. Park

Abstract:

Treatment for the non-matching interface is an important computational issue. To handle this problem, the method of Lagrange multipliers including classical and localized versions are the most popular technique. It essentially imposes the interface compatibility conditions by introducing Lagrange multipliers. However, the numerical system becomes unstable and inefficient due to the Lagrange multipliers. The interface element-independent formulation that does not include the Lagrange multipliers can be obtained by modifying the independent variables mathematically. Through this modification, more efficient and stable system can be achieved while involving equivalent accuracy comparing with the conventional method. A numerical example is conducted to verify the validity of the presented method.

Keywords: Element-independent formulation, non-matching interface, interface coupling, methods of Lagrange multipliers.

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Authors: M. Sharanreddy, P. K. Kulkarni

Abstract:

Brain tumor is inherently serious and life-threatening disease. Brain tumor builds the intracranial pressure in the brain, by shifting the brain or pushing against the skull, and also damaging nerves and healthy brain tissues. This intracranial pressure affects and interferes with normal brain functionality, which results in generation of abnormal electrical activities from brain. With recent development in the medical engineering and instruments, EEG instruments are able to record the brain electric activities with high accuracy, which establishes EEG as a primary tool for diagnosing the brain abnormalities. Research scholars and general physicians, often face difficulty in understanding EEG patterns. This paper presents the EEG patterns associated with brain tumor by combing medicine theory and neurologist experience. Paper also explains the pros-cons of the EEG based brain tumor identification.

Keywords: Brain tumor, Electroencephalogram (EEG).

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Authors: Ferenc Kovács, Gábor Hosszú

Abstract:

The paper presents an advanced control system for tennis ball throwing machines to improve their accuracy according to the ball impact points. A further advantage of the system is the much easier calibration process involving the intelligent solution of the automatic adjustment of the stroking parameters according to the ball elasticity, the self-calibration, the use of the safety margin at very flat strokes and the possibility to placing the machine to any position of the half court. The system applies mathematical methods to determine the exact ball trajectories and special approximating processes to access all points on the aimed half court.

Keywords: Control system, robot programming, robot control, sports equipment, throwing machine.

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Authors: A. Bajit, M. Nahid, A. Tamtaoui, E. H. Bouyakhf

Abstract:

In this paper, we propose a Perceptually Optimized Embedded ZeroTree Image Coder (POEZIC) that introduces a perceptual weighting to wavelet transform coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to the coding quality obtained using the SPIHT algorithm only. The paper also, introduces a new objective quality metric based on a Psychovisual model that integrates the properties of the HVS that plays an important role in our POEZIC quality assessment. Our POEZIC coder is based on a vision model that incorporates various masking effects of human visual system HVS perception. Thus, our coder weights the wavelet coefficients based on that model and attempts to increase the perceptual quality for a given bit rate and observation distance. The perceptual weights for all wavelet subbands are computed based on 1) luminance masking and Contrast masking, 2) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting, 3) the Wavelet Error Sensitivity WES used to reduce the perceptual quantization errors. The new perceptually optimized codec has the same complexity as the original SPIHT techniques. However, the experiments results show that our coder demonstrates very good performance in terms of quality measurement.

Keywords: DWT, linear-phase 9/7 filter, 9/7 Wavelets Error Sensitivity WES, CSF implementation approaches, JND Just Noticeable Difference, Luminance masking, Contrast masking, standard SPIHT, Objective Quality Measure, Probability Score PS.

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Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: Deep learning, long-short-term memory, energy, renewable energy load forecasting.

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Authors: Teh Raihana Nazirah Roslan, Siti Zulaiha Ibrahim, Sharmila Karim

Abstract:

A warrant is a financial contract that confers the right but not the obligation, to buy or sell a security at a certain price before expiration. The standard procedure to value equity warrants using call option pricing models such as the Black–Scholes model had been proven to contain many flaws, such as the assumption of constant interest rate and constant volatility. In fact, existing alternative models were found focusing more on demonstrating techniques for pricing, rather than empirical testing. Therefore, a mathematical model for pricing and analyzing equity warrants which comprises stochastic interest rate and stochastic volatility is essential to incorporate the dynamic relationships between the identified variables and illustrate the real market. Here, the aim is to develop dynamic pricing formulations for hybrid equity warrants by incorporating stochastic interest rates from the Cox-Ingersoll-Ross (CIR) model, along with stochastic volatility from the Heston model. The development of the model involves the derivations of stochastic differential equations that govern the model dynamics. The resulting equations which involve Cauchy problem and heat equations are then solved using partial differential equation approaches. The analytical pricing formulas obtained in this study comply with the form of analytical expressions embedded in the Black-Scholes model and other existing pricing models for equity warrants. This facilitates the practicality of this proposed formula for comparison purposes and further empirical study.

Keywords: Cox-Ingersoll-Ross model, equity warrants, Heston model, hybrid models, stochastic.

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Authors: Irtaza M. Syed, Kaamran Raahemifar

Abstract:

This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize the TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of a boost converter (BC), maximum power point tracking (MPPT) control, and a three-leg voltage source inverter (VSI). The operational model of VSI is used to synthesize the sinusoidal current and track the reference. The model is validated using a 35.7 kW PV system in Matlab/Simulink. Implementation results show simplicity and accuracy, as well as reliability of the model.

Keywords: Model predictive control, three phase voltage source inverter, PV system, Matlab/Simulink.

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Authors: M. Ghanadi, M. Rezazadeh*, M. Ardeshiri, R. Gholipour Peyvandi, M. Jafarzadeh, M. Shahriari, M.Rezaei Rad, Z. Gholamzadeh

Abstract:

A 3D industrial computed tomography (CT) manufactured based on a first generation CT systems, single-source – single-detector, was evaluated. Operation accuracy assessment of the manufactured system was achieved using simulation in comparison with experimental tests. 137Cs and 60Co were used as a gamma source. Simulations were achieved using MCNP4C code. Experimental tests of 137Cs were in good agreement with the simulations

Keywords: Gamma source, Industrial CT, MCNP4C, Operation assessment

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Authors: Venus Marza, Amin Seyyedi, Luiz Fernando Capretz

Abstract:

Software estimation accuracy is among the greatest challenges for software developers. This study aimed at building and evaluating a neuro-fuzzy model to estimate software projects development time. The forty-one modules developed from ten programs were used as dataset. Our proposed approach is compared with fuzzy logic and neural network model and Results show that the value of MMRE (Mean of Magnitude of Relative Error) applying neuro-fuzzy was substantially lower than MMRE applying fuzzy logic and neural network.

Keywords: Artificial Neural Network, Fuzzy Logic, Neuro-Fuzzy, Software Estimation

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Authors: Mohamed Belaid, Edison Muzenda, Getrude Mitilene, Mansoor Mollagee

Abstract:

A feasibility study for the design and construction of a pilot plant for the extraction of castor oil in South Africa was conducted. The study emphasized the four critical aspects of project feasibility analysis, namely technical, financial, market and managerial aspects. The technical aspect involved research on existing oil extraction technologies, namely: mechanical pressing and solvent extraction, as well as assessment of the proposed production site for both short and long term viability of the project. The site is on the outskirts of Nkomazi village in the Mpumalanga province, where connections for water and electricity are currently underway, potential raw material supply proves to be reliable since the province is known for its commercial farming. The managerial aspect was evaluated based on the fact that the current producer of castor oil will be fully involved in the project while receiving training and technical assistance from Sasol Technology, the TSC and SEDA. Market and financial aspects were evaluated and the project was considered financially viable with a Net Present Value (NPV) of R2 731 687 and an Internal Rate of Return (IRR) of 18% at an annual interest rate of 10.5%. The payback time is 6years for analysis over the first 10 years with a net income of R1 971 000 in the first year. The project was thus found to be feasible with high chance of success while contributing to socio-economic development. It was recommended for lab tests to be conducted to establish process kinetics that would be used in the initial design of the plant.

Keywords: Mechanical pressing, Net Present Value, Oilextraction, Project feasibility, Solvent extraction

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Authors: Siew-Li Ching, Maziani Sabudin

Abstract:

In this paper, we study on color transformation method on website images for the color blind. The most common category of color blindness is red-green color blindness which is viewed as beige color. By transforming the colors of the images, the color blind can improve their color visibility. They can have a better view when browsing through the websites. To transform colors on the website images, we study on two algorithms which are the conversion techniques from RGB color space to HSV color space and self-organizing color transformation. The comparative study focuses on criteria based on the ease of use, quality, accuracy and efficiency. The outcome of the study leads to enhancement of website images to meet the color blinds- vision requirements in perceiving image detailed.

Keywords: Color blind, color transformation, HSV (Hue, Saturation, Value), RGB (Red, Green, Blue).

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Authors: Ana Paula P. dos Santos, Claudia R. Andrade, Edson L. Zaparoli

Abstract:

Gas turbine air inlet cooling is a useful method for increasing output for regions where significant power demand and highest electricity prices occur during the warm months. Inlet air cooling increases the power output by taking advantage of the gas turbine-s feature of higher mass flow rate when the compressor inlet temperature decreases. Different methods are available for reducing gas turbine inlet temperature. There are two basic systems currently available for inlet cooling. The first and most cost-effective system is evaporative cooling. Evaporative coolers make use of the evaporation of water to reduce the gas turbine-s inlet air temperature. The second system employs various ways to chill the inlet air. In this method, the cooling medium flows through a heat exchanger located in the inlet duct to remove heat from the inlet air. However, the evaporative cooling is limited by wet-bulb temperature while the chilling can cool the inlet air to temperatures that are lower than the wet bulb temperature. In the present work, a thermodynamic model of a gas turbine is built to calculate heat rate, power output and thermal efficiency at different inlet air temperature conditions. Computational results are compared with ISO conditions herein called "base-case". Therefore, the two cooling methods are implemented and solved for different inlet conditions (inlet temperature and relative humidity). Evaporative cooler and absorption chiller systems results show that when the ambient temperature is extremely high with low relative humidity (requiring a large temperature reduction) the chiller is the more suitable cooling solution. The net increment in the power output as a function of the temperature decrease for each cooling method is also obtained.

Keywords: Absorption chiller, evaporative cooling, gas turbine, turbine inlet cooling.

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Authors: Vasundara Patel K. S., Harsha N. Bhushan, Kiran G. Gadag, Nischal Prasad B. N., Mohmmed Haroon

Abstract:

The most widely used semiconductor memory types are the Dynamic Random Access Memory (DRAM) and Static Random Access memory (SRAM). Competition among memory manufacturers drives the need to decrease power consumption and reduce the probability of read failure. A technology that is relatively new and has not been explored is the FinFET technology. In this paper, a single cell Schmitt Trigger Based Static RAM using FinFET technology is proposed and analyzed. The accuracy of the result is validated by means of HSPICE simulations with 32nm FinFET technology and the results are then compared with 6T SRAM using the same technology.

Keywords: Schmitt trigger based SRAM, FinFET, and Static Noise Margin.

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