Search results for: gate diffusion input

Authors: Y. Bentoutou

Abstract:

Memory Errors Detection and Correction aim to secure the transaction of data between the central processing unit of a satellite onboard computer and its local memory. In this paper, the application of a double-bit error detection and correction method is described and implemented in Field Programmable Gate Array (FPGA) technology. The performance of the proposed EDAC method is measured and compared with two different EDAC devices, using the same FPGA technology. Statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories onboard the first Algerian microsatellite Alsat-1 is given.

Keywords: Error Detection and Correction, On-board computer, small satellite missions.

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Authors: M. Mashayekhi, H. H. Ardakani, A. Omidian

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Among various testing methodologies, Built-in Self- Test (BIST) is recognized as a low cost, effective paradigm. Also, full adders are one of the basic building blocks of most arithmetic circuits in all processing units. In this paper, an optimized testable 2- bit full adder as a test building block is proposed. Then, a BIST procedure is introduced to scale up the building block and to generate a self testable n-bit full adders. The target design can achieve 100% fault coverage using insignificant amount of hardware redundancy. Moreover, Overall test time is reduced by utilizing polymorphic gates and also by testing full adder building blocks in parallel.

Keywords: BIST, Full Adder, Polymorphic Gate

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Authors: Andi Pawawoi, Syafii

Abstract:

Instantaneous electromagnetic torque of simple reflectance generator can be positive at a time and negative at other time. It is utilized to design a permanent magnet reluctance generator specifically. Generator is designed by combining two simple reluctance generators, consists of two rotors mounted on the same shaft, two output-windings and a field source of the permanent magnet. By this design, the electromagnetic torque on both rotor will be eliminated each other, so the input torque generator can be smaller. Rotor is expected only to regulate the flux flow to both output windings alternately, until the magnetic energy is converted into electrical energy, such as occurs in the transformer energy conversion. ​​The prototype trials have been made to test this design. The test result show that the new design of permanent magnets reluctance generator able to convert energy from permanent magnets into electrical energy, this is proven by the existence 167% power output compared to the shaft input power.

Keywords: Energy, Magnet permanent, Reluctance generator.

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Authors: Min-ju Kwon, Chae-won Kim, Jeong-yun Seo, Hee-guk Chae, Yong-seo Koo

Abstract:

In this paper, we presented the LDO (low-dropout) regulator which enhanced the PSRR by applying the constant current source generation technique through the BGR (Band Gap Reference) to form the noise sensing circuit. The current source through the BGR has a constant current value even if the applied voltage varies. Then, the noise sensing circuit, which is composed of the current source through the BGR, operated between the error amplifier and the pass transistor gate of the LDO regulator. As a result, the LDO regulator has a PSRR of -68.2 dB at 1k Hz, -45.85 dB at 1 MHz and -45 dB at 10 MHz. the other performance of the proposed LDO was maintained at the same level of the conventional LDO regulator.

Keywords: LDO regulator, noise sensing circuit, current reference, pass transistor.

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Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

Abstract:

In this paper, Least Mean Square (LMS) adaptive noise reduction algorithm is proposed to enhance the speech signal from the noisy speech. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is seen that the proposed LMS adaptive noise reduction algorithm reduces Mean square Error (MSE) and Log Spectral Distance (LSD) as compared to that of the earlier methods under various noise conditions with different input SNR levels. In addition, the proposed algorithm increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR improvement (ΔSNRseg) values; improves the Mean Opinion Score (MOS) as compared to that of the various existing LMS adaptive noise reduction algorithms. From these experimental results, it is observed that the proposed LMS adaptive noise reduction algorithm reduces the speech distortion and residual noise as compared to that of the existing methods.

Keywords: LMS, speech enhancement, speech quality, residual noise.

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Authors: Nelson K. Lujara

Abstract:

The paper presents a practical three-phase PWM inverter suitable for low voltage, low rating energy efficient systems. The work in the paper is conducted with the view to establishing the significance of the loss contribution from the PWM inverter in the determination of the complete losses of a photovoltaic (PV) arraypowered induction motor drive water pumping system. Losses investigated include; conduction and switching loss of the devices and gate drive losses. It is found that the PWM inverter operates at a reasonable variable efficiency that does not fall below 92% depending on the load. The results between the simulated and experimental results for the system with or without a maximum power tracker (MPT) compares very well, within an acceptable range of 2% margin.

Keywords: Energy, Inverter, Losses, Photovoltaic.

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Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil

Abstract:

Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuzzy weight sets (QFWS) using repeated simulation of the crisp neural network. Such type of fuzzy weight functions may be applied where we have multivariate crisp input that needs to be adjusted after iterative learning, like claim amount distribution analysis. As real data is subjected to noise and uncertainty, therefore, QFWS may be helpful in the simplification of such complex problems. Secondly, these QFWS provide good initial solution for training of fuzzy neural networks with reduced computational complexity.

Keywords: Crisp neural networks, fuzzy systems, extraction of logical rules, quasi-fuzzy numbers.

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Authors: E. Farshidi

Abstract:

This paper presents the design of a low power second-order continuous-time sigma-delta modulator for low power applications. The loop filter of this modulator has been implemented based on the nonlinear transconductance-capacitor (Gm-C) by employing current-mode technique. The nonlinear transconductance uses floating gate MOS (FG-MOS) transistors that operate in weak inversion region. The proposed modulator features low power consumption (<80uW), low supply voltage (1V) and 62dB dynamic range. Simulation results by HSPICE confirm that it is very suitable for low power biomedical instrumentation designs.

Keywords: Sigma-delta, modulator, Current-mode, Nonlinear Transconductance, FG-MOS.

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Authors: T.C. Manjunath, Student Member, B. Bandyopadhyay

Abstract:

This paper features the mathematical modeling of a single input single output based Timoshenko smart beam. Further, this mathematical model is used to design a multirate output feedback based discrete sliding mode controller using Bartoszewicz law to suppress the flexural vibrations. The first 2 dominant vibratory modes is retained. Here, an application of the discrete sliding mode control in smart systems is presented. The algorithm uses a fast output sampling based sliding mode control strategy that would avoid the use of switching in the control input and hence avoids chattering. This method does not need the measurement of the system states for feedback as it makes use of only the output samples for designing the controller. Thus, this methodology is more practical and easy to implement.

Keywords: Smart structure, Timoshenko beam theory, Discretesliding mode control, Bartoszewicz law, Finite Element Method, State space model, Vibration control, Mathematical model, SISO.

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Authors: M. A. Hasan, M. R. Islam, R. A. Tarefder

Abstract:

Currently, there are few user friendly Weigh-in- Motion (WIM) data analysis softwares available which can produce traffic input data for the recently developed AASHTOWare pavement Mechanistic-Empirical (ME) design software. However, these softwares have only rudimentary Quality Control (QC) processes. Therefore, they cannot properly deal with erroneous WIM data. As the pavement performance is highly sensible to the quality of WIM data, it is highly recommended to use more refined QC process on raw WIM data to get a good result. This study develops a userfriendly software, which can produce traffic input for the ME design software. This software takes the raw data (Class and Weight data) collected from the WIM station and processes it with a sophisticated QC procedure. Traffic data such as traffic volume, traffic distribution, axle load spectra, etc. can be obtained from this software; which can directly be used in the ME design software.

Keywords: Weigh-in-motion, software, axle load spectra, traffic distribution, AASHTOWare.

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Authors: R. Khaniki, M.B. Menhaj, H. Eliasi

Abstract:

This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.

Keywords: Generalized Predictive Control (GPC), TemperatureControl, Global Linearizing Control (GLC), Batch Reactor.

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Authors: R. Khaniki, M.B. Menhaj, H. Eliasi

Abstract:

This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.

Keywords: Generalized Predictive Control (GPC), TemperatureControl, Global Linearizing Control (GLC), Batch Reactor.

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Authors: Mounir Ait kerroum, Ahmed Hammouch, Driss Aboutajdine

Abstract:

Texture information plays increasingly an important role in remotely sensed imagery classification and many pattern recognition applications. However, the selection of relevant textural features to improve this classification accuracy is not a straightforward task. This work investigates the effectiveness of two Mutual Information Feature Selector (MIFS) algorithms to select salient textural features that contain highly discriminatory information for multispectral imagery classification. The input candidate features are extracted from a SPOT High Resolution Visible(HRV) image using Wavelet Transform (WT) at levels (l = 1,2). The experimental results show that the selected textural features according to MIFS algorithms make the largest contribution to improve the classification accuracy than classical approaches such as Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA).

Keywords: Feature Selection, Texture, Mutual Information, Wavelet Transform, SVM classification, SPOT Imagery.

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Authors: Y. J. Chen, T. M. Yue, Z. N. Guo

Abstract:

This paper reports the effects of laser energy on the characteristics of bubbles generated in the weld zone and the formation of new chemical bonds at the Polyethylene Terephthalate (PET)/Ti joint interface in laser joining of PET to Ti. The samples were produced by using different laser energies ranging from 1.5 J – 6 J in steps of 1.5 J, while all other joining parameters remained unchanged. The types of chemical bonding at the joint interface were analysed by the x-ray photoelectron spectroscopy (XPS) depth-profiling method. The results show that the characteristics of the bubbles and the thickness of the chemically bonded interface, which contains the laser generated bonds of Ti–C and Ti–O, increase markedly with increasing laser energy input. The tensile failure load of the joint depends on the combined effect of the amount and distribution of the bubbles formed and the chemical bonding intensity of the joint interface.

Keywords: Laser direct joining, Ti/PET interface, laser energy, XPS depth profiling, chemical bond, tensile failure load.

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Authors: Abdelghani Dendouga, Nour-Eddine Bouguechal, Souhil Kouda, Samir Barra

Abstract:

A behavioral model of a second order switchedcapacitor Sigma-Delta modulator is presented. The purpose of this work is the presentation of a behavioral model of a second order switched capacitor ΣΔ modulator considering (Error due to Clock Jitter, Thermal noise Amplifier Noise, Amplifier Slew-Rate, Non linearity of amplifiers, Gain error, Charge Injection, Clock Feedthrough, and Nonlinear on-resistance). A comparison between the use of MOS switches and the use transmission gate switches use is analyzed.

Keywords: Charge injection, clock feed through, Sigma Deltamodulators, Sigma Delta non-idealities, switched capacitor.

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Authors: Khaing Win Mar, Thinn Thu Naing

Abstract:

Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.

Keywords: Precipitation prediction, monthly precipitation, neural network models, Myanmar.

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Authors: Mandeep Kumar, Hari Singh

Abstract:

The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents the optimization aspects of a Wire Electrical Discharge Machining operation using Inconel X-750 as work material. The objective considered in this study is minimization of the dimensional deviation. Six input process parameters of WEDM namely spark gap voltage, pulse-on time, pulse-off time, wire feed rate, peak current and wire tension, were chosen as variables to study the process performance. Taguchi's design of experiments methodology has been used for planning and designing the experiments. The analysis of variance was carried out for raw data as well as for signal to noise ratio. Four input parameters and one two-factor interaction have been found to be statistically significant for their effects on the response of interest. The confirmation experiments were also performed for validating the predicted results.

Keywords: ANOVA, DOE, inconel, machining, optimization.

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Authors: Hong Bae Kim, Chang Ho Hur

Abstract:

Many governments have tried to reduce CO₂ emissions which are believed to be the main cause for global warming. The deployment of electric automobiles is regarded as an effective way to reduce CO₂ emissions. The Korean government has planned to deploy about 200,000 electric automobiles. The policy for the deployment of electric automobiles aims at not only decreasing gasoline consumption but also increasing electricity production. However, if an electricity consuming regions is not consistent with an electricity producing region, the policy generates environmental problems between regions. Hence, this paper has established the energy multi-region input-output model to specifically analyze the impacts of the deployment of electric automobiles on regional energy consumption and CO₂ emissions. Finally, the paper suggests policy directions regarding the deployment of electric automobiles.

Keywords: Electric automobiles, CO2 emissions, regional imbalances in electricity production and consumption, energy multi-region input-output model.

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Authors: Isao Taguchi, Yasuo Sugai

Abstract:

This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.

Keywords: data selection, function approximation problem, multistage leaning, neural network, voluntary oscillation.

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Authors: Brian A. Smith, Ronald W. McClendon, Gerrit Hoogenboom

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The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the duration of prior weather data included in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17°C and 0.16°C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons. Networks instantiating the same model but with different initial random weights often led to different prediction errors. These results strongly suggest that ANN model developers should consider instantiating and training multiple networks with different initial weights to establish preferred model parameters.

Keywords: Decision support systems, frost protection, fruit, time-series prediction, weather modeling

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Authors: A. Karsenty, A. Chelly

Abstract:

Ultrathin (UTD) and Nanoscale (NSD) SOI-MOSFET devices, sharing a similar W/L but with a channel thickness of 46nm and 1.6nm respectively, were fabricated using a selective “gate recessed” process on the same silicon wafer. The electrical transport characterization at room temperature has shown a large difference between the two kinds of devices and has been interpreted in terms of a huge unexpected series resistance. Electrical characteristics of the Nanoscale device, taken in the linear region, can be analytically derived from the ultrathin device ones. A comparison of the structure and composition of the layers, using advanced techniques such as Focused Ion Beam (FIB) and High Resolution TEM (HRTEM) coupled with Energy Dispersive X-ray Spectroscopy (EDS), contributes an explanation as to the difference of transport between the devices.

Keywords: Nanoscale Devices, SOI MOSFET, Analytical Model, Electron Transport.

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Authors: Hayam M. Ibrahim, Ferial M. Abu-Salem

Abstract:

Medicinal plants are now gaining attractiveness in treatment of bacterial infections and food preservation. The objective of this study was to assess antibacterial activity of some medicinal plants on pathogenic bacteria. Screening of antibacterial activity of aqueous and methanol extracts of some plants: Jojoba, Ginger, Sage, Thyme and Clove against Bacillus cereus, Salmonella typhimurium, Staphylococcus aureus, Clostridium perfringens and Escherichia coli were investigated. Antibacterial activity was performed by agar diffusion and disc diffusion method. Jatropha, Jojoba, Clove and Ginger extracts showed notable bacterial activity in the first screening step then selected to be tested against Bacillus cereus (Gram+), Staphylococcus aureus (Gram+) and Salmonella typhimurium (Gram−) and their effect was compared using antibiotics as control. Screening results showed potential antibacterial activity of the tested plant extracts against the screened bacterial strains. It was found that methanol extracts exhibited higher antibacterial activity than aqueous extracts. Methanol extract of Jatropha showed the highest inhibition zone against Staphylococcus aureus (Gram+) with 24.00 mm diameter, compared to the other plant extracts followed by clove. Meanwhile, the inhibition zones of methanol extracts of Jojoba and Ginger were the same (12mm).The Gram-positive bacteria were found to be more sensitive to aqueous and methanol extracts than Gram-negative bacteria.

Keywords: Antibacterial activity, Food-borne pathogenic bacteria, Medicinal plants, Plant extracts.

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Authors: Wipassorn Vinicchayakul, Pichaya Supanakoon, Sathaporn Promwong

Abstract:

A human’s hand localization is revised by using radar cross section (RCS) measurements with a minimum root mean square (RMS) error matching algorithm on a touchless keypad mock-up model. RCS and frequency transfer function measurements are carried out in an indoor environment on the frequency ranged from 3.0 to 11.0 GHz to cover federal communications commission (FCC) standards. The touchless keypad model is tested in two different distances between the hand and the keypad. The initial distance of 19.50 cm is identical to the heights of transmitting (Tx) and receiving (Rx) antennas, while the second distance is 29.50 cm from the keypad. Moreover, the effects of Rx angles relative to the hand of human factor are considered. The RCS input parameters are compared with power loss parameters at each frequency. From the results, the performance of the RCS input parameters with the second distance, 29.50 cm at 3 GHz is better than the others.

Keywords: Radar cross section (RCS), fingerprint-based localization, minimum root mean square (RMS) error matching algorithm, touchless keypad model.

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Authors: M. Jagabar Sathik, K. Ramani

Abstract:

In this paper family of multilevel inverter topology with reduced number of power switches is presented. The proposed inverter can generate both even and odd level. The proposed topology is suitable for symmetric structure. The proposed symmetric inverter results in reduction of power switches, power diode and gate driver circuits and also it may further minimize the installation area and cost. To prove the superiority of proposed topology is compared with conventional topologies. The performance of this symmetric multilevel inverter has been tested by computer based simulation and prototype based experimental setup for nine-level inverter is developed and results are verified.

Keywords: Cascaded H- Bridge (CHB), Multilevel Inverter (MLI), Nearest Level Modulation (NLM), Total Harmonic Distortion (THD).

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Authors: Fatima Zohra Rahou, A. Guen Bouazza, B. Bouazza

Abstract:

Invention of transistor is the foundation of electronics industry. Metal Oxide Semiconductor Field Effect Transistor (MOSFET) has been the key for the development of nanoelectronics technology. In the first part of this manuscript, we present a new generation of MOSFET transistors based on SOI (Silicon-On-Insulator) technology. It is a partially depleted Silicon-On-Insulator (PD SOI MOSFET) transistor simulated by using SILVACO software. This work was completed by the presentation of some results concerning the influence of parameters variation (channel length L and gate oxide thickness Tox) on our PDSOI n-MOSFET structure on its drain current and kink effect.

Keywords: SOI technology, PDSOI MOSFET, FDSOI MOSFET, Kink Effect, SILVACO TCAD.

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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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Authors: Swadhin Ku. Mishra, Sidhartha Panda, Simanchala Padhy, C. Ardil

Abstract:

In this paper, real-coded genetic algorithm (RCGA) optimization technique has been applied for large-scale linear dynamic multi-input-multi-output (MIMO) system. The method is based on error minimization technique where the integral square error between the transient responses of original and reduced order models has been minimized by RCGA. The reduction procedure is simple computer oriented and the approach is comparable in quality with the other well-known reduction techniques. Also, the proposed method guarantees stability of the reduced model if the original high-order MIMO system is stable. The proposed approach of MIMO system order reduction is illustrated with the help of an example and the results are compared with the recently published other well-known reduction techniques to show its superiority.

Keywords: Multi-input-multi-output (MIMO) system.Modelorder reduction. Integral squared error (ISE). Real-coded geneticalgorithm

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Authors: Mazhar B. Tayel, Amr H. Yassin

Abstract:

A proposed small-signal model parameters for a pseudomorphic high electron mobility transistor (PHEMT) is presented. Both extrinsic and intrinsic circuit elements of a smallsignal model are determined using genetic algorithm (GA) as a stochastic global search and optimization tool. The parameters extraction of the small-signal model is performed on 200-μm gate width AlGaAs/InGaAs PHEMT. The equivalent circuit elements for a proposed 18 elements model are determined directly from the measured S- parameters. The GA is used to extract the parameters of the proposed small-signal model from 0.5 up to 18 GHz.

Keywords: PHEMT, Genetic Algorithms, small signal modeling, optimization.

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Authors: Fl. Loury, P. Sablonière, C. Lamoureux, G. Magnier, Th. Gutierrez

Abstract:

A method is proposed to extract faithful representative patterns from data set of observations when they are suffering from non-negligible fluctuations. Supposing time interval between measurements to be extremely small compared to observation time, it consists in defining first a subset of intermediate time intervals characterizing coherent behavior. Data projection on these intervals gives a set of curves out of which an ideally “perfect” one is constructed by taking the sup limit of them. Then comparison with average real curve in corresponding interval gives an efficiency parameter expressing the degradation consecutive to fluctuation effect. The method is applied to sunlight data collected in a specific place, where ideal sunlight is the one resulting from direct exposure at location latitude over the year, and efficiency is resulting from action of meteorological parameters, mainly cloudiness, at different periods of the year. The extracted information already gives interesting element of decision, before being used for analysis of plant control.

Keywords: Base Input Reconstruction, Data Mining, Efficiency Factor, Information Pattern Operator.

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Authors: R. Akbari, M. A. Ehyaei, R. Shahi Shavvon

Abstract:

Nowadays, solar energy is used for energy purposes such as the use of thermal energy for domestic, industrial and power applications, as well as the conversion of the sunlight into electricity by photovoltaic cells. In this study, the thermodynamic simulation of the solar Rankin cycle with phase change material (paraffin) was first studied. Then energy and exergy analyses were performed. For optimization, a single and multi-objective genetic optimization algorithm to maximize thermal and exergy efficiency was used. The parameters discussed in this paper included the effects of input pressure on turbines, input mass flow to turbines, the surface of converters and collector angles on thermal and exergy efficiency. In the organic Rankin cycle, where solar energy is used as input energy, the fluid selection is considered as a necessary factor to achieve reliable and efficient operation. Therefore, silicon oil is selected for a high-temperature cycle and water for a low-temperature cycle as an operating fluid. The results showed that increasing the mass flow to turbines 1 and 2 would increase thermal efficiency, while it reduces and increases the exergy efficiency in turbines 1 and 2, respectively. Increasing the inlet pressure to the turbine 1 decreases the thermal and exergy efficiency, and increasing the inlet pressure to the turbine 2 increases the thermal efficiency and exergy efficiency. Also, increasing the angle of the collector increased thermal efficiency and exergy. The thermal efficiency of the system was 22.3% which improves to 33.2 and 27.2% in single-objective and multi-objective optimization, respectively. Also, the exergy efficiency of the system was 1.33% which has been improved to 1.719 and 1.529% in single-objective and multi-objective optimization, respectively. These results showed that the thermal and exergy efficiency in a single-objective optimization is greater than the multi-objective optimization.

Keywords: Exergy analysis, Genetic algorithm, Rankine cycle, Single and Multi-objective function.

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