Search results for: input data stream

Authors: R. Bardera Mora

Abstract:

Launch and recovery helicopter wind envelope for a ship type was determined as the first step to the helicopter qualification program. Flight deck velocities data were obtained by means of a two components laser Doppler anemometer testing a 1/50th model in the wind tunnel stream. Full-scale flight deck measurements were obtained on board the ship using a sonic anemometer. Wind tunnel and full-scale measurements were compared, showing good agreement and finally, a preliminary launch and recovery helicopter wind envelope for this specific ship was built.

Keywords: Flight deck flow, relative wind, ship airwake, wind envelope

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Authors: Abu Bakar Sambah, Fusanori Miura

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Remote sensing image processing, spatial data analysis through GIS approach, and analytical hierarchy process were introduced in this study for assessing the vulnerability area and inundation area due to tsunami hazard in the area of Rikuzentakata, Iwate Prefecture, Japan. Appropriate input parameters were derived from GSI DEM data, ALOS AVNIR-2, and field data. We used the parameters of elevation, slope, shoreline distance, and vegetation density. Five classes of vulnerability were defined and weighted via pairwise comparison matrix. The assessment results described that 14.35km² of the study area was under tsunami vulnerability zone. Inundation areas are those of high and slightly high vulnerability. The farthest area reached by a tsunami was about 7.50km from the shoreline and shows that rivers act as flooding strips that transport tsunami waves into the hinterland. This study can be used for determining a priority for land-use planning in the scope of tsunami hazard risk management.

Keywords: AHP, GIS, remote sensing, tsunami vulnerability.

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Authors: P. Beraldi, M. E. Bruni

Abstract:

Data Envelopment Analysis (DEA) is one of the most widely used technique for evaluating the relative efficiency of a set of homogeneous decision making units. Traditionally, it assumes that input and output variables are known in advance, ignoring the critical issue of data uncertainty. In this paper, we deal with the problem of efficiency evaluation under uncertain conditions by adopting the general framework of the stochastic programming. We assume that output parameters are represented by discretely distributed random variables and we propose two different models defined according to a neutral and risk-averse perspective. The models have been validated by considering a real case study concerning the evaluation of the technical efficiency of a sample of individual firms operating in the Italian leather manufacturing industry. Our findings show the validity of the proposed approach as ex-ante evaluation technique by providing the decision maker with useful insights depending on his risk aversion degree.

Keywords: DEA, Stochastic Programming, Ex-ante evaluation technique, Conditional Value at Risk.

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Authors: Ahmad M. Sarhan, Omar I. Al Helalat

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In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.

Keywords: ANN, Backpropagation, Gaussian, LMS, MSE, Neuron, standard deviation, Widrow-Hoff rule.

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Authors: M. Rychlik, R. Roszak, M. Morzynski, M. Nowak, H. Hausa, K. Kotecki

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Article presents the geometry and structure reconstruction procedure of the aircraft model for flatter research (based on the I22-IRYDA aircraft). For reconstruction the Reverse Engineering techniques and advanced surface modeling CAD tools are used. Authors discuss all stages of data acquisition process, computation and analysis of measured data. For acquisition the three dimensional structured light scanner was used. In the further sections, details of reconstruction process are present. Geometry reconstruction procedure transform measured input data (points cloud) into the three dimensional parametric computer model (NURBS solid model) which is compatible with CAD systems. Parallel to the geometry of the aircraft, the internal structure (structural model) are extracted and modeled. In last chapter the evaluation of obtained models are discussed.

Keywords: computer modeling, numerical simulation, Reverse Engineering, structural model

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Authors: V.Manikandan, N.Devarajan

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The main objective of this work is to provide a fault detection and isolation based on Markov parameters for residual generation and a neural network for fault classification. The diagnostic approach is accomplished in two steps: In step 1, the system is identified using a series of input / output variables through an identification algorithm. In step 2, the fault is diagnosed comparing the Markov parameters of faulty and non faulty systems. The Artificial Neural Network is trained using predetermined faulty conditions serves to classify the unknown fault. In step 1, the identification is done by first formulating a Hankel matrix out of Input/ output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out in online. Fault residues are extracted making a comparison of the first five Markov parameters of faulty and non faulty systems. The proposed diagnostic approach is illustrated on benchmark problems with encouraging results.

Keywords: Artificial neural network, Fault Diagnosis, Identification, Markov parameters.

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Authors: Mountassar Maamoun, Abdelhamid Meraghni, Abdelhalim Benbelkacem, Daoud Berkani

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This paper describes an efficient hardware implementation of a new technique for interfacing the data exchange between the microprocessor-based systems and the external devices. This technique, based on the use of software/hardware system and a reduced physical address, enlarges the interfacing capacity of the microprocessor-based systems, uses the Direct Memory Access (DMA) to increases the frequency of the new bus, and improves the speed of data exchange. While using this architecture in microprocessor-based system or in computer, the input of the hardware part of our system will be connected to the bus system, and the output, which is a new bus, will be connected to an external device. The new bus is composed of a data bus, a control bus and an address bus. A Xilinx Integrated Software Environment (ISE) 7.1i has been used for the programmable logic implementation.

Keywords: Interfacing, Software/hardware System, CPLD, programmable logic, DMA.

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

Abstract:

The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-Doppler features.

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Authors: Lucy Patricia Onundo, Wilfred Njoroge Mwema

Abstract:

The rapidly diminishing fossil fuel reserves, their exorbitant cost and the increasingly apparent negative effect of fossil fuels to climate changes is a wake-up call to explore renewable energy. Wind, bio-fuel and solar power have already become staples of Kenyan electricity mix. The potential of electric power generation from marine tidal currents is enormous, with oceans covering more than 70% of the earth. However, attempts to harness marine tidal energy in Kenya, has yet to be studied thoroughly due to its promising, cyclic, reliable and predictable nature and the vast energy contained within it. The high load factors resulting from the fluid properties and the predictable resource characteristics make marine currents particularly attractive for power generation and advantageous when compared to others. Global-level resource assessments and oceanographic literature and data have been compiled in an analysis of the technology-specific requirements for tidal energy technologies and the physical resources. Temporal variations in resource intensity as well as the differences between small-scale applications are considered.

Keywords: Energy data assessment, environmental legislation, renewable energy, tidal-in-stream turbines.

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Authors: S. Areerachakul

Abstract:

Nowadays, the increase of human population every year results in increasing of water usage and demand. Saen Saep canal is important canal in Bangkok. The main objective of this study is using Artificial Neural Network (ANN) model to estimate the Chemical Oxygen Demand (COD) on data from 11 sampling sites. The data is obtained from the Department of Drainage and Sewerage, Bangkok Metropolitan Administration, during 2007-2011. The twelve parameters of water quality are used as the input of the models. These water quality indices affect the COD. The experimental results indicate that the ANN model provides a high correlation coefficient (R=0.89).

Keywords: Artificial neural network, chemical oxygen demand, estimate, surface water.

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Authors: John Kabuba

Abstract:

The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ionexchange.

Keywords: Clinoptilolite, loading, modeling, Neural network.

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Authors: Marina Velikova, Hennie Daniels, Ad Feelders

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In many data mining applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. In this paper we consider partially monotone prediction problems, where the target variable depends monotonically on some of the input variables but not on all. We propose a novel method to construct prediction models, where monotone dependences with respect to some of the input variables are preserved by virtue of construction. Our method belongs to the class of mixture models. The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. By using simulation and real case studies, we demonstrate the application of our method. To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. The results show that our approach outperforms partially monotone linear models in terms of accuracy. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

Keywords: mixture models, monotone neural networks, partially monotone models, partially monotone problems.

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Authors: Chih Chin Yang, Chih Yu Lee, Jing Yi Wang, Mei Zhen Xue, Chia Yueh Wu

Abstract:

A well-defined frequency multiple dividing (FMD) circuit using a resonant model is presented in this research. The basic component of a frequency multiple divider as used in a resonant model is established by compositing a well-defined resonant effect of negative differential resistance (NDR) characteristics which possesses a wider operational region and high operational current at a bias voltage of about 1.15 V. The resonant model is then applied in the frequency dividing circuit with the above division ratio (R_D) of 200 at the signal input of middle frequency. The division ratio also exists at the input of a low frequency signal.

Keywords: Divider, frequency, resonant model.

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Authors: M. Hammerton, J. Trevathan, T. Myers, W. Read

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The transfer rate of messages in distributed sensor network applications is a critical factor in a system's performance. The Sensor Abstraction Layer (SAL) is one such system. SAL is a middleware integration platform for abstracting sensor specific technology in order to integrate heterogeneous types of sensors in a network. SAL uses Java Remote Method Invocation (RMI) as its connection method, which has unsatisfying transfer rates, especially for streaming data.  This paper analyses different connection methods to optimize data transmission in SAL by replacing RMI.  Our results show that the most promising Java-based connections were frameworks for Java New Input/Output (NIO) including Apache MINA, JBoss Netty, and xSocket. A test environment was implemented to evaluate each respective framework based on transfer rate, resource usage, and scalability. Test results showed the most suitable connection method to improve data transmission in SAL JBoss Netty as it provides a performance enhancement of 68%.

Keywords: Wireless sensor networks, remote method invocation, transmission time.

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Authors: Do Hoai Nam, Keiko Udo, Akira Mano

Abstract:

This paper presents the combination of different precipitation data sets and the distributed hydrological model, in order to examine the flood runoff reproductivity of scattered observation catchments. The precipitation data sets were obtained from observation using rain-gages, satellite based estimate (TRMM), and numerical weather prediction model (NWP), then were coupled with the super tank model. The case study was conducted in three basins (small, medium, and large size) located in Central Vietnam. Calculated hydrographs based on ground observation rainfall showed best fit to measured stream flow, while those obtained from TRMM and NWP showed high uncertainty of peak discharges. However, calculated hydrographs using the adjusted rainfield depicted a promising alternative for the application of TRMM and NWP in flood modeling for scattered observation catchments, especially for the extension of forecast lead time.

Keywords: Flood forecast, rainfall-runoff model, satellite rainfall estimate, numerical weather prediction, quantitative precipitation forecasting.

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Authors: Alexandre Boum, Salomon Madinatou

Abstract:

This paper aims at bringing a scientific contribution to the cardiac arrhythmia biomedical diagnosis systems; more precisely to the study of the amelioration of cardiac arrhythmia classification performance using artificial neural network, adaptive neuro-fuzzy and fuzzy inference systems classifiers. The purpose of this amelioration is to enable cardiologists to make reliable diagnosis through automatic cardiac arrhythmia analyzes and classifications based on high confidence classifiers. In this study, six classes of the most commonly encountered arrhythmias are considered: the Right Bundle Branch Block, the Left Bundle Branch Block, the Ventricular Extrasystole, the Auricular Extrasystole, the Atrial Fibrillation and the Normal Cardiac rate beat. From the electrocardiogram (ECG) extracted parameters, we constructed a matrix (360x360) serving as an input data sample for the classifiers based on neural networks and a matrix (1x6) for the classifier based on fuzzy logic. By varying three parameters (the quality of the neural network learning, the data size and the quality of the input parameters) the automatic classification permitted us to obtain the following performances: in terms of correct classification rate, 83.6% was obtained using the fuzzy logic based classifier, 99.7% using the neural network based classifier and 99.8% for the adaptive neuro-fuzzy based classifier. These results are based on signals containing at least 360 cardiac cycles. Based on the comparative analysis of the aforementioned three arrhythmia classifiers, the classifiers based on neural networks exhibit a better performance.

Keywords: Adaptive neuro-fuzzy, artificial neural network, cardiac arrythmias, fuzzy inference systems.

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Authors: S. Saisundar, Shan Jiang, Kevin T. C. Chai, David Nuttman, Minkyu Je

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Keywords: Analog to digital converter, cyclic, gain-boosting, pipelined.

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Authors: Insung Jung, Gi-Nam Wang

Abstract:

The objective of this paper is to a design of pattern classification model based on the back-propagation (BP) algorithm for decision support system. Standard BP model has done full connection of each node in the layers from input to output layers. Therefore, it takes a lot of computing time and iteration computing for good performance and less accepted error rate when we are doing some pattern generation or training the network. However, this model is using exclusive connection in between hidden layer nodes and output nodes. The advantage of this model is less number of iteration and better performance compare with standard back-propagation model. We simulated some cases of classification data and different setting of network factors (e.g. hidden layer number and nodes, number of classification and iteration). During our simulation, we found that most of simulations cases were satisfied by BP based using exclusive connection network model compared to standard BP. We expect that this algorithm can be available to identification of user face, analysis of data, mapping data in between environment data and information.

Keywords: Neural network, Back-propagation, classification.

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Authors: Florin Gorunescu

Abstract:

Diagnosis can be achieved by building a model of a certain organ under surveillance and comparing it with the real time physiological measurements taken from the patient. This paper deals with the presentation of the benefits of using Data Mining techniques in the computer-aided diagnosis (CAD), focusing on the cancer detection, in order to help doctors to make optimal decisions quickly and accurately. In the field of the noninvasive diagnosis techniques, the endoscopic ultrasound elastography (EUSE) is a recent elasticity imaging technique, allowing characterizing the difference between malignant and benign tumors. Digitalizing and summarizing the main EUSE sample movies features in a vector form concern with the use of the exploratory data analysis (EDA). Neural networks are then trained on the corresponding EUSE sample movies vector input in such a way that these intelligent systems are able to offer a very precise and objective diagnosis, discriminating between benign and malignant tumors. A concrete application of these Data Mining techniques illustrates the suitability and the reliability of this methodology in CAD.

Keywords: Endoscopic ultrasound elastography, exploratorydata analysis, neural networks, non-invasive cancer detection.

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Authors: J. R. Wang, Y. Chiang, W. S. Hsu, H. C. Chen, S. H. Chen, J. H. Yang, S. W. Chen, C. Shih

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In this study, the RASCAL and ALOHA codes are used to establish an analysis methodology for hydrogen fluoride (HF) evaluation. There are three main steps in this study. First, the UF₆ data were collected. Second, one postulated case was analyzed by using the RASCAL and UF₆ data. This postulated case assumes that fire occurring and UF₆ is releasing from a building. Third, the results of RASCAL for HF mass were as the input data of ALOHA. Two postulated cases of HF were analyzed by using ALOHA code and the results of RASCAL. These postulated cases assume fire occurring and HF is releasing with no raining (Case 1) or raining (Case 2) condition. According to the analysis results of ALOHA, the HF concentration of Case 2 is smaller than Case 1. The results can be a reference for the preparing of emergency plans for the release of HF.

Keywords: RASCAL, ALOHA, UF6, hydrogen fluoride.

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Authors: K.M. Faraoun, A. Boukelif

Abstract:

This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so become much easier. Contrary to the existing GP-classification techniques, the proposed one use a dynamic repartition of the transformed data in separated intervals, the efficacy of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were first performed using the Fisher-s Iris dataset, and then, the KDD-99 Cup dataset was used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperform the existing GP-classification methods [1],[2] and [3], and give a very accepted results compared to other existing techniques proposed in [4],[5],[6],[7] and [8].

Keywords: Genetic programming, patterns classification, intrusion detection

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Authors: G. S. Ayyappan, Srinivas Kota, Jaffer R. C. Sheriff, C. Prakash Chandra Joshua

Abstract:

In the present scenario of energy crises, energy conservation in the electrical machines is very important in the industries. In order to conserve energy, one needs to monitor the performance of an induction motor on-site and in-situ. The instruments available for this purpose are very meager and very expensive. This paper deals with the design and development of induction motor performance analyser on-line, on-site, and in-situ. The system measures only few electrical input parameters like input voltage, line current, power factor, frequency, powers, and motor shaft speed. These measured data are coupled to name plate details and compute the operating efficiency of induction motor. This system employs the method of computing motor losses with the help of equivalent circuit parameters. The equivalent circuit parameters of the concerned motor are estimated using the developed algorithm at any load conditions and stored in the system memory. The developed instrument is a reliable, accurate, compact, rugged, and cost-effective one. This portable instrument could be used as a handy tool to study the performance of both slip ring and cage induction motors. During the analysis, the data can be stored in SD Memory card and one can perform various analyses like load vs. efficiency, torque vs. speed characteristics, etc. With the help of the developed instrument, one can operate the motor around its Best Operating Point (BOP). Continuous monitoring of the motor efficiency could lead to Life Cycle Assessment (LCA) of motors. LCA helps in taking decisions on motor replacement or retaining or refurbishment.

Keywords: Energy conservation, equivalent circuit parameters, induction motor efficiency, life cycle assessment, motor performance analysis.

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Authors: Abu Bakar Munir, Siti Hajar Mohd Yasin, Firdaus Muhammad-Sukki

Abstract:

This paper seeks to analyse the benefits of big data and more importantly the challenges it pose to the subject of privacy and data protection. First, the nature of big data will be briefly deliberated before presenting the potential of big data in the present days. Afterwards, the issue of privacy and data protection is highlighted before discussing the challenges of implementing this issue in big data. In conclusion, the paper will put forward the debate on the adequacy of the existing legal framework in protecting personal data in the era of big data.

Keywords: Big data, data protection, information, privacy.

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Authors: Kasim Terzic, Bernd Neumann

Abstract:

Scene interpretation systems need to match (often ambiguous) low-level input data to concepts from a high-level ontology. In many domains, these decisions are uncertain and benefit greatly from proper context. This paper demonstrates the use of decision trees for estimating class probabilities for regions described by feature vectors, and shows how context can be introduced in order to improve the matching performance.

Keywords: Classification, Decision Trees, Interpretation, Vision

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Authors: Ramamurthy S, Ranjan P V, Raghavendiran T A

Abstract:

Reduced switching loss favours Pulse Skipping Modulation mode of switching dc-to-dc converters at light loads. Under certain conditions the converter operates in discontinuous conduction mode (DCM). Inductor current starts from zero in each switching cycle as the switching frequency is constant and not adequately high. A DC-to-DC buck converter is modelled and simulated in this paper under DCM. Effect of ESR of the filter capacitor in input current frequency components is studied. The converter is studied for its operation under input voltage and load variation. The operating frequency is selected to be close to and above audio range.

Keywords: Buck converter, Discontinuous conduction mode, Electromagnetic Interference, Pulse Skipping Modulation.

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Authors: Oscar J.G. Somsen, Fok Bolderheij

Abstract:

Electro-optical devices are increasingly used for military sea-, land- and air applications to detect, recognize and track objects. Typically, these devices produce video information that is presented to an operator. However, with increasing availability of electro-optical devices the data volume is becoming very large, creating a rising need for automated analysis. In a military setting, this typically involves detecting and recognizing objects at a large distance, i.e. when they are difficult to distinguish from background and noise. One may consider combining multiple images from a video stream into a single enhanced image that provides more information for the operator. In this paper we investigate a simple algorithm to enhance simulated images from a military context and investigate how the enhancement is affected by various types of disturbance.

Keywords: Electro-Optics, Automated Image alignment

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Authors: Pawalai Kraipeerapun, Chun Che Fung, Kok Wai Wong

Abstract:

This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.

Keywords: Multiclass classification, feed-forward backpropagation neural network, interval neutrosophic sets, uncertainty.

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Authors: Gilberto Gonzalez-A, Ignacio Rodríguez- A., Dunia Nuñez-P

Abstract:

A bond graph model of a two degrees of freedom PUMA is described. System inversion gives the system input required to generate a given system output. In order to get the system inversion of the PUMA manipulator, a linearization of the nonlinear bond graph is obtained. Hence, the bicausality of the linearized bond graph of the PUMA manipulator is applied. Thus, the bicausal bond graph provides a systematic way of generating the equations of the system inversion. Simulation results to verify the calculated input for a given output are shown.

Keywords: Bond graph, system inversion, bicausality, PUMA manipulator

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Authors: Ramamurthy S, Vanaja Ranjan P

Abstract:

A DC-to-DC converter for applications involving a source with widely varying voltage conditions with loads requiring constant voltage from full load down to no load is presented. The switching regulator considered is a Buck converter with Pulse Skipping Modulation control whereby pulses applied to the switch are blocked or released on output voltage crossing a predetermined value. Results of the study on the performance of regulator circuit are presented. The regulator regulates over a wide input voltage range with slightly higher ripple content and good transient response. Input current spectrum indicates a good EMI performance with crowding of components at low frequency range.

Keywords: DC/DC Converter, Pulse Skipping Modulation, Buckregulator, Modulation Factor, Electromagnetic Interference

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Authors: S. N. Deepa, G. Sugumaran

Abstract:

This paper proposes a new version of the Particle Swarm Optimization (PSO) namely, Modified PSO (MPSO) for model order formulation of Single Input Single Output (SISO) linear time invariant continuous systems. In the General PSO, the movement of a particle is governed by three behaviors namely inertia, cognitive and social. The cognitive behavior helps the particle to remember its previous visited best position. In Modified PSO technique split the cognitive behavior into two sections like previous visited best position and also previous visited worst position. This modification helps the particle to search the target very effectively. MPSO approach is proposed to formulate the higher order model. The method based on the minimization of error between the transient responses of original higher order model and the reduced order model pertaining to the unit step input. The results obtained are compared with the earlier techniques utilized, to validate its ease of computation. The proposed method is illustrated through numerical example from literature.

Keywords: Continuous System, Model Order Formulation, Modified Particle Swarm Optimization, Single Input Single Output, Transfer Function Approach

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