Search results for: Artificial Intelligent

Authors: Aurobi Das, V. Balakrishnan

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This paper focuses on the integration of hybrid renewable energy resources available in remote isolated islands of Sundarban-24 Parganas-South of Eastern part of India to National Grid of conventional power supply to give a Smart-Grid scenario. Before grid-integration, feasibility of optimization of hybrid renewable energy system is monitored through an Intelligent Controller proposed to be installed at Moushuni Island of Sundarban. The objective is to ensure the reliability and efficiency of the system to optimize the utilization of the hybrid renewable energy sources and also a proposition of how theses isolated Hybrid Renewable Energy Systems at remote islands can be grid-connected is analyzed towards vision of green smart-grid.

Keywords: Intelligent controller, hybrid renewable, solar photo voltaic, smart-grid.

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Authors: W. Walke, J. Przondziono, K. Nowińska

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The study comprises evaluation of suitability of passive layer created on the surface of AISI 316L stainless steel for products that are intended to have contact with blood. For that purpose, prior to and after chemical passivation, samples were subject to 7 day exposure in artificial plasma at the temperature of T=37°C. Next, tests of metallic ions infiltration from the surface to the solution were performed. The tests were performed with application of spectrometer JY 2000, by Yobin – Yvon, employing Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES). In order to characterize physical and chemical features of electrochemical processes taking place during exposure of samples to artificial plasma, tests with application of electrochemical impedance spectroscopy were suggested. The tests were performed with application of measuring unit equipped with potentiostat PGSTAT 302n with an attachment for impedance tests FRA2. Measurements were made in the environment simulating human blood at the temperature of T=37°C. Performed tests proved that application of chemical passivation process for AISI 316L stainless steel used for production of goods intended to have contact with blood is well-grounded and useful in order to improve safety of their usage.

Keywords: AISI 316L stainless steel, chemical passivation, artificial plasma, ions infiltration, EIS.

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Authors: W. K. Wong, Z. X. Guo, P.Y. Mok

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This paper presents a multi-objective order allocation planning problem with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization process, a Monte Carlo simulation technique and a heuristic pruning technique, is proposed to handle this problem. Experiments based on industrial data are conducted to validate the proposed model. Results show that (1) the proposed model can effectively solve the investigated problem by providing effective production decision-making solutions, which outperformsan NSGA-II-based optimization process and an industrial method.

Keywords: Multi-objective order allocation planning, Pareto optimization, Memetic algorithm, Mento Carlo simulation

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Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Mobile ad hoc network, MANET, intrusion detection system, back propagation algorithm, neural networks, traffic table, multilayer perceptron, feed-forward back-propagation, network simulator 2.

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Authors: Govind Shay Sharma, Randhir Singh Baghel

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The stock management of raw materials and finished goods is a significant issue for industries in fulfilling customer demand. Optimization of inventory strategies is crucial to enhancing customer service, reducing lead times and costs, and meeting market demand. This paper suggests finding an approach to predict the optimum stock level by utilizing past stocks and forecasting the required quantities. In this paper, we utilized Artificial Neural Network (ANN) to determine the optimal value. The objective of this paper is to discuss the optimized ANN that can find the best solution for the inventory model. In the context of the paper, we mentioned that the k-means algorithm is employed to create homogeneous groups of items. These groups likely exhibit similar characteristics or attributes that make them suitable for being managed using uniform inventory control policies. The paper proposes a method that uses the neural fit algorithm to control the cost of inventory.

Keywords: Artificial Neural Network, inventory management, optimization, distributor center.

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Authors: N. Drir, L. Barazane, M. Loudini

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It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now.

Keywords: Maximum power point tracking, neural networks, photovoltaic, P&O.

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Authors: Muhammad M. A. S. Mahmoud

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Natural gas sweetening process is a controlled process that must be done at maximum efficiency and with the highest quality. In this work, due to complexity and non-linearity of the process, the H2S gas separation and the intelligent fuzzy controller, which is used to enhance the process, are simulated in MATLAB – Simulink. New design of fuzzy control for Gas Separator is discussed in this paper. The design is based on the utilization of linear state-estimation to generate the internal knowledge-base that stores input-output pairs. The obtained input/output pairs are then used to design a feedback fuzzy controller. The proposed closed-loop fuzzy control system maintains the system asymptotically-stability while it enhances the system time response to achieve better control of the concentration of the output gas from the tower. Simulation studies are carried out to illustrate the Gas Separator system performance.

Keywords: Gas separator, gas sweetening, intelligent controller, fuzzy control.

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Authors: Pranee Pathomchaiwat

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The purposes of this research are 1) to study English language learning strategies used by the fourth-year students majoring in English and Business English, 2) to study the English language learning strategies which have an affect on English learning achievement, and 3) to compare the English language learning strategies used by the students majoring in English and Business English. The population and sampling comprise of 139 university students of the Suan Sunandha Rajabhat University. Research instruments are language learning strategies questionnaire which was constructed by the researcher and improved on by three experts and the transcripts that show the results of English learning achievement. The questionnaire includes 1) Language Practice Strategy 2)Memory Strategy 3) Communication Strategy 4)Making an Intelligent Guess or Compensation Strategy 5) Self-discipline in Learning Management Strategy 6) Affective Strategy 7)Self-Monitoring Strategy 8) Self-studySkill Strategy. Statistics used in the study are mean, standard deviation, T-test and One Way ANOVA, Pearson product moment correlation coefficient and Regression Analysis. The results of the findings reveal that the English language learning strategies most frequently used by the students are affective strategy, making an intelligent guess or compensation strategy, self-studyskill strategy and self-monitoring strategy respectively. The aspect of making an intelligent guess or compensation strategy had the most significant affect on English learning achievement. It is found that the English language learning strategies mostly used by the Business English major students and moderately used by the English major students. Their language practice strategies uses were significantly different at the 0.05 level and their communication strategies uses were significantly different at the 0.01 level. In addition, it is found that the poor students and the fair ones most frequently used affective strategy while the good ones most frequently used making an intelligent guess or compensation strategy. KeywordsEnglish language, language learning strategies, English learning achievement, and students majoring in English, Business English. Pranee Pathomchaiwat is an Assistant Professor in Business English Program, Suan Sunandha Rajabhat University, Bangkok, Thailand (e-mail: [email protected]).

Keywords: English language, language learning strategies, English learning achievement, students majoring in English, Business English

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Authors: T. R. Alencar, P. T. Leite

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Several optimization algorithms specifically applied to the problem of Operation Planning of Hydrothermal Power Systems have been developed and are used. Although providing solutions to various problems encountered, these algorithms have some weaknesses, difficulties in convergence, simplification of the original formulation of the problem, or owing to the complexity of the objective function. Thus, this paper presents the development of a computational tool for solving optimization problem identified and to provide the User an easy handling. Adopted as intelligent optimization technique, Genetic Algorithms and programming language Java. First made the modeling of the chromosomes, then implemented the function assessment of the problem and the operators involved, and finally the drafting of the graphical interfaces for access to the User. The program has managed to relate a coherent performance in problem resolution without the need for simplification of the calculations together with the ease of manipulating the parameters of simulation and visualization of output results.

Keywords: Energy, Optimization, Hydrothermal Power Systemsand Genetic Algorithms

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Authors: B. Thiagarajan, R. Bremananth

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Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different characteristics and treatments. Brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Locating the tumor within MR (magnetic resonance) image of brain is integral part of the treatment of brain tumor. This segmentation task requires classification of each voxel as either tumor or non-tumor, based on the description of the voxel under consideration. Many studies are going on in the medical field using Markov Random Fields (MRF) in segmentation of MR images. Even though the segmentation process is better, computing the probability and estimation of parameters is difficult. In order to overcome the aforementioned issues, Conditional Random Field (CRF) is used in this paper for segmentation, along with the modified artificial bee colony optimization and modified fuzzy possibility c-means (MFPCM) algorithm. This work is mainly focused to reduce the computational complexities, which are found in existing methods and aimed at getting higher accuracy. The efficiency of this work is evaluated using the parameters such as region non-uniformity, correlation and computation time. The experimental results are compared with the existing methods such as MRF with improved Genetic Algorithm (GA) and MRF-Artificial Bee Colony (MRF-ABC) algorithm.

Keywords: Conditional random field, Magnetic resonance, Markov random field, Modified artificial bee colony.

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Authors: Gonzalo Rubio, Eusebio Valero, Sven Lanzan

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The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations. As a results of this, Computational Fluid Dynamic (CFD) solvers are widely used in the aeronautical field. These solvers require the correct selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on the proper choice of these parameters. In this paper we create an expert system capable of making an accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver. Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time required for the convergence of a CFD solver.

Keywords: Artificial Neural Network, Computational Fluid Dynamics, Optimization

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Authors: Z. M. Najmi

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Over the years, the question around Artificial Intelligence has always been one with many answers. Whether by means of use in business and industry or complicated algorithmic programming, management of these technologies has always been the core focus. More recently, technologies have been questioned in industry and society alike as to whether they have improved human-centred design, assisted choices and objectives, and had a hand in systematic processes across the board. With these questions the answer may lie within AI technologies, and the steps needed in removing common human error. Elements such as Machine Learning, Deep Learning, Recommender Systems and Natural Language Processing will all be features to consider moving forward. Our previous intervention with AI applications has resulted in increased productivity, however, raised concerns for the continuation of traditional human-centred occupations. Emerging technologies such as Augmented Reality and Virtual Reality have all played a part in this during AI’s prominent rise. As mentioned, AI has been constantly under the microscope; the benefits and drawbacks may seem endless is wide, but AI is something we must take notice of and adapt into our everyday lives. The aim of this paper is to give an overview of the technologies surrounding A.I. and its’ related technologies. A comprehensive review has been written as a timeline of the developing events and key points in the history of Artificial Intelligence. This research is gathered entirely from secondary research, academic statements of knowledge and gathered to produce an understanding of the timeline of AI.

Keywords: Artificial Intelligence, Deep Learning, Augmented Reality, Reinforcement Learning, Machine Learning, Supervised Learning.

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Authors: Daniel Rivero, Julián Dorado, Juan R. Rabuñal, Alejandro Pazos, Javier Pereira

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The development of Artificial Neural Networks (ANNs) is usually a slow process in which the human expert has to test several architectures until he finds the one that achieves best results to solve a certain problem. This work presents a new technique that uses Genetic Programming (GP) for automatically generating ANNs. To do this, the GP algorithm had to be changed in order to work with graph structures, so ANNs can be developed. This technique also allows the obtaining of simplified networks that solve the problem with a small group of neurons. In order to measure the performance of the system and to compare the results with other ANN development methods by means of Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases. The results of those comparisons show that the system achieves good results comparable with the already existing techniques and, in most of the cases, they worked better than those techniques.

Keywords: Artificial Neural Networks, Evolutionary Computation, Genetic Programming.

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Authors: E. I. Fandi, E. A. Alfandi, M. A. Alrabib

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Implementing of a continues flow gas lift system for one vertical oil well producer in Wafa field was investigated under five reservoir pressures and their dependent parameters. Well 03 producers were responded positively to the gas lift system despite of the high well head operating pressures. However, the flowing bottom hole pressures were reduced by a ratio from 6 to 33 % in the case A3 for example, for the design runs conducted under the existing operating conditions for years 2003, 2006 and 2009. This reduction in FBHP has increased the production rate by a ratio from 12 to 22.5%. The results indicated that continues flow gas lift system is a good candidate as an artificial lift system to be considered for the one vertical producer covered by this study. Most significantly, timing for artificial lift by a gas lift system for this field is highly dependent on the amount of gas available at the time of implementation because of the high gas production rate from the top of the reservoir. 

Keywords: Gas lift, Wafa field, Ghadamis Basin, Artificial lift, Libya.

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Authors: Vinay Chandwani, Vinay Agrawal, Ravindra Nagar

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Artificial Neural Networks (ANN) trained using backpropagation (BP) algorithm are commonly used for modeling material behavior associated with non-linear, complex or unknown interactions among the material constituents. Despite multidisciplinary applications of back-propagation neural networks (BPNN), the BP algorithm possesses the inherent drawback of getting trapped in local minima and slowly converging to a global optimum. The paper present a hybrid artificial neural networks and genetic algorithm approach for modeling slump of ready mix concrete based on its design mix constituents. Genetic algorithms (GA) global search is employed for evolving the initial weights and biases for training of neural networks, which are further fine tuned using the BP algorithm. The study showed that, hybrid ANN-GA model provided consistent predictions in comparison to commonly used BPNN model. In comparison to BPNN model, the hybrid ANNGA model was able to reach the desired performance goal quickly. Apart from the modeling slump of ready mix concrete, the synaptic weights of neural networks were harnessed for analyzing the relative importance of concrete design mix constituents on the slump value. The sand and water constituents of the concrete design mix were found to exhibit maximum importance on the concrete slump value.

Keywords: Artificial neural networks, Genetic algorithms, Back-propagation algorithm, Ready Mix Concrete, Slump value.

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Authors: Sheng-Shih Wang, Wei-Ting Wang

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This paper designs an intelligent guidance system, based on wireless sensor networks, for efficient parking in parking lots. The proposed system consists of a parking space allocation subsystem, a parking space monitoring subsystem, a driving guidance subsystem, and a vehicle detection subsystem. In the system, we propose a novel and effective virtual coordinate system for sensing and displaying devices to determine the proper vacant parking space and provide the precise guidance to the driver. This study constructs a ZigBee-based wireless sensor network on Arduino platform and implements the prototype of the proposed system using Arduino-based complements. Experimental results confirm that the proposed prototype can not only work well, but also provide drivers the correct parking information.

Keywords: Arduino, Parking guidance, Wireless sensor network, ZigBee.

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Authors: S. Minhas, C. Juzek, U. Berger

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The planning in manufacturing system is becoming complicated day by day due to the expanding networks and shortage of skilled people to manage change. Consequently, faster lead time and rising demands for eco-efficient evaluation of manufacturing products and processes need exploitation of new and intelligent knowledge management concepts for manufacturing planning. This paper highlights motivation for incorporation of new features in the manufacturing planning system. Furthermore, it elaborates requirements for the development of intelligent knowledge management concept to support planning related decisions. Afterwards, the derived concept is presented in this paper considering two case studies. The first case study is concerned with the automotive ramp-up planning. The second case study specifies requirements for knowledge management system to support decisions in eco-efficient evaluation of manufacturing products and processes

Keywords: Ramp-up, Environmental impact, Knowledge management.

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Authors: H. Zainuddin, F. Hanafi, M. H. Hairi, A. Aman, M.H.N. Talib

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This paper investigates the effects of knowledge-based acceleration feedback control integrated with Automatic Generation Control (AGC) to enhance the quality of frequency control of governing system. The Intelligent Acceleration Feedback Controller (IAFC) is proposed to counter the over and under frequency occurrences due to major load change in power system network. Therefore, generator tripping and load shedding operations can be reduced. Meanwhile, the integration of IAFC with AGC, a well known Load-Frequency Control (LFC) is essential to ensure the system frequency is restored to the nominal value. Computer simulations of frequency response of governing system are used to optimize the parameters of IAFC. As a result, there is substantial improvement on the LFC of governing system that employing the proposed control strategy.

Keywords: Knowledge-based Supplementary Control, Acceleration Feedback, Load Frequency Control, Automatic Generation Control.

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Authors: Sachin Man Bajimaya, SangChul Park, Gi-Nam Wang

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The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.

Keywords: Artificial Neural Network (ANN), Indirect Extrusion, Finite Element Analysis, MES.

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Authors: Ming-Hui Lee, Tsung-Chien Chen, Yuh-Shiou Tai

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The intelligent fuzzy input estimator is used to estimate the input force of the rigid bar structural system in this study. The fuzzy Kalman filter without the input term and the fuzzy weighting recursive least square estimator are two main portions of this method. The practicability and accuracy of the proposed method were verified with numerical simulations from which the input forces of a rigid bar structural system were estimated from the output responses. In order to examine the accuracy of the proposed method, a rigid bar structural system is subjected to periodic sinusoidal dynamic loading. The excellent performance of this estimator is demonstrated by comparing it with the use of difference weighting function and improper the initial process noise covariance. The estimated results have a good agreement with the true values in all cases tested.

Keywords: Fuzzy Input Estimator, Kalman Filter, RecursiveLeast Square Estimator.

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Authors: O. S. Ebrahim, K. O. Shawky, M. A. Badr, P. K. Jain

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This paper describes an efficient; low-cost; high-availability; induction motor (IM) drive system with intelligent supervisory control for water distribution networks including storage tank. To increase the operational efficiency and reduce cost, the IM drive system includes main pumping unit and an auxiliary voltage source inverter (VSI) fed unit. The main unit comprises smart star/delta starter, regenerative fluid clutch, switched VAR compensator, and hysteresis liquid-level controller. Three-state energy saving mode (ESM) is defined at no-load and a logic algorithm is developed for best energetic cost reduction. To reduce voltage sag, the supervisory controller operates the switched VAR compensator upon motor starting. To provide smart star/delta starter at low cost, a method based on current sensing is developed for interlocking, malfunction detection, and life–cycles counting and used to synthesize an improved fuzzy logic (FL) based availability assessment scheme. Furthermore, a recurrent neural network (RNN) full state estimator is proposed to provide sensor fault-tolerant algorithm for the feedback control. The auxiliary unit is working at low flow rates and improves the system efficiency and flexibility for distributed generation during islanding mode. Compared with doubly-fed IM, the proposed one ensures 30% working throughput under main motor/pump fault conditions, higher efficiency, and marginal cost difference. This is critically important in case of water networks. Theoretical analysis, computer simulations, cost study, as well as efficiency evaluation, using timely cascaded energy-conservative systems, are performed on IM experimental setup to demonstrate the validity and effectiveness of the proposed drive and control.

Keywords: Artificial Neural Network, ANN, Availability Assessment, Cloud Computing, Energy Saving, Induction Machine, IM, Supervisory Control, Fuzzy Logic, FL, Pumped Storage.

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Authors: J. Mlýnek, R. Srb

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This article is focused on the calculation of heat radiation intensity and its optimization on an aluminum mould surface. The inside of the mould is sprinkled with a special powder and its outside is heated by infra heaters located above the mould surface, up to a temperature of 250°C. By this way artificial leathers in the car industry are produced (e. g. the artificial leather on a car dashboard). A mathematical model of heat radiation of infra heaters on a mould surface is described in this paper. This model allows us to calculate a heat-intensity radiation on the mould surface for the concrete location of infra heaters above the mould surface. It is necessary to ensure approximately the same heat intensity radiation on the mould surface by finding a suitable location for the infra heaters, and in this way the same material structure and color of artificial leather. In the model we have used a genetic algorithm to optimize the radiation intensity on the mould surface. Experimental measured values for the heat radiation intensity by a sensor in the surroundings of an infra heater are used for the calculation procedures. A computational procedure was programmed in language Matlab.

Keywords: Genetic algorithm, mathematical model of heat radiation, optimization of radiation intensity, software implementation

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Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian

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Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.

Keywords: Desalting unit, Crude oil, Neural Networks, Simulation, Recovery, Separation.

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Authors: Seyed Hossein Iranmanesh, Mansoureh Zarezadeh

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This paper presents an application of Artificial Neural Network (ANN) to forecast actual cost of a project based on the earned value management system (EVMS). For this purpose, some projects randomly selected based on the standard data set , and it is produced necessary progress data such as actual cost ,actual percent complete , baseline cost and percent complete for five periods of project. Then an ANN with five inputs and five outputs and one hidden layer is trained to produce forecasted actual costs. The comparison between real and forecasted data show better performance based on the Mean Absolute Percentage Error (MAPE) criterion. This approach could be applicable to better forecasting the project cost and result in decreasing the risk of project cost overrun, and therefore it is beneficial for planning preventive actions.

Keywords: Earned Value Management System (EVMS), Artificial Neural Network (ANN), Estimate At Completion, Forecasting Methods, Project Performance Measurement.

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Authors: Krishna S. Raghuwaiya, Shonal Singh, Jito Vanualailai

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In this paper, the trajectory tracking problem for carlike mobile robots have been studied. The system comprises of a leader and a follower robot. The purpose is to control the follower so that the leader-s trajectory is tracked with arbitrary desired clearance to avoid inter-robot collision while navigating in a terrain with obstacles. A set of artificial potential field functions is proposed using the Direct Method of Lyapunov for the avoidance of obstacles and attraction to their designated targets. Simulation results prove the efficiency of our control technique.

Keywords: Control, Trajectory Tracking, Lyapunov.

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Authors: Gayadhar Panda, Sidhartha Panda, C. Ardil

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The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.

Keywords: Automatic Generation Control (AGC), Dynamic Model, Two-area Power System, Fuzzy Logic Controller, Neural Network, Hybrid Neuro-Fuzzy(HNF).

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Authors: G. R. Rameshkumar, B. V. A Rao, K. P. Ramachandran

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Misalignment and unbalance are the major concerns in rotating machinery. When the power supply to any rotating system is cutoff, the system begins to lose the momentum gained during sustained operation and finally comes to rest. The exact time period from when the power is cutoff until the rotor comes to rest is called Coast Down Time. The CDTs for different shaft cutoff speeds were recorded at various misalignment and unbalance conditions. The CDT reduction percentages were calculated for each fault and there is a specific correlation between the CDT reduction percentage and the severity of the fault. In this paper, radial basis network, a new generation of artificial neural networks, has been successfully incorporated for the prediction of CDT for misalignment and unbalance conditions. Radial basis network has been found to be successful in the prediction of CDT for mechanical faults in rotating machinery.

Keywords: Coast Down Time, Misalignment, Unbalance, Artificial Neural Networks, Radial Basis Network.

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Authors: Commander Sunil Tyagi

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Effectiveness of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) classifiers for fault diagnosis of rolling element bearings are presented in this paper. The characteristic features of vibration signals of rotating driveline that was run in its normal condition and with faults introduced were used as input to ANN and SVM classifiers. Simple statistical features such as standard deviation, skewness, kurtosis etc. of the time-domain vibration signal segments along with peaks of the signal and peak of power spectral density (PSD) are used as features to input the ANN and SVM classifier. The effect of preprocessing of the vibration signal by Discreet Wavelet Transform (DWT) prior to feature extraction is also studied. It is shown from the experimental results that the performance of SVM classifier in identification of bearing condition is better then ANN and pre-processing of vibration signal by DWT enhances the effectiveness of both ANN and SVM classifier

Keywords: ANN, Artificial Intelligence, Fault Diagnosis, Pattern Recognition, Rolling Element Bearing, SVM. Wavelet Transform

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Authors: A. M. N. El-Khoja, A. F. Ashour, J. Abdalhmid, X. Dai, A. Khan

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In recent years, waste tyre problem is considered as one of the most crucial environmental pollution problems facing the world. Thus, reusing waste rubber crumb from recycled tyres to develop highly damping concrete is technically feasible and a viable alternative to landfill or incineration. The utilization of waste rubber in concrete generally enhances the ductility, toughness, thermal insulation, and impact resistance. However, the mechanical properties decrease with the amount of rubber used in concrete. The aim of this paper is to develop artificial neural network (ANN) models to predict the compressive strength of rubberised concrete (RuC). A trained and tested ANN was developed using a comprehensive database collected from different sources in the literature. The ANN model developed used 5 input parameters that include: coarse aggregate (CA), fine aggregate (FA), w/c ratio, fine rubber (Fr), and coarse rubber (Cr), whereas the ANN outputs were the corresponding compressive strengths. A parametric study was also conducted to study the trend of various RuC constituents on the compressive strength of RuC.

Keywords: Rubberized concrete, compressive strength, artificial neural network, prediction.

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Authors: A.Qaderi, A. Heydarinasab, M. Ardjmand

Abstract:

Poly-β-hydroxybutyrate (PHB) is one of the most famous biopolymers that has various applications in production of biodegradable carriers. The most important strategy for enhancing efficiency in production process and reducing the price of PHB, is the accurate expression of kinetic model of products formation and parameters that are effective on it, such as Dry Cell Weight (DCW) and substrate consumption. Considering the high capabilities of artificial neural networks in modeling and simulation of non-linear systems such as biological and chemical industries that mainly are multivariable systems, kinetic modeling of microbial production of PHB that is a complex and non-linear biological process, the three layers perceptron neural network model was used in this study. Artificial neural network educates itself and finds the hidden laws behind the data with mapping based on experimental data, of dry cell weight, substrate concentration as input and PHB concentration as output. For training the network, a series of experimental data for PHB production from Hydrogenophaga Pseudoflava by glucose carbon source was used. After training the network, two other experimental data sets that have not intervened in the network education, including dry cell concentration and substrate concentration were applied as inputs to the network, and PHB concentration was predicted by the network. Comparison of predicted data by network and experimental data, indicated a high precision predicted for both fructose and whey carbon sources. Also in present study for better understanding of the ability of neural network in modeling of biological processes, microbial production kinetic of PHB by Leudeking-Piret experimental equation was modeled. The Observed result indicated an accurate prediction of PHB concentration by artificial neural network higher than Leudeking- Piret model.

Keywords: Kinetic Modeling, Poly-β-Hydroxybutyrate (PHB), Hydrogenophaga Pseudoflava, Artificial Neural Network, Leudeking-Piret

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