Search results for: radial basic function neural networks.

Authors: Hamid Reza Boveiri

Abstract:

In this paper, a new proposed system for Persian printed numeral characters recognition with emphasis on representation and recognition stages is introduced. For the first time, in Persian optical character recognition, geometrical central moments as character image descriptor and fuzzy min-max neural network for Persian numeral character recognition has been used. Set of different experiments on binary images of regular, translated, rotated and scaled Persian numeral characters has been done and variety of results has been presented. The best result was 99.16% correct recognition demonstrating geometrical central moments and fuzzy min-max neural network are adequate for Persian printed numeral character recognition.

Keywords: Fuzzy min-max neural network, geometrical centralmoments, optical character recognition, Persian digits recognition, Persian printed numeral characters recognition.

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Authors: Mangesh R. Phate, V. H. Tatwawadi

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This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.

The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.

Keywords: Field data based model, Artificial neural network, Simulation, Convectional Turning, Material removal rate.

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Authors: A.Khaja Kamaluddin, B.Muhammed Yousoof

Abstract:

Routing places an important role in determining the quality of service in wireless networks. The routing methods adopted in wireless networks have many drawbacks. This paper aims to review the current routing methods used in wireless networks. This paper proposes an innovative solution to overcome the problems in routing. This solution is aimed at improving the Quality of Service. This solution is different from others as it involves the resuage of the part of the virtual circuits. This improvement in quality of service is important especially in propagation of multimedia applications like video, animations etc. So it is the dire need to propose a new solution to improve the quality of service in ATM wireless networks for multimedia applications especially during this era of multimedia based applications.

Keywords: Packet buffering, Routing Table, Virtual Circuits (VC)

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Authors: Peter Pfeifer, Julian Pfeifer, Niko Pfeifer

Abstract:

Information processing is at the focus of brain and cognition research. This work has a different perspective, it starts with behaviors. The detailed analysis of behaviors leads to the discovery that a significant proportion of them are based on only five basic drives. These basic drives are combinable, and the combinations result in the diversity of human behavior and thinking. The key elements are drive memories. They collect memories of drive-related situations and feelings. They contain variations of basic drives in numerous areas of life and build combinations with different meanings depending on the area. Human thinking could be explained with variations on these nested combinations of basic drives.

Keywords: Cognitive modeling, psycholinguistics, psychology, psychophysiology of cognition.

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Authors: Aaqif Afzaal Abbasi, Javaid Iqbal, Akhtar Nawaz Malik

Abstract:

4G Communication Networks provide heterogeneous wireless technologies to mobile subscribers through IP based networks and users can avail high speed access while roaming across multiple wireless channels; possible by an organized way to manage the Quality of Service (QoS) functionalities in these networks. This paper proposes the idea of developing a novel QoS optimization architecture that will judge the user requirements and knowing peak times of services utilization can save the bandwidth/cost factors. The proposed architecture can be customized according to the network usage priorities so as to considerably improve a network-s QoS performance.

Keywords: QoS, Network Coverage Boundary, ServicesArchives Units (SAU), Cumulative Services Archives Units (CSAU).

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Authors: Hongjoong Sin, Jangsoo Lee, Sungju Lee, Seunghwan Yoo, Sanghyuck Lee, Jaesik Lee, Yongjun Lee, Sungchun Kim

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Wireless sensor networks are consisted of hundreds or thousands of small sensors that have limited resources. Energy-efficient techniques are the main issue of wireless sensor networks. This paper proposes an energy efficient agent-based framework in wireless sensor networks. We adopt biologically inspired approaches for wireless sensor networks. Agent operates automatically with their behavior policies as a gene. Agent aggregates other agents to reduce communication and gives high priority to nodes that have enough energy to communicate. Agent behavior policies are optimized by genetic operation at the base station. Simulation results show that our proposed framework increases the lifetime of each node. Each agent selects a next-hop node with neighbor information and behavior policies. Our proposed framework provides self-healing, self-configuration, self-optimization properties to sensor nodes.

Keywords: Agent, Energy Efficiency, Genetic algorithm, Wireless Sensor Networks.

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Authors: Zied Trifa, Maher Khemakhem

Abstract:

The survey and classification of the different security attacks in structured peer-to-peer (P2P) overlay networks can be useful to computer system designers, programmers, administrators, and users. In this paper, we attempt to provide a taxonomy of structured P2P overlay networks security attacks. We have specially focused on the way these attacks can arise at each level of the network. Moreover, we observed that most of the existing systems such as Content Addressable Network (CAN), Chord, Pastry, Tapestry, Kademlia, and Viceroy suffer from threats and vulnerability which lead to disrupt and corrupt their functioning. We hope that our survey constitutes a good help for who-s working on this area of research.

Keywords: P2P, Structured P2P Overlay Networks, DHT, Security, classification

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Authors: Nor Azlina Ab. Aziz, Kamarulzaman Ab. Aziz, Wan Zakiah Wan Ismail

Abstract:

Coverage is one of the main research interests in wireless sensor networks (WSN), it is used to determine the quality of service (QoS) of the networks. Therefore this paper aims to review the common strategies use in solving coverage problem in WSN. The strategies studied are used during deployment phase where the coverage is calculated based on the placement of the sensors on the region of interest (ROI). The strategies reviewed are categorized into three groups based on the approaches used, namely; force based, grid based or computational geometry based approach.

Keywords: Computational geometry, coverage, Delaunay triangulation, force, grid, Voronoi diagram, wireless sensor networks.

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

Abstract:

Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: ANN, DWT, GLCM, KNN, ROI, artificial neural networks, discrete wavelet transform, gray-level co-occurrence matrix, k-nearest neighbor, region of interest.

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

Abstract:

The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: Analog circuits, digital circuits, memristors, neuromorphic computing systems.

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Authors: Jittarat Satonsaowapak, Thanatchai. Kulworawanichpong., Ratchadaporn Oonsivilai, Anant Oonsivilai

Abstract:

The use of renewable energy sources becomes more necessary and interesting. As wider applications of renewable energy devices at domestic, commercial and industrial levels has not only resulted in greater awareness, but also significantly installed capacities. In addition, biomass principally is in the form of woods, which is a form of energy by humans for a long time. Gasification is a process of conversion of solid carbonaceous fuel into combustible gas by partial combustion. Many gasifier models have various operating conditions; the parameters kept in each model are different. This study applied experimental data, which has three inputs, which are; biomass consumption, temperature at combustion zone and ash discharge rate. One output is gas flow rate. For this paper, neural network was used to identify the gasifier system suitable for the experimental data. In the result,neural networkis usable to attain the answer.

Keywords: Gasifier System, Identification, Neural Network

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Authors: Elkington Sibusiso Mnguni

Abstract:

Although South Africa has made good progress in providing basic water and sanitation services to its citizens, there is still a large section of the population that has no access to these services. This paper reviews the performance of the government’s municipal infrastructure grant programme in providing basic water and sanitation services which are part of the constitutional requirements to the citizens. The method used to gather data and information was a desk top study which sought to review the progress made in rolling out the programme. The successes and challenges were highlighted and possible solutions were identified that can accelerate the elimination of the remaining backlogs and improve the level of service to the citizens. Currently, approximately 6.5 million citizens are without access to basic water services and approximately 10 million are without access to basic sanitation services.

Keywords: Grant, municipal infrastructure, sanitation, services, water.

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Authors: Wei-Han Wang, Hsiang-Yu Yu, Mau-Sun Hua

Abstract:

Theory of Mind (ToM) refers to the ability to infer another’s mental state. With appropriate ToM, one can behave well in social interactions. A growing body of evidence has demonstrated that patients with temporal lobe epilepsy (TLE) may damage ToM by affecting on regions of the underlying neural network of ToM. However, the question of whether there is cerebral laterality for ToM functions remains open. This study aimed to examine whether there is cerebral lateralization for ToM abilities in TLE patients. Sixty-seven adult TLE patients and 30 matched healthy controls (HC) were recruited. Patients were classified into right (RTLE), left (LTLE), and bilateral (BTLE) TLE groups on the basis of a consensus panel review of their seizure semiology, EEG findings, and brain imaging results. All participants completed an intellectual test and four tasks measuring basic and advanced ToM. The results showed that, on all ToM tasks, (1) each patient group performed worse than HC; (2) there were no significant differences between LTLE and RTLE groups; and (3) the BTLE group performed the worst. It appears that the neural network responsible for ToM is distributed evenly between the cerebral hemispheres.

Keywords: Cerebral lateralization, social cognition, temporal lobe epilepsy, theory of mind.

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Authors: Wanqing You, Kai Qian, Xi He, Ying Qian

Abstract:

In this paper, the potential security issues brought by the virtualization of a Software Defined Networks (SDN) would be analyzed. The virtualization of SDN is achieved by FlowVisor (FV). With FV, a physical network is divided into multiple isolated logical networks while the underlying resources are still shared by different slices (isolated logical networks). However, along with the benefits brought by network virtualization, it also presents some issues regarding security. By examining security issues existing in an OpenFlow network, which uses FlowVisor to slice it into multiple virtual networks, we hope we can get some significant results and also can get furtherdiscussions among the security of SDN virtualization.

Keywords: FlowVisor, Network virtualization, Potential threats, Possible solutions.

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Authors: Igor Astrov, Mikhail Pikkov

Abstract:

This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. With the SA strategy, we proposed a multirate neural control of an AUV trajectory using neural network model reference controller for a nontrivial mid-small size AUV "r2D4" stochastic model. This control system has been demonstrated and evaluated by simulation of diving maneuvers using software package Simulink. From the simulation results it can be seen that the chosen AUV model is stable in the presence of high noise, and also can be concluded that the fast SA of similar AUV systems with economy in energy of batteries can be asserted during the underwater missions in search-and-rescue operations.

Keywords: Autonomous underwater vehicles, multirate systems, neurocontrollers, situational awareness.

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Authors: Achela K. Fernando, Xiujuan Zhang, Peter F. Kinley

Abstract:

A feed-forward, back-propagation Artificial Neural Network (ANN) model has been used to forecast the occurrences of wastewater overflows in a combined sewerage reticulation system. This approach was tested to evaluate its applicability as a method alternative to the common practice of developing a complete conceptual, mathematical hydrological-hydraulic model for the sewerage system to enable such forecasts. The ANN approach obviates the need for a-priori understanding and representation of the underlying hydrological hydraulic phenomena in mathematical terms but enables learning the characteristics of a sewer overflow from the historical data. The performance of the standard feed-forward, back-propagation of error algorithm was enhanced by a modified data normalizing technique that enabled the ANN model to extrapolate into the territory that was unseen by the training data. The algorithm and the data normalizing method are presented along with the ANN model output results that indicate a good accuracy in the forecasted sewer overflow rates. However, it was revealed that the accurate forecasting of the overflow rates are heavily dependent on the availability of a real-time flow monitoring at the overflow structure to provide antecedent flow rate data. The ability of the ANN to forecast the overflow rates without the antecedent flow rates (as is the case with traditional conceptual reticulation models) was found to be quite poor.

Keywords: Artificial Neural Networks, Back-propagationlearning, Combined sewer overflows, Forecasting.

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Authors: Dongwan Kang, Joohyung Oh, Chaetae Im

Abstract:

Recent communications environment significantly expands the mobile environment. The popularization of smartphones with various mobile services has emerged, and smartphone users are rapidly increasing. Because of these symptoms, existing wired environment in a variety of mobile traffic entering to mobile network has threatened the stability of the mobile network. Unlike traditional wired infrastructure, mobile networks has limited radio resources and signaling procedures for complex radio resource management. So these traffic is not a problem in wired networks but mobile networks, it can be a threat. In this paper, we analyze the security threats in mobile networks and provide direction to solve it.

Keywords: 3G, Core Network Security, GTP, Mobile NetworkSecurity

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Authors: J. Levendovszky, I. Koncz, P. Boros

Abstract:

The paper is concerned with developing stochastic delay mechanisms for efficient multicast protocols and for smooth mobile handover processes which are capable of preserving a given Quality of Service (QoS). In both applications the participating entities (receiver nodes or subscribers) sample a stochastic timer and generate load after a random delay. In this way, the load on the networking resources is evenly distributed which helps to maintain QoS communication. The optimal timer distributions have been sought in different p.d.f. families (e.g. exponential, power law and radial basis function) and the optimal parameter have been found in a recursive manner. Detailed simulations have demonstrated the improvement in performance both in the case of multicast and mobile handover applications.

Keywords: Multicast communication, stochactic delay mechanisms.

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Authors: János Levendovszky, András Oláh

Abstract:

In this paper, novel statistical sampling based equalization techniques and CNN based detection are proposed to increase the spectral efficiency of multiuser communication systems over fading channels. Multiuser communication combined with selective fading can result in interferences which severely deteriorate the quality of service in wireless data transmission (e.g. CDMA in mobile communication). The paper introduces new equalization methods to combat interferences by minimizing the Bit Error Rate (BER) as a function of the equalizer coefficients. This provides higher performance than the traditional Minimum Mean Square Error equalization. Since the calculation of BER as a function of the equalizer coefficients is of exponential complexity, statistical sampling methods are proposed to approximate the gradient which yields fast equalization and superior performance to the traditional algorithms. Efficient estimation of the gradient is achieved by using stratified sampling and the Li-Silvester bounds. A simple mechanism is derived to identify the dominant samples in real-time, for the sake of efficient estimation. The equalizer weights are adapted recursively by minimizing the estimated BER. The near-optimal performance of the new algorithms is also demonstrated by extensive simulations. The paper has also developed a (Cellular Neural Network) CNN based approach to detection. In this case fast quadratic optimization has been carried out by t, whereas the task of equalizer is to ensure the required template structure (sparseness) for the CNN. The performance of the method has also been analyzed by simulations.

Keywords: Cellular Neural Network, channel equalization, communication over fading channels, multiuser communication, spectral efficiency, statistical sampling.

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Authors: Axay J Mehta, Hema A Mehta, T.C.Manjunath, C. Ardil

Abstract:

In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.

Keywords: Power system, Load forecasting, Neural Network, Neuron, Stabilization, Network structure, Load.

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Authors: S. Cagin, X. Fischer, E. Delacourt, N. Bourabaa, C. Morin, D. Coutellier, B. Carré, S. Loumé

Abstract:

The complexity of scavenging by ports and its impact on engine efficiency create the need to understand and to model it as realistically as possible. However, there are few empirical scavenging models and these are highly specialized. In a design optimization process, they appear very restricted and their field of use is limited. This paper presents a comparison of two methods to establish and reduce a model of the scavenging process in 2-stroke diesel engines. To solve the lack of scavenging models, a CFD model has been developed and is used as the referent case. However, its large size requires a reduction. Two techniques have been tested depending on their fields of application: The NTF method and neural networks. They both appear highly appropriate drastically reducing the model’s size (over 90% reduction) with a low relative error rate (under 10%). Furthermore, each method produces a reduced model which can be used in distinct specialized fields of application: the distribution of a quantity (mass fraction for example) in the cylinder at each time step (pseudo-dynamic model) or the qualification of scavenging at the end of the process (pseudo-static model).

Keywords: Diesel engine, Design optimization, Model reduction, Neural network, NTF algorithm, Scavenging.

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Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

Abstract:

End milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product and surface roughness of the produced job plays an important role. In general, the surface roughness affects wear resistance, ductility, tensile, fatigue strength, etc., for machined parts and cannot be neglected in design. In the present work an experimental investigation of end milling of aluminium alloy with carbide tool is carried out and the effect of different cutting parameters on the response are studied with three-dimensional surface plots. An artificial neural network (ANN) is used to establish the relationship between the surface roughness and the input cutting parameters (i.e., spindle speed, feed, and depth of cut). The Matlab ANN toolbox works on feed forward back propagation algorithm is used for modeling purpose. 3-12-1 network structure having minimum average prediction error found as best network architecture for predicting surface roughness value. The network predicts surface roughness for unseen data and found that the result/prediction is better. For desired surface finish of the component to be produced there are many different combination of cutting parameters are available. The optimum cutting parameter for obtaining desired surface finish, to maximize tool life is predicted. The methodology is demonstrated, number of problems are solved and algorithm is coded in Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

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Authors: Fan-Yong Meng

Abstract:

In this paper, we first introduce the model of games on augmenting systems with a coalition structure, which can be seen as an extension of games on augmenting systems. The core of games on augmenting systems with a coalition structure is defined, and an equivalent form is discussed. Meantime, the Shapley function for this type of games is given, and two axiomatic systems of the given Shapley function are researched. When the given games are quasi convex, the relationship between the core and the Shapley function is discussed, which does coincide as in classical case. Finally, a numerical example is given.

Keywords: Cooperative game, augmenting system, Shapley function, core.

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Authors: Maja Sarevska, Abdel-Badeeh M. Salem

Abstract:

This paper considers the problem of Null-Steering beamforming using Neural Network (NN) approach for antenna array system. Two cases are presented. First, unlike the other authors, the estimated Direction Of Arrivals (DOAs) are used for antenna array weights NN-based determination and the imprecise DOAs estimations are taken into account. Second, the blind null-steering beamforming is presented. In this case the antenna array outputs are presented at the input of the NN without DOAs estimation. The results of computer simulations will show much better relative mean error performances of the first NN approach compared to the NNbased blind beamforming.

Keywords: Beamforming, DOAs, neural network.

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Authors: F. Rasi Maezabadi, M. Masdari, M. R. Soltani

Abstract:

Series of experimental tests were conducted on a section of a 660 kW wind turbine blade to measure the pressure distribution of this model oscillating in plunging motion. In order to minimize the amount of data required to predict aerodynamic loads of the airfoil, a General Regression Neural Network, GRNN, was trained using the measured experimental data. The network once proved to be accurate enough, was used to predict the flow behavior of the airfoil for the desired conditions. Results showed that with using a few of the acquired data, the trained neural network was able to predict accurate results with minimal errors when compared with the corresponding measured values. Therefore with employing this trained network the aerodynamic coefficients of the plunging airfoil, are predicted accurately at different oscillation frequencies, amplitudes, and angles of attack; hence reducing the cost of tests while achieving acceptable accuracy.

Keywords: Airfoil, experimental, GRNN, Neural Network, Plunging.

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Authors: M. N. Moschakis, V. V. Dafopoulos, I. G. Andritsos, E. S. Karapidakis, J. M. Prousalidis

Abstract:

This paper deals with the effect of a power transformer’s vector group on the basic voltage sag characteristics during unbalanced faults at a meshed or radial power network. Specifically, the propagation of voltage sags through a power transformer is studied with advanced short-circuit analysis. A smart method to incorporate this effect on analytical mathematical expressions is proposed. Based on this methodology, the positive effect of transformers of certain vector groups on the mitigation of the expected number of voltage sags per year (sag frequency) at the terminals of critical industrial customers can be estimated.

Keywords: Balanced and unbalanced faults, industrial design, phase shift, power quality, power systems, voltage sags (or dips).

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Authors: Chang-Hung Hsu, Chun-Yao Lee, Guan-Lin Liao, Yung-Tsan Jou, Jin-Maun Ho, Yu-Hua Hsieh, Yi-Xing Shen

Abstract:

This article simulates the wind generator set which has two fault bearing collar rail destruction and the gear box oil leak fault. The electric current signal which produced by the generator, We use Empirical Mode Decomposition (EMD) as well as Fast Fourier Transform (FFT) obtains the frequency range-s signal figure and characteristic value. The last step is use a kind of Artificial Neural Network (ANN) classifies which determination fault signal's type and reason. The ANN purpose of the automatic identification wind generator set fault..

Keywords: Wind-driven generator, Fast Fourier Transform, Neural network

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Authors: Tossaporn Kachanubal, Somkait Udomhunsakul

Abstract:

In this paper, we proposed a method to classify each type of natural rock texture. Our goal is to classify 26 classes of rock textures. First, we extract five features of each class by using principle component analysis combining with the use of applied spatial frequency measurement. Next, the effective node number of neural network was tested. We used the most effective neural network in classification process. The results from this system yield quite high in recognition rate. It is shown that high recognition rate can be achieved in separation of 26 stone classes.

Keywords: Texture classification, SFM, neural network, rock texture classification.

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

Abstract:

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: M. Sedighizadeh, A. Rezazadeh

Abstract:

Movable power sources of proton exchange membrane fuel cells (PEMFC) are the important research done in the current fuel cells (FC) field. The PEMFC system control influences the cell performance greatly and it is a control system for industrial complex problems, due to the imprecision, uncertainty and partial truth and intrinsic nonlinear characteristics of PEMFCs. In this paper an adaptive PI control strategy using neural network adaptive Morlet wavelet for control is proposed. It is based on a single layer feed forward neural networks with hidden nodes of adaptive morlet wavelet functions controller and an infinite impulse response (IIR) recurrent structure. The IIR is combined by cascading to the network to provide double local structure resulting in improving speed of learning. The proposed method is applied to a typical 1 KW PEMFC system and the results show the proposed method has more accuracy against to MLP (Multi Layer Perceptron) method.

Keywords: Adaptive Control, Morlet Wavelets, PEMFC.

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