Search results for: Radial basis function networks

Authors: Akram M. Othman, May H. Riadh

Abstract:

This research work is aimed at speech recognition using scaly neural networks. A small vocabulary of 11 words were established first, these words are “word, file, open, print, exit, edit, cut, copy, paste, doc1, doc2". These chosen words involved with executing some computer functions such as opening a file, print certain text document, cutting, copying, pasting, editing and exit. It introduced to the computer then subjected to feature extraction process using LPC (linear prediction coefficients). These features are used as input to an artificial neural network in speaker dependent mode. Half of the words are used for training the artificial neural network and the other half are used for testing the system; those are used for information retrieval. The system components are consist of three parts, speech processing and feature extraction, training and testing by using neural networks and information retrieval. The retrieve process proved to be 79.5-88% successful, which is quite acceptable, considering the variation to surrounding, state of the person, and the microphone type.

Keywords: Feature extraction, Liner prediction coefficients, neural network, Speech Recognition, Scaly ANN.

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Authors: Nasser Mohamed Ramli, Mohamad Syafiq Mohamad

Abstract:

Many types of controllers were applied on the continuous stirred tank reactor (CSTR) unit to control the temperature. In this research paper, Proportional-Integral-Derivative (PID) controller are compared with Fuzzy Logic controller for temperature control of CSTR. The control system for temperature non-isothermal of a CSTR will produce a stable response curve to its set point temperature. A mathematical model of a CSTR using the most general operating condition was developed through a set of differential equations into S-function using MATLAB. The reactor model and S-function are developed using m.file. After developing the S-function of CSTR model, User-Defined functions are used to link to SIMULINK file. Results that are obtained from simulation and temperature control were better when using Fuzzy logic control compared to PID control.

Keywords: CSTR, temperature, PID, fuzzy logic.

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Authors: F. Smach, M. Atri, J. Mitéran, M. Abid

Abstract:

Face detection and recognition has many applications in a variety of fields such as security system, videoconferencing and identification. Face classification is currently implemented in software. A hardware implementation allows real-time processing, but has higher cost and time to-market. The objective of this work is to implement a classifier based on neural networks MLP (Multi-layer Perceptron) for face detection. The MLP is used to classify face and non-face patterns. The systm is described using C language on a P4 (2.4 Ghz) to extract weight values. Then a Hardware implementation is achieved using VHDL based Methodology. We target Xilinx FPGA as the implementation support.

Keywords: Classification, Face Detection, FPGA Hardware description, MLP.

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Authors: Rana Khalid Naeem, Waseem Ahmed Khan, Muhammad Akhtar, Asif Mansoor

Abstract:

The Navier Stokes Equations (NSE) for an incompressible fluid of variable viscosity in the presence of an unknown external force in Von-Mises system x,\ are transformed, and some new exact solutions for a class of flows characterized by equation y f x a\b for an arbitrary state equation are determined, where f x is a function, \ the stream function, a z 0 and b are the arbitrary constants. In three, out of four cases, the function f x is arbitrary, and the solutions are the solutions of the flow equations for all the flows characterized by the equationy f x a\b. Streamline patterns for some forms of f x in unbounded and bounded regions are given.

Keywords: Bounded and unbounded region, Exact solution, Navier Stokes equations, Streamline pattern, Variable viscosity, Von- Mises system

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Authors: Amin Sedighfar, M. R. Moniri

Abstract:

Determination of state of charge (SOC) in today’s world becomes an increasingly important issue in all the applications that include a battery. In fact, estimation of the SOC is a fundamental need for the battery, which is the most important energy storage in Hybrid Electric Vehicles (HEVs), smart grid systems, drones, UPS and so on. Regarding those applications, the SOC estimation algorithm is expected to be precise and easy to implement. This paper presents an online method for the estimation of the SOC of Valve-Regulated Lead Acid (VRLA) batteries. The proposed method uses the well-known Kalman Filter (KF), and Neural Networks (NNs) and all of the simulations have been done with MATLAB software. The NN is trained offline using the data collected from the battery discharging process. A generic cell model is used, and the underlying dynamic behavior of the model has used two capacitors (bulk and surface) and three resistors (terminal, surface, and end), where the SOC determined from the voltage represents the bulk capacitor. The aim of this work is to compare the performance of conventional integration-based SOC estimation methods with a mixed algorithm. Moreover, by containing the effect of temperature, the final result becomes more accurate. 

Keywords: Kalman filter, neural networks, state-of-charge, VRLA battery.

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Authors: Kittipong Tripetch

Abstract:

This paper proposes a study of input impedance of 2 types of CMOS active inductors. It derives 2 input impedance formulas. The first formula is the input impedance of the grounded active inductor. The second formula is the input impedance of the floating active inductor. After that, these formulas can be used to simulate magnitude and phase response of input impedance as a function of current consumption with MATLAB. Common mode rejection ratio (CMRR) of the fully differential bandpass amplifier is derived based on superposition principle. CMRR as a function of input frequency is plotted as a function of current consumption. 

Keywords: Grounded active inductor, floating active inductor, Fully differential bandpass amplifier.

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Authors: Y. Harold Robinson, M. Rajaram, E. Golden Julie, S. Balaji

Abstract:

A mobile ad hoc network (MANET) is a wireless communication network where nodes that are not within direct transmission range establish their communication via the help of other nodes to forward data. Routing protocols in MANETs are usually categorized as proactive. Tree Based Opportunistic Routing (TBOR) finds a multipath link based on maximum probability of the throughput. The simulation results show that the presented method is performed very well compared to the existing methods in terms of throughput, delay and routing overhead.

Keywords: Mobile ad hoc networks, opportunistic data forwarding, proactive Source routing, BFS.

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Authors: Sandeep Chandana, Rene V. Mayorga

Abstract:

The paper presents a new hybridization methodology involving Neural, Fuzzy and Rough Computing. A Rough Sets based approximation technique has been proposed based on a certain Neuro – Fuzzy architecture. A New Rough Neuron composition consisting of a combination of a Lower Bound neuron and a Boundary neuron has also been described. The conventional convergence of error in back propagation has been given away for a new framework based on 'Output Excitation Factor' and an inverse input transfer function. The paper also presents a brief comparison of performances, of the existing Rough Neural Networks and ANFIS architecture against the proposed methodology. It can be observed that the rough approximation based neuro-fuzzy architecture is superior to its counterparts.

Keywords: Boundary neuron, neuro-fuzzy, output excitation factor, RANFIS, rough approximation, rough neural computing.

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Authors: Donglan Zha, Jiansong Si

Abstract:

Because of its flexibility, CES attracts much interest in economic growth and programming models, and the macroeconomics or micro-macro models. This paper focuses on the development, estimating methods of CES production function considering energy as an input. We leave for future research work of relaxing the assumption of constant returns to scale, the introduction of potential input factors, and the generalization method of the optimal nested form of multi-factor production functions.

Keywords: Bias of technical change, CES production function, elasticity of substitution, energy input.

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Authors: Eun-Jun Yoon, Kee-Young Yoo

Abstract:

In order to guarantee secure communication for wireless sensor networks (WSNs), many user authentication schemes have successfully drawn researchers- attention and been studied widely. In 2012, He et al. proposed a robust biometric-based user authentication scheme for WSNs. However, this paper demonstrates that He et al.-s scheme has some drawbacks: poor reparability problem, user impersonation attack, and sensor node impersonate attack.

Keywords: Security, authentication, biometrics, poor reparability, impersonation attack, wireless sensor networks.

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Authors: Stefan Rass

Abstract:

Quantum cryptography offers a way of key agreement, which is unbreakable by any external adversary. Authentication is of crucial importance, as perfect secrecy is worthless if the identity of the addressee cannot be ensured before sending important information. Message authentication has been studied thoroughly, but no approach seems to be able to explicitly counter meet-in-the-middle impersonation attacks. The goal of this paper is the development of an authentication scheme being resistant against active adversaries controlling the communication channel. The scheme is built on top of a key-establishment protocol and is unconditionally secure if built upon quantum cryptographic key exchange. In general, the security is the same as for the key-agreement protocol lying underneath.

Keywords: Meet-in-the-middle attack, quantum key distribution, quantum networks, unconditionally secure authentication.

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Authors: Won-Ho Kim, Chang-Ho Hyun, Moon-Jung Kim

Abstract:

In this paper, zigbee communication based wireless energy surveillance system is presented. The proposed system consists of multiple energy surveillance devices and an energy surveillance monitor. Each different standby power-off value of electric device is set automatically by using learning function of energy surveillance device. Thus adaptive standby power-off function provides user convenience and it maximizes the energy savings. Also, power consumption monitoring function is helpful to reduce inefficient energy consumption in home. The zigbee throughput simulator is designed to evaluate minimum transmission power and maximum allowable information quantity in the proposed system. The test result of prototype has been satisfied all the requirements. The proposed system has confirmed that can be used as an intelligent energy surveillance system for energy savings in home or office.

Keywords: Energy monitoring system, Energy surveillance system, Energy sensor network, Energy savings.

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Authors: Wen-Lung Huang, Shih-Pin Chen

Abstract:

This paper investigates the optimization problem of multi-product aggregate production planning (APP) with fuzzy data. From a comprehensive viewpoint of conserving the fuzziness of input information, this paper proposes a method that can completely describe the membership function of the performance measure. The idea is based on the well-known Zadeh-s extension principle which plays an important role in fuzzy theory. In the proposed solution procedure, a pair of mathematical programs parameterized by possibility level a is formulated to calculate the bounds of the optimal performance measure at a . Then the membership function of the optimal performance measure is constructed by enumerating different values of a . Solutions obtained from the proposed method contain more information, and can offer more chance to achieve the feasible disaggregate plan. This is helpful to the decision-maker in practical applications.

Keywords: fuzzy data, aggregate production planning, membership function, parametric programming

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Authors: Jin-Hong Kim

Abstract:

Roles of the vegetation to mitigate the erosion of the stream bed or to facilitate the deposition of the fine sediments by the species of the aquatic plants were presented. Field investigation on the estimation of the change of the bed level and the estimation of the flow characteristics were performed. The results showed that Phragmites japonica has the mitigation function of 0.3m-0.4m of the erosion in the range of higher than 1.0m/s of flow velocity at the vegetated region. Phragmites communis has the mitigation function of 0.2m-0.3m of the erosion in the range of higher than 0.7m/s of flow velocity at the vegetated region. Salix gracilistyla has greater role than Phragmites japonica and Phragmites communis to sustain the stable channel. It has the mitigation function of 0.4m-0.5m of the erosion in the range of higher than 1.4m/s of flow velocity. Miscanthus sacchariflorus has a weak role compared with that of Phragmites japonica and Salix gracilistyla, but it has still function for sustaining the stable bed. From these results, the vegetation has effective roles to mitigate the erosion or to facilitate the deposition of the stream bed.

Keywords: Aquatic plants, Phragmites japonica, Phragmites communis, Miscanthus sacchariflorus, Salix gracilistyla.

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Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: Deep learning, artificial neural networks, energy price forecasting, Turkey.

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Authors: Kemal Bicakci, Yusuf Uzunay

Abstract:

It is well-known that in wireless local area networks, authenticating nodes by their MAC addresses is not secure since it is very easy for an attacker to learn one of the authorized addresses and change his MAC address accordingly. In this paper, in order to prevent MAC address spoofing attacks, we propose to use dynamically changing MAC addresses and make each address usable for only one session. The scheme we propose does not require any change in 802.11 protocols and incurs only a small performance overhead. One of the nice features of our new scheme is that no third party can link different communication sessions of the same user by monitoring MAC addresses therefore our scheme is preferable also with respect to user privacy.

Keywords: Authentication, MAC address spoofing, security, wireless networks.

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Authors: Tomohiro Hachino, Kenji Shimoda, Hitoshi Takata

Abstract:

This paper presents a method of model selection and identification of Hammerstein systems by hybridization of the genetic algorithm (GA) and particle swarm optimization (PSO). An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The weighting parameters of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. On the other hand, the adjusting parameters of the ACF model structure are properly selected by the hybrid algorithm of the GA and PSO, where the Akaike information criterion is utilized as the evaluation value function. Simulation results are shown to demonstrate the effectiveness of the proposed hybrid algorithm.

Keywords: Hammerstein system, identification, automatic choosing function model, genetic algorithm, particle swarm optimization.

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Authors: K. Hosseini, MD., R. Mazaheri, MD., H.R. Khoddami Vishteh, MD., M.A. Mansournia, MD., H. Angoorani, MD

Abstract:

Background: Tissue Doppler Echocardiography (TDE) assesses diastolic function more accurately than routine pulse Doppler echo. Assessment of the effects of dynamic and static exercises on the heart by using TDE can provides new information about the athlete-s heart syndrome. Methods: This study was conducted on 20 elite wrestlers, 14 endurance runners at national level and 21 non-athletes as the control group. Participants underwent two-dimensional echocardiography, standard Doppler and TDE. Results: Wrestlers had the highest left ventricular mass index, enddiastolic inter-ventricular septum thickness and left ventricular Posterior wall thickness. Runners had the highest Left ventricular end-diastolic volume, LV ejection fraction, stroke volume and cardiac output. In TDE, the early diastolic velocity of mitral annulus to the late diastolic velocity ratio in athletic groups was greater than the controls with no significant difference. Conclusion: In spite of cardiac morphological changes in athletes, TDE shows that cardiac diastolic function won-t be adversely affected.

Keywords: Tissue Doppler Echocardiography, Diastolic function, Athlete's heart syndrome, Static exercise, Dynamic exercise

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Authors: Chiou-Yng Lee, Wen-Yo Lee, Chieh-Tsai Wu, Cheng-Chen Yang

Abstract:

Shifted polynomial basis (SPB) is a variation of polynomial basis representation. SPB has potential for efficient bit level and digi -level implementations of multiplication over binary extension fields with subquadratic space complexity. For efficient implementation of pairing computation with large finite fields, this paper presents a new SPB multiplication algorithm based on Karatsuba schemes, and used that to derive a novel scalable multiplier architecture. Analytical results show that the proposed multiplier provides a trade-off between space and time complexities. Our proposed multiplier is modular, regular, and suitable for very large scale integration (VLSI) implementations. It involves less area complexity compared to the multipliers based on traditional decomposition methods. It is therefore, more suitable for efficient hardware implementation of pairing based cryptography and elliptic curve cryptography (ECC) in constraint driven applications.

Keywords: Digit-serial systolic multiplier, elliptic curve cryptography (ECC), Karatsuba algorithm (KA), shifted polynomial basis (SPB), pairing computation.

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Authors: S. Adhirai, R. P. Mahapatra, Paramjit Singh

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Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.

Keywords: Optimization, optimal value, objective function, optimization problems, meta-heuristic optimization algorithms, Whale Optimization Algorithm, Implementation, MATLAB.

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Authors: Sadi Vural, Hiromi Tomii, Hironori Yamauchi

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As far as the latest technological improvements are concerned, digital systems more become popular than the past. Despite this growing demand to the digital systems, content copy and attack against the digital cinema contents becomes a serious problem. To solve the above security problem, we propose “traceable watermarking using Hash functions for digital cinema system. Digital Cinema is a great application for traceable watermarking since it uses watermarking technology during content play as well as content transmission. The watermark is embedded into the randomly selected movie frames using CRC-32 techniques. CRC-32 is a Hash function. Using it, the embedding position is distributed by Hash Function so that any party cannot break off the watermarking or will not be able to change. Finally, our experimental results show that proposed DWT watermarking method using CRC-32 is much better than the convenient watermarking techniques in terms of robustness, image quality and its simple but unbreakable algorithm.

Keywords: Decoder, Digital content, JPEG2000 Frame, System-On-Chip, traceable watermark, Hash Function, CRC-32.

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Authors: Labiba Gilani, Asifullah Khan, Anwar M. Mirza

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This paper introduces a technique of distortion estimation in image watermarking using Genetic Programming (GP). The distortion is estimated by considering the problem of obtaining a distorted watermarked signal from the original watermarked signal as a function regression problem. This function regression problem is solved using GP, where the original watermarked signal is considered as an independent variable. GP-based distortion estimation scheme is checked for Gaussian attack and Jpeg compression attack. We have used Gaussian attacks of different strengths by changing the standard deviation. JPEG compression attack is also varied by adding various distortions. Experimental results demonstrate that the proposed technique is able to detect the watermark even in the case of strong distortions and is more robust against attacks.

Keywords: Blind Watermarking, Genetic Programming (GP), Fitness Function, Discrete Cosine Transform (DCT).

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Authors: Mojca Ciglaric, Andrej Krevl, Matjaž Pancur, Tone Vidmar

Abstract:

P2P Networks are highly dynamic structures since their nodes – peer users keep joining and leaving continuously. In the paper, we study the effects of network change rates on query routing efficiency. First we describe some background and an abstract system model. The chosen routing technique makes use of cached metadata from previous answer messages and also employs a mechanism for broken path detection and metadata maintenance. Several metrics are used to show that the protocol behaves quite well even with high rate of node departures, but above a certain threshold it literally breaks down and exhibits considerable efficiency degradation.

Keywords: Network dynamics, overlay network, P2P system, routing efficiency.

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Authors: Sami Baraketi, Jean-Marie Garcia, Olivier Brun

Abstract:

Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods

Keywords: WDM, lightpath, RWA, wavelength fragmentation, optimization, linear programming, heuristic

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Authors: Viktor R. Stoynov, Zlatka V. Valkova-Jarvis

Abstract:

The intelligent management and optimisation of radio resource technologies will lead to a considerable improvement in the overall performance in Next Generation Networks (NGNs). Carrier Aggregation (CA) technology, also known as Spectrum Aggregation, enables more efficient use of the available spectrum by combining multiple Component Carriers (CCs) in a virtual wideband channel. LTE-A (Long Term Evolution–Advanced) CA technology can combine multiple adjacent or separate CCs in the same band or in different bands. In this way, increased data rates and dynamic load balancing can be achieved, resulting in a more reliable and efficient operation of mobile networks and the enabling of high bandwidth mobile services. In this paper, several distinct CA deployment strategies for the utilisation of spectrum bands are compared in indoor-outdoor scenarios, simulated via the recently-developed Realistic Indoor Environment Generator (RIEG). We analyse the performance of the User Equipment (UE) by integrating the average throughput, the level of fairness of radio resource allocation, and other parameters, into one summative assessment termed a Comparative Factor (CF). In addition, comparison of non-CA and CA indoor mobile networks is carried out under different load conditions: varying numbers and positions of UEs. The experimental results demonstrate that the CA technology can improve network performance, especially in the case of indoor scenarios. Additionally, we show that an increase of carrier frequency does not necessarily lead to improved CF values, due to high wall-penetration losses. The performance of users under bad-channel conditions, often located in the periphery of the cells, can be improved by intelligent CA location. Furthermore, a combination of such a deployment and effective radio resource allocation management with respect to user-fairness plays a crucial role in improving the performance of LTE-A networks.

Keywords: Comparative factor, carrier aggregation, indoor mobile network, resource allocation.

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Authors: A. Safari, R. Jahani, H. A. Shayanfar, J. Olamaei

Abstract:

This paper shows the results obtained in the analysis of the impact of distributed generation (DG) on distribution losses and presents a new algorithm to the optimal allocation of distributed generation resources in distribution networks. The optimization is based on a Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO) aiming to optimal DG allocation in distribution network. Through this algorithm a significant improvement in the optimization goal is achieved. With a numerical example the superiority of the proposed algorithm is demonstrated in comparison with the simple genetic algorithm.

Keywords: Distributed Generation, Distribution Networks, Genetic Algorithm, Particle Swarm Optimization.

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Authors: John N. Ellinas, Panagiotis Kenterlis

Abstract:

The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The current paper presents an approach for still image digital watermarking in which the watermark embedding process employs the wavelet transform and incorporates Human Visual System (HVS) characteristics. The sensitivity of a human observer to contrast with respect to spatial frequency is described by the Contrast Sensitivity Function (CSF). The strength of the watermark within the decomposition subbands, which occupy an interval on the spatial frequencies, is adjusted according to this sensitivity. Moreover, the watermark embedding process is carried over the subband coefficients that lie on edges where distortions are less noticeable. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency.

Keywords: Image watermarking, wavelet transform, human visual system, contrast sensitivity function.

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Authors: Qingqing Wang, Shouming Zhong

Abstract:

This paper is concerned with the stability problem with two additive time-varying delay components. By choosing one augmented Lyapunov-Krasovskii functional, using some new zero equalities, and combining linear matrix inequalities (LMI) techniques, two new sufficient criteria ensuring the global stability asymptotic stability of DNNs is obtained. These stability criteria are present in terms of linear matrix inequalities and can be easily checked. Finally, some examples are showed to demonstrate the effectiveness and less conservatism of the proposed method.

Keywords: Neural networks, Globally asymptotic stability, LMI approach, Additive time-varying delays.

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Authors: Mario Gonzalez, David Dominguez, Francisco B. Rodriguez

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The model of neural networks on the small-world topology, with metric (local and random connectivity) is investigated. The synaptic weights are random, driving the network towards a chaotic state for the neural activity. An ordered macroscopic neuron state is induced by a bias in the network connections. When the connections are mainly local, the network emulates a block-like structure. It is found that the topology and the bias compete to influence the network to evolve into a global or a block activity ordering, according to the initial conditions.

Keywords: Block attractor, random interaction, small world, spin glass.

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Authors: Yuzhi Cai

Abstract:

In this talk, we introduce a newly developed quantile function model that can be used for estimating conditional distributions of financial returns and for obtaining multi-step ahead out-of-sample predictive distributions of financial returns. Since we forecast the whole conditional distributions, any predictive quantity of interest about the future financial returns can be obtained simply as a by-product of the method. We also show an application of the model to the daily closing prices of Dow Jones Industrial Average (DJIA) series over the period from 2 January 2004 - 8 October 2010. We obtained the predictive distributions up to 15 days ahead for the DJIA returns, which were further compared with the actually observed returns and those predicted from an AR-GARCH model. The results show that the new model can capture the main features of financial returns and provide a better fitted model together with improved mean forecasts compared with conventional methods. We hope this talk will help audience to see that this new model has the potential to be very useful in practice.

Keywords: DJIA, Financial returns, predictive distribution, quantile function model.

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