Search results for: Wireless mesh network

Authors: Piyangkun Kukutapan, Siridech Boonsang

Abstract:

The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application.

Keywords: Maximum power point tracking, multilayer perceptron neural network, optimal duty cycle.

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

Abstract:

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: Mohammad Heidari

Abstract:

Horizontal wells are proven to be better producers because they can be extended for a long distance in the pay zone. Engineers have the technical means to forecast the well productivity for a given horizontal length. However, experiences have shown that the actual production rate is often significantly less than that of forecasted. It is a difficult task, if not impossible to identify the real reason why a horizontal well is not producing what was forecasted. Often the source of problem lies in the drilling of horizontal section such as permeability reduction in the pay zone due to mud invasion or snaky well patterns created during drilling. Although drillers aim to drill a constant inclination hole in the pay zone, the more frequent outcome is a sinusoidal wellbore trajectory. The two factors, which play an important role in wellbore tortuosity, are the inclination and side force at bit. A constant inclination horizontal well can only be drilled if the bit face is maintained perpendicular to longitudinal axis of bottom hole assembly (BHA) while keeping the side force nil at the bit. This approach assumes that there exists no formation force at bit. Hence, an appropriate BHA can be designed if bit side force and bit tilt are determined accurately. The Artificial Neural Network (ANN) is superior to existing analytical techniques. In this study, the neural networks have been employed as a general approximation tool for estimation of the bit side forces. A number of samples are analyzed with ANN for parameters of bit side force and the results are compared with exact analysis. Back Propagation Neural network (BPN) is used to approximation of bit side forces. Resultant low relative error value of the test indicates the usability of the BPN in this area.

Keywords: Artificial Neural Network, BHA, Horizontal Well, Stabilizer.

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Authors: Diego Garijo

Abstract:

A numerical approach for solving constant-coefficient differential equations whose solutions exhibit boundary layer structure is built by inserting Bernstein Partition of Unity into Galerkin variational weak form. Due to the reproduction capability of Bernstein basis, such implementation shows excellent accuracy at boundaries and is able to capture sharp gradients of the field variable by p-refinement using regular distributions of equi-spaced evaluation points. The approximation is subjected to convergence experimentation and a procedure to assemble the discrete equations without a background integration mesh is proposed.

Keywords: Bernstein polynomials, Galerkin, differential equation, boundary layer.

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Authors: Pedram Behyar, Maryam Heidari, Zahra Bayat

Abstract:

the purpose of this research is to identify and clarify factors which have positive effect among user satisfaction and their social networking through websites. The examined factors in this research are; innovation, ease of use, trustworthy and customer support which are defined as satisfaction factors. To obtain reliable research approaches and to have better result in this research four hypothesizes used to test. This hypothesis testing has been done by correlation, regression and test of normality by using “SPSS16" also the data which was analyzed by this software. this data was gathered from prepaid questionnaire.

Keywords: Customer Satisfaction, Social Network Website

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Authors: M. Raciti Castelli, S. De Betta, E. Benini

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The optimal grid spacing and turbulence model for the 2D numerical analysis of a vertical-axis water turbine (VAWaterT) operating in a 2 m/s freestream current has been investigated. The results of five different spatial domain discretizations and two turbulence models (k-ω SST and k-ε RNG) have been compared, in order to gain the optimal y+ parameter distribution along the blade walls during a full rotor revolution. The resulting optimal mesh has appeared to be quite similar to that obtained for the numerical analysis of a vertical-axis wind turbine.

Keywords: CFD, vertical axis water turbine, NACA 0025, blade y+.

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Authors: Chokri Baccouch, Chayma Bahhar, Hedi Sakli, Nizar Sakli, Taoufik Aguili

Abstract:

This paper presents a hybrid system solar cell antenna for 5G mobile communications networks. We propose here a solar cell antenna with either a front face collection grid or mesh patch. The solar cell antenna of our contribution combines both optical and radiofrequency signals. Thus, we propose two solar cell antenna structures in the frequency bands of future 5G standard respectively in both 2.6 and 3.5 GHz bands. Simulation using the Advanced Design System (ADS) software allows us to analyze and determine the antenna parameters proposed in this work such as the reflection coefficient (S11), gain, directivity and radiated power.

Keywords: Patch antenna, solar cell, DC, RF, 5G.

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Authors: S.Ponmalar, S.Sundaravadivelu

Abstract:

Traditional optical networks are gradually evolving towards intelligent optical networks due to the need for faster bandwidth provisioning, protection and restoration of the network that can be accomplished with devices like optical switch, add drop multiplexer and cross connects. Since dense wavelength multiplexing forms the physical layer for intelligent optical networking, the roll of high speed all optical switch is important. This paper analyzes such an ultra-high speed polymer electro-optic switch. The performances of the 2x2 optical waveguide switch with rectangular, triangular and trapezoidal grating profiles on various device parameters are analyzed. The simulation result shows that trapezoidal grating is the optimized structure which has the coupling length of 81μm and switching voltage of 11V for the operating wavelength of 1550nm. The switching time for this proposed switch is 0.47 picosecond. This makes the proposed switch to be an important element in the intelligent optical network.

Keywords: Intelligent optical network, optical switch, electrooptic effect, coupled mode theory, waveguide grating structures

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Authors: S. Beyou, B. Grondin-Perez, M. Benne, C. Damour, J.-P. Chabriat

Abstract:

The industrial process of the sugar cane crystallization produces a residual that still contains a lot of soluble sucrose and the objective of the factory is to improve its extraction. Therefore, there are substantial losses justifying the search for the optimization of the process. Crystallization process studied on the industrial site is based on the “three massecuites process". The third step of this process constitutes the final stage of exhaustion of the sucrose dissolved in the mother liquor. During the process of the third step of crystallization (Ccrystallization), the phase that is studied and whose control is to be improved, is the growing phase (crystal growth phase). The study of this process on the industrial site is a problem in its own. A control scheme is proposed to improve the standard PID control law used in the factory. An auto-tuning PID controller based on instantaneous linearization of a neural network is then proposed.

Keywords: Auto-tuning, PID, Instantaneous linearization, Neural network, Non linear process, C-crystallisation.

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Authors: Hamed Rafiei, Masoud Rabbani

Abstract:

This paper deals with the project selection problem. Project selection problem is one of the problems arose firstly in the field of operations research following some production concepts from primary product mix problem. Afterward, introduction of managerial considerations into the project selection problem have emerged qualitative factors and criteria to be regarded as well as quantitative ones. To overcome both kinds of criteria, an analytic network process is developed in this paper enhanced with fuzzy sets theory to tackle the vagueness of experts- comments to evaluate the alternatives. Additionally, a modified version of Least-Square method through a non-linear programming model is augmented to the developed group decision making structure in order to elicit the final weights from comparison matrices. Finally, a case study is considered by which developed structure in this paper is validated. Moreover, a sensitivity analysis is performed to validate the response of the model with respect to the condition alteration.

Keywords: Analytic network process, Fuzzy sets theory, Nonlinear programming, Project selection.

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Authors: R. Chithra, B. Kalavathi, J. Christy Lavanya

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WiMAX is a telecommunications technology and it is specified by the Institute of Electrical and Electronics Engineers Inc., as the IEEE 802.16 standard. The goal of this technology is to provide a wireless data over long distances in a variety of ways. IEEE 802.16 is a recent standard for mobile communication. In this paper, we provide an overview of various key management algorithms to provide security for WiMAX.

Keywords: Broadcast, Rekeying, Scalability, Secrecy, Unicast, WiMAX.

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Authors: Shivaleela E. S., Shrikant S. Tangade

Abstract:

In this paper optical code-division multiple-access (OCDMA) packet network is considered, which offers inherent security in the access networks. Two types of random access protocols are proposed for packet transmission. In protocol 1, all distinct codes and in protocol 2, distinct codes as well as shifted versions of all these codes are used. O-CDMA network performance using optical orthogonal codes (OOCs) 1-D and two-dimensional (2-D) wavelength/time single-pulse-per-row (W/T SPR) codes are analyzed. The main advantage of using 2-D codes instead of onedimensional (1-D) codes is to reduce the errors due to multiple access interference among different users. In this paper, correlation receiver is considered in the analysis. Using analytical model, we compute and compare packet-success probability for 1-D and 2-D codes in an O-CDMA network and the analysis shows improved performance with 2-D codes as compared to 1-D codes.

Keywords: Optical code-division multiple-access, optical CDMA correlation receiver, wavelength/time optical CDMA codes.

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Authors: Hazem M. El-Bakry, Ahmed Atwan

Abstract:

Computer networks are essential part in computerbased information systems. The performance of these networks has a great influence on the whole information system. Measuring the usability criteria and customers satisfaction on small computer network is very important. In this article, an effective approach for measuring the usability of business network in an information system is introduced. The usability process for networking provides us with a flexible and a cost-effective way to assess the usability of a network and its products. In addition, the proposed approach can be used to certify network product usability late in the development cycle. Furthermore, it can be used to help in developing usable interfaces very early in the cycle and to give a way to measure, track, and improve usability. Moreover, a new approach for fast information processing over computer networks is presented. The entire data are collected together in a long vector and then tested as a one input pattern. Proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.

Keywords: Usability Criteria, Computer Networks, Fast Information Processing, Cross Correlation, Frequency Domain.

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Authors: P. Parvasi, R. Janamiri, A. Sinkakarimi, I. Mahdavi, M. Safdari, M. H. Sedaghat, A. Hosseini, M. Karimi

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In this work study the location of interface in a stirred vessel with a Concave impeller by computational fluid dynamic was presented. To modeling rotating the impeller, sliding mesh (SM) technique was used and standard k-ε model was selected for turbulence closure. Mean tangential, radial and axial velocities and also turbulent kinetic energy (k) and turbulent dissipation rate (ε) in various points of tank was investigated. Results show sensitivity of system to location of interface and radius of 7 to 10cm for interface in the vessel with existence characteristics cause to increase the accuracy of simulation.

Keywords: CFD, Interface, Concave impeller, turbulence model.

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Authors: D. B. L. Bong, K.C. Lai, A. Joseph

Abstract:

The uses of road map in daily activities are numerous but it is a hassle to construct and update a road map whenever there are changes. In Universiti Malaysia Sarawak, research on Automatic Road Extraction (ARE) was explored to solve the difficulties in updating road map. The research started with using Satellite Image (SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space Segmentation & Edge Detection (Hybrid SCSS-EDGE) algorithm was developed to extract roads automatically from satellite-taken images. In order to extract the road network accurately, the satellite image must be analyzed prior to the extraction process. The characteristics of these elements are analyzed and consequently the relationships among them are determined. In this study, the road regions are extracted based on colour space elements and edge details of roads. Besides, edge detection method is applied to further filter out the non-road regions. The extracted road regions are validated by using a segmentation method. These results are valuable for building road map and detecting the changes of the existing road database. The proposed Hybrid Simple Colour Space Segmentation and Edge Detection (Hybrid SCSS-EDGE) algorithm can perform the tasks fully automatic, where the user only needs to input a high-resolution satellite image and wait for the result. Moreover, this system can work on complex road network and generate the extraction result in seconds.

Keywords: Road Network Recognition, Colour Space, Edge Detection, Urban Planning.

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Authors: Eric Filiol, Edouard Franc, Alessandro Gubbioli, Benoit Moquet, Guillaume Roblot

Abstract:

Worm propagation profiles have significantly changed since 2003-2004: sudden world outbreaks like Blaster or Slammer have progressively disappeared and slower but stealthier worms appeared since, most of them for botnets dissemination. Decreased worm virulence results in more difficult detection. In this paper, we describe a stealth worm propagation model which has been extensively simulated and analysed on a huge virtual network. The main features of this model is its ability to infect any Internet-like network in a few seconds, whatever may be its size while greatly limiting the reinfection attempt overhead of already infected hosts. The main simulation results shows that the combinatorial topology of routing may have a huge impact on the worm propagation and thus some servers play a more essential and significant role than others. The real-time capability to identify them may be essential to greatly hinder worm propagation.

Keywords: Combinatorial worm, worm spreading, worm virulence, stealth worm, spreading simulation, vertex cover, networktopology, WAST simulator, SuWAST simulator.

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Authors: Yogesh Aggarwal, Paratibha Aggarwal

Abstract:

The paper presents a comparative performance of the models developed to predict 28 days compressive strengths using neural network techniques for data taken from literature (ANN-I) and data developed experimentally for SCC containing bottom ash as partial replacement of fine aggregates (ANN-II). The data used in the models are arranged in the format of six and eight input parameters that cover the contents of cement, sand, coarse aggregate, fly ash as partial replacement of cement, bottom ash as partial replacement of sand, water and water/powder ratio, superplasticizer dosage and an output parameter that is 28-days compressive strength and compressive strengths at 7 days, 28 days, 90 days and 365 days, respectively for ANN-I and ANN-II. The importance of different input parameters is also given for predicting the strengths at various ages using neural network. The model developed from literature data could be easily extended to the experimental data, with bottom ash as partial replacement of sand with some modifications.

Keywords: Self compacting concrete, bottom ash, strength, prediction, neural network, importance factor.

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Authors: Frank Emmert-Streib, Matthias Dehmer, Jing Liu, Max Muhlhauser

Abstract:

The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.

Keywords: Graph similarity, generalized trees, graph alignment, DNA microarray data, cervical cancer.

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Authors: S. I. Abu Alasal, M. M. Esbeih, E. R. Fayyad, R. S. Gharaibeh, M. Z. Ali, A. A. Freewan, M. M. Jamhawi

Abstract:

This research proposes a novel reconstruction protocol for restoring missing surfaces and low-quality edges and shapes in photos of artifacts at historical sites. The protocol starts with the extraction of a cloud of points. This extraction process is based on four subordinate algorithms, which differ in the robustness and amount of resultant. Moreover, they use different -but complementary- accuracy to some related features and to the way they build a quality mesh. The performance of our proposed protocol is compared with other state-of-the-art algorithms and toolkits. The statistical analysis shows that our algorithm significantly outperforms its rivals in the resultant quality of its object files used to reconstruct the desired model.

Keywords: Meshes, Point Clouds, Surface Reconstruction Protocols, 3D Reconstruction.

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Authors: Aziah Khamis, H. Shareef

Abstract:

The purpose of planned islanding is to construct a power island during system disturbances which are commonly formed for maintenance purpose. However, in most of the cases island mode operation is not allowed. Therefore distributed generators (DGs) must sense the unplanned disconnection from the main grid. Passive technique is the most commonly used method for this purpose. However, it needs improvement in order to identify the islanding condition. In this paper an effective method for identification of islanding condition based on phase space and neural network techniques has been developed. The captured voltage waveforms at the coupling points of DGs are processed to extract the required features. For this purposed a method known as the phase space techniques is used. Based on extracted features, two neural network configuration namely radial basis function and probabilistic neural networks are trained to recognize the waveform class. According to the test result, the investigated technique can provide satisfactory identification of the islanding condition in the distribution system.

Keywords: Classification, Islanding detection, Neural network, Phase space.

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Authors: Simon Thiel, Stavros Dalakakis, Dieter Roller

Abstract:

This paper outlines the development of a learning retrieval agent. Task of this agent is to extract knowledge of the Active Semantic Network in respect to user-requests. Based on a reinforcement learning approach, the agent learns to interpret the user-s intention. Especially, the learning algorithm focuses on the retrieval of complex long distant relations. Increasing its learnt knowledge with every request-result-evaluation sequence, the agent enhances his capability in finding the intended information.

Keywords: Reinforcement learning, learning retrieval agent, search in semantic networks.

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Authors: M. Khajezadeh, M. Saied Fallah Niasar, S. Ali Asli, D. Davani Davari, M. Godarzi, Y. Asgari

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This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix.

Keywords: Artificial neural network, portfolio analysis, BCG matrix, Ansoff matrix.

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Authors: K. Govender, M. Sinclair

Abstract:

This paper demonstrates a proof of concept whereby shorter trips and land use densification can be promoted through an alternative approach to planning and implementation of road infrastructure in the South African context. It briefly discusses how the development of the Compact City concept relies on a combination of promoting shorter trips and densification through a change in focus in road infrastructure provision. The methodology developed in this paper uses a traffic model to test the impact of synthesized deterrence functions on congestion locations in the road network through the assignment of traffic on the study network. The results from this study demonstrate that intelligent planning of road infrastructure can indeed promote reduced urban sprawl, increased residential density and mixed-use areas which are supported by an efficient public transport system; and reduced dependence on the freeway network with a fixed road infrastructure budget. The study has resonance for all cities where urban sprawl is seemingly unstoppable.

Keywords: Compact cities, densification, road infrastructure planning, transportation modeling.

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Authors: L.Ramesh, S.P.Chowdhury, S.Chowdhury, A.A.Natarajan, C.T.Gaunt

Abstract:

Accurate loss minimization is the critical component for efficient electrical distribution power flow .The contribution of this work presents loss minimization in power distribution system through feeder restructuring, incorporating DG and placement of capacitor. The study of this work was conducted on IEEE distribution network and India Electricity Board benchmark distribution system. The executed experimental result of Indian system is recommended to board and implement practically for regulated stable output.

Keywords: Distribution system, Distributed Generation LossMinimization, Network Restructuring

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Authors: R. Shivarudraswamy, D. N. Gaonkar

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The continued interest in the use of distributed generation in recent years is leading to the growth in number of distributed generators connected to distribution networks. Steady state voltage rise resulting from the connection of these generators can be a major obstacle to their connection at lower voltage levels. The present electric distribution network is designed to keep the customer voltage within tolerance limit. This may require a reduction in connectable generation capacity, under utilization of appropriate generation sites. Thus distribution network operators need a proper voltage regulation method to allow the significant integration of distributed generation systems to existing network. In this work a voltage rise problem in a typical distribution system has been studied. A method for voltage regulation of distribution system with multiple DG system by coordinated operation distributed generator, capacitor and OLTC has been developed. A sensitivity based analysis has been carried out to determine the priority for individual generators in multiple DG environment. The effectiveness of the developed method has been evaluated under various cases through simulation results.

Keywords: Distributed generation, voltage control, sensitivity factor.

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Authors: Laila Y. Fannas, Ahmed Y. Ben Sasi

Abstract:

This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.

Keywords: Signature Recognition, Artificial Neural Network, Angle Features.

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Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: Convolutional neural network, discrete wavelet transform, deep learning, heart sound classification.

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Authors: A. Sina, A. Aeenmehr, H. Mohamadian

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Optimization and control of reactive power distribution in the power systems leads to the better operation of the reactive power resources. Reactive power control reduces considerably the power losses and effective loads and improves the power factor of the power systems. Another important reason of the reactive power control is improving the voltage profile of the power system. In this paper, voltage and reactive power control using Neural Network techniques have been applied to the 33 shines- Tehran Electric Company. In this suggested ANN, the voltages of PQ shines have been considered as the input of the ANN. Also, the generators voltages, tap transformers and shunt compensators have been considered as the output of ANN. Results of this techniques have been compared with the Linear Programming. Minimization of the transmission line power losses has been considered as the objective function of the linear programming technique. The comparison of the results of the ANN technique with the LP shows that the ANN technique improves the precision and reduces the computation time. ANN technique also has a simple structure and this causes to use the operator experience.

Keywords: voltage control, linear programming, artificial neural network, power systems

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: Identification, Neural networks, Predictive control, Transient stability, UPFC.

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Authors: I. Lucas, N. Schomberg, F-A. Couturier

Abstract:

Most empirical studies have analyzed how liquidity risks faced by individual institutions turn into systemic risk. Recent banking crisis has highlighted the importance of grasping and controlling the systemic risk, and the acceptance by Central Banks to ease their monetary policies for saving default or illiquid banks. This last point shows that banks would pay less attention to liquidity risk which, in turn, can become a new important channel of loss. The financial regulation focuses on the most important and “systemic” banks in the global network. However, to quantify the expected loss associated with liquidity risk, it is worth to analyze sensitivity to this channel for the various elements of the global bank network. A small bank is not considered as potentially systemic; however the interaction of small banks all together can become a systemic element. This paper analyzes the impact of medium and small banks interaction on a set of banks which is considered as the core of the network. The proposed method uses the structure of agent-based model in a two-class environment. In first class, the data from actual balance sheets of 22 large and systemic banks (such as BNP Paribas or Barclays) are collected. In second one, to model a network as closely as possible to actual interbank market, 578 fictitious banks smaller than the ones belonging to first class have been split into two groups of small and medium ones. All banks are active on the European interbank network and have deposit and market activity. A simulation of 12 three month periods representing a midterm time interval three years is projected. In each period, there is a set of behavioral descriptions: repayment of matured loans, liquidation of deposits, income from securities, collection of new deposits, new demands of credit, and securities sale. The last two actions are part of refunding process developed in this paper. To strengthen reliability of proposed model, random parameters dynamics are managed with stochastic equations as rates the variations of which are generated by Vasicek model. The Central Bank is considered as the lender of last resort which allows banks to borrow at REPO rate and some ejection conditions of banks from the system are introduced.

Liquidity crunch due to exogenous crisis is simulated in the first class and the loss impact on other bank classes is analyzed though aggregate values representing the aggregate of loans and/or the aggregate of borrowing between classes. It is mainly shown that the three groups of European interbank network do not have the same response, and that intermediate banks are the most sensitive to liquidity risk.

Keywords: Systemic Risk, Financial Contagion, Liquidity Risk, Interbank Market, Network Model.

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