Search results for: fuzzy boundary layer.

Authors: M.A.Behrang, M. Ghalambaz, E. Assareh, A.R. Noghrehabadi

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Fluid flow and heat transfer of vertical full cone embedded in porous media is studied in this paper. Nonlinear differential equation arising from similarity solution of inverted cone (subjected to wall temperature boundary conditions) embedded in porous medium is solved using a hybrid neural network- particle swarm optimization method. To aim this purpose, a trial solution of the differential equation is defined as sum of two parts. The first part satisfies the initial/ boundary conditions and does contain an adjustable parameter and the second part which is constructed so as not to affect the initial/boundary conditions and involves adjustable parameters (the weights and biases) for a multi-layer perceptron neural network. Particle swarm optimization (PSO) is applied to find adjustable parameters of trial solution (in first and second part). The obtained solution in comparison with the numerical ones represents a remarkable accuracy.

Keywords: Porous Media, Ordinary Differential Equations (ODE), Particle Swarm Optimization (PSO), Neural Network (NN).

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Authors: Emrah Bulut, Okan Duru, Shigeru Yoshida

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In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.

Keywords: C-means clustering, Fuzzy time series, Multi-variate design

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Authors: N. Samarinas, C. Evangelides, C. Vrekos

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The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.

Keywords: Classification, fuzzy logic, tolerance relations, rainfall data.

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Authors: Nesapriya. P., S. Rajalaxmi

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This paper is based on the bridgeless single-phase Ac–Dc Power Factor Correction (PFC) converters with Fuzzy Logic Controller. High frequency isolated Cuk converters are used as a modular dc-dc converter in Discontinuous Conduction Mode (DCM) of operation of Power Factor Correction. The aim of this paper is to simplify the program complexity of the controller by reducing the number of fuzzy sets of the Membership Functions (MFs) and to improve the efficiency and to eliminate the power quality problems. The output of Fuzzy controller is compared with High frequency triangular wave to generate PWM gating signals of Cuk converter. The proposed topologies are designed to work in Discontinuous Conduction Mode (DCM) to achieve a unity power factor and low total harmonic distortion of the input current. The Fuzzy Logic Controller gives additional advantages such as accurate result, uncertainty and imprecision and automatic control circuitry. Performance comparisons between the proposed and conventional controllers and circuits are performed based on circuit simulations.

Keywords: Fuzzy Logic Controller (FLC), Bridgeless rectifier, Cuk converter, Pulse Width Modulation (PWM), Power Factor Correction, Total Harmonic Distortion (THD).

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Authors: Kevin Fong-Rey Liu, Cheng-Wu Chen, Ken Yeh, Han-Hsi Liang

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Environmental impact assessment (EIA) is a procedure tool of environmental management for identifying, predicting, evaluating and mitigating the adverse effects of development proposals. EIA reports usually analyze how the amounts or concentrations of pollutants obey the relevant standards. Actually, many analytical tools can deepen the analysis of environmental impacts in EIA reports, such as life cycle assessment (LCA) and environmental risk assessment (ERA). Life cycle impact assessment (LCIA) is one of steps in LCA to introduce the causal relationships among environmental hazards and damage. Incorporating the LCIA concept into ERA as an integrated tool for EIA can extend the focus of the regulatory compliance of environmental impacts to determine of the significance of environmental impacts. Sometimes, when using integrated tools, it is necessary to consider fuzzy situations due to insufficient information; therefore, ERA should be generalized to fuzzy risk assessment (FRA). Finally, the use of the proposed methodology is demonstrated through the study case of the expansion plan of the world-s largest plastics processing factory.

Keywords: Fuzzy risk analysis, life cycle impact assessment, fuzzy logic, environmental impact assessment

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Authors: Erika Martinez Ramirez, Rene V. Mayorga

Abstract:

Nowadays, more engineering systems are using some kind of Artificial Intelligence (AI) for the development of their processes. Some well-known AI techniques include artificial neural nets, fuzzy inference systems, and neuro-fuzzy inference systems among others. Furthermore, many decision-making applications base their intelligent processes on Fuzzy Logic; due to the Fuzzy Inference Systems (FIS) capability to deal with problems that are based on user knowledge and experience. Also, knowing that users have a wide variety of distinctiveness, and generally, provide uncertain data, this information can be used and properly processed by a FIS. To properly consider uncertainty and inexact system input values, FIS normally use Membership Functions (MF) that represent a degree of user satisfaction on certain conditions and/or constraints. In order to define the parameters of the MFs, the knowledge from experts in the field is very important. This knowledge defines the MF shape to process the user inputs and through fuzzy reasoning and inference mechanisms, the FIS can provide an “appropriate" output. However an important issue immediately arises: How can it be assured that the obtained output is the optimum solution? How can it be guaranteed that each MF has an optimum shape? A viable solution to these questions is through the MFs parameter optimization. In this Paper a novel parameter optimization process is presented. The process for FIS parameter optimization consists of the five simple steps that can be easily realized off-line. Here the proposed process of FIS parameter optimization it is demonstrated by its implementation on an Intelligent Interface section dealing with the on-line customization / personalization of internet portals applied to E-commerce.

Keywords: Artificial Intelligence, Fuzzy Logic, Fuzzy InferenceSystems, Nonlinear Optimization.

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Authors: Ismail Saritas, Abdullah Adiyaman

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In this study, a fuzzy-logic based control system was designed to ensure that time and energy is saved during the operation of load elevators which are used during the construction of tall buildings. In the control system that was devised, for the load elevators to work more efficiently, the energy interval where the motor worked was taken as the output variable whereas the amount of load and the building height were taken as input variables. The most appropriate working intervals depending on the characteristics of these variables were defined by the help of an expert. Fuzzy expert system software was formed using Delphi programming language. In this design, mamdani max-min inference mechanism was used and the centroid method was employed in the clarification procedure. In conclusion, it is observed that the system that was designed is feasible and this is supported by statistical analyses..

Keywords: Fuzzy Logic Control, DC Motor, Load Elevators, Power Control.

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Authors: Mustafa Resa Becan, Ahmet Kuzucu

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A fuzzy predictive pursuit guidance is proposed as an alternative to the conventional methods. The purpose of this scheme is to obtain a stable and fast guidance. The noise effects must be reduced in homing missile guidance to get an accurate control. An aerodynamic missile model is simulated first and a fuzzy predictive pursuit control algorithm is applied to reduce the noise effects. The performance of this algorithm is compared with the performance of the classical proportional derivative control. Stability analysis of the proposed guidance method is performed and compared with the stability properties of other guidance methods. Simulation results show that the proposed method provides the satisfying performance.

Keywords: Fuzzy, noise effect, predictive, pursuit.

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Authors: M.A. Lazim, M. Suriani

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Knowing consumers' preferences and perceptions of the sensory evaluation of drink products are very significant to manufacturers and retailers alike. With no appropriate sensory analysis, there is a high risk of market disappointment. This paper aims to rank the selected coffee products and also to determine the best of quality attribute through sensory evaluation using fuzzy decision making model. Three products of coffee drinks were used for sensory evaluation. Data were collected from thirty judges at a hypermarket in Kuala Terengganu, Malaysia. The judges were asked to specify their sensory evaluation in linguistic terms of the quality attributes of colour, smell, taste and mouth feel for each product and also the weight of each quality attribute. Five fuzzy linguistic terms represent the quality attributes were introduced prior analysing. The judgment membership function and the weights were compared to rank the products and also to determine the best quality attribute. The product of Indoc was judged as the first in ranking and 'taste' as the best quality attribute. These implicate the importance of sensory evaluation in identifying consumers- preferences and also the competency of fuzzy approach in decision making.

Keywords: fuzzy decision making, fuzzy linguistic, membership function, sensory evaluation,

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Authors: Zoheir Tir, Rachid Abdessemed

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This paper presents a new control scheme to control a brushless doubly fed induction generator (BDFIG) using back-to-back PWM converters for wind power generation. The proposed control scheme is a New Self-Tuning Fuzzy Proportional-Derivative Controller (NSTFPDC). The goal of BDFIG control is to achieve a similar dynamic performance to the doubly fed induction generator (DFIG), exploiting the well-known induction machine vector control philosophy. The performance of NSTFPDC controller has been investigated and compared with the two controllers, called Proportional–Integral (PI) and PD-like Fuzzy Logic controller (PD-like FLC) based BDFIG. The simulation results demonstrate the effectiveness and the robustness of the NSTFPDC controller.

Keywords: Brushless Doubly Fed Induction Generator (BDFIG), PI controller, PD-like Fuzzy Logic controller, New Self-Tuning Fuzzy Proportional-Derivative Controller (NSTFPDC), Scaling factor, back-to-back PWM converters, wind energy system.

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Authors: H. M. Côrtes, J. I. Da Silva Filho, M. F. Blos, B. S. Zanon

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In a modern society the factor corresponding to the increase in the level of quality in industrial production demand new techniques of control and machinery automation. In this context, this work presents the implementation of a Paraconsistent-Fuzzy Digital PID controller. The controller is based on the treatment of inconsistencies both in the Paraconsistent Logic and in the Fuzzy Logic. Paraconsistent analysis is performed on the signals applied to the system inputs using concepts from the Paraconsistent Annotated Logic with annotation of two values (PAL2v). The signals resulting from the paraconsistent analysis are two values defined as Dc - Degree of Certainty and Dct - Degree of Contradiction, which receive a treatment according to the Fuzzy Logic theory, and the resulting output of the logic actions is a single value called the crisp value, which is used to control dynamic system. Through an example, it was demonstrated the application of the proposed model. Initially, the Paraconsistent-Fuzzy Digital PID controller was built and tested in an isolated MATLAB environment and then compared to the equivalent Digital PID function of this software for standard step excitation. After this step, a level control plant was modeled to execute the controller function on a physical model, making the tests closer to the actual. For this, the control parameters (proportional, integral and derivative) were determined for the configuration of the conventional Digital PID controller and of the Paraconsistent-Fuzzy Digital PID, and the control meshes in MATLAB were assembled with the respective transfer function of the plant. Finally, the results of the comparison of the level control process between the Paraconsistent-Fuzzy Digital PID controller and the conventional Digital PID controller were presented.

Keywords: Fuzzy logic, paraconsistent annotated logic, level control, digital PID.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

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This paper introduces new algorithms (Fuzzy relative of the CLARANS algorithm FCLARANS and Fuzzy c Medoids based on randomized search FCMRANS) for fuzzy clustering of relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd) in which the within cluster dissimilarity of each cluster is minimized in each iteration by recomputing new medoids given current memberships, FCLARANS minimizes the same objective function minimized by FCMdd by changing current medoids in such away that that the sum of the within cluster dissimilarities is minimized. Computing new medoids may be effected by noise because outliers may join the computation of medoids while the choice of medoids in FCLARANS is dictated by the location of a predominant fraction of points inside a cluster and, therefore, it is less sensitive to the presence of outliers. In FCMRANS the step of computing new medoids in FCMdd is modified to be based on randomized search. Furthermore, a new initialization procedure is developed that add randomness to the initialization procedure used with FCMdd. Both FCLARANS and FCMRANS are compared with the robust and linearized version of fuzzy c-medoids (RFCMdd). Experimental results with different samples of the Reuter-21578, Newsgroups (20NG) and generated datasets with noise show that FCLARANS is more robust than both RFCMdd and FCMRANS. Finally, both FCMRANS and FCLARANS are more efficient and their outputs are almost the same as that of RFCMdd in terms of classification rate.

Keywords: Data Mining, Fuzzy Clustering, Relational Clustering, Medoid-Based Clustering, Cluster Analysis, Unsupervised Learning.

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Authors: A. Faro, D. Giordano, M. Pennisi, G. Scarciofalo, C. Spampinato, F. Tramontana

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The paper proposes a methodology to process the signals coming from the Transcranial Magnetic Stimulation (TMS) in order to identify the pathology and evaluate the therapy to treat the patients affected by demency diseases. In particular, a fuzzy model is developed to identify the demency of the patients affected by Subcortical Ischemic Vascular Dementia (SIVD) and to measure the effect of a repetitive TMS on their motor performances. A tool is also presented to support the mentioned analysis.

Keywords: TMS, EMG, fuzzy logic, transcranial magnetic stimulation.

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Authors: Talaat S. El-Danaf

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In this paper, we develop quartic nonpolynomial spline method for the numerical solution of third order two point boundary value problems. It is shown that the new method gives approximations, which are better than those produced by other spline methods. Convergence analysis of the method is discussed through standard procedures. Two numerical examples are given to illustrate the applicability and efficiency of the novel method.

Keywords: Quartic nonpolynomial spline, Two-point boundary value problem.

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Authors: Debajyoti Mishra, Urmila Bhanja

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In this paper, a novel fuzzy approach is developed while solving the Dynamic Routing and Wavelength Assignment (DRWA) problem in optical networks with Wavelength Division Multiplexing (WDM). In this work, the effect of nonlinear and linear impairments such as Four Wave Mixing (FWM) and amplifier spontaneous emission (ASE) noise are incorporated respectively. The novel algorithm incorporates fuzzy logic controller (FLC) to reduce the effect of FWM noise and ASE noise on a requested lightpath referred in this work as FWM aware fuzzy dynamic routing and wavelength assignment algorithm. The FWM crosstalk products and the static FWM noise power per link are pre computed in order to reduce the set up time of a requested lightpath, and stored in an offline database. These are retrieved during the setting up of a lightpath and evaluated online taking the dynamic parameters like cost of the links into consideration.

Keywords: Amplifier spontaneous emission (ASE), Dynamic routing and wavelength assignment, Four wave mixing (FWM), Fuzzy rule based system (FRBS).

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Authors: Sri Hartati, 1Agus Harjoko, Brad G. Nickerson

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A prototype of an anomaly detection system was developed to automate process of recognizing an anomaly of roentgen image by utilizing fuzzy histogram hyperbolization image enhancement and back propagation artificial neural network. The system consists of image acquisition, pre-processor, feature extractor, response selector and output. Fuzzy Histogram Hyperbolization is chosen to improve the quality of the roentgen image. The fuzzy histogram hyperbolization steps consist of fuzzyfication, modification of values of membership functions and defuzzyfication. Image features are extracted after the the quality of the image is improved. The extracted image features are input to the artificial neural network for detecting anomaly. The number of nodes in the proposed ANN layers was made small. Experimental results indicate that the fuzzy histogram hyperbolization method can be used to improve the quality of the image. The system is capable to detect the anomaly in the roentgen image.

Keywords: Image processing, artificial neural network, anomaly detection.

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Authors: Lazim Abdullah, N orhanadiah Zam

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Increasing number of vehicles and lack of awareness among road users may lead to road accidents. However no specific literature was found to rank vehicles involved in accidents based on fuzzy variables of road users. This paper proposes a ranking of four selected motor vehicles involved in road accidents. Human and non-human factors that normally linked with road accidents are considered for ranking. The imprecision or vagueness inherent in the subjective assessment of the experts has led the application of fuzzy sets theory to deal with ranking problems. Data in form of linguistic variables were collected from three authorised personnel of three Malaysian Government agencies. The Multi Criteria Decision Making, fuzzy TOPSIS was applied in computational procedures. From the analysis, it shows that motorcycles vehicles yielded the highest closeness coefficient at 0.6225. A ranking can be drawn using the magnitude of closeness coefficient. It was indicated that the motorcycles recorded the first rank.

Keywords: Road accidents, decision making, closeness coefficient, fuzzy number

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Authors: Ayman A. Aly, Abdallah A. Alshnnaway

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The general idea behind the filter is to average a pixel using other pixel values from its neighborhood, but simultaneously to take care of important image structures such as edges. The main concern of the proposed filter is to distinguish between any variations of the captured digital image due to noise and due to image structure. The edges give the image the appearance depth and sharpness. A loss of edges makes the image appear blurred or unfocused. However, noise smoothing and edge enhancement are traditionally conflicting tasks. Since most noise filtering behaves like a low pass filter, the blurring of edges and loss of detail seems a natural consequence. Techniques to remedy this inherent conflict often encompass generation of new noise due to enhancement. In this work a new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of three stages. (1) Define fuzzy sets in the input space to computes a fuzzy derivative for eight different directions (2) construct a set of IFTHEN rules by to perform fuzzy smoothing according to contributions of neighboring pixel values and (3) define fuzzy sets in the output space to get the filtered and edged image. Experimental results are obtained to show the feasibility of the proposed approach with two dimensional objects.

Keywords: Additive noise, edge preserving filtering, fuzzy image filtering, noise reduction, two dimensional mechanical images.

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Authors: Joon Ho Kim, Tae Kwon Ha

Abstract:

Effect of aging treatment on microstructural aspects of interfacial layers of the Cu/Al clad sheet produced by differential speed rolling (DSR) process were studied by electron back scattered diffraction (EBSD). Clad sheet of Al/Cu has been fabricated by using DSR, which caused severe shear deformation between Al and Cu plate to easily bond to each other. Rolling was carried out at 100^oC with speed ratio of 2, in which the total thickness reduction was 45%. Interface layers of clad sheet were analyzed by EBSD after subsequent annealing at 400^oC for 30 to 120min. With increasing annealing time, thickness of interface layer and fraction of high angle grain boundary were increased and average grain size was decreased.

Keywords: Aluminum/Copper clad sheet, differential speed rolling, interface layer, microstructure, annealing, electron back scattered diffraction.

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Authors: Rishabh Sharma, Renu Vig, Neeraj Sharma

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The main design parameter of WSN (wireless sensor network) is the energy consumption. To compensate this parameter, hierarchical clustering is a technique that assists in extending duration of the networks life by efficiently consuming the energy. This paper focuses on dealing with the WSNs and the FIS (fuzzy interface system) which are deployed to enhance the BEENISH protocol. The node energy, mobility, pause time and density are considered for the selection of CH (cluster head). The simulation outcomes exhibited that the projected system outperforms the traditional system with regard to the energy utilization and number of packets transmitted to sink.

Keywords: Wireless sensor network, sink, sensor node, routing protocol, fuzzy rule, fuzzy inference system.

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Authors: Dinesh Acharya U., N. V. Subba Reddy, Krishnamoorthi Makkithaya

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The recognition of handwritten numeral is an important area of research for its applications in post office, banks and other organizations. This paper presents automatic recognition of handwritten Kannada numerals based on structural features. Five different types of features, namely, profile based 10-segment string, water reservoir; vertical and horizontal strokes, end points and average boundary length from the minimal bounding box are used in the recognition of numeral. The effect of each feature and their combination in the numeral classification is analyzed using nearest neighbor classifiers. It is common to combine multiple categories of features into a single feature vector for the classification. Instead, separate classifiers can be used to classify based on each visual feature individually and the final classification can be obtained based on the combination of separate base classification results. One popular approach is to combine the classifier results into a feature vector and leaving the decision to next level classifier. This method is extended to extract a better information, possibility distribution, from the base classifiers in resolving the conflicts among the classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy k-NN) as base classifier for individual feature sets, the results of which together forms the feature vector for the final k Nearest Neighbor (k-NN) classifier. Testing is done, using different features, individually and in combination, on a database containing 1600 samples of different numerals and the results are compared with the results of different existing methods.

Keywords: Fuzzy k Nearest Neighbor, Multiple Classifiers, Numeral Recognition, Structural features.

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Authors: Wen-Jye Shyr, Chia-Ming Lin, Hung-Yun Feng

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The purpose of this study was to develop an energy management system for university campuses based on the Internet of Things (IoT) technique. The proposed IoT technique based on WebAccess is used via network browser Internet Explore and applies TCP/IP protocol. The case study of IoT for lighting energy usage management system was proposed. Structure of proposed IoT technique included perception layer, equipment layer, control layer, application layer and network layer.

Keywords: Energy management, IoT technique, Sensor, WebAccess

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Authors: Aziz Sezgin

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We consider the problem of stabilization of an unstable heat equation in a 2-D, 3-D and generally n-D domain by deriving a generalized backstepping boundary control design methodology. To stabilize the systems, we design boundary backstepping controllers inspired by the 1-D unstable heat equation stabilization procedure. We assume that one side of the boundary is hinged and the other side is controlled for each direction of the domain. Thus, controllers act on two boundaries for 2-D domain, three boundaries for 3-D domain and ”n” boundaries for n-D domain. The main idea of the design is to derive ”n” controllers for each of the dimensions by using ”n” kernel functions. Thus, we obtain ”n” controllers for the ”n” dimensional case. We use a transformation to change the system into an exponentially stable ”n” dimensional heat equation. The transformation used in this paper is a generalized Volterra/Fredholm type with ”n” kernel functions for n-D domain instead of the one kernel function of 1-D design.

Keywords: Backstepping, boundary control, 2-D, 3-D, n-D heat equation, distributed parameter systems.

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Authors: Xinhua Zhang, Kelin Li

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In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.

Keywords: Exponential stability, stochastic fuzzy cellular neural networks, time-varying delays, impulses, reaction-diffusion terms.

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Authors: E. Farshidi

Abstract:

In this paper, based on a novel synthesis, a set of new simplified circuit design to implement the linguistic-hedge operations for adjusting the fuzzy membership function set is presented. The circuits work in current-mode and employ floating-gate MOS (FGMOS) transistors that operate in weak inversion region. Compared to the other proposed circuits, these circuits feature severe reduction of the elements number, low supply voltage (0.7V), low power consumption (<200nW), immunity from body effect and wide input dynamic range (>60dB). In this paper, a set of fuzzy linguistic hedge circuits, including absolutely, very, much more, more, plus minus, more or less and slightly, has been implemented in 0.18 mm CMOS process. Simulation results by Hspice confirm the validity of the proposed design technique and show high performance of the circuits.

Keywords: Current-mode, Linguistic-Hedge, Fuzzy Logic, lowpower

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Authors: Tae Kyung Kim, Hee Suk Seo

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Adapting various sensor devices to communicate within sensor networks empowers us by providing range of possibilities. The sensors in sensor networks need to know their measurable belief of trust for efficient and safe communication. In this paper, we suggested a trust model using fuzzy logic in sensor network. Trust is an aggregation of consensus given a set of past interaction among sensors. We applied our suggested model to sensor networks in order to show how trust mechanisms are involved in communicating algorithm to choose the proper path from source to destination.

Keywords: Fuzzy, Sensor Networks, Trust.

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Authors: Preeda Sansakorn, Min An

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In order to be capable of dealing with uncertainties, subjectivities, including vagueness arising in building construction projects, the application of fuzzy reasoning technique based on fuzzy set theory is proposed. This study contributes significantly to the development of a fuzzy reasoning safety risk assessment model for building construction projects that could be employed to assess the risk magnitude of each hazardous event identified during construction, and a third parameter of probability of consequence is incorporated in the model. By using the proposed safety risk analysis methodology, more reliable and less ambiguities, which provide the safety risk management project team for decision-making purposes.

Keywords: Safety risks assessment, building construction safety, fuzzy reasoning, construction risk assessment model, building construction projects.

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Authors: Kamaljit Singh Sokhal, Gangacharyulu Dasoraju, Vijaya Kumar Bulasara

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Biopolymers are popular in many areas, like petrochemicals, food industry and agriculture due to their favorable properties like environment-friendly, availability, and cost. In this study, a biopolymer gum Arabic was used to find its effect on the pressure drop at various concentrations (100 ppm – 300 ppm) with various Reynolds numbers (10000 – 45000). A rheological study was also done by using the same concentrations to find the effect of the shear rate on the shear viscosity. Experiments were performed to find the effect of injection of gum Arabic directly near the boundary layer and to investigate its effect on the maximum possible drag reduction. Experiments were performed on a test section having i.d of 19.50 mm and length of 3045 mm. The polymer solution was injected from the top of the test section by using a peristaltic pump. The concentration of the polymer solution and the Reynolds number were used as parameters to get maximum possible drag reduction. Water was circulated through a centrifugal pump having a maximum 3000 rpm and the flow rate was measured by using rotameter. Results were validated by using Virk's maximum drag reduction asymptote. A maximum drag reduction of 62.15% was observed with the maximum concentration of gum Arabic, 300 ppm. The solution was circulated in the closed loop to find the effect of degradation of polymers with a number of cycles on the drag reduction percentage. It was observed that the injection of the polymer solution in the boundary layer was showing better results than premixed solutions.

Keywords: Drag reduction, shear viscosity, gum Arabic, injection point.

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Authors: Md. Fazlul Karim, Ahmad Izani Ismail, Mohammed Ashaque Meah

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This paper focuses on the development of a 2-D boundary fitted and nested grid (BFNG) model to compute the tsunami propagation of Indonesian tsunami 2004 along the coastal region of Penang in Peninsular Malaysia.

In the presence of a curvilinear coastline, boundary fitted grids are suitable to represent the model boundaries accurately. On the other hand, when large gradient of velocity within a confined area is expected, the use of a nested grid system is appropriate to improve the numerical accuracy with the least grid numbers.

This paper constructs a shallow water nested and orthogonal boundary fitted grid model and presents computational results of the tsunami impact on the Penang coast due to the Indonesian tsunami of 2004. The results of the numerical simulations are compared with available data.

Keywords: Boundary Fitted Nested Model, Tsunami, Penang Island, 2004 Indonesian Tsunami.

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Authors: Hae Young Lee, Tae Ho Cho

Abstract:

In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false reports during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and overhead. In this paper, we propose a fuzzy logic for determining a security threshold value in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the number of partitions in a global key pool, the number of compromised partitions, and the energy level of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.

Keywords: Fuzzy logic, security, sensor network.

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