Search results for: Hybrid evolutionary optimization algorithm

Authors: Abdul Rauf, Muhammad Irfan, Muhammad Nadeem, Ishtiaq Ahmed, Hafiz Muhammad Nasir Iqbal

Abstract:

Rhizopus oligosporus was used in the present study for the production of protease enzyme under SSF. Sunflower meal was used as by-product of oil industry incorporated with organic salts was employed for the production of protease enzyme. The main purpose of the present was to study different parameters of protease productivity, its yields and to optimize basal fermentation conditions. The optimal conditions found for protease production using sunflower meal as a substrate in the present study were inoculum size (1%), substrate (Sunflower meal), substrate concentration (20 g), pH (3), cultivation period (72 h), incubation temperature (35oC), substrate to diluent-s ratio (1:2) and tween 81 (1 mL). The maximum production of protease in the presence of cheaper substrate at low concentration and stability at acidic pH, these characteristics make the strain and its enzymes useful in different industry.

Keywords: Acidic protease, Rhizopus oligosporus, Mediaoptimization, Solid state Fermentation

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Authors: Kozo Tanigawa, Toshihiko Sasama, Kenichi Takahashi, Takao Kawamura, Kazunori Sugahara

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In recent years, the number of the cases of information leaks is increasing. Companies and Research Institutions make various actions against information thefts and security accidents. One of the actions is adoption of the crime prevention system, including the monitoring system by surveillance cameras. In order to solve difficulties of multiple cameras monitoring, we develop the automatic human tracking system using mobile agents through multiple surveillance cameras to track target persons. In this paper, we develop the monitor which confirms mobile agents tracing target persons, and the simulator of video picture analysis to construct the tracking algorithm.

Keywords: Human tracking, mobile agent, monitoring, simulate.

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Authors: Hossein Nezamabadi-pour, Saeid Saryazdi

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In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.

Keywords: Object-based image retrieval, DCT domain, Image indexing, Image classification.

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

Abstract:

This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: Vehicle classification, signal processing, road traffic model, magnetic sensing.

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Authors: Horng-Yuan Wu, Chin-Yuan Hsu, Tsair-Fwu Lee

Abstract:

A novel three-phase active power filter (APF) circuit with photovoltaic (PV) system to improve the quality of service and to reduce the capacity of energy storage capacitor is presented. The energy balance concept and sampling technique were used to simplify the calculation algorithm for the required utility source current and to control the voltage of the energy storage capacitor. The feasibility was verified by using the Pspice simulations and experiments. When the APF mode was used during non-operational period, not only the utilization rate, power factor and power quality could be improved, but also the capacity of energy storage capacitor could sparing. As the results, the advantages of the APF circuit are simplicity of control circuits, low cost, and good transient response.

Keywords: active power filter, sampling, energy-storagecapacitor, harmonic current, energy balance.

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Authors: Ahmed K. Hasan, A. A. Zaidan, Anas Majeed, B. B. Zaidan, Rosli Salleh, Omar Zakaria, Ali Zuheir

Abstract:

Wireless mesh networks based on IEEE 802.11 technology are a scalable and efficient solution for next generation wireless networking to provide wide-area wideband internet access to a significant number of users. The deployment of these wireless mesh networks may be within different authorities and without any planning, they are potentially overlapped partially or completely in the same service area. The aim of the proposed model is design a new model to Enhancement Throughput of Unplanned Wireless Mesh Networks Deployment Using Partitioning Hierarchical Cluster (PHC), the unplanned deployment of WMNs are determinates there performance. We use throughput optimization approach to model the unplanned WMNs deployment problem based on partitioning hierarchical cluster (PHC) based architecture, in this paper the researcher used bridge node by allowing interworking traffic between these WMNs as solution for performance degradation.

Keywords: Wireless Mesh Networks, 802.11s Internetworking, partitioning Hierarchical Cluste.

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Authors: A. Hamidi, S. Khelladi, L. Illoul, A. Tcharkhtchi

Abstract:

Reactive rotational molding (RRM) is a process to manufacture hollow plastic parts with reactive material has several advantages compared to conventional roto molding of thermoplastic powders: process cycle time is shorter; raw material is less expensive because polymerization occurs during processing and high-performance polymers may be used such as thermosets, thermoplastics or blends. However, several phenomena occur during this process which makes the optimization of the process quite complex. In this study, we have used a mixture of isocyanate and polyol as a reactive system. The chemical transformation of this system to polyurethane has been studied by thermal analysis and rheology tests. Thanks to these results of the curing process and rheological measurements, the kinetic and rheokinetik of polyurethane was identified. Smoothed Particle Hydrodynamics, a Lagrangian meshless method, was chosen to simulate reactive fluid flow in 2 and 3D configurations of the polyurethane during the process taking into account the chemical, and chemiorehological results obtained experimentally in this study.

Keywords: Reactive rotational molding, free surface flows, simulation, smoothed particle hydrodynamics, surface tension.

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Authors: C. Ardil

Abstract:

Fuzzy set and intuitionistic fuzzy set are dealing with the imprecision and uncertainty inherent in a complex decision problem. However, sometimes these theories are not sufficient to model indeterminate and inconsistent information encountered in real-life problems. To overcome this insufficiency, the neutrosophic set, which is useful in practical applications, is proposed, triangular neutrosophic numbers and trapezoidal neutrosophic numbers are examined, their definitions and applications are discussed. In this study, a decision making algorithm is developed using neutrosophic set processes and an application is given in fighter aircraft selection as an example of a decision making problem. The estimation of the fighter aircraft selection with the neutrosophic multiple criteria decision analysis method is examined.  

Keywords: neutrosophic set, multiple criteria decision making analysis, fighter aircraft selection, MCDMA, neutrosophic numbers

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

Abstract:

We propose the use of Reinforcement Learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making make it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and a statistical analysis of the results. We study generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: Inventory Management, Reinforcement Learning, Supply Chain Optimization, Uncertainty.

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Authors: M. Rahou, A. Cheikh, F. Sebaa

Abstract:

Manufacturing tolerancing is intended to determine the intermediate geometrical and dimensional states of the part during its manufacturing process. These manufacturing dimensions also serve to satisfy not only the functional requirements given in the definition drawing, but also the manufacturing constraints, for example geometrical defects of the machine, vibration and the wear of the cutting tool. In this paper, an experimental study on the influence of the wear of the cutting tool (systematic dispersions) is explored. This study was carried out on three stages .The first stage allows machining without elimination of dispersions (random, systematic) so the tolerances of manufacture according to total dispersions. In the second stage, the results of the first stage are filtered in such way to obtain the tolerances according to random dispersions. Finally, from the two previous stages, the systematic dispersions are generated. The objective of this study is to model by the least squares method the error of manufacture based on systematic dispersion. Finally, an approach of optimization of the manufacturing tolerances was developed for machining on a CNC machine tool

Keywords: Dispersions, Compensation, modeling, manufacturing Tolerance, machine tool.

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Authors: B. Elshafei, X. Mao

Abstract:

The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: Data fusion, Gaussian process regression, signal denoise, temporal extrapolation.

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Authors: H. Meradi, S. Bouhouche, M. Lahreche

Abstract:

In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.

Keywords: LD converter, bath temperature, neural networks.

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Authors: Mohd Sazli Saad, Hishamuddin Jamaluddin, Intan Zaurah Mat Darus

Abstract:

This paper presents the development of an active vibration control using direct adaptive controller to suppress the vibration of a flexible beam system. The controller is realized based on linear parametric form. Differential evolution optimisation algorithm is used to optimize the controller using single objective function by minimizing the mean square error of the observed vibration signal. Furthermore, an alternative approach is developed to systematically search for the best controller model structure together with it parameter values. The performance of the control scheme is presented and analysed in both time and frequency domain. Simulation results demonstrate that the proposed scheme is able to suppress the unwanted vibration effectively.

Keywords: flexible beam, finite difference method, active vibration control, differential evolution, direct adaptive controller

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Authors: Carlos Ramirez, Carlos Concha, Benjamin Valdes

Abstract:

Emotions are related with learning processes and physiological signals can be used to detect them for the personalization of learning resources and to control the pace of instruction. A model of relevant emotions has been developed, where specific combinations of emotions and cognition processes are connected and integrated with the concept of 'flow', in order to improve learning. The cardiac pulse is a reliable signal that carries useful information about the subject-s emotional condition; it is detected using a classroom chair adapted with non invasive EMFi sensor and an acquisition system that generates a ballistocardiogram (BCG), the signal is processed by an algorithm to obtain characteristics that match a specific emotional condition. The complete chair system is presented in this work, along with a framework for the personalization of learning resources.

Keywords: Ballistocardiogram, emotions in learning, noninvasive sensors, personalization of learning resources.

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Authors: Rahib Abiyev, Vasif H. Abiyev, C. Ardil

Abstract:

In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.

Keywords: Fuzzy logic, neural network, neuro-fuzzy system, neuro-fuzzy prediction.

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Authors: João V. Staub de Melo, Glicério Trichês, Liseane P. Thives

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This paper presents the results of the development of asphalt nanocomposites containing carbon nanotubes (CNTs) with high resistance to permanent deformation, aiming to increase the performance of asphalt surfaces in relation to the rutting problem. Asphalt nanocomposites were prepared with the addition of different proportions of CNTs (1%, 2% and 3%) in relation to the weight of asphalt binder. The base binder used was a conventional binder (50-70 penetration) classified as PG 58-22. The optimum percentage of CNT addition in the asphalt binder (base) was determined through the evaluation of the rheological and empirical characteristics of the nanocomposites produced. In order to evaluate the contribution and the effects of the nanocomposite (optimized) in relation to the rutting, the conventional and nanomodified asphalt mixtures were tested in a French traffic simulator (Orniéreur). The results obtained demonstrate the efficient contribution of the asphalt nanocomposite containing CNTs to the resistance to permanent deformation of the asphalt mixture.

Keywords: Asphalt nanocomposites, asphalt mixtures, carbon nanotubes, nanotechnology, permanent deformation.

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Authors: T. Kjosmoen, T. Ryen, T. Eftestøl

Abstract:

In biological and biomedical research motif finding tools are important in locating regulatory elements in DNA sequences. There are many such motif finding tools available, which often yield position weight matrices and significance indicators. These indicators, p-values and E-values, describe the likelihood that a motif alignment is generated by the background process, and the expected number of occurrences of the motif in the data set, respectively. The various tools often estimate these indicators differently, making them not directly comparable. One approach for comparing motifs from different tools, is computing the E-value as the product of the p-value and the number of possible alignments in the data set. In this paper we explore the combinatorics of the motif alignment models OOPS, ZOOPS, and ANR, and propose a generic algorithm for computing the number of possible combinations accurately. We also show that using the wrong alignment model can give E-values that significantly diverge from their true values.

Keywords: Motif alignment, combinatorics, p-value, E-value, OOPS, ZOOPS, ANR.

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Authors: O. Srihakulung, Y. Soongsumal

Abstract:

Among the chemicals used for ammunition production, TNT (Trinitrotoluene) play a significant role since World War I and II. Various types of military weapon utilize TNT in casting process. However, the TNT casting process for warhead is difficult to control the cooling rate of the liquid TNT. This problem occurs because the casting process lacks the equipment to detect the temperature during the casting procedure This study presents the temperature detected by infrared camera to illustrate the cooling rate and cooling zone of curing, and demonstrates the optimization of TNT condition to reduce the risk of air gap occurred in the warhead which can result in the destruction afterward. Premature initiation of explosive-filled projectiles in response to set-back forces during gunfiring cause by casting defects. Finally the study can help improving the process of the TNT casting. The operators can control the curing of TNT inside the case by rising up the heating rod at the proper time. Consequently this can reduce tremendous time of rework if the air gaps occur and increase strength to lower elastic modulus. Therefore, it can be clearly concluded that the use of Infrared Cameras in this process is another method to improve the casting procedure.

Keywords: Infrared camera, TNT casting, warhead, curing.

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Authors: Slimane Ouhmad, Abdellah Halimi

Abstract:

In this work, neural networks methods MLP type were applied to a database from an array of six sensors for the detection of three toxic gases. The choice of the number of hidden layers and the weight values are influential on the convergence of the learning algorithm. We proposed, in this article, a mathematical formula to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases and optimized the computation time. The model presented here has proven to be an effective application for the fast identification of toxic gases.

Keywords: Back-propagation, Computing time, Fast identification, MLP neural network, Number of neurons in the hidden layer.

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Authors: Tamanna Siddiqui, M. Afshar Alam

Abstract:

Censored Production Rule is an extension of standard production rule, which is concerned with problems of reasoning with incomplete information, subject to resource constraints and problem of reasoning efficiently with exceptions. A CPR has a form: IF A (Condition) THEN B (Action) UNLESS C (Censor), Where C is the exception condition. Fuzzy CPR are obtained by augmenting ordinary fuzzy production rule “If X is A then Y is B with an exception condition and are written in the form “If X is A then Y is B Unless Z is C. Such rules are employed in situation in which the fuzzy conditional statement “If X is A then Y is B" holds frequently and the exception condition “Z is C" holds rarely. Thus “If X is A then Y is B" part of the fuzzy CPR express important information while the unless part acts only as a switch that changes the polarity of “Y is B" to “Y is not B" when the assertion “Z is C" holds. The proposed approach is an attempt to discover fuzzy censored production rules from set of discovered fuzzy if then rules in the form: A(X) ÔçÆ B(Y) || C(Z).

Keywords: Uncertainty Quantification, Fuzzy if then rules, Fuzzy Censored Production Rules, Learning algorithm.

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Authors: Avinesh Prasad, Bibhya Sharma, Jito Vanualailai

Abstract:

In this paper, we propose a solution to the motion control problem of a 2-link revolute manipulator arm. We require the end-effector of the arm to move safely to its designated target in a priori known workspace cluttered with fixed circular obstacles of arbitrary position and sizes. Firstly a unique velocity algorithm is used to move the end-effector to its target. Secondly, for obstacle avoidance a turning angle is designed, which when incorporated into the control laws ensures that the entire robot arm avoids any number of fixed obstacles along its path enroute the target. The control laws proposed in this paper also ensure that the equilibrium point of the system is asymptotically stable. Computer simulations of the proposed technique are presented.

Keywords: 2-link revolute manipulator, motion control, obstacle avoidance, asymptotic stability.

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Authors: Onur Ayaz, Nuray Ucar, Elif Bahar, Oguzhan Ucar, Mustafa Oksuz, Aysen Onen, Mehmet Ucar, Ezgi İşmar, Ali Demir

Abstract:

In this study, single nozzle method used for electrospinning technique which composite polymer solution with cellulose nanowiskers (CNW) was treated by ultrasonic sonificator have been compared with coaxial (double) nozzle method, in terms of mechanical, thermal and morphological properties of composite nanofiber. The effect of water content in composite polymer solution on properties of nanofiber has also been examined. It has been seen that single nozzle method which polymer solution does not contain water has better results than that of coaxial method, in terms of mechanical, thermal and morphological properties of nanofiber. However, it is necessary to make an optimization study on setting condition of ultrasonic treatment to get better dispersion of CNW in composite nanofiber and to get better mechanical and thermal properties

Keywords: cellulose nanowhiskers, coaxial nozzle, composite nanofiber, electrospinning

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Authors: Mohammed H. Rady, Mohd Sukri Mustapa, S Shamsudin, M. A. Lajis, A. Wagiman

Abstract:

The present study is aimed at investigating microhardness and density of aluminium alloy chips when subjected to various settings of preheating temperature and preheating time. Three values of preheating temperature were taken as 450 °C, 500 °C, and 550 °C. On the other hand, three values of preheating time were chosen (1, 2, 3) hours. The influences of the process parameters (preheating temperature and time) were analyzed using Design of Experiments (DOE) approach whereby full factorial design with center point analysis was adopted. The total runs were 11 and they comprise of two factors of full factorial design with 3 center points. The responses were microhardness and density. The results showed that the density and microhardness increased with decreasing the preheating temperature. The results also found that the preheating temperature is more important to be controlled rather than the preheating time in microhardness analysis while both the preheating temperature and preheating time are important in density analysis. It can be concluded that setting temperature at 450 °C for 1 hour resulted in the optimum responses.

Keywords: AA6061, density, DOE, hot extrusion, microhardness.

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Authors: Oguz Ustun, Erdal Bekiroglu

Abstract:

In the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised backpropagation learning algorithm are used in training of the ANN based position estimator. A 4-phase SRM have been used to verify the accuracy and feasibility of the proposed position estimator. Simulation results show that the proposed position estimator gives precise and accurate position estimations for both under the low and high level reference speeds of the SRM

Keywords: Artificial neural networks, modeling andsimulation, position observer, switched reluctance motor.

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Authors: Ionel Zagan, Vasile Gheorghita Gaitan

Abstract:

The development of CPUs and of real-time systems based on them made it possible to use time at increasingly low resolutions. Together with the scheduling methods and algorithms, time organizing has been improved so as to respond positively to the need for optimization and to the way in which the CPU is used. This presentation contains both a detailed theoretical description and the results obtained from research on improving the performances of the nMPRA (Multi Pipeline Register Architecture) processor by implementing specific functions in hardware. The proposed CPU architecture has been developed, simulated and validated by using the FPGA Virtex-7 circuit, via a SoC project. Although the nMPRA processor hardware structure with five pipeline stages is very complex, the present paper presents and analyzes the tests dedicated to the implementation of the CPU and of the memory on-chip for instructions and data. In order to practically implement and test the entire SoC project, various tests have been performed. These tests have been performed in order to verify the drivers for peripherals and the boot module named Bootloader.

Keywords: Hardware scheduler, nMPRA processor, real-time systems, scheduling methods.

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Authors: M. G. C. A. Cimino, G. Frosini, B. Lazzerini, F. Marcelloni

Abstract:

In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition data sets affected by noise and outliers. Robust fuzzy C-means (robust-FCM) is certainly one of the most known among these algorithms. In robust-FCM, noise is modeled as a separate cluster and is characterized by a prototype that has a constant distance δ from all data points. Distance δ determines the boundary of the noise cluster and therefore is a critical parameter of the algorithm. Though some approaches have been proposed to automatically determine the most suitable δ for the specific application, up to today an efficient and fully satisfactory solution does not exist. The aim of this paper is to propose a novel method to compute the optimal δ based on the analysis of the distribution of the percentage of objects assigned to the noise cluster in repeated executions of the robust-FCM with decreasing values of δ . The extremely encouraging results obtained on some data sets found in the literature are shown and discussed.

Keywords: noise prototype, robust fuzzy clustering, robustfuzzy C-means

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Authors: T. Hirner, J. Polec

Abstract:

This paper describes a new method of unequal error protection (UEP) for region of interest (ROI) with embedded zerotree wavelet algorithm (EZW). ROI technique is important in applications with different parts of importance. In ROI coding, a chosen ROI is encoded with higher quality than the background (BG). Unequal error protection of image is provided by different coding techniques. In our proposed method, image is divided into two parts (ROI, BG) that consist of more important bytes (MIB) and less important bytes (LIB). The experimental results verify effectiveness of the design. The results of our method demonstrate the comparison of the unequal error protection (UEP) of image transmission with defined ROI and the equal error protection (EEP) over multiple noisy channels.

Keywords: embedded zerotree wavelet (EZW), equal error protection (EEP), region of interest (ROI), RS code, unequal error protection (UEP)

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Authors: Zhiying Zhang, Chongdu Cho

Abstract:

Accurate evaluation of damping ratios involving soilstructure interaction (SSI) effects is the prerequisite for seismic design of in-situ buildings. This study proposes a combined approach to identify damping ratios of SSI systems based on ambient excitation technique. The proposed approach is illustrated with main test process, sampling principle and algorithm steps through an engineering example, as along with its feasibility and validity. The proposed approach is employed for damping ratio identification of 82 buildings in Xi-an, China. Based on the experimental data, the variation range and tendency of damping ratios of these SSI systems, along with the preliminary influence factor, are shown and discussed. In addition, a fitting curve indicates the relation between the damping ratio and fundamental natural period of SSI system.

Keywords: Damping ratio, seismic design, soil-structure interaction, system parameter identification.

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Authors: Aichun Feng, Zhi-Min Chen, Jing Tang Xing

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A two-dimensional linear wave-body interaction problem can be solved using a desingularized integral method by placing free surface Rankine sources over calm water surface and satisfying boundary conditions at prescribed collocation points on the calm water surface. A new free-surface Rankine source distribution scheme, determined by the intersection points of free surface and body surface, is developed to reduce numerical computation cost. Associated with this, a new treatment is given to the intersection point. The present scheme results are in good agreement with traditional numerical results and measurements.

Keywords: Source point distribution, panel method, Rankine source, desingularized algorithm.

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Authors: Swapnil Gupta, C. Pandu Rangan

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A uniquely restricted matching is defined to be a matching M whose matched vertices induces a sub-graph which has only one perfect matching. In this paper, we make progress on the open question of the status of this problem on interval graphs (graphs obtained as the intersection graph of intervals on a line). We give an algorithm to compute maximum cardinality uniquely restricted matchings on certain sub-classes of interval graphs. We consider two sub-classes of interval graphs, the former contained in the latter, and give O(|E|^2) time algorithms for both of them. It is to be noted that both sub-classes are incomparable to proper interval graphs (graphs obtained as the intersection graph of intervals in which no interval completely contains another interval), on which the problem can be solved in polynomial time.

Keywords: Uniquely restricted matching, interval graph, design and analysis of algorithms, matching, induced matching, witness counting.

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