Search results for: function analysis

Authors: Yong-Taeg Oh, Won Song, Seok-Eui Choi, Bo-Ra Koo, Dong-Chan Shin

Abstract:

ZnO+Ga2O3 functionally graded thin films (FGTFs) were examined for their potential use as Solar cell and organic light emitting diodes (OLEDs). FGTF transparent conducting oxides (TCO) were fabricated by combinatorial RF magnetron sputtering. The composition gradient was controlled up to 10% by changing the plasma power of the two sputter guns. A Ga2O3+ZnO graded region was placed on the top layer of ZnO. The FGTFs showed up to 80% transmittance. Their surface resistances were reduced to < 10% by increasing the Ga2O3: pure ZnO ratio in the TCO. The FGTFs- work functions could be controlled within a range of 0.18 eV. The controlled work function is a very promising technology because it reduces the contact resistance between the anode and Hall transport layers of OLED and solar cell devices.

Keywords: Work Function, TCO, Functionally Graded Thin Films, Resistance, Transmittance.

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Authors: Yuri Boiko, Iluju Kiringa, Tet Yeap

Abstract:

Current drainage attack suggested previously is further studied in regular settings of closed-loop controlled Brushless DC (BLDC) motor with Kalman filter in the feedback loop. Modeling and simulation experiments are conducted in a MATLAB environment, implementing the closed-loop control model of BLDC motor operation in position sensorless mode under Kalman filter drive. The current increase in the motor windings is caused by the controller (p-controller in our case) affected by false data injection of substitution of the angular velocity estimates with distorted values. Operation of multiplication to distortion coefficient, values of which are taken from the distortion function synchronized in its periodicity with the rotor’s position change. A saw function with a triangular tooth shape is studied herewith for the purpose of carrying out the bias injection with current drainage consequences. The specific focus here is on how the asymmetry of the tooth in the saw function affects the flow of current drainage. The purpose is two-fold: (i) to produce and collect the signature of an asymmetric saw in the attack for further pattern recognition process, and (ii) to determine conditions of improving stealthiness of such attack via regulating asymmetry in saw function used. It is found that modification of the symmetry in the saw tooth affects the periodicity of current drainage modulation. Specifically, the modulation frequency of the drained current for a fully asymmetric tooth shape coincides with the saw function modulation frequency itself. Increasing the symmetry parameter for the triangle tooth shape leads to an increase in the modulation frequency for the drained current. Moreover, such frequency reaches the switching frequency of the motor windings for fully symmetric triangular shapes, thus becoming undetectable and improving the stealthiness of the attack. Therefore, the collected signatures of the attack can serve for attack parameter identification via the pattern recognition route.

Keywords: Bias injection attack, Kalman filter, BLDC motor, control system, closed loop, P-controller, PID-controller, current drainage, saw-function, asymmetry.

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Authors: Rongtao Ding, Xinhao Ji, Linting Zhu

Abstract:

In recent years, the relevance feedback technology is regarded in content-based image retrieval. This paper suggests a neural networks feedback algorithm based on the radial basis function, coming to extract the semantic character of image. The results of experiment indicated that the performance of this relevance feedback is better than the feedback algorithm based on Single-RBF.

Keywords: Image retrieval, relevance feedback, radial basis function.

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Authors: Jeries J. Abou-Hanna

Abstract:

Several techniques exist for determining stress-intensity factors in linear elastic fracture mechanics analysis. These techniques are based on analytical, numerical, and empirical approaches that have been well documented in literature and engineering handbooks. However, not all techniques share the same merit. In addition to overly-conservative results, the numerical methods that require extensive computational effort, and those requiring copious user parameters hinder practicing engineers from efficiently evaluating stress-intensity factors. This paper investigates the prospects of reducing the complexity and required variables to determine stress-intensity factors through the utilization of the stress gradient and a weighting function. The heart of this work resides in the understanding that fracture emanating from stress concentration locations cannot be explained by a single maximum stress value approach, but requires use of a critical volume in which the crack exists. In order to understand the effectiveness of this technique, this study investigated components of different notch geometry and varying levels of stress gradients. Two forms of weighting functions were employed to determine stress-intensity factors and results were compared to analytical exact methods. The results indicated that the “exponential” weighting function was superior to the “absolute” weighting function. An error band +/- 10% was met for cases ranging from a steep stress gradient in a sharp v-notch to the less severe stress transitions of a large circular notch. The incorporation of the proposed method has shown to be a worthwhile consideration.

Keywords: Fracture mechanics, finite element method, stress intensity factor, stress gradient.

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Authors: Mahasen M. Abdel-Mageed, H. S. Zaghloul

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Annihilations, phase shifts, scattering lengths and elastic cross sections of low energy positrons scattering from magnesium atoms were studied using the least-squares variational method (LSVM). The possibility of positron binding to the magnesium atoms is investigated. A trial wave function is suggested to represent e+-Mg elastic scattering and scattering parameters were derived to estimate the binding energy and annihilation rates. The trial function is taken to depend on several adjustable parameters, and is improved iteratively by increasing the number of terms. The present results have the same behavior as reported semi-empirical, theoretical and experimental results. Especially, the estimated positive scattering length supports the possibility of positronmagnesium bound state system that was confirmed in previous experimental and theoretical work.

Keywords: Bound wave function, Positron Annihilation, scattering phase shift, scattering length.

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Authors: Truong Nguyen Luan Vu, Moonyong Lee

Abstract:

The interactions between input/output variables are a very common phenomenon encountered in the design of multi-loop controllers for interacting multivariable processes, which can be a serious obstacle for achieving a good overall performance of multiloop control system. To overcome this impediment, the decomposed dynamic interaction analysis is proposed by decomposing the multiloop control system into a set of n independent SISO systems with the corresponding effective open-loop transfer function (EOTF) within the dynamic interactions embedded explicitly. For each EOTF, the reduced model is independently formulated by using the proposed reduction design strategy, and then the paired multi-loop proportional-integral-derivative (PID) controller is derived quite simply and straightforwardly by using internal model control (IMC) theory. This design method can easily be implemented for various industrial processes because of its effectiveness. Several case studies are considered to demonstrate the superior of the proposed method.

Keywords: Multi-loop PID controller, internal model control(IMC), effective open-loop transfer function (EOTF)

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Authors: Miklos Palfia

Abstract:

An iterative definition of any n variable mean function is given in this article, which iteratively uses the two-variable form of the corresponding two-variable mean function. This extension method omits recursivity which is an important improvement compared with certain recursive formulas given before by Ando-Li-Mathias, Petz- Temesi. Furthermore it is conjectured here that this iterative algorithm coincides with the solution of the Riemann centroid minimization problem. Certain simulations are given here to compare the convergence rate of the different algorithms given in the literature. These algorithms will be the gradient and the Newton mehod for the Riemann centroid computation.

Keywords: Means, matrix means, operator means, geometric mean, Riemannian center of mass.

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Authors: Dimitris Varsamis, Christos Talagkozis, Alkiviadis Tsimpiris, Paris Mastorocostas

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The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the execution time. Specifically, a new function in MATLAB for serial k-means algorithm is developed, which meets all the requirements for the conversion to a function in MATLAB with parallel computations. Additionally, two different variants for the definition of initial values are presented. In the sequel, the parallel approach is presented. Finally, the performance tests for the computation times respect to the numbers of features and classes are illustrated.

Keywords: K-means algorithm, clustering, parallel computations, MATLAB.

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Authors: Ryad Zemouri, Paul Ciprian Patic

Abstract:

An adaptive software reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available software failure time data, Fuzzy Min-Max algorithm is used to globally optimize the number of the k Gaussian nodes. The corresponding optimized neural network architecture is iteratively and dynamically reconfigured in real-time as new actual failure time data arrives. The performance of our proposed approach has been tested using sixteen real-time software failure data. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to next-steppredictability compared to existing neural network model for failure time prediction.

Keywords: Neural network, Prediction error, Recurrent RadialBasis Function Network, Reliability prediction.

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Authors: Ye. Shapovalov, V. Shapovalov, O. Stryzhak, A. Salyuk

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Over the past decades, amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of data. The approach to systematization of scientific information on the production of biogas based on the ontological IT platform “T.O.D.O.S.” has been developed. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform “T.O.D.O.S.”. An ontological graph with a ranking function for previous scientific research and for a system of selection of microorganisms has been worked out. These systems provide high performance of information management of scientific information.

Keywords: Ontology-based analysis, analysis of scientific data, methanogenesys, microorganism hierarchy, T.O.D.O.S.

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Authors: Young-Seok Choi

Abstract:

We present a family of data-reusing and affine projection algorithms. For identification of a noisy linear finite impulse response channel, a partial knowledge of a channel, especially noise, can be used to improve the performance of the adaptive filter. Motivated by this fact, the proposed scheme incorporates an estimate of a knowledge of noise. A constraint, called the adaptive noise constraint, estimates an unknown information of noise. By imposing this constraint on a cost function of data-reusing and affine projection algorithms, a cost function based on the adaptive noise constraint and Lagrange multiplier is defined. Minimizing the new cost function leads to the adaptive noise constrained (ANC) data-reusing and affine projection algorithms. Experimental results comparing the proposed schemes to standard data-reusing and affine projection algorithms clearly indicate their superior performance.

Keywords: Data-reusing, affine projection algorithm, error constraint, system identification.

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Authors: Linjun Wu, Shihua Zhu, Xingle Feng

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In Orthogonal Frequency Division Multiplexing (OFDM) systems, the peak to average power ratio (PAR) is much high. The clipping signal scheme is a useful method to reduce PAR. Clipping the OFDM signal, however, increases the overall noise level by introducing clipping noise. It is necessary to recover the figure of the original signal at receiver in order to reduce the clipping noise. Considering the continuity of the signal and the figure of the peak, we obtain a certain conic function curve to replace the clipped signal module within the clipping time. The results of simulation show that the proposed scheme can reduce the systems? BER (bit-error rate) 10 times when signal-to-interference-and noise-ratio (SINR) equals to 12dB. And the BER performance of the proposed scheme is superior to that of kim's scheme, too.

Keywords: Orthogonal Frequency Division Multiplexing, Peak-to-Average Power Ratio, clipping time, conic function.

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Authors: M. Byzedi, B. Saghafian

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Droughts are complex, natural hazards that, to a varying degree, affect some parts of the world every year. The range of drought impacts is related to drought occurring in different stages of the hydrological cycle and usually different types of droughts, such as meteorological, agricultural, hydrological, and socioeconomical are distinguished. Streamflow drought was analyzed by the method of truncation level (at 70% level) on daily discharges measured in 54 hydrometric stations in southwestern Iran. Frequency analysis was carried out for annual maximum series (AMS) of drought deficit volume and duration series. Some factors including physiographic, climatic, geologic, and vegetation cover were studied as influential factors in the regional analysis. According to the results of factor analysis, six most effective factors were identified as area, rainfall from December to February, the percent of area with Normalized Difference Vegetation Index (NDVI) <0.1, the percent of convex area, drainage density and the minimum of watershed elevation that explained 90.9% of variance. The homogenous regions were determined by cluster analysis and discriminate function analysis. Suitable multivariate regression models were evaluated for streamflow drought deficit volume with 2 years return period. The significance level of regression models was 0.01. The results showed that the watershed area is the most effective factor with high correlation with deficit volume. Also, drought duration was not a suitable drought index for regional analysis.

Keywords: Iran, Streamflow drought, truncation level method, regional analysis.

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Authors: R. Oonsivilai, A. Oonsivilai

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Realistic systems generally are systems with various inputs and outputs also known as Multiple Input Multiple Output (MIMO). Such systems usually prove to be complex and difficult to model and control purposes. Therefore, decomposition was used to separate individual inputs and outputs. A PID is assigned to each individual pair to regulate desired settling time. Suitable parameters of PIDs obtained from Genetic Algorithm (GA), using Mean of Squared Error (MSE) objective function.

Keywords: Gas Turbine, PID, Genetic Algorithm, Transfer function.Mean of Squared Error

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Authors: Amir Hajian, Sepehr Damavandinejadmonfared

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In this paper the issue of dimensionality reduction is investigated in finger vein recognition systems using kernel Principal Component Analysis (KPCA). One aspect of KPCA is to find the most appropriate kernel function on finger vein recognition as there are several kernel functions which can be used within PCA-based algorithms. In this paper, however, another side of PCA-based algorithms -particularly KPCA- is investigated. The aspect of dimension of feature vector in PCA-based algorithms is of importance especially when it comes to the real-world applications and usage of such algorithms. It means that a fixed dimension of feature vector has to be set to reduce the dimension of the input and output data and extract the features from them. Then a classifier is performed to classify the data and make the final decision. We analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in this paper and investigate the optimal feature extraction dimension in finger vein recognition using KPCA.

Keywords: Biometrics, finger vein recognition, Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA).

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Authors: M. Azadbakht, Y. Fadakar

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Since fuel must be injected with appropriate pressure and time for accurate performance of diesel engines, then proper function of engine is influenced by accurate function of injector pump. At first total pump was designed by SolidWorks 2012 software. Then the total relationship of rotor, roller, internal cam ring, pole shoe and plunger in injector pump in MF285 tractor and their performance was shown. During suction state rollers connect with dents in internal cam ring and in pressure course pole shoes have drawer move in rotor and perform tappet action between rollers and plungers. The maximum stress was obtained by using analysis of finite element method. The maximum stress in contact surface of roller and internal cam ring and on roller surface. The maximum amount of this stress is 288.12 MPa. According to conducted analyses, the minimum value for safety factor is related to roller surface and it equals to 2.0477.

Keywords: Rotary injector pump, MF285 tractor, finite element, stress.

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Authors: Domenica Tambasco, Riina Bray

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Fibromyalgia is a chronic condition characterized by widespread musculoskeletal pain, fatigue, and reduced function. Management of symptoms include medications, physical treatments and mindfulness therapies. Myofascial Release is a modality that has been successfully applied in various musculoskeletal conditions. However, to the author’s best knowledge, it is not yet recognized as a self-management therapy option in Fibromyalgia. In this study, we investigated whether Self-applied Myofascial Release (SMR) is associated with overall improved function and symptoms in Fibromyalgia. Eligible adult patients with a confirmed diagnosis of Fibromyalgia at Women’s College Hospital were recruited to SMR. Sessions ran for 1 hour once a week for 7 weeks, led by the same two physiotherapists knowledgeable in this physical treatment modality. The main outcome measure was an overall impact score for function and symptoms based on the validated assessment tool for fibromyalgia, the Revised Fibromyalgia Impact Questionnaire (FIQR), measured pre- and post-intervention. Both descriptive and analytical methods were applied and reported. We analyzed results using a paired t-test to determine if there was a statistically significant difference in mean FIQR scores between initial (pre-intervention) and final (post-intervention) scores. A clinically significant difference in FIQR was defined as a reduction in score by 10 or more points. Our pilot study showed that SMR appeared to be a safe and effective intervention for our fibromyalgia participants and the overall impact on function and symptoms occurred in only 7 weeks. Further studies with larger sample sizes comparing SMR to other physical treatment modalities (such as stretching) in an randomized control trial (RCT) are recommended.

Keywords: Fibromyalgia, myofascial release, fibromyalgia impact questionnaire, fibromyalgia assessment status.

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Authors: S. Anna Durai, E. Anna Saro

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In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Counter Propagation Neural Network, it takes longer time to converge. The reason for this is that the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbor with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative Distribution Function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used the Counter Propagation Neural Network yield high compression ratio as well as it converges quickly.

Keywords: Correlation, Counter Propagation Neural Networks, Cummulative Distribution Function, Image compression.

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Authors: Mohammad Reza Ghasemi, Ali Ehsani

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In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.

Keywords: Composite Laminates, GA, Multi-objectiveOptimization, Neural Networks, RBFNN.

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Authors: Maumita Bhattacharya

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Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model presented in our earlier work [14] reduced computation time by controlled use of meta-models to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. Situations like model formation involving variable input dimensions and noisy data certainly can not be covered by this assumption. In this paper we present an enhanced version of DAFHEA that incorporates a multiple-model based learning approach for the SVM approximator. DAFHEA-II (the enhanced version of the DAFHEA framework) also overcomes the high computational expense involved with additional clustering requirements of the original DAFHEA framework. The proposed framework has been tested on several benchmark functions and the empirical results illustrate the advantages of the proposed technique.

Keywords: Evolutionary algorithm, Fitness function, Optimization, Meta-model, Stochastic method.

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Authors: Mohammed Mohsin Ali H., Mohamed Haneef

Abstract:

The connecting rod transmits the piston load to the crank causing the latter to turn, thus converting the reciprocating motion of the piston into a rotary motion of the crankshaft. Connecting rods are subjected to forces generated by mass and fuel combustion. This study investigates and compares the fatigue behavior of forged steel, powder forged and ASTM a 514 steel cold quenched connecting rods. The objective is to suggest for a new material with reduced weight and cost with the increased fatigue life. This has entailed performing a detailed load analysis. Therefore, this study has dealt with two subjects: first, dynamic load and stress analysis of the connecting rod, and second, optimization for material, weight and cost. In the first part of the study, the loads acting on the connecting rod as a function of time were obtained. Based on the observations of the dynamic FEA, static FEA, and the load analysis results, the load for the optimization study was selected. It is the conclusion of this study that the connecting rod can be designed and optimized under a load range comprising tensile load and compressive load. Tensile load corresponds to 360^o crank angle at the maximum engine speed. The compressive load is corresponding to the peak gas pressure. Furthermore, the existing connecting rod can be replaced with a new connecting rod made of ASTM a 514 steel cold quenched that is 12% lighter and 28% cheaper.

Keywords: Connecting rod, ASTM a514 cold quenched steel, static analysis, fatigue analysis, stress life approach.

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Authors: S. Sarumathi, N. Shanthi, P. Ranjetha

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Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand.

Keywords: Cluster Ensemble, Consensus Function, CSPA, Diversity, HGPA, MCLA.

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Authors: L. Mirtskhulava, M. Khunjgurua, N. Lomineishvili, K. Bakuria

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Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.

Keywords: Exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability.

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Authors: Eleni Aggelogiannaki, Haralambos Sarimveis

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In this work, a radial basis function (RBF) neural network is developed for the identification of hyperbolic distributed parameter systems (DPSs). This empirical model is based only on process input-output data and used for the estimation of the controlled variables at specific locations, without the need of online solution of partial differential equations (PDEs). The nonlinear model that is obtained is suitably transformed to a nonlinear state space formulation that also takes into account the model mismatch. A stable robust control law is implemented for the attenuation of external disturbances. The proposed identification and control methodology is applied on a long duct, a common component of thermal systems, for a flow based control of temperature distribution. The closed loop performance is significantly improved in comparison to existing control methodologies.

Keywords: Hyperbolic Distributed Parameter Systems, Radial Basis Function Neural Networks, H∞ control, Thermal systems.

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Authors: Geun-Il Park, Jae-Won Lee, Dou-Youn Lee, Jung-Won Lee, Kwang-Wook Kim, Kee-Chan Song

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Thermochemcial characteristics of powder fabricated using oxidation treatment of spent PWR fuel and SIMFUEL were evaluated for recycling of spent fuel such as DUPIC process. Especially, the influence of spent fuel burn-ups on the powder fabrication characteristics was experimentally evaluated, ranging from 27,300 to 65,000 MWd/tU. Densities of powder manufactured from an oxidation, OREOX and the milling processes at the same process conditions were compared as a function of the fuel burn-ups respectively. Also, based on chemical analysis results, homogeneity of fissile elements in oxidized powder was confirmed.

Keywords: Spent PWR fuel, DUPIC, Oxidation, OREOX, Powder, Chemical analysis

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Authors: Rana Yousef, Khalil el Hindi

Abstract:

The behavior of Radial Basis Function (RBF) Networks greatly depends on how the center points of the basis functions are selected. In this work we investigate the use of instance reduction techniques, originally developed to reduce the storage requirements of instance based learners, for this purpose. Five Instance-Based Reduction Techniques were used to determine the set of center points, and RBF networks were trained using these sets of centers. The performance of the RBF networks is studied in terms of classification accuracy and training time. The results obtained were compared with two Radial Basis Function Networks: RBF networks that use all instances of the training set as center points (RBF-ALL) and Probabilistic Neural Networks (PNN). The former achieves high classification accuracies and the latter requires smaller training time. Results showed that RBF networks trained using sets of centers located by noise-filtering techniques (ALLKNN and ENN) rather than pure reduction techniques produce the best results in terms of classification accuracy. The results show that these networks require smaller training time than that of RBF-ALL and higher classification accuracy than that of PNN. Thus, using ALLKNN and ENN to select center points gives better combination of classification accuracy and training time. Our experiments also show that using the reduced sets to train the networks is beneficial especially in the presence of noise in the original training sets.

Keywords: Radial basis function networks, Instance-based reduction, PNN.

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Authors: Oscar Javier Herrera, Manuel Ángel Camacho

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This paper addresses a cutting edge method of business demand forecasting, based on an empirical probability function when the historical behavior of the data is random. Additionally, it presents error determination based on the numerical method technique ‘propagation of errors.’ The methodology was conducted characterization and process diagnostics demand planning as part of the production management, then new ways to predict its value through techniques of probability and to calculate their mistake investigated, it was tools used numerical methods. All this based on the behavior of the data. This analysis was determined considering the specific business circumstances of a company in the sector of communications, located in the city of Bogota, Colombia. In conclusion, using this application it was possible to obtain the adequate stock of the products required by the company to provide its services, helping the company reduce its service time, increase the client satisfaction rate, reduce stock which has not been in rotation for a long time, code its inventory, and plan reorder points for the replenishment of stock.

Keywords: Demand Forecasting, Empirical Distribution, Propagation of Error.

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Authors: Loai AbdAllah, Mahmoud Kaiyal

Abstract:

Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: Missing values, distance metric, Bhattacharyya distance.

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Authors: Kai-Thorsten Wirt

Abstract:

In this paper we present the design of a new encryption scheme. The scheme we propose is a very exible encryption and authentication primitive. We build this scheme on two relatively new design principles: t-functions and fast pseudo hadamard transforms. We recapitulate the theory behind these principles and analyze their security properties and efficiency. In more detail we propose a streamcipher which outputs a message authentication tag along with theencrypted data stream with only little overhead. Moreover we proposesecurity-speed tradeoffs. Our scheme is faster than other comparablet-function based designs while offering the same security level.

Keywords: Cryptography, Combined Primitives, Stream Cipher, MAC, T-Function, FPHT.

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Authors: Sheroz Khan, A. F. Salami, W. A. Lawal, AHM Zahirul Alam, Shihab Abdel Hameed, M. J. E.Salami

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In this paper, a multi-branch power line is modeled using ABCD matrix to show its worth as a communication channel. The model is simulated using MATLAB in an effort to investigate the effects of multiple loading, multipath, and those as a result of load mismatching. The channel transfer function is obtained and investigated using different cable lengths, and different number of bridge taps under given loading conditions.

Keywords: Power line Communication, Transfer Function, Channel Modeling, Signal Transmission.

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