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Fractional linear programming models are widely used in real life complications. The fractional transportation problem (FTP) in particular is wide spread where the goal is the optimization of the ratio between 2 cost functions or damage functions or demand functions. But in view of the uncertainty involved in the parameters, fuzzy techniques become more essential. In the paper, we discuss a solution for a fully fuzzy multi objective FTP with the use of triangular fuzzy numbers without transforming it to a crisp equivalent to the existing model. The algorithm is explained with a numerical example with a better solution. [ABSTRACT FROM AUTHOR]

In this paper we present a new algorithm for the optimal solution to bottleneck transportation problem with imprecise coefficients without changing to its crisp equivalent form which is very distinct from other existing methods. Here all the variables are assumed to be triangular fuzzy numbers. Using a parametric form of triangular fuzzy numbers such as left fuzziness index, right fuzziness index, modal value, we obtain all the efficient solutions to a bottleneck-cost fuzzy transportation problem. This method enables the decision maker to find next efficient solution from the current efficient solution which varies from other methods. A numerical example is solved to illustrate the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we discuss a fuzzy transportation problem(FTP) involving trapezoidal fuzzy numbers. We planned another algorithm to find an initial basic fuzzy feasible solution which will be very nearer to the optimal solution. The optimal solution of the given fuzzy transportation problems is verified by using the fuzzy version of Modified Distribution method without transforming into its correspondent classical form. A few numerical problems are solved to illustrate the proposed algorithm and the solution is compared with the solutions obtained by using an existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper we propose a modified transitive closure algorithm to solve traveling salesman problems (TSP). The algorithm is coded in Java in such a way that, finite number of objectives can be fed into the program directly. Upon satisfying the route conditions of TSPs, optimal solutions of each objective function are displayed on the screen. Benchmark problems from the TSP LIB are also illustrated to show the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

The demand for electricity has increased With the increase in the number of electrical gadgets and appliances, it is now very important to emphasize in the concept of demand side management to meet the required demand of electricity. DSM assures efficient use of available energy resources. DSM along with smart grid allows the consumer to have details of power consumption information and allows the sup pliers to lower the overall load demand during the peak period and modify the load curve. Necessary data of the energy consumption and price have been taken from different papers. In this research work, the primary aim is to mainly lower the overall load demand and thus the operational cost and utility bills. The work is done in different areas – residential, commercial and industrial. To get the optimized result, Meta- heuristic algorithm was developed to minimize the problem. Because of the flexibility, gradient free mechanisms and local optima avoidance of the algorithms, meta-heuristic techniques are gaining popular. The evolutionary algorithms used to solve the demand side management problem are Particle Swarm Optimization, Firefly Algorithm and Salp Swarm Algorithm. The three algorithms are used to obtain the objective of the project work in three different areas- residential, commercial and industrial area. Firefly algorithm is found out to provide the best cost as compared to other two algorithms PSO and SSA. Firefly Algorithm is able to provide better accuracy. [ABSTRACT FROM AUTHOR]

Electrochemical machining (ECM) process has a wide capability to generate complex shapes on different materials which are occasionally difficult to cut. Its ability to machine a variety of materials makes it an extensively accepted non-traditional machining process in modern day manufacturing sector. Thus, selection of the optimal input parameters for an ECM process is crucial for its efficient utilization. In this paper, a comparative analysis is made among four metaheuristics, i.e. firefly algorithm (FA), differential evolution (DE), ant colony optimization (ACO) algorithm and teaching-learning-based optimization (TLBO) algorithm to discover the optimal values of various control parameters for an ECM process. Dimensional inaccuracy, tool life and material removal rate are the three responses considered which are subjected to temperature, choking and passivity constraints. The TLBO algorithm shows the best performance among the others without violating any of the constraints. The paired t-test is also performed to prove the efficacy of TLBO algorithm over the other optimization techniques. The results derived from these algorithms are finally compared with those obtained by the past researchers using other optimization methods for both single and multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, Dimensional synthesis of adjustable four-bar mechanism to generate accurate and uninterrupted path of Square is carried out. Harmonic Spacing (HS) algorithm is used to improve positioning of precision points along the intended profile path to enable robust synthesis. Reconstructed adjustable parameter curve (RAPC) is employed to extract adjustable link length value of intermediary path points along the intended path conforming to equally incremented crank rotation value. Optimization employing Genetic Algorithm (GA) followed by Pattern Search (PS) is implemented using MATLAB codes in the synthesis of mechanism. Simulation of motion of adjustable mechanism to generate intended coupler path is carried out to verify the dimensions of the links and other parameters of the mechanism. Results show precise, continuous and near perfect coupler path of square is generated. [ABSTRACT FROM AUTHOR]

A new evolutionary algorithm called colliding bodies optimization (CBO) considered in this paper to solve optimal power flow (OPF) problem in a power system network. CBO is based on natural phenomenon of one dimensional collision between two objectives. It does not have any control parameters and simple in structure. IEEE 30-bus system with various objective functions like, minimization of total fuel cost with valve point loading effect, minimization of emission profile are considered to test the feasibility and effectiveness of the CBO algorithm. In comparison with the other existed methods presented in the literature, the proposed CBO algorithm gives better optimal solutions for single objective OPF problems. [ABSTRACT FROM AUTHOR]

The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners (physicians) to accelerate diagnosis and treatment processes. In this paper, the sparse matrix approach was incorporated in neural network learning algorithm for scalability, minimize higher memory usage/storage capacity, enhancing implementation time and speed up the analysis of the data. The proposed intelligent classification system maximizes the intelligently classification of data and minimizes the number of trends inaccurately identified. For robustness, the proposed method was tested with three different datasets, namely, Hepatitis, SPECT Heart and Cleveland Heart. Therefore, an attempt was made to determine the performance indicators efficacy. Compared to some similar existing methods, the approach presented achieves improved performance. The program used for implementation of the proposed model is MATLAB R2016a (version 9.0) and executed in the 4.0 GB RAM processor of PC Intel Pentium Quad Core N3700. [ABSTRACT FROM AUTHOR]

2 Satisfiability (2SAT) is a language that bridges real life application to neural network optimization. 2SAT is an interesting paradigm because the outcome of this logical rule is always positive. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when logical rule is embedded to HNN. Increasing the storage capacity during learning phase of HNN has been a challenging problem for most neural network researchers. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to decipher several combinatorial optimization problems. In this paper, metaheuristic algorithm ACO will be proposed in learning 2SAT programming. To this end, all the learning model will be tested in a new restricted learning environment. In circumstances of where HNN model was not given unlimited iteration until all 2SAT clauses were fully satisfied, we would like to determine the better performing search method between exhaustive search and ACO. Results acquired from the computer simulation showed that ACO outperformed exhaustive search in restricted learning environment. [ABSTRACT FROM AUTHOR]

Clonal Selection Algorithm (CSA) is a sturdy evolutionary algorithm that mimics the biological immune system mechanism, frequently implemented as a searching technique in solving complex modelling tasks such as pattern recognition and combinatorial optimization. Subsequently, 3-Satisfiability logic mining is the fundamental medium to induce real data sets in a more precise manner. In this paper, a modified clonal selection algorithm with Hopfield neural network (HNN) is proposed for 3-Satisfiability data mining. Somatic hypermutation operator in CSA can reduce the iterations during HNN training phase and retrieve the best logical rule for a minimal testing error. In addition, 3- Satisfiability logical rule provides better attributes representation of the data set. The proposed method will be applied on Amazon's Employees Resources Access data set to predict the approval or denial for an unseen set of employees in the future. The performance evaluation of the proposed method will be compared with exhaustive search (ES) by computing plausible errors such as root mean square error (RMSE), mean absolute error (MAE), sum of squared error (SSE), accuracy and computational time. The computational simulations are carried out by manipulating different number of neurons to verify the capability of the proposed method in data mining. The experimental results have shown a better performance of the proposed model in training and retrieval phase of the Amazon's Employees Resources Access data set in comparison with ES used in this research. [ABSTRACT FROM AUTHOR]

2 Satisfiability (2SAT) logic programming has been a prominent logical rule that defines the structure of Radial Basis Function Neural Network. Training Radial Basis Function Neural Network with logic 2 Satisfiability is an optimization task since it is desired to find the optimal output weights during the training process. In this paper, artificial immune system (AIS) algorithm will be introduced to facilitate the training of RBFNN-2SAT. AIS is used for updating the output weights during training RBFNN-2SAT. In this study, the effectiveness of our hybrid computing paradigm, namely RBFNN-2SATAIS can be estimated by evaluating its testing data result using the root mean square error (RMSE) and computation time (CT). The obtained findings show that the proposed method was effective for achieving acceptable results for 2SAT logic rule. [ABSTRACT FROM AUTHOR]

Hopfield Neural Network (HNN) is a sort of neural network that is strongly dependent to energy minimization of solution. Although HNN managed to solve various optimization problem, the output of HNN suffered from a lack of interpretability and variation. This has severely limited the practical usability of HNN in doing logic programming. Inspired by random neuron perturbation, Estimation of Distribution Algorithm (EDA) has been proposed to explore various optimal neuron state. EDAs employs a probabilistic model to sample the neuron state in order to move toward the various optimal location of global minimum energy. In this paper, a new Mutation Hopfield Neural Network (MHNN) will be proposed to do k Satisfiability programming. Based on the experimental result, the proposed MHNN has outperformed conventional HNN in various performance metric. [ABSTRACT FROM AUTHOR]

In this paper micro-electro-mechanical systems (MEMS) deformable mirror (DM) for its use in adaptive optics in Indus-2 is studied. DMs are used to correct optical aberrations by precisely adjusting the deflection of mirror surface. A piezo based micro-actuator array of 19 keystone shape elements in diameter of 10 mm on silicon of 20 µm thickness is considered for the study. The finite element method (FEM) simulations were carried out using COMSOL Multiphysics 5.4 to estimate the displacement profile. In order to evaluate the applicability of this MEMS based DM in adaptive optics system, piezo response function matrix was determined by applying dc voltage of 1volt to each actuator one by one while keeping the others at zero voltage. The optimum voltage of each actuator to produce a target profile was determined using TLBO algorithm in MATLAB. In this way the obtained voltages are used to generated the low order Zernike mode shapes. [ABSTRACT FROM AUTHOR]

The Artificial Tree (AT) algorithm and Flower Pollination (FP) algorithm are algorithms inspired by life cycle of plants. AT algorithm is transformed by the growth law of trees while the FP algorithm is related with flower proliferation role in plants. This paper proposes these algorithms to solve the Uncapacitated Facility Location Problem (UFLP). Minimizing the sum of the fixed setup costs and customers serving costs is the objective of UFLP. Based on the computational result in given UFLP small number of data, both algorithms gave excellent result finding the minimum total costs. On the other hand, the AT algorithm performed better in bigger size of data than the FP algorithms. Moreover, higher number of population and iteration provided better computational performance in both algorithms. Higher number of switch probability of FP algorithm and constant maximum search number could help the algorithms to find better solution in solving the UFLP. [ABSTRACT FROM AUTHOR]

The Classification of microarray data are a challenging task because it has many features (genes) and a few samples in gene expression data. Feature selection on microarray data is very important processing in the classification method. Feature selection can produce fewer features to improve classification accuracy in high dimensional data. In this research, we compare two methods, namely kernel principal component analysis (Kernel PCA) and support vector machine - recursive feature elimination (SVM-RFE). Both are suitable methods for the selection of features. Kernel PCA is an extension of PCA using techniques of kernel methods, which works better on complicated spatial structures of high dimensional features. While SVM-RFE is an algorithm to select genes according to their weights. In this paper, the data taken from the National Center Biotechnology Information (NCBI) for Dengue fever microarray dataset. We choose the Support Vector Machine Classifier to classify our binary classes (dengue or health). From the experimental results, Kernel PCA and SVM-RFE have similarity accuracy (ACC), area under the curve (AUC), precision and recall but for running time, Kernel PCA only requires a short computational time than SVM-RFE for classification dengue and healthy patients on a dataset. So, Kernel PCA method as a feature selection is very helpful in improving the accuracy of classification performance and reducing the time consumption for the dengue microarray dataset. [ABSTRACT FROM AUTHOR]

There are several methods to solve decision making problem based on Pythagorean fuzzy soft sets (PFSS). If there is a case in which corresponding to some parameters we do not have any information regarding the universal set, then the existing decision making algorithms in literature fail to serve their purpose. This paper introduces a novel method to handle incomplete information in the case of PFSS and introduces an algorithm for decision making problem for incomplete PFSS. [ABSTRACT FROM AUTHOR]

In this paper, the problem of projective synchronization scheme of fractional-order energy resource system based on Takagi-Sugeno fuzzy model is investigated. The scheme is applied to achieve projective synchronization of both three and four dimensional fractional order energy resource systems via linear control approach. A fuzzy linear control and projective schemes are proposed to synchronizes the energy resource system. Numerical simulations of fractional-order energy resource systems are performed by using the Caputo version and a predictor-corrector algorithm for fractional-order differential equations, which is a generalization of the Adams-Bashforth-Moulton method. Finally some numerical simulations show the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Projection depth is an important concept of non-parametric inference on multivariate data analysis and is the most widely used as statistical depth notion among all the existing depth procedures. This depth procedure was based on Stahel-Donoho multivariate location and scatter estimator and its outlyingness. Further, the adjusted outlyingness concept was used to compute projection depth and namely adjusted projection depth. In both the depth procedures, exact, fixed and random algorithms have been used to compute projection depth values. In this paper, the Gram-Schmidt Orthonormalization (GSO) algorithm is proposed to compute adjusted projection depth in order to improve the accuracy measure. The superiority of the GSO algorithm based adjusted projection depth over the exact, random and fixed algorithms has been demonstrated by applying it in the context of classification analysis by computing average misclassification error rate under real and simulation environments. [ABSTRACT FROM AUTHOR]

In this paper a new procedure namely Modern Zero Suffix (MOZES) method is proposed to find the IFBS it converges to optimal solution for the fuzzy transportation problem. A new algorithm is generated to find the optimal solution for the fuzzy transportation problem with the aid of above said notion. The supply and demand are represented in terms of unique fuzzy number called Interval Valued Triangular Fuzzy Number (IVTFN). A relevant numerical illustrations are given to justify the above proposed notion. [ABSTRACT FROM AUTHOR]

In this paper, we steady natural convection in a discrete heat transfer and the effect of Rayleigh number in a two- dimensional square enclosure with an inner square block. The present problem taken by three different cases were containing on the partially heated which left vertical wall maintained at high temperature Th(Th > Tc). The right vertical wall considered at Tc and other horizontal walls and the inner square block should be thermally insulated. The mathematical model considered as mass, momentum and energy equation solved to using the simple algorithm by Finite Volume Method (FVM) on a flounder grid system consisting of modified Alternating Direction Implicit Method (ADIM) which are used to solve vorticity-stream function formulation.This result found that the streamlines and isotherms achieved a maximum for the distribution of partial heater and also increasing Rayleigh number. [ABSTRACT FROM AUTHOR]

This paper analyses the characteristics of flexible job shop scheduling problem (FJSP), and proposes a genetic simulated annealing algorithm to solve the scheduling problem. Considering the balance of the utilization rate of each machine, the maximum utilization rate of machine, the maximum completion time and other performance indicators are more reasonable, a genetic simulated annealing algorithm combining global search and local search is designed. Through the design of reasonable crossover operator, the global search ability of the genetic simulated annealing algorithm is improved. In addition, the precocity is improved by the simulated annealing algorithm, and the quality of understanding is improved. And finally, the effectiveness and feasibility of the algorithm is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

To solve many complex scientific problems, grid computing is used by many researchers for utilizing and sharing large-scale resources which is increasing expeditiously, in the distributed heterogeneous system. Computational grid, Data grid and Service grid are the three categories in which grid can be classified. Grid computing is also known as a type of parallel and distributed computing system, which allows sharing, collecting and selection of geographically distributed resources depending on their component such as performance, availability, capability, user QoS requirement, and cost during run time. Job scheduling plays very important role in grid computing system, which is one of the key research area in grid computing. This paper proposed a new scheduling algorithm called Feed forward neural network in grid computing system (FFNNGC), which is used to train the network. The training of the network is done until the network converges to a minimum error solution. To verify the proposed scheduling algorithm performance, a grid computing network is used which is based on Globus Toolkit 4. The proposed method is applied in real life problems such as vowels classification or voice recognition problems. Experiments were performed under these real life problems and the results indicated that the proposed method is helpful in such types of problems. [ABSTRACT FROM AUTHOR]

The paper considers simple algorithms for identifying a person for use in household appliances. The main requirements for algorithms are their ease of implementation using inexpensive microcontroller systems and simple CCD cameras. To make it easier to find eyes in images, an IR illumination system was developed. [ABSTRACT FROM AUTHOR]

In this paper, we presented exponentially fitted spline method to solve SPDDE with dual layer. At first, the given second order differential-difference equation is replaced by an asymptotically proportionate second order singular perturbation problem. At that point, a fitting factor is brought into the exponentially fitted spline Method. The value of fitting factor is obtained by the singular perturbations theory. The Thomas algorithm is used to solve the tridiagonal system obtained by the method. The result of the delay and also advance parameters on the boundary layer(s) has likewise been evaluated as well as represented in charts. The applicability of the proposed plan is actually confirmed through executing it on model examples. To show the accuracy of the method, the results are presented in terms of maximum absolute errors for arbitrary λ1, λ2 such that λ1 +λ2 =1/2. [ABSTRACT FROM AUTHOR]

Methods of processing quantum data become more important as quantum computing devices improve their quality towards fault tolerant universal quantum computers. These methods include discrimination and filtering of quantum states given as an input to the device that may find numerous applications in quantum information technologies. In the present paper, we address a scheme of a classification of input states, which is nondestructive and deterministic for certain inputs, while probabilistic, in general case. This can be achieved by incorporating phase estimation algorithm into the hybrid quantum-classical computation scheme, where quantum block is trained classically. We perform proof-of-principle implementation of this idea using superconducting quantum processor of IBM Quantum Experience. Another aspect we are interested in is a mitigation of errors occurring due to the quantum device imperfections. We apply a series of heuristic tricks at the stage of classical postprocessing in order to improve raw experimental data and to recognize patterns in them. These ideas may find applications in other realization of hybrid quantum-classical computations with noisy quantum machines. [ABSTRACT FROM AUTHOR]

One of the specific matrices having special characteristics and applications is centrosymmetric matrix. Recently, the algorithm of determinant this matrix by applying the algorithm of lower Hessenberg’s determinant was computed. Detection of the determinant in the beginning process is needed on those areas, therefore in this paper, the necessary and sufficient of the algorithm is presented. Some numerical examples of determinant of centrosymmetric matrix are given for deeper understanding. [ABSTRACT FROM AUTHOR]

The Consultative Committee for Space Data Systems (CCSDS) recommends AES algorithm as a cryptographic algorithm to maintain confidentiality and data security between spacecraft and ground systems. In implementation, there are several AES algorithm operating modes including Cipher-Block Chaining (CBC), Electronic Codebook (EBC), Cipher Feedback (CFB), Output Feedback (OFB), and counter mode (CTR). One of the operating modes of AES algorithm recommended by CCSDS is CTR because it has advantages in terms of efficiency of implementation and offers a high level of data security. LAPAN-A4 as the next generation of LAPAN satellites also uses CCSDS standard in terms of data exchange between satellites and ground stations including telemetry and telecommand (TTC) data and satellite payload data. In order to protect LAPAN-A4 satellite data, especially TTC data, in this paper AES algorithm was implemented in CTR operating mode on the FPGA which will then be implemented on LAPAN-A4 satellite onboard data handling (OBDH) as one of LAPAN-A4 satellite data security systems. To overcome the limited resource of FPGA, optimization is also done using basic iterative AES hardware reused method, which is to reuse hardware that has been designed n times for same operation for AES algorithm. From the results of implementation and optimization, efficiency of logic gates used is increasing almost a quarter less than the previous research. [ABSTRACT FROM AUTHOR]

Recent years have observed an immense refinement in the domain of object localization, especially over remote sensing images. Object localization aims to predict the objects within an image as well as its boundaries. Achieving accurate object localization over remote sensing images is always challenging due to the complex context information. The paper mainly concentrates on solving the problem of accurate object localization using a three-stage localization pipeline. The introductory stage in the pipeline focus on generating regions of interest (RoI) called candidate regions using selective search algorithm. Then, each of these candidate features is extracted by passing it through a hybrid convolutional neural network (CNN) model. Finally, to improve the localization accuracy we propose an optimal object localization technique called Unsupervised Score Based Bounding Box Regression using Feature Descriptors (USBBBR-FD) algorithm integrated with non-maximum suppression (NMS) algorithm to optimize the bounding boxes of regions which are detected as objects. Analysis shows that the detection precision of the hybrid CNN model is higher than that of any other single CNN model and the dimension-reduction CNN model performs far better than retrained CNN models. Experiments show that the proposed USBBBR-FD algorithm can more accurately locate objects within remote sensing images and also shows robustness in complex background as compared with traditional features extraction methods. [ABSTRACT FROM AUTHOR]

Percolation theory spans a wide area of application ranging from social science, geology, soil science, to complex material structure. Here in this paper, a high speed Monte Carlo program is developed using Python language to find out the site percolation threshold precisely in the square lattice. The coding is done by using the inbuilt libraries of Python named NumPy, SciPy, Matplotlib etc. The cluster identification and numbering is based on Hoshen-Kopelman(HK) algorithm which consumes low computer memory with small computation time compared to other methods. The percolation threshold (pc) computed in our case was is 0.5924528 which is consistent with series expansion method. We have also characterized the percolation by demonstrating normalized mass of percolating cluster (Npc), normalized size of spanning cluster (Nsc) and the ratio of Nsc /Npc. Present work is our generous effort to represent Python as an efficient tool for coding in percolation theory. The realizations of percolations by means of HK algorithm using Python language is reported for the first time as far as our knowledge is concerned. [ABSTRACT FROM AUTHOR]

The paper proposes the algorithm for the determination of the coefficients of the Williams series expansion in notched semidisks with different angles of the notch. The algorithm is based on the experimental procedure of the photoelasticity method and the finite element analysis. The large series of experiments for semidiscs was performed. Digital photoelasticity method is used to analyze experimentally the complete Williams series expansion of the stress and displacement fields in the vicinity of the crack tip in isotropic linear elastic plates under Mixed Mode loading. The distribution of the isochromatic fridge patterns is employed for obtaining the stress field near the crack tip by the use of the complete Williams asymptotic expansion for various classes of the experimental specimens (plates with two collinear cracks under tensile loading and under mixed mode loading conditions). The higher order terms of the Williams series expansion are taken into account and the coefficients of the higher order terms are experimentally obtained. The stress field equation of Williams up to fifty terms in each in mode I and mode II has been considered. The comparison of the experimental results and the calculations performed with finite element analysis has shown the importance and significant advantages of photoelastic observations for the multi-parameter description of the stress field in the neighborhood of the crack tip. [ABSTRACT FROM AUTHOR]

The Hyperbolic MBO (HMBO) algorithm has been modified to handle the problem of obstacle. In this paper, we use this algorithm to simulate the movement of interface regarding the curvature dependent acceleration. The initial curves and the obstacle are considered as closed curves. An obstacle is placed within the initial curve. We examine the curve movement. In the obstacle problem, we obtain that the curve movement does not exceed the obstacle. After touching the obstacle, the curve attaches to it. [ABSTRACT FROM AUTHOR]

As medical image segmentation is the present day challenge and a demand for researchers, several attempts have been made in this field. Fuzzy c-means clustering (FCM) is an algorithm that is affected by noise, thus objective-function is generally initiated with local-spatial information to enhance the robustness. But it’s implementation often results in rise in time complexity, caused by a repetitive computation of the distance separating pixels of local-spatial neighbours and clustering-centres. In order to solve this problem, a Modified FCM (MFCM) method is proposed in this paper. Initially, Image enhancement is performed using Contrast Limited Adaptive Histogram Equalization (CLAHE) then, the local-spatial data of images is subsumed into the MFCM algorithm with introduction of morphological-reconstruction operation (morphological opening operation) to assure detail preservation and noise immunity of images. Thirdly, alteration of membership partition build on the distance between pixels enclosing local-spatial neighbours and centres of clusters is exchanged with local membership filtering. Algorithm is quite efficient as median-membership filtering is capable of improvising partition matrix s ystematically. Hence, we propose that the explained algorithm achieves better results in less time. [ABSTRACT FROM AUTHOR]

In recent years, a 3D laser scanner has increasing attention as one of the modern technologies to digitizing the 3D object surface in manner to obtain a mathematical representation of object’s and surface reconstruction. Due to limitations of a 3D laser scanners, the row point cloud, which are acquired from these techniques, included some undesired information such as noise points and associated huge number of points in point cloud. In the present paper, a new proposed point cloud simplification algorithm for scanned object using 3D laser scanner (Matter and Form) has been adopted in manner to extract the necessary geometric features which represented by a 3D surface curvature. This algorithm based on instantaneous calculation of 3D Gaussian curvature for each data point. The curvature value indicates both quality of the topical calculations and a noise isolating and detection in the point cloud set. A MATLAB environment has been adopted to construct a proposed point cloud simplification algorithm program, this program has been proved using a practically case studies. The results proved the effectiveness of the proposed algorithm in local calculation of 3D curvature and detection and isolate the noise points in point cloud where the percent of data which was ignored as noisy data point were (49.29%) and (57.30%), of total data point number after applying the algorithm for the first and second proposed case studies respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the identification of linear dynamic systems using a new hybrid topology based on firefly algorithm with addition of fixed nonlinearities in the delta domain. The global search is carried out by the bacterial foraging algorithm (BFA), while the firefly algorithm (FA) is used to fine-tune solutions in hybrid topology. For identification in the delta domain, a test system with different polynomial nonlinearities was used. Delta operator presents a unified approach for system modelling, matching continuous and discrete-time results at a high sampling rate. An unbiased signal, contaminated with white noise, was taken up as an input for the estimation of unknown parameters of the modelsas well as coefficients of the polynomial nonlinearities. Not only does the hybrid algorithm outperform the originator heuristics of which it is made up, but it also outshines some of the latest approaches to heuristics recorded in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid firefly algorithm is used to identify dynamic systems considering nonlinearities in the delta domain. In the hybrid method, the search space is diversified globally by the firefly algorithm (FA), while the gray wolf optimizer (GWO) intensifies the solutions through local search. For system identification in the delta domain, a test model with polynomial nonlinearities was considered. Modelling in the delta domain unifies continuous-time systems at higher sampling frequencies with discrete-time results. Pseudo-random binary sequence poisoned with noise with a signal-to-noise ratio of 50 dB, was used as the input signal for the estimation of unknown model parameters and the static nonlinear coefficients. Not only does the hybrid algorithm outperform the parent algorithms of which it is made up, but also better than some standard and latest heuristic approaches published in the literature. [ABSTRACT FROM AUTHOR]

The paper illustrates the design and verification of a model-based diagnostic algorithm for fault detection and isolation of a Diesel Particulate Filter (DPF). The algorithm relies on the Structural Analysis theory, which identifies proper fault indicators (i.e., residuals) upon the knowledge of the mathematical relationships within the system model. The main innovation of the proposed work consists in applying for the first time such theory to a Diesel after-treatment system. To check theoretical fault detectability and isolability, the approach requires only qualitative correlations among the physical variables and parameters representative of the faults that may occur into the system, without the need for quantitative information. Nevertheless, to build the residuals, the form of the equations needs to be defined and properly implemented. The mathematical model developed in this work is a lumped dynamic model that accounts for two main control volumes, one related to the Diesel Oxidation Catalyst (DOC) and the other to the DPF. Mass and energy balance equations are introduced in both volumes. Within the DPF, soot inlet flow is related to the engine exhaust flow, and its oxidation kinetics is modelled by means of Arrhenius-type equation. Four faulty states are introduced into this study with respect to: i) variation in fuel-post injection amount (i.e., injector malfunction), ii) partial or missed fuel combustion in the DOC, iii) partial or missed residual fuel combustion in the DPF, and iv) degradation of the substrate viscosity. Each faulty state is modelled through a dedicated parameter and the overall model is then used to perform faults isolability through Structural Analysis. The theoretical results show that the proposed diagnostic algorithm can detect and correctly isolate all the considered faults. Then, residuals are built and verified in simulated environment on specific cases, proving the capability of the algorithm to successfully detect and isolate the considered faults. [ABSTRACT FROM AUTHOR]

This work investigates the use of predicting algorithms for energy consumption in the tertiary sector. Tertiary sector is fast-growing, in fact it used 23% of Italian electricity in 2000 reaching the 35% in 2016. The focus of this paper is on banker institutions spread across the Italian country. Several algorithms are taken into account and compared in order to find the best solution. The proposed algorithms underwent a training period where the parameter with higher impact on the overall consumption are taken into account. Once the model was trained, the last year of historical data was used to verify the quality of the proposed approach. Final remarks discuss possible algorithm refinement as well as its use for the quick detection and correction of anomalies in the energy use profile curves. [ABSTRACT FROM AUTHOR]

This article is devoted to the actual problem of building virtual network channels that do not depend on the physical topology and data connections. The paper proposes a new method of tunneling differing from the known ones by using a wide range of service protocols as carriers, which allows increasing the overall bandwidth and reliability of the network tunnels. For the implementation of the proposed method in the most common service protocols, potentially redundant packet header fields are defined and the method for including useful data in them is described. The article presents data presentation formats for such packets and describes methods of their formation. Special attention is paid to the agreement algorithm for parameters of the virtual channel, including a set of carrier protocols, as well as diagrams of the client-server interaction of the information system components when establishing a tunnel connection are given. [ABSTRACT FROM AUTHOR]

In this paper we propose a hybrid six-step method with algebraic order the highest possible. The area of application of this newly obtained algorithms is the numerical solution of second order initial and/or boundary-value problems with solutions which present periodical and/or oscillating behavior. [ABSTRACT FROM AUTHOR]

Non-negative matrix factorization with sparseness constraints (SNMF) has become a widely used tool for keeping the main features of the original data as well as reducing the storage space. Most proposed SNMF problems are commonly solved using the multiplicative update rules. However, the popular multiplicative update rules have been shown to give poor convergence. In this paper, a comparative study of two gradient-based algorithms with strong optimization property and its implementation on SNMF are described. The objective of this study is to investigate whether these two gradient-based algorithms will provide an improvement over the multiplicative update rules in terms of convergence and sparseness. We conduct several experiments on the ORL face database. Through the experimental results, we can see that these two gradient-based algorithms do actually achieve stronger optimization property and higher sparseness of the factor matrices. [ABSTRACT FROM AUTHOR]

A Courant-Beltrami penalty function is the square of the absolute value penalty function for inequality constraints, and it penalizes any violations of the constraints function gi (x) ≤ 0 but does not prevent such violations. In this paper, we modified a Courant-Beltrami penalty function method for solving constrained nonlinear programming problems. The results obtained indicate that the modified Courant-Beltrami (MCB) penalty function method provides the approximate optimal solutions and improved objective value using a fixed penalty parameter. Moreover, we investigate and proved MCB penalty function convergence, which has been supported by some numerical test examples. Furthermore, a quasi-newton algorithm was adopted to implement the result on MATLAB2018a via routine function fminunc for unconstrained optimization, the results have shown that the MCB penalty function method converges to a local minimum faster than the existing Courant-Beltrami penalty function method. [ABSTRACT FROM AUTHOR]

In this paper we propose three attacks on the prime power modulus N = prq for r ≥ 2. The first attack is based on the equation eX − NY +(qr + pru)Y = Z for suitable positive integer u. Using continued fraction we show that Y/X can be recovered among the convergents of the continued fraction expansion of e/N. Also we show that the number of such exponents is at least N5r−7/6(r+1)−ε where ε ≥ 0 is arbitrarily small for large N. Hence one can factor the prime power modulus N = prq in polynomial time. For i = 1,…,k, with k ≥ 2 and r ≥ 2 the second and third attacks works when attacks k RSA public keys (Ni, ei) are such that there exist k relations of the form eix−Niyi+(qri+priu)yi=zi or of the shape eixi−Niy+(qri+priu)y=zi where the parameters x, xi, y, yi, zi are suitably small in terms of the prime factors of the moduli. Based on LLL algorithm we show that our attack enable us to simultaneously factor the k prime power RSA moduli Ni. [ABSTRACT FROM AUTHOR]

In the present paper, the initial value problem for the third order partial differential equation with time delay is studied. The first and second order of accuracy difference schemes for the numerical solution of the third order partial differential equation with time delay are presented. The illustrative numerical results are provided. [ABSTRACT FROM AUTHOR]

In the present paper, a space-dependent identification problem for parabolic-elliptic equations is studied. The first and second order of accuracy difference schemes for the numerical solution of a source identification problem for one dimensional parabolic-elliptic equation are presented. Numerical results are given. [ABSTRACT FROM AUTHOR]

This paper discusses a quadcopter swarm collision avoidance algorithm “CA algorithm”. The algorithm builds a repulsive force field around each quadcopter and around the obstacles in the environment. The forces values which affect one quadcopter is proportional to the invers of the distances between it and the other quadcopters in the swarm, it is affected by the speeds too to improve the response of the quadcopters. The algorithm parameters were calculated so the quadcopters avoid the collisions and achieve the wanted reaction characteristics. The algorithm was tested using Matlab, Simulink and the results were discussed. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of external ellipsoidal estimation of the reachable set of the bilinear control system with uncertainties in the system matrix and in the initial states. It is assumed that the unknown initial states of the system belong to a given symmetric star-shaped set of a special type. The matrix of linear terms in the phase velocities of the system is unknown. The dynamic of the system is complicated by the presence of bilinear components in the right-hand side of the system differential equation. In this study, a new class of uncertainties in the parameters of the system matrix is considered. Under such constraints, the dynamical system is nonlinear, and the reachable set loses the property of convexity. The Minkowski gauge functions are used for the description of star-shaped reachable sets. The main result is the algorithm that allows constructing external ellipsoidal estimates of the reachable set for the considered system based on the technique of set-valued estimation. [ABSTRACT FROM AUTHOR]

The construction of multiple partitions in consensus clustering with random initialization and various parameters values of clustering algorithm enable us to measure the stability of objects (points) in a cluster. The procedure to indicate stability is aided by the co-occurrences of pair objects (i,j ) allocated to the same cluster in each partition. In this paper, we propose a voting-merged method - a combination of voting-based method and merging process. Our experiment with simulations and real datasets shows better performance for well-separated clusters and low degree of overlapping. [ABSTRACT FROM AUTHOR]

This paper presents a Proposed Java Algorithm for the Default-Recovery Rates (DRR) Model. The DRR Model is the extension to the Black-Scholes-Merton Model focusing on calculating the default and recovery rates of a firm. Default risk is one of the crucial risks in the risk management area that should be managed effectively. The world financial turmoil today will see more poor firms landing to defaults and bankruptcies. The probabilistic assessment of their financial growth would at least minimize the unfavorable impact. Although there are several efforts, instruments and methods used to manage the risk, it is said to be insufficient. To the best of our knowledge, there has been limited innovation in developing the default risk mathematical model into a java program. Therefore, through this study, default risk is predicted quantitatively using the Proposed Java Algorithm. The DRR Model has been integrated in the form of java algorithm and code. The Proposed Java Algorithm is implemented by calculating the default and recovery rates of a company and hence, predicting its level of default risk. It is found that the default risk is predicted high equivalent to the company poor financial performance. This shows that the default and recovery rates predicted by the DRR Model contain significant information on companies’ performance. In addition, the proposed java Algorithm can be one of the enhancements to the credit risk modeling field in producing a user-friendly application run by a java program. [ABSTRACT FROM AUTHOR]

Modified embedding interpolation and extrapolation model in Lanczos-types algorithms (MEIEMLA) is well-known as a strategy to avoid breakdown in Lanczos-type algorithms by taking advantage of the patterns which persistently exist in the iterates generated by a Lanczos-type algorithm. It is the improved EIEMLA by interpolating all vector solutions S = x1, x2,…,xk generated by a Lanczos-type algorithm, at one time by using PCHIP. Note, the previous EIEMLA interpolates the vector solutions by arranging S such that it contains all of the ith entries of iterates xk, for i = 1,2,…,n, and n is the dimension of the system. As a result, MEIEMLA performed the solutions of several systems of linear equations (SLEs) more efficient than EIEMLA. However, once running the MEIEMLA, it cannot convergent quickly. It is, therefore, important to put MEIEMLA in a restarting framework. In this paper, we combined the MEIEMLA and restarting in order for Lanczos-types algorithms getting rid off breakdown as well as enhancing their stability. Numerical results are presented and compared with other existing restarting strategies in Lanczos algorithms, including with REIEMLA. The results showed that the restarting MEIEMLA (RMEIEMLA) performed better than other existing restarting strategies. [ABSTRACT FROM AUTHOR]