Computational algorithms to simulate the steel continuous casting

Computational simulation is a very powerful tool to analyze industrial processes to reduce operating risks and improve profits from equipment. The present work describes the development of some computational algorithms based on the numerical method to create a simulator for the continuous casting process, which is the most popular method to produce steel products for metallurgical industries. The kinematics of industrial processing was computationally reproduced using subroutines logically programmed. The cast steel by each strand was calculated using an iterative method nested in the main loop. The process was repeated at each time step （？t） to calculate the casting time, simultaneously, the steel billets produced were counted and stored. The subroutines were used for creating a computational representation of a continuous casting plant （CCP） and displaying the simulation of the steel displacement through the CCP. These algorithms have been developed to create a simulator using the programming language C＋＋. Algorithms for computer animation of the continuous casting process were created using a graphical user interface （GUI）. Finally, the simulator functionality was shown and validated by comparing with the industrial information of the steel production of three casters.

SUBSTRUCTURE COMPUTATIONAL ALGORITHM FOR EXACT ANALYTIC METHOD

In[1], the exact analytic method for the solution of differential equation with variable coefficients was suggested and an analytic expression of solution was given by initial parameter algorithm. But to some problems such as the bending, free vibration and buckling of nonhomogeneous long cylinders, it is difficult to obtain their solutions by the initial parameter algorithm on computer. In this paper, the substructure computational algorithm for the exact analytic method is presented through the bending of non-homogeneous long cylindrical shell. This substructure algorithm can he applied to solve the problems which can not he calculated by the initial parameter algorithm on computer. Finally, the problems can he reduced to solving a low order system of algehraic equations like the initial parameter algorithm Numerical examples are given and compared with the initial para-algorithm at the end of the paper, which confirms the correctness of the substructure computational algorithm.

COMPUTATIONAL AND ALGORITHMIC ASPECTS OF CARDINAL SPLINE-WAVELETS

Pascal triangles are formulated for computing the coefficients of the B-spline series representation of the compactly supported spline-wavelets with minimum support and their derivatives.It is shown that with the al- ternating signs removed,all these sequences are totally positive.On the other hand,truncations of the recipro- cal Euler-Frobenius polynomials lead to finite sequences for orthogonal wavelet decompositions.For this pur- pose,sharp estimates are given in terms of the exact reconstruction of these approximate decomposed compo- nents.

Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

Fast Multilevel CVT-Based Adaptive Data Visualization Algorithm

Efficient data visualization techniques are critical for many scientific applications. Centroidal Voronoi tessellation(CVT) based algorithms offer a convenient vehicle for performing image analysis,segmentation and compression while allowing to optimize retained image quality with respect to a given metric.In experimental science with data counts following Poisson distributions,several CVT-based data tessellation algorithms have been recently developed.Although they surpass their predecessors in robustness and quality of reconstructed data,time consumption remains to be an issue due to heavy utilization of the slowly converging Lloyd iteration.This paper discusses one possible approach to accelerating data visualization algorithms.It relies on a multidimensional generalization of the optimization based multilevel algorithm for the numerical computation of the CVTs introduced in[1],where a rigorous proof of its uniform convergence has been presented in 1-dimensional setting.The multidimensional implementation employs barycentric coordinate based interpolation and maximal independent set coarsening procedures.It is shown that when coupled with bin accretion algorithm accounting for the discrete nature of the data,the algorithm outperforms Lloyd-based schemes and preserves uniform convergence with respect to the problem size.Although numerical demonstrations provided are limited to spectroscopy data analysis,the method has a context-independent setup and can potentially deliver significant speedup to other scientific and engineering applications.

Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level.Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples.Here,we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.

An algorithm for computed tomography image reconstruction from limited-view projections

With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.

Application of the Fictitious Domain Method for Navier-Stokes Equations

To apply the fictitious domain method and conduct numericalexperiments, a boundary value problem for an ordinary differential equation is considered. The results of numerical calculations for different valuesof the iterative parameter τ and the small parameter ε are presented. Astudy of the auxiliary problem of the fictitious domain method for NavierStokes equations with continuation into a fictitious subdomain by highercoefficients with a small parameter is carried out. A generalized solutionof the auxiliary problem of the fictitious domain method with continuationby higher coefficients with a small parameter is determined. After all theabove mathematical studies, a computational algorithm has been developedfor the numerical solution of the problem. Two methods were used to solvethe problem numerically. The first variant is the fictitious domain methodassociated with the modification of nonlinear terms in a fictitious subdomain.The model problem shows the effectiveness of using such a modification. Theproposed version of the method is used to solve two problems at once that arisewhile numerically solving systems of Navier-Stokes equations: the problem ofa curved boundary of an arbitrary domain and the problem of absence of aboundary condition for pressure in physical formulation of the internal flowproblem. The main advantage of this method is its universality in developmentof computer programs. The second method used calculation on a uniform gridinside the area. When numerically implementing the solution on a uniformgrid inside the domain, using this method it’s possible to accurately take intoaccount the boundaries of the curved domain and ensure the accuracy of thevalue of the function at the boundaries of the domain. Methodical calculationswere carried out, the results of numerical calculations were obtained. Whenconducting numerical experiments in both cases, quantitative and qualitativeindicators of numerical results coincide.

Two-Dimensional Reconstruction of Heat Transfer in a Flat Flame Furnace through Computer-Based Tomography and Tunable-Diode-Laser Absorption Spectroscopy

To explore the inherent characteristics of combustion-induced heat transfer in a flat flame furnace,a sophisticated hybrid method is introduced by combining a computer-based tomography(CT)-algebraic iterative algorithm and Tunable Diode Laser Absorption Spectroscopy(TDLAS).This technique is used to analyze the distribution of vapor concentration and furnace temperature.It is shown that by using this strategy a variety of details can be obtained,which would otherwise be out of reach.

A fast MPC algorithm for reducing computation burden of MIMO

The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is proposed in this paper to solve this problem, in which real-time values are modulated to bit streams to simplify the multiplication. In addition, manipulated variables in the prediction horizon are deduced to the current control horizon approximately by a recursive relation to decrease the dimension of QR optimization. The simulation results demonstrate the feasibility of this fast algorithm for MIMO systems.

RSMD-repeat searcher and motif detector

The functionality of a gene or a protein depends on codon repeats occurring in it.As a consequence of their vitality in protein function and apparent involvement in causing diseases,an interest in these repeats has developed in recent years.The analysis of genomic and proteomic sequences to identify such repeats requires some algorithmic support from informatics level.Here,we proposed an offline stand-alone toolkit Repeat Searcher and Motif Detector（RSMD）,which uncovers and employs few novel approaches in identification of sequence repeats and motifs to understand their functionality in sequence level and their disease causing tendency.The tool offers various features such as identifying motifs,repeats and identification of disease causing repeats.RSMD was designed to provide an easily understandable graphical user interface（GUI）,for the tool will be predominantly accessed by biologists and various researchers in all platforms of life science.GUI was developed using the scripting language Perl and its graphical module PerlTK.RSMD covers algorithmic foundations of computational biology by combining theory with practice.

AN EFFICIENT FINITE-DIFFERENCE ALGORITHM FOR COMPUTING AXISYMMETRIC TRANSONIC NACELLE FLOW FIELDS

A finite difference method for computing the axisymmetric, transonic flows over a nacelle is presented in this paper. By use of the conservative full-potential equation, body-fitted grid, and the exact boundary conditions, a new AF scheme is constructed according to the criterion of optimum convergence. The proposed scheme has been applied to transonic nacelle flow problems. Computation for several nacelles shows the rapid convergence of this scheme and excellent agreement with the experimental results.

MONTE CARLO SIMULATION ON COMPUTATIONAL FINANCE FOR GRID COMPUTING

Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results.Monte Carlo methods are often used in simulating complex systems.Because of their reliance on repeated computation of random or pseudo-random numbers,these methods are most suited to calculation by a computer and tend to be used when it is infeasible or impossible to compute an exact result with a deterministic algorithm.In finance,Monte Carlo simulation method is used to calculate the value of companies,to evaluate economic investments and financial derivatives.On the other hand,Grid Computing applies heterogeneous computer resources of many geographically disperse computers in a network in order to solve a single problem that requires a great number of computer processing cycles or access to large amounts of data.In this paper,we have developed a simulation based on Monte Carlo method which is applied on grid computing in order to predict through complex calculations the future trends in stock prices.

A Survey on Algorithms for Intelligent Computing and Smart City Applications

With the rapid development of human society, the urbanization of the world’s population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is under excessive pressure, various natural resources and energy are increasingly scarce, and environmental pollution is increasing, etc. However, the original urban model has to be changed to enable people to live in greener and more sustainable cities, thus providing them with a more convenient and comfortable living environment. The new urban framework, the smart city, provides excellent opportunities to meet these challenges,while solving urban problems at the same time. At this stage, many countries are actively responding to calls for smart city development plans. This paper investigates the current stage of the smart city. First, it introduces the background of smart city development and gives a brief definition of the concept of the smart city. Second, it describes the framework of a smart city in accordance with the given definition. Finally, various intelligent algorithms to make cities smarter, along with specific examples, are discussed and analyzed.

Implementation of Computational Electromagnetic on Distributed Systems

Now the new generation of technology could raise the bar for distributedcomputing. It seems to be a trend to solve computational electromagnetic work on a distributedsystem with parallel computing techniques. In this paper, we analyze the parallel characteristics ofthe distributed system and the possibility of setting up a tightly coupled distributed system byusing LAN in our lab . The analysis of the performance of different computational methods, such asFEM, MOM, FDTD and finite difference method, are given. Our work on setting up a distributed systemand the performance of the test bed is also included . At last, we mention the implementation of oneof our computational electromagnetic codes.

A COMPUTATIONAL METHOD FOR FUZZY TIME SERIES FORECASTING BASED ON DIFFERENCE PARAMETERS

Present study proposes a method for fuzzy time series forecasting based on difference parameters.The developed method has been presented in a form of simple computational algorithm.It utilizes various difference parameters being implemented on current state for forecasting the next state values to accommodate the possible vagueness in the data in an efficient way.The developed model has been simulated on the historical student enrollments data of University of Alabama and the obtained forecasted values have been compared with the existing methods to show its superiority.Further,the developed model has also been implemented in forecasting the movement of market prices of share of State Bank of India(SBI)at Bombay Stock Exchange(BSE),India.

A computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory

Although emission spectral tomography （EST） combines emission spectral measurement with optical computed tomography （OCT）, it is difficult to gain transient emission data from a large number of views, therefore, high precision OCT algorithms with few views ought to be studied for EST application. To improve the reconstruction precision in the case of few views, a new computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory （multi-criterion simulated annealing reconstruction technique, MCSART） is proposed. This algorithm can suffice criterion of least squares, criterion of most uniformity, and criterion of most smoothness synchronously. We can get global optimal solution by MCSART algorithm with simulated annealing theory. The simulating experiment result shows that this algorithm is superior to the traditional algorithms under various noises.

Comparative Analysis of Regulatory Motif Discovery Tools for Transcription Factor Binding Sites

In the post-genomic era, identification of specific regulatory motifs or transcription factor binding sites （TFBSs） in non-coding DNA sequences, which is essential to elucidate transcriptional regulatory networks, has emerged as an obstacle that frustrates many researchers. Consequently, numerous motif discovery tools and correlated databases have been applied to solving this problem. However, these existing methods, based on different computational algorithms, show diverse motif prediction efficiency in non-coding DNA sequences. Therefore, understanding the similarities and differences of computational algorithms and enriching the motif discovery literatures are important for users to choose the most appropriate one among the online available tools. Moreover, there still lacks credible criterion to assess motif discovery tools and instructions for researchers to choose the best according to their own projects. Thus integration of the related resources might be a good approach to improve accuracy of the application. Recent studies integrate regulatory motif discovery tools with experimental methods to offer a complementary approach for researchers, and also provide a much-needed model for current researches on transcriptional regulatory networks. Here we present a comparative analysis of regulatory motif discovery tools for TFBSs.

Functional knowledge integration of the design process

Functional knowledge integration is the initial and core phase of a design process. It is the key phase to ensure that the functional requirement of the design product can be appropriately complied with, and its result is also the rudiment of the subsequent detailed design work. If this important phase can be supported by an increasingly distributed resource environment, and be automated such that its completion requires less manual work, the efficiency of the design process would be largely improved and its ability to promote innovation would be enhanced. Therefore, this study involved a detailed analysis of the functional knowledge integration of the design process, as well as the proposal of a corresponding running model. Based on the model, a computational algorithm and an evaluating method were established to automate functional knowledge integration. A corresponding computer program was developed to prove the feasibility of this approach, and it was used to design a solarpowered wiper blade.

Kernelization in Parameterized Computation: A Survey

Parameterized computation is a new method dealing with NP-hard problems, which has attracted a lot of attentions in theoretical computer science. As a practical preprocessing method for NP-hard problems, kernelizaiton in parameterized computation has recently become an active research area. In this paper, we discuss several kernelizaiton techniques, such as crown decomposition, planar graph vertex partition, randomized methods, and kernel lower bounds, which have been used widely in the kernelization of many hard problems.