Minimum Number Of Iterations Research Articles

Elements of the synthesis method for the layout of a front-line aircraft

The article describes a technique that allows to form a planned projection of the aircraft using the minimum number of iterations (refinements based on aerodynamic and weight calculations results). The elements of the technique are described via example of the development (synthesis) of the layout scheme of a front-line aircraft with two engines and internal cargo compartments, performed according to the normal balancing scheme. Synthesis is carried out on the basis of certain predesign parameters, limitations, as well as solutions set by the designer, based on the analysis of parametric ratios and statistical dependencies.

On the complexity of detecting positive eigenvectors of nonlinear cone maps

In recent work with Lins and Nussbaum the first author gave an algorithm that can detect the existence of a positive eigenvector for order-preserving homogeneous maps on the standard positive cone. The main goal of this paper is to determine the minimum number of iterations this algorithm requires. It is known that this number is equal to the illumination number of the unit ball of the variation norm. In this paper we determine its illumination number, and hence provide a sharp lower bound for the running time of the algorithm.

MAPE - An Alternative Fitness Metric for Prony Analysis of Power System Signals

Abstract Phasor Measurement Units have facilitated tracking of oscillations in power system response signals. This has provided an impetus for identifying unstable component modes directly from oscillatory signals. Prony analysis, the earliest method proposed for this purpose, throws up some trivial modes. These not only distract the analyzer but also prolong processing time thereby delaying corrective action. Hence the fitness metric chosen should serve to minimize the number of trivial modes. The conventional fitness metric is Signal-to-Noise Ratio (SNR), which is actually Signal-to- Estimation error Ratio (SER). This paper proposes that Mean Absolute Percentage Error (MAPE) can also serve well as a fitness metric. It is shown through case studies carried out on well-known four-machine power system that there are a few cases where MAPE performs better than SER while in some instances SER works better. This inference is verified even in the presence of measurement noise. Hence a novel fitness metric is proposed combining MAPE with SER. Case studies on simulated signals obtained from New England-power system prove that this novel metric can achieve considerable reduction in processing time. Besides, an exponential binary search has been suggested for determining the optimal model order in minimum number of iterations.

Optimal Path Planning using Hybrid Bat Algorithm and Cuckoo Search

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

Accuracy of SPECT/CT-based lung dose calculation for Holmium-166 hepatic radioembolization before OSEM convergence.

PurposeIn intra‐arterial hepatic radioembolization using Holmium‐166 (166Ho) microspheres, a predicted lung‐absorbed dose of more than 30 Gy is a contraindication for therapy. Therefore, scout imaging by means of quantitative SPECT of the lungs after a low‐dose pretreatment session is essential. Earlier we showed the superiority of Monte Carlo‐based iterative SPECT reconstructions over conventional reconstructions due to its quantitative nature, required for dosimetry, at the cost of substantial computation times. In clinical routine, however, the limited available time between scout imaging and therapy constrains its application. To reduce computation times, we investigated the minimum number of iterations required to guarantee a clinical acceptable accuracy in lung dose estimation using patient and phantom data.Methods 166Ho scout SPECT data (range: 222‐283 MBq) were used from 10 patients. SPECT images were Monte Carlo‐based OSEM reconstructed (effective iterations: 240). Additionally, the 4D XCAT anthropomorphic phantom was used to mimic studies with an injected scout activity of 250 MBq and with varying lung‐absorbed doses ranging from 0.9 to 225 Gy for a therapeutic dosage of 15 GBq. These studies were reconstructed in the same way as the patient data, and were also reconstructed using a clinically available, standard OSEM algorithm for comparison. Lung‐absorbed dose was determined using VOI analysis as a function of iterations.ResultsThe estimated lung‐absorbed dose in nine patients ranged upon MC‐based OSEM convergence from 0 to 0.26 Gy for a therapeutic dosage. One patient had an estimated lung absorbed‐dose for a therapeutic dosage of 20.3 Gy upon MC‐based OSEM convergence, or 18.4 Gy after 40 iterations (−9%). The phantom data showed that the lung‐absorbed dose upon OSEM convergence was underestimated by 15% as compared to the actual simulated lung dose, and the dose after 40 iterations was underestimated by 9% as compared to the dose upon convergence. Both underestimations were irrespective of the magnitude of the lung‐absorbed dose (0.9 to 225 Gy) and thus can be easily corrected for. The quantitative accuracy of the MC‐based OSEM reconstructions (40 iterations, before convergence) outperformed the clinical OSEM reconstruction while estimating the lung dose.ConclusionsThe number of effective iterations necessary for quantitative estimation of the lung dose using MC‐based OSEM can be reduced from 240 to 40. The resulting sixfold reduction in calculation time enables processing of the scout images before therapy administration.

Effects of vertex degrees on the zero-forcing number and propagation time of a graph

In this paper, we present new bounds on the zero-forcing propagation time and zero-forcing number. Zero-forcing is based on iterating the following color change rule. If a vertex is colored and has only one neighbor that is not colored, then that uncolored neighbor becomes colored in the next iteration. The zero-forcing number is the size of a minimum set of vertices such that repeated iterations of the color change rule will color the entire graph. The zero-forcing propagation time is the minimum number of iterations required to color the entire graph from a minimum zero-forcing set. We present new bounds on the zero-forcing propagation time for cubic graphs. These bounds reduce the known bounds that are based on the minimum degree of the graph. We introduce zero-forcing forts, a special type of subgraph that must contain a vertex in any minimum zero-forcing set, and use these forts to bound the zero-forcing number of a graph. We also show that the zero-forcing number of a graph is bounded from below by its branchwidth.

A Precise Algorithm of Memristor Switching to a State with Preset Resistance

An algorithm of memristor switching with high precision to a state with preset resistance has been developed based on the application of voltage pulses with smoothly increasing amplitude and the duration varying randomly within preset limits. It is shown that the proposed algorithm can be implemented in memristor structures based on (Co40Fe40B20) x (LiNbO3)100–x nanocomposites with x ≈ 10 at. %. Optimum parameters are selected for the algorithm operation with a minimum number of iterations that allows the accuracy of resistance setting to be no worse than 0.5%. The obtained results can be used in the creation of neuromorphic systems.

Load flow analysis using generalised Hopfield neural network

This study proposes a generalised Hopfield neural network (GHNN) for solving non-linear load flow equations. The proposed method was formulated with appropriate energy function for performing load flow analysis of n -bus system. The intended method has the advantages of simple to use, more general application, faster convergence and better optimal solution over the conventional method of load flow using Newton-Raphson (NR) technique. The proposed method of GHNN has been used to solve the power flow equation by calculating the power mismatches and this constraint is used to formulate the energy function of Hopfield neural network (HNN). This energy function is used to derive the weights and bias values of the network. The optimal solution can be found, based on the minimisation of the energy function of continuous HNN. The suggested method was tested in a typical 3-bus and 5-bus power system. The mathematical equation of the proposed method was coded using Matlab/R2014a software. The simulation results obtained have shown that the proposed method is more efficient than NR method in terms of reduction in computational complexity and convergence time with minimum number of iterations.

Прецизионный алгоритм переключения мемристора в состояние с заданным сопротивлением

AbstractAn algorithm of memristor switching with high precision to a state with preset resistance has been developed based on the application of voltage pulses with smoothly increasing amplitude and the duration varying randomly within preset limits. It is shown that the proposed algorithm can be implemented in memristor structures based on (Co_40Fe_40B_20)_ x (LiNbO_3)_100– x nanocomposites with x ≈ 10 at. %. Optimum parameters are selected for the algorithm operation with a minimum number of iterations that allows the accuracy of resistance setting to be no worse than 0.5%. The obtained results can be used in the creation of neuromorphic systems.

Anti-continuous collisions user-based unpredictable iterative password salted hash encryption

Recently, providing services remotely over a network to enhance functionality and usability has become a trend. Applications regularly contain sensitive and personal information that should be managed and controlled by authorised persons. Thus, keeping the password security is important to prevent unauthorised users from impersonating other legal users. Hashing the password is one of the safety methods and means of preventing attacks. In this study, we proposed an iterative salted hash encryption mechanism named anti-continuous collision salted hash encryption (ACCSHE) that uses cryptographically secure pseudo random number generator (CSPRNG) and hashed with password data by secured hashing algorithm (SHA-256). The outcome manifests a flexibility of changing the results by controlling several key features, such as salt used, the range of the hashing process iterations or the minimum number of iterations without affecting the system's usability. The system efficiently prevented continues collision issue and effective processing time cost for an attacker.

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods.

Modeling of Step Voltage Regulators in Multiphase Load Flow Solution of Distribution Systems Using Newton's Method and Augmented Nodal Analysis

The increasing complexity of distribution systems requires general, efficient, and large-scale capable methods. This paper proposes a new technique to find the tap positions of step voltage regulators in multiphase load flow solvers in a direct and efficient manner. This is achieved by applying Newton method to the system of equations obtained by using the concept of augmented matrix formulation and adding the constraint equations. This approach allows employing the regulator equations directly. The regulators are modeled by taking into account the line drop compensator circuit with its settings, i.e., the desired voltage level, bandwidth, and R′ and X′ settings, which represent the scaled impedance parameters of the distribution feeder between the regulator and the load center at which the voltage is controlled. The limits of the regulators such as minimum and maximum tap positions are also accounted for. The proposed technique represents a voltage regulator with transformer equations using an augmented matrix formulation. The mismatch equations are developed using the desired voltage setting in constraint equations with transformation ratio of the transformer being the unknown variable. The Jacobian matrix, which is constructed using the augmented matrix formulation, is expanded to hold the constraint equations of voltage regulators. The proposed new method is tested on a variety of test circuits including the large-scale IEEE 8500 Node Test Feeder, and the minimum number of iterations reported in the literature is achieved as presented in this paper.

Modified simultaneous iterative reconstruction technique for fast, high‐quality CT reconstruction

Recent advances in computational power have made it possible to use iterative reconstruction (IR) algorithms in clinics for computed tomographic (CT) imaging. Many researchers prefer IR methods to analytical methods because they reduce radiation, image noise, and artefacts. Simultaneous Iterative Reconstruction Technique (SIRT) reduces the number of views needed for CT reconstruction. However, reconstructed images include ray artefacts that can make diagnosis difficult. This study proposes a modified IR algorithm for fast, high-quality CT reconstruction. The modified method incorporates geometric non-linear diffusion in the reconstruction estimate to minimise ray artefacts. This method also converges the algorithms into global minima much faster than other methods, using the minimum number of iterations. To meet the high computational demand of improved IR algorithms, a graphics processing unit was used in this study. The authors expect that the proposed technique can be used to reconstruct high-quality CT images faster and with minimal iterations.

An FPGA implementation of novel smart antenna algorithm in tracking systems for smart cities

The area of digital signal processing (DSP) faces a great challenge in suppressing the noise and interference in the transmitting signal. A huge number of applications are in need of such control methods for good audio communication, etc., and the most widespread method for achieving this is through COordinate Rotation DIgital Computer (CORDIC) based on QRD-RLS. However, this method requires several iterations for its calculations, and therefore in order to reduce the complexity of the calculations and for faster calculation with a minimum number of iterations, a modified CORDIC based on the QRD-RLS algorithm for the purpose of beamforming is proposed in this paper and named mixed scaling rotation coordinate rotation digital computer (MSR-CORDIC). In this work, beamforming and direction of arrival (DOA) estimation will be achieved using the MSR-CORDIC and MUSIC algorithms respectively. The proposed algorithm is developed using Verilog HDL and implemented using the Xilinx field-programmable gate array (FPGA). In all cases, the proposed algorithm shows remarkable improvement compared with the conventional SCC (space code correlator) beamforming algorithm.

Application of Machine Learning for Optimization of 3-D Integrated Circuits and Systems

The 3-D integration helps improve performance and density of electronic systems. However, since electrical and thermal performance for 3-D integration is related to each other, their codesign is required. Machine learning, a promising approach in artificial intelligence, has recently shown promise for addressing engineering optimization problems. In this paper, we apply machine learning for the optimization of 3-D integrated systems where the electrical performance and thermal performance need to be analyzed together for maximizing performance. In such systems, modeling can be challenging due to the multiscale geometries involved, which increases computation time per iteration. In this paper, we show that machine learning can be applied to such systems where multiple parameters can be optimized to achieve the desired performance using the minimum number of iterations. These results have been compared with other promising optimization methods in this paper. The results show that on an average, 4.4%, 31.1%, and 6.9% improvement in temperature gradient, CPU time, and skew are possible using machine learning, as compared with other methods.

Zero forcing propagation time on oriented graphs

Zero forcing is an iterative coloring procedure on a graph that starts by initially coloring vertices white and blue and then repeatedly applies the following rule: if any blue vertex has a unique (out-)neighbor that is colored white, then that neighbor is forced to change color from white to blue. An initial set of blue vertices that can force the entire graph to blue is called a zero forcing set. In this paper we consider the minimum number of iterations needed for this color change rule to color all of the vertices blue, also known as the propagation time, for oriented graphs. We produce oriented graphs with both high and low propagation times, consider the possible propagation times for the orientations of a fixed graph, and look at balancing the size of a zero forcing set and the propagation time.

Cost-effective task scheduling using hybrid approach in cloud

Cloud computing is the most recent computing paradigm where IT services are provided and delivered over the internet on demand. With increasing demand for cloud services models, some new challenges in cloud computing are also gaining importance. Increasing energy concern has not even spared cloud environment for considering energy conscious solutions. The scheduling problem for cloud environment has a lot of awareness as the cloud subscriber's tasks could be mapped to the available resources to achieve better results. Traditional heuristics are popularly used to obtain schedules for given tasks and resources. Best efforts results from these heuristics depend upon the tasks and resources at disposal. Meta-heuristic-based scheduling solutions are iteration-based solutions. Meta-heuristic performs search over solution space and converges in minimum number of iterations. A novel heuristic Nearest Neighbour (NN) and a hybrid variant of Particle Swarm Optimisation (PSO) as Nearest Neighbour Cost-Aware PSO (NNCA_PSO) have been proposed and implemented in this paper. Performance of NNCA_PSO has been evaluated for various parameters like makespan, energy consumption, utilisation and economic efficiency. NNCA_PSO has been proved to be stable like PSO and finds near-optimal solutions.

Cost-effective task scheduling using hybrid approach in cloud

Cloud computing is the most recent computing paradigm where IT services are provided and delivered over the internet on demand. With increasing demand for cloud services models, some new challenges in cloud computing are also gaining importance. Increasing energy concern has not even spared cloud environment for considering energy conscious solutions. The scheduling problem for cloud environment has a lot of awareness as the cloud subscriber's tasks could be mapped to the available resources to achieve better results. Traditional heuristics are popularly used to obtain schedules for given tasks and resources. Best efforts results from these heuristics depend upon the tasks and resources at disposal. Meta-heuristic-based scheduling solutions are iteration-based solutions. Meta-heuristic performs search over solution space and converges in minimum number of iterations. A novel heuristic Nearest Neighbour (NN) and a hybrid variant of Particle Swarm Optimisation (PSO) as Nearest Neighbour Cost-Aware PSO (NNCA_PSO) have been proposed and implemented in this paper. Performance of NNCA_PSO has been evaluated for various parameters like makespan, energy consumption, utilisation and economic efficiency. NNCA_PSO has been proved to be stable like PSO and finds near-optimal solutions.

An Optimal Cooling Schedule Using a Simulated Annealing Based Approach

Simulated annealing (SA) has been a very useful stochastic method for solving problems of multidimensional global optimization that ensures convergence to a global optimum. This paper proposes a variable cooling factor (VCF) model for simulated annealing schedule as a new cooling scheme to determine an optimal annealing algorithm called the Powell-simulated annealing (PSA) algorithm. The PSA algorithm is aimed at speeding up the annealing process and also finding the global minima of test functions of several variables without calculating their derivatives. It has been applied and compared with the SA algorithm and Nelder and Mead Simplex (NMS) methods on Rosenbrock valleys in 2 dimensions and multiminima functions in 3, 4 and 8 dimensions. The PSA algorithm proves to be more reliable and always able to find the optimum or a point very close to it with minimal number of iterations and computational time. The VCF compares favourably with the Lundy and Mees, linear, exponential and geometric cooling schemes based on their relative cooling rates. The PSA algorithm has also been programmed to run on android smartphone systems (ASS) that facilitates the computation of combinatorial optimization problems.

Fuzzy C-means Clustering with Temporal-based Membership Function

Objective: In this paper, a method is proposed to create clusters depending on temporal information. Despite its popularity, the FCM algorithm does not utilize temporal information in creating clusters, hence affecting the accuracy of clustering. This paper presents an improved Fuzzy C-means algorithm that incorporates temporal information into the membership function used for clustering. Methods: The proposed FCM algorithm employs temporal neighbouring of data points as the base of clustering. In order to evaluate the algorithm, experimental analysis was performed on three multi-labelled datasets, including a clinical free text (medical), textual email messages (Enron), and Bibtex. Finding: The experimental results show that the proposed function contributes a smaller value of objective function while using a minimum number of iterations. Application: The proposed work will benefit data mining in various domains such as information retrieval, healthcare, business management and many others. This is due to its ability in grouping data- points that are not mutually exclusive.