Single Iteration Research Articles

New Hybrid EC-Promethee Method with Multiple Iterations of Random Weight Ranges: Applied to the Choice of Policing Strategies

The decision-making process is part of everyday life for people and organizations. When modeling the solutions to problems, just as important as the choice of criteria and alternatives is the definition of the weights of the criteria. This study will present a new hybrid method for weighting criteria. The technique combines the ENTROPY and CRITIC methods with the PROMETHE method to create EC-PROMETHEE. The innovation consists of using a weight range per criterion. The construction of a weight range per criterion preserves the characteristics of each technique. Each weight range includes lower and upper limits, which combine to generate random numbers, producing “t” sets of weights per criterion, allowing “t” final rankings to be obtained. The alternatives receive a value corresponding to their position with each ranking generated. At the end of the process, they are ranked in descending order, thus obtaining the final ranking. The method was applied to the decision support problem of choosing policing strategies to reduce crime. The model used a decision matrix with twenty criteria and fourteen alternatives evaluated in seven different scenarios. The results obtained after 10,000 iterations proved consistent, allowing the decision maker to see how each alternative behaved according to the weights used. The practical implication observed concerning traditional models, where a single final ranking is generated for a single set of weights, is the reversal of positions after “t” iterations compared to a single iteration. The method allows managers to make decisions with reduced uncertainty, improving the quality of their decisions. In future research, we propose creating a web tool to make this method easier to use, and propose other tools are produced in Python and R.

Structural Time Series Model using Hamiltonian Monte Carlo for Rice Price

Although forecasts of future events are simply uncertain, predicting is one of the most important aspects of future planning. Accurate rice price predictions tend to be helpful for wholesalers, producers, and farmers to develop plans and strategies to reduce the risks that can be faced. Structural time series models are the most plausible alternative for long-term forecasting. This paper proposes an alternate method for modeling average rice prices using structural time series along with Bayesian parameter inference via Hamiltonian Monte Carlo (HMC). The model has been built using the monthly average wholesale rice price from January 2010 to December 2019. For working out both structural time series and HMC, the TensorFlow Probability Library was used. Linear trend, seasonal, and autoregressive components were combined as an additive model to the structural time model. The proposed Hamiltonian parameter produces an optimal acceptance rate. Their trace plot was used to diagnose the convergence of their chain. One of the predictive accuracy of models was assessed using the mean absolute percent error (MAPE). Through both single and multiple chain iterations, the prediction accuracy of a year-ahead is highly accurate, with MAPE less than 2%. Long-term iteration draws during Hamiltonian Monte Carlo should be considered when attempting to achieve more convergence.

Исследование влияния компонентов задачи оптимального управления на ее частоту для четырехногого шагающего робота

There exist a large number of classes of the walking robots differing in the number of limbs, principles and modes of motion and control. Each class of robots has its own advantages and disadvantages depending on the tasks to be solved. A quadruple walking robot is considered capable of moving over the rough terrain. Its advantages over the biped systems include higher stability, and over the six- and eight-legged systems - simpler control due to the smaller number of limbs, and the possibility of using animals as the prototypes. One of the important and not completely solved problems is development of approaches to control a quadruple walking robot, as well as establishing the influence of computational complexity of the control system separate elements on its overall operation frequency. Such robot control system is represented in the form of three modules: inverse kinematics, predictive control and model parameters calculation. Each module requires solving a certain problem. A study was conducted on the time distribution between the control system modules in a single iteration. Average time distribution values and their standard deviations were determined for the two robot motion modes: standing and walking. Results of the numerical experiments established that the standard deviations were not depending on the device motion mode, which indicated independence of the computational load created by the control system on the robot motion mode.

Robust Linear Decentralized Tracking of a Time-Varying Sparse Parameter Relying on Imperfect CSI

Robust linear decentralized tracking of a time varying sparse parameter is studied in a multiple-input multiple-output (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. Initially, assuming perfect CSI availability, a novel sparse Bayesian learning-based Kalman filtering (SBL-KF) framework is developed in order to track the time varying sparse parameter. Subsequently, an optimization problem is formulated to minimize the mean square error (MSE) in each time slot, followed by the design of a fast block coordinate descent (FBCD)-based iterative algorithm. A unique aspect of the proposed technique is that it requires only a single iteration per time slot to obtain the transmit precoder (TPC) matrices for all the sensor nodes (SNs) and the receiver combiner (RC) matrix for the fusion center (FC) in an online fashion. The recursive Bayesian Cramer Rao bound (BCRB) is also derived for benchmarking the performance of the proposed linear decentralized estimation (LDE) scheme. Furthermore, for considering a practical scenario having CSI uncertainty, a robust SBL-KF (RSBL-KF) is derived for tracking the unknown parameter vector of interest followed by the conception of a robust transceiver design. Our simulation results show that the schemes designed outperform both the traditional sparsity-agnostic KF and the state-of-the-art sparse reconstruction methods. Furthermore, as compared to the uncertainty-agnostic design, the robust transceiver architecture conceived is shown to provide improved estimation performance, making it eminently suitable for practical applications.

A New Extension of Optimal Auxiliary Function Method to Fractional Non-Linear Coupled ITO System and Time Fractional Non-Linear KDV System

In this article, we investigate the utilization of Riemann–Liouville’s fractional integral and the Caputo derivative in the application of the Optimal Auxiliary Function Method (OAFM). The extended OAFM is employed to analyze fractional non-linear coupled ITO systems and non-linear KDV systems, which feature equations of a fractional order in time. We compare the results obtained for the ITO system with those derived from the Homotopy Perturbation Method (HPM) and the New Iterative Method (NIM), and for the KDV system with the Laplace Adomian Decomposition Method (LADM). OAFM demonstrates remarkable convergence with a single iteration, rendering it highly effective. In contrast to other existing analytical approaches, OAFM emerges as a dependable and efficient methodology, delivering high-precision solutions for intricate problems while saving both computational resources and time. Our results indicate superior accuracy with OAFM in comparison to HPM, NIM, and LADM. Additionally, we enhance the accuracy of OAFM through the introduction of supplementary auxiliary functions.

Maximum Wireless Power Transmission Using Real-Time Single Iteration Adaptive Impedance Matching

Wireless power transfer (WPT) systems’ efficiency is significantly impacted by non-monotonic variations in the coupling coefficient. For very short distances or strong-coupling cases, the WPT efficiency is minimal at the natural resonant frequency, with two peaks around this frequency, known as the frequency splitting phenomenon. On the other hand, WPT capability decreases for long distances or weak coupling cases. Therefore, adaptive matching is required for WPT systems with varying distances, like wireless charging systems for electric vehicles (EVs). This paper first presents a detailed analysis of the frequency splitting phenomenon by studying the root locations of the WPT system’s transfer function. Then, a real-time fixed-frequency adaptive impedance matching (IM) method is proposed, in which the amplitude and phase of the input impedance is estimated using the average active power, the average reactive power, and the amplitude of input voltage. Unlike traditional search-and-find techniques, the proposed method calculates the optimal IM network parameters only in a single iteration, which improves the convergent speed. A scaled-down 20-Watt prototype controlled by the TMSF2812 is fabricated and used to validate the effectiveness of the proposed method over a wide range of coil-to-coil distances.

An Adaptive Sparse Channel Estimation Algorithm for OFDM Systems Based on Distributed Compressive Sensing

The current greedy iterative pursuit algorithms for sparse channel estimation in an orthogonal frequency division multiplexing (OFDM) system based on compressive sensing (CS) or distributed CS (DCS) have the disadvantages of relying on channel priori information as halting condition and having a low support searching efficiency. Under DCS framework, this letter proposes a unique halting condition for greedy algorithms by exploiting the delay correlation between adjacent symbol channels. Additionally, we present a segmented pruning strategy that supports to select multiple atoms in a single iteration to improve the support searching efficiency. Simulation results show that our algorithm can achieve more robust sparsity-adaptive channel estimation with reduced computational complexity compared to the traditional methods.

Investigating nonlinear forced vibration of functionally graded nanobeam based on the nonlocal strain gradient theory considering mechanical impact, electromagnetic actuator, thickness effect and nonlinear foundation

The present study proposes an investigation to nonlinear vibration response of a functionally graded (FG) nanobeam based on nonlocal strain gradient theory (NSGT) taking into consideration the longitudinal magnetic field and the thickness effect. The nonlinear terms from the governing equation are associated with the curvature of the nanobeam, the nonlinear foundation, the mean axial extension of the nanobeam and electromagnetic actuator for pinned-pinned nanobeam and the effect of discontinuities marked by the presence of the Dirac function (mechanical impact).The governing differential equation of motion and the boundary conditions are modeled within the framework of a simple supported Euler-Bernoulli nanobeam which accounts for the nonlinear von-Kármán strain and using so-called Hamilton's principle. To truncate the continuous system having an infinite degree of freedom, the Galerkin-Bubnov procedure is applied. The resulting nonlinear differential equation is studied by Optimal Auxiliary Functions Method. An explicit analytical solution is proposed for a complex problem near the primary resonance. Our proper procedure is a powerful tool to solve a nonlinear problem without presence of any small parameters. The main characteristic of our technique consists in the existence of some auxiliary functions derived from the expressions of the solution formed for initial linear equation and the form of nonlinear terms calculated for the solution of the linear equation. Also, the presence of some convergence-control parameters assures the rapid convergence of the solutions after only a single iteration. These parameters can be evaluated by means of some rigorous mathematical procedures. We have a large freedom to select the auxiliary functions and the number of convergence-control parameters. The proposed approach is proved to be very accurate, simple, and easy to implement for complicated nonlinear problems. The potential energy is analyzed in detail for different values of the parameters. The local stability of motion near the primary resonance is established using homotopy perturbation method.

Clustered Scheduling and Communication Pipelining for Efficient Resource Management of Wireless Federated Learning

This paper proposes using communication pipelining to enhance the convergence speed of federated learning in mobile edge computing applications. Due to limited wireless sub-channels, a subset of the total clients is scheduled in each iteration of federated learning algorithms. On the other hand, the scheduled clients wait for the slowest client to finish its computation. We propose to first cluster the clients based on the time they need per iteration to compute the local gradients of the federated learning model. Then, we schedule a mixture of clients from all clusters to send their local updates in a pipelined manner. In this way, instead of just waiting for the slower clients to finish their computations, more clients can participate in each iteration. While the time duration of a single iteration does not change, the proposed method can significantly reduce the number of required iterations to achieve a target accuracy. We provide a generic formulation for optimal client clustering under different settings, and we analytically derive an efficient algorithm for obtaining the optimal solution. We also provide numerical results to demonstrate the gains of the proposed method for different datasets and deep learning architectures.

IoT Resource Allocation and Optimization Using Improved Reptile Search Algorithm

The Internet of Things (IoT) is a dispersed network system that connects the world through the Internet. The architecture of IoT consists of more gateways and resources which cannot be allocated in a manual process. The allocation of resources in IoT is a challenging process due to the higher consumption of energy and high latency rate. To overcome the challenges in existing works, this research introduced an Improved Reptile Search Algorithm (IRSA) to solve the optimization problem which occurs during the time of allocation resources among IoT networks. IRSA employs the methodology of levy flight and cross-over to update the candidate position and enhance the search speed in a single iteration. The proposed method consumes less energy and has low latency during data transmission from User equipment (UE) to the base station.IRSA has been compared with the existing Scalable Resource Allocation Framework (SRAF) and Improved Chaotic Firefly Algorithm (ICFA). The obtained experimental results show that the proposed IRSA attained better performance with an allocation rate of 96.40% which is comparatively higher than SRAF and ICFA with 92.40% and 91.67% respectively.

Logistic Regression Matching Pursuit algorithm for text classification

Text classification is a challenging problem due to the high dimensionality of the text, which can limit classification performance. The orthogonal matching pursuit (OMP) algorithm is one of the most popular sparse recovery algorithms. An OMP based text classification algorithm, called the Logistic-OMP algorithm, was recently proposed by Skianis et al.. Simulation tests indicate that Logistic-OMP has excellent performance in text dimensionality reduction. This paper optimizes the Logistic-OMP algorithm, and proposes a new text classification algorithm called the Logistic Regression Matching Pursuit (LRMP) algorithm. The LRMP algorithm defines a new loss function and residual update function. It requires only one iteration to solve the negative log likelihood minimization problem, and its classification performance is guaranteed by the strong Wolfe condition, which makes it optimizes the classification accuracy while significantly speeding up the training speed. Simulation tests on topic classification and sentiment analysis from 20Newsgroups, Amazon product reviews, and movie reviews datasets show that the LRMP algorithm has a shorter computation time of a single iteration than the Logistic-OMP algorithm, with a total training time of 8.08%–21.16% shorter than that of the Logistic-OMP algorithm, and the memory usage is 3.20%–6.21% lower than that of the Logistic-OMP algorithm. Furthermore, the average Accuracy and F1-Score of the LRMP algorithm are improved by 1.61%–26.55% and 1.83%–25.46%, respectively, compared with the benchmark classifier. Compared with the advanced classifiers (including Logistic-OMP), the average Accuracy and F1-Score of the LRMP algorithm are improved by 0.46%–8.97% and 0.57%–9.40%, respectively.

A Quantum Parallel Markov Chain Monte Carlo

We propose a novel hybrid quantum computing strategy for parallel MCMC algorithms that generate multiple proposals at each step. This strategy makes the rate-limiting step within parallel MCMC amenable to quantum parallelization by using the Gumbel-max trick to turn the generalized accept-reject step into a discrete optimization problem. When combined with new insights from the parallel MCMC literature, such an approach allows us to embed target density evaluations within a well-known extension of Grover’s quantum search algorithm. Letting P denote the number of proposals in a single MCMC iteration, the combined strategy reduces the number of target evaluations required from O ( P ) to O ( P ) . In the following, we review the rudiments of quantum computing, quantum search and the Gumbel-max trick in order to elucidate their combination for as wide a readership as possible.

Full Waveform Inversion-Based Ultrasound Computed Tomography Acceleration Using Two-Dimensional Convolutional Neural Networks

AbstractUltrasound computed tomography (USCT) shows great promise in nondestructive evaluation and medical imaging due to its ability to quickly scan and collect data from a region of interest. However, existing approaches are a tradeoff between the accuracy of the prediction and the speed at which the data can be analyzed, and processing the collected data into a meaningful image requires both time and computational resources. We propose to develop convolutional neural networks (CNNs) to accelerate and enhance the inversion results to reveal underlying structures or abnormalities that may be located within the region of interest. For training, the ultrasonic signals were first processed using the full waveform inversion (FWI) technique for only a single iteration; the resulting image and the corresponding true model were used as the input and output, respectively. The proposed machine learning approach is based on implementing two-dimensional CNNs to find an approximate solution to the inverse problem of a partial differential equation-based model reconstruction. To alleviate the time-consuming and computationally intensive data generation process, a high-performance computing-based framework has been developed to generate the training data in parallel. At the inference stage, the acquired signals will be first processed by FWI for a single iteration; then the resulting image will be processed by a pre-trained CNN to instantaneously generate the final output image. The results showed that once trained, the CNNs can quickly generate the predicted wave speed distributions with significantly enhanced speed and accuracy.

Distributed Model Predictive Control for Heterogeneous Vehicle Platoon With Inter-Vehicular Spacing Constraints

This paper proposes a distributed control scheme for a platoon of heterogeneous vehicles based on the mechanism of model predictive control (MPC). The platoon composes of a group of vehicles interacting with each other via inter-vehicular spacing constraints, to avoid collision and reduce communication latency, and aims to make multiple vehicles driving on the same lane safely with a close range and the same velocity. Each vehicle is subject to both state constraints and input constraints, communicates only with neighboring vehicles, and may not know a priori desired setpoint. We divide the computation of control inputs into several local optimization problems based on each vehicle’s local information. To compute the control input of each vehicle based on local information, a distributed computing method must be adopted and thus the coupled constraint is required to be decoupled. This is achieved by introducing the reference state trajectories from neighboring vehicles for each vehicle and by employing the interactive structure of computing local problems of vehicles with odd indices and even indices. It is shown that the feasibility of MPC optimization problems is achieved at all time steps based on tailored terminal inequality constraints, and the asymptotic stability of each vehicle to the desired trajectory is guaranteed even under a single iteration between vehicles at each time. Finally, a comparison simulation is conducted to demonstrate the effectiveness of the proposed distributed MPC method for heterogeneous vehicle control with respect to normal and extreme scenarios.

An agnostic approach to ancient landscapes

We argue that the phenomenological or ‘agnostic’ approach to evolutionary systems advocated by Thomas Henry Huxley is applicable in anthropological archaeology and show how agnosticism helps defuse the tension between humanists, natural philosophers and natural historians in integrative research. We deploy problem-framing methods from policy-relevant research in a palaeoanthropological context, developing a model of complex (scale-dependent, irreversible) causality and applying it to the problem of human-landscape interaction and primate foot anatomy. We illustrate this process with a single iteration of the ‘project cycle’ focussed on human-landscape interaction. Modern humans are co-operative resilience feeders, exploiting complex causality by perturbing stable, unproductive landscapes and feeding on the fluxes of energy and resources released as they spring back. Is it possible that this resiliencefeeding is older than Homo sapiens?

Point Cloud Registration Methods for Long-Span Bridge Spatial Deformation Monitoring Using Terrestrial Laser Scanning

In recent years, efforts have been devoted to utilizing terrestrial laser scanning for bridge spatial performance inspection, but they are still restricted to small or medium-span bridges, like some historical heritages. Due to the large-scale dimensional features of long-span bridges, applications of 3D point cloud techniques still remain challenging, such as the extra-long scan range and extreme-small incidence angle when scanning a bridge with a span over 1000 m. Moreover, rare attempts can be found for the performance evaluation of point cloud registration methods for long-span bridges as well, which is a critical basis for further spatial deformation recognition on the point cloud data. Hence, in this study, a cross-evaluation of three iterative closest point (ICP) registration methods is conducted for long-span suspension bridges, namely, traditional ICP, kd-tree-based ICP, and feature point-based ICP algorithms. We conducted field laser scanning on the Ma’anshan Yangtze Bridge, an 1880 m long suspension bridge located in China. The results show that the feature point-based ICP algorithm outperforms the other two in terms of convergence rate and execution time for a single iteration due to the smaller number of registration points compared to the other two algorithms. Moreover, it also gives more precise values in terms of bridge tower spatial deformation identification. Meanwhile, due to efficient data organization, the kd-tree-based ICP algorithm takes less time for a single iteration than the traditional ICP algorithm. Finally, two suggestions for algorithm improvement in terms of efficiency optimization and accuracy improvement of long-span bridge deformation analysis are proposed based on the assessment results.

Stochastically Controlled Compositional Gradient for Composition Problems.

We consider composition problems of the form (1/n)∑i = 1n Fi (1/m)∑j = 1m Gj(x) , which are important for machine learning. Although gradient descent and stochastic gradient descent are straightforward solutions, the essential computation of G (x) = (1/m)∑j = 1mGj(x) in each single iteration is expensive, let alone for large m . In this article, we devise a stochastically controlled compositional gradient algorithm. Specifically, we introduce two variants of stochastically controlled technique to estimate the inner function G(x) and the gradient of the objective function, respectively. The computational cost is largely reduced. However, the natural needs of two stochastic subsets D1 and D2 form direct barriers to guarantee the convergence of the algorithm, especially the theoretical proof of the convergence. To this end, we present a general convergence analysis by proving | D1|=min{1/ϵ,m} and | D2|=min{1/ϵ,n } , through which the proposed method significantly improve composition algorithms under low target accuracy (i.e., 1/ϵ << m or n ) in both strongly convex and nonconvex settings. Comprehensive experiments demonstrate the superiority of the proposed method over existing methods.

Concatenated turbo polar-convolutional codes based on soft cancellation algorithm

This paper presents the Parallel and Serial Concatenated Turbo Polar-Convolutional Codes (CTPCCs) to improve the performance of polar codes in the finite code length regime. Both proposed concatenated codes use the Systematic Polar Code (SPC) and Recursive Systematic Convolutional (RSC) code as constituent codes. The iterative decoding algorithms of the proposed systems consist of a Soft Cancellation (SCAN) decoder with a single internal iteration for the SPC and SOVA or LogMap decoder for the RSC code. The simulation results show that the performance of the Parallel Concatenated Turbo Polar-Convolutional Code (PCTPCC) at low signal-to-noise ratios (SNRs) is better than that of the Serial Concatenated Turbo Polar-Convolutional Code (SCTPCC) and also better than the performance of other Turbo Polar Codes (TPCs). At BER=10−2, the proposed PCTPCC with SOVA and LogMap algorithms has about 0.3 dB and 0.25 dB performance gains over the SCTPCC models, respectively. In the high SNR regions, the performance of the SCTPCC with the LogMap algorithm significantly outperforms that of other models. At BER=10−6, the proposed SCTPCC models with SOVA and LogMap algorithms outperform the PCTPCC model by gains of 0.3 dB and 0.25 dB, respectively. The PCTPCC model with the SOVA decoder is superior to other models in terms of complexity.

Application of Iterative Three-Dimensional Laplace Transform Method for 2-Dimensional Nonlinear Klein-Gordon Equation

In the present study, the exact analytical solutions of the 2-dimensional nonlinear Klein-Gordon equation (NLKGE) is investigated using the 3-dimensional Laplace transform method in conjunction with the Daftardar-Gejji and Jafari Method (Iterative method). Through this method, the linear part of the problem is solved by using the 3-dimensional Laplace transform method, while the noise terms from the nonlinear part of the equation disappear through a successive iteration process of the Daftardar-Gejji and Jafari Method (DJM), where a single iteration gives the exact solution to the problem. Three test modeling problems from mathematical physics nonlinear Klein-Gordon equations are taken to confirm the performance and efficiency of the presented technique. For each illustrative example, the convergence of the novel iterative approach is shown. The findings also suggest that the proposed method could be applied to other types of nonlinear partial differential equation systems. HIGHLIGHTS Partial differential equations play a crucial role in the formulation of fundamental laws of nature and the mathematical analysis in different fields of studies The Klein-Gordon equation (KGE) is a nonlinear hyperbolic class of partial differential equations that occur in relativistic quantum mechanics and field theory, both of which are crucial to high-energy physicists The 3-dimensional Laplace transform method provides rapid convergence of the exact solution without any restrictive assumptions about the solution for linear differential equations The Laplace Transform method is often combined with other methods like the Daftardar-Gejji and Jafari (the new Iterative) method to solve complicated nonlinear differential equations The new iterative technique yields a series that can be summed up to get an analytical formula or used to build an appropriate approximation with a faster convergent series solution GRAPHICAL ABSTRACT

Parallel Jacobian-free Newton Krylov discrete ordinates method for pin-by-pin neutron transport models

A parallel Jacobian-Free Newton Krylov discrete ordinates method (comePSn_JFNK) is proposed to solve the multi-dimensional multi-group pin-by-pin neutron transport models, which makes full use of the good efficiency and parallel performance of the JFNK framework and the high accuracy of the Sn method for the large-scale models. In this paper, the k-eigenvalue and the scalar fluxes (rather than the angular fluxes) are chosen as the global solution variables of the parallel JFNK method, and the corresponding residual functions are evaluated by the Koch–Baker–Alcouffe (KBA) algorithm with the spatial domain decomposition in the parallel Sn framework. Unlike the original Sn iterative strategy, only a “flattened” power iterative process which includes a single outer iteration without nested inner iterations is required for the JFNK strategy. Finally, the comePSn_JFNK code is developed in C++ language and, the numerical solutions of the 2-D/3-D KAIST-3A benchmark problems and the 2-D/3-D full-core MOX/UOX pin-by-pin models with different control rod distribution show that comePSn_JFNK method can obtain significant efficiency advantage compared with the original power iteration method (comePSn) for the parallel simulation of the large-scale complicated pin-by-pin models.