AR Algorithm Research Articles

New analytic solutions in f(R)$$ f(R) $$‐cosmology from Painlevé analysis

Using the singularity analysis, we investigate the integrability properties and existence of analytic solutions in ‐cosmology. Specifically, for some power‐law ‐theories of particular interest, we apply the ARS algorithm to prove if the field equations possess the Painlevé property. Constraints for the free parameters of the power‐law models are derived, and new analytic solutions are derived, expressed in terms of Laurent expansions.

Minimal cut of strongly connected directed random graphs studied in virtual machine deployment

Abstract This paper first presents the basic concepts of random graph theory, including undirected, directed, and generalized random graphs. A random homomorphic number mapping is introduced to solve the cut distance problem of directed random graphs, and a minimum cut distance is proposed. Next, a virtual machine deployment strategy (VMDS) based on the minimum cut algorithm is proposed to construct VM clusters based on similarity and to cut VM clusters based on the minimum cut of directed random graphs. According to the results, the MAE (1.3875) and MSE (2.7783) of both the conventional AR algorithm and ES algorithm are the lowest. Compared to the MAE (1.7381) and MSE (3.7840) of the VMDS algorithm, both significantly decrease. The research results presented in this paper are useful for applying virtual machine deployment techniques in cloud computing.

Extremely Weak Signal Detection Algorithm of Multi-Pixel Photon Detector

For the detection of extremely weak signals in underwater long distance wireless optical communication, the Geiger mode of the multi pixel photon detector has serious pulse superposition effect and Poisson noise, which leads to counting difficulties and affects the measurement accuracy. This paper proposes a deconvolution joint generalized Anscombe transform (DUGAT) detection algorithm. First, the deconvolution filtering technology is used to process the weak signal to reduce the superposition effect and converge the pulse width. Secondly, the noise signal of Poisson+Gaussian (P+G) distribution is converted into Gaussian variable by using generalized Anscombe transform (GAT), which is equivalent to additive Gaussian noise signal. Finally, the symbols are counted by hard decision algorithm. The experimental results show that the sensitivity of this algorithm is improved by 8.57dB and 9.95dB respectively compared with the traditional AR and ML detection algorithms.

Multi-parameter infrared sensing method for operation state of power equipment

Aiming at the problems of difficult extraction and reconstruction of multi-parameter signals and poor perception effect of power equipment operation status, a multi-parameter infrared perception method of power equipment operation status is proposed. The multi-parameter (sound, light, electricity, vibration, heat, and environment) sensors of power equipment operation status are selected, the multi-parameter sensor integration framework is built based on the Internet of things technology, the multi-parameter sensors are effectively integrated, the power equipment operation status parameter signals are collected and reconstructed, the power equipment operation status perception model is built, the unsupervised classification and AR algorithm are introduced, and the reconstructed signals are fitted, to realize the infrared perception of power equipment operation status. The experimental data show that the integrity of power equipment operation state parameters and the accuracy of power equipment operation state perception obtained by the proposed method are higher, which proves that the proposed method has a good effect on power equipment operation state perception.

Optimal service decomposition for mobile augmented reality with edge cloud support

Mobile augmented reality (MAR) applications are starting to attract significant attention due to the enhanced capabilities stemming from both the network and the end devices that propel their realization. However, despite the progress on the end user devices, MAR applications are inherently hugely demanding in terms of computational and memory requirements since they combine, inter alia, video streams, computer generated images, intense computer vision algorithms and geolocation. To this end, edge cloud computing is envisioned as a key technology for supporting such applications where part of the computationally demanding algorithms could be offloaded to suitably selected edge clouds. Within that context the inherent user mobility should be considered to allow an efficient service continuum between edge and the end-terminal. To this end, in this paper, an optimal edge cloud resource MAR service decomposition is presented that takes explicitly into account the AR service composition as well as the inherent user mobility to proactively allocate resources to satisfy the required strict latency and frame accuracy requirements of MAR applications. In addition to the optimal decision making using mathematical programming, and as a mean to provide real-time decision making two advanced heuristic techniques are proposed. A Simulated Annealing based mobility aware AR algorithm (SAMAR) is developed to enhance computing efficiency and a Long Short-Term Memory (LSTM) neural network which is trained offline with optimal solutions. Numerical investigations reveal that significant gains can be achieved by the proposed schemes compare to a number of baseline previously proposed techniques.

Reform mode of sports training and competition organization based on data mining

SummaryChina has continuously strengthened its efforts in reforming the sports training and competition organization mode, issued a series of measures and made some innovations, but the current optimized policies and measures still have certain limitations. Therefore, this article first uses data mining technology to model students' sports training and competition mode, considering multi event competition, the scoring of the project itself, the uncertainty of the number of students in sports training and other factors. The results show that the average prediction accuracy of the time series data mining method applied in this article is 89.82%, which is 19.71% and 44.78% higher than that of ANN and traditional clustering methods, respectively. The average error rate of ARMA time series algorithm data mining is reduced by 21.95% and 18.89% compared with AR and Ma time series algorithm. In addition, compared with the convergence time of multidimensional sports data mining realized by AR algorithm and Ma algorithm, ARMA time series algorithm is shortened by 77.13% and 70.02%, respectively.

Construction and Application of Virtual Reality-Based Sports Rehabilitation Training Program.

This paper adopts virtual reality technology to conduct in-depth research and analysis on sports rehabilitation training, designs a corresponding sports rehabilitation training program, and applies it to practice. An AR algorithm based on dynamic target tracking under VSLAM is proposed. The algorithm can effectively reject dynamic targets in static scenes while ensuring that the virtual objects registered based on dynamic template target tracking are still in the world coordinate system of VSLAM. To facilitate patients' hand function rehabilitation training, this paper uses OpenPose for 2D gesture pose recognition, combines camera pose information and depth information provided by VSLAM to map key points of the hand into the world coordinate system, and then completes the interaction by collision detection algorithm. The virtual interaction module is implemented in this paper to meet the demand for multiuser off-site interaction in virtual training. This paper uses the Unity3D software and Photon Server server to create a VR virtual scene and design a user interaction mechanism to realize a system that supports multiple users to train together online, which effectively extends the application scope of the VR training system. The module utilizes Unity3D's VR development capabilities to develop VR virtual basketball gym scenes and single-player offline interaction mechanisms such as virtual user single shooter and shooter; then, Photon Server is used to design and implement a multiuser remote login system and a multiuser interpass mechanism, thus achieving the effect of multiplayer remote online interaction in the same VR space. Finally, the proposed module was validated, and the results proved the effectiveness of the sports rehabilitation training program.

Integrability, Variational Principle, Bifurcation, and New Wave Solutions for the Ivancevic Option Pricing Model

The Ivancevic option pricing model comes as an alternative to the Black‐Scholes model and depicts a controlled Brownian motion associated with the nonlinear Schrodinger equation. The applicability and practicality of this model have been studied by many researchers, but the analytical approach has been virtually absent from the literature. This study intends to examine some dynamic features of this model. By using the well‐known ARS algorithm, it is demonstrated that this model is not integrable in the Painlevé sense. He’s variational method is utilized to create new abundant solutions, which contain the bright soliton, bright‐like soliton, kinky‐bright soliton, and periodic solution. The bifurcation theory is applied to investigate the phase portrait and to study some dynamical behavior of this model. Furthermore, we introduce a classification of the wave solutions into periodic, super periodic, kink, and solitary solutions according to the type of the phase plane orbits. Some 3D‐graphical representations of some of the obtained solutions are displayed. The influence of the model’s parameters on the obtained wave solutions is discussed and clarified graphically.

Data Mining Technology Used in an Internet of Things-Based Decision Support System for Information Processing Intelligent Manufacturing

In recent years, database technology has improved significantly, and database management systems have gained widespread adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is placed on conceptualizing the decision support system's integrated performance.

Implementation of Recommendation Algorithm based on Recommendation Sessions in E-commerce IT System

The aim of the paper is to present a study as the implementation of the author’s Algorithm of the Recommendation Sessions ARS in an operating e‐commerce information system and to analyse basic parameters of the recommendation system created as a result of the implementation. The first part of the study contains a synthetic description of the area of recommendation systems. The next section presents the proprietary ARS recommendation algorithm based on recommendation sessions. The third part of the paper describes the mathematical model of the recommendation session built on the basis of the theory of graphs and networks, which such model makes the input data for the algorithm in question. The next part of the publication describes the possibilities of representing graph structures and the method of implementing a G graph (constituting a set of the recommendation session) in a relational database. The implementation of the ARS algorithm, based on the SQL standard, was also presented. The implementations in question have been developed on the basis of a working information system of the e‐commerce class. As a result of the implementation of the algorithm, a fully functional recommendation system was created, which can be adapted to various e‐commerce IT systems. The positive result of the work was confirmed by the research on the parameters of the recommendation system, included in the last part of the study.

Torque ripple and noise control of switched reluctance motor using an adaptive fuzzy PI control with the aid of AR algorithm

In recent days switched reluctance motor is widely used for numerous industrial applications due to its simple structure, minimum cost and maximum efficiency. Regardless of numerous exclusive benefits of the switched reluctance motor (SRM), acoustic noise of this motor is high and it is important to accomplish more analysis on the noise lessening, which is the primary goal of this paper. The major causes of acoustic noise in a SRM are torque ripple and radial magnetic force. Since radial magnetic force is highly influential by the design of motor, torque ripple control is analysed in this article for acoustic noise control. Torque ripple control of SRM is proposed using optimization in direct torque control (DTC) method. Nowadays, optimisation plays a crucial role in motor drives for enhanced control. In this paper, artificial raindrop algorithm is proposed in DTC of SRM to minimise torque ripple. Performance of proposed ARA based DTC of four-phase 8/6 SRM is analysed using Matlab and compared with the performance of fuzzy gain scheduling PI controller based DTC.

Digital Daimons: Algorithmic Rhetorics of Augmented Reality

This article develops a theoretical framework for studying the algorithmic underpinnings of contemporary augmented reality technologies. We delineate this framework through the rhetorical figure of the “daimon,” a greek mythological entity as well as a technical concept within computer science, to articulate an approach to AR algorithms as emergent, material processes that can create unpredicted, unintended effects. Ultimately, we argue that the conceptual framework of the “daimon” provides an interface through which writing and rhetoric scholars can better discern the algorithmic effects of emerging AR composing platforms.

Similarity inner solutions for the Pulsar equation

Lie symmetries are applied to classify the source of the magnetic field for the Pulsar equation near to the surface of the neutron star. We find that there are six possible different admitted Lie algebras. We apply the corresponding Lie invariants to reduce the Pulsar equation close to the surface to an ordinary differential equation. This equation is solved either with the use of Lie symmetries or the application of the ARS algorithm for singularity analysis to write the analytic solution as a Laurent expansion. These solutions are called inner solutions.

Analysis of Frequent Itemsets Mining Algorithm Againts Models of Different Datasets

Data mining is a study that uses statistical knowledge, mathematical calculations, artificial intelligence methods, machine learning by extracting and identifying useful information and related knowledge from various large databases.. One of them is looking for itemsets combination from the data stack, the search process can be done using the Apriori Association Rules algorithm, the FPGrowth Association Rule and Closed Association rule. The three algorithms are some of the implementations of frequent itemsets search methods. Two datasets will be tested, namely retail datasets and accidents datasets derived from fimi datasets, with the aim of knowing the behavior of the three algorithms against the dataset model we tested. Each algorithm will test both datasets, with minimum support values for retail datasets ranging from 0.01-0.05 and min-conf 0.01-0.05. Likewise on the accident min-sup and min-conf 0.6-1 datasets. For retail algorithm data with the fastest processing time, the FP-Growth AR algorithm is more than 85% compared to Apriori-AR, followed by AR-Apriori and Apriori-Close-AR. For detailed memory usage results the Apriori-AR algorithm is lighter and outperforms. Accident data the experimental results show that FP-Growth-AR is a little more memory efficient and the fastest process is Apriori-Close, while the Apriori-AR with the longest processing time.

Machine learning–based haptic‐enabled surgical navigation with security awareness

SummaryA novel security awareness surgical navigation system has been proposed for the accurate minimally invasive surgery with machine learning algorithms, haptic‐enabled devices, and customized surgical tools to guide the surgery with real‐time force and visual navigation. To provide a direct and simplified user interface during the operation, we combined traditional surgical guide images with AR‐based view and implemented a 3D reconstructed patient‐specific surgical environment includes with all surgical requisite details. In particular, we trained the surgical collected biomechanics haptic data by employed LSTM‐based RNN algorithm, and residual network for the intraoperative force manipulation prediction and classification, respectively. Experiments evaluation results on percutaneous therapy surgery demonstrated a higher performance and distinguished accuracy by the visual and haptic combined than the traditional navigation system. These preliminary study findings may suggested a new framework in the minimally invasive surgical navigation application and hint at the possibility integration of haptic, AR, and machine learning algorithms implementation in medical simulation. In addition, we take security into account when implementation this new framework.

Application of Stabilized AR‐z Spectrum in Harmonic Analysis for Geophysics

AbstractThe AR‐z spectrum devised in Ding and Chao (2015a) for analyzing harmonic signals (an extension of the frequency domain autoregressive AR algorithm developed by Chao and Gilbert (1980) based on Prony's method) proves to have significantly higher sensitivity and spectral resolution than the Fourier and maximum‐entropy spectra, especially when the signal‐to‐noise ratio is low. As such, it can be prone to random fluctuations in the form of spurious variances of the noise spectrum. Here we develop a stabilized AR‐z spectrum taking advantage of a Monte Carlo noise‐assisted bootstrap scheme, and demonstrate its effectiveness in obtaining more robust spectral estimates for single record. We apply the stabilized AR‐z spectrum, and compare it with the Fourier and maximum‐entropy spectra, to a number of global geophysical observables of the Earth, including the elastic free oscillations, length of day variation (ΔLOD), dynamic oblateness (ΔJ2), polar motion, Earth's nutation, and global mean sea level variation. We report the findings about hitherto unresolved fine structures, including those for the Mf and Mm tidal clusters in the ΔLOD, ΔJ2, polar motion and the Earth's nutation, and the modal splittings of the Earth's free oscillations. Furthermore, a number of long‐period signals of certain nominal periodicities are detected (or confirmed) in the stabilized AR‐z spectra, including the 6‐year signals in ΔLOD, the 18.6‐year signals expected in ΔLOD and ΔJ2, and certain quasiperiodic interannual to decadal periodicities whose origins have yet to be identified. Overall, the stabilized AR‐z spectrum demonstrates a momentous performance in the quest for numerical harmonic analysis methods.

Efficient distance-based representative skyline computation in 2D space

Representative skyline computation is a fundamental issue in database area, which has attracted much attention in recent years. A notable definition of representative skyline is the distance-based representative skyline (DBRS). Given an integer k, a DBRS includes k representative skyline points that aims at minimizing the maximal distance between a non-representative skyline point and its nearest representative. In the 2D space, the state-of-the-art algorithm to compute the DBRS is based on dynamic programming (DP) which takes O(km2) time complexity, where m is the number of skyline points. Clearly, such a DP-based algorithm cannot be used for handling large scale datasets due to the quadratic time cost. To overcome this problem, in this paper, we propose a new approximate algorithm called ARS, and a new exact algorithm named PSRS, based on a carefully-designed parametric search technique. We show that the ARS algorithm can guarantee a solution that is at most ?? larger than the optimal solution. The proposed ARS and PSRS algorithms run in O(klog2mlog(T/??)) and O(k2 log3m) time respectively, where T is no more than the maximal distance between any two skyline points. We also propose an improved exact algorithm, called PSRS+, based on an effective lower and upper bounding technique. We conduct extensive experimental studies over both synthetic and real-world datasets, and the results demonstrate the efficiency and effectiveness of the proposed algorithms.

Augmenting painted architectures for communicating cultural heritage

The paper presents a research under development at the University of Trieste to analyze a painted architecture by Paolo Veronese and to present the results using AR systems (Augmented Reality Systems). The canvas was painted in 1573 and it is now at the Gallerie dell’Accademia Museum in Venice. The aim of the research was to transform a two-dimensional work of art in a three dimensional one, allowing all the visitors of a museum to enter the space of the representation and perceive it in a more direct way. After the geometrical analysis of the picture, we started the digital restitution of the perspective references and proceed to model the virtual scene using Boolean primitives and applying all the textures to render the scene in a very realistic way. The further step was to convert the model into a dynamic form with AR algorithms and associate it with spatial references to allow users to do a virtual experience of it.

Subsidence Monitoring Model of AR and Kalman Hybrid Algorithm and its Application

The non-stationary time series data are dealt with the hybrid algorithm modal which combining the combined kalman and AR algorithm, and the modal was build, which the parameters stochastic variance of the AR modal was set in the state equations instead of extracting the tendency items from original data in conventional AR modals, and measure equations were constructed by observation data, then the AR modal can be solved by the Kalman algorithm. The deformation prediction results of the modal used in underground tunnel construction showed that this method was accuracy and feasible.

AR quivers, exceptional sequences and algorithms in derived Hall algebras

Consider the canonical isomorphism between the positive part U + of the quantum group Uq(g) and the Hall algebra H(Λ), where the semisimple Lie algebra g and the finite-dimensional hereditary algebra Λ share a Dynkin diagram. Chen and Xiao have given two algorithms to decompose the root vectors into linear combinations of monomials of Chevalley generators of U + , respectively induced by the braid group action on the exceptional sequences of Λ-modules and the structure of the Auslander-Reiten quiver of Λ. In this paper, we obtain the corresponding algorithms for the derived Hall algebra DH(Λ), which was introduced by Toen. We show that both algorithms are applicable to the lattice algebra and Heisenberg double in the sense of Kapranov. All the new recursive formulae have the same flavor with the quantum Serre relations.