Multi-step Algorithm Research Articles

A Novel Multi-Step Multi-Exposure PTV Algorithm With Adaptive Time Separation

This work proposes a new multi-step measurement algorithm for PTV to cope with high flow velocities that do not allow time-resolved measurements. The proposed methodology relies on an iterative process, whereby a double-frame single-pulse image acquisition is followed by a multi-exposure acquisition. The novelty of the approach lies in the adaptive selection of the time separation used in the second acquisition to maximise the measurement dynamic velocity range and minimise the occurrence of overlapping particle images. The adaptive selection is performed based on the velocity field obtained by the first double-frame single-pulse acquisition and aims to obtain a rather uniform particle displacement across the measurement volume. The proposed methodology is demonstrated based on flow measurements in the near-wake of a truncated cylinder at Re=33,000. The results obtained by the presented method are then compared to those retrieved via the state-of-the-art PTV algorithm Shake-The-Box, a double-frame single-exposure strategy and a standard double-frame double-exposure strategy with a fixed pulse separation time. The increment of the particle displacement in the regions characterized by low velocity leads to a reduction of the relative uncertainty of the evaluated velocity and a consequent increase of the achievable dynamic velocity range. Among the double-frame strategies, the requirement to allow PTV measurement at a relatively high speed, the proposed methodology achieves the lowest error with respect to the reference, given by STB measurement in this case.

Data-Driven-Based Event-Triggered Control for Nonlinear CPSs Against Jamming Attacks.

This brief considers the security control problem for nonlinear cyber-physical systems (CPSs) against jamming attacks. First, a novel event-based model-free adaptive control (MFAC) framework is established. Second, a multistep predictive compensation algorithm (PCA) is developed to make compensation for the lost data caused by jamming attacks, even consecutive attacks. Then, an event-triggering mechanism with the dead-zone operator is introduced in the adaptive controller, which can effectively save communication resources and reduce the calculation burden of the controller without affecting the control performance of systems. Moreover, the boundedness of the tracking error is ensured in the mean-square sense, and only the input/output (I/O) data are used in the whole design process. Finally, simulation comparisons are provided to show the effectiveness of our method.

Multi-Step Algorithm for Constructing Statistical Estimates Based on the Bayesian Approach in Measurement Problems

Multi-Step Algorithm for Constructing Statistical Estimates Based on the Bayesian Approach in Measurement Problems

Optimization of the Distribution of Resource Support at the Stages of Development and Operation of Digitalized Organizational Systems

The issues of optimizing the distribution of resource support in the management of digitalized organizational systems throughout the entire life cycle off their existence are considered. The characteristics off the stages off functioning and development are given, optimization models off these stages are formulated. The problem off determining the optimal transition from the stage off functioning to the stage of development is solved, the time intervals of these stages are determined. The task is to determine the resource support for these stages, optimize the distribution off resource support for the time intervals off functioning and development based on the methods off multi-alternative optimization. The solution off optimization problems is proposed to be carried out with the direct participation off an expert, who determines the main points in a multi-step algorithm for finding the optimal distribution off resources.

Numerical Stability and Performance of Semi-Explicit and Semi-Implicit Predictor–Corrector Methods

Semi-implicit multistep methods are an efficient tool for solving large-scale ODE systems. This recently emerged technique is based on modified Adams–Bashforth–Moulton (ABM) methods. In this paper, we introduce new semi-explicit and semi-implicit predictor–corrector methods based on the backward differentiation formula and Adams–Bashforth methods. We provide a thorough study of the numerical stability and performance of new methods and compare their stability with semi-explicit and semi-implicit Adams–Bashforth–Moulton methods and their performance with conventional linear multistep methods: Adams–Bashforth, Adams–Moulton, and BDF. The numerical stability of the investigated methods was assessed by plotting stability regions and their performances were assessed by plotting error versus CPU time plots. The mathematical developments leading to the increase in numerical stability and performance are carefully reported. The obtained results show the potential superiority of semi-explicit and semi-implicit methods over conventional linear multistep algorithms.

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial segments extracted from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlying arterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs.

An Algorithm for Finding Contradictions in Multiformat Data using Apache Spark

The quality of managerial decision-making is significantly influenced by the inconsistency and heterogeneity of information obtained from various sources with the inability to unambiguously determine their reliability, for example, social networks, electronic media, opinion polls, as well as the types of representations used, for example, texts, graphs or tables. The purpose of the work was to conduct theoretical and experimental studies that ensure the choice of methods and their implementation in the algorithm for processing multiformat data to solve the problem of inconsistency and heterogeneity of information. To achieve this goal, the following tasks were solved: comparative analysis of the possibilities of methods for finding contradictions in heterogeneous information: latent-semantic analysis, neural networks and others; development of an algorithm for intelligent processing of big data using the Apache Spark module; evaluation of the algorithms performance for obtaining a qualitative result within a given time interval. As a result of the research, in the framework of solving the problem of finding contradictions for processing media publications, it is proposed to consistently use latent semantic analysis to select articles on a given topic, and then the method of determining the tonality of articles, and for processing the results of sociological surveys, the method of calculating the integral indicator for the question selected from the questionnaire. Based on the selected methods, a multi-step algorithm was developed and then implemented in Python using the Apache Spark platform in the form of a software product registered in the Register of Computer Programs. Based on the results of the experiments conducted in the work, it was concluded that the use of the Apache Spark module with the developed algorithm makes it possible to ensure an effective search for contradictions in information with the fulfillment of the requirements for efficiency.

Clostridioides difficile infection (CDI) treatment outcomes and recurrence factor at a pediatric hospital

Background: CDI is the single most common cause of nosocomial diarrhea in both adults and children. Available data regarding treatment outcomes in hospitalized children remain limited. CDI recurrence in children has been reported in 20%–30% of cases. Consensus regarding the best testing method for CDI is lacking. The 2018 IDSA guideline recommends a multistep algorithm with detection of glutamate dehydrogenase antigen plus toxin, followed by detection of toxigenic C. difficle with nucleic acid amplification test (NAAT) if results are discordant. Methods: We included patients aged 1–26 years admitted from July 2020 through June 2021 with CDI symptoms and positive toxin or NAAT. Healthcare facility-onset CDI (HO-CDI) was defined as positive specimen collected >3 days after admission. Community-onset CDI (CO-CDI) was defined as positive specimen collected ≤3 days after admission. Community-onset healthcare facility-associated CDI (CO-HCFA-CDI) was defined as positive specimen from a patient who was discharged from the facility ≤4 weeks prior. Recurrence was defined as an episode of CDI occurring within 60 days after onset of a previous infection. Results: Mean age of the 63 patients meeting inclusion criteria was 11.2 years (range, 1–21 years). Most patients (n = 37; 58.7%) were male, tested negative for C. difficile toxins (n = 39; 61.9%), and had mild-to-moderate disease (n = 61; 96.8%). Patients with immunocompromising conditions were common, including malignancy (n = 38; 60.3%), inflammatory bowel disorder (n = 8; 12.7%), and history of solid organ transplant (n = 5; 7.9%). Previously healthy without chronic medical conditions were uncommon (n = 4; 6.3%). CO-CDI was most common (n = 26; 41.3%) followed by HO-CDI (n = 23; 36.5%). Also, 34 patients (53.9%) were exposed to antibiotics within the previous 30 days, 16 (47.0%) of whom received 2 or more antibiotics. Sulfamethoxazole–trimethoprim was the most prescribed agent (13; 38%), most (12; 92.3%) as prophylaxis for Pneumocystis jirovecii pneumonia. Furthermore, 42 patients (66.7%) were receiving gastric acid suppressant agents. Laxatives were given to 14 patients (22.2%) within 72 hours of testing, despite electronic reminders. Most were treated with oral vancomycin (n = 46; 73.0%). In addition, 5 patients (7.9%) did not receive CDI treatment at the discretion of the treating physician; all were toxin negative. CDI was cured in 58 patients (92.1%) with only 5 (7.9%) experiencing recurrence infection. Patients testing positive for C. difficile toxin were more likely to experience infection recurrence compared to those with a negative toxin screen: 4 of 24 (16.7%) versus 1 of 39 (2.6%) (P = .044). Conclusions: Most patients with CDI were treated with oral vancomycin at our institution. We observed significantly lower rate of recurrence than previously reported. Toxin-positive patients experienced higher recurrence rate. Prospective studies are needed to confirm our findings.Funding: NoneDisclosures: None

Joint trajectory and passive beamforming optimization in IRS-UAV enhanced anti-jamming communication networks

This paper investigates the anti-jamming communication scenario where an intelligent reflecting surface (IRS) is mounted on the unmanned aerial vehicle (UAV) to resist the malicious jamming attacks. Different from existing works, we consider the dynamic deployment of IRS-UAV in the environment of the mobile user and unknown jammer. Therefore, a joint trajectory and passive beamforming optimization approach is proposed in the IRS-UAV enhanced networks. In detail, the optimization problem is firstly formulated into a Markov decision process (MDP). Then, a dueling double deep Q networks multi-step learning algorithm is proposed to tackle the complex and coupling decision-making problem. Finally, simulation results show that the proposed scheme can significantly improve the anti-jamming communication performance of the mobile user.

Multi-step attack threat recognition algorithm based on attribute association in internet of things security

Multi-step attack threat recognition algorithm based on attribute association in internet of things security

Distributed multi-step subgradient projection algorithm with adaptive event-triggering protocols: a framework of multiagent systems

This paper discusses a convex optimisation problem with a common set of constraints in the framework of multi-agent systems. Each agent only exchanges information with its neighbours and collaboratively searches for the optimal solution of the global function. To this addressed problem, a distributed multi-step subgradient projection algorithm is developed, where an adaptive event-triggering protocol is designed to govern the information exchange. It is disclosed that the state of each agent representing the estimate of the optimal solution asymptotically converges to one of the optimal solutions under suitably chosen stepsizes and momentum parameters. Simulation results verify that the proposed algorithm has better convergence performance than the standard event-triggered subgradient projection algorithm. In addition, the communication frequency between agents can be effectively reduced to save communication resource consumption.

A scalable multi-step least squares method for network identification with unknown disturbance topology

Identification methods for dynamic networks typically require prior knowledge of the network and disturbance topology, and often rely on solving poorly scalable non-convex optimization problems. While methods for estimating network topology are available in the literature, less attention has been paid to estimating the disturbance topology, i.e., the (spatial) noise correlation structure and the noise rank in a filtered white noise representation of the disturbance signal. In this work we present an identification method for dynamic networks, in which an estimation of the disturbance topology precedes the identification of the full dynamic network with known network topology. To this end we extend the multi-step Sequential Linear Regression and Weighted Null Space Fitting methods to deal with reduced rank noise, and use these methods to estimate the disturbance topology and the network dynamics in the full measurement situation. As a result, we provide a multi-step least squares algorithm with parallel computation capabilities and that rely only on explicit analytical solutions, thereby avoiding the usual non-convex optimizations involved. Consequently we consistently estimate dynamic networks of Box Jenkins model structure, while keeping the computational burden low. We provide a consistency proof that includes path-based data informativity conditions for allocation of excitation signals in the experimental design. Numerical simulations performed on a dynamic network with reduced rank noise clearly illustrate the potential of this method.

Open Search of Peptide Glycation Products from Tandem Mass Spectra

Identification of chemically modified peptides in mass spectrometry (MS)-based glycation studies is a crucial yet challenging task. There is a need to establish a mode for matching tandem mass spectrometry (MS/MS) data, allowing for both known and unknown peptide glycation modifications. We present an open search approach that uses classic and modified peptide fragment ions. The latter are shifted by the mass delta of the modification. Both provide key structural information that can be used to assess the peptide core structure of the glycation product. We also leverage redundant neutral losses from the modification side chain, introducing a third ion class for matching referred to as characteristic fragment ions. We demonstrate that peptide glycation product MS/MS spectra contain multidimensional information and that most often, more than half of the spectral information is ignored if no attempt is made to use a multi-step matching algorithm. Compared to regular and/or modified peptide ion matching, our triple-ion strategy significantly increased the median interpretable fraction of the glycation product MS/MS spectra. For reference, we apply our approach for Amadori product characterization and identify all established diagnostic ions automatically. We further show how this method effectively applies the open search concept and allows for optimized elucidation of unknown structures by presenting two hitherto undescribed peptide glycation modifications with a delta mass of 102.0311 and 268.1768 Da. We characterize their fragmentation signature by integration with isotopically labeled glycation products, which provides high validity for non-targeted structure identification.

Predictive Finite-State Control—When to Use and When Not

A multistep predictive algorithm eliminates overshoots of the current vector outside the boundary circle while the processor identifies the next optimal switching state vector following a boundary transition. The predictions of the current vector trajectories refer to a simplified machine model consisting only of the leakage inductance. Nevertheless is good accuracy of the predictions achieved. Operation at low switching frequency minimizes both harmonic distortion and switching losses. Using higher switching frequencies does not lead to noticeable improvement over ordinary carrier modulation. Maximum fundamental output voltage is achieved by predictive overmodulation.

Design of district heating networks in built environments using GIS: A case study in Vitoria-Gasteiz, Spain

The efficient integration of high levels of industrial waste heat in low temperature district-heating networks is a promising technique that requires specific methodologies for its satisfactory implementation. This paper presents a novel methodology for assessing the energy and economic feasibility of new district-heating networks in existing urban areas for the integration of industrial waste heat sources. The methodology consists in an innovative multistep procedure using geographic information systems and data analysis tools, combining georeferenced data about buildings, industries and roads. The spatial distribution of the analysis area is divided into smaller buffers and grids, as a result, the routing design of the pipelines that makes up the district-heating topology is obtained under several assumptions. The methodology provides the most suitable area choice for the deployment of a district-heating, also implemented with a multi-step algorithm for routing the pipelines of the network. This methodology is applied to a particular case study located in Vitoria-Gasteiz (northern Spain). Different configurations for the district heating network are obtained with lengths of the network varying from 8 to 27 km. Payback values near to six years are achieved in most of the district-heating network configurations. The maximum payback period obtained within the configurations is 8.5 years. An economic sensitivity analysis is presented for the proposed optimal district-heating network configuration. The proposed methodology could be replicated for different case studies as long as the input data is available to the user.

Efficient phase-shift error compensation algorithm based on probability density function

In non-digital projection systems, the inaccuracy of phase shifts is one of the most important nonlinear errors. In this paper, an efficient and universal compensation algorithm is proposed, which does not need to find the exact phase shift amounts, but to compensate the periodical errors by a universal two-coefficient error model. With this model, only two error coefficients should to be searched for any multi-step phase shift algorithm. To find the error coefficients, a correlation process and a tool known as the probability density function (PDF) are used. First, a set of pre-simulated PDF curves are generated with the different error coefficients. Second, a practical PDF curve from captured fringes is calculated. Finally, the correlation process is carried out to locate the most similar simulated PDF curve, whose error coefficients are the practical ones. In this paper, we demonstrate the compensation for three, four and five-step phase-shifting algorithms separately. Experimental results show that this method has high universality, and can meet the requirements of rapid and high accuracy measurement.

Multi-step inertial forward-backward-half forward algorithm for solving monotone inclusion

In this article, we study a multi-step inertial forward-backward-half forward splitting algorithm to solve the solution of the sum of three operators with two of them being respectively, cocoercive and monotone-Lipschitz continuous. Notably, the convergence of the multi-step inertial forward-backward-half forward algorithm is established under the summability condition formulated on the basis of the iterative sequence in a Hilbert space setting. We also display two applications including problems that are: a composite monotone inclusion problem involving mixtures of linearly composed with parallel-sum type operators, and a composite convex optimization problem. As far as the resolvent of the composite operator is concerned, due to difficulty in calculation, then, the composite monotone inclusion problem is addressed by the proposed multi-step inertial algorithm together with the primal-dual idea. Experiments conducted on an image deblurring problem indicate the superiority and efficiency of the proposed method.

Nonlinear acceleration of momentum and primal-dual algorithms

We describe convergence acceleration schemes for multistep optimization algorithms where the underlying fixed-point operator is not symmetric. In particular, our analysis handles algorithms with momentum terms such as Nesterov’s accelerated method or primal-dual methods. The acceleration technique combines previous iterates through a weighted sum, whose coefficients are computed via a simple linear system. We analyze performance in both online and offline modes, and we study in particular a variant of Nesterov’s method that uses nonlinear acceleration at each iteration. We use Crouzeix’s conjecture to show that acceleration performance is controlled by the solution of a Chebyshev problem on the numerical range of a non-symmetric operator modeling the behavior of iterates near the optimum. Numerical experiments are detailed on logistic regression problems.

Cascade Forward Artificial Neural Network based Behavioral Predicting Approach for the Integrated Satellite-terrestrial Networks

In order to reduce the risk of authorized users being interrupted in the cognitive satellite wireless network, a multi-step prediction approach based on a cascaded forward artificial neural network is proposed to predict user behavior in the designed scenario. This approach uses the powerful learning ability of the cascaded forward network to analyze the historical spectrum occupancy records of licensed users, and then predict the user behavior in the next few time slots. The prediction result can help the base station in the cognitive network to schedule the dynamic access process of the cognitive users, and reduce the interference caused by the cognitive user to the authorized users. Finally, compared with traditional prediction algorithms, it is verified that the proposed multi-step prediction algorithm can effectively reduce the probability of spectrum conflicts.

Analyzing and evaluating quality of the multi-step radar target recognition algorithm with the ability to meet reliability of decisions

The paper presents the issue of improving the efficiency of the radar target recognition by flexibly extending the observation time, giving results of evaluating the quality metrics of “the multi-step recognition algorithm with the ability to meet the reliability of decisions” in the case of recognizing radar targets on Gaussian noise. In the paper, we use the simulation method to determine the probability matrix of conditional true/false recognition and the statistical properties of the required number of observation cycles. The simulation results show that the multi-step algorithm improves the probability of true recognition and allows to meet the reliability of the decisions with the average number of required observation cycles significantly smaller than that in the case of using the conventional one-step algorithm.