Online Calculation Research Articles

A tool to calculate the reverberation time of rooms with uneven absorption distribution

The reverberation time in rectangular rooms with uneven distribution of absorbing elements and surfaces, for example suspended ceilings, lead to a non-diffuse sound field. This implies longer reverberation times than predicted by sabines or eyrings reverberation formula. To enable a correct prediction of the reverberation times in such rooms, the only possibility was to apply room acoustic simulations, which require high effort to model the room and to perform the simulation. In 2021, a prediction method was published by Zhou et. al. to calculate the reverberation time in such rooms much more precisely than using sabines or eyrings formula, without the need for a room acoustic simulation. This method has been transformed into an online calculation tool, named "reverberate". In this contribution the methodology will be quickly reviewed and the online tool will be presented.

Toward Efficient Calculation of Inverses in Control Allocation for Safety-Critical Applications

Many control allocation algorithms require the calculation of (pseudo)inverses of control effectiveness matrices, also referred to as a B matrix, which for nonlinear systems might change over time. Such cases would require an online calculation of the respective inverses. Storage of all possible, offline precalculated inverses might exceed available memory sizes in common aircraft applications. This is especially relevant for systems with a high number of control effectors, like novel aircraft configurations. Several control allocation algorithms exist that require updates of the matrix to be inverted, the considered example being Redistributed Scaled Pseudoinverse. Within the Redistributed Scaled Pseudoinverse algorithm, the control allocation problem is solved iteratively by sequentially removing the columns of the B matrix that belong to saturated effectors. An approach using the Sherman–Morrison formula is presented in this study, which calculates the inverses based on recursive updates. This proposed approach has the following advantages over conventional Redistributed Scaled Pseudoinverse algorithm: reduced computational load and ease of protection against run-time errors. These make it a candidate for use in the context of safety-critical applications. The approach gives promising results and shows significant decrease of computational time. However, specific numerical challenges require additional investigations.

Collaborative Optimization of a Matrix Manufacturing System Based on Overall Equipment Effectiveness

When several traditional flow-shop lines operate in parallel, the operation mode with no communication between production lines will no longer be the optimal production paradigm. This paper describes matrix manufacturing systems (MMS) in a general manner from the perspective of related works, comparing different manufacturing organizational forms and their characteristics. Subsequently, MMS are extracted during the parallel production of multiple surface mount technology (SMT) lines. An overall equipment effectiveness (OEE) online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS. The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS. The efficiency of each matrix unit (MU) was calculated, and a collaborative optimization method was proposed based on an indicator (OEE). In this paper, an example of eight SMT lines is presented. The partitioning of MUs, OEE calculation of each MU, and the low OEE unit collaborative optimization method are described in detail. Through a case study, the architecture of the collaborative optimization model for the MMS was constructed and discussed. Finally, the improvement in the OEE proved the effectiveness and usability of the proposed architecture.

An active disturbance rejection stair-like generalized predictive control with time delay weakening for denitrification process in thermal power unit

The optimization control of the selective catalytic reduction (SCR) denitrification system holds significant importance in the production of the power generation industry. This paper presents an active disturbance rejection stair-like generalized predictive control with time delay weakening (WADRC-SGPC) to enhance the control performance in SCR denitrification system. Compared with classical generalized predictive control (GPC), active disturbance rejection generalized predictive control (ADRC-GPC) combines the advantages of ADRC, uses Extended State Observer (ESO) to compensate the uncertainty of the system, and regards the compensated system as a series integrator. Therefore, it has the advantages of no system identification and online calculation of Diophantine equation. In addition, a time delay weakening link is incorporated for the known part of the system model, which effectively improves the performance of the controller. The parameter selection issue of the weight function in time delay weakening is addressed using a hybrid strategy whale optimization algorithm. Finally, the superior performance of the proposed control strategy in terms of set-point tracking and disturbance rejection is verified through simulation experiments.

Determination of hydrostatic pressure on the plane surface of an arbitrary non-simmetrical form by the three-command method K123

Competitive struggle in the conditions of wide access to unlimited amounts of information and computerization of all branches of production and services imposes strict requirements on specialized knowledge, practical skills and general mathematical culture of future engineers. The online program of modern multivariate engineering calculation of the problem of determining the force of hydrostatic pressure on an element of a flat asymmetric surface with a curvilinear face is presented - https://www.k123.org.ua/jeh5.html. Algorithms are implemented according to the author's method of three commands K123 (c) Yu.D. Kopanytsia (hereinafter the K123 method). A client-server solution based on CGI technology with a web form for inputting source data has been implemented. The results of the online calculation were performed according to the new analytical dependencies obtained on the basis of the author's method K123. In parallel, the numerical algorithms of the author's method of three K123 commands and the conclusions of the corresponding calculations in tabular form were implemented. Realized generation of graphics by the server program based on the coordinates of the points obtained by iterative calculations according to the algorithms of the numerical implementation of the K123 method. The relative error of iterative calculations and the estimation of the accuracy of the result are automatically determined based on the proposed exact analytical dependencies.

Development and application of a population pharmacokinetic model repository for caffeine dose tailoring in preterm infants

ABSTRACT Background Considerable interindividual variability for the pharmacokinetics of caffeine in preterm infants has been demonstrated, emphasizing the importance of personalized dosing. This study aimed to develop and apply a repository of currently published population pharmacokinetic (PopPK) models of caffeine in preterm infants to facilitate model-informed precision dosing (MIPD). Research design and methods Literature search was conducted using PubMed, Embase, Scopus, and Web of Science databases. Relevant publications were screened, and their quality was assessed. PopPK models were reestablished to develop the model repository. Covariate effects were evaluated and the concentration–time profiles were simulated. An online simulation and calculation tool was developed as an instance. Results Twelve PopPK models were finally included in the repository. Preterm infants’ age and body size, especially the postnatal age and current weight, were identified as the most clinically critical covariates. Simulated blood concentration–time profiles across these models were comparable. Caffeine citrate-dose regimen should be adjusted according to the age and body size of preterm infants. The developed online tool can be used to facilitate clinical decision-making. Conclusions The first developed repository of PopPK models for caffeine in preterm infants has a wide range of potential applications in the MIPD of caffeine.

Optimal capacity configuration and operation strategy of typical industry load with energy storage in fast frequency regulation

As the potential and competent load-side resources for frequency response and control in modern power grids, typical industrial load can compensate for the deficiency of frequency response capability for new-type power systems. However, the operational flexibility is seriously enforced by the operation conditions uncertainties of industrial load. With “Online Calculation, and Real-time Matching” as the core, based on fuzzy mathematical theory, the coordinated operation strategy of typical industrial loads and energy storage systems (ESS) is proposed to finish fast frequency regulation (FFR) tasks. And an optimal capacity configuration model of industrial loads with ESSs is established to evaluate the whole economic profitability in FFR. Specially, in terms of algorithms, a novel Two-layer Parallel Particle Swarm Optimization and Genetic Algorithm (TPPSGA) is innovatively designed in this manuscript. Comparing with traditional heuristic algorithms, TPPSOGA improves computation speed by 6.84 times. Simulation results show that our proposed strategy can reasonably estimate the frequency capability of industrial load by membership degree function, and prove that the industrial load participating in FFR with the support of ESSs is able to get more revenue and stability than industrial load or ESSs separately is in regulation of FFR. The economic analyze in market price and ESS parameters can help the load agent to screen the suitable-type ESS and its capacity to serve for FFR and enhance the flexibility in load-side frequency regulation.

Loss Minimization Model Predictive Current Control for Linear Induction Motors

To further improve the operating efficiency of linear induction motor, this paper proposes a loss minimization model predictive current control method based on governable loss online calculation. First, the method derives the equivalent circuit of linear induction motor containing independent iron loss branches, and establishes the loss model containing iron loss. Second, the expressions of stator d‐axis current with governable losses and secondary flux amplitude are derived from theoretical analysis, and then a given value of stator d‐axis current is derived when the governable losses are minimal. Then, a chain observer is designed to observe the secondary flux and calculate the governable loss and stator d‐axis current under the current operation. Finally, a method is proposed to introduce the stator d‐axis current observation results into the model predictive current control, which ensures the loss suppression effect and improves the dynamic performance at the same time. In addition, this article also compares the loss minimization controllers with or without iron loss. The simulation results are consistent with the theory, and the optimal control of the secondary flux of the linear induction motor can be successfully realized, thus effectively reducing the energy loss of the motor in the dynamic operation process. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit

This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage (sICH) in the intensive care unit (ICU). We did a retrospective study in our study and identified cases of sICH from the MIMIC IV (n = 1486) and Zhejiang Hospital databases (n = 110). The model was constructed using features selected through LASSO regression. Among five well-known models, the selection of the best model was based on the area under the curve (AUC) in the validation cohort. We further analyzed calibration and decision curves to assess prediction results and visualized the impact of each variable on the model through SHapley Additive exPlanations. To facilitate accessibility, we also created a visual online calculation page for the model. The XGBoost exhibited high accuracy in both internal validation (AUC = 0.907) and external validation (AUC = 0.787) sets. Calibration curve and decision curve analyses showed that the model had no significant bias as well as being useful for supporting clinical decisions. XGBoost is an effective algorithm for predicting in-hospital mortality in patients with sICH, indicating its potential significance in the development of early warning systems.

Internal Temperature Estimation of Lithium Batteries Based on a Three-Directional Anisotropic Thermal Circuit Model

In order to improve the accuracy of internal temperature estimation in batteries, a 10-parameter time-varying multi-surface heat transfer model including internal heat production, heat transfer and external heat transfer is established based on the structure of a lithium iron phosphate pouch battery and its three directional anisotropic heat conduction characteristics. The entropy heat coefficient, internal equivalent heat capacity and internal equivalent thermal resistance related to the SOC and temperature state of the battery were identified using experimental tests and the least square fitting method, and were then used for online calculation of internal heat production and heat transfer in the battery. According to the time-varying and nonlinear characteristics of the heat transfer between the surface and the environment of the battery, an internal temperature estimation algorithm based on the square root cubature Kalman filter was designed and developed. By iteratively calculating the estimated surface temperature and the measured value, dynamic tracking and online correction of the internal temperature of the battery can be achieved. The verification results using FUDS and US06 dynamic working condition data show that the proposed method can quickly eliminate the influence of initial temperature deviations and accumulated process errors and has the characteristics of a high estimation accuracy and good robustness. Compared with the estimation results of the adaptive Kalman filter, the proposed method improves the estimation accuracy of FUDS and US06 working conditions by 67% and 54%, respectively, with a similar computational efficiency.

Dyslexia Articles Unboxed: Analyzing Their Readability Level.

Dyslexia is characterized by difficulties with fluent word recognition, decoding, or spelling, and it has been linked to family history. Given the impact of dyslexia on broad academic activities and well-being, ensuring that information about dyslexia is accessible to affected children and their families is vital. This study aims to assess the readability levels of dyslexia-related websites, with the hypothesis that such websites should be written at an appropriate readability level to accommodate those who may also have reading challenges. This study analyzes the readability of 50 web articles on dyslexia using 6 readability formulas: Flesch-Kincaid Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Score, Simple Measure of Gobbledygook (SMOG) Index, Coleman Liau Index, and Automated Readability Index. The search term "What is dyslexia" was used on Google. Each article was analyzed using the online calculation website WebFX. The readability goal for these websites was set at fifth grade, a level recommended for patients with reading challenges. The study found that among the 50 websites, the lowest median readability score was 11.8 (corresponding to a 12th-grade level) on the SMOG Index, while the highest scores were 15.5 on both the Gunning Fog Score and the Coleman Liau Index (indicative of college-level readability). Almost none of the websites had scores below a fifth-grade level. Most websites related to dyslexia are too complex. Tools such as readability metrics and sentence restructuring by AI can help make the information more accessible and understandable to the stakeholders.

First report of ivermectin resistance in cyathostomins (small strongyles) of horses in Argentina

In Argentina, as in the rest of the world, cyathostomins are the most common nematodes parasitizing horses. Control is based almost exclusively on the administration of benzimidazoles, pyrimidines, and macrocyclic lactones. However, intensive use of these drugs is resulting in the development of anthelmintic resistance (AR). For example, AR to benzimidazoles is currently distributed throughout Argentina, while incipient AR to pyrimidines (pyrantel embonate) is appearing in areas where this drug is used. Macrocyclic lactones and especially ivermectin, are by far the most used drugs by the vast majority of equine premises in the country. Although ivermectin has been used since 1982, its efficacy against equine strongylid parasites has remained very high until the present. In this study we report for the first time, the presence of a cyathostomin population with resistance to ivermectin in adult horses belonging to an equine premise located in central Argentina. Fecal egg count reduction tests (FECRT) were performed following the most recent guidelines of the World Association for the Advancement of Veterinary Parasitology (WAAVP) for the diagnosis of anthelmintic resistance (research protocol) and resistance was considered when the Upper 90% Credible Interval fell below the expected efficacy threshold of 99.9%. Calculations were carried out using two different online calculation interfaces suggested by WAAVP. For the 14-day post-treatment interval, ivermectin efficacy was 79.5% (90% Credible Interval: 68.1–88.8) and 79.3% (74.2–83.6.3%) with the two methods, respectively. At 19 days post treatment, fecal egg count reductions were 68.6% (50.5–83.1) and 68.4% (61.9–74.1), respectively. At both intervals, this cyathostomin population fullfilled the criteria for AR. These findings suggest dispersion of ivermectin resistant cyathostomins in Argentina. Given the widespread use of macrocyclic lactones, it is important that veterinarians and the equine industry promote a more selective and evidence-based use of these drugs and establish routine monitoring to determine anthelmintic field efficacy to detect treatment failures as early as possible and avoid potential health problems as well as further spread of resistant genes.

MHBase: A comprehensive database of short microhaplotypes for advancing forensic genetic analysis

Microhaplotypes (MHs) were first recommended by Prof. Kidd for use in forensics because they can improve human identification, kinship analysis, mixture deconvolution, and ancestry prediction. Since their introduction, extensive research has demonstrated the advantages of MHs in forensic applications and provided useful data for different populations. Currently, two databases, ALFRED (ALlele FREquency Database) and MicroHapDB (MicroHaplotype DataBase), house the published MH information and population data. We previously constructed a single nucleotide polymorphism SNP-SNP MH database (D-SNPsDB) of MHs within 50 bp on the whole human genome for 26 populations integrating basic data such as physical genome positions, mapping of variant identifiers (rsIDs), allele frequencies, and basic variant information. Building upon the previous research, we further selected MHs containing at least two variants (SNPs and/or insertions/deletions [InDels]) within a short DNA fragment (≤ 50 bp) in 26 populations based on the 1000 Genomes Project dataset (Phase 3) to construct a more comprehensive database. Subsequently, we established a user-friendly website that allows users to search the MH database (MHBase) based on their research objectives and study population to find suitable loci and provides other functions such as querying reported loci, performing online calculations using the PHASE software, and calculating ancestral-related parameters. The loci in the database are classified as SNP-based MHs, which include only SNPs, and InDel-including MHs, which contain at least one InDel. Here, we provide a detailed overview of the MHBase and an analysis of shared loci at the global and continental levels, ancestral markers, the genetic distance within loci, and mapping with the genome annotation file. The website is an accessible and useful tool for researchers engaged in marker discovery, population studies, assay development, and panel design.

Active Equalization of Lithium Battery Pack with Adaptive Control based on DC Energy Conversion Circuit

Background:: How to solve the inconsistency of battery pack is a key point to ensure reliable operation of electric vehicles. Battery equalization is an effective measure to address the inconsistency. Passive equalization method has poor efficiency and thermal management problems. Average voltage equalization method is only suitable for situations where there is a significant voltage difference between batteries. The SOC-based equalization method is relatively difficult and may inevitably lead to the accumulation of errors during the process. Objective:: In order to avoid the disadvantages of traditional control methods, a new control method is proposed to improve the accuracy and self-adaptation of active equalization, which is easy to be realized without online calculation. Methods:: Cascaded bidirectional Buck-boost circuit is adopted as the novel equalization topology. Based on fuzzy PID theory, an adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed in this paper. Parameter design of the fuzzy PID controller is carried out. A battery equalization system based on cascaded bidirectional Buck-boost circuit is designed and developed. Experimental verification is conducted on relevant hardware platforms. Results:: An adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed. Compared to passive equalization, this proposed method provides high efficiency. Regarding traditional voltage control, the method improves control reliability and flexibility. Compared to the average voltage equalization method, the approach needs less convergence time. Moreover, the control method is much easier to realize than the SOC-based equalization method. Conclusion:: By using the presented adaptive control based on DC energy conversion circuit, the degree of self-adaptation of the equalization process has been obtained as higher and the inconsistency as smaller.

Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals.

The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons. This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters. The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity. The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals. This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.

Online battery aging calculation in plug-in hybrid electric vehicle considering driving condition

ABSTRACT Online calculation of battery capacity loss and its instantaneous remaining life for electric and hybrid electric vehicles is a challenging subject, particularly for designing and optimizing vehicle energy management in real-life driving conditions. This paper presents an online method for estimating plug-in hybrid electric vehicle battery degradation, considering the driving conditions. For this purpose, a national parallel plug-in hybrid electric vehicle is employed, where its components have been sized based on some experimental map characteristics using a genetic algorithm. Also, a real-world driving cycle is developed based on the data gathered in the actual traffic conditions in Tehran. The vehicle’s energy management system is designed using an adaptive equivalent consumption minimization strategy. In addition, an empirical battery life model combined with an online cycle counting method is used for battery aging calculation. To evaluate the effectiveness of the proposed approach, the capacity fade of the vehicle battery over various driving cycles has been calculated. The comparison of the results confirms the efficacy of the proposed algorithm compared to the Rainflow counting algorithm. Applying the Rainflow to all driving cycles yields consistent outcomes. However, when employing the suggested method, capacity losses of approximately 2.4%, 1.9%, 2.1%, and 1.4% are detected in Tehran, FTP-75, WLTP, and NEDC, respectively, following one hundred driving cycles. This means the capacity loss increases by 35%, 50%, and 71% for FTP-75, WLTP, and Tehran, respectively, compared with the NEDC driving cycle.

A local ROM for Rayleigh–Bénard bifurcation problems

This work presents a local reduced-order method for computing bifurcation diagrams in 2D Rayleigh–Bénard convection problems. The proposed method is based on Proper Orthogonal Decomposition, and employs a reduced-order study of the regularity of solutions to detect new solution branches within the bifurcation diagram. The locality of the method is achieved through k-means clustering, and the selection of local problems in the online stage is done in the solution space. All hyperparameters of the reduced method, such as the number of clusters or the number of POD modes, are estimated by deterministic criteria. Furthermore, the offline–online splitting strategy for online calculations is explicitly outlined. The method is applied to a single-parameter problem and a two-parameter problem, showing its ability to rapidly compute bifurcation diagrams with small errors. Compared to a standard approach that samples each branch separately, the local approach produces more accurate results in less computational time.

A Fast Calculation Approach for Elastohydrodynamic Finite Line Contacts Applicable to Online Calculations of Rolling Bearing Models

Abstract Finite line contacts in rolling element bearings are usually under the regime of elastohydrodynamic lubrication (EHL). To obtain deeper insights into bearing performance, it is necessary to directly couple EHL contact models into bearing models. However, the existing EHL contact models are either too time consuming to be employed in the bearing model or too simplified to consider tilting contact behaviors and actual roller profiles. A fast calculation approach for EHL finite line contacts is proposed by combining the empirical film thickness formulas that have been developed for decades and an improved slicing technique that considers the coupling behaviors between slices. The proposed approach can not only predict the contact stiffness (normal contact stiffness and tilting contact stiffness) and contact states (contact pressure and film thickness) accurately but also is universal for different profiled contacts and material properties. The proposed approach costs only a few milliseconds for a single load case, which enables it to be directly employed in bearing models. Besides, the proposed approach is more of a framework, the use of which can be extended by involving different film thickness formulas and correction factors to consider complicated EHL behaviors such as thermal effects, shear thinning effects, surface roughness, lubricant starvation, and so on.

An integrated energy storage framework with significant energy management and absorption mechanism for machine learning assisted electric vehicle application

Regarding environmental friendliness, low maintenance needs, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) have become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energy flow system. As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when compared to solo energy sources. Additionally, the primary problem interaction applications, including such battery electric vehicles, are the energy storage system. Multiple energy storage technologies, including battery packs, flywheels, super-capacitors and fuel cells, are combined into a HESS due to their complementing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing backup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of power density. HESS needs a smart Energy Management System (EMS) to function properly since it combines the dynamic characteristics of a battery and a SuperCapacitor (SC). The motive of the study is to suggest an actual power management control system to accomplish these objectives. The plan is built using a wavelet transform, deep learning mechanism, and fuzzy logic together. A useful tool for separating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is challenging to immediately apply it in a system, though. Because of this, the traditional optimization-model-based facility energy management system encounters substantial difficulties with online forecast and calculation. To solve this problem, the paper proposes a ML technique dependent on a Long Short-Term Memory (LSTM). The suggested control system structure allows for the separation of the offline and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline stage. As a result, the supercapacitor receives an online calculation and distribution of the high-frequency power requirement. The SOC of the supercapacitor is kept within the appropriate range via fuzzy logic control. To evaluate the efficacy of the suggested energy management control technique, a 70 V battery with 92 V supercapacitor hybrid energy storage devices for hardware platforms have been created.

A time-varying driving style oriented model predictive control for smoothing mixed traffic flow

Connected and automated vehicles (CAVs) have great potential to smooth mixed traffic flow. To focus on the fact that the driving styles of HDVs are time-varying, a new control framework based on time-varying model predictive control (MPC) is proposed for mixed traffic flow in longitudinal control. Firstly, a genetic algorithm can be employed to calibrate the parameters of the car-following model based on offline trajectory data, representing the long-term driving style of HDVs. Secondly, the short-term driving style is recognized through modifying the long-term driving style based on real-time trajectory data within a sliding window. Then, this paper seeks to design a time-varying MPC control framework to realize a smoother mixed traffic flow. Finally, compared to the baseline model, the method we proposed achieves the high-performance both in fuel consumption and driving comfort under various scenarios, and maintains fast online calculations. Besides, it also displays the significant potential of saving fuel consumption in a large-scale traffic system, reducing by over 11.28% with 17∼33% penetration rates of CAVs.