High-accuracy Approach Research Articles

Electronic structure simulations in the cloud computing environment.

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi-Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery. We pay particular attention to the intricate workflows for performing complex chemistry simulations, associated data curation, and mechanisms for accuracy assessment, which is demonstrated with the Arrows automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities.

Deep learning facilitated superhigh-resolution recognition of structured light ellipticities.

Elliptical beams (EBs), an essential family of structured light, have been investigated theoretically due to their intriguing mathematical properties. However, their practical application has been significantly limited due to the inability to determine all their physical quantities, particularly the ellipticity factor, a unique parameter for EBs of different families. In this paper, to our knowledge, we proposed the first high-accuracy approach that can effectively distinguish EBs with an ellipticity factor difference of 0.01, equivalent to 99.9% field similarities. The method is based on a transformer deep learning (DL) network, and the accuracy has reached 99% for two distinct families of exemplified EBs. To prove that the high performance of this model can dramatically extend the practical aspect of EBs, we used EBs as information carriers in free-space optical communication for an image transmission task, and an error bit rate as low as 0.22% is achieved. Advancing the path of such a DL approach will facilitate the research of EBs for many practical applications such as optical imaging, optical sensing, and quantum-related systems.

Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

Carbon dioxide (CO2) is a crucial greenhouse gas, and its concentration and spatiotemporal characteristics are among the principal sources of uncertainty in global warming assessments. Satellite remote sensing is a widely adopted, high-accuracy approach for monitoring atmospheric CO2. However, limited swath width and cloud cover significantly reduce satellite observation coverage. This study addresses the temporal changes in CO2 concentration and utilizes a machine learning-based fusion of multiple data sources to generate daily, full-coverage, 0.05° spatial resolution column-averaged CO2 concentration data for China from 2015 to 2020. Ten-fold cross-validation yielded a determination coefficient R2 of 0.97, root mean square error of 0.92 ppm, and mean absolute error of 0.59 ppm. Compared to other datasets, this study’s dataset exhibits superior accuracy and spatiotemporal detail. Using the produced CO2 concentration data in this study, we conducted a spatiotemporal analysis of CO2 concentrations in China. The results indicate that, in general, the Western region exhibits a higher growth rate in CO2 concentration than the Eastern and Central regions, with areas of lower CO2 concentration experiencing higher growth rates while regions with higher CO2 concentration have lower growth rates. Moreover, the highest increase in CO2 concentration occurred in 2016, with a substantial decrease in CO2 concentration growth observed in 2018. Notably, the reduction in CO2 concentration in the Qinghai-Tibet Plateau region during the summer is considerably smaller than in other regions, possibly due to atmospheric transport from the Indian Peninsula.

Spectrally tunable light source based on deep neural network model

A spectrally tunable light source based on deep neural network (DNN) model is proposed in this work, which can reproduce arbitrary spectra accurately and rapidly. After calculating the scale factors using the trained DNN model, the target spectrum can be reproduced by regulating the combined monochromatic LEDs based on the pulse width modulation (PWM) signal with corresponding duty cycle. The standard solar spectrum and measured natural spectra at different times are reproduced using the proposed spectrally tunable light source. It is demonstrated that there is a good agreement between the target spectra and the corresponding reproduced spectra, with a fitted correlation index of above 0.9. Furthermore, the proposed spectrally tunable light source is able to transform spectrum at a frequency higher than 25 Hz and occupies only 750 KB memory space. As a result, the proposed spectrally tunable light source system can offer a high accuracy, fast speed and low cost approach to the development of specially light sources.

Analysis of Mesh Resolution Effect to Numerical Result of CFD-ROM: Turbulent Flow in Stationary Parallel Plate

Computational fluid dynamics (CFD) is extensively utilized to predict flow behaviour in various industries and applications. The Full Order Model (FOM) is a high-accuracy approach to flow modelling, but it requires significant computational resources due to its high order and thousands of variables. To address this problem, the Reduced Order Model (ROM) was developed. Despite the advancement brought by ROM, there is a notable gap in research concerning the impact of mesh configuration on CFD-ROM results. While the number of modes has been extensively studied for its influence on CFD-ROM, the mesh configuration, a critical aspect of the simulation process, has received relatively limited attention. This study investigates the effect of mesh resolution on numerical results in CFD-ROM concerning turbulent flow within stationary parallel plates. Employing rigorous methods, including Richardson Extrapolation, verification, validation, and error percentage. The results explicitly confirm that mesh resolution directly impacts the numerical results of the velocity field in CFD-ROM. It is found that there is a notable reduction in Convergence Grid Index (CGI) values for different mesh ratios: 6.401% for medium-to-coarse and 2.031% for fine-to-medium ratio. Thus, with the same mode number, mesh resolution selection can enhance the numerical result of the velocity field in CFD-ROM.

High Accuracy Approach for Crosstalk Effects Compensation on 2-D Resistive Sensor Array Readouts

High Accuracy Approach for Crosstalk Effects Compensation on 2-D Resistive Sensor Array Readouts

Self-sampling for human papilloma virus DNA as a strategy to increase access to cervical cancer screening: patients' and providers' perspectives on facilitators and barriers to scale.

Self-sampling represents a high accuracy approach to human papilloma virus DNA (HPV-DNA) testing that allows for privacy and autonomy. As part of a larger study to evaluate cervical cancer screening in Mozambique, we explored user-driven facilitators and barriers to, and provider perspectives on, self-sampling. Our study was conducted in 4 public health facilities in 2 districts in Mozambique. Women aged 30-49 were prospectively enrolled for HPV screening and were offered provider or self-collected sampling. We used enrolment data from 9014 participants to examine characteristics of women choosing self-sampling and conducted in depth interviews with 104 women and 15 providers to understand facilitators and barriers to self-sampling. 97.5% of participants chose self-sampling over provider sampling. Participant-reported barriers included fear about technique, discomfort and inadequate training. Facilitators to self-sampling included increased privacy and having been exposed to a peer who previously self-sampled. Providers expressed concern about their limited role in the screening process with a self-sampling technique. Self-sampling for HPV is an acceptable approach to cervical cancer screening but barriers such as fear of sampling incorrectly and discomfort with their bodies remain.

An advanced machine learning approach for high accuracy automated diagnosis of otitis media with effusion in different age groups using 3D wideband acoustic immittance

Wideband Acoustic Immittance (WAI) is a diagnostic tool for identifying middle ear dysfunction. The challenge to its widespread use is difficulty in interpreting the complex data. This study aimed to develop advanced Machine Learning (ML) tools to automatically diagnose ears with otitis media with effusion (OME) in different age groups from the WAI data. A total of 1177 sets of WAI data were collected from 551 normal middle ears and 626 ears with OME, divided into three age groups. A Titan IMP440 was used to measure wideband absorbance at frequencies from 226 to 8000 Hz, and pressure between +200 daPa and −300 daPa. A two-stage ML approach was used to achieve a highly accurate diagnosis of OME in each age group. In the first stage, a convolutional neural network (CNN) was developed to classify the WAI data set. In the second stage, another neural network with a self-attention mechanism was used to classify the most discriminative regions of the data. These regions were extracted areas that had the top 2.5 % most statistically significant difference between normal and OME ears in the training WAI data. Final classification considered outputs from the two stages. The two-stage ML approach achieved classification accuracy of 96.6 %, 94.1 %, and 90.7 % for the three age groups, respectively. The importance of this research is its contribution to the development of an automated diagnostic tool for OME. This tool will be easy to use, highly accurate, works across age groups and which will support clinicians in their diagnostic decisions.

Edge-Enhanced Object-Space Model Optimization of Tomographic Reconstructions for Additive Manufacturing.

Object-space model optimization (OSMO) has been proven to be a simple and high-accuracy approach for additive manufacturing of tomographic reconstructions compared with other approaches. In this paper, an improved OSMO algorithm is proposed in the context of OSMO. In addition to the two model optimization steps in each iteration of OSMO, another two steps are introduced: one step enhances the target regions' in-part edges of the intermediate model, and the other step weakens the target regions' out-of-part edges of the intermediate model to further improve the reconstruction accuracy of the target boundary. Accordingly, a new quality metric for volumetric printing, named 'Edge Error', is defined. Finally, reconstructions on diverse exemplary geometries show that all the quality metrics, such as VER, PW, IPDR, and Edge Error, of the new algorithm are significantly improved; thus, this improved OSMO approach achieves better performance in convergence and accuracy compared with OSMO.

A Martian sonic anemometer

On Mars, we have yet to fully quantify atmospheric turbulent transport [Banfield, JASA (2016)]. The key required instrument is a wind sensor that can resolve horizontal and vertical winds, and the perturbations associated with turbulent eddies. On the surface, 10–20Hz sampling and a sensitivity of 5 cm/s are required (McBean, 1972). Viking, Pathfinder, Curiosity, and Insight used hot film or hot wire anemometers that can be confused by radiative heating and sample at approximately 1 Hz. Sonic anemometry is an alternative high speed, high accuracy approach which uses differences in the acoustic time of flight to determine flow speed. However, operation on Mars can be challenging due to the low acoustic source strength and high attenuation in the thin (6––11 mbar) CO2 atmosphere, as well as large temperature variations (−100°C to 20°C). In this paper, we describe an instrument that addresses these challenges: a 3 axis ultrasonic anemometer using custom low impedance, wide band transducers, low noise electronics, and an optimized mechanical structure. With an earlier prototype in wind tunnel tests in 6 mbar CO2, we demonstrated resolution better than 10 cm/s, and an accuracy of 3% at flow speeds up to 12 m/s. Data from an instrument flight to 128 000 feet on a stratospheric balloon will be presented, and progress towards a flight qualifiable instrument will be described.

Numerical solution of distributed-order time fractional Klein–Gordon–Zakharov system

In this work, the distributed-order time fractional Klein–Gordon–Zakharov system is introduced by substituting the second-order temporal derivative with a distributed-order fractional derivative. The Caputo fractional derivative is utilized to define this kind of distributed-order fractional derivative. A high accuracy approach based on the Chebyshev cardinal polynomials is established for this system. The proposed method turns the fractional system solution into an algebraic system solution by approximating the unknown solution via these cardinal polynomials and engaging their derivative matrices (that are obtained in this paper). Some test problems are considered to investigate the capability and accuracy of this approach.

Nonlinear Iterative Learning Control: A Frequency-Domain Approach for Fast Convergence and High Accuracy

Iterative learning control (ILC) involves a trade-off between perfect, fast attenuation of iteration-invariant disturbances and amplification of iteration-varying ones. The aim of this paper is to develop a nonlinear ILC framework that achieves fast convergence, robustness, and low converged error values in ILC. To this end, the method includes a deadzone nonlinearity in the learning update, which uses the difference in amplitude characteristics of repeating and varying disturbances to modify the learning gain for each error sample. A criterion for monotonic convergence of the nonlinear ILC algorithm is provided, which is used in combination with system measurements to select suitable design parameters. The proposed algorithm is validated using simulations, in which fast convergence to low error values is demonstrated.

Adaptive online auto-tuning using Particle Swarm optimized PI controller with time-variant approach for high accuracy and speed in Dual Active Bridge converter

<abstract> <p>Electric vehicles (EVs) are an emerging technology that contribute to reducing air pollution. This paper presents the development of a 200 kW DC charger for the vehicle-to-grid (V2G) application. The bidirectional dual active bridge (DAB) converter was the preferred fit for a high-power DC-DC conversion due its attractive features such as high power density and bidirectional power flow. A particle swarm optimization (PSO) algorithm was used to online auto-tune the optimal proportional gain (<italic>K<sub>P</sub></italic>) and integral gain (<italic>K<sub>I</sub></italic>) value with minimized error voltage. Then, knowing that the controller with fixed gains have limitation in its response during dynamic change, the PSO was improved to allow re-tuning and update the new <italic>K<sub>P</sub></italic> and <italic>K<sub>I</sub></italic> upon step changes or disturbances through a time-variant approach. The proposed controller, online auto-tuned PI using PSO with re-tuning (OPSO-PI-RT) and one-time (OPSO-PI-OT) execution were compared under desired output voltage step changes and load step changes in terms of steady-state error and dynamic performance. The OPSO-PI-RT method was a superior controller with 98.16% accuracy and faster controller with 85.28 s<sup>-1</sup> average speed compared to OPSO-PI-OT using controller hardware-in-the-loop (CHIL) approach.</p> </abstract>

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images.

The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus's characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10-11 at 639 epoch, regression of -1.6 × 10-9, momentum gain (Mu) 1 × 10-9, and gradient value of 9.6852 × 10-8, which represent a network with a high ability to predict virus levels. The fully automated system enables virologists to take a high-accuracy approach to virus diagnosis, prevention of mutations, and life cycle and improvement of diagnostic reagents and drugs, adding a point of view to the advancement of medical virology.

Malicious attacks detection using GRU-BWFA classifier in pervasive computing

With the advanced trends in pervasive computing, the data users were subjected to face different kinds of attacks. Several algorithms for attack detection was performed with minimal accuracy in prediction and consideration of performance metrics was not effective. Hence effective and prompt detection of malicious attacks must be optimized in terms of confidentiality, privacy, availability and integrity. Accordingly, the proposed research paper provides an effective mechanism for detecting and classifying DDoS attacks such as TCP-SYN, UDP flood, ICMP echo, HTTP flood, Slow Loris Slow Post and Brute Force attack, by utilizing machine learning methods within the UNSW-NB15 dataset and NSL-KDD dataset. Significantly, Gated Recurrent Unit Neural Network based on Bidirectional Weighted Feature Averaging (GRU-BWFA) classifier is utilized as a proposed classifier approach for high detection rate and accuracy in distinguishing the mentioned DDoS attacks. Feature selection is performed using the Enhanced Salp Swarm Optimization technique to select the optimal features for identifying the attacks. The proposed classifier evaluates with other different classifiers which provide a detailed study in detecting DDoS attacks using the UNSW-NB15 dataset and NSL-KDD dataset. The proposed model results 0.9936 accuracy for UNSW-NB 15 dataset and 0.9918 accuracy for NSL-KDD dataset. Empirical findings indicate that machine learning methods are highly effective at detecting and classifying attacks with a higher accuracy rate.

A high-accuracy genotype classification approach using time series imagery

Genotype classification plays a vital role in cultivar evaluation, selection, and production. However, classifying plant genotypes by phenotypes remains an unresolved issue. In this paper, a high-accuracy approach is proposed for plant genotype classification. Based on the Densenet201 and bi-directional Long Short-Term Memory model (bi-directional LSTM), a Densenet201-BLSTM model is given in the approach for classifying various genotypes based on time series of plant images. The growth and development dynamic behaviours and important phenotypes of plants are bi-directionally encoded by the proposed Densenet201-BLSTM to model the complex relationship between phenotypes and genotypes. The accuracy of genotype classification obtained by the proposed DenseNet201-BLSTM model on the test dataset reaches 98.31%. The first attempt is made to classify genotypes of panicoid grain crops . What's more, the proposed genotype classification approach will be useful for the classification of progeny accessions based on their similarity to reference accessions. • A high-accuracy approach of plant genotype classification is proposed. • Genotypes were classified by simultaneously considering spatiotemporal features. • The first attempt is made to classify genotypes of panicoid grain crops. • This study provides the foundation for genotype–phenotype correlation research.

Time Series Forecasting Based on Support Vector Machine Using Particle Swarm Optimization

In recent years, due to the non-linear nature, complexity, and irregularity of time series, especially in energy consumption and climate, studying this field has become very important. Therefore, this study aims to provide a high accuracy and efficiency hybrid approach to time series forecasting. The proposed model is called EDFPSO-SVR (Empirical Mode Decomposition- Discrete Wavelet Transform- Feature selection with Particle Swarm Optimization-Support Vector Regression). In the proposed hybrid approach, the first step is to decompose the signal into the Intrinsic Mode Functions (IMF) component using the Empirical Mode Decomposition (EMD) algorithm. In the second step, each component is transformed into subsequences of approximation properties and details by converting the Wavelets. In the third step, the best feature is extracted by the PSO algorithm. The purpose of using the PSO algorithm is feature extraction and error minimization of the proposed approach. The fourth step, using time vector regression, has dealt with time series forecasting. Four data sets in two different fields have been used to evaluate the proposed method. The two datasets are electric load of England and Poland, and the other two datasets are related to the temperature of Australia and Belgium. Evaluation criteria include MSE, RMSE, MAPE, and MAE. The evaluation results of the proposed method with other Principal component analysis (PCA) feature extraction algorithms, and comparisons with methods and studies in this field, indicate the proper performance of the proposed approach.

Experimental Evaluation of the Forkbeard Ultrasonic Indoor Positioning System

Indoor positioning systems are crucial to provide location-based services in areas that are not covered by a Global Navigation Satellite System. Among the many technologies applied to this field, ultrasound has emerged as a potential low-cost, high-accuracy approach to positioning based on trilateration. Several ultrasonic systems have been proposed over the years. Of these, academic systems are typically prototypes that are unavailable to the public, whereas commercial systems generally do not provide characterization test results. In this work, we have conducted a detailed characterization study of a commercial indoor positioning system for smart devices developed by Forkbeard Technologies AS. We tested the system under static and dynamic conditions in a motion capture lab of approximately 150 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We considered different room occupancies, beacon configurations, and device positions. The results, given in terms of 2-D absolute errors at different confidence levels, show great variation depending on the aforementioned conditions. The worst case scenario corresponds to a pedestrian in motion in an office setup with four beacons, with the phone placed in the pocket. In this case, 80% of the errors were below 143 cm. The best results were obtained under static conditions using ten beacons, for which 80% of the errors were below 44 cm.

Resonant Frequency Ultrasonic P-Waves for Evaluating Uniaxial Compressive Strength of the Stabilized Slag–Cement Sediments

Abstract Marine sediments can be stabilized by ultra high-strength binders: cement, Cement Kiln Dust (CKD) and slag. The properties of the stabilized soil indicate potential to their reuse. This study investigated the performance of the unconfined compressive strength (UCS) in the marine sediments stabilized by binder (cement, CKD, slag), tested by ultrasonic P-waves. Materials include 194 specimens collected from the port of Gothenborg. The experiment was performed in Swedish Geotechnical Institute (SGI). The UCS of specimens stabilized by different ratio of binders (cement, CKD, slag) was tested by resonance frequencies of the elastic P-waves. The significant increase in the UCS (&gt;1500 kPa) was recorded for the highest values of CKD and cement, and low values of slag. The correlation profiles of low water/high binder (LW/HB) cement/slag (40/60%) were controlled by curing time. The slag–cement–CKD simplex tests demonstrated UCS of samples with low/high water content and various binder ratio of cement (kg/m3). The ratio of cement binder and curing time play a critical role in the increase of UCS followed by mechanical properties of specimens and intensity of stress. The highest values exceed 1000 m/s in P-waves. The results shown high accuracy (97%) and non-contacting approach for testing UCS of sediments. Seismic methods can be applied to test the UCS of the stabilized sediments, and also in-situ via seismic CPT, surface testing or cross hole seismic testing.

Virtual beam generation and guidance command calculation in FLS advanced approach process

FLS approach is an effective means to improve approach perception and reduce operational risk, and it is one of the key functions of flight management system of advanced commercial aircraft. This paper studies the generating virtual beam, calculating beam deviation and guidance command in FLS advanced approach guidance technology, and proposes a method of generating virtual beam based on navigation database information and a method of calculating beam deviation based on spatial geometric relationship. The guidance law was constructed based on angle deviation and lateral distance between the actual path and FLS beam, and the simulation test examle was designed to verity the deviation and guidance command calculation algorithm. The simulation results show that the FLS guidance algorithm proposed in this paper can achieve high-accuracy approach guidance.