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hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

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Computers, Materials & Continua 2022年第6期71卷 4659-4676页

作者： Radek Martinek Jan Baros Rene Jaros Lukas Danys Jan Nedoma VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Cybernetics and Biomedical Engineering70800Ostrava-PorubaCzechia VSB Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Telecommunications70800Ostrava-PorubaCzechia

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

关键词： 5G noise reduction hybrid algorithms speech recognition 5G data networks in-vehicle background noise

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Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

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Pedosphere 2022年第5期32卷 718-732页

作者： Khabat KHOSRAVI Phuong T.T.NGO Rahim BARZEGAR John QUILTY Mohammad T.AALAMI Dieu T.BUI Department of Watershed Management Engineering Ferdowsi University of MashhadMashhad 93(Iran) Department of Earth and Environment Florida International UniversityMiami 33199(USA) Institute of Research and Development Duy Tan UniversityDa Nang 550000(Vietnam) Department of Bioresource Engineering McGill UniversitySte Anne de Bellevue QC H9X(Canada) Faculty of Civil Engineering University of TabrizTabriz 51(Iran) Department of Civil and Environmental Engineering University of WaterlooWaterloo N2L 3G1(Canada) Department of Business and IT University of South-Eastern NorwayNotodden 3603(Norway)

Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and costly.Empirical and physical models have been developed to predict cumulative infiltration(CI),but are often inaccurate.In this study,several novel standalone machine learning algorithms(M5Prime(M5P),decision stump(DS),and sequential minimal optimization(SMO))and hybrid algorithms based on additive regression(AR)(i.e.,AR-M5P,AR-DS,and AR-SMO)and weighted instance handler wrapper(WIHW)(i.e.,WIHW-M5P,WIHW-DS,and WIHW-SMO)were developed for CI prediction.The Soil Conservation Service(SCS)model developed by the United States Department of Agriculture(USDA),one of the most popular empirical models to predict CI,was considered as a benchmark.Overall,154 measurements of CI(explanatory/input variables)were taken from 16 sites in a semi-arid region of Iran(Illam and Lorestan provinces).Six input variable combinations were considered based on Pearson correlations between candidate model inputs(time of measuring and soil bulk density,moisture content,and sand,clay,and silt percentages)and CI.The dataset was divided into two subgroups at random:70%of the data were used for model building(training dataset)and the remaining 30%were used for model validation(testing dataset).The various models were evaluated using different graphical approaches(bar charts,scatter plots,violin plots,and Taylor diagrams)and quantitative measures(root mean square error(RMSE),mean absolute error(MAE),Nash-Sutcliffe efficiency(NSE),and percent bias(PBIAS)).Time of measuring had the highest correlation with CI in the study area.The best input combinations were different for different algorithms.The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models.The AR-M5P model provided the most accurate CI predictions(RMSE=0.75 cm,MAE=0.59 cm,NSE=0.98),while the SCS model had the lowest performance(RMSE=4.77 cm,MAE=2.64 cm,NSE=0.23).The differences in RMSE between the best m

关键词： additive regression hybrid algorithms empirical model soil water infiltration weighted instances handler wrapper

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Application of Metaheuristic algorithms for Optimizing Longitudinal Square Porous Fins

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Computers, Materials & Continua 2021年第4期67卷 73-87页

作者： Samer H.Atawneh Waqar A.Khan Nawaf N.Hamadneh Adeeb M.Alhomoud College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Department of Mechanical Engineering College of EngineeringPrince Mohammad bin Fahd UniversityAl Khobar31952Saudi Arabia Department of Basic Sciences College of Science and Theoretical StudiesSaudi Electronic UniversityRiyadh11673Saudi Arabia

The objectives of this study involve the optimization of longitudinal porous fins of square cross-section using metaheuristic algorithms.A generalized nonlinear ordinary differential equation is derived using Darcy and Fourier’s laws in the energy balance around a control volume and is solved numerically using RFK 45 method.The temperature of the base surface is higher than the fin surface,and the fin tip is kept adiabatic or cooled by convection heat transfer.The other pertinent parameters include Rayleigh number(100≤Ra≤10^(4)),Darcy number,(10^(−4)≤Da≤10^(−2)),relative thermal conductivity ratio of solid phase to fluid(1000≤kr≤8000),Nusselt number(10≤Nu≤100),porosity(0.1≤φ≤0.9).The impacts of these parameters on the entropy generation rate are investigated and optimized using metaheuristic algorithms.In computer science,metaheuristic algorithms are one of the most widely used techniques for optimization problems.In this research,three metaheuristic algorithms,including the firefly algorithm(FFA),particle swarm algorithm(PSO),and hybrid algorithm(FFAPSO)are employed to examine the performance of square fins.It is demonstrated that FFA-PSO takes fewer iterations and less computational time to converge compared to other algorithms.

关键词： Optimization firefly algorithm particle swarm algorithm hybrid algorithms porous media entropy generation rate

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Noise Reduction in Industry Based on Virtual Instrumentation

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Computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend human-machine-interaction.In the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G deployment.This study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a dataset.The experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from Microsoft.We tested a hybrid algorithm for noise reduction and its impact on voice command recognition efficiency.Using virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested algorithms.Laser welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+ICA.Press workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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