Experimental Identification of Model Parameters and the Statistical Processing Using a Nonlinear Oscillator Applied to EEG Analysis

2018 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Stress State ◽  
Nonlinear Oscillator ◽  
Statistical Processing ◽  
Psychological State ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Model ◽  
Eeg Data ◽  
Machine Interface ◽  
Almost All

The nonlinear analysis may help to reveal the complex behavior of the Electroencephalogram (EEG) signal. In order to analyze the EEG in real time, we have proposed an EEG analysis model using a nonlinear oscillator with one degree of freedom and minimum required parameters. Our method identifies EEG model parameters experimentally. The purpose of this study is to examine the specific characteristic of model parameters. Validation of the method and investigation of characteristic of model parameters were conducted based on alpha frequency EEG data in both relax state and stress state. The results of the parameter identification with the time sliding window for 1 second show almost all of the identified parameters have a normal distribution spread around the average. The model outputs can closely match the complicated experimental EEG data. The results also showed that the existence of nonlinear term in the EEG analysis is crucial and the linearity parameter shows a certain tendency as the nonlinearity increases. Furthermore, the activities of EEG become linear on the mathematical model when suddenly change from the relax state to the stress state. The results indicate that our method may provide useful information in various field including the quantification of human mental or psychological state, diagnosis of brain disease such as epilepsy and design of brain machine interface.

scholarly journals Comprehensive Wind and Wave Statistics and Extreme Values for Design and Analysis of Marine Structures in the Adriatic Sea

2021 ◽  
Vol 9 (5) ◽  
pp. 522
Author(s):  
Marko Katalinić ◽  
Joško Parunov
Keyword(s):  
Adriatic Sea ◽  
Extreme Values ◽  
Wave Climate ◽  
Offshore Structures ◽  
Model Parameters ◽  
Peak Period ◽  
Significant Wave ◽  
Wave Statistics ◽  
Wave Heights ◽  
Almost All

Wind and waves present the main causes of environmental loading on seagoing ships and offshore structures. Thus, its detailed understanding can improve the design and maintenance of these structures. Wind and wave statistical models are developed based on the WorldWaves database for the Adriatic Sea: for the entire Adriatic Sea as a whole, divided into three regions and for 39 uniformly spaced locations across the offshore Adriatic. Model parameters are fitted and presented for each case, following the conditional modelling approach, i.e., the marginal distribution of significant wave height and conditional distribution of peak period and wind speed. Extreme significant wave heights were evaluated for 20-, 50- and 100-year return periods. The presented data provide a consistent and comprehensive description of metocean (wind and wave) climate in the Adriatic Sea that can serve as input for almost all kind of analyses of ships and offshore structures.

Identification of ’Effective’ Linear Joints Using Coupling and Joint Identification Techniques

1998 ◽  
Vol 120 (2) ◽  
pp. 331-338 ◽  
Author(s):  
Y. Ren ◽  
C. F. Beards
Keyword(s):  
Dynamic Properties ◽  
Real Life ◽  
Model Parameters ◽  
Coupling Technique ◽  
Alternative Approach ◽  
Identification Technique ◽  
Joint Identification ◽  
Stiff Joint ◽  
Almost All ◽  
Identification Techniques

Almost all real-life structures are assembled from components connected by various types of joints. Unlike many other parts, the dynamic properties of a joint are difficult to model analytically. An alternative approach for establishing a theoretical model of a joint is to extract the model parameters from experimental data using joint identification techniques. The accuracy of the identification is significantly affected by the properties of the joints themselves. If a joint is stiff, its properties are often difficult to identify accurately. This is because the responses at both ends of the joint are linearly-dependent. To make things worse, the existence of a stiff joint can also affect the accuracy of identification of other effective joints (the term “effective joints” in this paper refers to those joints which otherwise can be identified accurately). This problem is tackled by coupling these stiff joints using a generalized coupling technique, and then the properties of the remaining joints are identified using a joint identification technique. The accuracy of the joint identification can usually be improved by using this approach. Both numerically simulated and experimental results are presented.

scholarly journals Free Sunday-Loss, Deprivation or Welfare

2020 ◽  
pp. 10-22
Author(s):  
Elza Jurun ◽  
Nada Ratković ◽  
Bože Vuleta
Keyword(s):  
Public Opinion ◽  
Statistical Analysis ◽  
Statistical Processing ◽  
Education Level ◽  
Economic Issue ◽  
Human Right ◽  
The Republic ◽  
Almost All ◽  
Maximum Limit ◽  
Demographic Policy

This paper presents the part of results obtained by a comprehensive statistical analysis of public opinion in the issue of work-free Sunday, based on a survey undertaken in the Republic of Croatia in October 2017. The research has been made aiming at providing the answer to the crucial question of whether free Sunday can be considered only as of the economic issue or concerns deeply almost all the spheres of life in general. Moreover, the authors want to show and promote free Sunday as socio-economic phenomena which become a political and ideological issue as a fundamental human right and true notion of human freedom and welfare. Besides, as a member of the European Sunday Alliance, Croatia is the first EU member state which promotes free Sunday as one of the measures of active demographic policy. Along with the results of classical statistical processing of public opinion research, the methodology of this research has also involved the hypothesis testing about differences in the proportions as well as post-stratification of the two-step stratified random sample based on gender, age, size of residence, regions and education level. Even more, than two-thirds of respondents consider important or exceptionally important not-working on Sundays and support the maximum limit of that work.

scholarly journals An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

2021 ◽  
Author(s):  
Sheng Zhang ◽  
Joan Ponce ◽  
Zhen Zhang ◽  
Guang Lin ◽  
George Karniadakis
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Robustness Analysis ◽  
Data Driven ◽  
Model Parameters ◽  
Calibration Model ◽  
Epidemiological Models ◽  
Analysis Model ◽  
Identifiability Analysis

AbstractEpidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and forecasting with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible–exposed–infectious–recovered (SEIR) model, including new compartments and model vaccination in order to forecast the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately predict the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC’s government’s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

scholarly journals The impact of social media on young web users’ psychological well-being during the COVID-19 pandemic progression

2021 ◽  
Vol 10 (39) ◽  
pp. 50-61
Author(s):  
Alisar Hudimova ◽  
Ihor Popovych ◽  
Vita Baidyk ◽  
Olena Buriak ◽  
Olha Kechyk
Keyword(s):  
Social Media ◽  
Media Use ◽  
Well Being ◽  
Diagnostic Methods ◽  
Psychological State ◽  
Social Media Use ◽  
Psychological Well Being ◽  
Use Of Social Media ◽  
Almost All ◽  
The Impact

Aim. The present study empirically investigates and theoretically substantiates the results of the impact of social media on young web-users’ psychological well-being during the forced self-isolation caused by the progression of the COVID-19 pandemic (N = 254). Materials and methods. Standardized valid psycho-diagnostic methods, the author’s questionnaire (A. Hudimova, 2021), correlation and factor analyses were used to identify young web users’ patterns of social media involvement during the forced self-isolation. Results. The results show that during the global COVID-19 pandemic, young web users give preference for passive social media use rather than for communication. The obtained results showed an expansion in the time spent via social media by young web users. It was found that the progression of the COVID-19 pandemic is accompanied by the participants’ experience of negative emotions and fears of the unknown (r = .204; p <.01). It is substantiated that increasing immersion of young web users in social media is a kind of strategy to escape from bad thoughts (r = .271; p <.01). Significantly, it is stated that uncontrolled use of social media causes sleep disorders during isolation (r = .444; p <.01). Conclusions. The study proves that young people spend almost all day online due to the obsessive pattern of social media involvement and/or procrastination, which often provokes withdrawal syndrome upon the attempt to distract from them. The lack of controlled time spending on social media during self-isolation provokes an exacerbation of anxiety, apathy, depressed mood, and a sense of isolation from social reality. The obtained results provide evidence that the causal relations of passive social media use provoke an exacerbation of feelings of alienation, disrupt the healthy rhythm of sleep, and psychological state of young web-users during the progression of the COVID-19 pandemic.

Examination of Correlation Between Arousal Level and Auditory Environmental Change Using Rock and Classic Music

2017 ◽  
Author(s):  
Kentaro Miyago ◽  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Visual Cortex ◽  
Frontal Lobe ◽  
Traffic Accidents ◽  
Evaluation Method ◽  
Duffing Oscillator ◽  
Least Square Method ◽  
Arousal Level ◽  
Least Square ◽  
Model Parameters ◽  
Eeg Data

Recently, traffic accidents due to drowsy driving, operation mistake in the power plant by drowsiness and decrease arousal in employment during work have been attracted as problems. To avoid such an accident, arousal level could be quantitatively evaluated in real time. We suggested that the one of the parameters of Duffing oscillator parameters is related to the conventional arousal level using the EEG frequency component. However, in this examination, effects on the EEG from visual and active behavior were considered, but those from hearing also need to be investigated. In this paper, we performed the experiment in the musical environment using rock and classic music to investigate the model parameters for effect of the auditory stimulation, and acquired EEG data in Visual cortex and Frontal lobe. The acquired EEG data was used to identify the model parameters, which were identified solving the inverse problem by Least Square method. Results of investigating correlation between conventional arousal revel and model parameter shows a significant correlation in case of the auditory environmental situation. Moreover, Visual cortex is better than Frontal lobe as a measurement point in this evaluation method.

scholarly journals The information content of Cosmic Infrared Background anisotropies

2019 ◽  
Author(s):  
Robert Reischke ◽  
Vincent Desjacques ◽  
Saleem Zaroubi
Keyword(s):  
Power Spectrum ◽  
Cosmological Parameters ◽  
Model Parameters ◽  
Mass Relation ◽  
Infrared Background ◽  
Cross Correlations ◽  
The Mean ◽  
Cosmic Infrared Background ◽  
Almost All ◽  
Halo Occupation Distribution

Abstract We use analytic computations to predict the power spectrum as well as the bispectrum of Cosmic Infrared Background (CIB) anisotropies. Our approach is based on the halo model and takes into account the mean luminosity-mass relation. The model is used to forecast the possibility to simultaneously constrain cosmological, CIB and halo occupation distribution (HOD) parameters in the presence of foregrounds. For the analysis we use wavelengths in eight frequency channels between 200 and 900 GHz with survey specifications given by Planck and LiteBird. We explore the sensitivity to the model parameters up to multipoles of ℓ = 1000 using auto- and cross-correlations between the different frequency bands. With this setting, cosmological, HOD and CIB parameters can be constrained to a few percent. Galactic dust is modeled by a power law and the shot noise contribution as a frequency dependent amplitude which are marginalized over. We find that dust residuals in the CIB maps only marginally influence constraints on standard cosmological parameters. Furthermore, the bispectrum yields tighter constraints (by a factor four in 1σ errors) on almost all model parameters while the degeneracy directions are very similar to the ones of the power spectrum. The increase in sensitivity is most pronounced for the sum of the neutrino masses. Due to the similarity of degeneracies a combination of both analysis is not needed for most parameters. This, however, might be due to the simplified bias description generally adopted in such halo model approaches.

An Elastic-Plastic Solver of the Wheel-Rail Contact

2010 ◽  
Author(s):  
Constantin I. Ba˘rbiˆnt¸a˘ ◽  
Sulleyman Yaldiz ◽  
Alina Dragomir ◽  
Spiridon S. Cret¸u
Keyword(s):  
Plastic Deformation ◽  
Stress State ◽  
Plastic Material ◽  
Computer Code ◽  
Load Level ◽  
Analysis Model ◽  
Elastic Plastic ◽  
Circular Arcs ◽  
Plastic Analysis ◽  
Perfect Plastic

Wheel and rail in service undergo continual wear and plastic deformation at the surface, so that in time all wheel profiles will be different. The optimization by grinding a worn rail profile to minimize contact stresses requires the development of a software to reconstruct a rail profile using circular arcs. A working algorithm, able to be incorporated into a computer code, has been developed to solve the stress state in the general case of non-Hertzian contacts. To limit the pressure, an elastic-perfect plastic material has been incorporated into the computer code. The pressure distribution and the corresponding stresses states have been investigated for pure normal loadings, as well as for the combined, normal and tangential loadings. The elastic-plastic analysis model allows fast investigations regarding the influence of different parameters such as load level, contact geometry including the geometry of the worn profiles.

Model-Based EEG Analysis: Proposal and Verification of Mathematical Model for Application to Neurotechnology

2019 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Mathematical Model ◽  
Binary Classification ◽  
Least Square ◽  
Mental Condition ◽  
Support Vector ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Method ◽  
Model Based ◽  
Proposed Model

Abstract We have modeled dynamics of EEG with one degree of freedom nonlinear oscillator and examined the relationship between mental state of humans and model parameters simulating behavior of EEG. At the IMECE conference last year, Our analysis method identified model parameters sequentially so as to match the waveform of experimental EEG data of the alpha band using one second running window. Results of temporal variation of model parameters suggested that the mental condition such as degree of concentration could be directly observed from the dynamics of EEG signal. The method of identifying the model parameters in accordance with the EEG waveform is effective in examining the dynamics of EEG strictly, but it is not suitable for practical use because the analysis (parameter identification) takes a long time. Therefore, the purpose of this study is to test the proposed model-based analysis method for general application as a neurotechnology. The mathematical model used in neuroscience was improved for practical use, and the test was conducted with the cooperation of four subjects. model parameters were experimentally identified approximately every one second by using least square method. We solved a binary classification problem of model parameters using Support Vector Machine. Results show that our proposed model-based EEG analysis is able to discriminate concentration states in various tasks with an accuracy of over 80%.

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