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.

Study on Detection of Epileptic Discharges Based on a Duffing Oscillator Model

2014 ◽  
Author(s):  
Takahiro Murakami ◽  
Yasumi Ukida ◽  
Masami Fujii ◽  
Michiyasu Suzuki ◽  
Takashi Saito
Keyword(s):  
Duffing Oscillator ◽  
Time History ◽  
Least Square Method ◽  
Least Square ◽  
Quantitative Detection ◽  
Model Parameters ◽  
Appearance Time ◽  
Epileptic Discharges ◽  
History Of ◽  
Electrocorticogram Ecog

In order to establish a quantitative detection method for appearance in epileptic discharges (EDs), we propose using the model parameters in a Duffing oscillator, which is a nonlinear mathematical model. Extracting four frequency bands of delta, theta, alpha and beta waves from the time history of the electrocorticogram (ECoG) obtained from rats with induced EDs, we applied a sweep window to the time history for each band. So as to fit the equation for the Duffing oscillator to the time history of the ECoG, we used the least square method to determine the model parameters expressing characteristics of ECoG. The Duffing oscillator has three kinds of vibrational parameters and four kinds of parameters about the amplitude for the driving force with two predominant frequencies contained in ECoG. In order to examine the appearance time of the EDs and the change of ECoG characteristics, we determined the model parameters for each sweep window. When epilepsy occurs, we found that the amount of the parameters related to “conservation”, “dissipation” and “input quantities” increases. On the other hand, the parameter value corresponding to nonlinearity tends to decrease. It is found that the proposed method by the model parameters of the Duffing oscillator can be used in quantitative detection for EDs.

Research on Evaluation of Mental Stress Level Using a Modified Duffing Oscillator

2016 ◽  
Author(s):  
Kentaro Miyago ◽  
Kenyu Uehara ◽  
Koji Mori ◽  
Takashi Saito
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Evaluation Method ◽  
Duffing Oscillator ◽  
Arousal Level ◽  
Beta Band ◽  
Research On Evaluation ◽  
Eeg Data ◽  
The Mathematical Model ◽  
The Relationship

The decrease of arousal level has led to serious accidents in driving and operation of machine. A system to accurately evaluate arousal level in real time is required to prevent such accidents. In this paper, we examined whether parameters of the mathematical model, which are determined in each time shifting window using EEG data could be used as a method to evaluate the arousal level. The modified Duffing oscillator was proposed as a mathematical model to perform simplified parameters calculation and identification. It was applied to EEG data, which is obtained by EEG acquisition experimentation in alternative repeat of relaxed and concentrated state. As a result of the parameters identification in theta band, alpha band, beta band, and 4–30 Hz band, it is shown that each parameter is repeating increase and decrease with time progress of each experiment. Especially, the relationship between the conventional arousal level and parameter B, which is the coefficient of the proportional term in the modified Duffing oscillator shows a significant correlation. It is suggested that parameter B of the modified Duffing oscillator would evaluate the arousal level. Since this evaluation method of arousal level does not require analyzing for each band, it can be used to calculate the arousal level in real time.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals Grey power models based on optimization of initial condition and model parameters

2014 ◽  
Vol 4 (2) ◽  
pp. 370-382 ◽  
Author(s):  
Yunchol Jong ◽  
Sifeng Liu
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Model Parameters ◽  
Initial Condition ◽  
Content Type ◽  
Novel Approach ◽  
New Information ◽  
Power Models ◽  
Grey Verhulst Model ◽  
Verhulst Model

Purpose – The purpose of this paper is to propose a novel approach to improve prediction accuracy of grey power models including GM(1, 1) and grey Verhulst model. Design/methodology/approach – The modified new models are proposed by optimizing the initial condition and model parameters. The new initial condition consists of the first item and the last item of a sequence generated by applying the first-order accumulative generation operator on the sequence of raw data. Findings – It is shown that the newly modified grey power model is an extension of the previous optimized GM(1, 1) and grey Verhulst model. And the optimized initial condition reflected the principle of new information priority. Practical implications – The result of a numerical example indicates that the modified grey model presented in this paper with better prediction performance. Originality/value – The new initial condition are derived by weighted combination of the first item and the last item. The coefficients of weight obtained by the least square method.

Effect Factors Analysis of Flow Noise Generation from Perforated Duct Muffler Element

2011 ◽  
Vol 346 ◽  
pp. 204-209 ◽  
Author(s):  
Hai Jun Zhao ◽  
Zhao Xiang Deng
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Model Parameters ◽  
Noise Generation ◽  
Effect Factors ◽  
Expansion Chamber ◽  
Flow Noise ◽  
Factors Analysis ◽  
Structure Of Flow ◽  
Relationship Model

Flow noise regeneration from perforated tube muffler element was measured on the self-developing test bench, relationship model on total sound power of flow regeneration noise and structure parameters and work condition was established. Its model parameters were solved making use of hyper static least square method. Using the model effect factors of flow noise generation were discussed. Result shows that the reducing of the perforated diameter and the perforated part length is favor of the reduction of flow noise, and perforated ratio and expansion chamber diameter have less effect on flow noise. After analyzing spectrum structure of flow regeneration noise, it is displayed that with the increase of flow velocity projected peak value frequency has the trend of moving to middle and high, its intensity also becomes stronger, and sound energy in some the frequency accounts for about 60% of the total energy, Strophe number is the range of from 0.2 to 0.35.

scholarly journals A Precisely One-Step Registration Methodology for Optical Imagery and LiDAR Data Using Virtual Point Primitives

2021 ◽  
Vol 13 (23) ◽  
pp. 4836
Author(s):  
Chunjing Yao ◽  
Hongchao Ma ◽  
Wenjun Luo ◽  
Haichi Ma
Keyword(s):  
Urban Areas ◽  
Least Square Method ◽  
Least Square ◽  
Transformation Model ◽  
Model Parameters ◽  
Linear Features ◽  
One Step ◽  
Virtual Point ◽  
Optical Imagery ◽  
Point Data

The registration of optical imagery and 3D Light Detection and Ranging (LiDAR) point data continues to be a challenge for various applications in photogrammetry and remote sensing. In this paper, the framework employs a new registration primitive called virtual point (VP) that can be generated from the linear features within a LiDAR dataset including straight lines (SL) and curved lines (CL). By using an auxiliary parameter (λ), it is easy to take advantage of the accurate and fast calculation of the one-step registration transformation model. The transformation model parameters and λs can be calculated simultaneously by applying the least square method recursively. In urban areas, there are many buildings with different shapes. Therefore, the boundaries of buildings provide a large number of SL and CL features and selecting properly linear features and transforming into VPs can reduce the errors caused by the semi-discrete random characteristics of the LiDAR points. According to the result shown in the paper, the registration precision can reach the 1~2 pixels level of the optical images.

scholarly journals Marshall-Olkin Generalized Pareto Distribution: Bayesian and Non Bayesian Estimation

2020 ◽  
pp. 21-33 ◽  
Author(s):  
Hanan Haj AHmad ◽  
Ehab Almetwally
Keyword(s):  
Loss Function ◽  
Bayesian Method ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Model Parameters ◽  
Type I ◽  
Generalized Pareto

A new generalization of generalized Pareto Distribution is obtained using the generator Marshall-Olkin distribution (1997). The new distribution MOGP is more flexible and can be used to model non-monotonic failure rate functions. MOGP includes six different sub models: Generalized Pareto, Exponential, Uniform, Pareto type I, Marshall-Olkin Pareto and Marshall-Olkin exponential distribution. We consider different estimation procedures for estimating the model parameters, namely: Maximum likelihood estimator, Maximum product spacing, Least square method, weighted least square method and Bayesian Method. The Bayesian Method is considered under quadratic loss function and Linex loss function. Simulation analysis using MCMC technique is performed to compare between the proposed point estimation methods. The usefulness of MOGP is illustrated by means of real data set, which shows that this generalization is better fit than Pareto, GP and MOP distributions.

scholarly journals Pengaruh Masukan Kendali Terhadap Hasil Identifikasi Parameter Pesawat Udara Konfigurasi Konvensional Matra Terbang Longitudinal

2019 ◽  
Vol 7 (1) ◽  
pp. 25-30
Author(s):  
Eries Bagita Jayanti ◽  
Novita Atmasari ◽  
Hidayati Mardikasari ◽  
Ardian Rizaldi ◽  
Fuad Surastyo Pranoto ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Least Square Method ◽  
Least Square ◽  
Model Parameters ◽  
Control Field ◽  
Longitudinal Dimension ◽  
Navigation Data ◽  
Modeling Process ◽  
High Level ◽  
Input Form

Parameter identification is a process to get real characteristics of the motion dynamics of an object which can then be used to build the dynamics model of the object, which has a very high level of validity and accuracy. The modeling process is usually carried out using aircraft input data and the results of existing navigation data recording. From the data, the model parameters are estimated using the simple least square method. In this study, the simulation was carried out by varying the deflection input in the control field and simulation time. The input given to the longitudinal dimension is the deflection of the elevator control field. The results of parameter identification in the Corsair A-7A plane in the longitudinal dimension indicate that the input form 3-2-1 has a smaller error value than using doublet and pulse inputs. This shows that the input form 3-2-1 is most suitable for the longitudinal dimension among the given inputs.

scholarly journals Numerical approach for extraction of photovoltaic generator single-diode model parameters

2021 ◽  
Vol 2 (2) ◽  
pp. 58-66
Author(s):  
Abdelaaziz Benahmida ◽  
Noureddine Maouhoub ◽  
Hassan Sahsah
Keyword(s):  
Ideality Factor ◽  
Series Resistance ◽  
Least Square Method ◽  
Numerical Approach ◽  
Least Square ◽  
Physical Parameters ◽  
Model Parameters ◽  
Shunt Resistance ◽  
Key Points ◽  
Photovoltaic Generators

In this work, a numerical approach has been proposed to estimate the five single-diode circuit model physical parameters of photovoltaic generators from their experimental current-voltage characteristics. Linear least square method has been used to solve the system of three linear equations to express the shunt resistance, the saturation current and the photocurrent as a function of the series resistance and the ideality factor. Two key points have been used to solve the system of two nonlinear equations to extract values of series resistance and ideality factor. The advantage of the proposed method with respect of existing numerical techniques is that use only two key points of the experimental characteristic and need only two initial guesses and does not use any approximation. To evaluate the proposed method, three PV generators data have been used to compare the experimental and the theoretical curves. The application of the proposed method provides a good agreement with the experimental.

scholarly journals Modeling and parameters estimation of a Spatial Predator-Prey distribution

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Denis Ndanguza ◽  
Jean Pierre Muhirwa ◽  
Anatholie Uwimana
Keyword(s):  
Stability Analysis ◽  
Synthetic Data ◽  
Simulated Data ◽  
Least Square Method ◽  
Parameters Estimation ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Predator Prey ◽  
Monte Carlo Algorithms

Predator prey interactions are important in ecology and most of time in the analysis, the two antagonists are assumed to be in a closed system. The aim of this study is to model the unclosed predator-prey system. The model is built and simulated data are computed by adding noise on deterministic solution. Therefore, model parameters are estimated using least square method. We compute the two critical points and the stability analysis is carried out and results show that the population is stable at one critical point and unstable at (0,0). The model fits the synthetic data with coefficient of determination R2 = 0.9693 equivalent to 96.93%. Using the residual analysis to test the validity of the model, it is shown that there is no pattern between residuals. To strengthen the validity of the model, the Markov Chain Monte Carlo algorithms are used as an alternative method in parameters estimation. Diagnostics prove the chains’ convergence which is the sign of an accurate model. As conclusion, the model is accurate and it can be applied to real data.Keywords: predator-prey, spatial distribution, parameters, Metropolis-Hastings algorithm, model diagnostic, stability analysis

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