Vibration stability analysis of cantilever structure based on symbolic regression algorithm and modal analysis method

The vibration stability of cantilever mechanism under high-speed rotation directly affects the positioning accuracy. Modal analysis method is usually used to study the vibration stability. However, the traditional experimental modal analysis (EMA) method needs to measure the impulse excitation, while the operational modal analysis (OMA) method needs to satisfy the assumption of white noise. Therefore, the existing modal analysis methods cannot be applied to the study of vibration stability of high-frequency cantilever mechanism. In this paper, the symbolic regression (SR) algorithm is combined with the EMA method, and the robustness analysis and feasibility verification are carried out under the condition of adding noise. The validation of the new method is divided into two parts. In the first part, a three degree of freedom (DOF) linear model is constructed, and the modal parameters identified by SR method and state space method are compared. In the second part, the method is applied to identify the modal parameters of stepped bar. The results are compared with the results of LMS (Siemens’ Testlab). Based on the time-domain response signal only, the modal parameters are extracted by SR, and the main vibration frequency is extracted from the response signal. The system simulation and experimental results show the method provides a possibility to analyze the vibration stability of cantilever structure.

Acceptable Residual Stresses Value Criteria for Rail Stress State

According to simulating and experimental research, the negative linear relation between the level of residual stresses, assessed by the method of acoustic tensometry, and the divergence of the notch in the rail cut in accordance with clause 7.14 of GOST 51685-2013, was found. The correlation coefficient is 0.94. It was determined by the method of regression analysis that the sensitivity of inclination of the notch is the same for different rail casts with account of the measuring errors and it is minus 0.032, but the constant component of the expression is different for different casts of the rail. The constant component should be assessed according to the results of the tests of residual stresses by destructive methods (cutting the rail) in accordance with 7.14 GOST 51685-2013. After evaluating the constant component for each of the rail casts, the dependence can be obtained that describes the notch divergence depending on the level of the measured residual stresses. When the descrepancy of the notch does not exceed 2 mm, the stress state of the rail is acceptable by GOST 51685-2013; the predicted notch discrepancy of the notch is calculated according to the dependence given in the paper. Experimental evaluation of residual stresses was carried out using the EMA method of wave input-reception and the acoustoelasticity effect. Mathematical modeling was carried out by the finite element method using the Comsol Multiphysics environment. The size of the finite elements is adaptive, the size of the element ranges from 0.2 mm to 3 mm. To simulate residual stresses, distributed loads with a gradient character were applied to the rail, selected so that at a given level of stress across the rail elements, the stress transition had a smooth character.

Pandemic dynamics prediction in Java using the Moving Average method and the Knowledge Growing System (KGS)

This study aims to analyze the comparative performance of pandemic dynamics prediction methods on the island of Java, based on data from March to May 2020 covering the provinces of DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java. The prediction uses Knowledge Growing System (KGS) and time series models, namely Single Moving Average (SMA) and Exponential Moving Average (EMA). Based on the Mean Absolute Percentage Error (MAPE) computational results, the EMA method produces a lower error rate than the SMA method with 47.94 % on average. The KGS prediction with a Degree of Certainty (DoC) produced a trend analysis that the pandemic dynamics in DKI Jakarta province will decrease gradually if the current policy is still implemented. Whereas in the other provinces, the KGS predicted the pandemic dynamics trends will still increase.

Mood and Stress Evaluation of Adult Patients With Moyamoya Disease in Korea: Ecological Momentary Assessment Method Using a Mobile Phone App

Background Moyamoya disease (MMD) is a known progressive obstructive cerebrovascular disorder. Monitoring and managing mood and stress are critical for patients with MMD, as they affect clinical outcomes. The ecological momentary assessment (EMA) method is a longitudinal study design by which multiple variable assessments can be performed over time to detect momentary fluctuations and changes in psychological dimensions such as mood and stress over time. Objective This study aimed to identify predicting factors associated with momentary mood and stress at both the within-person and between-person levels and to examine individual fluctuation of mood over time in the short term using an EMA method combined with a mobile phone app. Methods Participants aged older than 18 years were recruited from a tertiary hospital in Seoul, Korea, between July 2018 and January 2019. The PsyMate scale for negative affect (NA) and positive affect (PA) and the Trier Inventory for Chronic Stress Scale were uploaded on patient mobile phones. Using a mobile app, data were collected four times a day for 7 days. Pearson correlations and mixed modeling were used to predict relationships between repeatedly measured variables at both the between-person and within-person levels. Results The mean age of the 93 participants was 40.59 (SD 10.06) years, 66 (71%) were female, and 71 (76%) were married. Participants provided 1929 responses out of a possible 2604 responses (1929/2604, 74.08%). The mean momentary NA and PA values were 2.15 (SD 1.12) and 4.70 (SD 1.31) out of 7, respectively. The momentary stress value was 2.03 (SD 0.98) out of 5. Momentary NA, PA, and stress were correlated (P<.001) and varied over time in relation to momentary variables. Common momentary variables associated with momentary mood and stress at both the within-person (level 1) and between-person (level 2) levels were identified. Momentary NA increased when being alone and being at the hospital at both levels, whereas momentary PA increased when eating or drinking, resting, being at a café, restaurant or a public place but decreased when being alone at both levels. Momentary stress increased when being at the office, at a public place, or as the time of the day went by but decreased when resting or during the weekend. Different factors affecting mood and stress at different levels were identified. Fluctuations in individual momentary mood over time at the within-person level were captured. Conclusions The EMA method using a mobile phone app demonstrated its ability to capture changes in mood and stress in various environmental contexts in patients with MMD. The results could provide baseline information for developing interventions to manage negative mood and stress of patients with MMD based on the identified predictors affecting mood and stress at two different levels.

Mood and Stress Evaluation of Adult Patients With Moyamoya Disease in Korea: Ecological Momentary Assessment Method Using a Mobile Phone App (Preprint)

BACKGROUND Moyamoya disease (MMD) is a known progressive obstructive cerebrovascular disorder. Monitoring and managing mood and stress are critical for patients with MMD, as they affect clinical outcomes. The ecological momentary assessment (EMA) method is a longitudinal study design by which multiple variable assessments can be performed over time to detect momentary fluctuations and changes in psychological dimensions such as mood and stress over time. OBJECTIVE This study aimed to identify predicting factors associated with momentary mood and stress at both the within-person and between-person levels and to examine individual fluctuation of mood over time in the short term using an EMA method combined with a mobile phone app. METHODS Participants aged older than 18 years were recruited from a tertiary hospital in Seoul, Korea, between July 2018 and January 2019. The PsyMate scale for negative affect (NA) and positive affect (PA) and the Trier Inventory for Chronic Stress Scale were uploaded on patient mobile phones. Using a mobile app, data were collected four times a day for 7 days. Pearson correlations and mixed modeling were used to predict relationships between repeatedly measured variables at both the between-person and within-person levels. RESULTS The mean age of the 93 participants was 40.59 (SD 10.06) years, 66 (71%) were female, and 71 (76%) were married. Participants provided 1929 responses out of a possible 2604 responses (1929/2604, 74.08%). The mean momentary NA and PA values were 2.15 (SD 1.12) and 4.70 (SD 1.31) out of 7, respectively. The momentary stress value was 2.03 (SD 0.98) out of 5. Momentary NA, PA, and stress were correlated (<i>P</i>&lt;.001) and varied over time in relation to momentary variables. Common momentary variables associated with momentary mood and stress at both the within-person (level 1) and between-person (level 2) levels were identified. Momentary NA increased when being alone and being at the hospital at both levels, whereas momentary PA increased when eating or drinking, resting, being at a café, restaurant or a public place but decreased when being alone at both levels. Momentary stress increased when being at the office, at a public place, or as the time of the day went by but decreased when resting or during the weekend. Different factors affecting mood and stress at different levels were identified. Fluctuations in individual momentary mood over time at the within-person level were captured. CONCLUSIONS The EMA method using a mobile phone app demonstrated its ability to capture changes in mood and stress in various environmental contexts in patients with MMD. The results could provide baseline information for developing interventions to manage negative mood and stress of patients with MMD based on the identified predictors affecting mood and stress at two different levels. CLINICALTRIAL

Investigating the Dynamics of Suicidal Ideation

Background: Although the fluctuating nature of suicidal ideation (SI) has been described previously, longitudinal studies investigating the dynamics of SI are scarce. Aim: To demonstrate the fluctuation of SI across 6 days and up to 60 measurement points using smartphone-based ecological momentary assessments (EMA). Method: Twenty inpatients with unipolar depression and current and/or lifetime suicidal ideation rated their momentary SI 10 times per day over a 6-day period. Mean squared successive difference (MSSD) was calculated as a measure of variability. Correlations of MSSD with severity of depression, number of previous depressive episodes, and history of suicidal behavior were examined. Results: Individual trajectories of SI are shown to illustrate fluctuation. MSSD values ranged from 0.2 to 21.7. No significant correlations of MSSD with several clinical parameters were found, but there are hints of associations between fluctuation of SI and severity of depression and suicidality. Limitations: Main limitation of this study is the small sample size leading to low power and probably missing potential effects. Further research with larger samples is necessary to shed light on the dynamics of SI. Conclusion: The results illustrate the dynamic nature and the diversity of trajectories of SI across 6 days in psychiatric inpatients with unipolar depression. Prediction of the fluctuation of SI might be of high clinical relevance. Further research using EMA and sophisticated analyses with larger samples is necessary to shed light on the dynamics of SI.

INFLUENCE OF DYNAMIC MAGNETIZATION TO IMPROVE THE EFFICIENCY OF ELECTROMAGNETIC-ACOUSTIC TRANSFORMATION WITH WAVEGUIDE CONTROL RODS

The disadvantage of the electromagnetic-acoustic (EMA) method receiving ultrasonic waves are low efficiency. The traditional way to enhance its effectiveness is increase the bias field. The aim of the study was research the way to improve the efficiency of the EMA transformation, using a time-varying bias field.The researches held with the help of a specially designed installation that allows the magnetization to be performed by a constant and alternating magnetic field (dynamic bias), synchronously with the passage of the received pulse. The object of the study were rods made of different grades of steel with a diameter of 4–6 mm, in which the symmetrical zero mode S0 of the rod wave was excited by the EMA method (in the frequency range of about 40 kHz). A comparative analysis of the amplitudes and form pulses of multiple reflections during static and dynamic reversal of magnetization and with a full cycle of magnetization reversal conducted.The result of the efficiency measurements EMA reception during static and dynamic bias found a significant (up to 5 times) increase in the signal amplitude on the receiving transducer. Taking into account that the main contribution to the excitation mechanism and the reception mechanism made the magnetostrictive effect on low frecuncy, it can assumed that using a dynamic bias field is impacting significant on the effective mobility of magnetic domains (that is changes the dynamic magnetic susceptibility of the material). It is established that it is possible to monitor steel at lower values of the bias field, and, consequently, to reduce the mass dimensions of the magnetic system.Thus, in the course of the researchers found of effect of dynamic bias and effect of dynamic bias increase acoustic pulse amplitude of the signal of the received EMA method. Using this method will improve the quality EMA testing by creating more efficient EMA transducer. Taking into account that the value of the detected effect depends significantly on the steel grade, we can assume its possible application in the methods of express analysis, estimation of structural and stressed states.

Variable-structure interacting multiple-model estimation for group targets tracking with random matrices

In order to improve the estimation performance of interacting multiple model tracking algorithm for group targets, the expected-mode augmentation variable-structure interacting multiple model (EMA-VSIMM) and the best model augmentation variable-structure interacting multiple model (BMA-VSIMM) tracking algorithms are presented in this paper. First, by using the EMA method, a more proper expected-mode set has been chosen from the basic model set of group targets, which can make the selected tracking models better match up to the true mode. The BMA algorithm uses a fixed parameter model of different structures to constitute a candidate model set and selects a minimum difference model from target state as the present extended model from the set of candidates at real time. Second, in the filtering process of VSIMM, the fusion estimation of extension state is implemented by the scalar coefficients weighting method, where weight coefficient is calculated by the trace of the corresponding covariance matrix of extension state. The performances of the proposed EMA-VSIMM and BMA-VSIMM algorithms are evaluated via simulation of a generic group targets maneuvering tracking problem.