Shell Buckling With Polymorphic Uncertain Surface Imperfections and Sensitivity Analysis

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Marc Fina ◽  
Lukas Panther ◽  
Patrick Weber ◽  
Werner Wagner
Keyword(s):  
Random Fields ◽  
Surrogate Model ◽  
Small Sample Size ◽  
Cylindrical Shells ◽  
Probabilistic Approach ◽  
Gaussian Random Fields ◽  
Small Sample ◽  
Load Factor ◽  
Statistical Parameters ◽  
Geometric Imperfections

Abstract In a probabilistic design approach for cylindrical shells, Gaussian random fields are used to simulate geometric imperfections. The shape of imperfections depends, among others, on the autocorrelation properties of the random field. Underlying uncertainties such as a small sample size or imprecise measurements make it practically impossible to define a crisp correlation function. For a more realistic description of the imprecise correlation structure, the classical probabilistic approach is extended to a fuzzy stochastic approach. More exactly, the polymorphic uncertainty approach is used taking into account natural variability and incompleteness. Consequently, geometric imperfections are represented as fuzzy probability based random fields. Therefore, the required correlation parameters are described as polymorphic uncertain parameters. The quantification of uncertainties is demonstrated on real data. Furthermore, the polynomial chaos surrogate model is used for the alpha-level optimization in the fuzzy analysis. The sensitivity indices as a by-product of the surrogate model show the influence of the input parameters on the statistical parameters of the critical buckling load factor. The main purpose of this paper is to show how the presented methods can support the design process of cylindrical shells.

Sampling-Based Reliability Analysis Using Deep Feedforward Neural Network (DFNN)

2020 ◽  
Author(s):  
Ungki Lee ◽  
Ikjin Lee
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Small Sample Size ◽  
Small Sample ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Performance Function ◽  
Design Point ◽  
Previous Design

Abstract Reliability analysis that evaluates a probabilistic constraint is an important part of reliability-based design optimization (RBDO). Inverse reliability analysis evaluates the percentile value of the performance function that satisfies the reliability. To compute the percentile value, analytical methods, surrogate model based methods, and sampling-based methods are commonly used. In case the dimension or nonlinearity of the performance function is high, sampling-based methods such as Monte Carlo simulation, Latin hypercube sampling, and importance sampling can be directly used for reliability analysis since no analytical formulation or surrogate model is required in these methods. The sampling-based methods have high accuracy but require a large number of samples, which can be very time-consuming. Therefore, this paper proposes methods that can improve the accuracy of reliability analysis when the number of samples is not enough and the sampling-based methods are considered to be better candidates. This study starts with the idea of training the relationship between the realization of the performance function at a small sample size and the corresponding true percentile value of the performance function. Deep feedforward neural network (DFNN), which is one of the promising artificial neural network models that approximates high dimensional models using deep layered structures, is trained using the realization of various performance functions at a small sample size and the corresponding true percentile values as input and target training data, respectively. In this study, various polynomial functions and random variables are used to create training data sets consisting of various realizations and corresponding true percentile values. A method that approximates the realization of the performance function through kernel density estimation and trains the DFNN with the discrete points representing the shape of the kernel distribution to reduce the dimension of the training input data is also presented. Along with the proposed reliability analysis methods, a strategy that reuses samples of the previous design point to enhance the efficiency of the percentile value estimation is explained. The results show that the reliability analysis using the DFNN is more accurate than the method using only samples. In addition, compared to the method that trains the DFNN using the realization of the performance function, the method that trains the DFNN with the discrete points representing the shape of the kernel distribution improves the accuracy of reliability analysis and reduces the training time. The proposed sample reuse strategy is verified that the burden of function evaluation at the new design point can be reduced by reusing the samples of the previous design point when the design point changes while performing RBDO.

scholarly journals Seafloor sediment classification of the Sylt Outer Reef, German Bight from 2016 -2018 using ensemble modelling 

2021 ◽  
Author(s):  
Daphnie Galvez ◽  
Svenja Papenmeier ◽  
Alexander Bartholomä ◽  
Karen Helen Wiltshire
Keyword(s):  
German Bight ◽  
Predictive Accuracy ◽  
Small Sample Size ◽  
Probabilistic Approach ◽  
Small Sample ◽  
Outer Reef ◽  
Sediment Distribution ◽  
Ensemble Modelling ◽  
Distribution Maps ◽  
Modelling Techniques

<p>Recent studies on seafloor mapping have presented different modelling methods to map and classify marine sediment distribution. However, are these methods classify different sediment classes the same way? And how do we choose the right model for a certain set of sediment classes? In this study, we aim to address these issues by using ensemble modelling to map the distribution of different sediment class on a dynamic, shallow continental shelf. Our data were derived from side-scan mosaics and multibeam data repeatedly collected from 2016 to 2018 in the Sylt Outer Reef (German Bight). We used a probabilistic approach for each class separately and then compared the predicted probability for each class, to see which class is more likely to be assigned to the location. Each sediment class was predicted using a combination of different classification modelling techniques, and then the result of these models was ensembled to produced one final prediction. This approach avoids selecting one single method, limits model selection bias and can provide information on the trends and variation across models.  Furthermore, we also looked on the temporal changes in sediment distributions by comparing the sediment class predictions from 2016 to 2018.</p><p>Our analysis suggest that combining different modelling techniques  (i.e. random forest, boosting regression trees etc.) provide higher predictive accuracy than using one single modelling method. The resulting sediment distribution maps are more objective and are produced faster than manual delineated maps often considered by stakeholders. We also identify some limitations in having small sample size and we proposed that by combining certain models and choosing the proper amount of pseudo-absence or background data can address this issue.</p>

scholarly journals The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2542 ◽  
Author(s):  
Tu T.N. Luong ◽  
Jong-Myon Kim
Keyword(s):  
Fundamental Problem ◽  
Small Sample Size ◽  
Real Life ◽  
Leak Detection ◽  
Small Sample ◽  
Training Data ◽  
Statistical Parameters ◽  
Detection Scheme ◽  
Operation Conditions ◽  
Water Pipelines

Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability.

Probing the ultrastructure of the mitotic and meiotic spindle in eggs: An expeditious approach based on semi-thin (0.25 μm) serial sections

1983 ◽  
Vol 41 ◽  
pp. 552-555
Author(s):  
Conly L. Rieder ◽  
S. Bowser ◽  
R. Nowogrodzki ◽  
K. Ross ◽  
G. Sluder
Keyword(s):  
Small Sample Size ◽  
Small Sample ◽  
Meiotic Spindle ◽  
Serial Sections ◽  
Mitotic Apparatus ◽  
Thin Sections ◽  
Cell Cycles ◽  
Ultrastructural Studies

Eggs have long been a favorite material for studying the mechanism of karyokinesis in-vivo and in-vitro. They can be obtained in great numbers and, when fertilized, divide synchronously over many cell cycles. However, they are not considered to be a practical system for ultrastructural studies on the mitotic apparatus (MA) for several reasons, the most obvious of which is that sectioning them is a formidable task: over 1000 ultra-thin sections need to be cut from a single 80-100 μm diameter egg and of these sections only a small percentage will contain the area or structure of interest. Thus it is difficult and time consuming to obtain reliable ultrastructural data concerning the MA of eggs; and when it is obtained it is necessarily based on a small sample size.We have recently developed a procedure which will facilitate many studies concerned with the ultrastructure of the MA in eggs. It is based on the availability of biological HVEM's and on the observation that 0.25 μm thick serial sections can be screened at high resolution for content (after mounting on slot grids and staining with uranyl and lead) by phase contrast light microscopy (LM; Figs 1-2).

Validating the Factor Structure of the Stigma of Suicide Scale–Short Form Spanish Version Among Healthcare Students

Crisis ◽  
2020 ◽  
pp. 1-5
Author(s):  
Ruthmarie Hernández-Torres ◽  
Paola Carminelli-Corretjer ◽  
Nelmit Tollinchi-Natali ◽  
Ernesto Rosario-Hernández ◽  
Yovanska Duarté-Vélez ◽  
...  
Keyword(s):  
Factor Structure ◽  
Structural Equation ◽  
Short Form ◽  
Small Sample Size ◽  
Small Sample ◽  
Spanish Version ◽  
Equation Modeling ◽  
Cross Sectional ◽  
Healthcare Students ◽  
Spanish Speaking

Abstract. Background: Suicide is a leading cause of death among Spanish-speaking individuals. Suicide stigma can be a risk factor for suicide. A widely used measure is the Stigma of Suicide Scale-Short Form (SOSS-SF; Batterham, Calear, & Christensen, 2013 ). Although the SOSS-SF has established psychometric properties and factor structure in other languages and cultural contexts, no evidence is available from Spanish-speaking populations. Aim: This study aims to validate a Spanish translation of the SOSS-SF among a sample of Spanish-speaking healthcare students ( N = 277). Method: We implemented a cross-sectional design with quantitative techniques. Results: Following a structural equation modeling approach, a confirmatory factor analysis (CFA) supported the three-factor model proposed by Batterham and colleagues (2013) . Limitations: The study was limited by the small sample size and recruitment by availability. Conclusion: Findings suggest that the Spanish version of the SOSS-SF is a valid and reliable tool with which to examine suicide stigma among Spanish-speaking populations.

An Initial Investigation of Suicide Attempt Disclosures Among US Veterans

Crisis ◽  
2020 ◽  
pp. 1-7
Author(s):  
Brooke A. Ammerman ◽  
Sarah P. Carter ◽  
Heather M. Gebhardt ◽  
Jonathan Buchholz ◽  
Mark A. Reger
Keyword(s):  
Suicide Attempt ◽  
Mental Health Professionals ◽  
Help Seeking ◽  
Psychiatric Treatment ◽  
Small Sample Size ◽  
Romantic Partners ◽  
Small Sample ◽  
Professional Care ◽  
Patient Disclosure ◽  
Previous Suicide Attempt

Abstract. Background: Patient disclosure of prior suicidal behaviors is critical for effectively managing suicide risk; however, many attempts go undisclosed. Aims: The current study explored how responses following a suicide attempt disclosure may relate to help-seeking outcomes. Method: Participants included 37 veterans with a previous suicide attempt receiving inpatient psychiatric treatment. Veterans reported on their most and least helpful experiences disclosing their suicide attempt to others. Results: Veterans disclosed their suicide attempt to approximately eight individuals. Mental health professionals were the most cited recipient of their most helpful disclosure; romantic partners were the most common recipient of their least helpful disclosures. Positive reactions within the context of the least helpful disclosure experience were positively associated with a sense of connection with the disclosure recipient. Positive reactions within the most helpful disclosure experience were positively associated with the likelihood of future disclosure. No reactions were associated with having sought professional care or likelihood of seeking professional care. Limitations: The results are considered preliminary due to the small sample size. Conclusion: Findings suggest that while positive reactions may influence suicide attempt disclosure experiences broadly, additional research is needed to clarify factors that drive the decision to disclose a suicide attempt to a professional.

Investigating the Dynamics of Suicidal Ideation

Crisis ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 65-69 ◽  
Author(s):  
Nina Hallensleben ◽  
Lena Spangenberg ◽  
Thomas Forkmann ◽  
Dajana Rath ◽  
Ulrich Hegerl ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Small Sample Size ◽  
Unipolar Depression ◽  
Small Sample ◽  
History Of ◽  
Severity Of Depression ◽  
Ecological Momentary ◽  
Depressive Episodes ◽  
Ema Method ◽  
Shed Light

Abstract. 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.

Participant Reactions to Suicide-Focused Research

Crisis ◽  
2020 ◽  
Vol 41 (5) ◽  
pp. 367-374
Author(s):  
Sarah P. Carter ◽  
Brooke A. Ammerman ◽  
Heather M. Gebhardt ◽  
Jonathan Buchholz ◽  
Mark A. Reger
Keyword(s):  
Medical Records ◽  
Research Study ◽  
Small Sample Size ◽  
Psychiatric Inpatient ◽  
Small Sample ◽  
Inpatient Unit ◽  
Online Laboratory ◽  
Psychiatric Inpatient Unit ◽  
Before And After ◽  
Participant Reactions

Abstract. Background: Concerns exist regarding the perceived risks of conducting suicide-focused research among an acutely distressed population. Aims: The current study assessed changes in participant distress before and after participation in a suicide-focused research study conducted on a psychiatric inpatient unit. Method: Participants included 37 veterans who were receiving treatment on a psychiatric inpatient unit and completed a survey-based research study focused on suicide-related behaviors and experiences. Results: Participants reported no significant changes in self-reported distress. The majority of participants reported unchanged or decreased distress. Reviews of electronic medical records revealed no behavioral dysregulation and minimal use of as-needed medications or changes in mood following participation. Limitations: The study's small sample size and veteran population may limit generalizability. Conclusion: Findings add to research conducted across a variety of settings (i.e., outpatient, online, laboratory), indicating that participating in suicide-focused research is not significantly associated with increased distress or suicide risk.

Measurements method of the audio recordings acoustic quality indicator prepared for registration and processing in the Unified Biometric System

2019 ◽  
pp. 40-46 ◽  
Author(s):  
V.V. Savchenko ◽  
A.V. Savchenko
Keyword(s):  
Quality Control ◽  
Small Sample Size ◽  
Personal Data ◽  
Digital Processing ◽  
Small Sample ◽  
Practical Implementation ◽  
Acoustic Measurements ◽  
Pitch Frequency ◽  
Wide Range ◽  
Automated Quality Control

We consider the task of automated quality control of sound recordings containing voice samples of individuals. It is shown that in this task the most acute is the small sample size. In order to overcome this problem, we propose the novel method of acoustic measurements based on relative stability of the pitch frequency within a voice sample of short duration. An example of its practical implementation using aninter-periodic accumulation of a speech signal is considered. An experimental study with specially developed software provides statistical estimates of the effectiveness of the proposed method in noisy environments. It is shown that this method rejects the audio recording as unsuitable for a voice biometric identification with a probability of 0,95 or more for a signal to noise ratio below 15 dB. The obtained results are intended for use in the development of new and modifying existing systems of collecting and automated quality control of biometric personal data. The article is intended for a wide range of specialists in the field of acoustic measurements and digital processing of speech signals, as well as for practitioners who organize the work of authorized organizations in preparing for registration samples of biometric personal data.

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