Decoding pilot behavior consciousness of EEG, ECG, eye movements via an SVM machine learning model

2020 ◽  
Vol 11 (04) ◽  
pp. 2050028
Author(s):  
Xiashuang Wang ◽  
Guanghong Gong ◽  
Ni Li ◽  
Li Ding ◽  
Yaofei Ma
Keyword(s):  
Standard Deviation ◽  
Power Spectra ◽  
Behavior Modeling ◽  
Physiological Characteristics ◽  
Physiological Data ◽  
Future Research ◽  
Support Vector ◽  
Modeling Methods ◽  
Multimodal Features ◽  
Pilot Behavior

To decode the pilot’s behavioral awareness, an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data. Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves. The experiment starts directly from the multimodal physiological characteristics to explore pilots’ behavior. Electroencephalography, electrocardiogram, and eye movement were recorded simultaneously. Extracted multimodal features of ground missions, air missions, and cruise mission were trained to generate support vector machine behavior model based on supervised learning. The results showed that different behaviors affects different multiple rhythm features, which are power spectra of the [Formula: see text] waves of EEG, standard deviation of normal to normal, root mean square of standard deviation and average gaze duration. The different physiological characteristics of the pilots could also be distinguished using an SVM model. Therefore, the multimodal physiological data can contribute to future research on the behavior activities of pilots. The result can be used to design and improve pilot training programs and automation interfaces.

scholarly journals Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN: a “Conscious-Lab” development

2021 ◽  
Author(s):  
Mehdi Alidokht ◽  
Samaneh Yazdani ◽  
Esmaeil Hadavandi ◽  
Saeed Chehreh Chelgani
Keyword(s):  
Laboratory Experiments ◽  
Scaling Up ◽  
Dense Medium ◽  
Support Vector ◽  
Coal Processing ◽  
Separation Device ◽  
Modeling Methods ◽  
Machine Learning Methods ◽  
Coal Washery ◽  
First Time

AbstractTri-flo cyclone, as a dense-medium separation device, is one of the most typical environmentally friendly industrial techniques in the coal washery plants. Surprisingly, no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses (yield and recovery). To fill this gap, this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method (boosted-neural network “BNN”) which is able to linearly and nonlinearly assess multivariable correlations among all variables, rank them based on their effectiveness and model their produced responses. These assessments and modeling were considered a new concept called “Conscious Laboratory (CL)”. CL can markedly decrease the number of laboratory experiments, reduce cost, save time, remove scaling up risks, expand maintaining processes, and significantly improve our knowledge about the modeled system. In this study, a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo separators. Well-known machine learning methods, random forest, and support vector regression were developed to validate BNN outcomes. The comparisons indicated the accuracy and strength of BNN over the examined traditional modeling methods. In a sentence, generating a novel BNN within the CL concept can apply in various energy and coal processing areas, fill gaps in our knowledge about possible interactions, and open a new window for plants' fully automotive process.

The Importance of the Posterolateral Area of the Diaphragm Muscle for Palpation and for the Treatment of Manual Osteopathic Medicine

2021 ◽  
pp. 1-9
Author(s):  
Bruno Bordoni ◽  
Stevan Walkowski ◽  
Allan Escher ◽  
Bruno Ducoux
Keyword(s):  
Functional Anatomy ◽  
Diaphragm Muscle ◽  
Physiological Data ◽  
Future Research ◽  
Physical Aspect ◽  
Osteopathic Medicine ◽  
Self Healing ◽  
Hands On ◽  
Critical Issues ◽  
Solid Foundation

The eupneic act in healthy subjects involves a coordinated combination of functional anatomy and neurological activation. Neurologically, a central pattern generator, the components of which are distributed between the brainstem and the spinal cord, are hypothesized to drive the process and are modeled mathematically. A functionally anatomical approach is easier to understand although just as complex. Osteopathic manipulative treatment (OMT) is part of osteopathic medicine, which has many manual techniques to approach the human body, trying to improve the patient’s homeostatic response. The principle on which OMT is based is the stimulation of self-healing processes, researching the intrinsic physiological mechanisms of the person, taking into consideration not only the physical aspect, but also the emotional one and the context in which the patient lives. This article reviews how the diaphragm muscle moves, with a brief discussion on anatomy and the respiratory neural network. The goal is to highlight the critical issues of OMT on the correct positioning of the hands on the posterolateral area of the diaphragm around the diaphragm, trying to respect the existing scientific anatomical-physiological data, and laying a solid foundation for improving the data obtainable from future research. The correctness of the position of the operator’s hands in this area allows a more effective palpatory perception and, consequently, a probably more incisive result on the respiratory function.

scholarly journals Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1105 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Standard Deviation ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Curve Analysis ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Features Selection ◽  
Power Curve ◽  
Working Parameters ◽  
Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

scholarly journals Changes in the Food-Related Behaviour of Italian Consumers during the COVID-19 Pandemic

Foods ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 169
Author(s):  
Rosa Maria Fanelli
Keyword(s):  
Standard Deviation ◽  
Food Consumption ◽  
Consumer Behaviour ◽  
Eating Habits ◽  
Small Sample ◽  
Future Research ◽  
Critical Approach ◽  
Pilot Survey

The principal aim of this study is to explore the effects of the first lockdown of the Coronavirus Disease 2019 (COVID-19) pandemic on changes in food consumption and food-related behaviour on a diverse sample of Italian consumers aged ≥18 years. To achieve this aim, the research path starts with an investigation of some of the first few studies conducted on Italian consumers. It then reports the findings of a pilot survey carried out on a small sample of Italian consumes who live in Molise. The studies chosen for investigation were published as articles or research reports. In total, six relevant studies were chosen, each involving a different sized sample of Italian consumers. The average number of respondents is 2142, with a standard deviation of 1260.56. A distinction is made between the results of the articles, the research reports, and the pilot survey. The latter was conducted to develop and validate the components of a new questionnaire and, furthermore, to assess changes in the eating habits of individuals during the COVID-19 pandemic. The results suggest that the effects of the pandemic on consumer behaviour can, above all, be grouped into changes related to shopping for food, eating habits, and food-related behaviour. This article can serve as the basis for future research in this area as it identifies and highlights key changes, in addition to comparing the earliest evidence available, using a critical approach.

scholarly journals Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6172
Author(s):  
Jiaming Wang ◽  
Rui Cheng ◽  
Mei Liu ◽  
Pin-Chao Liao
Keyword(s):  
Human Computer Interaction ◽  
Industrial Revolution ◽  
Safety Management ◽  
Research Progress ◽  
Physiological Data ◽  
Future Research ◽  
Research Subjects ◽  
Management Platform ◽  
Computer Interaction ◽  
Hazard Recognition

Human–computer interaction, an interdisciplinary discipline, has become a frontier research topic in recent years. In the fourth industrial revolution, human–computer interaction has been increasingly applied to construction safety management, which has significantly promoted the progress of hazard recognition in the construction industry. However, limited scholars have yet systematically reviewed the development of human–computer interaction in construction hazard recognition. In this study, we analyzed 274 related papers published in ACM Digital Library, Web of Science, Google Scholar, and Scopus between 2000 and 2021 using bibliometric methods, systematically identified the research progress, key topics, and future research directions in this field, and proposed a research framework for human–computer interaction in construction hazard recognition (CHR-HCI). The results showed that, in the past 20 years, the application of human–computer interaction not only made significant contributions to the development of hazard recognition, but also generated a series of new research subjects, such as multimodal physiological data analysis in hazard recognition experiments, development of intuitive devices and sensors, and the human–computer interaction safety management platform based on big data. Future research modules include computer vision, computer simulation, virtual reality, and ergonomics. In this study, we drew a theoretical map reflecting the existing research results and the relationship between them, and provided suggestions for the future development of human–computer interaction in the field of hazard recognition from a practical perspective.

scholarly journals Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

2021 ◽  
Vol 12 ◽  
Author(s):  
Mahdi Mohseni ◽  
Volker Gast ◽  
Christoph Redies
Keyword(s):  
Sentence Length ◽  
Classification Model ◽  
Future Research ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Lexical Diversity ◽  
Long Range Correlations ◽  
Central Hypothesis ◽  
Fractal Patterns ◽  
Fractal Spectrum

This study investigates global properties of three categories of English text: canonical fiction, non-canonical fiction, and non-fictional texts. The central hypothesis of the study is that there are systematic differences with respect to structural design features between canonical and non-canonical fiction, and between fictional and non-fictional texts. To investigate these differences, we compiled a corpus containing texts of the three categories of interest, the Jena Corpus of Expository and Fictional Prose (JEFP Corpus). Two aspects of global structure are investigated, variability and self-similar (fractal) patterns, which reflect long-range correlations along texts. We use four types of basic observations, (i) the frequency of POS-tags per sentence, (ii) sentence length, (iii) lexical diversity, and (iv) the distribution of topic probabilities in segments of texts. These basic observations are grouped into two more general categories, (a) the lower-level properties (i) and (ii), which are observed at the level of the sentence (reflecting linguistic decoding), and (b) the higher-level properties (iii) and (iv), which are observed at the textual level (reflecting comprehension/integration). The observations for each property are transformed into series, which are analyzed in terms of variance and subjected to Multi-Fractal Detrended Fluctuation Analysis (MFDFA), giving rise to three statistics: (i) the degree of fractality () of the fractal spectrum. The statistics thus obtained are compared individually across text categories and jointly fed into a classification model (Support Vector Machine). Our results show that there are in fact differences between the three text categories of interest. In general, lower-level text properties are better discriminators than higher-level text properties. Canonical fictional texts differ from non-canonical ones primarily in terms of variability in lower-level text properties. Fractality seems to be a universal feature of text, slightly more pronounced in non-fictional than in fictional texts. On the basis of our results obtained on the basis of corpus data we point out some avenues for future research leading toward a more comprehensive analysis of textual aesthetics, e.g., using experimental methodologies.

Support Vector Machine

2009 ◽  
pp. 501-522 ◽  
Author(s):  
A. B.M. Shawkat Ali
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
System Analysis ◽  
Learning Algorithm ◽  
Mathematical Formulation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Support Vector ◽  
Analysis And Design ◽  
Simple Demonstration

From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.

scholarly journals Identifying Potential Leakage Zones in an Irrigation Supply Channel by Mapping Soil Properties Using Electromagnetic Induction, Inversion Modelling and a Support Vector Machine

Soil Systems ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 25
Author(s):  
Ehsan Zare ◽  
Nan Li ◽  
Tibet Khongnawang ◽  
Mohammad Farzamian ◽  
John Triantafilis
Keyword(s):  
Electrical Conductivity ◽  
Support Vector Machine ◽  
Chemical Properties ◽  
Exchange Capacity ◽  
Future Research ◽  
Support Vector ◽  
Stream Channels ◽  
Supply Channel ◽  
Physical And Chemical ◽  
And Chemical Properties

The clay alluvial plains of Namoi Valley have been intensively developed for irrigation. A condition of a license is water needs to be stored on the farm. However, the clay plain was developed from prior stream channels characterised by sandy clay loam textures that are permeable. Cheap methods of soil physical and chemical characterisations are required to map the supply channels used to move water on farms. Herein, we collect apparent electrical conductivity (ECa) from a DUALEM-421 along a 4-km section of a supply channel. We invert ECa to generate electromagnetic conductivity images (EMCI) using EM4Soil software and evaluate two-dimensional models of estimates of true electrical conductivity (σ—mS m−1) against physical (i.e., clay and sand—%) and chemical properties (i.e., electrical conductivity of saturated soil paste extract (ECe—dS m−1) and the cation exchange capacity (CEC, cmol(+) kg−1). Using a support vector machine (SVM), we predict these properties from the σ and depth. Leave-one-site-out cross-validation shows strong 1:1 agreement (Lin’s) between the σ and clay (0.85), sand (0.81), ECe (0.86) and CEC (0.83). Our interpretation of predicted properties suggests the approach can identify leakage areas (i.e., prior stream channels). We suggest that, with this calibration, the approach can be used to predict soil physical and chemical properties beneath supply channels across the rest of the valley. Future research should also explore whether similar calibrations can be developed to enable characterisations in other cotton-growing areas of Australia.

Support Vector Machines and Affective Science

2020 ◽  
Vol 12 (4) ◽  
pp. 297-308
Author(s):  
Chris H. Miller ◽  
Matthew D. Sacchet ◽  
Ian H. Gotlib
Keyword(s):  
Support Vector Machines ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Data Driven ◽  
Future Research ◽  
Support Vector ◽  
Predictive Algorithm ◽  
Affective Science ◽  
Vector Machines ◽  
Potential Applications

Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

scholarly journals Borderline personality disorder classification based on brain network measures during emotion regulation

2020 ◽  
Author(s):  
Henk Cremers ◽  
Linda van Zutphen ◽  
Sascha Duken ◽  
Gregor Domes ◽  
Andreas Sprenger ◽  
...  
Keyword(s):  
Emotion Regulation ◽  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Brain Network ◽  
Borderline Personality ◽  
Future Research ◽  
Support Vector ◽  
Classification Analysis ◽  
Network Analyses ◽  
Network Measures

AbstractBorderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.

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