scholarly journals Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yong-Zhi Li ◽  
Guo-Zheng Li ◽  
Jian-Yi Gao ◽  
Zhi-Feng Zhang ◽  
Quan-Chun Fan ◽  
...  
Keyword(s):  
Feature Selection ◽  
European Space Agency ◽  
Feature Selection Method ◽  
Space Mission ◽  
Medical Data ◽  
Diagnostic Methods ◽  
Classification Model ◽  
Syndrome Differentiation ◽  
Spleen Deficiency ◽  
Space Agency

Mars500 study was a psychological and physiological isolation experiment conducted by Russia, the European Space Agency, and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission. In this paper, we present data mining methods to mine medical data collected from the crew consisting of six spaceman volunteers. The synthesis of the four diagnostic methods of TCM, inspection, listening, inquiry, and palpation, is used in our syndrome differentiation. We adopt statistics method to describe the syndrome factor regular pattern of spaceman volunteers. Hybrid optimization based multilabel (HOML) is used as feature selection method and multilabelk-nearest neighbors (ML-KNN) is applied. According to the syndrome factor statistical result, we find that qi deficiency is a base syndrome pattern throughout the entire experiment process and, at the same time, there are different associated syndromes such as liver depression, spleen deficiency, dampness stagnancy, and yin deficiency, due to differences of individual situation. With feature selection, we screen out ten key factors which are essential to syndrome differentiation in TCM. The average precision of multilabel classification model reaches 80%.

scholarly journals Prisma: A New Space Mission For Stellar Physics

1993 ◽  
Vol 137 ◽  
pp. 812-819
Author(s):  
T. Appourchaux ◽  
D. Gough ◽  
P. Hyoyng ◽  
C. Catala ◽  
S. Frandsen ◽  
...  
Keyword(s):  
European Space Agency ◽  
Real Space ◽  
Space Mission ◽  
Economic Conditions ◽  
X Ray ◽  
Space Agency ◽  
New Space

PRISMA (Probing Rotation and Interior of Stars: Microvariability and Activity) is a new space mission of the European Space Agency. PRISMA is currently in a Phase A study with 3 other competitors. PRISMA is the only ESA-only mission amongst those four and only one mission will be selected in Spring 1993 to become a real space mission.The goal of the Phase A study is to determine whether the payload of PRISMA can be accommodated on a second unit of the X-ray Multi-Mirror (XMM) bus; and whether the budget of the PRISMA mission can be kept below 265 MAU (’88 Economic conditions). The XMM mission is an approved cornerstone and is in a Phase A together with PRISMA.

scholarly journals Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2910
Author(s):  
Kei Suzuki ◽  
Tipporn Laohakangvalvit ◽  
Ryota Matsubara ◽  
Midori Sugaya
Keyword(s):  
Feature Selection ◽  
Single Channel ◽  
Feature Selection Method ◽  
Classification Model ◽  
Features Selection ◽  
Selection Methods ◽  
Emotion Classification ◽  
Model Based ◽  
Physiological Indexes ◽  
Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

Feature Selection Method for Hydraulic System Faults Diagnosis Based on GA-PLS

2010 ◽  
Vol 44-47 ◽  
pp. 1130-1134
Author(s):  
Sheng Li ◽  
Pei Lin Zhang ◽  
Bing Li
Keyword(s):  
Feature Selection ◽  
Hydraulic System ◽  
Nearest Neighbor ◽  
Feature Selection Method ◽  
Original Data ◽  
Selection Method ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbor Algorithm ◽  
Faults Diagnosis

Feature selection is a key step in hydraulic system fault diagnosis. Some of the collected features are unrelated to classification model, and some are high correlated to other features. These features are harmful for establishing classification model. In order to solve this problem, genetic algorithm-partial least squares (GA-PLS) is proposed for selecting the representative and optimal features. K nearest neighbor algorithm (KNN) is used for diagnosing and classifying hydraulic system faults. For expressing better performance of GA-PLS, the original data of a model engineering hydraulic system is used, and the results of GA-PLS are compared with all feature used and GA. The experimental results show that, the proposed feature method can diagnose and classify hydraulic system faults more efficiently with using fewer features.

scholarly journals The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4749
Author(s):  
Shaorong Zhang ◽  
Zhibin Zhu ◽  
Benxin Zhang ◽  
Bao Feng ◽  
Tianyou Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Extraction Time ◽  
Classification Model ◽  
Discrete Wavelet ◽  
New Feature

The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

scholarly journals Coordination within the remote sensing payload on the Solar Orbiter mission

2020 ◽  
Vol 642 ◽  
pp. A6 ◽  
Author(s):  
F. Auchère ◽  
V. Andretta ◽  
E. Antonucci ◽  
N. Bach ◽  
M. Battaglia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Planning Process ◽  
European Space Agency ◽  
Space Mission ◽  
Integrated System ◽  
Solar Interior ◽  
Space Agency ◽  
Synergistic Approach ◽  
Communication Scheme ◽  
Cross Calibration

Context. To meet the scientific objectives of the mission, the Solar Orbiter spacecraft carries a suite of in-situ (IS) and remote sensing (RS) instruments designed for joint operations with inter-instrument communication capabilities. Indeed, previous missions have shown that the Sun (imaged by the RS instruments) and the heliosphere (mainly sampled by the IS instruments) should be considered as an integrated system rather than separate entities. Many of the advances expected from Solar Orbiter rely on this synergistic approach between IS and RS measurements. Aims. Many aspects of hardware development, integration, testing, and operations are common to two or more RS instruments. In this paper, we describe the coordination effort initiated from the early mission phases by the Remote Sensing Working Group. We review the scientific goals and challenges, and give an overview of the technical solutions devised to successfully operate these instruments together. Methods. A major constraint for the RS instruments is the limited telemetry (TM) bandwidth of the Solar Orbiter deep-space mission compared to missions in Earth orbit. Hence, many of the strategies developed to maximise the scientific return from these instruments revolve around the optimisation of TM usage, relying for example on onboard autonomy for data processing, compression, and selection for downlink. The planning process itself has been optimised to alleviate the dynamic nature of the targets, and an inter-instrument communication scheme has been implemented which can be used to autonomously alter the observing modes. We also outline the plans for in-flight cross-calibration, which will be essential to the joint data reduction and analysis. Results. The RS instrument package on Solar Orbiter will carry out comprehensive measurements from the solar interior to the inner heliosphere. Thanks to the close coordination between the instrument teams and the European Space Agency, several challenges specific to the RS suite were identified and addressed in a timely manner.

scholarly journals Icarus: In-situ monitoring of the surface degradation on a near-Sun asteroid

2021 ◽  
Author(s):  
Mikael Granvik ◽  
Tuomas Lehtinen ◽  
Andrea Bellome ◽  
Joan-Pau Sánchez
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Bulk Composition ◽  
European Space Agency ◽  
Space Mission ◽  
In Situ Monitoring ◽  
The Sun ◽  
Spacecraft Design ◽  
Cost Constraints ◽  
Space Agency

<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>Icarus is a mission concept designed to record the activity of an asteroid during a close encounter with the Sun. The primary science goal of the mission is to unravel the nontrivial mechanism(s) that destroy asteroids on orbits with small perihelion distances. Understanding the destruction mechanism(s) allows us to constrain the bulk composition and interior structure of asteroids in general. The Icarus mission does not only aim to achieve its science goals but also functions as a technical demonstration of what a low-cost space mission can do. The proposed space segment will include a single spacecraft capable of surviving and operating in the harsh environment near the Sun. The spacecraft design relies on the heritage of missions such as Rosetta, MESSENGER, Parker Solar Probe, BepiColombo, and Solar Orbiter. The spacecraft will rendezvous with an asteroid during its perihelion passage and records the changes taking place on the asteroid’s surface. The primary scientific payload has to be capable of imaging the asteroid’s surface in high resolution using visual and near-infrared channels as well as collecting and analyzing particles that are ejected from the asteroid. The payload bay also allows for additional payloads relating to, for example, solar research. The Icarus spacecraft and the planned payloads have high technology readiness levels and the mission is aimed to fit the programmatic and cost constraints of the F1 mission (Comet Interceptor) by the European Space Agency. Considering the challenging nature of the Icarus trajectory and the fact that the next F-class mission opportunity (F2) is yet to be announced, we conclude that Icarus is feasible as an F-class mission when certain constraints such as a suitable launch configuration are met (e.g., if EnVision is selected as M5). A larger mission class, such as the M class by the European Space Agency, would be feasible in all circumstances.</p> </div> </div> </div>

scholarly journals An improved tree model based on ensemble feature selection for classification

2019 ◽  
pp. 1290-1307 ◽  
Author(s):  
CHANDRALEKHA MOHAN ◽  
SHENBAGAVADIVU NAGARAJAN
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Ensemble Methods ◽  
Classification Performance ◽  
Medical Data ◽  
Random Trees ◽  
Machine Learning Techniques ◽  
Tree Model ◽  
Wrapper Method ◽  
Clinical Conditions

Researchers train and build specific models to classify the presence and absence of a disease and the accuracy of such classification models is continuously improved. The process of building a model and training depends on the medical data utilized. Various machine learning techniques and tools are used to handle different data with respect to disease types and their clinical conditions. Classification is the most widely used technique to classify disease and the accuracy of the classifier largely depends on the attributes. The choice of the attribute largely affects the diagnosis and performance of the classifier. Due to growing large volumes of medical data across different clinical conditions, the need for choosing relevant attributes and features still lacks method to handle datasets that target specific diseases. This study uses an ensemble-based feature selection using random trees and wrapper method to improve the classification. The proposed ensemble learning classification method derives a subset using the wrapper method, bagging, and random trees. The proposed method removes the irrelevant features and selects the optimal features for classification through probability weighting criteria. The improved algorithm has the ability to distinguish the relevant features from irrelevant features and improve the classification performance. The proposed feature selection method is evaluated using SVM, RF, and NB evaluators and the performances are compared against the FSNBb, FSSVMb, GASVMb, GANBb, and GARFb methods. The proposed method achieves mean classification accuracy of 92% and outperforms the other ensemble methods.

Development of a classifier with analysis of feature selection methods for COVID-19 diagnosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hetal Chauhan ◽  
Kirit Modi ◽  
Saurabh Shrivastava
Keyword(s):  
Decision Making ◽  
Feature Selection ◽  
Specific Treatment ◽  
Diagnostic Methods ◽  
Classification Model ◽  
Selection Methods ◽  
Medical Resources ◽  
Content Type ◽  
Patient Reported ◽  
Diagnosis Model

Purpose The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making. Design/methodology/approach In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score. Findings The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset. Originality/value Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.

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