scholarly journals Testing a Neural Network Accelerator on a High-Altitude Balloon

2019 ◽  
Author(s):  
Gilbert Clark ◽  
Geoffrey Landis ◽  
Ethan Barnes ◽  
Blake LaFuente ◽  
Kristina Collins
Keyword(s):  
Neural Network ◽  
High Altitude

scholarly journals Neural network correlates of high‐altitude adaptive genetic variants in Tibetans: A pilot, exploratory study

2020 ◽  
Vol 41 (9) ◽  
pp. 2406-2430 ◽  
Author(s):  
Zhiyue Guo ◽  
Cunxiu Fan ◽  
Ting Li ◽  
Luobu Gesang ◽  
Wu Yin ◽  
...  
Keyword(s):  
Neural Network ◽  
High Altitude ◽  
Genetic Variants ◽  
Exploratory Study

scholarly journals Short-term Vehicle Speed Prediction by Time Series Neural Network in High Altitude Areas

2019 ◽  
Vol 304 ◽  
pp. 032072
Author(s):  
Feng Shi ◽  
Yong Wang ◽  
Jun Chen ◽  
Jian Wang ◽  
Yanjun Hao ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
High Altitude ◽  
Vehicle Speed ◽  
Short Term ◽  
Speed Prediction

scholarly journals Research on Data Processing Method of High Altitude Meteorological Parameters Based on Neural Network

2015 ◽  
Vol 7 (1) ◽  
pp. 1597-1604
Author(s):  
Min Qiu ◽  
Yan Nan Mu ◽  
Xiu Ping Zhang ◽  
Dong Yang
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
High Altitude ◽  
Meteorological Parameters ◽  
Processing Method ◽  
Data Processing Method

Research on Prediction Model of Discharge Voltage under High-Altitude Condition Based on BP ANN

2014 ◽  
Vol 492 ◽  
pp. 153-161 ◽  
Author(s):  
Xiao Qing Luo ◽  
Feng Huo ◽  
Qi Ming Ye
Keyword(s):  
Neural Network ◽  
Environmental Factors ◽  
High Altitude ◽  
Maximum Error ◽  
Discharge Voltage ◽  
Air Gap ◽  
Test Results ◽  
The Neural Network ◽  
Test Voltage ◽  
Bp Neural Network Model

To obtain a mathematical model capable of predicting the 50% discharge voltage of air-gap under high-altitude conditions, we used the test results of air-gap in high altitude areas to establish the 50% discharge voltage BP neural network model of air-gap under high-altitude conditions. We used the model to forecast the 50% discharge voltage of air-gap under high-altitude conditions. The result shows that the maximum error between forecast voltage and test voltage is 1.42%, which certificates the possibility of using the neural network to build the multidimensional and nonlinear relationship between the environmental factors and discharge voltage. At the same time, we can simulate and analyze the effect of environmental factors on discharge voltage of air-gap with the help of model that we established, analysis showed that there was a positive correlation between the environmental factors, such as temperature, humidity and atmosphere pressure, and discharge voltage of air-gap.

Acrophobia Quantified by EEG Based on CNN Incorporating Granger Causality

2020 ◽  
pp. 2050069
Author(s):  
Fo Hu ◽  
Hong Wang ◽  
Qiaoxiu Wang ◽  
Naishi Feng ◽  
Jichi Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Altitude ◽  
Granger Causality ◽  
Brain Network ◽  
Classification Performance ◽  
Classification Task ◽  
Detection Methods ◽  
Practical Applications ◽  
Functional Brain Network

The aim of this study is to quantify acrophobia and provide safety advices for high-altitude workers. Considering that acrophobia is a fuzzy quantity that cannot be accurately evaluated by conventional detection methods, we propose a comprehensive solution to quantify acrophobia. Specifically, this study simulates a virtual reality environment called High-altitude Plank Walking Challenge, which provides a safe and controlled experimental environment for subjects. Besides, a method named Granger Causality Convolutional Neural Network (GCCNN) combining convolutional neural network and Granger causality functional brain network is proposed to analyze the subjects’ noninvasive scalp EEG signals. Here, the GCCNN method is used to distinguish the subjects with severe acrophobia, moderate acrophobia, and no acrophobia in a three-class classification task or no acrophobia and acrophobia in a two-class classification task. Compared with the mainstream methods, the GCCNN method achieves better classification performance, with an accuracy of 98.74% for the two-class classification task (no acrophobia versus acrophobia) and of 98.47% for the three-class classification task (no acrophobia versus moderate acrophobia versus severe acrophobia). Consequently, our proposed GCCNN method can provide more accurate quantitative results than the comparative methods, making it to be more competitive in further practical applications.

scholarly journals Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Jiong Huang ◽  
Fulin Dang
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Big Data ◽  
High Altitude ◽  
Classification Accuracy ◽  
Chronic Obstructive ◽  
Ecg Signal ◽  
Obstructive Pulmonary Disease ◽  
Chronic Pulmonary Heart Disease

This study explores the risk factors of chronic pulmonary heart disease (CPHD) induced by plateau chronic obstructive pulmonary disease (COPD) based on intelligent medical treatment and big data of electrocardiogram (ECG) signal. Based on GPU, a wavelet algorithm is introduced to extract features of ECG signal, and it was combined with generalized regression neural network (GRNN) to improve classification accuracy. From June 2018 to December 2020, 10,185 patients diagnosed with COPD in the plateau area by pulmonary function testing, ECG, and chest X-ray at X Hospital are taken as the research objects to evaluate the distribution of CPHD incidence at different ages and altitudes. The running time of GTX780Ti is about 15 times shorter than that of CPU. The accuracy of N detection based on the GPU-accelerated neural network model reached 98.06%. Accuracy (Acc), sensitivity (Se), specificity (Sp), and positive rate (PR) of V were 99.03%, 89.17%, 98.92%, and 93.18%, respectively. The Acc, Se, Sp, and PR of S were 99.54%, 86.22%, 99.74%, and 92.56%, respectively. The GRNN classification accuracy was up to 98%. 19% of COPD patients were diagnosed with CPHD, including 1,409 males (72.82%) and 526 females (36.24%). The highest prevalence of CPHD was 64.60% when the altitude was 1,900–2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m. The highest prevalence of CPHD was 63.77% at the age of 61–70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%. Therefore, the GPU-based neural network model improved the classification accuracy of ECG signals. Age and altitude were risk factors for CPHD induced by high-altitude COPD, which provided a reference for the prevention, diagnosis, and treatment of CPHD in high-altitude areas.

Contrast of Filament Bright Rims

1994 ◽  
Vol 144 ◽  
pp. 365-367
Author(s):  
E. V. Kononovich ◽  
O. B. Smirnova ◽  
P. Heinzel ◽  
P. Kotrč
Keyword(s):  
High Altitude ◽  
Line Profile ◽  
Line Center ◽  
Magnetic Structures ◽  
The Mean

AbstractThe Hα filtergrams obtained at Tjan-Shan High Altitude Observatory near Alma-Ata (Moscow University Station) were measured in order to specify the bright rims contrast at different points along the line profile (0.0; ± 0.25; ± 0.5; ± 0.75 and ± 1.0 Å). The mean contrast value in the line center is about 25 percent. The bright rims interpretation as the bases of magnetic structures supporting the filaments is suggested.

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