scholarly journals Deep Learning-Based Real-Time Auto Classification of Smartphone Measured Bridge Vibration Data

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2710
Author(s):  
Ashish Shrestha ◽  
Ji Dang
Keyword(s):  
Real Time ◽  
Classification Performance ◽  
Classification Model ◽  
Mobile Platforms ◽  
Accelerometer Sensor ◽  
Vibration Data ◽  
Practical Feasibility ◽  
Term Monitoring

In this study, a simple and customizable convolution neural network framework was used to train a vibration classification model that can be integrated into the measurement application in order to realize accurate and real-time bridge vibration status on mobile platforms. The inputs for the network model are basically the multichannel time-series signals acquired from the built-in accelerometer sensor of smartphones, while the outputs are the predefined vibration categories. To verify the effectiveness of the proposed framework, data collected from long-term monitoring of bridge were used for training a model, and its classification performance was evaluated on the test set constituting the data collected from the same bridge but not used previously for training. An iOS application program was developed on the smartphone for incorporating the trained model with predefined classification labels so that it can classify vibration datasets measured on any other bridges in real-time. The results justify the practical feasibility of using a low-latency, high-accuracy smartphone-based system amid which bottlenecks of processing large amounts of data will be eliminated, and stable observation of structural conditions can be promoted.

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

scholarly journals Pulse Radar with Field-Programmable Gate Array Range Compression for Real Time Displacement and Vibration Monitoring

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 82
Author(s):  
Mihai-Liviu Tudose ◽  
Andrei Anghel ◽  
Remus Cacoveanu ◽  
Mihai Datcu
Keyword(s):  
Real Time ◽  
Cross Correlation ◽  
Vibration Monitoring ◽  
Time Range ◽  
Pulse Radar ◽  
Analog Front End ◽  
Compression Time ◽  
Field Programmable ◽  
Term Monitoring

This paper aims to present the basic functionality of a radar platform for real time monitoring of displacement and vibration. The real time capabilities make the radar platform useful when live monitoring of targets is required. The system is based on the RF analog front-end of a USRP, and the range compression (time-domain cross-correlation) is implemented on the FPGA included in the USRP. Further processing is performed on the host computer to plot real time range profiles, displacements, vibration frequencies spectra and spectrograms (waterfall plots) for long term monitoring. The system is currently in experimental form and the present paper aims to prove its functionality. The precision of this system is estimated (using the 3σ approximation) at 0.6 mm for displacement measurements and 1.8 mm for vibration amplitude measurements.

scholarly journals Atmospheric water vapour content over La Silla Paranal Observatory

2009 ◽  
Vol 5 (H15) ◽  
pp. 537-537
Author(s):  
R. Querel ◽  
F. Kerber ◽  
R. Hanuschik ◽  
G. Lo Curto ◽  
D. Naylor ◽  
...  
Keyword(s):  
Real Time ◽  
Water Vapour ◽  
Atmospheric Water ◽  
Water Vapour Content ◽  
Atmospheric Water Vapour ◽  
Infrared Wavelengths ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Principle Source

Water vapour is the principle source of opacity at infrared wavelengths in the earth's atmosphere. Measurements of atmospheric water vapour serve two primary purposes when considering operation of an observatory: long-term monitoring of precipital water vapour (PWV) is useful for characterizing potential observatory sites, and real-time monitoring of PWV is useful for optimizing use, in particular for mid-IR observations.

scholarly journals A real-time wireless wearable electroencephalography system based on Support Vector Machine for encephalopathy daily monitoring

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877956 ◽  
Author(s):  
Qing Zhang ◽  
Pingping Wang ◽  
Yan Liu ◽  
Bo Peng ◽  
Yufu Zhou ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Early Warning ◽  
Mobile Terminal ◽  
Support Vector ◽  
Brain Health ◽  
Intelligent Support ◽  
Daily Monitoring ◽  
Term Monitoring

Wearable electroencephalography systems of out-of-hospital can both provide complementary recordings and offer several benefits over long-term monitoring. However, several limitations were present in these new-born systems, for example, uncomfortable for wearing, inconvenient for retrieving the recordings by patients themselves, unable to timely provide accurate classification, and early warning information. Therefore, we proposed a wireless wearable electroencephalography system for encephalopathy daily monitoring, named as Brain-Health, which focused on the following three points: (a) the monitoring device integrated with electroencephalography acquisition sensors, signal processing chip, and Bluetooth, attached to a sport hat or elastic headband; (b) the mobile terminal with dedicated application, which is not only for continuous recording and displaying electroencephalography signal but also for early warning in real time; and (c) the encephalopathy’s classification algorithm based on intelligent Support Vector Machine, which is used in a new application of wearable electroencephalography for encephalopathy daily monitoring. The results showed a high mean accuracy of 91.79% and 93.89% in two types of classification for encephalopathy. In conclusion, good performance of our Brain-Health system indicated the feasibility and effectiveness for encephalopathy daily monitoring and patients’ health self-management.

Experimental investigation into the use of vibration data for long-term monitoring of an all-composite bridge

2000 ◽  
Author(s):  
Colin P. Ratcliffe ◽  
John W. Gillespie, Jr. ◽  
Dirk Heider ◽  
Douglas A. Eckel II ◽  
Roger M. Crane
Keyword(s):  
Experimental Investigation ◽  
Vibration Data ◽  
Composite Bridge ◽  
Long Term Monitoring ◽  
Term Monitoring

scholarly journals Primary biliary cholangitis – a new nosological unit in the classification of liver diseases (literature review and own clinical observation)

2019 ◽  
pp. 76-82
Author(s):  
T. V. Penkina ◽  
E. A. Shikina ◽  
D. T. Dicheva ◽  
O. E. Berezutskaya ◽  
N. L. Golovkina ◽  
...  
Keyword(s):  
Biochemical Parameters ◽  
Clinical Observation ◽  
Primary Biliary Cholangitis ◽  
Routine Practice ◽  
Biliary Cirrhosis ◽  
Long Term Monitoring ◽  
Definition Of ◽  
Term Monitoring

Identification of changes in biochemical parameters of liver functional activity during screening studies requires additional examination of the patient in order to determine the genesis of the disease. In recent years, in routine practice, the most frequently used is an isolated definition of the level of transaminases (ALT, AST), which does not allow timely detection of latent cholestasis syndrome. Primary biliary cholangitis (PBC), previously referred to as primary biliary cirrhosis, is a relatively rare chronic autoimmune cholesthetic liver disease, predominantly affecting middle-aged women and prone to progressing liver cirrhosis. The recommendations of AASLD and EASL note the need for long-term monitoring of patients with ongoing UDCA therapy and regular diagnostic studies to identify signs of disease progression. A clinical example of successful treatment of a patient with PBC with the Russian drug Exhol® is described.

scholarly journals Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot

2021 ◽  
Vol 15 ◽  
Author(s):  
Neethu Robinson ◽  
Tushar Chouhan ◽  
Ernest Mihelj ◽  
Paulina Kratka ◽  
Frédéric Debraine ◽  
...  
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Brain Computer Interface ◽  
Brain Activation ◽  
Classification Performance ◽  
Open Loop ◽  
Computer Interface ◽  
Single Subject ◽  
User Engagement

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.

scholarly journals Phytoindication of environmental mutagenesis in the in-situ conditions using the pollen of selected species of native flora

2021 ◽  
Author(s):  
Karol Mičieta
Keyword(s):  
Pollen Grains ◽  
Heavy Load ◽  
Native Flora ◽  
In Situ Conditions ◽  
Environmental Mutagenesis ◽  
Term Monitoring ◽  
Long Term Experience

The aim of this study is to provide an effective method for indicating ecogenotoxicity in the environment using pollen grains and microspores of selected species of the native flora in the in situ conditions. In the report, we summarize the results of long-term experience with the benefits of native flora species as bioindicators of polluted environments. We present the current results of long-term monitoring of phytoindication of ecogenotoxicity in Bratislava and selected traffic junctions in Slovakia. The increase of pollen grain abortion in the group of localities exposed to a heavy load of traffic pollution demonstrates the ecogenotoxic impact of traffic emissions in the environment. The detailed practical methodological tools and possible difficulties with the classification of abortivity of microspores and pollen grains of these plant species are discussed.

scholarly journals Indoor Thermal Environment Long-Term Data Analytics Using IoT Devices in Korean Apartments: A Case Study

2020 ◽  
Vol 17 (19) ◽  
pp. 7334
Author(s):  
Hyunjun Yun ◽  
Jinho Yang ◽  
Byong Hyoek Lee ◽  
Jongcheol Kim ◽  
Jong-Ryeul Sohn
Keyword(s):  
Real Time ◽  
Thermal Environment ◽  
Temperature Management ◽  
Living Room ◽  
Indoor Thermal Environment ◽  
Long Term Monitoring ◽  
Iot Devices ◽  
Term Monitoring

IoT-based monitoring devices can transmit real-time and long-term thermal environment data, enabling innovative conversion for the evaluation and management of the indoor thermal environment. However, long-term indoor thermal measurements using IoT-based devices to investigate health effects have rarely been conducted. Using apartments in Seoul as a case study, we conducted long-term monitoring of thermal environmental using IoT-based real-time wireless sensors. We measured the temperature, relative humidity (RH), and CO2 in the kitchen, living room, and bedrooms of each household over one year. In addition, in one of the houses, velocity and globe temperatures were measured for multiple summer and autumn seasons. Results of our present study indicated that outdoor temperature is an important influencing factor of indoor thermal environment and indoor RH is a good indicator of residents’ lifestyle. Our findings highlighted the need for temperature management in summer, RH management in winter, and kitchen thermal environment management during summer and tropical nights. This study suggested that IoT devices are a potential approach for evaluating personal exposure to indoor thermal environmental risks. In addition, long-term monitoring and analysis is an efficient approach for analyzing complex indoor thermal environments and is a viable method for application in healthcare.

Sign in / Sign up

Export Citation Format

Share Document