scholarly journals Analysis of high-rise building safety detection methods based on big data and artificial intelligence

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093530
Author(s):  
Jiaojiao Xu ◽  
Chuanjie Yan ◽  
Yangyang Su ◽  
Yong Liu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Model Building ◽  
Test System ◽  
Dynamic Monitoring ◽  
Detection Methods ◽  
Sensor Technology ◽  
Land Resource ◽  
Time Dynamic ◽  
High Rise

With rapid industrialization, the construction of high-rise buildings is a good and effective solution to the rational and effective use of land resources and alleviation of existing land resource tensions. Especially in the construction process, if there is a problem with the pile foundation, the building will inevitably be tilted, which will directly affect the personal safety of the construction workers and resident users. The experiments in this article use the concept of big data to divide the system into modules such as data collection, data preprocessing, feature extraction, prediction model building, and model application in order to provide massive data storage and parallel computing services to form a security test system. The experimental data show that wireless sensor technology is applied to the inclination monitoring of buildings, and a monitoring system based on wireless inclination sensors is designed to enable real-time dynamic monitoring of buildings to ensure human safety. When the experimental model frame is stable under normal environmental conditions, a nonstationary vibration is artificially produced for a period of time from the outside world, which is about 60 s higher than the traditional method, and the efficiency is also increased by about 80%, a situation where a building has a reversible tilt change.

scholarly journals Methodology of Adaptive Prognostics and Health Management Using Streaming Data in Big Data Environment

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianshe Feng
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Adaptive Sampling ◽  
Health Management ◽  
Streaming Data ◽  
Sensor Technology ◽  
Selection Strategy ◽  
Prognostics And Health Management ◽  
Online Environment ◽  
Stream Data

Prognostics and health management (PHM) has gradually become an essential technique to improve the availability and efficiency of industrial systems. With the rapid advancement of sensor technology and communication technology, a huge amount of real-time data is generated from various applications industry, which brings new challenges to PHM in the context of big data streams. On one hand, high-volume stream data places a heavy demand on data storage, communication, and PHM modeling. On the other hand, continuous fluctuation and drift are essential properties of stream data in an online environment, which requires the PHM model to be capable to capture the new formation in stream data adaptively and continuously. This research proposes a systematic methodology to develop an effective online evolving PHM method with adaptive sampling mechanism against continuous stream data. An adaptive sample selection strategy is developed to effectively select the representative samples in both off-line and online environment. Meanwhile, a probabilistic theory-based modeling approach is developed to update the model with newly selected samples. Finally, the whole methodology is validated with real-world industrial cases. The result comparison between the proposed methodology and state-of-art methods verifies the superiority of the proposed method.

The Software Design of Inverter Test System Based on VC and NI Controls

2011 ◽  
Vol 467-469 ◽  
pp. 2002-2006
Author(s):  
Wen Lun Cao ◽  
Bei Chen ◽  
Yu Yao He
Keyword(s):  
Data Storage ◽  
Performance Test ◽  
Rotation Speed ◽  
Test System ◽  
Dynamic Monitoring ◽  
Hardware Platform ◽  
Current Voltage ◽  
Three Phase ◽  
Balance Analysis ◽  
Bus Voltage

The I/O performance test system of inverter is designed. ADlink PCI board and signal regulating panel are core of hardware platform. The software uses the mixed programs, that is, the graph control of Labview and Microsoft Visual C++. Based on the eight analog inputs, two analog outputs, three digital inputs and four digital outputs provided with specific application, we fulfill the following assignments: the dynamic monitoring of three-phase input current/voltage, DC-bus voltage and actual rotation speed, curve drawing and data storage. The computer could give the enable signal and analog speed of inverter, besides the failures are read and displayed. Finally the harmonic analysis and the balance analysis of three-phase current/voltage are realized.

scholarly journals Prediction of lameness using automatically recorded activity, behavior and production data in post-parturient Irish dairy cows

2021 ◽  
Vol 74 (1) ◽  
Author(s):  
G. M. Borghart ◽  
L. E. O’Grady ◽  
J. R. Somers
Keyword(s):  
Dairy Cows ◽  
Milk Production ◽  
Model Building ◽  
Learning Algorithm ◽  
Live Weight ◽  
Sensor Data ◽  
Detection Methods ◽  
Sensor Technology ◽  
Practical Implementation ◽  
Locomotion Score

Abstract Background Although visual locomotion scoring is inexpensive and simplistic, it is also time consuming and subjective. Automated lameness detection methods have been developed to replace the visual locomotion scoring and aid in early and accurate detection. Several types of sensors are measuring traits such as activity, lying behavior or temperature. Previous studies on automatic lameness detection have been unable to achieve high accuracy in combination with practical implementation in a on farm commercial setting. The objective of our research was to develop a prediction model for lameness in dairy cattle using a combination of remote sensor technology and other animal records that will translate sensor data into easy to interpret classified locomotion information for the farmer. During an 11-month period, data from 164 Holstein-Friesian dairy cows were gathered, housed at an Irish research farm. A neck-mounted accelerometer was used to gather behavioral metrics, additional automatically recorded data consisted of milk production and live weight. Locomotion scoring data were manually recorded, using a one-to-five scale (1 = non-lame, 5 = severely lame). Locomotion scores where then used to label the cows as sound (locomotion score 1) or unsound (locomotion score ≥ 2). Four supervised classification models, using a gradient boosted decision tree machine learning algorithm, were constructed to investigate whether cows could be classified as sound or unsound. Data available for model building included behavioral metrics, milk production and animal characteristics. Results The resulting models were constructed using various combinations of the data sources. The accuracy of the models was then compared using confusion matrices, receiver-operator characteristic curves and calibration plots. The model which achieved the highest performance according to the accuracy measures, was the model combining all the available data, resulting in an area under the curve of 85% and a sensitivity and specificity of 78%. Conclusion These results show that 85% of this model’s predictions were correct in identifying cows as sound or unsound, showing that the use of a neck-mounted accelerometer, in combination with production and other animal data, has potential to replace visual locomotion scoring as lameness detection method in dairy cows.

Secure big data storage and sharing scheme for cloud tenants

2015 ◽  
Vol 12 (6) ◽  
pp. 106-115 ◽  
Author(s):  
Hongbing Cheng ◽  
Chunming Rong ◽  
Kai Hwang ◽  
Weihong Wang ◽  
Yanyan Li
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Sharing Scheme ◽  
Big Data Storage
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