A tnGAN-based Leak Detection Method for Pipeline Network Considering Incomplete Sensor Data

2020 ◽  
pp. 1-1
Author(s):  
Xuguang Hu ◽  
Huaguang Zhang ◽  
Dazhong Ma ◽  
Rui Wang
Keyword(s):  
Detection Method ◽  
Leak Detection ◽  
Sensor Data ◽  
Pipeline Network

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

Data processing techniques of hydraulic bed sensor and doppler radar for elder care

2017 ◽  
Author(s):  
◽  
Bo-Yu Su
Keyword(s):  
Heart Rate ◽  
Doppler Radar ◽  
Detection Method ◽  
Blood Pressure Measurement ◽  
Fall Detection ◽  
The United States ◽  
Heart Beat ◽  
Body Motion ◽  
Sensor Data ◽  
Heart Rate Monitoring

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Population aging is a common phenomenon in a society. The developed country like the United States, eldercare is becoming an important issue nowadays. There are many aspects we need to address for eldercare, including - circulatory system, alimentary system, nervous system and so on. In this research study, we focus on the heart rate monitoring and estimation using a hydraulic bed sensor. In addition, we also develop the fall detection technique using a Doppler radar. The hydraulic bed sensor for heart rate monitoring is placed under the mattress. The sensor system contains four tubes filled with water and uses the pressure sensor to obtain the Ballistocardiogram (BCG) signal. The BCG signal contains the information of heart beat, respiratory rate and body motion. Two algorithms are developed to process the bed sensor data. One uses the Hilbert transform and the other is based on the energy. By using the algorithms we developed, we can extract the heart beat information to estimate the heart rate. The system has been validated in a well controlled lab environment and a nursing house. In addition to the heart rate, the relative blood pressure measurement by using two features extracted from the bed sensor signal has also been developed and validated with 48 people data. The results show high correlation coefficient with the groundtruth. The Doppler radar for human fall detection is mounted in the ceiling. The radar senses the motion of an object and produces outputs based on the Doppler shift effect. We propose an effective method based on Wavelet Transform (WT) for fall vs. nonfall classification. The proposed fall detection classi er can distinguish between the fall and daily activities. The good performance of the proposed detection method has been validated through the data from the lab and in-home environments, with the falls from stunt actors and senior residents. To further improve the performance, we introduce an additional radar mounted on the wall. Based on the same detection method as when using one radar, we extract and concatenate the features from two radars for classification. The result shows outstanding improvement.

Simulation of flow characteristics for leak detection in oil and gas pipeline network

2021 ◽  
Author(s):  
B. Manshoor ◽  
A. Khalid ◽  
I. Zaman ◽  
D. Hissein Didane ◽  
N. F. F. Zulkefli
Keyword(s):  
Oil And Gas ◽  
Flow Characteristics ◽  
Leak Detection ◽  
Gas Pipeline ◽  
Pipeline Network

An Anomaly Detection Method Based on GCN and Correlation of High Dimensional Sensor Data in Power Grid System

2021 ◽  
pp. 444-454
Author(s):  
Liu Weiwei ◽  
Lei Shuya ◽  
Zheng Xiaokun ◽  
Li Han ◽  
Wang Xinyu ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Power Grid ◽  
Sensor Data ◽  
High Dimensional ◽  
Grid System

Development of a timesaving leak detection method for brick-type packages

2007 ◽  
Vol 82 (3) ◽  
pp. 324-332 ◽  
Author(s):  
Vijaykrishna Sivaramakrishna ◽  
Frank Raspante ◽  
Sevugan Palaniappan ◽  
Melvin A. Pascall
Keyword(s):  
Detection Method ◽  
Leak Detection
Sign in / Sign up

Export Citation Format

Share Document