scholarly journals Real-Time Diagnosis of Island Landslides Based on GB-RAR

2020 ◽  
Vol 8 (3) ◽  
pp. 192 ◽  
Author(s):  
Deming Ma ◽  
Yongsheng Li ◽  
Jianwei Cai ◽  
Bingquan Li ◽  
Yanxiong Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Rapid Development ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Measurement Technology ◽  
Monitoring Method ◽  
Geological Disasters ◽  
Landslide Mass ◽  
Time Diagnosis

Landslides are one of the most frequent and serious geological disasters that threaten people’s lives and property safety. In recent years, with the rapid development of the coastal economy and the increasingly strained spatial resources, the island development activities have become extremely rapid, resulting in the frequent occurrence of landslides on the island. We selected Beichangshan Island in the north of China as the research area. By using high-precision ground-based real aperture radar (GB-RAR) measurement technology, the displacement changes of potential landslides are monitored continuously and dynamically to realize the real-time diagnosis and early warning of island landslides. At the same time, the data interpretation method and key processing flow are described in detail. The results show that during the whole monitoring process, an area of obvious change is found, which is mainly located in the middle of the landslide mass. The mean velocity rate shows a nonlinear deformation trend. The maximum deformation of the landslide in the five selected points reaches 4.5 mm, which indicates that the area is in an unstable stage. The deformation monitoring ability of GB-RAR technology to identify the sub-millimeter level is demonstrated, and the monitoring method is verified. The validity and reliability of the method can be applied to real-time dynamic fine deformation diagnosis of island landslides. Its accuracy can meet the needs of dynamic change monitoring of island landslides, and it can become an important tool and means for early warning and treatment of landslides. The research is conducive to further enriching and improving the monitoring method system of island geological disasters in China, provides a scientific basis and technical support for early warning and disaster prevention and mitigation of island landslides, and can be popularized and applied in the monitoring of island landslides.

scholarly journals Multistage identification method for real-time abnormal events of streaming data

2019 ◽  
Vol 15 (12) ◽  
pp. 155014771989454
Author(s):  
Hao Luo ◽  
Kexin Sun ◽  
Junlu Wang ◽  
Chengfeng Liu ◽  
Linlin Ding ◽  
...  
Keyword(s):  
Real Time ◽  
Coal Mine ◽  
Early Warning ◽  
Sliding Window ◽  
Geological Structure ◽  
Streaming Data ◽  
Event Monitoring ◽  
Monitoring Method ◽  
Identification Method ◽  
Abnormal Events

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events (“the coal mine disaster events”). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms of coal mine disasters, adjusting the early warning level according to the identification extent of the disaster signal, which increases the early warning level gradually with the successful result of the matching of 1/ N size of the template, and the piecewise aggregate approximation method is used to optimize the calculation process. Experimental results show that the method proposed in this article is more accurate and be used in real time.

scholarly journals An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Development ◽  
Automatic Monitoring ◽  
Deformation Monitoring ◽  
Open Pit Mine ◽  
Slope Deformation ◽  
Open Pit ◽  
Gis Technology ◽  
Monitoring Method

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

scholarly journals Early Warning Indicators of Landslides Based on Deep Displacements: Applications on Jinping Landslide and Wendong Landslide, China

2021 ◽  
Vol 9 ◽  
Author(s):  
He Chen ◽  
Guo Li ◽  
Rui Fang ◽  
Min Zheng
Keyword(s):  
Early Warning ◽  
Deformation Monitoring ◽  
Landslide Risk ◽  
Displacement Method ◽  
Monitoring Method ◽  
Deformation Stages ◽  
Early Warning Indicators ◽  
Displacement Based ◽  
Warning Indicators ◽  
Monitoring And Early Warning

Real-time monitoring and early warning have great significance in reducing/avoiding the consequences caused by landslides. The deep displacement-based monitoring method has been proven to be a suitable solution for landslide risk management. However, the early warning indicators based on the deep displacement method need to be fully understood. This paper reports on an investigation into early warning indicators and deformation monitoring of several natural landslides. A series of indicators using the profiles of the accumulative displacement, kinetic energy, and their rates against time for early warning are developed and calibrated by monitoring and analyzing a natural landslide. The early warning indicators are then applied to monitor and identify the different deformation stages of the Jinping County North Landslide and the Wendong Town Landslide.

scholarly journals A Real-Time Deformation Monitoring Method for Large-Scale Structure Based on Relay Camera

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chuan He ◽  
Lianxiong Liu ◽  
Changhua Hu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Large Scale Structure ◽  
Deformation Monitoring ◽  
Scale Structure ◽  
Monitoring Method ◽  
The Real ◽  
Intrinsic Parameters ◽  
Time Deformation ◽  
Extrinsic Parameters

In the process of the deformation monitoring for large-scale structure, the mobile vision method is often used. However, most of the existent researches rarely consider the real-time property and the variation of the intrinsic parameters. This paper proposes a real-time deformation monitoring method for the large-scale structure based on a relay camera. First, we achieve the real-time pose-position relationship by using the relay camera and the coded mark points whose coordinates are known. The real-time extrinsic parameters of the measuring camera are then solved according to the constraint relationship between the relay camera and the measuring camera. Second, the real-time intrinsic parameters of the measuring camera are calculated based on the real-time constraint relationship among the extrinsic parameters, the intrinsic parameters, and the fundamental matrix. Finally, the coordinates of the noncoded measured mark points, which are affixed to the surface of the structure, are achieved. Experimental results show that the accuracy of the proposed method is higher than 1.8 mm. Besides, the proposed method also possesses the real-time and automation property.

scholarly journals Developing an Intelligent Monitoring Technology for Airport Stone Column Machines

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3050
Author(s):  
Ke Tang ◽  
Haiwen Yuan ◽  
Jianxun Lv ◽  
Fengchen Chen
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Satellite System ◽  
Third Party ◽  
Stone Columns ◽  
Stone Column ◽  
Measurement Technology ◽  
Construction Quality ◽  
Monitoring Method ◽  
Intelligent Monitoring

Most of the construction machinery for vibro-sinking stone columns, which are widely used in China, needs to be improved in terms of degree of automation. Engineering quality control is mainly carried out post-inspection; consequently, it is difficult to control the construction quality in real time. According to the construction characteristics of traditional stone column machines, we established the theory and model for the real-time monitoring of stone column construction, as well as put forward an intelligent monitoring method for stone column machines. With the comprehensive application of critical technologies such as the Global Navigation Satellite System (GNSS) measurement technology, laser ranging sensors, and massive data processing, an intelligent data acquisition technique and associated monitoring equipment for stone column construction machines are developed. The data acquisition and storage of crucial construction parameters, such as pile depth, pile point co-ordinates, bearing layer current, and reverse insertion times, are realized. A large number of actual construction data are collected and the construction quality parameters of stone column machines are obtained. By comparison with third-party detection data, it is verified that the intelligent monitoring technique for stone column machines proposed in this paper is feasible.

scholarly journals Study on debris flow risk monitoring and Early Warning in Nujiang Prefecture, Yunnan Province, China

2021 ◽  
Vol 906 (1) ◽  
pp. 012003
Author(s):  
Qianrui Huang ◽  
Shuran Yang ◽  
Xianfeng Cheng ◽  
Yungang Xiang
Keyword(s):  
Debris Flow ◽  
Early Warning ◽  
Yunnan Province ◽  
Local Economic Development ◽  
Research Field ◽  
Monitoring Method ◽  
Flow Monitoring ◽  
Geological Disasters ◽  
Geological Disaster ◽  
Monitoring And Early Warning

Abstract Debris flow is the mainly the geological disasters in Nujiang Prefecture, while precipitation is the trigger of it, how to implement debris flow forecast based on precipitation monitoring data or forecast data is a hot issue in current debris flow disaster research field. Because of the special geomorphology in Nujiang Prefecture, due to the influence of human activities, geological disasters occur frequently, severely affect the local economic development. As a demonstration area of geological disaster monitoring and early warning in Yunnan Province, to build a well-developed geological disaster warning system, it is very important to spread it to other parts of Yunnan province. Based on the analysis of the current situation of geological disasters in Nujiang Prefecture, adopt appropriate monitoring method and calculation method to select the primary sites for debris flow monitoring and early warning in the Nu River basin for research.

Intelligent image recognition system for detecting abnormal features of scenic spots based on deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5149-5159
Author(s):  
Kainan Liu ◽  
Meiyun Zhang ◽  
Mohammed K. Hassan
Keyword(s):  
Real Time ◽  
Image Recognition ◽  
Early Warning ◽  
Predictive Ability ◽  
Recognition System ◽  
Recall Rate ◽  
Monitoring Method ◽  
Optical Flow Method ◽  
Scenic Spot ◽  
Expression Model

To monitor the scene anomaly in real-time through video and image and identify the emergencies, try to respond quickly at the beginning of the emergency and reduce the loss. This paper mainly focuses on the realization of the image recognition system for the anomalous characteristics of tourism emergencies. The problem is to study the number of people in the scenic spot based on scenic spot monitoring. The video-based population anomaly monitoring method has improved the AUC index of the W-SFM method by 0.423, and the AUC has increased by 0.0844 compared with the optical flow method; Degree-enhanced algorithm (BCOF), by grasping the micro-blog data related to the scenic spot, comprehensively predicts the overall comfort of the current tourists in the scenic spot, and establishes a tourist state expression model. Compared with the BN algorithm and the NEG algorithm, the BCOF algorithm is the accuracy and the recall rate of tourists in the scenic spots was improved by 14% and 18% respectively. The image recognition system of tourism emergency anomaly was established, and the early warning model of tourism emergency based on group intelligence perception was used to implement early warning on scenic spots. Monitoring, can achieve an overall accuracy of 83.33%, the model has a strong predictive ability, and achieves a scenic spot Real-time monitoring of events.

Research on Aero-Engine Lubricating Real-Time Oil Measurement Method

2014 ◽  
Vol 644-650 ◽  
pp. 840-844
Author(s):  
Bao Feng Shan ◽  
Fan Chen Mo ◽  
Jing Chun Li ◽  
Zhi Ping Tian
Keyword(s):  
Real Time ◽  
Measurement System ◽  
Measurement Method ◽  
Spatial Databases ◽  
Rapid Development ◽  
Time Measurement ◽  
Lubricating Oil ◽  
Measurement Technology ◽  
Real Time Measurement ◽  
Aero Engine

With the rapid development of aviation technology, the performance of aero-engine continues to increase in China. However, Chinese Aero-engine lubricating oil real-time measurement technology is not mature, active airplane almost have no Aero-engine lubricating oil real-time measurement system, It requires further in-depth study.By research on Aero-engine lubricating oil measurement technology. First, it outlined the method and principles of measurement, and based on UG Open the analysis of Aero-engine lubricating oil system was developed. Through which an analysis of the model of Aero-engine lubricating oil, the spatial databases about sensor depth of immersion oil, aircraft attitude and fuel volume were built.,which can provide the data for the subsequent lubricating real-time oil measuring analysis .

scholarly journals Development of smart boulders to monitor mass movements via the Internet of Things: a pilot study in Nepal

2021 ◽  
Vol 9 (2) ◽  
pp. 295-315
Author(s):  
Benedetta Dini ◽  
Georgina L. Bennett ◽  
Aldina M. A. Franco ◽  
Michael R. Z. Whitworth ◽  
Kristen L. Cook ◽  
...  
Keyword(s):  
Debris Flow ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Area Network ◽  
Accelerometer Data ◽  
Warning Systems ◽  
2015 Gorkha Earthquake ◽  
Landslide Mass ◽  
Landslide Body

Abstract. Boulder movement can be observed not only in rockfall activity, but also in association with other landslide types such as rockslides, soil slides in colluvium originating from previous rockslides, and debris flows. Large boulders pose a direct threat to life and key infrastructure in terms of amplifying landslide and flood hazards as they move from the slopes to the river network. Despite the hazard they pose, boulders have not been directly targeted as a mean to detect landslide movement or used in dedicated early warning systems. We use an innovative monitoring system to observe boulder movement occurring in different geomorphological settings before reaching the river system. Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway is subjected to periodic landsliding and floods during the monsoons and was heavily affected by coseismic landslides during the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads, and other key infrastructure, such as hydropower plants, is observed every year. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels before the monsoon season of 2019. The trackers, equipped with accelerometers, can detect small angular changes in the orientation of boulders and large forces acting on them. The data can be transmitted in real time via a long-range wide-area network (LoRaWAN®) gateway to a server. Nine of the tagged boulders registered patterns in the accelerometer data compatible with downslope movements. Of these, six lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement of the landslide mass. Three boulders located in a debris flow channel show sharp changes in orientation, likely corresponding to larger free movements and sudden rotations. This study highlights the fact that this innovative, cost-effective technology can be used to monitor boulders in hazard-prone sites by identifying the onset of potentially hazardous movement in real time and may thus establish the basis for early warning systems, particularly in developing countries where expensive hazard mitigation strategies may be unfeasible.

scholarly journals Development of smart boulders to monitor mass movements via the Internet of Things: A pilot study in Nepal

2020 ◽  
Author(s):  
Benedetta Dini ◽  
Georgina L. Bennett ◽  
Aldina M. A. Franco ◽  
Michael R. Z. Whitworth ◽  
Kristen L. Cook ◽  
...  
Keyword(s):  
Debris Flow ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Accelerometer Data ◽  
Warning Systems ◽  
2015 Gorkha Earthquake ◽  
Landslide Mass ◽  
Landslide Body ◽  
Rock Slides

Abstract. Boulder movement can be observed not only in rock fall activity, but also in association with other landslide types such as rock slides, soil slides in colluvium originated from previous rock slides and debris flows. Large boulders pose a direct threat to life and key infrastructure, amplifying landslide and flood hazards, as they move from the slopes to the river network. Despite the hazard they pose, boulders have not been directly targeted as a mean to detect landslide movement or used in dedicated early warning systems. We use an innovative monitoring system to observe boulder movement occurring in different geomorphological settings, before reaching the river system. Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway is subjected to periodic landsliding and floods during the monsoons and was heavily affected by coseismic landslides during the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads and other key infrastructure, such as hydropower plants, is observed every year. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels, before the monsoon season of 2019. The trackers, equipped with accelerometers, can detect small angular changes in boulders orientation and large forces acting on them. The data can be transmitted in real time, via a long-range wide area network (LoRaWAN®) gateway to a server. Nine of the tagged boulders registered patterns in the accelerometer data compatible with downslope movements. Of these, six lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement consistent with the landslide mass. Three boulders, located in a debris flow channel, show sharp changes in orientation, likely corresponding to larger free movements and sudden rotations. This study highlights that this innovative, cost-effective technology can be used to monitor boulders in hazard prone sites, identifying in real time the onset of movement, and may thus set the basis for early warning systems, particularly in developing countries, where expensive hazard mitigation strategies may be unfeasible.

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