The Intelligent Monitoring System

1990 ◽  
Vol 80 (6B) ◽  
pp. 1833-1851 ◽  
Author(s):  
Thomas C. Bache ◽  
Steven R. Bratt ◽  
James Wang ◽  
Robert M. Fung ◽  
Cris Kobryn ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Monitoring System ◽  
Database Management System ◽  
Automated System ◽  
Seismic Events ◽  
Process Data ◽  
Intelligent Monitoring ◽  
Seismic Arrays ◽  
Interactive Analysis ◽  
Knowledge Based

Abstract The Intelligent Monitoring System (IMS) is a computer system for processing data from seismic arrays and simpler stations to detect, locate, and identify seismic events. The first operational version processes data from two high-frequency arrays (NORESS and ARCESS) in Norway. The IMS computers and functions are distributed between the NORSAR Data Analysis Center (NDAC) near Oslo and the Center for Seismic Studies (Center) in Arlington, Virginia. The IMS modules at NDAC automatically retrieve data from a disk buffer, detect signals, compute signal attributes (amplitude, slowness, azimuth, polarization, etc.), and store them in a commercial relational database management system (DBMS). IMS makes scheduled (e.g., hourly) transfers of the data to a separate DBMS at the Center. Arrival of new data automatically initiates a “knowledge-based system (KBS)” that interprets these data to locate and identify (earthquake, mine blast, etc.) seismic events. This KBS uses general and area-specific seismological knowledge represented in rules and procedures. For each event, unprocessed data segments (e.g., 7 min for regional events) are retrieved from NDAC for subsequent display and analyst review. The interactive analysis modules include integrated waveform and map display/manipulation tools for efficient analyst validation or correction of the solutions produced by the automated system. Another KBS compares the analyst and automatic solutions to mark overruled elements of the knowledge base. Performance analysis statistics guide subsequent changes to the knowledge base so it improves with experience. The IMS is implemented on networked Sun workstations, with a 56 kbps satellite link bridging the NDAC and Center computer networks. The software architecture is modular and distributed, with processes communicating by messages and sharing data via the DBMS. The IMS processing requirements are easily met with major processes (i.e., signal processing, KBS, and DBMS) on separate Sun 4/2xx workstations. This architecture facilitates expansion in functionality and number of stations. The first version was operated continuously for 8 weeks in late-1989. The Center functions were then transferred to NDAC for subsequent operation. Later versions will be distributed among NDAC, Scripps/IGPP (San Diego), and the Center to process data from many stations and arrays. The IMS design is ambitious in its integration of many new computer technologies, but the operational performance of the first version demonstrates its validity. Thus, IMS provides a new generation of automated seismic event monitoring capability.

Initial results from the Intelligent Monitoring System

1990 ◽  
Vol 80 (6B) ◽  
pp. 1852-1873 ◽  
Author(s):  
Steven R. Bratt ◽  
Henry J. Swanger ◽  
Richard J. Stead ◽  
Floriana Ryall ◽  
Thomas C. Bache
Keyword(s):  
Knowledge Base ◽  
Monitoring System ◽  
Seismic Data ◽  
Data Interpretation ◽  
Local Network ◽  
Intelligent Monitoring ◽  
Knowledge Based ◽  
Interpretation Process ◽  
The University ◽  
Initial Results

Abstract The Intelligent Monitoring System (IMS) integrates advanced technologies in a knowledge-based distributed system that automates most of the seismic data interpretation process. Results from IMS during its first 8 weeks of operation (1 October through 25 November 1989) are analyzed to evaluate its performance. During this test period, the IMS processed essentially all data recorded by the NORESS and ARCESS high-frequency arrays in Norway. The emphasis was on detection and location of regional events within 2,000 km of these arrays. All events were reviewed and corrected if necessary by a skilled analyst. The final IMS Bulletin for the period includes 1,580 regional events (∼280 events/day). Approximately 55 per cent were smaller than MLg 1, with the largest just over MLg 3. Comparison of IMS locations in southern Finland and northwestern USSR (800 to 900 km from both arrays) with event locations from the University of Helsinki's local network bulletin are used to assess the detection and location capabilities of the system. Two or more phases (minimum needed to locate) were detected for 96 per cent of the events with magnitude greater than 2.5. The median separation between the IMS and Helsinki locations for all common events was 23.5 km. A consistent bias in arrival-time and azimuth residuals was observed for events in small geographic areas, indicating that refined travel-time models and path corrections could further improve location accuracy. The knowledge base in this first version of IMS was based on analysis of NORESS data, and many of the errors in interpretation corrected by the analysts can be attributed to differences encountered when this knowledge is used to interpret ARCESS data. Nevertheless, nearly 60 per cent of the events appearing in the final bulletin are automatic solutions approved without change or moved (by analyst corrections) less than 25 km from the automatic locations. The IMS had the most difficulty interpreting the overlapping signals generated by closely spaced explosions commonly detonated at mines in the Kola Peninsula and northern Sweden. Using the knowledge acquisition facilities included in the system, the deficiencies responsible for these and other errors are isolated, leading to development of new knowledge to be incorporated in the next version of the IMS knowledge base.

Knowledge-based interpretation of seismic data in the Intelligent Monitoring System

1993 ◽  
Vol 83 (5) ◽  
pp. 1507-1526 ◽  
Author(s):  
Thomas C. Bache ◽  
Steven R. Bratt ◽  
Henry J. Swanger ◽  
Gregory W. Beall ◽  
Frederick K. Dashiell
Keyword(s):  
Expert System ◽  
Bayesian Analysis ◽  
Monitoring System ◽  
High Frequency ◽  
Distributed Processing ◽  
Neural Nets ◽  
Compact Representation ◽  
Seismic Events ◽  
Automated Identification ◽  
Intelligent Monitoring

Abstract The Intelligent Monitoring System (IMS) provides a new capability for automated and interactive analysis of data to detect and locate seismic events recorded by a network of seismic stations. IMS integrates emerging technologies in artificial intelligence, database management, computer graphics, and distributed processing into an operational system used for routine bulletin production and associated research tasks. The first version of IMS (Bache et al., 1990a,b; Bratt et al., 1990) was designed for detection and location of regional events recorded by the two high-frequency arrays in Norway (NORESS and ARCESS). This has been extensively revised and expanded to become IMS Version 2 which is designed to detect and locate all seismic events recorded by an arbitrary seismic network. Since March 1991 it has been operated continuously to process the data from four high-frequency arrays (adding FINESA in Finland and GERESS in Germany). For some periods data from as many as seven 3-component stations in Eurasia have also been included in the processing. The most important new element is ESAL (Expert System for Association and Location) which interprets signal detections to form and locate seismic events. It is programmed in the ART expert system shell which provides the knowledge representation framework and inference mechanisms for complex and knowledge-rich rule-based reasoning. The current version of ESAL represents knowledge through approximately 200 ART rules that are configured through about 300 user-specified parameters and tables. The IMS architecture and operational procedures are designed to facilitate acquisition of new knowledge for ESAL. Knowledge acquisition methods being used include: Bayesian analysis, training neural-nets, statistical analysis to estimate parameters configuring rules, computing fuzzy-logic membership functions, and formulating new rules. Only the Bayesian probabilities are discussed in detail here. They provide a compact representation of complex knowledge about station-specific differences in phase characteristics. As an example, we describe the rules used for automated identification of detected regional Sn, Lg, and Rg phases. Using a Bayesian analysis technique, we quantify the differences in S-phase characteristics. The data show that they fall into two classes with GERESS distinct from the three Fennoscandia arrays.

scholarly journals Smart Parking System

2019 ◽  
Vol 9 (2) ◽  
pp. 584-587
Keyword(s):  
Monitoring System ◽  
Automated System ◽  
Human Beings ◽  
Intelligent Monitoring ◽  
Severe Problem ◽  
Parking System ◽  
Smart Parking

Now days in many multiplex systems there is a severe problem for car parking systems? There are many slots for car parking, so to park a car one has to look for the all lanes. Moreover there is a lot of men labor involved for this process for which there is lot of investment. Conventionally, car parking systems does not have any intelligent monitoring system. Parking slots are monitored generally by human beings. All vehicles enter into the parking area and time waste for searching for a vacant parking slot. Sometimes it creates blockage. Conditions become worse when there are multiple parking lanes and each lane have multiple parking slots.So the need is to develop a system which indicates directly which parking slot is vacant in any lane. The project involves a system including infrared transmitter and receiver in every lane and a indicator. The designed system works on the basis of an IOT module connected to a Wi-Fi module and a website that shows vacancy of parking lanes.Use of automated system for car parking monitoring will reduce the efforts.So the man entering parking area can view using IoT module involved and can decide which slot to enter so as to park the car.

scholarly journals Improvement of the automated seismic and acoustic monitoring system “Prognoz-ADS” main elements

2020 ◽  
Vol 192 ◽  
pp. 04014
Author(s):  
Pavel Anikin ◽  
Gennady Kursakin ◽  
Iuliia Fedotova
Keyword(s):  
Rock Mass ◽  
Monitoring System ◽  
Experimental Studies ◽  
Automated System ◽  
Seismic Events ◽  
Frequency Range ◽  
Industrial Testing ◽  
Control Zone ◽  
Accuracy Measurement ◽  
Highly Sensitive

The results of theoretical and experimental studies have established the need to improve and modernize the highly sensitive piezoelectric resonant type PeA12 converters used in the seismic monitoring system, which have been successfully used for more than 30 years in the rock mass at a number of rockburst hazardous mines. The main problem of the accuracy measurement by the geophone PeA12 (and other models based on it) is due to the presence of several resonances in the operating frequency band. The developed upgraded model of the AP2088 converter has successfully passed industrial testing as part of the automated monitoring system “Prognoz-ADS” at rockburst hazardous mine. During the test period (more than 1 year), seismoacoustic events were registered and verified in the rock mass, including rock burst and shocks in the rock mass. Thus, the use of highly sensitive (u10 V/m•s-2) piezoacoustic converters AP2088 as part of the automated system provides registration of acoustic emission in the rock mass in the frequency range from 0.1 to 10 kHz with the energy of seismic events from 10 to 106 J, which will increase the reliability of the forecast of geodynamic phenomena and technogenic seismicity in the control zone of the system.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

Design and Realization of CNG Intelligent Monitoring System

2013 ◽  
Vol 380-384 ◽  
pp. 761-764
Author(s):  
Zhen Hua Wang ◽  
Ge Fei Yu
Keyword(s):  
Monitoring System ◽  
Detection System ◽  
Short Message ◽  
Danger Signal ◽  
Intelligent Monitoring ◽  
Alarm Signal ◽  
Filling Station ◽  
Temperature Detection ◽  
Arm Microprocessor ◽  
Monitoring Information

One CNG remote intelligent monitoring system is designed and realized in this article. The monitoring system can receive real time monitoring information and monitor environment of CNG filling station by using GSM short message platform , terminal PC and cell phone based on ARM microprocessor, PTM100GSM module, pressure and temperature detection system, when the pressure, temperature or consistence of gas storage well is over the threshold , the monitoring system will send the alarm signal. Its proved that the monitoring system works stably and reliably and can effectively monitor fatal public danger signal.

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