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.

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.

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.

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

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