CN103116961A - Enclosed space fire hazard detection alarm system and alarm method based on electronic nose technology - Google Patents

Enclosed space fire hazard detection alarm system and alarm method based on electronic nose technology Download PDF

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CN103116961A
CN103116961A CN2013100261435A CN201310026143A CN103116961A CN 103116961 A CN103116961 A CN 103116961A CN 2013100261435 A CN2013100261435 A CN 2013100261435A CN 201310026143 A CN201310026143 A CN 201310026143A CN 103116961 A CN103116961 A CN 103116961A
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CN103116961B (en
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张永明
王晓伟
方俊
王进军
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University of Science and Technology of China USTC
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Abstract

The invention provides an enclosed space fire hazard detection alarm system and an alarm method based on an electronic nose technology. The alarm system comprises a sampling pipe, a filter device, three gas sensors, a temperature sensor, a sensor power supply unit, a sensor control unit, a signal collecting unit, a signal conditioning unit, a monitoring host, a sound-light alarm device, a flowmeter, a vacuum pump, a three-way electromagnetic valve and a tail gas processing device. By means of the system and the method, real-time on-line analysis and study and intelligent judgement can be achieved, a fire hazard can be found in a very early stage, false alarm rate and report-failure rate can be reduced, and damage to persons and equipment in an enclosed space of the first hazard can be reduced to the larger extent.

Description

A kind of confined space fire detection alarm system and method based on Electronic Nose Technology
Technical field
The invention belongs to the fire detection technology field, be specifically related to confined space fire detection alarm system and method based on Electronic Nose Technology.
Background technology
Confined space refers to import and export limited, and natural ventilation is bad, the unconventional finite space of isolation relative to the external world.Common confined space mainly comprises some power distribution cabinet, machine room, goods and materials freight house, aircraft hold and space capsule etc.In case this type of confined space breaking out of fire, the smog of generation, poison gas and heat will certainly be assembled at short notice in a large number, and personnel and equipment are caused great infringement.Therefore, early stage fire detecting and alarm seems particularly important.
At present, the variation of visible smokescope realizes reporting to the police during mainly for fire in confined space, reports by mistake, fails to report than being easier to be subjected in air interferences such as dust, steam to produce.The fire characteristic γ-ray emission occurs extremely early stage in fire, prior to the appearance of visible smog.And, CO and CO that natural fire produces 2Very regular Deng the characteristic gas concentration change, be beneficial to detection.But gas sensor has selectivity (namely for the multiple gases cross sensitivity), uses single sensor to carry out the warning of fire characteristic detection of gas and easily is subjected to the impact of other gas or environmental factor and causes wrong report.And utilize Electronic Nose Technology to address this problem well.Electronic Nose Technology mainly is comprised of gas sensor array, Signal Pretreatment and pattern-recognition three parts.Be applied at present the algorithm for pattern recognition that the Electronic Nose Technology in detection field adopts and mainly contain BP neural network, support vector machine (SVM) etc.This type of Algorithm for Training time is long, and complex structure is unfavorable for the online updating network model, thereby causes the generation of reporting by mistake, failing to report.
Summary of the invention
The technology of the present invention is dealt with problems: overcome the deficiencies in the prior art, a kind of confined space fire detection alarm system and method based on Electronic Nose Technology is provided, extract fire characteristic gas O 2, CO, CO 2Changing Pattern, on-line analysis, study, intelligent decision, so that at utmost point early detection fire, and false alarm reduction, rate of failing to report, reduce to a greater extent fire to the infringement of confined space personnel and equipment.
The technology of the present invention solution: a kind of confined space fire detection alarm system based on Electronic Nose Technology, its structure as shown in Figure 1, it is made of sampling pipe, filtration unit, three gas sensors, temperature sensor, sensor power supply unit, sensor control unit, signal gathering unit, signal condition unit, monitoring host computer, acoustic-optic alarm, flowmeter, vacuum pump, three-way solenoid valve, exhaust gas processing device etc.
Gas sensor gathers the O at monitored scene through sampling pipe 2, CO, CO 2Deng signal, the PNN network model that input has trained after the data pre-service, prediction fire probability U iIf fire probability is greater than given threshold values U d1, begin to record pre-warning time t (i), otherwise data inputted the online training program of PNN network model.Carry out the secondary judgement, if fire probability is more than or equal to given threshold values U d2, directly open acoustic-optic alarm and connect exhaust gas processing device; Otherwise, judge that whether pre-warning time t (i) is more than or equal to given threshold values t d: be to open acoustic-optic alarm and connect exhaust gas processing device; No, data are inputted the online training program of PNN network model.Fig. 2 is real-time Fire process flow diagram in the present invention.
The present invention is based on Electronic Nose Technology, adopt three to O 2, CO, CO 2The gas sensor of cross sensitivity and a temperature sensor form the sensor array image data, adopt probabilistic neural network (PNN) as its algorithm for pattern recognition.In order to reduce model complexity and training difficulty, add filtration unit and adopt flowmeter control by the gas flow rate of sensor at the sampling pipe end.Filtration unit has two purposes: the smoke particle in (1) sampling by filtration gas extends sensor life-time; (2) dry sample gas reduces water vapor to the impact of gas sensor.When determining optimum gas velocity, configurable many group O 2, CO, CO 2The mixed gas sample changes flow, obtains that under multithread speed, the gas sensor response curve judges.
The present invention adopts probabilistic neural network model (PNN) to carry out pattern-recognition.PNN utilizes Parzen window method to come the estimated probability density function based on the Bayes classification theory decision-making, and have many good performances: (1) PNN training speed is fast, is beneficial to real-time application; (2) decision-making can realize Bayes Optimum; (3) fault-tolerant ability is strong; When (4) adding new training sample, needn't again train network.
Need it is carried out off-line training before adopting the PNN network model.The off-line training flow process as shown in Figure 3.At first the on-the-spot disaster hidden-trouble of confined space is investigated, and carried out the simulated fire experiment with this.Obtain the simulated fire field data by gas sensor and temperature sensor.Because the present invention is devoted to Real Time Monitoring, therefore can only choose gas sensor response S i, gas sensor rate of change Δ S iWith temperature-responsive value T iCarry out pre-service as individual features.Data preprocessing method also directly affects the operating characteristic of system.Adopt array normalization can reach good effect, computing formula is:
X i , j ′ = X i , j / ( 1 n Σ i X i , j 2 ) 1 / 2
Wherein, X i,jJ eigenwert when representing the i time measurement, Value after representation transformation.With the data after normalization Carry out the PCA principal component analysis (PCA), by dimensionality reduction, (the individual orthogonal characteristic variable of N<j), namely major component, make it to reflect data to seek N Principal character, the scale of compression legacy data matrix.When a current N principal component contributor rate reaches 90%, think that this N major component can reflect Principal character, be used for the former data of match.Utilize the top n major component, namely characteristic parameter, form new sample set X={x i,n| n=1,2 ..., N}, and be applied to the training of newly-built PNN network model.When the output expectation meets the demands, stop training, the PNN network model that forms is input in monitoring host computer.
The present invention can write PNN network model training sample with the data that obtain in real time and on line monitoring, it is carried out retraining, thereby optimize the PNN network model.Online training flow process as shown in Figure 4.The monitoring site data are sent into online training program after judging through fire.For at sequence of threads, when system stablized for a long time, the data that write training sample constantly were tending towards identical, and the waste storage space, the model ability of reduction model.Therefore, needing whether system is in stable state judges.Set threshold values θ, when satisfying:
‖X(i+1)-X(i)‖<θ
Think that system enters stable state, thereby stop online training, keep the PNN network model constant.When system is not in stable state, with the output of the fire hazard monitoring master routine desired output as this sample, add the training sample set of PNN network model to train online, until the test sample book desired output satisfies condition.The new PNN network model that obtains writes monitoring host computer again, carries out follow-up monitoring.
When fire probability is near 0.5, judge whether that directly breaking out of fire is unreasonable.The present invention introduces pre-warning time fire probability is carried out intelligent decision, increases the warning fiduciary level.Pre-warning time is defined as follows:
t ( i ) = t ( i - 1 ) + 1 , U i > U d 1 t ( i - 1 ) · u [ t ( i - 1 ) - t ( i - p ) ] , U i ≤ U d 1
U () representation unit step signal, and i ∈ [0, T).T is cycle length, when i=T, and the replacement pre-warning time.As fire probability U iGreater than U d1The time, start pre-warning time timing t (i).If U iGreater than U d2, directly start sound and light alarm, connect exhaust gas processing device and the pre-warning time t (i) that resets; No, need further judgement.Judge that whether pre-warning time t (i) is more than or equal to given threshold values t dIf,, start and report to the police, record and replacement pre-warning time t (i); No, with data Input Online training program and the pre-warning time t (i) that resets.
A kind of confined space fire detecting and alarm method based on Electronic Nose Technology is characterized in that performing step is as follows:
(1) at first confined space is carried out fire accident investigation, find out its main fire risk, and carry out simulated experiment for these several principal risks.
(2) according to the simulated experiment the data obtained, the PNN network model is carried out off-line training.After data normalization, carry out the PCA principal component analysis (PCA), and be applied to the training of newly-built PNN network model.When the output expectation meets the demands, stop training, the PNN network model that forms is input in monitoring host computer.
(3) the PNN network model that trains is write monitoring host computer.The systematic sampling pipe is placed in confined space to be monitored, and three-way solenoid valve is connected air, and turn on sensor power supply unit and control module are opened vacuum pump and begun to bleed, and starts monitoring host computer.The data that gather from monitoring site obtain fire probability by the PNN network model, carry out real-time fire identification in conjunction with the pre-warning time judgment mechanism.
(4) utilize the monitoring site real time data that obtains that the PNN network model is trained online.At first whether system being in stable state judges.When system is in stable state, stop online training, keep the PNN network model constant; When system is not in stable state, data are added the training sample set of PNN network model to train online, obtain new PNN network model, and again write monitoring host computer, in order to carry out follow-up monitoring.
The present invention's advantage compared with prior art is:
The gas concentration that produces prior to smog when (1) the present invention utilizes Electronic Nose Technology to process fire changes, and time of fire alarming is prior to conventional smoke detector and the video smoke detector of the conventional use of confined space, and has reduced wrong report, rate of failing to report.
(2) the present invention adopts the PNN network model that serious forgiveness is stronger, be more conducive to online training, and introduces the stable state judgement, realizes confined space fire Real Time Monitoring and online updating.
(3) the present invention introduces the pre-warning time judgment mechanism, makes the method more intelligent, increases the fiduciary level of reporting to the police.
Description of drawings
Fig. 1 is the structural representation that the present invention is based on the confined space fire detection alarm system of Electronic Nose Technology;
Fig. 2 is real-time Fire process flow diagram in the present invention;
Fig. 3 is probabilistic neural network in the present invention (PNN) off-line training process flow diagram;
Fig. 4 is that probabilistic neural network in the present invention (PNN) is trained process flow diagram online.
Embodiment
As shown in Figure 1, the confined space fire detection alarm system that the present invention is based on Electronic Nose Technology comprises; Sampling pipe, filtration unit, three gas sensors, temperature sensor, sensor power supply unit, sensor control unit, signal gathering unit, signal condition unit, monitoring host computer, acoustic-optic alarm, flowmeter, vacuum pump, three-way solenoid valve and exhaust gas processing device; Add filtration unit and adopt flowmeter to control the gas flow rate that passes through three gas sensors at the sampling pipe end.
The sensor power supply unit provides power supply, its output terminal connecting sensor for three gas sensors and temperature sensor; Sensor control unit control three gas sensors and temperature sensor unlatching, close its output terminal connecting sensor; Three sensors are respectively O 2Sensor, CO sensor and CO 2Sensor; Signal gathering unit is used for gathering the data of three gas sensors and temperature sensor, and its input end is connected with sensor, and output terminal is connected with the signal condition unit; The signal condition unit can amplify the signal that collects, filtering, and its input end is connected with signal gathering unit, and output terminal is connected with monitoring host computer; Monitoring host computer is provided with fire hazard monitoring master routine and PNN Neural Network Online training algorithm, can the Real time identification fire and provide warning, online updating PNN neural network and storage data, and its output terminal connects acoustic-optic alarm and three-way solenoid valve.
Sampling pipe gathers, carries confined space gas on-site data, and the one end is as in confined space, and the other end connects filtration unit; Water vapor in the filtration unit filtering gas and smoke particle reduce the impact on sensor, and the one end connects sampling pipe, and the other end connects gas analysis chamber by the road; Gas analysis chamber is used for placing three gas sensors and temperature sensor, and its other end is the connection traffic meter by the road; Flow velocity in the flowmeter pilot piping, its other end connects vacuum pump by the road; Vacuum pump is bled, and for systematic sampling provides power, its other end is the connecting tee solenoid valve by the road; Three-way solenoid valve is controlled the flow direction of tail gas, and its other end connects exhaust gas processing device by the road; Exhaust gas processing device can be processed CO, the CO in sample gas 2Deng harmful gas, prevent from polluting.
Under normal condition, three-way solenoid valve is communicated with air, and flowmeter is controlled the vacuum pump speed of evacuation, and the gas of confined space is by sampling pipe, and device enters gas analysis chamber after filtration.Place 3 gas sensors and a temperature sensor in gas analysis chamber, guarantee its normal operation by power supply unit and control module.The signal of four sensor collections is sent into monitoring host computer analysis after signal gathering unit and signal condition cell processing.During breaking out of fire, solenoid valve is connected exhaust gas processing device, and monitoring host computer is controlled acoustic-optic alarm and reported to the police.
Method of the present invention is applied to some equipment machine room, and step is as follows:
(1) at first such confined space is carried out fire accident investigation, find that main fire risk is the overheated or short circuits of the nonmetallic materials such as cable, circuit board wherein.Therefore, can carry out simulated experiment for these several situations.
(2) to PNN network model off-line training, as shown in Figure 3.Obtain the simulated fire field data by gas sensor and temperature sensor.Choose gas sensor response S i, gas sensor rate of change Δ S iWith temperature-responsive value T iCarry out pre-service as individual features.Adopt array normalization can reach good effect, computing formula is:
X i , j ′ = X i , j / ( 1 7 Σ i X i , j 2 ) 1 / 2
Wherein, X i,jJ eigenwert when representing the i time measurement, Value after representation transformation.With the data after normalization Carry out the PCA principal component analysis (PCA).Front 3 principal component contributor rates reach 90%, can be used for the former data of match.Utilize front 3 major components, namely characteristic parameter, form new sample set X={x i,n| n=1,2,3}, and be applied to the training of newly-built PNN network model.When the output expectation meets the demands, stop training, the PNN network model that forms is input in monitoring host computer.
(3) real-time Fire in the present invention, as shown in Figure 2, gas sensor gathers the O at monitored scene through sampling pipe 2, CO, CO 2Deng signal, the PNN network model that input has trained after the data pre-service, prediction fire probability U iIf fire probability is greater than given threshold values U d1=0.4, begin to record pre-warning time t (i), otherwise data are inputted the online training program of PNN network model.Carry out the secondary judgement, if fire probability is more than or equal to given threshold values U d2=0.6, directly open acoustic-optic alarm and connect exhaust gas processing device; Otherwise, judge that whether pre-warning time t (i) is more than or equal to given threshold values t d=15: be, open acoustic-optic alarm, connect exhaust gas processing device, and data are inputted the online training program of PNN network model; No, data are inputted the online training program of PNN network model.
Pre-warning time is defined as follows:
t ( i ) = t ( i - 1 ) + 1 , U i > U d 1 t ( i - 1 ) · u [ t ( i - 1 ) - t ( i - p ) ] , U i ≤ U d 1
U () representation unit step signal, and i ∈ [0, T).T=20 is cycle length, when i=T, and the replacement pre-warning time.Get p=5, if namely represent continuous 4 U iAll less than or equal to U d1, pre-warning time t (i) resets.
(4) the PNN network model is trained online, as shown in Figure 4.The monitoring site data are sent into online training program after judging through the fire hazard monitoring master routine.When whether system is in stable state and judges, set threshold values θ=0.01, when satisfying:
‖X(i+1)-X(i)‖<θ
Think that system enters stable state, thereby stop online training, keep the PNN network model constant.When system is not in stable state, with the output of the fire hazard monitoring master routine desired output as this sample, add the training sample set of PNN network model to train online, until the test sample book desired output satisfies condition.The new PNN network model that obtains writes monitoring host computer again, carries out follow-up monitoring.
In a word, when the present invention utilizes Electronic Nose Technology to process fire, gas concentration changes, and time of fire alarming is shorter, and has reduced wrong report, rate of failing to report; Adopt the PNN network model, and introduce the stable state judgement, realize Real Time Monitoring and online updating; Introduce the pre-warning time judgment mechanism, more intelligent, reliable.The present invention can be applicable to the fire detecting and alarm of the confined spaces such as power distribution cabinet, machine room, goods and materials freight house, aircraft hold and space capsule.
Instructions of the present invention does not elaborate the known technology that part belongs to those skilled in the art.
The above; only be the embodiment in the present invention; but protection scope of the present invention is not limited to this; anyly be familiar with the people of this technology in the disclosed technical scope of the present invention; can understand conversion or the replacement expected; all should be encompassed in of the present invention comprise scope within, therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.

Claims (5)

1. confined space fire detection alarm system based on Electronic Nose Technology is characterized in that comprising: sampling pipe, filtration unit, three gas sensors, temperature sensor, sensor power supply unit, sensor control unit, signal gathering unit, signal condition unit, monitoring host computer, acoustic-optic alarm, flowmeter, vacuum pump, three-way solenoid valve and exhaust gas processing device;
The sensor power supply unit provides power supply, its output terminal connecting sensor for three gas sensors and temperature sensor; Sensor control unit control three gas sensors and temperature sensor unlatching, close its output terminal connecting sensor; Three sensors are respectively O 2Sensor, CO sensor and CO 2Sensor; Signal gathering unit is used for gathering the data of three gas sensors and temperature sensor, and its input end is connected with sensor, and output terminal is connected with the signal condition unit; The signal condition unit to the signal that collects amplify, filtering, its input end is connected with signal gathering unit, output terminal is connected with monitoring host computer; Monitoring host computer is provided with fire hazard monitoring master routine and PNN Neural Network Online training algorithm, can the Real time identification fire and provide warning, online updating PNN neural network and storage data, and its output terminal connects acoustic-optic alarm and three-way solenoid valve;
Sampling pipe gathers, carries confined space gas on-site data, and the one end is as in confined space, and the other end connects filtration unit; Water vapor in the filtration unit filtering gas and smoke particle reduce the impact on sensor, and the one end connects sampling pipe, and the other end connects gas analysis chamber by the road; Gas analysis chamber is used for placing three gas sensors and temperature sensor, and its other end is the connection traffic meter by the road; Flow velocity in the flowmeter pilot piping, its other end connects vacuum pump by the road; Vacuum pump is bled, and for systematic sampling provides power, its other end is the connecting tee solenoid valve by the road; Three-way solenoid valve is controlled the flow direction of tail gas, and its other end connects exhaust gas processing device by the road; Exhaust gas processing device can be processed CO, the CO in sample gas 2Harmful gas prevents from polluting;
Monitoring host computer implementation procedure: the O at monitored scene 2, CO, CO 2And temperature signal, the PNN neural network model that input has trained after the data pre-service, the prediction fire probability also carries out the pre-warning time judgement, if judgement breaking out of fire, open acoustic-optic alarm, connect exhaust gas processing device, and the online training program of PNN network model that the data input has been trained; If judge not breaking out of fire, continue monitoring, and data are inputted the online training program of PNN network model; Before training online, carry out the stable state judgement, if judgement is in stable state, return to the fire hazard monitoring master routine; If not, sample is added the training of PNN neural network model, obtain new PNN network model, and write the fire hazard monitoring master routine.
2. the confined space fire detection alarm system based on Electronic Nose Technology according to claim 1, it is characterized in that: the described PNN network model that has trained adopts the off-line training mode, the off-line training process is: at first the on-the-spot disaster hidden-trouble of confined space is investigated, and carried out the simulated fire experiment with this; Obtain the simulated fire field data by gas sensor and temperature sensor, choose gas sensor response S i, gas sensor rate of change Δ S iWith temperature-responsive value T iCarry out pre-service as individual features, data preprocessing method adopts the array method for normalizing, and computing formula is:
X i , j ′ = X i , j / ( 1 n Σ i X i , j 2 ) 1 / 2
Wherein, X i,jJ eigenwert when representing the i time measurement, Value after representation transformation;
With the data after normalization Carry out the PCA principal component analysis (PCA), by dimensionality reduction, (the individual orthogonal characteristic variable of N<j), namely major component, make it to reflect data to seek N Principal character, the scale of compression legacy data matrix; When a current N principal component contributor rate reaches 90%, think that this N major component can reflect Principal character, be used for the former data of match; Utilize the top n major component, namely characteristic parameter, form new sample set X={x i,n| n=1,2 ..., N}, and be applied to the training of newly-built PNN network model; When the output expectation meets the demands, stop training, the PNN network model that forms is write monitoring host computer.
3. the confined space fire detection alarm system based on Electronic Nose Technology according to claim 1, it is characterized in that: the online training program of described PNN network model is embodied as: whether system is in stable state judges; Set threshold values θ, when satisfying:
‖X(i+1)-X(i)‖<θ
Think that system enters stable state, thereby stop online training, keep the PNN network model constant; When system is not in stable state, with the output of the fire hazard monitoring master routine desired output as this sample, add the training sample set of PNN network model to train online, until the test sample book desired output satisfies condition; The new PNN network model that obtains writes monitoring host computer again, carries out follow-up monitoring.
4. the confined space fire detection alarm system based on Electronic Nose Technology according to claim 1, it is characterized in that: described pre-warning time t (i) is defined as follows:
t ( i ) = t ( i - 1 ) + 1 , U i > U d 1 t ( i - 1 ) · u [ t ( i - 1 ) - t ( i - p ) ] , U i ≤ U d 1
U () representation unit step signal, and i ∈ [0, T), T is cycle length, when i=T, and replacement pre-warning time t (i).
5. confined space fire detecting and alarm method based on Electronic Nose Technology is characterized in that performing step is as follows:
(1) at first confined space is carried out fire accident investigation, find out its main fire risk, and carry out simulated experiment for these several principal risks;
(2) according to the simulated experiment the data obtained, the PNN network model is carried out off-line training;
After data normalization, carry out the PCA principal component analysis (PCA), and be applied to the training of newly-built PNN network model; When the output expectation meets the demands, stop training, the PNN network model that forms is input in monitoring host computer;
(3) the PNN network model that trains is write monitoring host computer;
The systematic sampling pipe is placed in confined space to be monitored, and three-way solenoid valve is connected air, and turn on sensor power supply unit and control module are opened vacuum pump and begun to bleed, and starts monitoring host computer.The data that gather from monitoring site obtain fire probability by the PNN network model, carry out real-time fire identification in conjunction with the pre-warning time judgment mechanism;
(4) utilize the monitoring site real time data that obtains that the PNN network model is trained online;
At first whether system being in stable state judges; When system is in stable state, stop online training, keep the PNN network model constant; When system is not in stable state, data are added the training sample set of PNN network model to train online, obtain new PNN network model, and again write monitoring host computer, in order to carry out follow-up monitoring.
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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103366495A (en) * 2013-07-11 2013-10-23 合肥工业大学 Air-breathing high-sensitivity smoke particle detector and application thereof
CN103389230A (en) * 2013-07-22 2013-11-13 中国人民解放军后勤工程学院 Novel sampling and monitoring system for open space oil material fire pollution
CN103646490A (en) * 2013-12-20 2014-03-19 中国科学技术大学 Composite detection technology based aspirated aircraft cargo space fire detection device
CN103996263A (en) * 2014-05-11 2014-08-20 中国科学技术大学 Air suction type airplane cargo fire disaster detector with smog gas sensing function
CN105185022A (en) * 2015-10-21 2015-12-23 国家电网公司 Transformer substation fire detection system based on multi-sensor information combination and detection information combination method
CN107085079A (en) * 2017-05-12 2017-08-22 盐城工学院 A kind of Artificial Olfactory device and organic pollution control device
CN107331102A (en) * 2017-08-30 2017-11-07 国网安徽省电力公司电力科学研究院 Intelligent concentration suction-type cable fire pole early warning device and method
CN109615814A (en) * 2018-12-03 2019-04-12 中国船舶重工集团公司第七〇九研究所 Radiation resistance combined type fire detecting arrangement
CN111311869A (en) * 2020-02-14 2020-06-19 清华大学合肥公共安全研究院 Fire safety monitoring method and system based on area alarm model and cloud platform
CN111443173A (en) * 2020-03-03 2020-07-24 南京农业大学 Method for detecting adulterated illegal cooking oil in edible peanut oil based on electronic nose method

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6166647A (en) * 2000-01-18 2000-12-26 Jaesent Inc. Fire detector
KR20040080664A (en) * 2003-03-12 2004-09-20 신화전자주식회사 Fire-warning facilities with electronic nose
US20040204915A1 (en) * 2002-07-19 2004-10-14 Cyrano Sciences Inc. Chemical and biological agent sensor array detectors
US20060006997A1 (en) * 2000-06-16 2006-01-12 U.S. Government In The Name Of The Secretary Of Navy Probabilistic neural network for multi-criteria fire detector
CN101241019A (en) * 2008-02-28 2008-08-13 河北工业大学 Robot bionic olfactory system
CN101769889A (en) * 2010-01-06 2010-07-07 浙江清华长三角研究院 Electronic nose system for detecting quality of agricultural products

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6166647A (en) * 2000-01-18 2000-12-26 Jaesent Inc. Fire detector
US20060006997A1 (en) * 2000-06-16 2006-01-12 U.S. Government In The Name Of The Secretary Of Navy Probabilistic neural network for multi-criteria fire detector
US20040204915A1 (en) * 2002-07-19 2004-10-14 Cyrano Sciences Inc. Chemical and biological agent sensor array detectors
KR20040080664A (en) * 2003-03-12 2004-09-20 신화전자주식회사 Fire-warning facilities with electronic nose
CN101241019A (en) * 2008-02-28 2008-08-13 河北工业大学 Robot bionic olfactory system
CN101769889A (en) * 2010-01-06 2010-07-07 浙江清华长三角研究院 Electronic nose system for detecting quality of agricultural products

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
BANCHA CHARUMPOM ETC.: "Classifying Smokes Using an Electronic Nose and Neural Networks", 《SLCE 2002》 *
TORU FUJINAKA ETC.: "Intelligent Electronic Nose Systems for Fire Detection Systems Based on Neural Networks", 《THE SECOND INTERNATIONAL CONFERENCE ON ADVANCED ENGINEERING COMPUTING AND APPLICATIONS IN SCIENCES》 *
吕振等: "基于仿生嗅觉的电子鼻在煤矿火灾预报识别的应用研究", 《计算机测量与控制》 *
方向生: "基于电子鼻技术的电气火灾预警系统研究", 《中国博士学位论文全文数据库》 *
秦树基等: "人工神经网络对电子鼻性能的影响", 《同济大学学报(自然科学版)》 *
耿志广等: "基于人工神经网络的电子鼻对混合气体检测研究", 《现代计算机》 *

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* Cited by examiner, † Cited by third party
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CN107331102A (en) * 2017-08-30 2017-11-07 国网安徽省电力公司电力科学研究院 Intelligent concentration suction-type cable fire pole early warning device and method
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