WO2016033186A1 - Système de surveillance chimique - Google Patents

Système de surveillance chimique Download PDF

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Publication number
WO2016033186A1
WO2016033186A1 PCT/US2015/046957 US2015046957W WO2016033186A1 WO 2016033186 A1 WO2016033186 A1 WO 2016033186A1 US 2015046957 W US2015046957 W US 2015046957W WO 2016033186 A1 WO2016033186 A1 WO 2016033186A1
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WIPO (PCT)
Prior art keywords
chemical
feature vector
dynamic feature
regression
detection
Prior art date
Application number
PCT/US2015/046957
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English (en)
Inventor
Chengmeng HSIUNG
Jing Li
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Eloret Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Eloret Corporation filed Critical Eloret Corporation
Publication of WO2016033186A1 publication Critical patent/WO2016033186A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
    • G01N33/0034—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array comprising neural networks or related mathematical techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
    • G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
    • G01N27/12—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body in dependence upon absorption of a fluid; of a solid body in dependence upon reaction with a fluid, for detecting components in the fluid
    • G01N27/125—Composition of the body, e.g. the composition of its sensitive layer
    • G01N27/127—Composition of the body, e.g. the composition of its sensitive layer comprising nanoparticles

Definitions

  • the disclosed technology relates to a chemical monitoring system.
  • the chemical monitoring system can use Dynamic Pattern Recognition (or DPR) where an instrument continuously monitors ambient air or some other atmosphere for detection and identification of chemical gases not usually present.
  • DPR Dynamic Pattern Recognition
  • SPR Static Pattern Recognition
  • a button is pressed telling the chemical monitoring system a starting time and an ending time of a sample period, e.g., an exhaled breath of a person, for detection and identification of chemicals not usually present, e.g. certain drugs or cancers.
  • the chemical monitoring system of the disclosed technology is capable of receiving raw data signals from a nanochemical sensor. These data signals are filtered for noise and outlier signals are rejected. The filtered signals are sent to a dynamic feature vector calculator and the resultant is compared to an event detection model containing a plurality of chemical detection vectors. (The event detection model was built using a machine learning algorithm and training data sets.) If it is found that the dynamic feature vector matches or closely matches a chemical detection vector within the event detection model a certain number of times, e.g., five matches in a row, an alarm can be triggered displaying a specific chemical that was identified within the gas mixture.
  • the data signals can be sent to a regression dynamic feature vector calculator where the resultant is compared to a regression model containing a plurality of chemical concentration vectors.
  • the regression model was also built using a machine learning algorithm and training data sets.). Based upon the regression dynamic feature vector matching or closely matching a chemical concentration vector within the regression model, a concentration of the specific chemical within the gas mixture can be determined and displayed.
  • a computer-implemented method for monitoring for a chemical event in a gas mixture can comprise: receiving data signals from at least one sensor on a timed basis; calculating a dynamic feature vector for event detection using the data signals; comparing the dynamic feature vector for event detection to an event detection model containing a plurality of chemical detection vectors; determining if the dynamic feature vector for event detection matches a chemical detection vector within the event detection model thereby identifying a specific chemical within the gas mixture;
  • the method can further comprise the steps of:
  • the method can further comprise the steps of: displaying the concentration of the specific chemical. In some implementations, the method can further comprise the steps of: filtering the data signals for noise and median readings. In some implementations, the method can further comprise the steps of: normalizing and auto scaling the dynamic feature vector for event detection. In some implementations, the data signals can be electrical resistance readings of a nanochemical sensor. In some
  • the gas mixture can be ambient air.
  • the timed basis is in a range of once every 1 to 60 seconds on continuous basis.
  • a system for monitoring for a chemical event in a gas mixture comprising: at least chemical detection unit having a chemical detection software application installed thereon; and a sensor, which sensor is coupled to the at least one mobile device, the chemical detection software application programmed to:
  • One advantage of the disclosed technology is that the dynamic feature vector corrects for baseline drift.
  • Figure 1 is a block diagram of an example of a system used with the disclosed technology
  • Figures 2a-b are a flow chart showing an example process of the disclosed technology
  • Figures 3a-b are a flow chart showing an example process of the disclosed technology
  • Figure 4 is a block diagram of an example of a system used with the disclosed technology
  • Figure 5 is a flow chart showing an example process of the disclosed technology.
  • Figure 6 is a block diagram of an example of a system used with the disclosed technology.
  • the disclosed technology relates to a chemical monitoring system for a gas mixture, e.g., ambient air or an exhaled breath of a person.
  • a gas mixture e.g., ambient air or an exhaled breath of a person.
  • the chemical monitoring system is capable of receiving raw data signals from an array of nanochemical sensors. These data signals can be filtered for noise and outlier readings can be rejected. The filtered signals are sent to a dynamic feature vector calculator and the resultant is compared to an event detection model containing a plurality of chemical detection vectors. (The event detection model was built using a machine learning algorithm and training data sets.) If it is found that the dynamic feature vector matches or nearly matches a chemical detection vector within the event detection model a certain number of times, e.g., five matches in a row, an alarm can be triggered displaying a specific chemical that was identified within the gas mixture.
  • the data signals are sent to a regression dynamic feature vector calculator where the resultant is compared to a regression model containing a plurality of chemical concentration vectors.
  • the regression model was also built using a machine learning algorithm and training data sets.). Based upon the regression dynamic feature vector matching or nearly matching a chemical concentration vector within the regression model, a concentration of the specific chemical within the gas mixture can be determined and displayed.
  • the chemical monitoring system 10 includes one or more sensors 12 electrically coupled to a chemical monitoring unit 14 for sensing ambient air 16.
  • These sensors 12 can be nanosensors that are capable of detecting chemicals and volatile organic compounds within the ambient air 16 using carbon nanotubes but other sensors are contemplated.
  • Nanosensor technology uses nanostructures, e.g., single walled carbon nanotubes (SWNTs), combined with a silicon-based microfabrication and micromachining process.
  • SWNTs single walled carbon nanotubes
  • IDE interdigitated electrode
  • Each sensor in the array can consist of a nanostructure— chosen from many different categories of sensing material— and an interdigitated electrode (IDE) as a transducer.
  • IDE interdigitated electrode
  • These chemical sensors can be one type of electrochemical sensor that implies the transfer of charge from one electrode to another. This means that at least two electrodes constitute an electrochemical cell to form a closed electrical circuit.
  • the electron configuration is changed in the nanostructured sensing device, therefore, the changes in the electronic signal such as current or voltage can be observed before and during an exposure to a gas species.
  • the concentration of the chemical species, such as gas molecules can be measured.
  • the chemical monitoring unit 10 can be a computing device for receiving these measurements and processing these measurements in integrated algorithms that monitor for specific chemical events and are capable of identification of chemical species and the prediction of concentration for the identified chemical species.
  • the chemical monitoring unit 10 can include classification models built using machine learning algorithms, e.g., pattern recognition algorithms that aim to provide a reasonable answer for all possible inputs and to perform "most likely” matching of the inputs, taking into account their statistical variation.
  • machine learning algorithms e.g., pattern recognition algorithms that aim to provide a reasonable answer for all possible inputs and to perform "most likely” matching of the inputs, taking into account their statistical variation.
  • the first classification model is the event detection model.
  • the event detection models can be used as a classification algorithm that discriminates between a benign state vs a chemical detected state. That is, known benign state samples and known chemical state samples are sent to a machine learning algorithm in order to define a classification model.
  • the machine learning algorithm can use ensemble learning techniques, but any type of machine learning technique can be used.
  • Ensemble learning is a machine learning technique where multiple algorithms are trained to solve the same problem. In contrast to ordinary machine learning approaches that try to learn one hypothesis from training data. Ensemble methods try to construct a set of hypotheses and combine them into one useable model. That is, an ensemble is a supervised learning algorithm that can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis or model.
  • the disclosed technology will use the chemicals detected state as samples representative of data that the trained system should classify as positive, e.g. belonging to the object class of chemicals found in sample, and the benign state samples as samples representative of data that the systems should classify as negative, e.g. not belonging to the object class of chemicals not found in sample.
  • the computerized system can "learn" features of the positive samples that are not present in the negative samples and vice versa. The system can then look for these features in an unknown sample, and when they are present or absent, declare that the sample is a positive or negative sample as the case may be.
  • the process of providing test samples to the system and allowing or "teaching" the system to learn prominent features is known as training the classifier.
  • two sample groups are used but in some implementations multiple sample groups may be used.
  • training an object classification or object detection system involves extracting features in either a supervised or unsupervised manner to learn the differences and similarities between the positive and negative samples. Once determined, these indicators can be applied to new samples to determine whether they should be classified as belonging to the object class or not belonging to the object class.
  • the event detection model can be built using support vector machine (SVM).
  • SVM is a pattern classifier that selects a hyper-plane based on maximizing a separation margin between classes. Its solution can depend on a small subset of training examples, e.g., dynamic or static support vectors. And it can be easily extended to deal with datasets with nonlinear separation through the kernel mapping scheme. Confidence in the form of decision value or probability can be calculated together with the winner class for a predicted chemical compound. Typically, normalization followed by autoscaling
  • pretreatments is performed on the support vectors before they are sent for SVM modeling.
  • the second classification model is the regression model. Once the "chemicals detected" state has been confirmed, the algorithm will trigger the regression calculation step where a regression model based on detected chemical will be used to calculate that chemical's concentration.
  • the regression model can be built using multivariate Partial Least Square (PLS). For example, a regression model can be built by first receiving a set of known samples from a sensor. The sample data can be filtered for noise and autoscaled or mean centered. A dynamic training model is then created using machine learning vectors for the sample data and a non-agent can be added to the training model for robustness.
  • PLS Partial Least Square
  • the chemical monitoring unit 10 can also include an event detection vector calculator and a regression vector calculator.
  • the event detection vector can use a conventional feature vector scheme (see equation (1), below) or a differencing feature vector scheme (see equation (2) or (3) below) where the differencing feature vector (diffFV) compensates for a signal having significant baseline drift.
  • Rt is a resistance reading at the current time
  • t is the resistance reading at a fixed time step, t-w called a moving window width, before the current time.
  • FVt is the current feature vector and FV0 is the feature vector at a fixed time step, w, before the current time translating into a formula of:
  • DiffFVt (Rt-R0)/R0 - (Rtl - R01)/R01 -— (3)
  • Rt is a resistance reading at the current time
  • t is the resistance reading at a fixed time step
  • w before the current time
  • Rtl is the resistance reading at a fixed time step
  • w before the current time
  • R01 is the resistance reading at a fixed time step 2w before current time.
  • the dynamic feature vector for regression is calculated by the same formula as for conventional event detection (or equation (1) ). The only difference is the calculation of R0 which will be the latched reading at the time before event detection was just triggered (or one time step before sensors' response fall inside the target chemical's decision boundary) translating into same formula like equation (1) above:
  • Rt is a resistance reading at the current time
  • t is the resistance reading at the latched reading before the start of the event.
  • the algorithm of the chemical detection unit 10 integrates signal preprocessing and postprocessing with a dynamic training model built from median virtual sensors for continuous monitoring of a chemical or a few chemicals present in an ambient environment.
  • FIGs 2a-2b is a flowchart of an embodiment of the algorithm of the present invention.
  • a detection software unit can be uploaded to a computing device, e.g., a cell phone, laptop or any other portable device.
  • the detection unit can include an event detection model and a regression model. Since model files are loaded as structure data type, they can be unwrapped into each parameter's variable data type and stored in the computing device. These model parameters can be used in both the classification and regression calculations' models parameters settings.
  • a nanosensor can be coupled to the computing device. (Step 2). The sensor can read raw responses based on electric resistance readings from the sensor array. These responses can be received by the detection software unit. (Step 3).
  • the responses can be, e.g., multivariable time series data.
  • the responses can be applied to a filter (Step 4), e.g., Savitzky-Golay filtering, to remove noisy data and a median reading can be taken from a subset of redundant sensors to remove the influence of any outlier sensor(s).
  • a filter e.g., Savitzky-Golay filtering
  • These treated responses can be sent to a calculator for calculating a dynamic feature vector for event detection, as described above.
  • the calculated feature vector is applied to the event detection model to predict class assignment and confidence.
  • Step 6 If a match is found (Step 7), to ensure detection, alarm logic can be applied. That is, usually when we normalize sensors' response to ambient air, because it has the effect of amplifying responses, it will generate random noisy dynamic feature vectors.
  • alarm logic can be used that sets a certain alarming threshold in the form of a consecutive triggering alarm (this means the current response falls inside the decision boundary of a certain chemical class) for say 5 time steps before the alarm is triggered and the chemical class assignment is predicted. (Step 8).
  • Step 9 If an alarm threshold is reached, trigger an alarm and display the detected chemical.
  • resistance readings can be retrieved for calculating a dynamic feature vector for regression, as described above.
  • Step 10 The calculated regression vector is applied to the regression model to calculate concentration reading.
  • Step 11 The concentration reading or indicator can then be displayed.
  • Step 12 The concentration reading or indicator can then be displayed.
  • FIG. 3a-3b is a flowchart of an embodiment of the algorithm of the present invention.
  • a device can receive data signals from at least one sensor on a timed basis.
  • Step Al A dynamic feature vector is calculated using the data signals.
  • Step A2 The vector is compared to an event detection model containing a plurality of chemical detection vectors to determine if the dynamic feature vector matches a chemical detection vector within the event detection model thereby identifying a specific chemical within the gas mixture.
  • Step A3 If a match is found (Step A4), these steps are repeated until a threshold number of matches occur in consecutive order.
  • Step A5 is a threshold number of matches occur in consecutive order.
  • a regression dynamic feature vector is calculated and compared to a regression model containing a plurality of chemical concentration vectors. (Step A6). A concentration of the specific chemical within the gas mixture is then determined based upon the regression dynamic feature vector matching a chemical concentration vector within the regression model.
  • the chemical monitoring system 20 includes one or more sensors 22 electrically coupled to a chemical monitoring unit 24 for sensing exhaled air 26.
  • the chemical monitoring system 20 can be used to sample a single event, e.g., a single breath from a person.
  • the unit 24 can have a button that instructs the device to begin to analyze an incoming samples for a number of cycles, e.g., once a second for ten seconds. These samples can be applied to static vector calculations that are similar to the dynamic vector calculations, above.
  • FVt is the end feature vector and FV0 is the start feature vector translating into a formula of:
  • DiffFVt (Rt-R0)/R0 - (Rtl - R01)/R01
  • Rt is a resistance reading at an end time
  • t RO is the resistance reading at a fixed time step
  • w before the current time
  • R01 is the resistance reading at 2*w time steps before current time
  • Rtl is the resistance reading at a fixed time step w time steps before current time.
  • Rt is a resistance reading at current time, t
  • R0 is the resistance reading at a fixed time step, w, before the current time.
  • FIG. 5 is a flowchart of an embodiment of the algorithm of the present invention.
  • a device for monitoring for a chemical event in a sample of exhaled air using a sensor and a mobile computing device A chemical detection unit is loaded onto the mobile unit device.
  • the device receives data signals from at least one sensor.
  • a dynamic feature vector is calculated using the data signals (Step Bl).
  • the vector is compared to an event detection model containing a plurality of chemical detection vectors. (Step B2). If the dynamic feature vector matches a chemical detection vector within the event detection model thereby identifying a specific chemical within the gas mixture. (Step B3).
  • a regression dynamic feature vector is calculated (Step B5) and compared to a regression model containing a plurality of chemical concentration vectors. (Step B6). A concentration of the specific chemical within the gas mixture is determined based upon the regression dynamic feature vector matching a chemical concentration vector within the regression model. (Step B7).
  • FIG. 6 is a schematic diagram of an example of a chemical detection system
  • the chemical detection system 100 can include a sensor array 110, chemical detection unit 130, one or more processors 121, application programming interface (API) 125, one or more display devices 127, e.g., CRT, LCD, one or more interfaces 123, input devices 126, e.g., touchscreen, keyboard, mouse, scanner, activation button, etc., and one or more computer-readable mediums 124. These components exchange communications and data using one or more buses, e.g., EISA, PCI, PCI Express, etc.
  • the term "computer-readable medium” refers to any non-transitory medium that participates in providing instructions to processor 121 for execution.
  • the computer-readable mediums further include operating system 122.
  • the operating system 122 can be multi-user, multiprocessing, multitasking, multithreading, real-time, near real-time and the like.
  • the operating system 122 can perform basic tasks, including but not limited to: recognizing input from input device 126; sending output to display devices 127; keeping track of files and directories on computer-readable mediums 124, e.g., memory or a storage device; controlling peripheral devices, e.g., disk drives, printers, etc.; and managing traffic on the one or more buses.
  • the operating system 122 can also run algorithms (e.g. detection vector calculator 131 and regression vector calculator 134) associated with the system 100, accessing the detection model 132 and regression model 135, and running the detection comparator 133 and regression comparator 136.
  • Implementations of the subject matter and the operations described in this specification can be done in electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the subject matter described in this specification can be done as one or more computer programs, e.g., one or more modules of computer program instructions, encoded on a computer storage media for execution by, or to control the operation of, data processing apparatus.
  • the program instructions can be encoded on an artificially- generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus.
  • the computer storage medium can be, or can be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them.
  • the operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer- readable storage devices or received from other sources.
  • data processing apparatus encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or combinations of them.
  • the apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
  • the apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a repository management system, an operating system, a cross- platform runtime environment, e.g., a virtual machine, or a combination of one or more of them.
  • code that creates an execution environment for the computer program in question e.g., code that constitutes processor firmware, a protocol stack, a repository management system, an operating system, a cross- platform runtime environment, e.g., a virtual machine, or a combination of one or more of them.
  • the apparatus and execution environment can realize various different computing model infrastructures, e.g., web services, distributed computing and grid computing infrastructures.
  • a computer program also known as a program, software, software
  • a computer program can, but need not, correspond to a file in a file system.
  • a program can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g., files that store one or more modules, sub-programs, or portions of code.
  • a computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
  • processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.
  • a processor can receive instructions and data from a read-only memory or a random access memory or both.
  • the elements of a computer comprise a processor for performing or executing instructions and one or more memory devices for storing instructions and data.
  • a computer can also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks.
  • a computer need not have such devices.
  • a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, e.g., a universal serial bus (USB) flash drive, to name just a few.
  • Devices suitable for storing computer program instructions and data include all forms of non- volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
  • the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
  • implementations of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
  • keyboard and a pointing device e.g., a mouse or a trackball
  • Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, thought or tactile input.
  • a computer can interact with a user by sending documents to and receiving documents from a device that is

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Abstract

La présente invention concerne un dispositif pour la surveillance d'un événement chimique dans un mélange gazeux. Le dispositif peut recevoir des signaux de données à partir d'un capteur et calculer un vecteur de caractéristique dynamique à l'aide de ces signaux de données. Le vecteur est comparé à un modèle de détection d'événement contenant une pluralité de vecteurs de détection chimique. Si le vecteur correspond à un vecteur de détection chimique à l'intérieur du modèle de détection d'événement, les étapes ci-dessus sont répétées jusqu'à ce qu'un nombre seuil de correspondances se produise dans un ordre consécutif. Lorsque le nombre seuil est atteint, un vecteur de caractéristique dynamique de régression est calculé en utilisant les signaux de données et est comparé à un modèle de régression contenant une pluralité de vecteurs de concentration chimique. Une concentration du produit chimique spécifique dans le mélange gazeux est ensuite déterminée.
PCT/US2015/046957 2014-08-26 2015-08-26 Système de surveillance chimique WO2016033186A1 (fr)

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EP3746800A1 (fr) * 2018-01-29 2020-12-09 Stratuscent Inc. Système de détection chimique
EP3936861A1 (fr) * 2020-07-10 2022-01-12 Infineon Technologies AG Dispositif de détection de gaz pour détecter une ou plusieurs gaz dans un mélange de gaz
WO2022191173A1 (fr) * 2021-03-12 2022-09-15 パナソニックＩｐマネジメント株式会社 Procédé d'identification de gaz et système d'identification de gaz
WO2023037999A1 (fr) * 2021-09-07 2023-03-16 パナソニックＩｐマネジメント株式会社 Procédé d'analyse de gaz et système d'analyse de gaz

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US20020094531A1 (en) * 1999-06-14 2002-07-18 Frederic Zenhausern Apparatus and method for monitoring molecular species within a medium
US20060155486A1 (en) * 2004-10-07 2006-07-13 Walsh Alicia M Computer-implemented system and method for analyzing mixtures of gases
US20140223995A1 (en) * 2013-01-31 2014-08-14 Sensirion Ag Portable sensor device with a gas sensor and method for operating the same

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Publication number Priority date Publication date Assignee Title
EP3746800A1 (fr) * 2018-01-29 2020-12-09 Stratuscent Inc. Système de détection chimique
EP3746800A4 (fr) * 2018-01-29 2021-11-10 Stratuscent Inc. Système de détection chimique
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EP3936861A1 (fr) * 2020-07-10 2022-01-12 Infineon Technologies AG Dispositif de détection de gaz pour détecter une ou plusieurs gaz dans un mélange de gaz
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WO2022191173A1 (fr) * 2021-03-12 2022-09-15 パナソニックＩｐマネジメント株式会社 Procédé d'identification de gaz et système d'identification de gaz
WO2023037999A1 (fr) * 2021-09-07 2023-03-16 パナソニックＩｐマネジメント株式会社 Procédé d'analyse de gaz et système d'analyse de gaz

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