546 results about "Olfactory perception" patented technology

An artificial intelligence system based on spatial-temporal information pairs is provided by the present disclosure. By integrally deploying paired vision, auditory and olfactory acquisition devices, a device that can collect the data within a 720-degree area is constructed, and the multi-dimensional continuous spatial-temporal information pairs such as positions, morphologies, motion states, sounds and odors from the ambient environment of the acquisition device or a same spatial object in the environment are recorded in real time. These information pairs can not only contain spatial relationships and a clock attribute, but also contain rich label attributes, for example, identifiers of acquisition devices, and a name, a category and a behavior pattern of the spatial object.

The invention relates to the technical field of emotion recognition, in particular to an odor-induced EEG emotion recognition method and system based on a space-time stratified attention mechanism, and the method comprises the steps: presenting odor stimulation to a testee through an olfactory stimulation device, and synchronously collecting an EEG signal and a free association voice text of the testee; performing semantic analysis on the free association voice text, and extracting keywords and corresponding emotion dimension tags; carrying out space-time hierarchical feature extraction on the EEG signal; constructing a space-time hierarchical attention mechanism model; and on the basis of the weighted spatial-temporal features and semantic tags, node connection weights of the individualized knowledge graph are updated through a graph neural network, and an emotion recognition result is generated. According to the method, spatial attention and time attention are combined, key spatio-temporal characteristics are dynamically focused, the limitation of a fixed weight of a traditional method is avoided, and the pertinence and integrity of neural signal representation are remarkably improved.

Described herein are wearable sensory stimulation and monitoring devices that can be worn around the back of the neck along and in contact with the spine. Apparatuses described herein stimulate one or more senses including auditory, tactile or olfactory. Apparatuses are also described that allow for sensing and monitoring of physiologic parameters such as heart rate, blood pressure and movement. The device also has features that allow for easy attachment or integration to articles worn on the head such as augmented reality and virtual reality accessories to provide visual stimulation and entertainment.

The invention provides a multi-scene fusion perception system and method of a humanoid robot olfaction sensor, and relates to the technical field of intelligent sensors, and the system comprises an olfaction perception optimization module which is used for obtaining an olfaction perception optimization network; the first matrix module is used for monitoring a gas pipeline to obtain a first matrix; the second matrix module is used for performing interference compensation according to a real-time environment; the third matrix module is used for correcting according to the state of the sensor; the leakage detection module is used for detecting pipeline leakage; and the suppression verification module is used for carrying out false alarm suppression verification on the pipeline leakage detection result to obtain a pipeline early warning signal. The gas leakage detection method and device can solve the technical problem that the gas leakage detection precision is limited due to the fact that the gas leakage detection device is prone to environmental interference and high in false alarm rate in the prior art, and can adjust the multi-node olfaction sensing network in real time through historical data, conduct multi-level correction on monitoring data and introduce false alarm suppression verification, thereby improving the detection accuracy of gas leakage. The accuracy of gas leakage detection in a multi-scene environment is improved.

The invention provides an olfactory function evaluation method for human olfactory detection, and belongs to the technical field of olfactory detection based on machine learning. The method comprises the following steps: firstly, collecting representative odor substances covering different types, releasing odor samples with different concentrations through an odor release device, collecting data after ensuring that a test environment is stable through air purification and environment regulation and control, and designing an olfactory function evaluation model based on multi-dimensional comprehensive analysis and judgment; information processing and feature extraction are carried out on the response condition of the subject after the subject contacts the odor sample, and the mining depth of feedback data is further improved by using a hierarchical data processing method, so that the evaluation precision is improved. Meanwhile, during actual training of the model, a model training strategy for adaptive parameter adjustment is designed. According to the method, the optimized model is applied to an actual olfactory function evaluation scene, comprehensive and accurate evaluation and analysis of the human olfactory function can be realized, and the olfactory health condition can be judged in an auxiliary manner.

The invention discloses a generative odor real-time synthesis method and system based on cross-modal potential space mapping, and belongs to the technical field of artificial intelligence and olfaction calculation. The method comprises the following steps: acquiring a multi-modal input stream of a current scene, and extracting an emotion semantic feature vector by using a deep neural network; mapping the semantic features into target odor chemical feature vectors by using nonlinear projection through a pre-constructed vision-smell joint embedding space; constructing a convex optimization model based on olfactory perception, and calculating a basic liquid optimal mixing proportionality coefficient matrix capable of fitting the target vector; the matrix is converted into a micro-fluidic driving signal, and the target smell is synthesized in situ in the micro-fluidic chip. The invention further discloses a self-adaptive cleaning logic and olfactory fatigue compensation mechanism based on scene mutation detection. The method solves the problems that in the prior art, label matching is dependent, new smell cannot be synthesized, and dynamic transition is lacked, and olfactory replicating and real-time generation of abstract semantic scenes are achieved.

The invention relates to the technical field of power equipment monitoring, in particular to a multi-mode sensor power equipment state monitoring system based on AI smell. The data acquisition module is used for acquiring multi-dimensional sensing data around the electrical equipment; the AI olfaction processing module is used for converting original sensor data into standardized odor feature fingerprints by performing spatial-temporal feature extraction and multi-modal feature fusion on the multi-dimensional perception data; the overheating state analysis module is used for generating an overheating comprehensive index and an equipment state evaluation result based on the smell characteristic fingerprints; the multi-stage early warning and response module is used for carrying out early warning grade judgment based on the overheating comprehensive index and the equipment state evaluation result and generating graded early warning information; and the storage module is used for storing historical monitoring data and early warning event records. According to the invention, through system integration of the multi-mode sensor array of the bionic olfaction mechanism, AI feature fusion processing and intelligent analysis decision, active sensing and accurate identification of the overheating state of the power equipment are realized.

The invention discloses an image description method, device and equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: describing original image data based on a preset to-be-described feature, and obtaining an initial description text, the initial description text represents description information corresponding to features to be described in the original image data; semantic enhancement processing is conducted on the initial description text from preset five-sense dimensions, an enhanced description text is obtained, and the five-sense dimensions comprise the visual dimension, the tactile dimension, the auditory dimension, the taste dimension and the olfactory dimension; and generating comprehensive description information of the original image data based on the initial description text and the enhanced description text. According to the method and the device, the performance of the multi-modal large model in the cross-modal generation task can be improved.

The present invention relates to a screening method for odor-suppressing materials. A screening method according to the present invention includes: causing a test substance and an odor-causing substance to act on at least one olfactory receptor polypeptide selected from the group consisting of olfactory receptor OR2AG2 and polypeptides that include an amino acid sequence having 80% or greater identity with the amino acid sequence of the aforementioned olfactory receptor and exhibit responsiveness to odor-causing substances; measuring the response of the olfactory receptor polypeptide to the odor-causing substance; and, on the basis of the measured response, identifying test substances that suppress a response of the olfactory receptor polypeptide as candidate substances for an odor-suppressing material.

The invention discloses a system and a method for monitoring olfactory smell in a pension community based on Internet of Things, and relates to the technical field of pension, a TVOC sensor and an ammonia gas sensor are adopted to capture volatile organic compounds released by sweat in real time, and temperature, humidity and air fluidity data are combined to calculate a dynamic air quality index, so that the olfactory smell in the pension community is monitored. The defect that a traditional method only depends on temperature and humidity or CO2 concentration monitoring is overcome; secondly, in the air quality state analysis process, AI analysis is adopted for sweat and odor concentration prediction and pollution trend modeling, a long-short-term memory network LSTM is introduced for time sequence analysis, in combination with air quality change rate and humidity anomaly detection, it is guaranteed that air pollution can be timely and early recognized and predicted, and the problem of monitoring blind areas is avoided; and in the aspect of air optimization regulation and control, the air volume and the purification intensity of the air purification equipment are dynamically adjusted through ventilation control in combination with an air quality optimization instruction and an old people fitness activity mode.

The invention provides a total sensory data processing method and device based on AR glasses and electronic equipment, and relates to the field of data processing. In the method, total sensory data for a target environment sent by a multi-modal sensor is obtained, and the multi-modal sensor is located on AR glasses; performing feature extraction on the total sensory data by adopting a multi-modal dynamic fusion network to obtain a visual feature, an auditory feature, an olfactory feature and a tactile feature; determining a synchronization event from the visual feature, the auditory feature, the olfactory feature and the tactile feature through a time alignment model; and according to the synchronization event, generating a multi-modal feedback strategy, and controlling the AR glasses to execute the multi-modal feedback strategy. By implementing the technical scheme provided by the invention, the use experience of a wearer of the AR glasses can be improved.

The invention discloses an olfactory navigation rat robot based on a brain-computer interface technology and a navigation method. The olfactory navigation rat robot comprises a biological rat body, a signal acquisition stimulation backpack and a host, an invasive cortex array electrode is implanted in a rat olfactory bulb area, a field potential signal is accurately recorded, and motion control and respiration signal recording are achieved through the implanted electrode; the signal acquisition stimulation knapsack acquires electrophysiological signals of the rat in the movement process, and an online odor classification model is constructed by combining acquired respiratory signals or a neural network respiratory signal fitting model; based on a multi-normal form olfactory bulb signal database of rat individuals, a transfer learning technology is adopted to adapt to odor decoding tasks under different experimental conditions. The natural olfactory capability of the rat is utilized to construct an autologous neural signal closed-loop system, so that the accurate control of efficient site traversal and specific smell search on the rat robot is realized, and the accuracy and adaptability of olfactory navigation tasks are remarkably improved.

The invention discloses a multi-mode perception collaboration and real-time synchronization system, which breaks through the limitation of vision / hearing in the prior art, covers vision, hearing, smell and movement, and realizes more accurate cooperation through a perception layer, a control layer and an execution layer. According to the invention, an immersive experience space full of shock and reality can be created, synchronous output of visual, auditory, tactile and olfactory signals is realized by means of high-precision equipment, and a series of cross-sensory signal superposition schemes are designed on the basis of response characteristics of human physiology and psychology to various sensory stimuli. Therefore, the overall perception of the experiencer to the space atmosphere is enhanced. Meanwhile, through weight distribution and conflict resolution, the system can flexibly adjust the sensing mode according to the environment and the user state. The multi-device cooperation error can be controlled at millisecond level and millimeter level, and the natural fusion of virtuality and reality is realized.

To provide a method for screening lipid deterioration odor suppressing materials, a lipid deterioration odor suppressing agent, a method for producing food and beverages containing the agent, and food and beverages containing the agent.SOLUTION: Provided is a method for screening lipid deterioration odor suppressing materials, comprising the following steps (1) to (3): (1) bringing a test substance and a lipid deterioration odor causative substance into contact with an olfactory receptor polypeptide that contains an amino acid sequence of OR2 J3 and / or an amino acid sequence that has at least 90% identity with the amino acid sequence of OR2 J3 and is responsive to the lipid deterioration odor causative substance; (2) measuring a response of the olfactory receptor polypeptide to the lipid deterioration odor causative substance; and (3) identifying a test substance that suppresses the response of the olfactory receptor polypeptide based on the measured response.SELECTED DRAWING: Figure 7

Aspects of the present disclosure relate to an apparatus for predicting an olfactory profile of a molecule, the apparatus comprising memory circuitry, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to obtain a first representation of the molecule and a second representation of the molecule, process the first representation using at least one first machine-learning model to obtain a first predicted olfactory profile of the molecule, process the second representation using at least one second machine-learning model to obtain a second predicted olfactory profile of the molecule, process the first predicted olfactory profile and the second predicted olfactory profile, or a combined version of the first predicted olfactory profile and the second predicted olfactory profile, using a third machine-learning model, the third machine-learning model being trained to output a third predicted olfactory profile of the molecule.

The invention discloses an outdoor environment health monitoring method based on an olfactory chip, and relates to the technical field of environment health monitoring. A traditional sensor and an olfactory chip are adopted at a monitoring node at the same time, so that synchronous acquisition of temperature, humidity, gas concentration and complex smell fingerprints is realized; the limitation of identifying multi-component pollutants in a complex environment of an industrial area by a single data source is fundamentally made up; the traditional method is difficult to distinguish cross interference of various gases, however, in the invention, a deep neural network is combined with an attention mechanism to extract implicit features of sensing data, and data cross correction is realized through space-time correlation analysis, so that the accuracy of pollutant identification is remarkably improved; further introducing a graph neural network to carry out modeling on spatio-temporal data of each monitoring node, capturing space structure information of the whole monitoring network, and forming a fusion feature vector with relatively high distinguishing capability; the problem of signal cross interference when various malodorous gases are mixed in an industrial area is effectively solved.

The invention provides a molecular smell recognition method, device and equipment, a medium and a product, and the method comprises the steps: obtaining a plurality of gas molecules in a target region; performing substance identification on the plurality of gas molecules, and determining the substance structure of each gas molecule; determining the SMILES formula of each gas molecule according to the substance structure of each gas molecule; carrying out standardization treatment on the SMILES formula of each gas molecule to obtain a target SMILES formula of each gas molecule; generating a target virtual code according to the plurality of target SMILES formulas and preset olfactory receptor information; inputting the target virtual code and a plurality of target SMILES into a trained OR-POM model, and determining a smell label of each gas molecule; and determining the odor in the target area and an odor space map in the target space according to the odor label of each gas molecule. According to the SMILES type of the gas molecules and olfactory receptor information, stereoisomerous information analysis of the odor molecules can be carried out, and the odor of the gas molecules can be coded and identified.

Provided are a method and the like for efficiently screening for a candidate substance for a starting material that suppresses hexanal odor. The screening method according to the present invention comprises the steps of: adding a test substance and hexanal to a specific olfactory receptor polypeptide; a step for measuring the response of the olfactory receptor polypeptide to hexanal; and identifying, on the basis of the measured response, a test substance that inhibits the response of the olfactory receptor polypeptide as a candidate substance for a raw material that inhibits hexanal odor.

The invention discloses a vehicle-mounted interaction system and method for adjusting the emotion of a driver, a vehicle and a medium, and relates to the technical field of intelligent driving. The vehicle-mounted interaction system comprises an emotion sensing module, a data processing module and a feedback control module which are connected with one another, the emotion sensing module is used for acquiring multi-modal information of a driver, and the multi-modal information comprises facial data, physiological data and voice data; the data processing module is used for fusing the multi-modal information and determining an emotional state level of the driver; and the feedback control module is used for controlling operation parameters of the multi-channel actuator according to the emotional state level, and the multi-channel actuator comprises an auditory channel, a visual channel, an olfactory channel and a tactile channel. By adopting the vehicle-mounted interaction system, the emotion of the driver can be actively sensed and cooperatively adjusted, and the driving safety and comfort are effectively improved.

The invention discloses a system and method for performing cranial nerve stimulation based on olfaction, hearing and synchronous magnetomotive stimulation, and the system comprises a magnetic therapy bearing main body which is a magnetic therapy helmet, serves as a basic bearing structure of the system, and is used for installing all functional assemblies, providing magnetic therapy physical auxiliary stimulation, and achieving the cooperation of magnetic therapy and multi-mode stimulation; the olfactory stimulation subsystem is used for providing olfactory stimulation with precise regulation and control; the auditory stimulation subsystem is used for providing personalized audio stimulation; the magnetomotive stimulation subsystem is used for providing magnetomotive stimulation; the nerve stimulation cooperation subsystem is used for planning multi-mode stimulation as a whole and achieving regulation and control. Through the synergistic effect of olfactory stimulation, auditory stimulation and magnetomotive stimulation, three major neural pathways of olfactory, auditory and somatosensory can be activated at the same time, multi-dimensional nerve resonance is formed, the brain wave synchronization degree is remarkably improved, and the adjuvant therapy effect on Alzheimer's disease, cognitive impairment and other diseases is superior to that of single-mode stimulation.

The invention discloses an interactive seven-stringed plucked instrument music body and mind healing system, relates to the technical field of intelligent health management, and constructs an immersive body and mind conditioning environment by integrating multi-sensory stimulation of sound, light and smell and a seven-stringed plucked instrument music healing function. The system comprises a simulated seven-stringed plucked instrument sound box, an intelligent seven-stringed plucked instrument base, a millimeter wave radar, an infrared camera, a directional microphone, an intelligent aromatherapy machine and an intelligent light module, physiological and psychological data of a user are collected in real time, and a physical and psychological state report is generated through AI model analysis. And dynamically matching guqin music tracks (such as'ghost blue 'for relieving anxiety), fragrance types (such as sandalwood for soothing nerves) and intelligent light scenes (simulating morning dawn / moonlight). A non-contact sensing technology is innovatively adopted, and a seven-stringed plucked instrument tone color characteristic and a natural light environment algorithm are combined, so that cooperative adjustment of music frequency, light color temperature and fragrance concentration is realized, the problems of single sense organ and insufficient dynamic adaptation of a traditional healing system are solved, and a personalized and non-intrusive body and mind conditioning scheme is provided for sub-health people.

To enable installing, at appropriate positions, respective sensors for acquiring various kinds of information necessary for controlling a robot in a multimodal manner; and to acquire information from the respective sensors in an appropriate state and at appropriate timing.SOLUTION: In a hand tool 50, sensors having respective different information acquisition functions are attached to respective finger parts 22A, 22B, 22C, 22D, and 22E. In other words, sensors for acquiring various kinds of information necessary for controlling a robot are attached. A visual sensor 51A for detecting visual information is attached to a thumb, an auditory sensor 51B for detecting auditory information is attached to an index finger, an olfactory sensor 51C is attached to a middle finger, a tactile sensor 51D for detecting tactile information is attached to a ring finger, and a taste sensor 51E for detecting taste information is attached to a little finger.SELECTED DRAWING: Figure 4

The invention relates to computer implemented methods for the prediction of the smell of a molecule, comprising providing a first database comprising the structure, preferably the three- dimensional (3D) and / or chemical structure, of one or more olfactory receptor (OR), training a machine learning model or AI on the data comprised within the first database, providing a second database comprising (i) the (3D) structure of one or more odorant molecule and (ii) a respective textual description of the odor sensation induced by said one or more odor molecule in a human subject, training a machine learning (ML) model or artificial intelligence (AI) on the data comprised within the second database, fitting the (3D) structure of the one or more odor molecule to the (3D) structure of the one or more OR, thereby determining an affinity score for each fitted combination of the one or more odor molecule with a respective olfactory receptor, wherein the fitting comprises the use and training of a ML model or AI, wherein the affinity score is indicative for the degree of fit of a respective odor molecule to the OR, creating a comprehensive third database or third data embedding structure / layer comprising the data of the first database, the second database and the affinity score determined for each fitted combination of a respective odor molecule with a respective olfactory receptor, providing the (3D) structure of at least a first odor molecule of interest, which is not comprised within the second database, and predicting an odor sensation induced by the at least first odor molecule of interest in a human subject using machine learning or AI considering the data comprised within the third database or third data embedding structure / layer.

The utility model discloses a cleaning robot with an olfactory perception positioning structure, and relates to the technical field of cleaning machines. The cleaning robot with the olfactory perception positioning structure comprises a cleaning robot body, an excrement cleaning assembly is installed on one side of the cleaning robot body, and a cleaning roller assembly for cleaning pet urine is installed on the side, away from the excrement cleaning assembly, of the cleaning robot body. A cleaning assembly for cleaning the cleaning roller assembly is installed on the inner wall of the cleaning robot body, a gas sensor for identifying pet excrement and urine smell is fixedly connected to the upper portion of the cleaning robot body, and the gas sensor can position the position of excrement according to the excrement gas molecule concentration; according to the pet excrement cleaning device, the smell sensor is arranged, so that the device can position the smell of pet excrement according to smell perception, the excrement cleaning assembly or the cleaning roller assembly is used for accurately cleaning the excrement, and pollution of the pet excrement to air and the environment is reduced.

The invention discloses a method for predicting an interaction relationship between aroma substances based on a decision tree algorithm. The method comprises the following steps: constructing an aroma substance interaction relationship database; training the C5.0 decision tree prediction model based on the Booting algorithm by taking data of the aroma substance interaction relationship database as a training test data set to obtain an optimal prediction model; and utilizing the optimal prediction model to obtain the interaction relationship of the aroma substance combination to be predicted. According to the method disclosed by the invention, the decision tree model is adopted to carry out association modeling on binding energy data and sensory experiment results, and a Boosting algorithm is introduced to optimize model parameters, so that deep fusion of olfactory receptor binding energy and sensory evaluation data is realized; and through multi-dimensional data integration, the aroma substance interaction prediction precision is remarkably improved, and the method has wide applicability and good application prospects.

The invention relates to an odor molecule and olfactory receptor reaction prediction method and device based on a multi-mode deep learning network, and the method comprises the steps: firstly, extracting physical and chemical descriptors of odor molecules as the characteristics of the physical and chemical descriptors through the interaction data between the odor molecules and olfactory receptors, and employing a deep learning network model to predict the reaction between the odor molecules and the olfactory receptors; the method comprises the following steps: firstly, judging whether an unknown odor molecule can excite an olfactory receptor or not, secondly, judging the possibility of reaction between the odor molecule and each olfactory receptor through a network calculation model by adopting odor molecule sequence data, and then, further introducing odor molecule structure image data, and calculating the reaction probability between the odor molecule and each olfactory receptor. And finally, in order to improve the accuracy and reliability of a recognition result, determining a final reaction olfactory receptor of the odorant molecule through a calculation result obtained through analysis, namely two groups of probability distributions. According to the method, the recognition accuracy of the odor molecules and the olfactory receptors is remarkably improved, meanwhile, the dependence on an experimental method is reduced, and a new tool is provided for chemical and biological research.

The invention discloses an olfactory reconstruction electrical stimulation regulation and control system for a nerve prosthesis, and the system comprises a sensor, an electrode array, a control unit, a signal processing module, a wireless transmission module, an electrode driving module, and a feedback collection module. The olfactory bulb, the olfactory epithelium or the olfactory cortex are electrically stimulated through the array electrode, and the smell sensing process is simulated. According to the invention, olfaction signal transmission is reconstructed by accurately regulating and controlling electrical stimulation parameters, and a patient with olfaction loss is helped to recover perception of surrounding environment smell. The system has the advantages of high precision, controllability, personalized adaptation and the like, and an innovative technical scheme is provided for perception recovery of olfactory deficiency patients.

Disclosed are bioadaptive environments, such as immersive multi-sensory environments that respond to biofeedback from a subject within the environment, and provide interactive and synergistic visual, auditory, tactile, and olfactory stimuli. In some aspects the bioadaptive environments are designed to enhance the therapeutic benefits from states of increased neuroplasticity and non-ordinary or altered states of consciousness, including both pharmacologically induced and non-pharmacologically induced. In some aspects, the bioadaptive environments are designed to enhance the setting for and subjective experience of psychedelics. In some aspects, disclosed bioadaptive environments enhance the therapeutic benefits of psychedelics and other plasticity-inducing agents, including when used to treat mental health disorders and neurodegenerative disorders, as well as for the improvement of wellbeing.

The disclosure relates a method for olfactory training and a computer program for use in the method for olfactory training, using a nose plug being adapted for insertion into a nostril of a human subject, wherein the nose plug comprises a structural molded material and a fragrance substance or mixture of fragrance substances experienced as an odor, said 5 fragrance substance or mixture of fragrance substances being evenly dispersed in the structural molded material. The disclosure further relates to a kit of nose plugs for use in an olfactory training program and a nose plug for use in an olfactory training program.

A scent generator includes a supply chamber configured for receiving a volume of an olfactory stimulant, the supply chamber including an electrically conductive foam configured to volatilize a portion of the olfactory stimulant; and a power supply configured to provide an electrical signal to the electrically conductive foam and cause the olfactory stimulant to volatilize and be ejected from the supply chamber. A pest trap using the scent generator and a computer program product for controlling the scent generator are disclosed.