13467 results about "Breathing" patented technology

The invention discloses a coal mine ventilation system intelligent regulation and control method based on environmental perception, and relates to the technical field of coal mine ventilation system intelligent regulation and control, and the method comprises the following steps: collecting environmental data of different areas of a mine, including gas concentration, temperature and humidity, air velocity and oxygen content, and monitoring the operation state of ventilation equipment in real time; data of all the sensors are synchronously processed through the data fusion technology, and a unified environment sensing model is generated. According to the invention, through cooperative work of the expert system and deep learning, real-time monitoring and emergency plan execution under extreme working conditions are realized, and the response speed and decision accuracy are improved. A reinforcement learning algorithm is adopted to optimize ventilation line switching, the adaptive capacity and redundancy guarantee of the system are enhanced, and it is ensured that airflow is not interrupted at the critical moment. Through abnormality comparison of the knowledge base and the rule base, false alarm and misjudgment are reduced, system efficiency is improved, operation burden is relieved, and safety and stable operation of mine personnel are guaranteed to the maximum extent.

The invention relates to the technical field of mine ventilation, in particular to a mine ventilation dynamic regulation and control method and system based on a space-time diagram convolutional network, and the regulation and control method comprises the following steps: 1, carrying out the modeling and data collection of a ventilation system; step 2, modeling and anomaly prediction of the graph convolutional network; step 3, generation and execution of a ventilation equipment coordinated regulation and control strategy; step 4, edge node calibration and model optimization; and 5, performing full-process control and emergency response. A ventilation network topological structure and multi-modal time series data are fused through a space-time diagram convolutional network, a joint model containing spatial feature extraction and time series modeling is constructed, 30-minute early warning of ventilation abnormity and propagation path prediction are achieved, the lag response limitation that a traditional system only depends on a single parameter threshold value is broken through, and the risk pre-judgment capability is improved.

The invention discloses a tunnel segmented exhaust method and system based on train working conditions, a medium and a product, and relates to the technical field of ventilation. The method comprises the steps that a target tunnel is divided into a plurality of control sections according to fan installation configuration of the target tunnel; acquiring train passing data and train configuration information of the control section, and calculating a flue gas growth value of the control section in a preset time period; collecting flue gas concentration data of the control section, and calculating a ventilation demand index of the control section in combination with the flue gas growth value; collecting wind direction parameters of the control sections, determining a ventilation control relationship between the adjacent control sections based on the ventilation demand indexes and the wind direction parameters, and generating a ventilation cooperation scheme; according to the ventilation cooperation scheme, operation parameters of the tunnel fans in all the control sections are calculated, and control instructions corresponding to the operation parameters are sent to the tunnel fans. By implementing the application, the fan operation can be adjusted according to the actual train operation condition, and the ventilation effect is optimized.

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention relates to the technical field of data processing, in particular to a pneumology department intelligent early warning system based on data fusion. The method comprises the following steps: monitoring and collecting multi-source physiological data of a patient to obtain original physiological data; preprocessing the original physiological data to obtain preprocessed physiological data; performing intelligent fusion processing on the preprocessed physiological data to obtain comprehensive physiological feature data; performing anomaly detection analysis on the comprehensive physiological feature data to obtain an analysis result of the breathing state; performing risk grade division based on the analysis result of the breathing state; and making medical suggestions based on the risk levels. The technical problems that in the implementation process of intelligent early warning of the pneumology department, a current respiration monitoring system is inaccurate in multi-source physiological data fusion and low in patient health condition judgment accuracy are solved.

The invention relates to the technical field of granary ventilation and energy consumption optimization, in particular to an intelligent granary ventilation and energy consumption optimization decision-making method based on reinforcement learning, and the method comprises the steps: obtaining the internal and external multi-dimensional state data of a granary, respectively constructing a basic state vector, a space correlation feature vector and a grain condition change trend vector based on the multi-dimensional state data inside and outside the granary; combining the basic state vector, the space correlation feature vector and the grain condition change trend vector into a space-time feature tensor, inputting the space-time feature tensor into a space-time topology perception decision model based on a graph attention network, and outputting an optimal composite action vector for controlling ventilation equipment; and analyzing and converting the optimal composite action vector to drive ventilation equipment of the granary to execute control. According to the method, through fusion of space-time topology perception and deep reinforcement learning, accurate prediction and active regulation and control of the granary state are realized, and energy consumption of a ventilation system can be reduced to the greatest extent while grain storage safety is ensured.

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention relates to the field of grain depot ventilation management, and discloses an automatic management system for ventilation in intelligent grain depot production, which comprises an environmental parameter monitoring module, a data processing and analysis module, an intelligent ventilation decision module, a ventilation equipment linkage control module and a dynamic feedback optimization module, the environmental parameter monitoring module is used for collecting temperature, humidity and gas concentration data in the bin in real time through the Internet of Things communication unit; the data processing and analyzing module is connected with the environment parameter monitoring module; the intelligent ventilation decision-making module receives the multi-dimensional data atlas, analyzes the temperature gradient, the humidity sensitivity index and the mildew risk value in the atlas through a dynamic classification algorithm, and fuses external meteorological data; the ventilation equipment linkage control module is used for receiving the instruction sequence of the intelligent ventilation decision module; the dynamic feedback optimization module is used for monitoring the efficiency coefficient and the grain pile environment change rate in the ventilation process; the system has the advantages that the ventilation precision and efficiency are improved, and granary environment management is optimized.

In some implementations, a low dead space airway adapter for sampling fluid flowing through a ventilation assembly includes an outer wall configured to couple to an endotracheal (ET) tube adapter and an internal projection positioned at least partially within an internal cavity defined by the outer wall and configured to extend within an internal cavity of the ET tube adapter when the airway adapter is in use. The internal projection can include a fluid passageway configured for fluid communication with at least a portion of the internal cavity of the ET tube adapter and a free end comprising an opening in fluid communication with the fluid passageway. At least a portion of the free end can be chamfered around the opening and configured to contact an inner surface of the ET tube adapter when the outer wall is coupled to the ET tube adapter.

The invention discloses an intelligent regulation and control method and system for dust removal and air control of an underground high-gas heading face, and relates to the field of intelligent control. Firstly, an environment parameter set and an equipment parameter set are obtained in real time; constructing a digital twinborn model of the tunneling face and the ventilation system based on the environmental parameter set and the equipment parameter set, and simulating gas concentration and dust concentration under different working conditions; the prediction model predicts the gas emission amount trend and the dust concentration trend; and inputting the environment parameter set, the equipment parameter set and the risk level into an optimization model, and generating a target decision instruction by the optimization model. The operation parameters in the ventilation system can be timely regulated and controlled in real time based on the monitored environment parameter set of the tunneling face and the equipment parameter set in the ventilation system, so that the ventilation and dust removal efficiency of the ventilation system is improved. Moreover, the decision instruction is generated by referring to the trend of the gas emission amount and the dust concentration in the future, so that the accuracy of the decision instruction of the ventilation system is further improved.

The invention relates to the field of mine ventilation, in particular to an intelligent adjusting method and system for mine ventilation. The method comprises the following steps: constructing a dynamic topology model taking a roadway as an edge and taking a fan and an air door as nodes based on a mine CAD drawing; acquiring the actual wind pressure and the actual wind volume of each roadway branch in real time, performing inversion to obtain the current wind resistance, and updating the wind resistance attribute of the edge in the dynamic topology model by using the current wind resistance; calculating the optimal air volume of the roadway branch by using the updated dynamic topology model; based on the optimal air volume, target regulation and control parameters of the draught fan and the air door are obtained through solving by means of a particle swarm algorithm; and according to the target regulation and control parameters, the fan and the air door are adjusted through a fuzzy PID algorithm. The problem that in the prior art, mine ventilation adjustment is not intelligent and safe enough is solved, the theoretical optimal air volume is obtained through deep analysis of the mine, then ventilation adjustment is conducted step by step, and the scientificity and efficiency of ventilation adjustment are improved.

The invention relates to the technical field of mine ventilation control, in particular to a mine fan ventilation control method and system based on digital twinning. The method comprises the following steps: carrying out data preprocessing on acquired mine monitoring data; performing feature extraction on the preprocessed mine monitoring data; utilizing a random forest algorithm to predict the relationship between the data characteristics and the fan air volume; a digital twinborn model is used for resolving the relation between the data characteristics and the fan air volume, and a fan ventilation control strategy is obtained; and generating a PLC control instruction based on the fan ventilation control strategy. And through multi-source data fusion and deep analysis, the real-time ventilation requirement of the mine can be accurately grasped. Based on random forest algorithm prediction and digital twinborn model calculation, dynamic and accurate adjustment of the air volume of the draught fan is achieved, it is ensured that the concentration of harmful gas in all underground areas is maintained within a safety threshold value, the oxygen content is sufficient, an ideal environment is created for personnel operation and equipment operation, and the safety and reliability of a ventilation system are remarkably improved.

The invention provides a mobile henhouse ventilation regulation and control method and system based on an environment model, and relates to the technical field of intelligent breeding, and the method comprises the steps: obtaining original temperature data, original humidity data, a three-dimensional space wind speed component, an opening geometric distribution data set, and original latitude and longitude coordinate data and time data; calculating a dynamic environment weight coefficient matrix according to the original latitude and longitude coordinate sequence and the time data; generating a self-adaptive heat exchange flow field according to the dynamic environment weight coefficient matrix, the original temperature data and the original humidity data; according to the self-adaptive heat exchange flow field and the trepanning geometric distribution data set, a ventilation compensation amount demand curve is obtained through calculation; generating a ventilation intensity adjusting instruction set according to the ventilation compensation amount demand curve; and according to the ventilation intensity adjusting instruction set, outputting an optimal ventilation control parameter. According to the method, ventilation response self-adaptive accurate matching, airflow dead zone self-suppression and energy consumption-effect dynamic balance under the moving sudden change working condition are achieved.

The invention relates to a dynamic regulation and control method and system for mine ventilation. The method comprises the following steps: acquiring multi-source data in a mine, and preprocessing the multi-source data to obtain a standardized data set; calculating the risk of each region in the mine based on the standardized data set to obtain a risk prediction matrix; based on the standardized data set and the risk prediction matrix, the air volume demand of each area is calculated, and an air volume demand table is obtained; based on the air volume demand table, an optimization proposition corresponding to the air volume demand is constructed and solved, and a solution set of the optimization proposition is obtained; mapping the solution set based on a preset rule to obtain a feasible allocation scheme; and based on the feasible allocation scheme, generating an equipment cooperation instruction, and obtaining a security instruction set. According to the method, dynamic factors can be considered, the air volume is dynamically adjusted, multi-fan cooperation is achieved, and the effects of real-time sensing and autonomous dynamic decision making of mine ventilation are achieved.

The invention relates to the technical field of frequency conversion control, and discloses a frequency conversion control method and system for a nuclear power station ventilation system, and the method comprises the steps: collecting the multi-region real-time operation data of the nuclear power station ventilation system, and carrying out the standardization processing, and obtaining a standardized data set; performing fan performance curve fitting and differential pressure field feature mapping on the standardized data set to obtain differential pressure field mapping features; the differential pressure field mapping features are input into an active neural differential pressure field network model for frequency conversion parameter prediction, and target frequency conversion control parameters are obtained; feedforward-feedback combined control is conducted on the target frequency conversion control parameters through the multi-mode frequency conversion controller, the fan rotating speed and the air valve opening value are obtained, the target frequency conversion control parameters can be generated in a self-adaptive mode, the control performance is remarkably improved, and smooth transition of the nuclear power station ventilation system is achieved.

Disclosed herein are systems and methods for providing targeted temperature management (TTM) therapy to a patient. Systems included herein provide an airflow to the patient in additional to a liquid flow that define a thermal energy exchange with the patient. Various systems may provide air at a defined TTM temperature to a thermal contact pad, a mattress or a ventilator that delivers the TTM air to the patient via the ventilation therapy. Also disclosed herein are systems, devices, and methods for preventing, managing, and / or removing perspiration moisture from between the thermal contact pad and the patient. Disclosed herein is a thermal contact pad includes a wicking material to draw moisture away from the patient. Disclosed herein also is a thermal contact pad including airflow that draws moisture away from the patient.

The invention relates to the technical field of energy and energy conservation, in particular to a ventilation air conditioner control method based on load prediction. The technical problems that in the prior art, a ventilation air conditioner control method has certain hysteresis quality, advanced adjustment cannot be achieved, and autonomous adjustment and control cannot be conducted on the occasion with large flow are solved. According to the technical scheme, the ventilation air conditioner control method based on load prediction comprises the steps of analyzing the relation between the operation parameters of each component device of an air conditioner system and the building cooling load according to a model prediction result, fitting the change of the relation into a curve, and compiling an operation control scheme under typical working conditions according to the fitted relation curve. The building cooling load prediction value within a period of time can be obtained according to the algorithm, the system formulates the air conditioner system control strategy according to the prediction value, the problems of unreasonable building ventilation air conditioner operation mode and control lag can be solved, the operation energy efficiency of the air conditioner system is improved, on-demand cooling is effectively achieved, and the energy consumption is reduced. And intelligent building operation management is powerfully supported.

The invention discloses an energy consumption optimization control method and system for heating and ventilation equipment, and the method comprises the steps: carrying out the comprehensive evaluation of the energy consumption condition of the heating and ventilation equipment, and recognizing the current energy consumption condition and energy-saving potential of the equipment; the system is connected with heating and ventilation equipment through the Internet of Things technology, and collects parameters of the heating and ventilation equipment in real time. Based on the preprocessed data, establishing energy consumption models of the water chilling unit, the cooling tower, the freezing pump and the cooling pump; building an energy consumption prediction regression model by adopting a decision tree algorithm, and predicting the total energy consumption of the heating and ventilation system in real time; according to the predicted energy consumption data, an optimization control strategy of the heating and ventilation equipment is formulated in combination with a set energy consumption control objective function; and applying the formulated optimization control strategy to the heating and ventilation equipment, carrying out real-time monitoring and feedback, and carrying out iterative optimization on the control strategy according to an actual effect. According to the method, comprehensive evaluation, real-time monitoring and optimal control of the energy consumption of the heating and ventilation equipment can be realized, and a powerful guarantee is provided for energy conservation and emission reduction of a building.

The invention provides a small sample respiratory disease pattern recognition method based on a diffusion model. The method comprises the following steps: firstly, for common respiratory disease types, collecting and classifying data of three respiratory modes: normal respiration, pause respiration and deep respiration which respectively correspond to respiratory diseases such as normal respiration, obstructive apnea and asthma in medicine; secondly, learning an internal distribution rule of data by using a radio frequency diffusion model, and generating diversified and high-quality radio frequency time sequence data on the basis of limited real samples so as to expand a data set; and thirdly, combining the generated data with real data, extracting an accurate respiration signal through a one-dimensional time sequence extraction algorithm, and converting a time sequence into a two-dimensional image by utilizing a Gramian Angle Field (GAF) method, so that the time dependency relationship of the time sequence is intuitively presented in the image. And finally, a convolutional neural network (CNN)-Transform deep learning model is adopted to carry out feature extraction and classification on the image data, and then multi-scale feature analysis and breathing mode recognition are carried out. According to the method, the recognition precision and generalization ability of the model are remarkably improved under the small sample condition, and an accurate and reliable solution is provided for non-contact breathing mode recognition.

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

A facial basin assembly includes a basin with a convex top side and a concave bottom side. The assembly features a mouthpiece to facilitate user breathing while their face is submerged in liquid. The mouthpiece is connected to a coupling device via one or more air flow conduits. This coupling device is designed to be removably inserted through a ledge of the basin and is adapted to direct the user's exhalation toward the bottom of the basin. The basin comprises a base portion and a curved portion, the latter allowing exhaled breath to escape into the environment.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention relates to the technical field of mine intelligent ventilation, and discloses a mine intelligent ventilation system which comprises a mine ventilation environment parameter monitoring system, an air door and air window automation and remote control system, a local ventilator intelligent control system, a main ventilator intelligent monitoring system and an analysis decision platform. In the mine ventilation environment parameter monitoring system, according to the principle of mine ventilation resistance measurement, a ventilation route of a mine is selected, multi-parameter sensors are arranged at nodes, air pressure, temperature, humidity, wind speed and wind quantity at the starting end and the tail end of each roadway of the mine are monitored in real time, and node air density is calculated. According to the intelligent ventilation system for the mine, real-time wind measurement improves the wind measurement precision and frequency of the mine and reduces the workload of manual wind quantity detection, meanwhile, the number of wind measurement staff can be properly reduced, manual inspection tour and unattended operation of wind measurement work are achieved, and therefore expenditure is reduced.

The invention relates to an intelligent ventilation system in a tunnel and a control method thereof, and belongs to the technical field of tunnel ventilation. The intelligent ventilation system in the tunnel comprises a data acquisition unit, a ventilation unit, a control unit, a data storage unit and a data analysis unit, wherein the data acquisition units are mounted in detection sections in a tunnel, and each data acquisition unit comprises a COVI detector, a wind speed and direction detector and a traffic flow detection module; when the CO concentration and the VI exceed threshold values, the ventilation unit is controlled to enter a corresponding working mode; the data analysis unit is used for generating an early warning model according to the data of the data storage unit, and the early warning model is used for obtaining CO concentration and VI data in the future # imgabs0 # time according to the wind speed and wind direction data, the traffic flow, the vehicle type, the fuel oil type data, the CO concentration and the VI change. According to the ventilation system, intervention can be conducted before the pollution concentration is increased, the period of insufficient ventilation is effectively avoided, and it is guaranteed that good air quality is always kept in the tunnel.

The invention relates to the technical field of mine intelligent ventilation and poison filtration, and discloses a mine intelligent ventilation and poison filtration dynamic regulation and control method and system.The mine intelligent ventilation and poison filtration dynamic regulation and control method comprises the steps that a neural physical hybrid model is constructed, physical constraint conditions are combined with a deep neural network, and mine airflow field distribution is predicted; decomposing the neural physical hybrid model into three hierarchical sub-models, and deploying the three hierarchical sub-models to corresponding computing nodes to realize a hierarchical computing architecture; establishing a multi-level cooperation mechanism based on federal learning, and realizing distributed model training and optimization; constructing a task allocation decision model, and dynamically determining an optimal execution hierarchy of an airflow field calculation task according to task characteristics, resource states and real-time requirements; dynamically adjusting the precision and performance balance of airflow field calculation; according to the method, the key technical problems in the mine ventilation and poison filtration system are solved through the neural physical hybrid model, the multi-level distributed computing architecture and the federal learning collaboration mechanism.

The present invention discloses an air purifier, belonging to the technical field of ventilation devices. The air purifier comprises: a shell, an air inlet is provided at the lower end of the shell, an air inlet grid is provided on the air inlet, an air outlet is provided at the upper end of the shell, an air outlet grid is provided on the air outlet; a fan is provided in the shell; a lighting mechanism is provided at the lower end of the shell; a filter is provided in the shell; a disinfection mechanism is provided in the shell, and the disinfection mechanism is used to disinfect the air inside the shell. Dust and harmful substances will remain in the device after being purified by the air purifier. The disinfection mechanism automatically disinfects and sterilizes the internal space of the device after the air purification work is completed, which can replace the user to disassemble the device for manual disinfection. On the one hand, it reduces the user's operating burden, and on the other hand, it avoids the user from contacting with harmful substances, thereby ensuring the safety of the user.

The invention relates to the technical field of vehicle safety monitoring, and discloses a non-contact type in-vehicle driver respiratory rate detection method and device. Physiological signal data are collected through a millimeter wave radar, infrared thermal imaging and a microphone array, breathing frequency band signals are extracted through self-adaptive wavelet transform, and breathing signal waveforms are reconstructed through time-frequency joint analysis. A model formed by combining a multi-channel convolutional neural network and a bidirectional gating circulation unit is adopted to extract a respiratory rhythm feature vector, interference is eliminated based on a dynamic time warping algorithm, and a respiratory frequency value is estimated by applying methods such as variational mode decomposition. And processing abnormal points and missing data through a sliding window mechanism, and outputting a stable monitoring result. The invention further provides a sensor data synchronous calibration and model online updating method. According to the invention, the breathing frequency can be detected with high precision in a complex in-vehicle environment, and the safety and intelligent level of the vehicle are improved.

A method for treating Chronic Obstructive Pulmonary Disease (COPD) or chronic bronchitis to alleviate the discomforts of breathing by using non-thermal electroporation energy to ablate diseased portions of the lung including the bronchus, airways and alveoli which, in effect, opens the restrictive diseased portions thereby maximizing the overall surface area thereof causing improved airflow and uninhibited breathing.

A ventilation system includes: a housing including a first inlet drawing outdoor into the housing, a second inlet drawing room air into the housing, a first outlet discharging outdoor air into an indoor space, and a second outlet discharging room air to an outdoor space; a total heat exchanger exchanging heat between the outdoor air and the room air; a first heat exchanger between the first inlet and the total heat exchanger; a second heat exchanger between the first inlet and the first heat exchanger; a compressor supplying a refrigerant to the first heat exchanger and the second heat exchanger; a four-way valve switching a flow direction of the refrigerant; a first expansion device at an inlet of the first heat exchanger expanding the refrigerant; a second expansion device between the first heat exchanger and the second heat exchanger and expanding the refrigerant; and at least one processor.