534 results about "Warning system" patented technology

The invention discloses an earth and rockfill dam seepage-deformation early warning method and system based on time-space combined anomaly, and belongs to the field of dam body safety data research. The method comprises the following steps: constructing a spatio-temporal topological graph based on an engineering coordinate system, integrating multi-dimensional data by nodes, and constructing a dynamic adjacency matrix according to spatial distance and seepage relevance; extracting features by using a space-time diagram convolutional network, a self-loop mechanism and cross-layer attention; and executing dual-drive early warning through standard threshold preliminary screening, multi-scale LSTM prediction and a time decay evidence theory. The system comprises a sensor network and an intelligent computing module, and the intelligent computing module has adaptive modeling and visualization functions. According to the scheme, seepage-deformation space-time correlation quantitative analysis is achieved, the hysteresis effect is captured, the threshold value is dynamically corrected, multi-source evidences are fused, the early warning timeliness and accuracy are improved, and the risk of false alarm and missing alarm is reduced.

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention relates to the technical field of fire early warning, in particular to an early fire early warning system based on AI image recognition, and the system comprises an image collection module which is used for obtaining the video stream data of a monitoring area in real time; an AI image analysis module which is in communication connection with the image acquisition module and is used for receiving the video stream data and carrying out real-time analysis on video frames based on a pre-trained fire identification model so as to extract visual features related to the fire; and the early warning judgment module is in communication connection with the AI image analysis module and is used for receiving an analysis result of the visual features. According to the early fire early warning system and method based on AI image recognition, through video image analysis, the system can recognize weak flame or smoke characteristics at the initial stage of a fire and when naked eyes do not obviously see the characteristics, and the delay problem that a traditional smoke-sensing and temperature-sensing detector needs to wait for physical parameters to be diffused to the detector to be triggered is solved.

The invention discloses an air conditioner energy consumption adaptive management system and method based on dynamic feature selection, and relates to the technical field of air conditioner energy consumption management, and the method comprises the steps: building a system energy consumption digital twin model as a theoretical optimal energy consumption baseline; operating parameters are collected in real time, and key parameter subsets are screened through working condition recognition and feature importance dynamic evaluation; inputting the key parameters into the model to obtain theoretical energy consumption, and comparing the theoretical energy consumption with a measured value to generate an energy consumption deviation rate; smooth processing is carried out on the deviation ratio sequence, recognition is carried out in combination with a dynamic threshold value and various anomaly detection algorithms, and grading early warning is triggered; the system comprises four core modules, namely a digital twinborn model construction module, a key parameter dynamic screening module, an energy consumption deviation calculation module and a grading early warning triggering module. The method can adapt to different working conditions, accurately capture energy consumption abnormities, and effectively improve the intelligent level and accuracy of energy efficiency management of the air conditioning system.

A mobile track safety device (1.1), in particular a track worker warning system, for securing a work area (2) on a track section (3.1, 3.2) comprises a signal processing unit (19) for processing digital information, wherein the signal processing unit (19) has at least one interface (6) for receiving a position of at least one moving hazard object (8, 12), in particular a rail vehicle (12), from a tracking device (13.2, 17), and for receiving a position of at least one moving protection object (8, 16), in particular a track worker (16), from a tracking device (13.2, 17) and for issuing a safety command to secure the work area (2), wherein the signal processing unit (19) is configured to determine a hazard potential by comparing the positions of the at least one hazard object (8, 12) and the at least one protection object (8, 16) and to issue the safety command via the interface (6) depending on the determined hazard potential. A method for securing a work area (2) on a track section (3.1, 3.2), in particular by means of such a track safety device (1.1).

The invention relates to the field of coal mine geological disaster monitoring, in particular to a coal mine surface slope remote sensing data fusion monitoring method and system, and the method comprises the steps: collecting multi-source data through a satellite-unmanned aerial vehicle-ground three-stage network, and carrying out the cleaning, correction and standardization preprocessing; utilizing an improved LSTM model which introduces an attention mechanism and a geological parameter correction item to predict slope displacement and risk levels; distributing monitoring resources through a PSO (Particle Swarm Optimization) algorithm, dynamically adjusting an equipment state through a fuzzy PID (Proportion Integration Differentiation) algorithm, and fusing multi-source data through a Bayesian algorithm to generate a high-dimensional matrix; and finally, storing data based on the alliance chain and performing early warning based on a dynamic threshold value. The system correspondingly comprises six core modules. The problems of narrow coverage, poor timeliness and low data utilization rate of traditional monitoring are solved, millimeter-level precision monitoring is realized, the early warning response is less than or equal to 5 minutes, the cost is reduced by more than 40%, and the method is suitable for safety and ecological monitoring of various coal mine slopes.

The invention provides a sleep apnea real-time early warning system based on heart rate variability analysis, belongs to the technical field of computer data processing, and provides a data processing method which is used for dynamically selecting a monitoring terminal, adaptively adjusting an analysis threshold value based on a sleep situation and carrying out time sequence correlation verification on a heart rate and a sound signal. And the sleep apnea event monitoring accuracy and the system operation efficiency are improved. The monitoring terminals are dynamically selected, the predictive switching strategy is adopted, continuity and high quality of heart rate data streams input into the data processing system are ensured, the data acquisition modes caused by body position changes of the user in the sleep period are switched, subsequent analysis errors are avoided, and the user experience is improved. By determining situation information such as sleeping postures of a user in real time and dynamically adjusting an analysis threshold value of heart rate variability, a data processing model can intelligently adjust the sensitivity of the data processing model according to the risk level, and deep time sequence correlation verification is carried out in combination with heart rate and sound signals, so that collaborative analysis of multi-modal information is realized.

The invention discloses a vehicle blind area dynamic enhancement and warning system for a low-light environment, and relates to the technical field of automobile active safety, and the system comprises a short wave infrared imaging module which is used for capturing a passive thermal feature image of a scene behind the side of a vehicle; the multi-source signal acquisition module is used for acquiring a vehicle bus signal representing a vehicle motion state and an operation intention of a driver in real time; the data processing and risk assessment module is connected with the short wave infrared imaging module and the multi-source signal acquisition module, and is used for performing fusion processing on the thermal feature image and the vehicle bus signal, identifying a blind area target and calculating a dynamic risk level of the blind area target; the grading warning and intervention module is connected with the data processing and risk assessment module; the short-wave infrared imaging technology which is sensitive to night light and high in anti-interference capacity is adopted, the imaging problem under extremely low illumination is fundamentally solved, and stable detection of vehicles and pedestrians in a long distance in a completely light-source-free environment is achieved.

The invention belongs to the field of safety monitoring of wind power equipment, and provides a multi-source data fusion and safety early warning system and method in the hoisting process of a wind turbine generator, and the system comprises a data collection and preprocessing module which collects and preprocesses multi-source sensor data such as a tower inclination angle and a suspension arm inclination angle; the state manifold construction module is used for mapping the preprocessed data to a multi-dimensional space to construct a state manifold and generating an ideal geodesic line; the trajectory analysis and risk assessment module is used for calculating the deviation degree between the real-time state trajectory and the ideal geodesic line and generating a risk score; the dynamic threshold generation module constructs a risk tensor field based on environmental parameters, and extracts a risk threshold curved surface after wind speed adjustment; the early warning decision module is used for comparing the risk score with a threshold curved surface to determine an early warning level and sending information; and the visual display module is used for displaying the state manifold, the track, the tensor field and the early warning information. By introducing a differential geometry theory, state manifold representation and geodesic deviation measurement are constructed, and accurate assessment of the hoisting process risk is realized.

The invention discloses an intelligent fault diagnosis early warning system for mine electromechanical equipment, which comprises a data sensing module used for collecting multi-mode state data of the mine electromechanical equipment and carrying out anti-interference preprocessing, and an edge intelligent module used for receiving the preprocessed data and executing fault diagnosis and dynamic threshold adjustment. The cloud evolution module is used for receiving a diagnosis result and original data and completing self-evolution through data enhancement and model updating, and the application service module provides graded early warning, residual life prediction and operation and maintenance decisions based on the diagnosis result and evolution information; the method effectively captures early weak fault features through multi-modal data deep fusion in combination with a physical mechanism model and a data-driven algorithm, greatly improves the recognition accuracy of major faults, and improves the recognition accuracy of the major faults through deploying a lightweight CNN-LSTM model at an edge end and focusing on model evolution and data enhancement at a cloud end. And collaboration of edge quick response and cloud continuous optimization is realized.

The invention is suitable for the field of intelligent illumination control and energy-saving management, and provides an illumination state monitoring management method and system for energy-saving operation of a subway station, and the method comprises the steps: continuously collecting the electrical characteristics, optical performance parameters, equipment basic attributes and historical operation records of illumination nodes, and carrying out the fusion to form analysis basic data; identifying potential fault risk nodes based on the data; simulating a fault influence by using a digital twin model and comparing the fault influence with a safety standard; and starting preventive optical compensation or compensation-combined graded early warning according to a comparison result. The system correspondingly comprises a parameter acquisition module, a risk analysis module, an influence analysis module and an optical compensation and early warning module. According to the invention, fault advanced pre-judgment, accurate compensation and efficient operation and maintenance are realized, the energy-saving and safe operation requirements are balanced, and the intelligent management level of the illumination system is improved.

The invention provides a personnel safety early warning and prediction method and system based on dynamic perception of an unmanned aerial vehicle. The method comprises the following steps: collecting real-time video data of a construction site through an inspection unmanned aerial vehicle; collecting high-precision pose data of the unmanned aerial vehicle; processing the video data, identifying personnel and construction equipment, and obtaining geographic coordinates of the personnel and the construction equipment; performing risk analysis in combination with the dynamic electronic fence of the construction equipment and the behavior intention prediction result of the personnel, and generating personnel-related safety early warning; the system comprises a data acquisition module, a data processing and identification module, a prediction analysis module, a risk research and judgment module and an early warning execution and feedback module. According to the invention, personnel and equipment high-precision positioning, accurate behavior intention prediction and multi-dimensional risk early warning personnel safety early warning and prediction can be realized.

The invention relates to a multi-factor dynamic correction investment benefit intelligent evaluation system, in particular to the field of investment evaluation, and aims to capture macroscopic event and project risk index impact in real time and dynamically generate factor impact coefficients through an event-driven factor correction module, so that the response speed and sensitivity of a model to project sudden change are remarkably improved, and the evaluation efficiency is improved. The self-adaptive weight optimization module utilizes a deep reinforcement learning framework to continuously optimize factor weight distribution based on revenue after risk adjustment in a rolling time window so as to realize self-adaptive adjustment of an investment strategy and optimization of a revenue-risk ratio, and the abnormal conduction early warning module constructs a dynamic directed graph and combines a graph neural network technology to realize early warning of the abnormal conduction. According to the method, abnormal factor conduction paths can be perspectively recognized, systematic engineering risks can be effectively warned, a strategy execution module fuses weight optimization results and risk warning signals, rebalance is executed under the condition that investment cost and risk control constraints are strictly met, and finally a project configuration scheme giving consideration to revenue, robustness and real-time performance is generated.

A warning system includes one or more processors configured to execute area detection processing of detecting an area of interest that is potentially unsafe for a user in a real space, execute distance determination processing of determining a distance between the user and the area of interest, and execute vibration generation processing of generating vibration that causes the user to sense a force in a direction away from the area of interest on a basis of a positional relationship between the user and the area of interest in a case where determination is made that the distance between the user and the area of interest is less than a threshold value.

The invention discloses an intelligent adjusting method and a control system for a windscreen wiper. The intelligent adjusting method comprises the following steps: acquiring real-time rainfall, visibility, vehicle speed, glass pollutant type and scraper abrasion loss data; inputting a windscreen wiper control decision model to output wiping frequency and strength; after executing the action, comparing with a glass definition correction model; and monitoring windscreen wiper part data to generate fault early warning. The system comprises a multi-source data acquisition module, an intelligent decision adjustment module, a self-adaptive optimization module and a fault monitoring and early warning module. Through multi-source data linkage and dynamic model correction, the problem that in the prior art, the wiping effect is poor due to the fact that wiper blade abrasion and pollutant types are ignored is solved, and the adaptability and safety of the windscreen wiper are improved.

The invention discloses a flight safety warning method based on pilot operation intention recognition, which comprises the following steps: S1, collecting EEG data of a subject in each stage of simulated flight through an electroencephalogram signal data collection device; s2, carrying out preprocessing operation on the collected EEG data; s3, extracting and fusing the electroencephalogram signals by constructing a convolutional gradient attention network, and mapping fused features to an operation intention of a pilot; s4, calculating safety risk factors according to the deviation between the flight state and the preset flight envelope, the pilot operation intention and the external environment factors; s5, comparing the risk factor with a preset risk threshold, and dividing the risk into a high level, a middle level and a low level; and S6, the cockpit touch alarm system triggers multi-mode alarm in the cockpit according to the risk level, and adjusts the flight state in real time. The flight accident risk caused by human factors can be reduced, and the aviation safety is improved.

The invention relates to the technical field of fire early warning, in particular to a fire early warning system based on the Internet of Things, which comprises a fire early warning center, a data acquisition unit, a fire distinguishing unit, a node verification unit, a radiation verification unit, a fire visual unit and an early warning visual unit, according to the invention, the environment information is collected through a plurality of fire monitoring devices, the comprehensive coverage and real-time monitoring of a target area are realized, the fire hazard can be found in time, the accuracy of fire judgment is improved, the situations of false alarm and missing alarm are reduced, and meanwhile, the fire authenticity analysis of two dimensions of node request verification and optical radiation verification is accompanied, so that the accuracy of fire judgment is improved. The method is advantaged in that misjudgment caused by a single factor is reduced, fire hazard early warning accuracy is further improved, information feedback visual analysis is carried out from angles of fire hazard diffusion paths, diffusion grades and the like, information of fire hazard diffusion directions, fire hazard positions and the like can be visually known based on the acquired visual fire hazard model, and targeted fire hazard emergency response decision making is further facilitated.

An elevator system having: a hoistway; an elevator car movable within the hoistway; a controller operationally coupled to the elevator car; a counterweight coupled to the elevator car and movable within the hoistway; and an approaching-hazard-warning-system that has: a warning system controller; a range finding sensor, operationally coupled to the warning system controller, and mounted to one of the elevator car, the counterweight and the hoistway wall, wherein, while operating in an inspection mode, the warning system controller is configured to receive sensor data from the range finding sensor, detect when a distance between the elevator car and the counterweight is less than a stop distance while the elevator car and the counterweight are moving toward each other, and execute a first responsive action including one or more of slowing the elevator car by communicating with the car controller, stopping the elevator car and sounding a first audible alert.

The application relates to the technical field of garbage disposal, and discloses a warning system and method for a garbage drop-off port, which comprises a collection unit, a judgment unit and a processing unit, wherein the collection unit collects image data of the garbage drop-off port, obtains a usage area proportion of the garbage drop-off port, and judges whether the garbage is full; the judgment unit collects weight data of the garbage bin, judges whether the garbage is overweight; when the garbage is overweight, the processing unit determines a cleaning priority level according to the overweight data; when it is judged that the garbage is not overweight, the collection unit collects garbage proportion data in the garbage bin to judge whether misjudgment occurs; when it is judged that misjudgment occurs, congestion early warning is carried out; and the processing unit collects temperature data and rotten matter proportion in the garbage bin to judge whether the cleaning priority level is adjusted. The application realizes accurate monitoring and management of the garbage drop-off port, improves the efficiency and quality of garbage disposal, and solves the problems existing in the traditional garbage disposal mode.

The gynecological urethral catheterization pain management integrated workstation comprises a main control terminal, a self-adaptive urethral catheterization support with force feedback, a three-dimensional navigation module, a consumable management module and a complication early warning system. The main control terminal is connected with the self-adaptive urinary catheterization support, the three-dimensional navigation module, the consumable management module and the complication early warning system through a network. By means of the AR dynamic navigation and pressure feedback linkage function, the operation time is effectively shortened, the operation efficiency is improved, and the qualified rate of independent operation of new nurses is increased. In the aspect of pain management, the individualized NRS evaluation module evaluates the pain, timely intervention is performed, and the pain of the patient is reduced. The complication prevention and control effect is obvious, the damage to the urethra is reduced, and the urinary tract infection incidence rate is reduced. Meanwhile, complete tracing of consumables is achieved through the RFID consumable management and electronic medical record docking function, and data support is provided for continuous improvement of nursing quality.

The invention is applied to the technical field of data processing and computer system monitoring, and discloses a data warehouse monitoring and warning method, which comprises the following steps of: configuring a baseline to monitor and warn a task, configuring a warning rule to monitor and warn the task, configuring data quality monitoring to monitor and warn, and warning through a telephone warning system. According to the data warehouse monitoring alarm method, aiming at the problem of alarm delay existing in a traditional scheme, the problem is solved through a multi-dimensional trigger mechanism: an early warning allowance is set by baseline monitoring, early warning is triggered in advance before a task is close to timeout, and a task alarm rule responds to key nodes such as'errors after automatic rerun 'in real time; data quality monitoring immediately triggers detection after a task is successfully executed, abnormal data is ensured to be found in time, a telephone alarm system is linked with a trigger through file monitoring, rapid transmission of abnormal information and a multi-link real-time triggering mechanism are achieved, and timely processing of the data warehouse task is ensured.

This document describes systems and methods for indicating to a user equipment (UE) that a public warning system (PWS) is to be, and is being broadcasted, and for the UE to acquire the PWS broadcast.

The invention belongs to the technical field of medical monitoring and early warning, and relates to an electroencephalogram early warning system for central nervous adverse events in an acupuncture treatment course, and the system comprises an appliance integration and time synchronization association module which is used for synchronously generating an operation mark signal and a skin conductance signal; the reference state vector construction module and the safety stress vector construction module are respectively used for establishing an individualized physiological baseline vector and a safety stress limit vector; the real-time state vector generation module is used for generating a real-time physiological state vector; the dynamic weight adjusting module is used for generating a correction vector; the analysis and risk judgment module is used for analyzing whether the state evolution trajectory meets a risk condition or not; the grading early warning execution module and the intervention instruction triggering module are used for executing grading early warning and closed-loop physical intervention; the technical problems that in an existing acupuncture early warning method, operation and physiological signal time sequence correlation is weak, and multi-system collaborative risk evolution is difficult to comprehensively capture through single-dimension monitoring are solved.

A heatstroke risk notification system that enables quick and effective responses tailored to the situation. [Solution] The heatstroke risk notification system comprises a monitored terminal that acquires location information and at least one of illuminance, ultraviolet intensity, external temperature, and internal temperature to determine the heatstroke risk and provide notification according to the risk level; a guardian terminal that receives and displays the risk level; and a server that relays the risk level between the monitored terminal and the guardian terminal. The monitored terminal includes a location acquisition unit, an environment acquisition unit, a determination unit, and a notification unit. The guardian terminal program receives and displays the risk level. The server device receives and transmits the risk level.

The solution presented in the invention relates to a loudspeaker unit (E) for a vehicle, for example as part of a vehicle acoustic warning system, having a loudspeaker housing (1) and a loudspeaker (2) accommodated in an inner cavity (123) of the loudspeaker housing (1), wherein the loudspeaker housing (1) has a first housing wall (10) to which the loudspeaker (2) is fixed on the inner side facing the inner cavity (123). For example, the first housing wall (10) is provided to be air-permeable in order to transmit the sound generated by the loudspeaker (2) outwards into the ambient space surrounding the loudspeaker housing (1), for which purpose the first housing wall has a gas-permeable grid area (100) comprising a plurality of channels (1003) each in cross section Z-shaped or S-shaped for transmitting sound.

Methods, systems, and devices for wireless communications are described. Generally, the described techniques may enable a user equipment (UE) to receive warning notifications while connected to a narrowband internet-of-things (NB-IoT) network. In some examples, the UE may periodically tune a radio frequency chain of the UE to another network that supports a public warning system to monitor for one or more paging messages, the one or more warning notifications, or both. In some cases, the UE may periodically tune to the other network entity in accordance with a sleep mode of the UE. Additionally, or alternatively, the NB-IoT network may transmit the one or more paging messages to the UE, and the UE may tune to the other network entity to receive the one or more warning notifications based on receiving the one or more paging messages.

The invention discloses a decompression sickness early warning research system and method based on an ultrasound contrast quantitative technology, and relates to the technical field of diving medicine monitoring, the decompression sickness early warning research system comprises an ultrasound detection unit, a control processing and early warning unit and a debugging control unit, the ultrasound detection unit and the control processing and early warning unit are underwater wearing parts, and the debugging control unit is an underwater wearing part. The debugging control unit is an overwater intelligent terminal part, the underwater wearing part is connected with the overwater intelligent terminal part through wireless signals, and the ultrasonic detection unit is composed of an ultrasonic transducer, a front-end module, an FPGA signal processing module and a communication interface module. By installing the ultrasonic detection unit, the function of improving the detection capacity is achieved, micro bubbles can be detected through the ultrasonic contrast technology, compared with traditional Doppler ultrasound and two-dimensional ultrasound, the detection sensitivity is greatly improved, the bubble forming trend can be found earlier, and the onset risk can be avoided earlier.

The present application relates to a kind of around rail type surgical auxiliary system and method.The system at least includes the annular floor track arranged around operating table.Multiple degree of freedom mechanical arm moves along annular floor track and is configured to perform the freedom operation in horizontal and / or vertical plane.Multiple degree of freedom mechanical arm is instructed to move along the annular floor track and is instructed to drive to complete the movement of several degrees of freedom.The present application improves the accuracy of risk identification in the process of surgery by the detection warning of multiple degree of freedom mechanical arm in around rail type surgical auxiliary system and the active warning of medical staff.Around rail type surgical auxiliary system is used for the taking and arrangement of surgical instrument on the one hand, and is used for recording the whole process of surgery on the other hand, and the relevant surgical data recorded is identified and judged, so as to form multi-direction, multi-angle warning system in operating room.

The proposed solution relates to a computer-implemented method for outputting an amplified audio output signal (S2) using an amplification unit (1) for an acoustic vehicle warning system (100) to generate sound audible from persons in the vicinity of a vehicle (200). The method involves receiving an audio input signal (S1) and a temperature value (T) from a temperature sensing unit (20). A control unit (10) of the amplification unit (1) adjusts the amplification of the audio input signal (S1), taking into account at least one temperature reduction measure depending on the temperature value (T), in order to generate the audio output signal (S2). According to the proposed solution, the temperature reduction measure comprises at least one of i.a. adjusting the audio input signal (S1) with respect to at least one frequency (F1) of the audio input signal (S1) while maintaining or increasing the gain of at least one other frequency (F2) of the audio input signal (S1), ii. reducing an operating frequency of the amplification unit (1), and iii. adding at least one frequency (F3) to the audio output signal (S2) that is not included in the audio input signal (S1).