59 results about "Sensor selection" patented technology

The invention relates to the technical field of brushless motor control systems, and discloses an intelligent redundant brushless motor Hall position correction system, which comprises a multi-sensor redundant layout module used for arranging multiple types of sensors, and each type of sensor provides rotor position information; and the data processing and fusion module is used for receiving and processing the data from the plurality of sensors, removing noise through filtering and denoising algorithms, and outputting an accurate rotor position estimation value. According to the invention, the problem of rotor position error caused by external interference is solved through multi-sensor redundancy layout and a Kalman filtering fusion algorithm; an intelligent fault detection and switching mechanism is introduced, and the fault tolerance capability and the emergency response capability are improved through a standby sensor selection algorithm; and real-time monitoring, remote diagnosis, fault early warning and parameter adjustment of the equipment are realized through cloud platform data transmission and remote monitoring technologies.

A system for predictive queue management of a monitored area, including a controller having a processor and sensors installed within a connected lighting system at optimal locations, is provided. The optimal locations are generated by a sensor selection model based on selection training data and potential sensor locations. The sensors capture optimized sensor data corresponding to 2024 / 037908 individuals in the monitored area, and may include PIR sensors, SPT sensors, and / or RF sensors. The processor then generates, based on the optimized data and a forecasting model, a queue volume prediction including a number of individuals that will need a service in the monitored area during a predetermined future time window. The processor then generates, based on recommender inputs including at least the queue volume prediction, a recommendation including a number of queues needed to process the queue volume prediction.

The invention relates to the technical field of data processing, and discloses a deep foundation pit monitoring data acquisition method and system. The method comprises the following steps: arranging sensors according to three depth levels of a pit top, a pit wall and a pit bottom of the deep foundation pit to obtain grouped data; calculating foundation pit deformation gradient, supporting structure stress, soil layer stability and underground water seepage parameters, and extracting space weight data; adopting a deep foundation pit space layering Gaussian hybrid clustering algorithm to screen representative monitoring sensors; inputting representative sensor data into the deep foundation pit long short-term memory network to predict foundation pit deformation; and dynamically adjusting the monitoring data acquisition frequency through game theory weight distribution and cloud model evaluation, and generating a data acquisition control instruction. The technical problems that in deep foundation pit monitoring, the data collection efficiency is low, representative sensor selection lacks scientific basis, and misjudgment is prone to occurring in single-index early warning are solved, and the intelligent level and prediction precision of deep foundation pit monitoring data collection are improved.

The present invention provides an avionics device and a signal processing method based on high-precision inertial navigation, which relate to the field of aviation navigation control technology. The invention uses environmental complexity assessment to drive sensor selection, optimizes the acquisition quality of inertial data such as angular velocity and acceleration, and pixel coordinates, and optical data such as specific data in real time, reduces redundancy, utilizes spectral decomposition and adaptive filtering, dynamically cleans signals for environmental noise in different frequency bands, retains key features, uses spatiotemporal feature reconstruction to generate high-dimensional feature vectors, enhances the expressiveness of low-quality data, dynamically adjusts fusion weights based on signal-to-noise ratio and consistency index, combines predictive error correction to eliminate long-term drift, ensures processing delay is less than 10ms with hardware support, and maintains data stability through anti-interference shielding. The invention breaks through the dependence of traditional navigation on external signals, realizes long-term high-precision navigation, is particularly suitable for aviation covert missions and civil aviation flights in severe weather, and improves mission success rate and safety.

1. The name of the design product: digital time and tooth type selection graphical user interface of electronic equipment. 2. The use of the design product: for an electronic device. 3. The design points of the design product: the interface content of the graphical user interface in the screen. 4. The picture or photo that best indicates the design points: front view. 5. The rear view, left view, right view, top view, and bottom view are conventional designs, and the rear view, left view, right view, top view, and bottom view are omitted. 6. The use of the graphical user interface: for adult and minor tooth type display, adult and minor selection, exposure film or sensor selection, time display, operation display, and power display. The front view is the initial interface.

The invention discloses a base station of a robot and a robot system. The base station of the robot comprises a base station main body; according to the in-place detection assembly, a pedal is movably arranged on the upper side of a base plate and rotatably connected with the base plate, and at least part of the pedal extends out of the upper side of a base station body; one of the sensor and the fitting piece is arranged on the pedal, the other one is arranged on the base station main body, and the fitting piece and the sensor are selectively aligned or staggered. Therefore, the pedal is movably arranged on the upper side of the base plate and rotatably connected with the base plate, and when the robot reaches the base station body and drives the pedal to rotate downwards around the rear end, the matching piece and the sensor which are originally aligned with each other are staggered, or the matching piece and the sensor which are originally staggered with each other are aligned with each other; therefore, the judgment of the base station on the in-place of the robot is more accurate, and the in-place judgment principle is simpler and more direct.

The invention relates to the technical field of transformer fault diagnosis, in particular to a device and a method for detecting dissolved gas in oil. The device comprises a detection sensor, a signal conversion module and a diagnosis module. Compared with the prior art, the performance bottleneck of a traditional gas sensor is fundamentally broken through through an original cantilever beam double-sided heterogeneous MOFs modification structure and an in-situ growth process: the in-situ growth process ensures firm combination of a sensitive coating and a substrate, and the defects that the coating is easy to fall off and poor in uniformity in a traditional coating method are remarkably overcome; a reliable hardware basis is provided for long-term online monitoring; and a double-sided heterogeneous modification strategy enables a single sensing unit to generate collaborative sensing and differential response to various fault gases, so that the core problems of poor selectivity and serious cross interference of a traditional sensor are effectively solved.

The invention discloses a power transformer fault automatic identification and grade determination method, and relates to the technical field of power system equipment state monitoring and fault diagnosis, and the method comprises the following steps: 1, carrying out the cross-domain fusion of multi-modal signal collection, including sensor type selection and layout, time-space synchronous triggering and signal transmission; 2, constructing a transfer learning framework for cross-field coupling feature mining, wherein the transfer learning framework comprises digital twin model construction, transfer learning algorithm optimization and coupling feature modulation and demodulation; according to the invention, through cross-domain fusion of multi-modal signal acquisition, cross-field coupling information is comprehensively reserved; coupling features are accurately mined based on a digital twin model and transfer learning optimization; the hidden fault recognition accuracy and the grade judgment adaptability are improved through an attention enhancement framework; the limitation of traditional single physical field analysis is broken through on the whole, the cost is reduced, meanwhile, early warning and full-life-cycle accurate management and control of various hidden faults are achieved, and support is provided for power grid safety.

A controller-configurable input signal conditioning (C-CISC) circuit includes an input node, a first switch, a second switch, and a multiplexer which are operatively coupled with an output pin of a microcontroller. First and second signal conditioning branches of the C-CISC circuit are operatively coupled with an input node and a sensor signal input pin of the microcontroller controller via a first input and a second input of a multiplexer. The C-CISC circuit is configurable in response to a first output of the sensor selection output pin to provide an analog signal input received at the input node to the sensor signal input pin and configurable in response to a second output of the sensor selection output pin to provide a single edge nibble transmission (SENT) signal input received at the input node to the sensor signal input pin.

The present invention provides a two-layer dynamic information update method and product for energy harvesting networks, which relates to the field of edge computing technology. The technical solution provided by the embodiment of the present invention can realize the internal and external two-layer dynamic transmission and power adjustment mechanism of sensor group scheduling and intra-group sensor scheduling based on KKT (Karush-Kuhn-Tucker) conditions, and achieves a significant improvement in system performance while ensuring the quality of updated data. The strategy has the following advantages: (1) Accurate sensor selection mechanism: Establish a multi-dimensional evaluation model, comprehensively consider indicators such as sensor energy status, channel quality, and data importance, and select the optimal sensor combination through KKT condition optimization to ensure maximum system update efficiency; (2) Efficient power adjustment strategy: Dynamically adjust the transmission power to adapt to real-time changes in channel conditions and energy supply to achieve optimal allocation of energy resources and avoid ineffective energy consumption; (3) Significant performance improvement: Effectively reduce the number of invalid updates, significantly improve update efficiency, and maximize the overall system performance while ensuring data quality and AoI requirements.

The invention relates to the field of equipment control, in particular to a control method of a multi-fan array. The method comprises the steps that fan impeller angular displacement signals are collected through a displacement sensor array and converted into digital input signals, differential processing is conducted on the signals, a phase deviation value is calculated, a pulse width modulation coefficient is generated, PWM control parameters are synchronously transmitted through an EtherCAT bus to drive the direct-current brushless fan, a closed-loop feedback mechanism is established, and a master-slave type topological structure is adopted. The method also comprises the technical means of displacement sensor selection, bus setting, dynamic compensation algorithm, main control unit configuration, redundancy fault tolerance, topology identification, environmental parameter compensation, energy recovery, coding variable bit width and the like. According to the invention, the synchronization error lt of more than multiple fans is realized; the anti-interference capability, the time synchronization precision and the fault switching speed are improved, and the technical effects of self-adaptive load balancing, wind speed stable control, reduction of the overall power consumption of the system, self-adaptive adjustment of the data throughput and the like are achieved.

In some aspects, a gesture tracking device may receive availability information from a sensor system including a plurality of sensors based on a current operating condition associated with the plurality of sensors. The attitude tracking device may select a set of sensor modalities associated with the sensor system based on the availability information. The gesture tracking device may select a gesture tracking model based on the selected set of sensor modalities and one or more key performance indicator (KPI) requirements related to a current context associated with a gesture tracking configuration of the client application. The attitude tracking device may estimate an attitude associated with the object using an attitude tracking model based on sensor inputs associated with one or more sensors selected from the plurality of sensors. Numerous other aspects are described.

The invention relates to the technical field of safety monitoring, in particular to a toilet fall detection system based on multi-sensor fusion. Comprising a data processing unit, an alarm module, a millimeter wave radar module mounted on a ceiling of a toilet, an infrared sensor mounted on a wall surface of the toilet, and a piezoelectric vibration sensor arranged at the bottom of a wash basin of the toilet, the data processing unit is electrically connected with the millimeter wave radar module, the alarm module, the infrared proximity sensor and the piezoelectric vibration sensor. The problems that the false alarm rate is high due to the fact that an existing single-sensor tumble detection scheme cannot effectively distinguish normal low-posture actions such as tumble and squatting and kneeling in a bathroom scene, and installation is complex and reliability is poor due to the fact that a sensor type selection or installation mode is not suitable for the environment that the bathroom is humid and the space is limited are solved.

The invention discloses a charging device and a base station of a robot. The charging device comprises a charging arm; the in-place detection assembly comprises a touch part, a sensor and a matching part, one end of the touch part is rotatably arranged in the charging arm, and at least part of the touch part extends out of the charging arm to be used for abutting against the robot; one of the sensor and the matching piece is arranged on the touch piece, the other one is arranged on the charging arm, and the matching piece and the sensor are selectively aligned or staggered. Therefore, one end of the touch piece is rotatably arranged in the charging arm, and when the robot reaches the base station main body, abuts against the touch piece and drives the touch piece to rotate, the matching piece and the sensor which are originally aligned with each other are staggered with each other, or the matching piece and the sensor which are originally staggered with each other are aligned with each other; therefore, the in-place judgment of the robot by the charging arm can be more accurate, and the in-place judgment principle is simpler and more direct.

This application relates to the field of data processing technology and discloses a method and system for acquiring monitoring data in deep foundation pits. The method includes: deploying sensors at three depth levels—the top, walls, and bottom of the foundation pit—to obtain grouped data; calculating the foundation pit deformation gradient, support structure stress, soil stability, and groundwater seepage parameters, and extracting spatial weight data; using a spatially layered Gaussian mixture clustering algorithm to select representative monitoring sensors; inputting the representative sensor data into a long short-term memory network (LSTM) to predict foundation pit deformation; and dynamically adjusting the monitoring data acquisition frequency through game theory weight allocation and cloud model evaluation to generate data acquisition control commands. This application solves the technical problems of low data acquisition efficiency, lack of scientific basis for representative sensor selection, and susceptibility to misjudgment in single-index early warning systems in deep foundation pit monitoring, thereby improving the intelligence level and prediction accuracy of deep foundation pit monitoring data acquisition.

This invention relates to an advanced method for detecting and analyzing scent profiles using an intelligent gas sensor array system. The method involves acquiring an initial data set from a sensor array exposed to a gas mixture, followed by performing a coarse analysis to determine a superset of scent profiles, which includes various subset scent profiles within the gas mixture. Based on this preliminary analysis, a set of refinement parameters is established, leading to the adjustment of operational parameters of the sensor array. These parameters include sampling rate, duration of sensing, sensor selection, heating profile, and energy feed to selected sensors, which are tailored to enhance the specificity and accuracy of the subsequent analysis. The method further includes acquiring an expanded data set under these optimized conditions, allowing for a refined analysis of the gas mixture. This refined analysis is specifically designed to accurately identify the presence of target scent profiles, utilizing the enhanced sensitivity and specificity provided by the adjusted sensor array settings.

In predictive maintenance performed by analyzing observation data that is time-series data acquired from a plurality of sensors, it is possible to detect a sign of occurrence of an abnormality by invariant analysis not only when a relationship between sensors shows linearity but also when the relationship shows nonlinearity.SOLUTION: The abnormality predictor detection system and the like acquire observation data of each of a plurality of sensors, select, for each of the plurality of sensors, a pair having the strongest causal relationship based on the observation data of each of the plurality of sensors among pairs obtained by combining each of the plurality of sensors with another sensor, generate, for each of the pairs selected for each of the plurality of sensors, a covariant relationship model indicating a relationship between a pair of sensors constituting the pair and evaluating collapse of the relationship from a normal state, and detect collapse of the relationship in a normal state between the pair of sensors constituting the pair based on the covariant relationship model.SELECTED DRAWING: Figure 7

A system for creating a virtual field device is provided. The system includes a processor having memory that when executed causes the processor to provide a sensor selection user interface element configured to receive user selection of at least one process sensor providing high-speed, unfiltered measurement data. The processor also provides a processing selection user interface element configured to receive user selection of at least one processing action to perform on the high-speed, unfiltered measurement data to generate a process output. The processor also provides an output selection user interface element configured to receive user selection of at least one remote device to which the process output is communicated. A computer-implemented method of creating a virtual field device is also provided.

The present invention provides a defect inspection system with which it is possible to accurately inspect a defect in an inspection object. A defect inspection system according to the present invention comprises: a camera (100) for capturing an image of an inspection object (101); a database (500); and a computer (120) provided with a defect screening unit (104), a sensor selection unit (107), and a measurement position calculation unit (108). In the database (500), the sensor used in the inspection of a defect in the inspection object (101) is stored together with characteristic data pertaining to the sensor in accordance with the type of the defect. The defect screening unit (104) obtains, from the image captured by the camera (100), the type of the defect and the occurrence range of the defect in the inspection object (101). The sensor selection unit (107) refers to the database (500) and selects a sensor to be used for inspection in accordance with the type of the defect. The measurement position calculation unit (108) determines, for the selected sensor, a measurement start position and a measurement end position for the inspection on the basis of the characteristic data and the occurrence range of the defect.

A system for testing a sensor and a hub is provided. The system includes a testing control device and a sensor testing device. The testing control device is configured to select a testing profile based at least in part on the sensor, and transmit a testing initiation signal based at least in part on the testing profile. The sensor testing device is configured to perform testing of the sensor and the hub in response to the testing initiation signal, the testing including rotating the sensor along at least one axis based at least in part on the testing profile, and transmit test data to the testing control device based at least in part on the testing of the sensor and the hub. The testing control device is configured to receive the test data from the sensor testing device.

The invention discloses a scene-based sensor type selection method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a verification risk level corresponding to a target scene feature of a vehicle and obtaining characteristic parameters of each type of candidate sensors; selecting at least one group of candidate sensor combination in each candidate sensor based on a preset redundancy range and each characteristic parameter; determining a comprehensive utility index corresponding to each candidate sensor combination, and determining the candidate sensor combination with the maximum comprehensive utility index as a candidate sensor combination to be detected; and verifying the to-be-tested candidate sensor combination according to a verification configuration set corresponding to the verification risk level, and determining the verified to-be-tested candidate sensor combination as a target sensor combination of the vehicle. According to the invention, the problem of cost waste caused by sensor redundancy in the traditional scheme is solved, and the scene adaptability and reliability of the target sensor combination in practical application are enhanced.

To provide a defect inspection system capable of precisely inspecting a defect of an inspection object.SOLUTION: A defect inspection system comprises: a camera 100 for capturing an image of an inspection object 101; a database 500; and a computer 120 having a defect cleaning section 104, a sensor selection section 107, and a measurement position calculation section 108. In the database 500, a sensor used for inspection of a defect of the inspection object 101 is stored along with characteristic data of the sensor according to the type of the defect. The defect screening section 104 obtains the type of the defect and an occurrence range of the defect in the inspection object 101 from the image captured by the camera 100. The sensor selection section 107 refers to the database 500 to select a sensor used for inspection according to the type of the defect. The measurement position calculation section 108 obtains a measurement start position and a measurement ending position in the inspection on the basis of characteristic data and the occurrence range of the defect for the selected sensor.SELECTED DRAWING: Figure 1

Sensor diagnostics for in-cell touch screen controllers are disclosed. Apparatuses, methods, and systems to detect the presence of faults such as shorts and / or opens within a touch sensitive display panel are described herein. In one implementation, a touch screen controller is disclosed that includes one or more receive channels, where each receive channel is configured to scan a corresponding group of sensors of a plurality of sensors. The touch screen controller can also include a plurality of multiplexers, each configured to selectively couple a respective sensor of the plurality of sensors to a corresponding receive channel or a reference voltage. The touch screen controller can also include a processing device configured to detect one or more shorts based on DC current sensing. The touch screen controller can also detect one or more opens based on AC current sensing of each of the one or more receive channels.

The present invention provides a sensor power allocation method and device for real-time drone position and energy optimization. The method includes, based on physical layer security theory, real-time optimization of the drone's spatial position to ensure that more sensors are within the drone's communication coverage, and a sensor selection strategy to ensure that data transmitted by the selected sensors is secure. Subsequently, using energy efficiency as a performance indicator, the selected sensors are allocated power with optimal energy efficiency, enabling the sensors to transmit as much stored sensor data as possible to the drone within limited energy. This method can effectively address data security and energy limitation issues in drone-based sensor data acquisition processes. Through technical means such as real-time drone position optimization, secure sensor selection, and sensor power allocation, the method maximizes sensor energy utilization efficiency while ensuring data transmission security.

The invention relates to the technical field of hydraulic structure monitoring, and discloses an optical fiber-structural body coupling coordination degree evaluation method, which specifically comprises the following steps: step 1, hydraulic structure physical model building and data acquisition: building a hydraulic structure physical model, and burying distributed optical fibers in a key area of the hydraulic structure physical model; the optical fiber strain sensor is connected with the BOTDA demodulator and is used for acquiring optical fiber strain data; and structural body strain and displacement monitoring is carried out by using a DIC. The method not only considers the strain data of the optical fiber, but also introduces the strain data of the structural body, and enables the evaluation of the coupling coordination degree to be more comprehensive and scientific through the time sequence analysis of the data. According to the coupling coordination degree evaluation result, the arrangement position of the optical fiber and the model selection of the sensor are guided, so that the performance of the whole optical fiber monitoring system is optimized, the adaptability and the reliability of the system in a complex environment are improved, and parts which are not involved in the device are the same as those in the prior art or can be realized by adopting the prior art.

The present invention relates to a method, in particular a computer-implemented method, for generating a training data set (TD) based on an input training data set (ITD) containing several input training images (I i ) comprises, which are received from at least one sensor, a computer program and a computer-readable (storage) medium. For each input training image (I i The procedure includes the following steps: - Determining a depth map (DM) that corresponds to the initial training image (I i ) corresponds to, and determining a three-dimensional point cloud (PC) based on the depth map (DM); - Selecting at least one first virtual position (P v ) for the sensor, which differs from a home position (P) corresponding to a sensor position for recording the input training data set (ITD); - Projecting (Proj) the point cloud (PC) onto a virtual image (I) v ), that of the first virtual position (P v ) corresponds; - Determining at least one image area (i) of the virtual image (I v ) with missing or insufficient point cloud (PC) information and replacing at least one specific image area (i) with a substitute image area (i R ), which was generated by using a generative image generation model (IGM); - Obtaining a training data image (I) of the training dataset (TD) by using an image rendering model (RM) based on the virtual image (I) v ).

This invention provides a tokamak magnetic sensor layout optimization design method, comprising: data preparation, constructing a sensor signal matrix and an objective variable matrix; filtering out sensor signals with low signal-to-noise ratio; constructing an optimization objective function of appropriate form; determining the priority selection order of sensors based on the optimization objective function using a first-order incremental selection method; optimizing the hyperparameters in the optimization objective function using a grid search method to determine the final optimization objective function, the corresponding sensor selection priority order, and the minimum number of sensors; based on the sensor signal sorting results, selecting a type of sensor signal with an incrementally increasing number starting from the minimum number to obtain usable layout schemes; and defining the layout with the minimum total number of sensors among the usable layout schemes that have passed verification testing as the optimal layout. This invention can significantly reduce the number of magnetic sensors while meeting the plasma reconstruction accuracy requirements, and the reconstruction accuracy is significantly higher than that of a uniform distribution with the same number of sensors.

The invention relates to a hydrazine gas sensor based on a sea urchin-shaped ZnO / SiC nano composite material and a preparation method of the hydrazine gas sensor, and belongs to the technical field of semiconductor gas sensors. Aiming at the problems that the existing hydrazine detection technology is complex in equipment and poor in real-time performance and the traditional ZnO sensor is low in selectivity, insufficient in stability and the like, the ZnO / SiC nano composite material is synthesized by adopting a solvothermal method through a triple cooperation strategy of morphology regulation and control, heterostructure construction and defect engineering. The material takes cubic silicon carbide (3C-SiC) as a core, zinc oxide grows on the surface to form an urchin-shaped nano structure, and a material system is optimized by controlling the addition amount of SiC. The sensing performance is innovatively improved through the synergistic effect of a three-dimensional mesoporous diffusion channel, n-n heterojunction interface charge separation and oxygen vacancy mediated surface activation. The response value of the prepared sensor to 50ppm hydrazine at 160 DEG C can reach 45, the response / recovery time is only 22s / 13s, the lower detection limit is as low as 1ppm, and the sensor has excellent selectivity and long-term stability of more than 30 days. Compared with a traditional ZnO sensor, the prepared sensor is low in raw material cost, high in sensitivity and low in working temperature. The invention provides a new material solution for developing portable hydrazine detection equipment with high sensitivity and quick response, and is suitable for the fields of aerospace fuel monitoring, chemical safety early warning and the like.

The application discloses a fracturing expansion state evaluation method based on a wellhead acoustic emission signal root mean square value, which comprises the following steps: step 1, acoustic emission sensor selection; step 2, acoustic emission sensor arrangement; step 3, acoustic emission signal acquisition; step 4, acoustic emission signal early stage filtering; in view of the maximum peak value of the acoustic emission signal, a commonly used threshold value of the fracturing field environment noise is used to preliminarily filter the signal to filter out most of the interference information; step 5, acoustic emission root mean square value envelope analysis after filtering; step 6, root mean square value curve trend analysis; taking a continuous n-point drop as a condition for judging the drop, wherein n is greater than or equal to 6, all falling edges of the curve are found, that is, points at which the root mean square value curve suddenly drops; and step 7, fracturing expansion state evaluation. The application can provide accurate and reliable basis for shale oil fracturing state evaluation, greatly reduces exploration and exploitation costs, and further improves the level and efficiency of shale oil exploration and development.