3334 results about "Estimation result" patented technology

The exercise assisting system includes: a display device including a display screen displaying an image to a user; a comparison image storing unit storing a comparison image representing an image of an exerciser performing a predetermined exercise; a comparison image display unit displaying the comparison image stored in the storing unit on the screen; a mirror image displaying means displaying a mirror image of the user so as to overlap the comparison image; a characteristic amount extraction unit detecting positions of sampling points of a body of the user and calculating a characteristic amount representing a posture of the user based on the position; a posture estimation unit comparing the characteristic amount from the extraction unit with a criterion amount representing a posture of the exerciser and estimating a deviation between postures of the user and the exerciser; and a presentation unit presenting an estimation result of the estimation unit.

The invention provides a combined estimation method for lithium ion battery state of charge, state of health and state of function. The combined estimation method comprises the steps that the state of he---alth of a battery is estimated online: open circuit voltage and internal resistance are identified online by adopting a recursive least square method with a forgetting factor, the state of charge is indirectly acquired according to a pre-established OCV-SOC corresponding relation, and then the size of battery capacity is estimated according to cumulative charge and discharge electric charge between two SOC points; the state of charge of the battery is estimated online: the state of charge of the battery is estimated by adopting the Kalman filter algorithm based on a two-order RC equivalent circuit model, and the battery capacity parameter in the Kalman filter algorithm is updated according to the estimation result of battery capacity; and the state of function of the battery is estimated online: the maximum chargeable and dischargeable current is calculated based on the voltage limit and the current limit of the battery according to internal resistance obtained by online identification, and then the maximum chargeable and dischargeable function can be obtained through further calculation.

The invention provides an early warning method for preventing vehicle collision and a device. The method includes steps of acquiring video image information of the front of a vehicle, and obtaining first characteristic information of an obstacle target of the front of the vehicle according to the video image information; acquiring radar measurement information of the front of the vehicle, and determining second characteristic information of the obstacle target of the front of the vehicle according to the radar measurement information; fusing characteristics of the first characteristic information and the second characteristic information so as to determine final characteristic information of the obstacle target of the front of the vehicle; estimating collision risks to the obstacle target according to the final characteristic information, and realizing early warning for a driver according to a collision risk estimation result. The obstacle target of the front of the vehicle and state information of the obstacle target are identified by means of fusing information, which are acquired by double cameras and a radar device, of the front of the vehicle, target identification precision is improved, and a collision preventive alarm function of the vehicle can be well realized.

The circuit size of a diversity receiver for an orthogonal frequency division multiplexing signal is reduced, and the diversity effect is increased, by providing a power ratio comparator that calculates a difference value as a ratio of powers derived from channel estimation results for subcarrier components received from two antennas (11), (21) and compares the calculated difference value with a predetermined threshold, and a selective/equal gain combining selector (33) that outputs one of the received demodulated signals when the comparison result indicates that the calculated difference value is greater than the threshold value.

An interference signal removal system includes interference signal estimating means for estimating an interference signal contained in a received signal based on the received signal and a result obtained by removing an interference signal from the received signal, interference signal removing means for removing from the received signal the interference signal estimated by the interference signal estimating means; and interference signal estimation controlling means for storing the interference signal estimation result of the interference signal estimating means in memory and controlling the interference signal estimation by the interference signal estimating means so as to estimate an interference signal contained in the received signal based on a past interference signal estimation result stored in memory.

An adaptive subchannel and bit allocation method in a wireless communication system. A mobile station analyzes channel quality information of a subchannel at a predetermined period and determines a feedback band index with a maximum decision criterion. The mobile station feeds back the determined feedback band index with the maximum decision criterion to a base station. The base station generates band allocation information using the feedback information from the mobile station, and transmits the band allocation information to the mobile station. The mobile station transmits AMC information using the band allocation information received from the base station. The base station estimates a channel using the AMC information transmitted from the mobile station and allocates bits to the allocated band according to the channel estimation result.

Embodiments of the invention disclose an image-based vehicle damage estimation method and device and electronic equipment. The method comprises the following steps of: obtaining a to-be-processed image of vehicle damage estimation; identifying a vehicle component in the to-be-processed image and confirming a component area, in the to-be-processed image, of the vehicle component; recognizing damage parts and damage types in the to-be-processed image; determining a damage component in the to-be-processed image and a corresponding damage part and damage type according to the damage parts and the component area; and generating a maintenance scheme on the basis of information comprising the damage component, the damage parts and the damage types. By utilizing the method and device and the electronic equipment, specific information such as damage parts and degrees of vehicle components can be detected rapidly, correctly and reliably, the damage estimation result is more correct and reliable, and maintenance scheme information is provided for the users to rapidly and efficiently carry out vehicle damage estimation processing, so that the service experience of the users is greatly improved.

The invention discloses a driving range estimation method of an electric car. The driving range estimation method of the electric car comprises the following steps: 1.1 a planned route and the future microcosmic traffic state of the route are obtained according to a set departure site, a destination and the departure time; 1.2 an aggregative variable is calculated based on the obtained speed per second and an acceleration; 1.3 a built electric car electricity consumption rate model is selected according to different driving conditions, and electricity consumption per second is calculated by combining the calculated aggregative variable; and 1.4 residual electric quantity of a current battery is obtained, residual energy of the battery is calculated, and residual range is obtained through circular calculation by combing the electricity consumption of the car. The driving range estimation method of the electric car takes the influence of a real traffic state on the energy consumption of the electric car into account, and overcomes the defect that the estimation result of an existing method is not accurate enough.

The invention relates to an evaluating system and method for community influence in a social network. The method comprises the steps that a social network chart with social network users as nodes and user relationships as sides is built; according to the social network chart, the community structure of the social network is obtained by carrying out community division through the label propagation algorithm; according to the social network chart and matrixes which communities belong to, the parameter of the community influence is calculated, and the initial influence of each community is generated; according to the transmission probability model of the influence, an influence transmission probability matrix is generated; according to the influence transmission probability matrix and the community influence iterative computation model, the community influence is iterated and upgraded until the iteration end condition is met, the influence value of each community is obtained, and the sequence of the community influence, namely, the influence estimation result of each community in the social network is obtained after normalization. The system and method can effectively analyze the distribution of the community influence in the social network and can be used for high-influence community mining, thereby being capable of being applied to the fields of network marketing and the like.

An cost based optimizer optimizes access to at least a portion of hierarchically-organized documents, such as those formatted using eXtensible Markup Language (XML), by estimating a number of results produced by the access of the hierarchically-organized documents. Estimating the number of results comprises computing the cardinality of each operator executing query language expressions and further computing a sequence size of sequences of hierarchically-organized nodes produced by the query language expressions. Access to the hierarchically-organized documents is optimized using the structure of the query expression and/or path statistics involving the hierarchically-organized data. The cardinality and the sequence size are used to calculate a cost estimation for execution of alternate query execution plans. Based on the cost estimation, an optimal query execution plan is selected from among the alternate query execution plans.

The present invention discloses a high-precision satellite attitude determination method based on star sensor and gyroscope, which comprises the following steps: step 1. establishing a status equation of a satellite attitude determination system; step 2. establishing a measurement equation of the satellite attitude determination system; step 3. performing an online real-time model error estimation through predictive filtering; and step 4. performing a status estimation on a compensated model through 2-order interpolation filtering to obtain the attitude of a satellite. By applying predictive filtering to performing an online real-time model error estimation and correcting the system model, the invention overcomes the shortcoming existing in the conventional estimation process that error is processed into zero-mean white noise; and in addition, the invention can process any nonlinear system and noise conditions to obtain an estimation result of higher precision and is applicable to thefield of high-precision attitude determination.

A method and an apparatus are provided for adjusting a sampling rate based on a state estimation result. The method comprises receiving metrology data associated with processing of workpieces, estimating a next process state based on at least a portion of the metrology data and determining an error value associated with the estimated next process state. The method further comprises processing a plurality of workpieces based on the estimated next process state and adjusting a sampling protocol of the processed workpieces that are to be measured based on the determined error value.

The invention discloses a method for predicting service life of a product by an accelerated degradation testing based on degradation distribution non-stationary time series analysis. The method comprises the following steps of: 1, acquisition and preprocessing of test data; 2, parameter estimation of degradation distribution; 3, time series modeling of degradation distribution parameters; 4, accelerated degradation modeling based on the degradation distribution; and 5, service life prediction based on degradation distribution. The method can macroscopically describe degradation statistical regulation of all samples under accelerated stress, overall analyze the time series of the degradation distribution in an accelerated degradation process, can extrapolate the degradation distribution under the accelerated stress to normal stress to obtain product reliability reflecting the product accelerated degradation random process volatility regulation and service life relationship prediction, improve the credibility on estimation result of service life prediction and reliability, and save more time and is more efficient compared with performance degradation prediction under normal stress level.

An iterative mantissa calculator calculates a quotient mantissa for a divide mode or a result mantissa for a square-root mode. The calculator includes at least first and second summing devices. In the divide mode, each summing device calculates a respective estimated partial remainder W[j+1] for the next iteration, j+1, as 2*W[j]−S_j+1*D, where W[j] is the estimated partial remainder for the current iteration calculated during the prior iteration, S_j+1 is the quotient bit estimated for the next iteration, and D is the respective divisor bit. The estimated quotient bit for the next iteration is selected based on the calculated partial remainder. In the square-root mode, the first summing device calculates 2W[j]−2S[j]S_j+1, where W[j] is the estimated partial remainder and S_j+1 is the estimated result generated during the current iteration, j. A shift register shifts the value of the estimated result, S_j+1, to generate −S_j+1²·2^−(j+1), which is summed with the result from the first summing device to generate the estimated partial remainder for the square root mode.

The invention discloses an intelligent bus scheduling management system, and is applied to the fields of traffic and communication. The system comprises an electronic station board, a vehicular station, a scheduling station and at least one bus management center, wherein the electronic station board acquires bus route information of a passenger and sends the information to the bus management center; the vehicular station sends bus positioning information and passenger card-swiping records to the bus management center; and the bus management center counts and/or estimates the number of passengers waiting for a bus in each route according to the acquired information, plans a bus scheduling scheme according to a statistical result and/or an estimation result and the actual bus condition in each route, and sends a command to the scheduling station to schedule buses. The system schedules the buses according to passenger requirements and the actual bus condition to improve intelligence and efficiency, save energy and reduce emission, and ensures that the passengers select the optimal bus route, understand the current position of the bus, do not wait for the bus blindly and go out easily.

The invention provides a robot semantic SLAM method based on object instance matching, a processor and a robot. The robot semantic SLAM method comprises the steps that acquring an image sequence shotin the operation process of a robot, and conducting feature point extraction, matching and tracking on each frame of image to estimate camera motion; extracting a key frame, performing instance segmentation on the key frame, and obtaining all object instances in each frame of key frame; carrying out feature point extraction on the key frame and calculating feature point descriptors, carrying outfeature extraction and coding on all object instances in the key frame to calculate feature description vectors of the instances, and obtaining instance three-dimensional point clouds at the same time; carrying out feature point matching and instance matching on the feature points and the object instances between the adjacent key frames; and performing local nonlinear optimization on the pose estimation result of the SLAM by fusing the feature point matching and the instance matching to obtain a key frame carrying object instance semantic annotation information, and mapping the key frame intothe instance three-dimensional point cloud to construct a three-dimensional semantic map.

The invention relates to the technical field of storage battery state of charge prediction, and discloses an on-line feedback battery state of charge (SOC) predicting method. According to the method, SOC valuation model parameters are corrected according to historical data in the on-line operating process of a storage battery. The influence of temperature, coulomb efficiency and self discharge on battery SOC is considered, basic operating parameters of the storage battery are only required to be monitored, related coefficients are corrected as long as conditions are met in the operating process of the battery, coefficient values are repeatedly corrected, and an SOC estimation result is close to a true value along time, so the accuracy is high and the storage battery SOC can be predicted on line.

The invention discloses an accelerated degradation test prediction method based on fuzzy theory, which comprises the following steps: collecting test data; performing the analysis of regression aimingat performance degradation data under each stress level; extrapolating the performance degradation rate of the product under a normal stress level; estimating a diffusion coefficient sigma in an excursion Brownian motion with drift by adopting a maximum likelihood estimation method; establishing an accelerated degradation test life and reliability predication model based on the fussy theory; andpredicting the life and the reliability of the product by adopting the fussy life and reliability prediction model. The method firstly introduces the fussy concept into an accelerated degradation testto enable the prediction result of the accelerated degradation test to be more reasonable, avoids the condition of rash routine reliability estimation result through considering the fussiness fuzziness of the performance degradation threshold, solves the problem of failed performance degradation in the engineering reality, and is suitable for the accelerated degradation tests of step stress and progressive stress and unaccelerated performance degradation prediction for the problem of the performance degradation failure.

The invention discloses a construction method and system for a two-dimensional and three-dimensional joint model of an indoor environment. The method comprises: obtaining two-dimensional laser scanning data output by a two-dimensional laser scanning instrument arranged in a horizontal direction, and constructing a two-dimensional grid map; performing platform six-degree-of-freedom pose estimation according to data output by an inertial sensing unit and the two-dimensional grid map with an extended Kalman filtering method; obtaining two-dimensional laser scanning data output by two two-dimensional laser scanning instruments with bottoms mounted in a vertical direction, and constructing three-dimensional point cloud data of the indoor environment; registering the three-dimensional point cloud data to be a three-dimensional map by taking a platform six-degree-of-freedom pose estimation result as an initial rotary translation matrix; and performing closed-loop detection of the two-dimensional grid map by adopting a closed-loop detection principle in two-dimensional grid map construction, and obtaining globally optimized two-dimensional grid map and three-dimensional map through platform pose adjustment, thereby achieving an effect of efficiently and accurately constructing two-dimensional and three-dimensional maps.

The invention relates to a method and a device for monitoring and diagnosing the generating efficiency of a wind turbine generator in a real-time manner. The method comprises following steps: obtaining historical data information and power information of two groups of air blowers respectively; fitting power curves of two groups of air blowers respectively as per the historical data information and power information of two groups of air blowers; calculating wind speed distribution parameters of two groups of air blowers as per the historical data information and power information of two groups of air blowers; calculating generating capacity data of two groups of air blowers as per the power curves and wind speed distribution parameters of two groups of air blowers; processing the generating capacity data of two groups of air blowers to obtain result data, estimating the generating efficiency of air blowers as per a relation between the result data and a preset value, and optimizing the control systems of the air blowers as per the estimation result. The invention can perform real time monitoring to the generating efficiency of the wind turbine generator and estimates the generating efficiency as per monitoring data, adjusts the control optimizing scheme of the wind turbine generator as per the estimation result, and is favorable for improvement of the generating efficiency of the wind turbine generator.

The invention discloses a face recognition method based on deep transformation learning in an unconstrained scene. The method comprises the following steps: obtaining a face image and detecting face key points; carrying out transformation on the face image through face alignment, and in the alignment process, minimizing the distance between the detected key points and predefined key points; carrying out face attitude estimation and carrying out classification on the attitude estimation results; separating multiple sample face attitudes into different classes; carrying out attitude transformation, and converting non-front face features into front face features and calculating attitude transformation loss; and updating network parameters through a deep transformation learning method until meeting threshold requirements, and then, quitting. The method proposes feature transformation in a neural network and transform features of different attitudes into a shared linear feature space; by calculating attitude loss and learning attitude center and attitude transformation, simple class change is obtained; and the method can enhance feature transformation learning and improve robustness and differentiable deep function.