136 results about "Weight estimation" patented technology

A method of knowledge-based engineering cost and weight estimation of an HVAC air-handling assembly for a climate control system on a vehicle includes the steps of selecting a parametric model of an HVAC air-handling assembly design using a knowledge-based engineering library stored in a memory of a computer system. The method also includes the steps of selecting a component part from the parametric model of the HVAC air-handling assembly, determining a cost estimate of the component part using the knowledge-based engineering library and using a summation of the component part cost as the cost estimate of the HVAC air-handling assembly. The method further includes the steps of determining a weight estimate of the component part using the knowledge-based engineering library and using a summation of the component part weight as the weight estimate of the HVAC air-handling assembly. The method further includes the steps of using the cost and weight estimate in the HVAC air-handling assembly design.

A method for estimating weight of a payload held by load lifting machine 1, wherein the load lifting machine comprises lifting linkage with a boom 5 pivotably connected to a machine chassis 2, the method comprising calculating an estimation using the torque of the boom 5, torque of the boom during calibration lifts for first and second payloads, and dynamic torque adjustment determined for first and second payloads.

A system configured to estimate a change in temperature of a brake component of a vehicle brake during a braking operation. The system includes a weight estimation module for estimating a weight of a vehicle to which a brake is fitted, a volume estimation module for estimating a volume of a brake component, a temperature model module for calculating a change in temperature during brake application based on a relationship between the weight of the vehicle as estimated by the weight estimation module, the volume of the brake component as estimated by the volume estimation module, the deceleration of the vehicle, and further constants of the vehicle and / or the brake.

An antenna weight estimation method in a mobile communication system is capable of improving the accuracy of antenna weight estimation values, thereby improving communication quality and increasing radio channel capacity. It operates instantaneous estimation and accumulative estimation in parallel, in which the former is made for estimating the antenna weight used for received SNIR estimation for transmission power control, and the latter is made for demodulating the received data. Thus, it can obtain the antenna weight for the received SNIR measurement with a minimum delay, and at the same time, it can improve the reliability of the antenna weight for the data demodulation.

The invention provides a method of estimating the body size of a yak, based on 3D vision. The method of estimating the body size of a yak, based on 3D vision includes the steps: acquiring the side images of a yak by means of image acquisition equipment; extracting the foreground images from the side images; identifying the key points of the yak body from the extracted foreground images; and by means of the identified key points of the yak body, automatically extracting the body size information of the yak. The invention also provides a method of estimating the weight of a yak, based on 3D vision. The method of estimating the weight of a yak, based on 3D vision includes the steps: including the method of performing the method of estimating the body size of a yak; and taking the extracted body size information of the yak as input, and utilizing a yak weight estimation model to predict the weight value of the yak. Alternatively, the invention furthermore provides a method of estimating the weight of a yak, based on 3D vision. The method of estimating the weight of a yak, based on 3D vision includes the steps: acquiring the side images of a yak by means of image acquisition equipment; extracting the foreground images from the side images; and directly predicting the weight value from the foreground images, by means of a convolution neural network. The invention also provides a corresponding portable computer device.

The invention relates to an IoT (Internet of things)-based sheep growth monitoring and information service cloud platform comprehensive system, which comprises an information service cloud platform, the internet, subscriber terminal equipment, an IoT intelligent control integrated machine and a sheep growth condition acquisition device; the sheep growth condition acquisition device is connected with the IoT intelligent control integrated machine; the IoT intelligent control integrated machine is respectively connected with the information service cloud platform and the subscriber terminal equipment through the internet. The IoT-based sheep growth monitoring and information service cloud platform comprehensive system disclosed by the invention has the beneficial effects that a growth archive of noncontact measurement (body length, body height, body temperature and weight) and automatic recording is established for sheep; suspected diseased sheep can be discovered in advance by body temperature measurement, and early discovery and early isolation are achieved, so that the infection and spread of an epidemic disease are reduced; arrangement is performed on growth information by the information service cloud platform, a data source can be provided for mutton tracing, and a basis is provided for a specialist and a manager to adjust sheep farming flows; the IoT-based sheep growth monitoring and information service cloud platform comprehensive system also has a remote sheep catching function and a weight estimation function.

A center-of-gravity detection system includes a vehicle capable of carrying cargo and adapted to be towed by a towing vehicle, a shake detector configured to detect shakes in the directions of the self-weight and width of the towed vehicle during travel of the towed vehicle, and an arithmetic unit. The arithmetic unit is configured to derive, based upon physical quantities that correlate with the shakes, the location of the center of gravity, in three-dimensional space, of the towed vehicle.

The invention provides intelligent mobile electronic equipment with a size and weight estimation function. The intelligent mobile electronic equipment comprises a camera, an image processing unit, an estimating unit and a control unit. Images at multiple angles of a tested object are taken through the camera and then are synthesized by the image processing unit to obtain a three-dimensional image of the tested object, the estimation unit estimates the size and the volume of the tested object according to the three-dimensional image, the focal length and the object distance, and the weight of the tested object is calculated by multiplying the size and the volume by the density of the tested object. Therefore, the intelligent mobile electronic equipment is portable, and can be used for acquiring sizes and weights of objects such as human bodies, animals and foods at any place and at any time, and thus users can master the sizes and the weights of the users and other objects at any time.

A fluid-filled seat bladder assembly includes multiple capacitive sensing elements for detecting variation in bladder deformation due to occupant weight. The bladder is defined by upper and lower elastomeric sheets that are peripherally sealed by welding, and the capacitive sensors are defined by metalized films adjoining the upper and lower elastomeric sheets. The bladder fluid separating the upper and lower metalized films acts as a dielectric material, and the capacitance or electric field coupling of each sensor increases when the separation between the respective upper and lower films decreases due occupant seat weight. The cumulative change in capacitance or electric field coupling reflects the total seated weight, and the individual capacitance or electric field coupling changes provide a profile of the pressure distribution across the seat for improved occupant classification. The metalized films may be located exterior of the bladder, or may be are formed on interior surfaces of the upper and lower elastomeric bladder sheets.

The invention relates to a granary grain storage quantity detection method based on support vector regression. The method comprises the following steps: arranging two circles of pressure sensors on the bottom surface of a granary, detecting an output value of each sensor, and computing granary grain storage weight estimation (FOMRULA) according to a detection model (FOMRULA). Aiming at urgent need of the nation to national grain storage online quantity detection and specific requirement of the detection, the invention provides the granary grain storage quantity detection method based on the support vector regression according to the granary bottom surface pressure intensity distribution and pressure intensity measured value change characteristics; the provided method is high in detection precision, and suitable for the grain storage quantity detection in various granary structure types.

The invention relates to a grain bin stored-grain quantity detection method based on a detection point pressure intensity value sequence. A circle of pressure sensors are arranged on the bottom face of a grain bin and the vertical distance of each pressure sensor to a closest outer wall is d; and the output value of each sensor is detected and according to a detection model (as is shown in the specification), the stored-grain weight estimation W<^> of the grain bin is calculated. According to characteristics of pressure intensity distribution of the bottom face of the grain bin and change of pressure intensity measurement values, the invention proposes the grain bin stored-grain quantity detection method of a support vector regression model, based on the detection point pressure intensity value sequence. According to a corresponding sensor arrangement method, the core technology of the method includes two parts: the support vector regression grain bin stored-grain quantity detection model based on the detection point pressure intensity value sequence and a system calibration and modeling method. The grain bin stored-grain quantity detection method has the characteristics of being high in detection precision, high in universality and adaptive to stored-grain quantity detection of a plurality of kinds of grain bin structure types and the like.

The invention provides a method for weight estimation. The method comprises the following steps of obtaining a user behavior log and obtaining revealing information, click information and transaction information of an object based on the user behavior log; carrying out word segmentation on query information according to a preset rule to obtain word segmentation units, and respectively obtaining the revealing information, the click information and the transaction information of each word segmentation unit according to the emerging frequency of the word segmentation units in the revealing information, the click information and the transaction information of the object; determining a click rate and a click conversion rate of each word segmentation unit according to the revealing information, the click information and the transaction information of each word segmentation unit; determining a weight of each word segmentation unit as a weight corresponding to the object of each word segmentation unit according to the click rate and the click conversion rate of each word segmentation unit. The invention further provides a method for weight estimation for determining the weight of each object according to the current query information and the weight of each word segmentation unit. The invention further provides a method and a system for weight estimation. According to the method, the device and the system, the degree of accuracy for sorting is improved.

The invention relates to a grain bin stored-grain quantity detection method based on a structure self-adapting detection model. Two circles of pressure sensors are arranged on the bottom face of a grain bin and output values of the sensors are detected and according to the detection model (as is shown in the specification), the grain bin stored-grain weight estimation W<^> is calculated. The method has the characteristics of being high in detection precision and adaptive to stored-grain quantity detection of a plurality of kinds of grain bin structure types and the like.

Provided is an overturn preventing device for a dump vehicle equipped with a body (3) and hoist cylinders (5). The dump vehicle overturn preventing device comprises: a loaded weight estimation unit (32) which estimates loaded weight on the body; a vehicle rotation moment calculation unit (33) which calculates a vehicle rotation moment Mb caused by movement of the dump vehicle's load upon discharging of the load; a reference moment calculation unit (34) which determines a reference moment Ms not greater than an overturn threshold moment MI which is the minimum value of a rotation moment required to lift the front wheels (1) off the ground; a judgment unit (35) which judges whether or not the vehicle rotation moment Mb has exceeded the reference moment Ms; and a display device (37) which notifies the driver that there is a probability of an overturn of the vehicle when the vehicle rotation moment Mb is judged to have exceeded the reference moment Ms. The device thus prevents overturning of the vehicle caused by the load sliding down from the body in a lump.

Provided are an object verification apparatus and method. The object verification apparatus includes a matching unit performing a plurality of different matching algorithms on a query image and generating a plurality of scores; a score normalization unit normalizing each of the generated scores to be adaptive to the query image; a weight estimation unit estimating weights of the normalized scores based on the respective matching algorithms applied; and a score fusion unit fusing the normalized scores by respectively applying the weights estimated by the weight estimation unit to the normalized scores.

An automatic transmission control system for an automobile, comprising a vehicle weight estimation unit which estimates a vehicle weight of the automobile a torque estimation unit which estimates an output torque, an acceleration input unit which accepts an acceleration signal; a load estimation unit (110) which estimates a running load from the estimated vehicle weight, the estimated output torque and the accepted acceleration; a memory which stores a plurality of shift schedules therein; and a gear position determination unit (109) which includes the memory, and which selects one of the shift schedules in accordance with the vehicle weight and the estimated running load, so as to determine a gear position of an automatic transmission of the automobile in conformity with the selected shift schedule. An exact shift operation conformed to the vehicle weight and the running load can be performed, and an enhanced fuel consumption can be attained.

The invention relates to a task scheduling method in the field of high-performance clusters. The task scheduling method comprises the steps: after analysis of each working node of a Hadoop in a job submitting stage, acquiring an actual stage weight according with a task, after processing of a geometric mean method, establishing a reference standard of the stage weight for left tasks of the job; in a task feedback stage, adopting the reference standard for the left tasks of the job, and estimating the task residual execution time by combining with progresses of sub-stages; in a job feedback stage, solving a geometric mean of stage weights of all tasks by using a staging manner, and establishing a job name-stage weight mapping record to be used as a reference of executing subsequent jobs on the node. According to the task scheduling method, self-learning and information feedback can be respectively carried out on the task of each job in a multi-job parallel executing environment to obtain more accurate stage weight estimation, the accuracy of pre-estimating the task residual execution time is improved, the hit rate of selecting dated tasks is increased, and the optimal utilization of cluster resources is promoted.

A method for estimating weight of a payload held by load lifting machine 1, wherein the load lifting machine comprises lifting linkage with a boom 5 pivotably connected to a machine chassis 2, the method comprising calculating an estimation using the torque of the boom 5, torque of the boom during calibration lifts for first and second payloads, and dynamic torque adjustment determined for first and second payloads.

A vehicle occupant classification system classifies a vehicle occupant based on data from an array of sensors using a combination of weight estimation, pattern recognition, and evaluation of statistical characteristics. This occupant classification can then be used to make an airbag deployment state decision. The major modules of this system can include a calibration unit, a weight estimation module, a pattern module, and a decision-making module. The calibration unit can perform a calibration process to normalize sensor deflections using a known deflection force in order to compensate for variations in sensors and the effects of the seat trim and foam. The weight estimation module can perform a weight estimation process that uses calibration data from the calibration process and sensor data from the sensors to translate sensor deflection due to a vehicle occupant into a displacement value. The pattern module can look for traits in the pattern of sensor deflections that are common for objects other than people. Finally, the decision-making module can make a deployment state decision for the airbag by looking at displacement trends to evaluate occupant weight and movement, and by evaluating pattern information.

The invention discloses a rod-shaped platform chicken on-site weighing system and method. A weighing rod is connected between two weighing modules to form a rod-shaped weighing platform; the weighing rod transmits weights to the weighing modules; a camera is installed directly above the weighing rod; the weighing modules and the camera are all in connection with a computer; each weighing module comprises a housing, a pressure transducer, a control circuit and an iron block. The system obtains total weight data on a scale, obtains a chicken average weight estimated value on the rod, and a chicken average weight estimated value around the rod according to image information, obtains an effective single chicken average weight based on each weight data, prepares a graph according to a breeding period, and obtains average weight increase cases and individual difference changes of chicken flocks. The rod-shaped platform chicken on-site weighing system can replace heavy manual operation, eliminate human factor interference, obtain a whole weight increment curve of chicken flocks and individual weight difference changes as breeding references, and solve the problems of data abnormity and common results under weight of a weighing system.

The invention relates to the field of image processing, and specifically relates to a multi-source image fusion method based on enhanced learning. The method comprises the steps: decomposing a multi-source image to obtain a high-frequency coefficient signal and a low-frequency coefficient signal, and performing weighted reconstruction of the high-frequency coefficient signal and the low-frequencycoefficient signal to obtain a fusion image. The method adopts a Q learning algorithm in enhanced learning for the training of the weights of the high-frequency coefficient signal and the low-frequency coefficient signal, selects a weight which enables the fusion evaluation criteria to be optimal for the weighted fusion, and obtains the fusion coefficient signal. The fusion image is reconstructedto obtain the fusion image. Based on the Q learning algorithm, the method achieves the training of the weights of the high-frequency coefficient signal and the low-frequency coefficient signal obtained through the decomposition of the multi-source image, and improves the reliability and accuracy of weight estimation.

A method of measuring the weight of an aircraft is disclosed. The method comprises, in a self-propelled aircraft undercarriage having a electrical rotating machine, the steps of: measuring the current and voltage going into the rotating machine using current and voltage measuring means, calculating the power into the rotating machine, measuring the speed (or torque or acceleration) of said machine using speed sensing means, and comparing the power and speed (or torque or acceleration) results with a database of power and speed relationships of comparable aircraft of varying weights.

The invention belongs to the trust management field of large-scale distributed network application and particularly relates to a self-adapting dynamic trust weight evaluating method which is particularly fit for various large-scale open distributed applications of gridding, pervasive computing, P2P (peer-to-peer) computing, Ad hoc (mobile ad hoc networking), electronic commerce and the like based on Internet as a basic platform. On the basis of existing direct trust evaluating method based on historical function and a feedback trust polymerization technology based on path function, the invention provides a method for using two parameters including confidence factor and feedback factor to automatically establish and dynamically adjust weight calculated by fusing direct trust and feedback trust, the invention overcomes the defect of confirming the weight subjectively in common use before, thus leading the invention to have good scientificity and higher practical application value.

A moving image encoding device that encodes a difference between a predicted image and an original image is provided. The predicted image is generated using a weighted reference image, for which a weight is multiplied with and an offset is added to a reference image. The moving image encoding device includes an image data reduction section and a weighting estimation section. The image data reduction section reduces data volumes of the original image and the reference image while retaining characteristics of the original image and the reference image. The weighting estimation section estimates weighting parameters on the basis of image data of the original image and reference image of which the data volumes have been reduced by the image data reduction section.

A frame-based occupant weight estimation apparatus for a vehicle seat that is supported by two or more floor brackets secured to the vehicle floor spans an open space between the floor brackets to prevent shunting of the occupant weight through a foreign object lying on the floor between the floor brackets. The weight estimation apparatus includes a fluid filled bladder sandwiched between first and second rigid plates, and is held in place between the seat frame and the floor brackets by a set of fasteners. A spring preloads the bladder so that deviations of the bladder fluid pressure from the preload pressure will be indicative of occupant weight. The first plate spans the open space between the floor brackets so that the fluid pressure is indicative of occupant weight even in cases where a portion of the occupant weight is supported by a foreign object disposed between the floor brackets.

The invention relates to artificial intelligence, and discloses a live pig weight measurement method, a server and a computer readable storage medium. The method comprises the steps: obtaining collected multi-head live pig depth images; performing image segmentation on the live pig depth image according to a preset image segmentation algorithm to extract a live pig body image; extracting trainingcharacteristics from the live pig body image, wherein the training characteristics comprise one or more of the following characteristics: a live pig body projection area, a live pig body length, a live pig body height, a live pig body width and a live pig chest circumference; a live pig weight estimation model is obtained through training according to the training characteristics and the corresponding live pig weight information; And inputting the images of the to-be-detected live pigs into the live pig weight estimation model to obtain weight information of the to-be-detected live pigs. The live pig weight measurement model is obtained through training based on the neural network, the live pig weight can be intelligently measured through the model, measurement is convenient and fast, andmanpower can be effectively liberated.

The invention discloses a method and a device for enhancing an image spatial resolution, relates to the field of image information processing and aims at enhancing the image spatial resolution. The method comprises that: weight estimation is performed on modes of motion of pixel points in a low-resolution image, and at least one weight value of the pixel points is obtained, wherein at least one weight value is used for describing possibility of at least one mode of motion of the pixel points; at least one weight value is screened according to a preset algorithm, and abnormal weight values are eliminated; and at least one frame of the low-resolution image is fused according to the screened weight value so that a super-resolution image is obtained. The method and the device for enhancing the image spatial resolution are mainly applied to a super-resolution reconstruction technology.

The invention proposes a PID-based iterative learning control method based on a neural network, belonging to the field of control science and engineering. For a general nonlinear difference system, the invention gives an iterative learning PID controller, and then dynamic parameters in the PID controller are fitted by using three RBF neural networks. The weights of the neural networks are estimated according to a gradient descent method and a neural network weight update expression is obtained. Since an exact model of a controlled system is complex or unknown, a real value of a system pseudo partial derivative (PPD) in a neural network weight estimation expression cannot be obtained, and an estimated value is used for numerical value substitution. In this way, a nonlinear iterative learning control algorithm that can be run is generated. Finally, an example is given to demonstrate that the numerical simulation of the algorithm in a nonlinear system and a picking and placing robot system is effective. Therefore, this algorithm can be applied to a general (unknown) nonlinear differential system.