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119 results about "Estimated Weight" patented technology

Estimated weight definition is - the weight specified in tariffs and agreed upon by shippers and carriers to be that of certain commodities shipped in specified packages in order to avoid the weighing of each package.

Weight classification system

A method and apparatus is provided that classifies a seat occupant into one of several different weight classes based on an estimated value of the seat occupant weight. An occupant's measured weight varies when the occupant's seating position changes or when the vehicle travels over adverse road conditions. A plurality of weight sensors are used to measure the weight exerted by a seat occupant against a seat bottom and are used to determine center of gravity for the seat occupant. A seat belt force sensor is also used to assist in classifying the seat occupant. Compensation factors using the seat belt force and center of gravity information are used to generate an estimated weight value. The estimated value of the occupant weight is compared to a series of upper and lower weight thresholds assigned to each of the weight classes to generate an occupant weight sample class. Over a period of time, several estimated weight values are compared to the weight class thresholds. Once a predetermined number of consistent and consecutive occupant weight sample classes is achieved, the occupant is locked into a specific occupant weight class. When the weight class is locked, the separation value between the upper and lower thresholds is increased to account for minor weight variations due to adverse road conditions and changes in occupant position.
Owner:SIEMENS VDO AUTOMOTIVE CORP

Trigger word tagging system and method for biomedical events

The invention discloses a trigger word tagging system and method for biomedical events. The trigger word tagging system comprises a pretreatment module, a tagging model building module and a tagging module, wherein the pretreatment module is used for acquiring a training sample and a testing sample and comprises a word segmentation unit, a protein molecule identification unit, a feature extraction unit and a pre-tagging unit; the word segmentation unit is used for acquiring the word sequence of an original text; the protein molecule identification unit is used for identifying protein molecules and replacing with a standard mode to bring more convenience for feature extraction and trigger word tagging; the feature extraction unit is used for extracting the word forms, the word characteristics and other syntactic properties and semantic properties, and finally pre-tags the word sequence as a training and testing sample set; the tagging model building module is used for building a feature template, generating characteristic functions, and estimating weights corresponding to the characteristic functions to obtain a CRFs trigger word tagging model; the tagging module is used for trigger word tagging of an unknown test sequence and displays the result on a GUI interface.
Owner:NANJING UNIV OF POSTS & TELECOMM

Method for online parallel computing of recommended information, device for online parallel computing of recommended information, and server for online parallel computing of recommended information

The invention belongs to the technical field of Internet, and discloses a method for online parallel computing of recommended information, a device for online parallel computing of the recommended information, and a server for online parallel computing of the recommended information. The method for online parallel computing of the recommended information comprises the following steps: obtaining an application ID and information bit IDs of a first user in a current application, and obtaining an information list corresponding to each information bit ID; obtaining real-time data corresponding to the application ID in a real-time and online manner, pulling historical data corresponding to the application ID, and carrying out parallel computing of estimated weights of information to be recommended in each information list according to the real-time data and the historical data; screening the information to be recommended in each information list according to each estimated weight of each piece of information to be recommended; determining the information to be recommended that has been screened from each information list to be the recommended information of an information bit identified by the information bit ID corresponding to each information list. The invention has the advantages that the real-time data which is obtained in a real-time and online manner and the pulled historical data are used for the parallel computing of the estimated weights of the information to be recommended, and the recommended information is determined according to the estimated weights, so that the determined recommended information is more accurate.
Owner:SHENZHEN TENCENT COMP SYST CO LTD

Auxiliary truncation particle filtering method, device, target tracking method and device

The invention discloses an auxiliary truncation particle filtering method, a device, a target tracking method and a device. The auxiliary truncation particle filtering method comprises the steps of adopting an original prior probability density function as a first importance density function for filtering particles to obtain a first mean value and a first covariance value corresponding to a target state; in the original prior probability density function, importing the current observation information and the target characteristic information based on the truncation theory to construct a corrected prior probability density function; adopting the corrected prior probability density function as a second importance density function for filtering particles so as to obtain a second mean value and a second covariance value corresponding to the target state; respectively conducting the weighted treatment on first mean value, the first covariance value, the second mean value and the second covariance value according to the estimated weight of the target state so as to obtain a posterior probability density function corresponding to the target state, and completing the particle filtering process. In this way, the particle filtering accuracy and the particle filtering real-time performance are improved. Meanwhile, the rapid target-tracking problem of a target model in the uncertainty condition caused by the target maneuvering condition in the non-linear and non-gaussian environment can be solved.
Owner:KUNSHAN RUIXIANG XUNTONG COMM TECHCO
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