최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
DataON 바로가기다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
Edison 바로가기다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
Kafe 바로가기국가/구분 | United States(US) Patent 등록 |
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국제특허분류(IPC7판) |
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출원번호 | US-0188673 (2002-07-03) |
발명자 / 주소 |
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출원인 / 주소 |
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대리인 / 주소 |
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인용정보 | 피인용 횟수 : 397 인용 특허 : 29 |
Vehicle diagnostic system which diagnoses the state of the vehicle or the state of a component of the vehicle and generates an output indicative or representative thereof. A communications device transmits the output of the diagnostic system to a remote location, possibly via a satellite or the Inte
Vehicle diagnostic system which diagnoses the state of the vehicle or the state of a component of the vehicle and generates an output indicative or representative thereof. A communications device transmits the output of the diagnostic system to a remote location, possibly via a satellite or the Internet. The diagnostic system can include sensors mounted on the vehicle, each providing a measurement related to a state of the sensor or a measurement related to a state of the mounting location, and a processor coupled to the sensors and arranged to receive data from the sensors and process the data to generate the output indicative or representative of the state of the vehicle or its component. The processor may embody a pattern recognition algorithm trained to generate the output from the data received from the sensors and be arranged to control parts of the vehicle based on the output.
Vehicle diagnostic system which diagnoses the state of the vehicle or the state of a component of the vehicle and generates an output indicative or representative thereof. A communications device transmits the output of the diagnostic system to a remote location, possibly via a satellite or the Inte
Vehicle diagnostic system which diagnoses the state of the vehicle or the state of a component of the vehicle and generates an output indicative or representative thereof. A communications device transmits the output of the diagnostic system to a remote location, possibly via a satellite or the Internet. The diagnostic system can include sensors mounted on the vehicle, each providing a measurement related to a state of the sensor or a measurement related to a state of the mounting location, and a processor coupled to the sensors and arranged to receive data from the sensors and process the data to generate the output indicative or representative of the state of the vehicle or its component. The processor may embody a pattern recognition algorithm trained to generate the output from the data received from the sensors and be arranged to control parts of the vehicle based on the output. and predicting the dynamic response of the system at different time positions between the first time and the second time to define a dynamic operation path of the system between the current output value and the desired output value at the second time; and an optimizer for optimizing the operation of the dynamic controller at each of the different time positions from the first time to the second time in accordance with a predetermined optimization method that optimizes the objectives of the dynamic controller to achieve a desired dynamic operation path, such that the objectives of the dynamic predictive model vary as a function of time. 2. The dynamic controller of claim 1, wherein said dynamic predictive model comprises: a dynamic forward model operable to receive input values at each of said different time positions and map said received input values through a stored representation of the system to provide a predicted dynamic output value; an error generator for comparing the predicted dynamic output value to the desired output value and generating a primary error value as the difference therebetween for each of said time positions; an error minimization device for determining a change in the input value to minimize the primary error value output by said error generator; a summation device for summing said determined input change value with the original input value for each time position to provide a future input value; and a controller for controlling the operation of said error minimization device to operate under control of said optimizer to minimize said primary error value in accordance with said predetermined optimization method. 3. The dynamic controller of claim 2, wherein said controller controls the operation of said summation device to iteratively minimize said primary error value by storing the summed output from said summation device in a latch in a first pass through said error minimization device and input the latch contents to said dynamic forward model in a subsequent pass and for a plurality of subsequent passes, with the output of said error minimization device summed with the previous contents of said latch with said summation device, said latch containing the current value of the input on the first pass through said dynamic forward model and said error minimization device, said controller outputting the contents of said latch as the input to the system after said primary error value has been determined to meet the objectives in accordance with said predetermined optimization method.4. The dynamic controller of claim 2, wherein said dynamic forward model is a dynamic linear model with a fixed gain.5. The dynamic controller of claim 4 and further comprising a gain adjustment device for adjusting the gain of said linear model for substantially all of said time positions.6. The dynamic controller of claim 5, wherein said gain adjustment device comprises: a non-linear model for receiving an input value and mapping the received input value through a stored representation of the system to provide on the output thereof a predicted output value, and having a non-linear gain associated therewith; said linear model having parameters associated therewith that define the dynamic gain thereof; and a parameter adjustment device for adjusting the parameters of said linear model as a function of the gain of said non-linear model for at least one of said time positions. 7. The dynamic controller of claim 6, wherein said gain adjustment device further comprises an approximation device for approximating the dynamic gain for a plurality of said time positions between the value of the dynamic gain at said first time and the determined dynamic gain at the one of said time positions having the dynamic gain thereof determined by said parameter adjustment device.8. The dynamic controller of claim 7, wherein the one of said time positions at which said parameter adjustment device adjusts said parameters a s a function of the gain of said non-linear model corresponds to the maximum at the second time.9. The dynamic controller of claim 6, wherein said non-linear model is a steady-state model.10. The dynamic controller of claim 2, wherein said error minimization device includes a primary error modification device for modifying said primary error to provide a modified error value, said error minimization device optimizing the operation of the dynamic controller to minimize said modified error value in accordance with said predetermined optimization method.11. The dynamic controller of claim 10, wherein said primary error is weighted as a function of time from the first time to the second time.12. The dynamic controller of claim 11, wherein said weighting function decreases as a function of time such that said primary error value is attenuated at a relatively high value proximate to the first time and attenuated at a relatively low level proximate to the second time.13. The dynamic controller of claim 2, wherein said error minimization device receives said predicted output from said dynamic forward model and determines a change in the input value maintaining a constraint on the predicted output value such that minimization of the primary error value through a determined input change would not cause said predicted output from said dynamic forward model to exceed said constraint.14. The dynamic controller of claim 2, and further comprising a filter determining the operation of said error minimization device when the difference between the predicted manipulated variable and the desired output value is insignificant.15. The dynamic controller of claim 14, wherein said filter determines when the difference between the predicted manipulated variable and the desired output value is not significant by determining the accuracy of the model upon which the dynamic forward model is based.16. The dynamic controller of claim 15, wherein the accuracy is determined as a function of the standard deviation of the error and a predetermined confidence level, wherein said confidence level is based upon the accuracy of the training over the mapped space.17. A method for predicting an output value from a received input value, comprising the steps of: modeling a set of static data received from a system in a predictive static model over a first range, the static model having a static gain of K and modeling the static operation of the system; modeling a set of dynamic data received from the system in a predictive dynamic model over a second range smaller than the first range to model the dynamic operation of the system, the dynamic model having a dynamic gain k, and the operation of the dynamic model being independent of the operation of the static model; adjusting the gain of the dynamic model as a predetermined function of the gain of the static model to vary the model parameters of the dynamic model; predicting the dynamic operation of the system for a change in the input value between a first input value at a first time and a second input value at a second time; subtracting the input value from a steady-state input value previously determined and inputting the difference to the dynamic model and processing the input through the dynamic model to provide a dynamic output value; and adding the dynamic output value from the dynamic model to a steady-state output value previously determined to provide a predicted value. 18. The method of claim 17, wherein the predetermined function is an equality function wherein the static gain K is equal to the dynamic gain k.19. The method of claim 17, wherein the static model is a non-linear model.20. The method of claim 19, wherein the dynamic model for a given dynamic gain is linear.21. The method of claim 20, wherein the step of adjusting the gain of the dynamic model as a function of the predetermined function of the gain of the static model is a non-linear operation.22. The method of claim 17, wher ein the static model and the dynamic model are utilized in a control function to receive as inputs the manipulated inputs of the system, the actual output from the system in addition to a desired output value at which the system is to operate, and then perform the step of predicting future manipulated inputs required to achieve the desired output.23. The method of claim 22, and further comprising the step of optimizing the operation of the dynamic model in accordance with a predetermined optimization method between an initial steady-state value and a predicted final steady-state value that constitutes an input control variable to the system during the control operation.24. The method of claim 23, wherein the step of optimizing comprises determining the dynamic gain k for multiple positions between the input steady-state input value and the final predicted steady-state input value that comprises the input control values to the system.25. The method of claim 24, wherein the step of determining utilizes an algorithm that estimates the dynamic gain k independent of the operation of the static model.26. The method of claim 25, wherein the algorithm is a quadratic equation.27. The method of claim 23, wherein the step of predicting with the dynamic model utilized as a dynamic controller comprises the steps of: predicting the dynamic operation of the system from the initial steady-state input value to the predicted steady-state input value to provide a predicted dynamic operation; comparing the predicted dynamic operation to the desired steady-state value at the final desired output value and generating an error as the difference therebetween; determining a change in the input value for input to step of predicting the dynamic operation which will vary the input value thereto; and varying the change in the input value to minimize the error. 28. The method of claim 27, wherein the step of detennining the error comprises multiplying the determined error value by a predetermined weighting value that is a function of time from the first time to the second time.29. The method of claim 27, wherein the predetermined weighting function attenuates the error for values proximate in time to the initial steady-state value at the first time and decreases the attenuation value as time increases to the final steady-state value at the second time. odel.7. The method of claim 4 wherein the base set of documents is the result set of documents.8. The method of claim 4 wherein the base set of documents is user specified.9. The method of claim 1 wherein the input set of documents is a result set of Web pages generated by a Web search engine in response to a user query, and the first predetermined threshold is the median content ranking of the result set of Web pages.10. The method of claim 1 wherein the first predetermined threshold is determined interactively.11. The method of claim 1 wherein the first predetermined threshold is determined from the slope of a graph plotting the content ranking versus a similarity score.12. The method of claim 1 wherein the first predetermined threshold is computed as a fraction of a maximum content score of the input set of documents.13. The method of claim 1 further comprising measuring a weight dependant on the number and direction of edges connecting a particular subset document to determine the linkage ranking of the particular subset document.14. The method of claim 13 wherein the linkage rankings are normalized.15. The method of claim 13 further comprising dividing the weight of a particular document by the number of edges connected to the particular document from other documents when the other documents are stored at an identical site.16. The method of claim 13 further comprising dividing the weight of a particular document by the number of edges connecting the particular document to other documents when the other documents are stored at an identical site.17. A method for providing an output set of ranked documents, comprising: representing an input set of documents as a graph of nodes and directed edges in a memory, each node to represent one document, and each directed edge connecting a pair of nodes to represent a linkage between the pair of documents; ranking the input set of documents represented in the graph according to their contents; selecting a subset of documents from the input set of documents having a content ranking greater than a first predetermined threshold and deleting nodes in the graph representing all other documents wherein the first predetermined threshold is the median content ranking of the input set of documents; ranking the selected subset of documents according to their linkage; and selecting an output set of documents from the subset of documents having a linkage ranking greater than a second predetermined threshold. 18. The method of claim 17 wherein the input set of documents includes a result set of Web pages generated by a Web search engine in response to a user query.19. The method of claim 18 wherein the input set of documents includes Web pages directly linked to the result set of pages.20. The method of claim 19 further comprising measuring the similarity of the content of a particular input document to the content of a base set of documents to determine the content ranking of the particular document.21. The method of claim 20 wherein the similarity is measured according to a vector space model.22. The method of claim 20 wherein the similarity is measured according to a probabilistic mode.23. The method of claim 20 wherein the base set of documents is the result set of documents.24. The method of claim 20 wherein the base set of documents is user specified.25. The method of claim 17 wherein the input set of documents is a result set of Web pages generated by a Web search engine in response to a user query, and the first predetermined threshold is the median content ranking of the result set of Web pages.26. The method of claim 17 wherein the first predetermined threshold is determined interactively.27. The method of claim 17 wherein the first predetermined threshold is determined from the slope of a graph plotting the content ranking versus a similarity score.28. The method of claim 17 wherein the first predetermined threshold is computed as a fraction of a maximum content score of
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