273results about How to "Shorten study time" patented technology

Instrumentation for guiding a surgeon in performing a unicompartmental knee replacement includes a tibial block having a guide, and a milling tool adapted to engage the guide, such that the surgeon can mill the desired tibial bone bed by directing the milling tool along the guide. Further including a femoral jig having a cutting slot, a guide, and a milling tool adapted to engage the guide, such that a surgeon can cut a portion of the femur through the slot, and mill the femoral bone bed by directing the milling tool about the guide. A femoral trial removal clamp facilitates in removing the femoral trial prosthesis, and a spreader compression clamp aids in compressing the final prostheses as the bone cement cures.

The present system and method provide an automated approach to conditioning of content. The content is scanned for topics for conditioning, then supplemental content is identified and selected using search tools such as general search engines searching the Internet and search tools for private databases. The conditioned content may be customized for a particular user or category of users, including different materials for different users, in different formats including multimedia, based on a profile for the user(s) for which the personalized content is being prepared. The topics for conditioning may be identified using a text analysis engine, either alone or in combination with a rules engine, in the preferred embodiment.

A method is disclosed for adaptive learning of fundamental-frequency repeatable runout (1FRRO) compensation information in a disk drive. The disk drive includes a transducer head, a magnetic disk having a plurality of concentric data tracks defined by embedded servo wedges that provide position information, and a control system. In the method, 1FRRO compensation information is learned over a predetermined minimum number of disk revolutions. After the predetermined minimum number of disk revolutions, the 1FRRO compensation information is monitored for convergence while learning of the 1FRRO compensation information continues. Learning is terminated upon detection of convergence of the 1FRRO compensation information.

A gas turbine output learning circuit is configured to compute a current combustion gas temperature TIT at an inlet of a gas turbine by linear interpolation by use of two characteristic curves A and B respectively representing relations between a pressure ratio and an exhaust gas temperature in the cases of the combustion gas temperature at the inlet of the gas turbine at 1400° C. and 1500° C., then to compute ideal MW corresponding to this combustion gas temperature TIT at the inlet of the gas turbine by linear interpolation according to 1400° C.MW and 1500° C.MW (temperature controlled MW), and then to correct the 1400° C.MW and the 1500° C.MW so as to match the ideal MW with a measured gas turbine output (a power generator output).

A sample image is filtered through a circumferential filter 28, an image feature amount is learned in a discrimination section 30, an image G to be searched is filtered through the circumferential filter 28 to detect a rotation invariant image feature amount for each filtered area, and each detected image feature amount is inputted into the discrimination section 30. Thereby, it is discriminated whether or not a filtering area is considered to be face image at high speed. And the dimensional number of image feature amounts is greatly reduced, so that not only the discrimination work but also the learning time of the sample image are greatly reduced.

A device and method for operating the device that includes verifying a user, managing communication priorities, predicting application use, rewarding the user for device activity, posting social media status updates, and enhancing sleep patterns.

Described herein is, inter alia, a method and a device for providing a number of rental vehicles at a station, determining at least one of a number of rental vehicles available at the station and a number of vehicle-receiving spaces available at the station, and sending the at least one of a number of rental vehicles available at the station and a number of vehicle-receiving space available at the station to a user. A distance and historical use of stations are also considered to suggest preferred stations and provide incentives associated therewith. A graphical user interface and a systems adapted to carry the method are also provided hereby.

An anthropomorphic medical robot arm includes a base end, a first arm element, a base joint coupling the base end to the first arm element, a second arm element, a middle joint coupling the second arm element to the first arm element, a distal functional end, a distal joint coupling the distal functional end to the second arm element, and at least one selectively operable movement inhibitor operable on the base joint, middle joint and / or distal joint so as to restrict the functionally possible range of movement of the robot arm to the range of movement of a human arm.

The present invention provides an interface device between a prototype system and an HLA emulating system, wherein the interface device is composed of a collocation tool generating module, an XML collocation file analyzing module, an HLA system service accessing module, a prototype system service accessing module, a system service switching module, an emulation storing module and an upper code assistant generating module. The XML collocation file analyzing module, the HLA system service accessing module, the prototype system service accessing module, the system service switching module and the emulation storing module are arranged in the interface device. The collocation tool generating module and the upper code assistant generating module are arranged outside the interface device. The interface device aims at different prototype systems and automatically generates a basic code frame of interface device, customizes the action and capability of interface device with the collocation tool for realizing the accurate access from the prototype system to the HLA emulating system, and fully uses the advantages of HLA emulating mechanism for realizing the seamless access from the prototype system to the HLA system.

The invention relates to the field of new energy wind power generation system, in particular to a wind turbine health warning method, comprising the following steps: (1) collection of wind farm monitoring data: collecting and storing the monitoring data under the normal operation state of the wind farm; (2) selection and determination of the characteristic parameters related to the output power ofthe wind turbine; (3) establishment of wind turbine power forecasting model; (4) construction of standard residual set of health status; (5) acquisition of real-time residual set; (6) wind turbine health assessment and early warning. The invention converts the selected generator output power performance parameter into an appropriate distance measurement, and reflects the health state of the comprehensive generating capacity of the unit at the time point. The method can quantitatively evaluate the deterioration of the generating capacity of the unit under the generating operation condition, and has strong practicability.

A wearable apparatus for measuring position and action of an arm includes: a main frame worn on an upper body of a user; and an arm motion-measuring unit connected to a side of the main frame, having a plurality of joints, and worn on an arm of a user, in which at least any one of the joints of the arm motion-measuring unit has a straight-motional degree of freedom. Accordingly, an instructor can conveniently move both arms in the apparatus, can precisely instruct a two-arm robot in motions of the instructor's arms, can reduce learning time of the robot, and can make the robot quickly and accurately learn the motions.

The invention provides a device and method for carrying out subject training based on virtual reality. The device is realized through the following components: a virtual reality driving module for simulating a real car for a user to carry out simulated driving and subject training, a subject training module for splitting the subject training into a certain quantity of single trainings, a point position teaching module for teaching a student through a passing method of each subject and calling a pre-stored key operating instruction at a key point, and a scoring module for scoring a driving process of the user according to a subject examination scoring rule and judging whether a subject examination passes or not. The device and method, provided by the invention, have the beneficial effects that point positions are clear and the method is clear; the burdens of the students and trainers are reduced through a plurality of types of repeated learning, and contradictions between the students and the trainers are avoided; the learning time of the students in real cars on site is shortened and the operation cost of driver training schools and the trainers is greatly reduced; the subject is split into a plurality of single courses and each single course is split into a plurality of courses, so that the school effect of the students is improved.

A modeling method of dynamic soft measurement based on pulse response template and parameter optimization includes setting up convolution mode based on pulse response principle, using pulse response parameter as main identification object, generating a set of pulse response template and template parameter constraint scope base on production process mechanism and utilizing optimized means to identify pulse response template parameter for finalizing optimization-solution of dynamic soft measurement model parameter.

The invention discloses a real-time limit learning machine short-time traffic flow prediction method based on fusion. The method, for prediction technology in short-time unstable traffic flow scene, predicts short-time traffic flow on the basis of the realtimeness, the accuracy, and the reliability of short-time traffic flow and a fused real-time limit learning machine. The short-time traffic flow prediction method is based on a simplified single-implicit-strata feedforward neural network structure, may fast train historical data at a traffic flow peak value and updates reached data in an increment way, and saves learning time while guaranteeing certain prediction precision. Further, the method guarantees the stability and the robustness of short-time traffic flow prediction by using a fusing mechanism, performs reconstruction during a data missing and violent fluctuation period, and is short in training time. The root mean square error and the standard error percentage of a prediction result are both in a confidence region.

A graphical user interface that presents document organization in terms of objects readily familiar to the user is provided by the present invention. According to one embodiment of the present invention, a method for managing documents in a computer system includes a step of producing a representation of a document that indicates to the user the contents of the document. A step of associating the representation with the document in the memory of the computer is also part of the method. The method includes a step of displaying a document closet image on the display. The document closet image can include a plurality of document images. A step of selecting a document image from the document closet in response to an input from the user is also part of the method. The method can also include turning the document image selected for display to the user so that the user can readily view the representation of the document's contents.

The invention relates to a path planning obstacle avoidance control method for an autonomous underwater vehicle in a large-scale continuous obstacle environment. The invention relates to the technicalfield of underwater robot path obstacle avoidance planning, and the method comprises the steps: building a large-scale continuous obstacle simulation training environment, building the state and action of a deep reinforcement learning neural network through employing obstacle avoidance sensor information as the input and the navigation speed and yaw angular speed as the output, for a multi-targetstructure of a motion planning obstacle avoidance control process, performing modular design on a reward function, and in order to avoid system instability caused by sparse rewards, setting continuous rewards are set in combination with an artificial potential field method. According to the method, obstacle avoidance training is carried out on the autonomous underwater vehicle by utilizing an improved depth deterministic strategy gradient algorithm, and an obstacle avoidance strategy obtained by training is written into a robot lower computer control system; when the autonomous underwater vehicle runs in an underwater canyon, obstacle avoidance is carried out by using an obstacle avoidance strategy learned by training, and the autonomous underwater vehicle safely reaches a target area.

The invention discloses a real-time online tool wear monitoring method based on wavelet analysis and neutral network. The real-time online tool wear monitoring method comprises steps as follows: step one, detection and transmission of three-way cutting force; step two, wavelet analysis processing; step three, normalization processing; step four, determination of input of the neural network; step five, neural network processing. The method has simple steps and is convenient to implement, the tool wear state can be rapidly and efficiently detected, the processing quality can be guaranteed, and the production efficiency is improved.

The embodiment of the invention provides a cloud robot task scheduling method and system based on parallel reinforcement learning. The cloud robot task scheduling method includes the steps that original problems are loaded in a scheduling center, and the original problems are divided into a plurality of sub-problems; the sub-problems and a plurality of computational nodes are matched by the scheduling center; the sub-problems are sent to the computational nodes matched with the sub-problems by the scheduling center; the computational nodes carry out parallel reinforcement learning on the sub-problems and feed learning results of the sub-problems back to the scheduling center; and the scheduling center judges whether the original problems have converged or not according to the learning results of the sub-problems and preset convergence conditions, and if the original problems have converged, optimal strategies of the original problems are output. The method can obtain the precise optimal strategies and reduce the time cost.

The invention provides a deformation prediction method and apparatus based on Kalman filtering and a BP neural network, wherein the deformation prediction method based on the Kalman filtering and the BP neural network comprises: obtaining the deformation monitoring data of a monitored object in a project as a training sample; establishing a BP neural network topology model according to the training sample; learning the BP neural network topology model by using a Kalman filtering algorithm according to preset training parameters to adjust the weights of the neurons in the BP neural network topology model; and performing deformation prediction according to the BP neural network topology model with adjusted weights. The deformation prediction method based on Kalman filtering and a BP neural network can shorten the learning time of the BP neural network and improve the establishment efficiency of deformation prediction model in a deformation prediction process.

A control apparatus of an engine that includes an intake passage fuel injector and an in-cylinder fuel injector according to the invention is characterised by further including correcting means for selectively increase-correcting or decrease-correcting a fuel injection quantity to bring an air-fuel ratio close to a predetermined target air-fuel ratio; and switching means for dividing an increase amount or decrease amount of the fuel injection quantity into a distribution ratio that is to be divided between the two fuel injectors, and when executing a correction by the correcting means, making one of the fuel injectors responsible for the larger part of a ratio, making the other fuel injector responsible for the smaller part of the ratio, and switching the fuel injector responsible fore the larger part of the ratio according to the operating state of the engine.

The invention discloses a method for determining a distal femur cuneiform plane in a digital auxiliary mode. The method mainly comprises the following steps that 1, continuous sectional CT scanning is performed on a patient, and image data is obtained; 2, three-dimensional data of a knee joint prosthesis is obtained through reverse engineering means; 3, polygonal processing and precise curved surface processing are performed on the three-dimensional data of the thighbone and the knee joint prosthesis; 4, marking and checking of synostosis mark points are performed in computer aided design software; 5, the cuneiform plane is determined for performing cuneiform rehearsal. The method has the advantages that digital means is adopted before operation, synostosis mark points, axes, prosthesis models, cuneiform planes and the like are determined, and an operation path can be better planned, and an operation plan can be designed; the cuneiform plane does not need to be determined by opening a medullary space, wounds are reduced, and the bleeding volume and fat embolism occurrence probability are lowered; computer assistant positioning is performed, the positioning accuracy is improved, the operation effect is better, and the patient recovery time is shortened.

The invention belongs to the technical field of computer automation application, and particularly provides a machine room environmental data management and real-time analyzing and processing system. The system comprises a browser side OCX control and a server side. The system is a multilevel distributed computer monitoring network structurally. The whole monitoring system is composed of three parts: a monitoring center, a monitoring unit and a remote IE browsing station. The monitoring objects are power equipment and machine room environment in a communication machine room, and functions of real-time remote measurement, remote signaling, remote control and remote regulation of the machine room-power and machine room environment can be realized. Meanwhile, functions of automatic prompt alarm, display and printing of alarm information, alarm or fault classified statistics, fault repair dispatching and fault sheet management, automatic repair assistance and automatic repetition measurement and fault elimination can also be provided. The monitoring system has positive promotion effect in fully utilization of manpower resource, strengthening and maintenance of construction of support means, guaranteeing of equipment stable operation and machine room safety, enhancement of labor productivity and network maintenance level, realization of unattended operation from attended operation of the machine room and promotion of modernization of power supply equipment maintenance.

A gas turbine output learning circuit is configured to compute a current combustion gas temperature TIT at an inlet of a gas turbine by linear interpolation by use of two characteristic curves A and B respectively representing relations between a pressure ratio and an exhaust gas temperature in the cases of the combustion gas temperature at the inlet of the gas turbine at 1400° C. and 1500° C., then to compute ideal MW corresponding to this combustion gas temperature TIT at the inlet of the gas turbine by linear interpolation according to 1400° C.MW and 1500° C.MW (temperature controlled MW), and then to correct the 1400° C.MW and the 1500° C.MW so as to match the ideal MW with a measured gas turbine output (a power generator output).

The invention discloses an automatic three-dimensional model component category tagging method. The method comprises the following steps that in the training process, quick training is conducted according to a three-dimensional model tagging training set, and a quick tagging model used for classifying and tagging surface patches and mesh edges of an unknown three-dimensional model is obtained through training; in the tagging process, the quick tagging model, obtained through training, of the surface patches and mesh edges is utilized for classifying surface patches and mesh edges of a target model, classification probability distribution of the surface patches and mesh edges is obtained, a graph model is constructed, smoothing and optimizing of segmentation boundaries are conducted through multi-tag graph cut optimization, and therefore rapid automatic target three-dimensional model component category tagging is achieved.

The invention relates to a driver personal training driving analog machine and the method. The machine comprises a single-purpose PC machine, a communication unit, a data preprocessing unit, a display unit, a sound unit, a ware driving unit, a practice operation unit and a data collecting unit.

The invention discloses a three-decision unbalanced data oversampling method based on a Spark big data platform, and relates to a Spark big data technology in the field of data excavation. The method comprises the following steps: firstly, carrying out data transformation with an RDD (Resilient Distributed Dataset) of Spark to obtain a normalized sample set with the LabeledPoint format <label: [features]>, and dividing the sample set into a training set and a test set; secondly, carrying out data variation by adopting the RDD of Spark, calculating a distance between samples, determining the radius of a domain, and classifying the samples in the whole training set into positive domain samples, boundary domain samples and negative domain samples according to a neighborhood three-decision model; then respectively oversampling the boundary domain samples and the negative domain samples; and finally, calling a Spark Mllib machine learning algorithm to verify a sampling result. According to the three-decision unbalanced data oversampling method based on the Spark big data platform, the problem of classification of a large-scale unbalanced data set in the field of machine learning and mode recognition is effectively solved.

The embodiments of the present invention provide an intelligent key learning method and an intelligent key learning system, and relate to the technical field of vehicle keys. The intelligent key learning method is used for the intelligent key learning system. The intelligent key learning system comprises an offline diagnosis electric inspection device and a vehicle to be learned, wherein the offline diagnosis electric inspection device and the vehicle to be learned form communication connection. The intelligent key learning method comprises that the offline diagnosis electric inspection deviceacquires an identification code corresponding to a target intelligent key according to a learning starting command; the identification code is identified so as to acquire the feature data of the target intelligent key; and the feature data of the target intelligent key is stored in a vehicle to be learned so as to complete the learning on the target intelligent key by the vehicle to be learned. According to the present invention, with the method and the system, the activation of the intelligent key is not required during the intelligent key learning, such that the learning time is reduced, the radio interference during the learning is avoided, and the learning efficiency is improved.

The intelligent teaching system for simulative acupuncture and moxibustion consists of body's acupoint model, acupoint sensors, signal amplifying follower circuit, acupoint matrix keyboard circuit, matrix keyboard expanding circuit, CPU, terminal PC and control software. It features that each of the acupoint sensors is provided with three layers of conducting film corresponding to three states of 'contact acupoint', 'Diqi' and 'offsetting acupoint' separately, and that matrix keyboard circuit and CPU scanning matrix keyboard circuit are constituted to detect the state signal of the sensors, judge which acupoint to have acupuncture and moxibustion and control the terminal PC. The present invention makes the teaching of acupuncture and moxibustion intuitive and may be used widely in the teaching.

The invention discloses a short-term photovoltaic power generation prediction method considering the correlation degree of weather and meteorological factors. The method comprises the following steps:1, rejecting bad data through an iForest algorithm; 2, respectively calculating Pearson correlation coefficients R of the photovoltaic power generation power and the five meteorological factors underfour weather types, and normalizing the Pearson correlation coefficients R; 3, performing fuzzy clustering on the five meteorological factors of the to-be-measured day, and obtaining a correlation coefficient of the historical day and the to-be-measured day; 4, a correlation coefficient normalization value is introduced, and the correlation degree between a historical day and a to-be-measured dayis solved; and 5, taking the historical day with high correlation degree as historical data, inputting the historical data and the meteorological factors of the day to be measured into the improved ACO-BP neural network, and finally obtaining a predicted value of the solar photovoltaic power generation to be measured; and 6, determining a neural network correlation coefficient, and performing simulation. The method aims to improve the photovoltaic power generation prediction precision, improves the practicality of a prediction model, and plays a great role in the combination of scheduling andprediction.