276 results about "Time model" patented technology

A system and method for providing optimal insulin injections to a subject, using a controller, a continuous glucose monitor, and an insulin delivery unit is disclosed. The controller possesses a discrete-time, linear model predictive control law, means for sending information to the insulin delivery unit, and means for receiving information from the CGM. The control law implemented is derived from a discrete-time model of glucose insulin dynamics and an aggressiveness parameter. The result is that using only glucose measurements obtained from sensor readings and, prior values of external insulin infusion and meal and exercise announcement the optimal insulin injection necessary to safely regulate blood glucose can be calculated.

A method for the estimation of the state variables of nonlinear systems with exogenous inputs is based on improved extended Kalman filtering (EKF) type techniques. The method uses a discrete-time model, based on a set of nonlinear differential equations describing the system, that is linearized about the current operating point. The time update for the state estimates is performed using integration methods. Integration, which is accomplished through the use of matrix exponential techniques, avoids the inaccuracies of approximate numerical integration techniques. The updated state estimates and corresponding covariance estimates use a common time-varying system model for ensuring stability of both estimates. Other improvements include the use of QR factorization for both time and measurement updating of square-root covariance and Kalman gain matrices and the use of simulated annealing for ensuring that globally optimal estimates are produced.

The present invention is related to a system and method of considering time segments or intervals in a collaborative filtering model. The present invention extends collaborative filtering approaches by integrating considerations of temporality into the training and / or vote input associated with the usage of collaborative filtering models. The present invention also applies filtering to the output with temporal models, so as to view a most appropriate subset of recommended content, centering on content that may be available at a target time. The present invention applies time to a collaborative filtering model by allowing weight to be associated with selections within a current time segment, selections historically watched within the current time segment by the user and selections historically watched within the current time segment by a large group of users.

A vehicle environment monitoring system is provided that is based on a three-dimensional vector model. The three-dimensional vector model of the vehicle's environment is generated on the basis of the image data captured by at least one three-dimensional camera. Out of the image data, particular data are extracted for generating the three-dimensional vector model in order to reduce the data volume. For data extraction, a data extraction algorithm is applied that is determined in accordance with at least one parameter that relates to the situation of the vehicle. Therefore, targeted data extraction is performed for generating a three-dimensional model that is particularly adapted for an application that is desired in the current vehicle situation. The applications of the vector model include driver assistance, external monitoring and vehicle control, as well as recording in an event data recorder. In one implementation, a sequence of three-dimensional vector models, representing a three-dimensional space-and-time model, is generated.

The invention provides a system for adjusting the amount of data bandwidth provided to a mobile device comprising a bandwidth adjustment facilitator arranged to receive a request from the mobile device for adjusting the amount of data bandwidth; a bandwidth throttler in communication with the bandwidth adjustment facilitator; the bandwidth throttler adapted to cap or allocate excess available data bandwidth to the mobile device; wherein on receipt of the request, the bandwidth adjustment facilitator process the request and if the request is successfully processed, adjusts the data bandwidth provided to the mobile device via the bandwidth throttler. The system may further be adapted for billing / charging based on either pay-per-specified-time model or pay per action model.The invention is conveniently suited for use in telecommunications system and does not require modifications to be made to existing telecommunications system.

The present invention belongs to the data processing technology field, and discloses an urban bus dynamic scheduling optimization method and system. The method comprises: on the basis of obtaining information such as degree of congestion of a bus, a road congestion condition, bus real-time location and the like, establishing a three-layer scheduling decision model comprising a time frame bus-times model, a departing time decision model and a path changing model; realizing information interaction of people, vehicles and roads through an internet technology; determining optimal time frame bus times through the time frame bus-times model from the beginning of balancing bus operating cost, passenger waiting time and riding comfort; taking the maximum total meeting times and the minimum total meeting stations of an area as a target regulation departing moment through the departing time decision model so as to effectively reduce the probability of transferring and waiting; and employing the idea of being far away from a jamming road by employing the path changing model to effectively improve the running speed and reduce the following station passenger waiting time, and arranging response stations to allow passengers having missed the stations to get off.

The invention discloses a Gaussian mixture hidden Markov model and regression analysis remaining life prediction method. The Gaussian mixture hidden Markov model and regression analysis remaining life prediction method comprises the specific steps of 1 performing characteristic vector extraction through wavelet packet algorithm decomposition, 2 establishing Gaussian mixture hidden Markov model libraries of different fault modes; 3 establishing part failure time modes of different fault modes; 4 performing part fault mode recognition and failure state assessment; 5 utilizing regression analysis to predict remaining life. The Gaussian mixture hidden Markov model and regression analysis remaining life prediction method integrates data drive and probability statistics fully utilizes respective advantages that the hidden Markov models predict the remaining life and have randomness and real-timeliness; the par failure process is divided into multiple stages, the probability statistics and the regression analysis are adopted to correct current usage time, and the remaining life prediction accuracy is improved. The Gaussian mixture hidden Markov model and regression analysis remaining life prediction method has the advantages of being high in prediction accuracy, operation speed and real-timeliness, low in cost and the like.

Quantifying the impact of wasteful operations on a database system is provided. One or more operations that are determined to be wasteful are received. The impact of the wasteful operations on performance in a database may then be quantified. The database is monitored to determine when a wasteful operation is being performed. When a wasteful operation is detected, a time value is recorded of the time spent on processing the wasteful operation. The time value is stored and used to quantify an impact of a performance problem in a database. The time value may be stored and associated with other time values that are recorded for the same wasteful operation. Thus, the impact of wasteful operations that are performed and processed in a database may be determined.

Methods and apparatus are described in which, at design-time a thorough analysis and exploration is performed to represent a multi-objective “optimal” trade-off point or points, e.g. on Pareto curves, for the relevant cost (C) and constraint criteria. More formally, the trade-off points may e.g. be positions on a hyper-surface in an N-dimensional Pareto search space. The axes represent the relevant cost (C), quality cost (Q) and restriction (R) criteria. Each of these working points is determined by positions for the system operation (determined during the design-time mapping) for a selected set of decision knobs (e.g. the way data are organized in a memory hierarchy). The C-Q-R values are determined based on design-time models that then have to be “average-case” values in order to avoid a too worst-case characterisation. At processing time, first a run-time BIST manager performs a functional correctness test, i.e. checks all the modules based on stored self-test sequences and “equivalence checker” hardware. All units that fail are deactivated (so that they cannot consume any power any more) and with a flag the run-time trade-off controllers, e.g. Pareto controllers, are informed that these units are not available any more for the calibration or the mapping. At processing time, also a set of representative working points are “triggered” by an on-chip trade-off calibration manager, e.g. a Pareto calibration manager, that controls a set of monitors which measure the actual C-Q-R values and that calibrates the working points to their actual values. Especially timing monitors require a careful design because correctly calibrated absolute time scales have to be monitored.

The invention relates to a system and method for predicting the arrival time of buses in real time. The system comprises an intelligent acquiring module, a predicting processing module and a displaying module which are arranged on the bus; the intelligent acquiring module is used for acquiring the real-time position information of the buses and transmitting the real-time position information of the buses to the predicting processing module through a 3G network; the predicting processing module is used for establishing a road section consumption time model according to the received position information of the buses, predicting the arrival time of the buses of each road section and transmitting the arrival time of the buses to the displaying module through the 3G network; and the displaying module is used for displaying the received arrival time of the buses. The system is characterized in that the data reported in real time of the buses on the road section between the current bus and the station is used as the reference and a plurality of feature information is used for prediction so that the accuracy of predicting the arrival time of buses is improved. The system has good real-time performance, self adaptability and expandability.

The invention discloses a hazardous chemical substance transport scheduling method based on multi-target modeling optimization. The hazardous chemical substance transport scheduling method includes the steps of respectively building a path length model, a building time model, a vehicle fixed cost model and a risk model, carrying out per-unit and weighted processing on the four sub-models to obtain an evaluation function of hazardous chemical substance transport optimized scheduling, solving the models through an improved genetic algorithm including natural number coding, initial population recursion generation, an optimal storage strategy, improved matched intersecting and continuous three-time intersecting, and finally obtaining a hazardous chemical substance transport optimal path which is short in transport path, high in distribution efficiency, few in distribution vehicle and small in risk. According to the hazardous chemical substance transport scheduling method, four targets are considered at the same time, a decision maker can set different weight values according to self requirements, the searching direction of the genetic algorithm is determined through the weight values, fitness values are continuously iterated to be finally converged, and therefore the optimal path is obtained.

The invention provides a method and system for determining seismic slice isochronism. The method includes the steps that seismic data are collected; the seismic data are identified to obtain the isochronal maker bed of the seismic data and two events corresponding to the isochronal maker bed; according to the events, a stratum time model is established; according to the time model, Wheeler domain conversion is performed on the seismic data to obtain a geologic age domain amplitude data cube; reflection inclination angles corresponding to the geologic age domain amplitude data cube are determined; according to the reflection inclination angle and the geologic age domain amplitude data cube, the slice isochronism is determined; according to the slice isochronism, oil and gas exploration is performed. The problem that because the stratum deposition rate changes, whether seismic slices are isochronal or not is difficult to determine is solved, and the seismic slice isochronism is effectively judged.

The invention discloses an urban rail transit part route operation regulating method in an emergency scene. The method comprises the following steps of S1, obtaining the rain operation and route parameters of a rail route, the position of an emergency event, and the fault parameter, so that the interruption time is determined; S2, determining an alternative part route foldback scheme according to the train operation and route parameter, the position and the fault parameter of the emergency event, and the interruption time; S3, building the total target function of the rail route operation regulation according to a station total retention number model and the total delay time model of arrival and departure of all trains in the part route foldback scheme; S4, solving the total target function by using a particle swarm algorithm of the convergence factor and the weight; determining the optimum part route foldback scheme. When the part route foldback scheme is used for regulating the rail transit operation under the condition of the emergency event, the problem of station passenger retention can be well solved; the rail train is promoted to recover the normal operation.

An electric vehicle rapid charging demand scheduling method based on load space transfer comprises the steps of dynamically selecting a path of an urban road network and sequentially establishing a road section dynamic travel time model and a dynamic path selection model; establishing a charging navigation scheme considering the congestion degree of the charging station, wherein a user charging navigation model, a charging station group service capability optimization model and a charging selection model considering vehicle decision dynamic evolution are sequentially established; and establishing an ordered rapid charging strategy based on the interaction between the charging station and the electric vehicle, including formulating the cost of the charging station and establishing a master-slave game model of the charging station and the electric vehicle. Factors such as interaction influence of multi-vehicle charging selection and non-uniform facility utilization rate of the urban fastcharging station are considered in the electric vehicle fast charging demand scheduling process; the interactive influence of charging station selection and path selection between vehicles and the interactive game strategy between stations and vehicles are fully considered, and a more scientific and effective scheduling scheme can be provided for quick charging of electric vehicles.

The invention discloses a heterogeneous multi-kernel power capping method through coordination of DVFS and task mapping. The method comprises the steps that firstly, computational node power consumption, CPU power consumption and GPU power consumption scripts can be measured after program execution is completed for a heterogeneous system, then, selected parallel test benchmark programs are modified for obtaining the execution time of different kernel functions; different frequencies are set for a CPU and a GPU, application programs are operated only on the CPU and the GPU, detailed operation information is obtained and comprises the total execution time, the execution time of each kernel function, computational node power consumption, CPU power consumption and GPU power consumption; on thebasis of the operation information, a predicted model is designed and includes a predicted execution time model and a power consumption model; finally, on the basis of the predicted model, system power consumption and execution time under different CPU frequencies, GPU frequencies and task distribution schemes are obtained to be filled in a configuration table, and according to an improved greedyalgorithm, the best configuration scheme is found. By adopting the heterogeneous multi-kernel power capping method, the system power consumption budget is limited while the system performance can beimproved.

The invention relates to a model based on a cross-layer shuttle vehicle intensive automatic storage system and an optimization method of the model. Elevators and shuttle vehicles are respectively analyzed, and average service time models of the elevators or the shuttle vehicles at each stage in the stock output operation process of the cross-layer shuttle vehicle intensive automatic storage system are solved; according to the operation processes of the shuttle vehicles and the elevators, with a stock output task serving as a customer and the shuttle vehicles serving as service counters, and the shuttle vehicles serving as customers and the elevators serving as service counters, a multilevel semi-open-loop queueing network model is established for the stock output operation of the cross-layer shuttle vehicle intensive automatic storage system; an approximate average algorithm is used for solving the multilevel semi-open-loop queueing network model, and main performance parameters in evaluation indexes of the cross-layer shuttle vehicle intensive automatic storage system are obtained. By means of the model, the performance of the system under different goods shelf configurations can be accurately and effectively evaluated, the optimal shuttle vehicle and elevator configuration combinations can be obtained, and therefore the design of the system is guided, and the system running cost is saved.

In some embodiments, apparatus and systems, as well as methods, may operate to acquire fluid signature data representing a plurality of nuclear magnetic resonance echo trains associated with fluids in a material, to simultaneously invert a plurality of relaxation time models to provide inverted results, and to determine fluid properties using the inverted results. The relaxation time models may be associated with the fluids using the fluid signature data after constraining ratios of T1 relaxation times to T2 relaxation times.

The invention discloses an interval velocity model building method under complicated geological conditions, firstly solves the problem of time dimension fitting under complicated terrain conditions and also solves the problems of interval velocity filling and speed modeling on the basis of the solving of the problem of time dimension fitting. The interval velocity model building method disclosed by the invention has the following positive effects: in the interval forming process, data is pretreated by utilizing the concepts of grid and hanging wall and footwall of faults, the concept of group of faults is also introduced, so that an interval time model is built more efficiently; a certain number of primary interval velocity data is utilized to fit the velocities of the hanging wall and the footwall of the faults more accurately, so that a more accurate velocity model is obtained.

The invention provides a method for predicting the operational reliability of a power distribution network based on an ARIMA model, and the method comprises the steps: predicting the number of times of monthly power failures of a user through building the ARIMA model; enabling an unstable element failure time sequence to be converted into a stable time sequence, and then carrying out the regression of the lagged value of a dependent variable and the present value and lagged value of a random error term to build a user monthly power failure time model; sampling a shutdown point according to the prediction results; considering real-time load operation conditions; building a fault mode impact table based on a TLOC rule and a PLOC rule; calculating the system recovery time for the shutdown of equipment at each time, and finally obtaining a yearly reliability index. The method proposed by the invention guides the planning, design, operation and maintenance of a future power grid effectively and accurately, improves the accuracy of prediction and estimation of the operational reliability of the power distribution network, achieves the stable operation of the power distribution network, reduces the frequency of power failures, and reduces the power failure range.

The invention discloses a vehicle safety anti-collision control method combining safety distance and collision time, comprising the following steps of establishing a vehicle safety distance model; establishing a vehicle safety collision time model; and performing vehicle safety anti-collision decision. According to the method of the invention, an early warning distance is divided into three critical distances, namely a critical safety distance, a critical danger distance and a critical infinitesimal distance. Different safety prevention measures are provided for different distance phases, including safe driving tips, warnings, primary braking, secondary braking and emergency braking. Not only can ride comfort reduction caused by early braking be prevented, but also vehicle collision causedby delayed braking can be avoided. The method of the invention combines a safety distance model and a collision time model, which complement each other's deficiencies. The safety collision time modelcan compensate for the recognition error of a vehicle-mounted sensor on the safety distance; the safety distance model can make up for the infinite problem of TTC calculated by the safety collision time model.

The invention discloses a real-time dynamic flight path planning method based on linear programming. The real-time dynamic flight path planning method based on linear programming comprises the following steps: obtaining a coordinate point set included by obstacle boundary coordinate points according to morphological characteristic coordinates of an obstacle in a navigation coordinate system; obtaining a dynamic parameter constraint set of an aircraft according to an established discrete time model; and obtaining a flight path coordinate point set in the navigation coordinate system according to an optimal flight path target function on the basis of the coordinate point set included by the obstacle boundary coordinate points and the flight dynamic parameter constraint set. By virtue of the real-time dynamic flight path planning method based on linear programming, the technical problem in the prior art that the flight path cannot be automatically planned is solved; furthermore, the technical effect of automatically planning the flight path is achieved.

A system and methods for real-time model based vehicle structural anomaly detection are disclosed. A real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle is received, and the real-time measurement is compared to expected operation data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated, if the persistence exceeds a persistence threshold value.

The invention discloses a robust fuzzy control method for a hydraulic loop based on an uncertain discrete model, belonging to the technical field of plate and strip rolling. The method is used for realizing high-precision and stable control on a loop control system through designing a robust fuzzy state feedback controller based on an uncertain discrete time model. An uncertain continuous time model is established according to a dynamic model of the loop control system, a proper sampling period is selected, the obtained continuous model is discretized by using a zero-order holder method, then the uncertain discrete time model of the loop control system is obtained, and a robust fuzzy controller is designed on the basis. The robust fuzzy control method for the hydraulic loop based on the uncertain discrete model has the advantage of solving the problem that the traditional loop control method can not be used for eliminating perturbation existing in system parameters and steady state errors caused by outer interference in plate and strip hot continuous rolling processes.

The invention belongs to the field of trusted computing, and relates to an embedded software trusted attribute modeling and verification method. The method comprises the following steps that (1) a semi-formal model is built; (2) the semi-formal model is converted to a trusted embedded software model ZAM on a finite field, wherein the trusted embedded software model ZAM comprises a time model, a static model and a dynamic model; (3) a ZMTL timing sequence logic formula describing embedded software trusted attributes is given, and trusted attributes of the ZAM model are verified through an FZMCA algorithm. A trusted embedded software modeling and verification frame is divided into a UML / MARTE graphical layer, a model converting layer, a ZAM model layer and a model verification layer. Modeling and verifications can be conducted on the embedded software trusted attributes according to the method to reduce trusted attribute defects of embedded software in the developing process and reduce software failure probability.

A step response of a clock synchronous circuit including a bandwidth restriction effect of a transmission path is extracted from circuit data on a simulation subject. A second discrete time model is generated by applying the response function to a first discrete time model generated from the circuit data. Using the second discrete time model, clock edge timing and an effective signal value of a signal input to / output from the clock synchronous circuit at this timing are calculated for simulation execution. Analogically accurate simulation of a circuit operation around a sampling edge of a clock enables precise simulation with a minimum calculation in a short time. Accordingly, the invention can provide an accurate simulation method for accurately modeling an analog operation of a signal transmission circuit that inputs and outputs a high-speed signal, to calculate in a short time.

A system and method for provisioning Internet access to a computing device comprising an Internet access adjustment facilitator arranged to receive a request from the computing device for adjusting Internet access from a paid Internet access mode to a toll-free Internet access mode; and a whitelist in communication with the Internet access adjustment facilitator, the whitelist maintaining a list of web resources available for toll free access by the computing device; wherein upon successful processing of the request, the list of web resources are toll free for access by the computing device. The system may further be adapted for billing / charging based on either pay-per-specified-time model or pay per action model.

The embodiment of the invention provides a shaft temperature sensor detecting method and device. The method comprises the steps that if the state of a shaft temperature sensor is normal, the difference between the temperature value at the current time and the temperature reference value at the current time is acquired to acquire the temperature change value at the current time, and whether the temperature reference value of the current time is updated is related to whether the temperature change value of the previous time is greater than a preset hop threshold; if the temperature change valueat the current time is greater than the hop threshold, an abnormal hop occurs; the number of consecutive abnormal hops is counted; and if the number of consecutive abnormal hops is greater than a number threshold, the shaft temperature sensor is faulty. According to the embodiment of the invention, the problem of complex and long-time model constructing of state anomaly detection is overcome, andthe shaft temperature sensor is detected in real time.

The invention provides a predictive direct power control method of a three-phase grid connected rectifier based on an extended state observer, and belongs to the field of electronic power control technology. The predictive direct power control method is used for solving the problems of relatively poor anti-jamming performance, slow response speed and large overshoot of the predictive direct power control method of the three-phase grid connected rectifier in the prior art. The control of the predictive direct power control method provided by the invention is composed of two control rings: a voltage regulation ring is an outer ring and a power tracking ring is an inner ring. A PI controller is combined with the extended state observer to form the outer ring to resist external interference. Predictive control based on a system discrete time model forms the inner ring to directly control active power and reactive power. The extended state observer is added in the predictive direct power control, and the extended state observer deems the external interference as a new system state and estimates and compensates the new system state in a feedback manner. Practice proves that the extended state observer is a very effective manner of processing the system uncertainty and the external interference.

The invention belongs to the technical field of pattern recognition and computer vision, and discloses a visual object tracking method based on multiple model integration and structured depth characteristics. The method comprises the steps that modeling is conducted on the appearance of a tracking object by adopting multilayer structured depth network characteristics, network extraction characteristics have higher robustness on interference factors such as motion blur; low-level characteristics in structure characteristics can not only distinguish strong interfering targets easily but also make position estimation more accurate; high-level characteristics in the structured characteristics can separate the tracking object from a background easily. Accordingly, visual object tracking is conducted by means of long time and short time model merging, and the precision of position estimation is improved easily through a short-time model; the strong interfering targets which are similar are inhibited and tracked easily through the short-time model. The method has the advantages of being high in precision and robustness; the method can be used for application such as video monitoring, roadtraffic condition analysis and human-computer interaction.