363results about How to "Prediction is simple" patented technology

Systems, devices and methods for determining the concentration of physiological fluid analytes are provided. The subject systems have a plurality of biosensor devices present on a disposable cartridge. Each biosensor device includes a biosensor and a skin penetration means. In practicing the subject methods, a movement means of the device is used to move each biosensor device in a first direction that provides for penetration of the skin-piercing means into a skin layer followed by movement of the biosensor in a second direction that provides for removal of the skin-piercing means from the skin layer, where this movement profile provides for physiological fluid access and analyte concentration determination by the analyte sensor means. The subject systems, devices and methods for using the same find use in determining the concentration of a variety of different physiological fluid analytes, and are particularly suited for use in detection of physiological fluid glucose concentration.

An intra prediction method and apparatus. The intra prediction method of intra predicting a macroblock divided into a plurality of blocks includes intra predicting the plurality of blocks in a predetermined scanning order. One or more block among the plurality of blocks are intra predicted independently of their respective, immediate preceding block that is intra predicted immediately before the one or more blocks are intra predicted and are intra predicted in parallel with their respective, immediate preceding blocks.

This invention generally relates to a computer system and method for objectively analyzing the quality and cost of work performed by legal service providers. Price and quality of legal service providers can be compared to various benchmarks for by industry, region, complexity, corporate targets and the like. A user of the system can analyze the cost and quality of a corporation's legal counsel and offer suggestions and solutions that resolve issues identified by the system.

A rewriting apparatus of a vehicle program rewriting system predicts, on the basis of both a state of a battery at the time of starting rewriting the programs of ECUs and a scheduled process time period of rewiring the programs, a state of the battery after rewriting the programs, and executes rewriting the programs if the predicted state of the battery satisfies a condition on which the vehicle can be restarted.

The invention provides a method and system of the prediction of time series data. The method comprises the steps that wavelet decomposition is conducted on the sequence formed by t-1 moment data, n subsequences are obtained; stationary detection is conducted on n subsequences respectively; for non-stationary time series, an advance learning LSTM model is built using the t-1 moment data, and the values of t moment are predicted respectively, and forecasts of the non-stationary part are obtained by summing; similarly, for stationary sequences, ARMA models are respectively built and the values of t moment are predicted, and the forecasts on stationary part are obtained by summing; finally the prediction values of the non-stationary part and the stationary part at t moment are summed, to obtain the final forecast value. By the method and system of the prediction of time series data, by wavelet decomposition, the advantages of LSTM and ARMA are combined, in comparison with traditional methods, better effects are provided in dealing with non-stationary time series. In addition, by benefiting from the unique LSTM structure in the model, the forecasting and the generalization ability of the method is better, and the method is suitable for time series prediction in various fields.

A wireless sensor network and management method for same are disclosed. The wireless sensor network includes a plurality of nodes transitioning between a sleeping state, probing state, and working state. The management method enables the nodes to make a state transition according to sent and received messages.

In a vehicle speed control system, each node existing ahead of a vehicle is detected, and a stable running speed at which the vehicle runs stably when the vehicle passes over each node, and a deceleration needed to accelerate/decelerate the vehicle so that the vehicle speed becomes the stable running speed at each node by the time when the vehicle arrives at the node are successively calculated. On the basis of the deceleration at each node, a point at which the maximum deceleration value is achieved is selected, and the vehicle is subjected to deceleration control so that the vehicle speed becomes the stable running speed at the point. When a point at which the curve degree is maximum is different from a point at which the deceleration value is maximum and also the point is located ahead of the vehicle, further necessary deceleration control is carried out.

There is disclosed a picture synthesizing apparatus for superimposing an auxiliary image upon an image converted as if the image were photographed with a virtual viewpoint, displaying the image in a monitor, and providing the image such that an approach to a surrounding solid object can easily be predicted from the monitor image. The picture synthesizing apparatus includes: an image pickup unit 101 disposed in a car; a viewpoint change image synthesizing unit 102 for changing a viewpoint of the image obtained by the image pickup unit 101 and synthesizing the image; a car straight locus line generation unit 103 for generating a locus line at an arbitrary height of the car, when the car linearly advances; and a car locus line drawing unit 104 for drawing the locus line generated by the car straight locus line generation unit 103 on the image synthesized by the viewpoint change image synthesizing unit 102.

The invention relates to an automatic driving risk evaluation method considering uncertainty. The automatic driving risk evaluation method considering uncertainty comprises the steps of a priori environment map constructing step, a traffic participation vehicle state observing step, an own vehicle executing trajectory taking step and a self-adaptive multi-target tracking step based on priori knowledge. Through driving behavior likelihood estimation, orbifold matching and trajectory predication, running collision risk is estimated by using multiple safety margins, and finally, space-time running risk situation map is output for evaluation; and therefore, multi-safety-margin risk estimation of automatic driving can be realized, the adapting ability of automatic driving to the environment canbe improved, and the automatic driving process is more stable and reliable.

A radio quality degradation prediction system includes a wireless terminal and a monitoring server. The wireless terminal includes a positioning unit that determines a position of the wireless terminal and notifies the monitoring server of movement history of the wireless terminal via a wireless network, and a user notification unit that gives a warning to a user upon receiving a radio quality degradation alert at the wireless terminal. The monitoring server includes a future position prediction unit that predicts a future position of the wireless terminal based on the movement history of the wireless terminal, a radio propagation prediction unit that estimates radio quality in a service coverage area by using a radiowave propagation simulator, and a quality degradation prediction unit that predicts degradation of radio quality at the future position of the wireless terminal and transmits a radio quality degradation alert to the wireless terminal.

Systems and methods of the present disclosure facilitate managing information technology (IT) infrastructure. The system can include a server configured to retrieve data records for first and second IT assets, each asset comprising at least one of a computing device, a software application, an end user, a business unit, a piece of networking hardware, or an agreement for an IT service. The server can select a template comprising a relationship between the first and second data records. The system can include a link generator configured to generate a different relationship, absent from the template, between the first and second data records. The system can include a search engine configured to identify third and fourth IT assets that satisfy the second relationship, determine an indices of completeness for the third and fourth IT assets, and rank the third and fourth IT assets based on the indices to generate an ordered list.

The invention relates to an electromechanical device nonlinear failure prediction method, comprising the following steps: 1, obtain data which can represent the running state of a device and select a section continuous vibration signal which has a long course and is sensitive to the failure to analyze; 2, respectively carry out exceptional value elimination and missing data filling to the vibration data by a 3 sigma method and an interpolation method; 3, carry out noise reduction to the vibration signal by a lifting wavelet method; 4, decompose the vibration signal after the noise reduction to corresponding characteristic bandwidths; 5, obtain a low dimension manifold character by utilizing a typical predicted characteristic bandwidth and adopting a nonlinear manifold learning method through decoupling of topological mapping and non-failure energy information; 6, carry out intelligent failure prediction with long course trend in a time domain by utilizing a recurrent neural network which has the dynamic self-adaptive characteristic and a first dimension of the low dimension manifold character as a neural network input. The lifting wavelet method is adopted in the invention, the algorithm is simple, the arithmetic speed is high, and the used memory is less, thereby being suitable for the characteristic bandwidth abstraction of failure character. The electromechanical device nonlinear failure prediction method can be widely applied to the failure prediction of all kinds of electromechanical devices.

To provide a destination prediction apparatus which can predict a destination of a user by using a carried item carried by the user, the destination prediction apparatus according to the present invention includes: a position information detection unit which detects a present position of a mobile terminal; a carried item detection unit which detects the carried item of the user; a history accumulation unit which accumulates a history of date and time information detected by a clock unit, position information detected by the position information detection unit, and information of the carried item of the user detected by the carried item detection unit; a carry frequency judgment unit which calculates a frequency at which the carried item is carried; a causal relationship extraction unit which calculates an infrequently carried item and a related point; a destination prediction unit which predicts the destination using the position information, the carried item information, and a position causal relationship extracted by the causal relationship extraction unit; and the clock unit which detects the date and time information.

The present invention discloses a sharing bike attraction and generation prediction method based on an ARIMA (Autoregressive Integrated Moving Average Model). The method comprises the following stepsof: 1) collecting GPS positioning data of static parking positions of available bikes in an area, and continuously performing collection for assigned days; 2) obtaining geographic information data oftraffic zones in the area; 3) matching geographical location information of the sharing bikes to each traffic zone; 4) establishing a sharing bike trip total sample; 5) establishing a sharing bike available bike distribution spatial and temporal distribution thermodynamic chart, and a space thermodynamic chart of the number of times of attraction and generation of each zone; 6) establishing a timesequence of the number of travel times of each zone; 7) establishing an ARIMA prediction model after parameters are calibrated; and 8) predicting a sharing bike trip of each traffic zone in a next time aggregation interval. The demand prediction method employs position data of bikes to sense time-space features of the sharing bikes in a city and performs prediction of the time sequence so that adata support is provided for operation, management and scheduling of the sharing bikes.

The invention belongs to the field of traffic condition prediction, and relates to a method for discovering congestion points, congestion lines and congestion areas based on a composite network. The composite network in the method is composed of two or more sub networks and connecting edges between the sub networks. The sub networks are independent networks constituting the composite network. Thecomposite network includes an intersection network and a sensor network. The sensor network takes traffic sensors at intersections as nodes, and the sensors are related by connecting edges. The intersection network takes intersections as nodes, and connecting edges are established according to whether the intersections can be connected. On the basis of the intersection network, the definitions ofa congestion index, a congestion point, a congestion line and a congestion area are given. Calculation is carried out based on the network structure. The method specifically comprises the steps as follows: first, defining a congestion index Ti, a congestion point, a congestion line and a congestion area; then, quantifying the congestion level; and finally, analyzing and calculating congestion points, congestion lines and congestion areas. The method is designed ingeniously, has good operability and practicability, and has a wide market prospect.

Provided is A method of manufacturing diesel fuel, including: fractionating in a first fractionator a synthetic oil obtained by Fisher-Tropsch synthesis into at least two fractions of a middle fraction, and a wax fraction containing a wax component heavier than the middle fraction; hydroisomerizing the middle fraction by bringing the middle fraction into contact with a hydroisomerizing catalyst to produce a hydroisomerized middle fraction; hydrocracking the wax fraction by bringing the wax fraction into contact with a hydrocracking catalyst to produce a wax decomposition compound; fractionating in a second fractionator a mixture of the hydroisomerized middle fraction and the hydrocracked wax fraction into at least two fractions including a kerosene fraction and a gas oil fraction; and mixing the at least two fractions at a predetermined blend ratio to produce a diesel fuel having a kinematic viscosity at 30° C. of 2.5 mm²/s or more and a pour point of −7.5° C. or less.

An apparatus for controlling a voltage converting apparatus controls a voltage converting apparatus capable of performing one-arm drive using either a first arm or a second arm by alternatively switching on a first switching element and a second switching element each of which is connected to a reactor in series. The apparatus for controlling the voltage converting apparatus is provided with: a current detecting device for detecting a reactor current; an average value estimating device for estimating an average value of the reactor current in units of periods of a gate signal for changing on and off of each of the first switching element and the second switching element, by using the detected reactor current; and a controlling device for controlling operation of the voltage converting apparatus on the basis of the estimated average value of the reactor current.

An error tolerant computer controlled system comprises several computers working redundantly and controlling actuators based on signals from sensors and input devices. Each data item emitted by each computer is simultaneously sent through differing communication paths to each actuator, such that in normal operation each actuator receives each data item through several paths. This system continues to function properly even in case of a failure without requiring any re-routing of the data items, which makes it easier to design, analyze and test and thereby increases its reliability.

The invention belongs to the field of traffic condition prediction, and relates to a method for discovering a congestion time period based on a composite network. According to the method, a compositenetwork of an intersection network and a sensor network is constructed based on a complex network theory, and a congestion time period in the traffic network can be accurately judged. The method is implemented specifically according to the following steps: firstly, a congestion index Ti, a congestion time period and congestion level quantization are defined, then the congestion time period is calculated through establishing a mapping relation between the intersection network and the sensor network and performing congestion time period analysis, and finally frequent congestion time period analysis is performed. The model established according to the method is simple, the calculation data amount is small, the processing process is concise and clear, the operability and the practicability aregood, and effective implementation is facilitated. Meanwhile, the overall design is ingenious in conception, the required infrastructure are easy to obtain, the prediction process is simple, the application environment is friendly, and the market prospect is broad.