26628 results about "Prediction methods" patented technology

Accordingly, a virtual keypad system for inputting text is provided. A virtual keypad system includes a remote controller having at least one touchpad incorporated therein and divided into a plurality of touch zones. A display device is in data communication with the remote controller and is operable to display a user interface including a keypad, where each key of the keypad is mapped to a touch zone of the touchpad. A prediction module, in response to an operator pressing a given touch zone to select a particular character, performs one or more key prediction methods to predict one or more next plausible keys. A key mapping module remaps the touch zones of the touchpad to the keys of the keypad based on the one or more next plausible keys.

A mobile terminal (21) that appropriately predicts an application that a user is likely to use includes: an input device (106) that selects and executes any of two or more applications; a GPS reception unit (108); a behavior pattern extraction unit (205) that creates a usage prediction rule of the application executed on the input device (106), in association with a usage location detected by the GPS reception unit (108); and an information display control unit (204) that specifies, based on the usage prediction rule, an application corresponding to the current location detected by the GPS reception unit (108), and causes a display (105) to display the specified application as a prediction result.

An automatic playback overshoot correction system predicts the position in the program material where the user expects to be when the user stops the fast forward or reverse progression of the program material. The invention determines the position where the program material was stopped. The media controller transitions to the new mode that the user selected, starting at the stopped position with an overshoot correction factor added or subtracted from it. The invention adapts to the user by remembering how much the user corrects after he stops the fast forward or reverse mode. Correction factors are calculated using the user's corrections and adjusting the correction factors if the user continues to make corrections. The invention also uses a prediction method to correctly place the user within the program upon transition out of either mode and determines if the speed of the fast forward or reverse modes and then automatically subtracts or adds, respectively, a time multiple to the frame where the transition was detected and positions the user at the correct frame. The time multiple is fine tuned if the user is consistently correcting after the fast forward or rewind mode stops. Another method initially tests the user's reaction time using a test video and asks the user to press the fast forward or reverse button on his control device during the test video and then asks the user to position the video to the place that he expected the system to have been. This time span is then used whenever the user uses the fast forward or reverse modes and is adjusted with a multiple for each speed. A final method allows the user to simply set a sensitivity setting that the system will use as a correction factor and a multiple is subtracted or added to the release frame whenever the user uses the fast forward or reverse modes, respectively.

The invention discloses an attention CNNs and CCR-based text sentiment analysis method and belongs to the field of natural language processing. The method comprises the following steps of 1, training a semantic word vector and a sentiment word vector by utilizing original text data and performing dictionary word vector establishment by utilizing a collected sentiment dictionary; 2, capturing context semantics of words by utilizing a long-short-term memory (LSTM) network to eliminate ambiguity; 3, extracting local features of a text in combination with convolution kernels with different filtering lengths by utilizing a convolutional neural network; 4, extracting global features by utilizing three different attention mechanisms; 5, performing artificial feature extraction on the original text data; 6, training a multimodal uniform regression target function by utilizing the local features, the global features and artificial features; and 7, performing sentiment polarity prediction by utilizing a multimodal uniform regression prediction method. Compared with a method adopting a single word vector, a method only extracting the local features of the text, or the like, the text sentiment analysis method can further improve the sentiment classification precision.

A method and apparatus is provided for decoding an encoded baseline video stream and an enhancement stream. The baseline video stream is decoded, upscaled and enhanced by applying adaptive filters specified by the enhancement stream. Baseline upscaled images are then coded to motion compensate enhanced high resolution images using previously decoded enhanced images, thus recycling these enhanced images. The enhancement stream provides the best predictor method for the decoder to combine blocks from previous enhanced images and upscaled images to produce a motion compensated enhanced image. Likewise, forward and backward motion compensated images are blended according to feature classification and filter extraction methods provided by the enhancement stream to produce a bidirectionally predicted frame. Lastly, the decoder applies residual data from the enhancement stream to produce a completed enhanced image.

A video compression method, and more particularly, a prediction method for efficiently eliminating redundancy within a video frame, and a video compression method and an apparatus using the prediction method are provided. There is provided a method for encoding video based on a multi-layer structure, including performing intra-prediction on a current intra-block using images of neighboring intra-blocks of the current intra-block to obtain a prediction residual, performing prediction on the current intra-block using an image of a lower layer region corresponding to the current intra-block to obtain a prediction residual, selecting one of the two prediction residuals that offers higher coding efficiency, and encoding the selected prediction residual.

Vehicles (1-1) or ships (1-2) each carry a magnetic force line sensor (11), a GPS position detector (12), and a data transmitter (13) and travel within an observation area transmitting magnetic field data and position data of each point to an earthquake prediction center (4). A telluric current induction field estimation unit (43) of the earthquake prediction center (4) estimates telluric current induction fields based on the observation data that it receives and collects. A telluric current estimation unit (44) estimates telluric currents based on the results of estimating the telluric current induction fields. A telluric current induction field intensity change pattern generation unit (45) generates patterns that indicate the change over time of the intensity of telluric current induction fields. An earthquake prediction unit (46) analyzes the state of distribution of the telluric currents and the patterns of change in the intensities of the telluric current induction fields and estimates a seismofocal zone, seismic intensity, and time of occurrence of a seismic event.

The invention discloses a motion vector forecasting method in a resolution quantizing structure, wherein, forecasting motion vectors of macro blocks in enhancement layers are acquired by utilization of relativity of motion vectors of a time domain, a space domain and an interlayer domain. The realization process is that: on the time domain, candidate motion vectors of the time domain are acquired from motion vectors of macro blocks with the same position with the prior frame; on the space domain, candidate motion vectors of the space domain are acquired from motion vectors of adjacent blocks; on the interlayer domain, candidate motion vectors of the interlayer domain are acquired from corresponding motion vectors with low space domain resolution hierarchy; candidate motion vectors with minimum motion estimation cost are selected to be forecasting motion vectors by selection among the candidate motion vectors of the time domain, the space domain and the interlayer domain. The invention has the advantages of capability of acquiring more accurate forecasting motion vectors, capability of compressing code rate of motion vectors, and obtaining of performance gain.

A prediction method, merged method, has been introduced in the enhancement layer of a multiple layer video coding. This merged method was designed to efficiently handle the prediction of the non moving parts in coding of the enhancement layer VOP or frame. All the information for this merged mode prediction is obtained from the base layer, and no additional side information is transmitted. This prediction mode when used together with combination of the existing forward mode, backward mode, and interpolated mode, can improve the coding efficiency for enhancement layer video coding, especially in low bit rate coding. The method can be used in most multiple layer video coding schemes, especially in spatial scalability video coding.

An IVR system is disclosed in which customer experience is enhanced by improving the accuracy and intent prediction capabilities of an interactive voice response system. Customers are allowed to make a natural language queries to specify their intent, while the accuracy of traditional IVR systems is maintained by using key features in language along with the customer's past transactions, CRM attributes, and customer segment attributes to identify customer intent.

A driving behavior prediction apparatus includes, for accurately predicting a driving behavior, a position calculation unit for a subject vehicle position calculation, a route setting unit for setting a navigation route, a distance calculation unit for calculating a distance to a nearest object point, a parameter storage unit for storing a template weighting factor that reflects a driving operation tendency of a driver, a driving behavior prediction unit for predicting a driver's behavior based on vehicle information and the template weighting factor, a driving behavior recognition unit for recognizing driver's behavior at the object point, and a driving behavior learning unit for updating the template weighting factor so as to study the driving operation tendency of the driver in a case that a prediction result by the driving behavior prediction unit agrees with a recognition result by the driving behavior recognition unit.

An intra prediction method and a device using the intra prediction method are provided. The intra prediction method includes the steps of: deriving a current prediction mode as a prediction mode of a current block; constructing neighboring samples of the current block with available reference samples; filtering the available reference samples; and generating predicted samples of the current block on the basis of the filtered available reference samples. The filtering step includes performing the filtering using the available reference sample located in the prediction direction of the current prediction mode and a predetermined number of available reference samples neighboring to the prediction direction of the current prediction mode.

The invention relates to a bus arrival time prediction technique based on bus GPS data, which comprises sending out from the existing bus GPS data, making full use of auxiliary information such as pulling in, door opening and the like of a bus GPS apparatus, adopting a space dividing method to divide zones for bus lines, accounting the history arrival time of partitioned areas to each station in front according to the actual arrival time of a bus, and leading the bus arrival time to continuously do self-adapting update as the accumulation of history data, and meanwhile, predicating the bus arrival time by means of road traffic status which is calculated by GPS information of a taxi or road traffic status calculated by other systems, and improving the predicating accuracy.

An information processing apparatus includes: a touch detection unit detecting touch of an instruction object on an operation surface; a proximity detection unit detecting proximity of the instruction object to the operation surface; and a control unit detecting movement of the instruction object on or to the operation surface based on the detection result of the touch detection unit and the detection result of the proximity detection unit and starting a process corresponding to an input operation predicted from the detected movement of the instruction object.

A mobile terminal that appropriately predicts an application that a user is likely to use that includes: an input device that selects and executes any of two or more applications; a GPS reception unit; a behavior pattern extraction unit that creates a usage prediction rule of the application executed on the input device , in association with a usage location detected by the GPS reception unit; and an information display control unit that specifies, based on the usage prediction rule, an application corresponding to the current location detected by the GPS reception unit , and causes a display to display the specified application as a prediction result.

A video coding method using a multi-layer structure, and more particularly, a method and apparatus for facilitating a search for an intra-prediction mode in an upper layer using an intra-prediction mode in a lower layer while efficiently and compressively encoding the searched intra-prediction mode are provided. The intra-prediction method includes searching for an optimum prediction mode of a current block among a predetermined number of intra-prediction modes, and obtaining a directional difference between the searched optimum prediction mode and an optimum prediction mode of a lower layer block corresponding to the current block.

An object path prediction method, apparatus, and program and an automatic operation system that can secure safety even in situations that can actually occur are provided. For this purpose, a computer having a storage unit that stores the position of an object and the internal state including the speed of the object reads the position and internal state of the object from the storage unit, generates trajectories in a space-time consisting of time and space from changes of the positions that can be taken by the object with the passage of time based on the read position and internal state of the object, and predicts probabilistically paths of the object by using the generated trajectories.

The invention discloses a power grid security situation predicting method based on an improved deep learning model and belongs to the technical field of power system safety. The power grid security situation predicting method includes: performing power grid security situation evaluation through power grid data collection and preprocessing; aiming at the characteristic that indicator data of power grid security situation evaluation are high in relevance and dimension, providing an improved self-coding network method to lower dimension of the indicator data, and utilizing a data sample after dimension reduction and a power grid security situation value corresponding to a next time monitoring point; adopting an improved deep belief network to build a deep learning situation predicting model with multi-input and multi-output for power grid security situation prediction. By the power grid security situation predicting method, speed and accuracy of power grid security situation prediction can be improved effectively.

Wavefront aberration of a projection optical system is measured and information on the wavefront aberration is obtained (step 102). Furthermore, a pattern of a reticle is transferred onto a wafer via a projection optical system (steps 104 to 108). Then, the waver on which the pattern is transferred is developed, and line width measurement is performed on the resist image formed on the wafer and line width difference of images of a first line pattern extending in a predetermined direction and a second line pattern that is orthogonal to the first line pattern is measured (steps 112 to 118). And, according to a value of the 12th term of the Zernike polynomial, which is an expansion of the wavefront aberration, and the line width difference, the projection optical system is adjusted so that magnitude of the 9th term (a low order spherical aberration term) is controlled (steps 120 to 124).

A preference predicting method compares a subject user's play list with a plurality of other user's play lists and generates suggested new entertainment product or service selections to the subject user. In an embodiment of the method, the user's play list is compared to stored play lists to identify, on a selection by selection basis, how many selection titles from the user are found on each of the stored play lists. This comparison step generates a peer comparison group of the stored play lists having at least a selected number (e.g., fifty) selection title matches. The peer comparison group entries having a selected number of the user's play list selection titles are identified as liking the same selections and each identified play list is searched to identify a selection title not included in the user's play list, thereby generating a predicted selection title for the subject user.

The invention belongs to the technical field of network security and particularly relates to an attack-oriented network security situation prediction method, device and system. The method comprises the following steps: detecting and collecting alarm data and network environment operation and maintenance information in a network countermeasure environment, obtaining an element set required by network security situation prediction, wherein the element set comprises three types of information of an attacker, a defense party and a network environment; evaluating the attacker capability and the level of the defense party, establishing a dynamic Bayesian attack graph, and calculating an attack phase number and an attack state occurrence probability vector; and combining a vulnerability scoring standard and network asset information, and performing time-space dimension quantification on the network security situation value. According to the method, dynamic association of the situation elements of the defense party, the attacker, the environment information and the like is achieved, the actual environment of the network is better conformed to, the future situation and the attack occurrencetime can be accurately predicted, higher prediction efficiency is achieved, and storage scale and timeliness of network security situation awareness are optimized, so as to provide more effective guidance for network protection.

The present invention relates to technologies for integrated circuits and Large Area Integrated Circuits (LAICs), which are integrated circuits made from photo-repetition of one or several reticle image fields, stitched together on at least one lithographic process layer. It also relates to a specific class of LAIC that can connect to the contacts of other ICs placed on its surface, where specific contact detection algorithms means are disclosed. The innovations include means for defect tolerance of serial communication links, means for efficient diagnosis of short and stuck-at faults in regular reconfigurable network, means for a programmable interposer for rapid prototyping of 3D stacked chips, means to build efficient large area micro-system devices (LAMS), with distributed and configurable hierarchical structures for power supply, thermal regulation and signal propagation, means to reduce mechanical / thermal / thermo-mechanical issues in LAMS devices, means to propagate analog signal on a configurable digital network, means to predict thermo-mechanical stress peaks.

The invention belongs to the petroleum exploration field, and concretely relates to a compact sandstone reservoir complex netted fracture prediction method. The method comprises the steps of: building a geological structure model and a fracture growth model; testing magnitudes and directions of ancient and modern crustal stresses; completing a rock mechanic parameter experiment; testing rock mechanic parameters and fracture stress sensitivities; developing a fracture rock multistage composite rupture criterion; performing a rock deformation physical test to obtain a peak value intensity; building a relation model between single axle state stress-strain and fracture bulk density; building a relation model between triaxial state stress-strain and fracture bulk density and occurrence; building a relation model between single axle state stress-strain and fracture bulk density; calculating and stimulating fracture parameters under modern conditions; and verifying the reliability of a fracture quantitative prediction result. The method can accurately obtain compact sandstone reservoir complex netted fracture parameters, and perform quantitative characterization, is suitable for quantitative prediction of any fracture mainly with a brittle reservoir, and reduces exploitation risks and costs.

The invention provides a method for predicting a dolomite reservoir. The method includes acquiring seismic attributes of a target stratum, increasing the signal-to-noise ratio of the seismic attributes, adopting the seismic attributes with the signal-to-noise ratio increased to perform post-stack wave impedance inversion; adopting the seismic attributes with the signal-to-noise ratio increased to perform pre-stack elastic parameter inversion; adopting the results of the post-stack wave impedance inversion and / or the pre-stack elastic parameter inversion to predict the dolomite reservoir of the target stratum. By an effective predicting method technical system of seismic data quality evaluation, pre-stack depth processing, post-stack attribute analysis and post-stack wave impedance inversion lithologic identification, pre-stack attribute analysis and pre-stack elastic parameter inversion of a high-quality reservoir and hydrocarbon detection of the dolomite reservoir, the dolomite reservoir can be effectively predicted.

The invention provides a combined wind power prediction method suitable for a distributed wind power plant. The method comprises the following steps: step 1, acquiring data and pre-processing; step 2, utilizing a training sample set and a prediction sample set which are normalized to build a wind speed prediction model based on a radial basis function neural network and predict the wind speed and variation trend of distribution fans at the next moment; step 3, building a distributed wind power plant area CFD (computational fluid dynamics) model and externally deducing the prediction wind speed of each fan in the plant area according to factors such as the terrain, coarseness and wake current influence of a distributed wind field; step 4, acquiring the power data of an SCADA (supervisory control and data acquisition) system fan of the distributed wind field; and step 5, adopting correlation coefficients. The invention firstly provides a double-layer combined neural network to respectively predict the wind speed and power. Models are respectively built through adopting appropriate efficient neural network types, and improved particle swarm optimization with ideas of 'improvement', 'variation' and 'elimination' is additionally added to optimize the neural network, so that the speed and precision of modeling can be effectively improved, and the decoupling between wind speed and power is realized.

The invention provides a visual significance prediction method based on a generative adversarial network. which mainly comprises the steps of constructing a saliency generative adversarial network (SalGAN) based on two deep convolutional neural network (DCNN) modules, and training. Specifically, the method comprises the steps of constructing the saliency generative adversarial network (SalGAN) based on two deep convolutional neural network (DCNN) modules, wherein the saliency generative adversarial network comprises a generator and a discriminator, and the generator and the discriminator are combined for predicating a visual saliency graph of a preset input image; and training filter weight in the SalGAN through perception loss which is caused by combining content loss and adversarial loss. A loss function in the method is combination of error from the discriminator and cross entropy relative to calibrated true data, thereby improving stability and convergence rate in adversarial training. Compared with further training of individual cross entropy, adversarial training improves performance so that higher speed and higher efficiency are realized.

A method of computing a manufacturing yield of an integrated circuit having device shapes includes sub-dividing the integrated circuit into failure mechanism subdivisions (each of the failure mechanism subdivisions includes one or more failure mechanism and each of the failure mechanisms includes one or more defect mechanisms), partitioning the failure mechanism subdivisions by area into partitions, pre-processing the device shapes in each partition, computing an initial average number of faults for each of the failure mechanisms and for each partition by numerical integration of an average probability of failure of each failure mechanism, (the numerical integration produces a list of defect sizes for each defect mechanism, and the computing of the initial average includes setting a maximum integration error limit, a maximum sample size for a population of each defect size, and a maximum number of allowable faults for each failure mechansim), and computing a final average number of faults for the integrated circuit by iterativelly reducing a statistical error of the initial average number of faults for each of the failure mechanisms until the statistical error is below an error limit.

Active sample collaborative prediction method, system and program storage device are provided. A method in one aspect may include determining approximation X for matrix Y using collaborative prediction, said matrix Y being sparse initially and representing pairwise measurement values; selecting one or more unobserved entries from said matrix Y representing active samples using said approximation X and an active sample heuristic; obtaining values associated with said unobserved entries; inserting said values to said matrix Y; and repeating the steps of determining, selecting, obtaining and inserting until a predetermined condition is satisfied.

The present invention represents a procedure for compensating the heave movement of offshore cranes. The dynamic model of the compensation actuator (hydraulically operated winch) and the load hanging on a rope are derived. Based on this model, a path-tracking control unit is developed. To compensate the movement of the ship / watercraft caused by waves, the heave movement is defined as a time-varying disturbance and is analyzed with respect to uncoupling conditions. With a model expansion, these conditions are satisfied, and an inversion-based uncoupling control law is formulated. To stabilize the system, an observer is used for reconstructing the unknown state by means of a force measurement. Furthermore, the compensation efficiency can be improved by predicting the heave movement. There is proposed a prediction method in which no ship / watercraft models or properties are required. The simulation and measurement results validate the heave compensation method.