208 results about "Long-term prediction" patented technology

Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.

A multi-rate speech codec supports a plurality of encoding bit rate modes by adaptively selecting encoding bit rate modes to match communication channel restrictions. In higher bit rate encoding modes, an accurate representation of speech through CELP (code excited linear prediction) and other associated modeling parameters are generated for higher quality decoding and reproduction. A speech encoder employing various encoding schemes based upon parameters including an available transmission bit rate. In addition, the speech encoder is operable to identify and apply an optimal encoding scheme for a given speech signal. The speech encoder may be applied code-excited linear prediction when the available bit rate is above a predetermined upper threshold. Pitch preprocessing, including continuous warping, may be applied when it is below a predetermined lower threshold. The encoder considers varying characteristics of the speech signal including the long term prediction mode of a previous frame, and a spectral difference between the line spectral frequencies of a current and a previous frame, a predicted pitch lag, an open loop pitch lag, a closed loop pitch lag, a pitch gain, and a pitch correlation.

A method of concealing transmission error in a digital audio signal in which a signal that has been decoded after transmission is received, the samples decoded while the transmitted data is valid are stored, at least one short-term prediction operator and one long-term prediction operator are estimated as a function of stored valid samples, and any missing or erroneous samples in the decoder signal are generated using the operators estimated in this way, the method being characterized in that the energy of the synthesized signal as generated in this way is controlled by means of a gain that is computed and adapted sample by sample.

Base layer coding section 101 encodes an input signal to obtain base layer coded information. Base layer decoding section 102 decodes the base layer coded information to obtain a base layer decoded signal and long term prediction information (pitch lag). Adding section 103 inverts the polarity of the base layer decoded signal to add to the input signal, and obtains a residual signal. Enhancement layer coding section 104 encodes a long term prediction coefficient calculated using the long term prediction information and the residual signal to obtain enhancement layer coded information. Base layer decoding section 152 decodes the base layer coded information to obtain the base layer decoded signal and long term prediction information. Using the long term prediction information, enhancement layer decoding section 153 decodes the enhancement layer coded information to obtain an enhancement layer decoded signal. Adding section 154 adds the base layer decoded signal and enhancement layer decoded signal to obtain a speech/sound signal. It is thereby possible to implement scalable coding with small amounts of calculation and coded information.

A three-dimensional urban traffic road network global state prediction method under a large data environment is disclosed. Aiming at a coupling multi-layer large-scale urban traffic road network, a global traffic mode containing coupling information is defined, a method of extracting a main road network characteristic is provided, a high dimension state time sequence is converted into a discrete state type sequence, and difficulties of high dimension data in processing and prediction aspects are simplified. From aspects of traffic periodicity, delay performance and other characteristics, a historical state transfer information database containing a multielement traffic characteristic is constructed, mass data information and a data driving idea are fully used and a Markov transition probability theory is applied so as to realize multi-step global traffic state prediction. Through the global traffic prediction, a traffic state of each area can be acquired one time. Compared to an existing traffic prediction method, by using the method of the invention, a long-term prediction result is acquired rapidly and a good prediction output effect is possessed.

Techniques for performing adaptive and robust prediction. Prediction techniques are adaptive in that they use a minimal amount of historical data to make predictions, the amount of data being selectable. The techniques are able to learn quickly about changes in the workload traffic pattern and make predictions, based on such learning, that are useful for proactive response to workload changes. To counter the increased variability in the prediction as a result of using minimal history, robustness is improved by checking model stability at every time interval and revising the model structure as needed to meet designated stability criteria. Furthermore, the short term prediction techniques can be used in conjunction with a long term forecaster.

A method, device, and apparatus provide the ability to predict a portion of a polyphonic audio signal for compression and networking applications. The solution involves a framework of a cascade of long term prediction filters, which by design is tailored to account for all periodic components present in a polyphonic signal. This framework is complemented with a design method to optimize the system parameters. Specialization may include specific techniques for coding and networking scenarios, where the potential of each enhanced prediction is realized to considerably improve the overall system performance for that application. One specific technique provides enhanced inter-frame prediction for the compression of polyphonic audio signals, particularly at low delay. Another specific technique provides improved frame loss concealment capabilities to combat packet loss in audio communications.

A speech coding method of significantly reducing error propagation due to voice packet loss, while still greatly profiting from a pitch prediction or Long-Term Prediction (LTP), is achieved by limiting or reducing a pitch gain only for the first subframe or the first two subframes within a speech frame. The method is used for a voiced speech class; a pitch cycle length is compared to a subframe size to decide to reduce the pitch gain for the first subframe or the first two subframes within the frame. Speech coding quality loss due to the pitch gain reduction is compensated by increasing a bit rate of a second excitation component or adding one more stage of excitation component only for the first subframe or the first two subframes within the speech frame.

The invention discloses a prediction method based on a time sequence. The prediction method comprises the following steps of: continuing an original time sequence by using a mean generating function (MGF) method to obtain continuation sequences; screening the continuation sequences by using an optimal subset regression (OSR) method to obtain an optimal subset; by using the optimal subset which is obtained in the preceding step and using the original time sequence as output, training a back propagation (BP) neural network to obtain a BP neural network prediction model; and predicting by using the BP neural network prediction model. By combining the MGF method, the OSR method and the BP neural network, a novel MGF-OSR-BP prediction model is established. By focusing on the prediction model and construction of a learning matrix, the accuracy rate of prediction is higher, and reference can be provided for medium-and long-term prediction research of similar time sequence elements.

A method of determining the reliability of long-term predicted orbit data, includes: determining the reliability of long-term predicted orbit data, which is acquired by predicting a satellite orbit in a target period of at least one day, using predicted position data including predicted positions of a positioning satellite in time series and actual position data including actual positions of the positioning satellite corresponding to the predicted positions.

The invention discloses a fault prediction method and device for industrial equipment based on an LSTM circulating neural network, wherein the method comprises the following steps of: acquiring a state monitoring data set of a plurality of sensors at the periphery of target equipment, wherein the state monitoring data set comprises monitoring data from 0 moment to a current moment; selecting a prediction characteristics containing preset fault information from the state monitoring data set by utilizing a characteristic selection standard, wherein the characteristic selection standard comprisesa correlation index and a monotonicity index; performing characteristic conversion on the prediction characteristics to obtain a prediction characteristic vector; and performing single-step fault prediction, long-term fault prediction and residual life prediction on the target equipment according to the prediction characteristic vector and a fault prediction network model. The method can effectively avoid insufficient prediction precision caused by unreasonable preset fault threshold, can give a confidence interval under the occasion of single-step performance prediction, and can achieve long-term prediction of performance and residual service life of the equipment.

A method and apparatus for prediction in a speech-coding system is provided herein. The method of a 1^st order long-term predictor (LTP) filter, using a sub-sample resolution delay, is extended to a multi-tap LTP filter, or, viewed from another vantage point, the conventional integer-sample resolution multi-tap LTP filter is extended to use sub-sample resolution delay. This novel formulation of a multi-tap LTP filter offers a number of advantages over the prior-art LTP filter configurations. Particularly, defining the lag with sub-sample resolution makes it possible to explicitly model the delay values that have a fractional component, within the limits of resolution of the over-sampling factor used by the interpolation filter. The coefficients of such a multi-tap LTP filter are thus largely freed from modeling the effect of delays that have a fractional component. Consequently their main function is to maximize the prediction gain of the LTP filter via modeling the degree of periodicity that is present and by imposing spectral shaping.

The invention discloses a spacecraft lithium ion battery cycle life prediction method. The method comprises the following steps: firstly collecting capacity data of a lithium ion battery; computing ahealth state time sequence SOHBAT of the lithium ion battery; decomposing the health state time sequence SOHBAT of the lithium ion battery by applying an empirical mode decomposition model; predictinga global degradation trend of the SOHBAT based on an ARIMA model; predicting the local regeneration and fluctuation of the SOHBAT based on the GPR model; integrating prediction results of the ARIMA model and the GPR model, and acquiring the spacecraft lithium ion battery cyclic life prediction. The global degradation trend and the local capacity regeneration and fluctuation phenomenon of the battery health state time sequence SOHBAT can be effectively extracted by utilizing the empirical mode decomposition method, the global trend and local fluctuation phenomenon of the battery health changecan be simulated by utilizing each of the ARIMA model and the GPR model, the capacity regeneration and fluctuation prediction problem in the battery performance degradation can be effectively solved,so that real health degradation trend of the lithium battery can be captured by integrating the models, and the accuracy of the lithium battery long-term prediction is improved.

The invention discloses a cloud computing system load predicting method capable of automatically adjusting parameters, which comprises the following steps: at the moment t, computing the actual load O(t) of a system at the moment t through system call; executing short-term prediction; computing alpha (t) and E(t) by utilizing the O(t) value and historical data; executing long-term prediction; computing alpha T(t) and ET(t) by utilizing the O(t) and the historical data; combining the short-term prediction and the long-term prediction; when t is less than T, outputting the O(t) and switching to the next step; otherwise, taking the maximum value or average value of E(t-1) and ET(t-T) as the output at the moment t; and updating the historical data, waiting the moment t+1 and switching to the first step. In the invention, the alpha (t) and the alpha T(t) are computed in real time through error functions, thereby enhancing the prediction accuracy of classic EWMA (Exponentially Weighted Moving Average); the requirement that a prediction value is slightly larger than an actual value can be met by expanding the alpha (t) and the alpha T(t) to an interval (-1, 1); and the responsiveness of the prediction to the load periodicity of a cloud computing platform is enhanced by introducing a long-term prediction module.

The invention relates to the technical field of intelligent traffic, and discloses a parking guidance method and device, and a medium. The method comprises the following steps: receiving a parking lotberth prediction request sent by a client, wherein the berth prediction request comprises a starting point and a destination of travel; predicting an arrival moment of the destination according to the starting point and the destination of travel; determining a parking lot in a designated range with the destination as a benchmark; and according to a pre-built long-term prediction model, determining an unoccupied berth number of each parking lot within the designated range at the arrival moment, and sending the unoccupied berth number to the client for display, wherein the long-term predictionmodel is a model which is built according to historical parking data of the parking lot in advance and can predict the unoccupied berth number of each parking lot in different time periods. The futureunoccupied berths are predicted through the long-term prediction model, so that parking guidance is facilitated. In addition, the unoccupied berths can be viewed in real time through a short-term prediction model, so that the berth guidance efficiency is further improved.

The invention belongs to the building energy consumption prediction field, and provides an energy utilization management method so as to achieve the purpose of energy saving and amend the energy saving weak links. Therefore, the adopted technical scheme of the method is that the office energy consumption prediction method comprises the following steps: 1) establishing a people-in-the-room rate model; 2) establishing a device hourly power model; 3) establishing a lamp hourly power model; 4) calculating hourly power of indoor consumed energy according to the hourly number of people obtained from the step 1) and the hourly power obtained from the step 2)- the step 4), and with the running time being combined, obtaining daily average power consumption amount of target offices in the long-term prediction; and 5) providing a power consumption optimization scheme. The method is mainly applied to the building energy consumption management.

The invention discloses a power load medium and long term prediction method based on an improved grey prediction model. In order to solve the problem that large errors are easily generated in a fixed model prediction time varying system, a model updating algorithm is provided, in other words, a method combining a sliding window algorithm with a traditional grey prediction model is adopted. The prediction model is recursively updated in a mode that a part of old data are deleted and a part of new data are introduced in a modeling sequence, key quantity a&b related to the mathematic model is decomposed, a recursive mathematical formula is simplified, and the influence of the length of a sliding window on modeling accuracy is discussed.