46 results about "Combinatorial algorithms" patented technology

Embodiments include a codec for use in a videoconferencing or similar system includes a video encoder pipeline that has a pre-processor component that is optimized to detect faces and compress the facial video data in an optimum manner. The codec has a pre-processing step that analyzes each frame on a per macroblock basis to determine the mathematical activity level per block. The activity level calculation is used as a parameter to the bitrate control module of the encoder to control the quantization, and thus the fine grained quality of the output data. An object detection module (e.g., a face detector) is placed in the pre-processing step. The object detection data is then combined with the activity level and object detection certainty value through a combinatorial algorithm comprising a weighted average or normalized multiplication process.

Methods for determining a trailer status are disclosed herein. An example method includes capturing a three-dimensional image and a two-dimensional image. The three-dimensional image may comprise three-dimensional image data, and the two-dimensional image may comprise two-dimensional image data. The example method may further include determining a first trailer status based on the three-dimensional image data, and determining a second trailer status based on the two-dimensional image data. The example method may further include comparing the first trailer status to the second trailer status to determine a final trailer status.

The invention discloses a method for determining the optimal back pressure of a direct air-cooling unit. The method combines mechanism analysis with historical data, and based on historical operation data, combines Particle Swarm Optimization (PSO) and XGBoost (eXtreme Gradient Boosting, XGBoost) combined algorithm to determine the specific enthalpy value of the exhaust steam at the final stage of the steam turbine; the equivalent enthalpy drop method is used to calculate the micro-increased power characteristics of the group; combined with the frequency conversion characteristics of the air-cooled fan to calculate the fan power consumption, when the difference between the two is the largest, that is, the unit is in The optimum back pressure under this condition. The method for determining the optimal back pressure of the unit proposed by the invention can provide the optimal back pressure value of the unit under multiple working conditions, assist in realizing the energy-saving and optimal operation of the cold end of the air-cooling unit, and help to deeply tap the energy-saving potential of the direct air-cooling unit.

The invention provides a GNSS / MINS (global navigation satellite system / micro-electro-mechanical systems inertial navigation system) super-deep combination navigation method, aiming to realize super-deep combination of a GNSS and an MINS. The GNSS uses a direct location estimation method to estimate location errors, speed error and clock errors according to location and speed auxiliary information provided by an INS (inertial navigation system) to close loops; a system takes an MIMU (MINS inertial measurement unit) and a base band correlator as sensors to realize all the navigation functions in a top combinational algorithm, in other words, the algorithm takes the GNSS base band correlator as the sensor of sensitive space-time locating fields and takes the MIMU as the sensor of sensitive inertance fields to realize integral combination of the GNSS and the MINS; and by the aid of the INS, the GNSS combines multichannel information to perform vector phase discrimination and vector locating. The invention further provides super-deep combination system and device applied to the method. The method, the system and the device have the advantages of high navigation accuracy, good dynamic property, high GNSS tracking sensitivity, high GNSS anti-interference performance, wide GNSS dynamic pulling range and the like; and in theory, the dynamic range is limited by trends of the MINS, and -160dBM (decibels above one milliwatt in 600 ohms) signal tracking can be realized by the GNSS by the aid of the MINS.

The present invention relates to a method of calculating a Topography Adjusted Vegetation Index (TAVI) based on a band ratio model and a solar altitude angle. The method includes the following steps: obtaining the apparent reflectance data of a remote sensing image through image preprocessing, analyzing the quality of the image and numerical distribution, calculating a Shadow Vegetation Index (SVI), and constructing a TAVI combinational algorithm:TAVI=BirBr+f⁡(Δ)·1Br,calculating an adjustment factor f(Δ) with the solar altitude angle, and finally obtaining anti-topographic effect TAVI vegetation information. The TAVI in the present invention is composed of two band ratio submodels RVI and SVI, the denominators of both of which are red band data of a remote sensing image, and the adjustment factor f(Δ), which is calculated by a solar altitude angle as a parameter with a sensor factor applied, has great physical significance. The TAVI calculation method does not need digital elevation model (DEM) data and remote sensing image classification when not depending on ground survey data, and ensures that the interference of the topographic effects with the vegetation information can be effectively eliminated by the TAVI, thereby avoiding the problem of reduced inversion accuracy of ground vegetation information due to different registration accuracy of a remote sensing image and DEM data.

Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and / or the item data to provide recommendations. The system also includes a batch-process results data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing. The algorithm engine returns results of called composable algorithms designated as being executable using on-demand processing by executing the executable code thereof, and returns results of called composable algorithms designated as being executable using batch-demand-processing by obtaining the results from the batch-process results data store.

A transparent floor tile screen with complete transparency and force perception, including an electromechanical structure and a software module; the electromechanical structure includes a transparent liquid crystal screen, a pressure sensing array, a transparent plastic layer, a metal bracket, a communication module, a paddle array, and a microcomputer; the said The software module includes screen combination algorithm and pressure analysis algorithm. By realizing the above content, a transparent floor tile screen based on a transparent LCD screen and a pressure-sensing array is realized. The floor tile screen can directly see the scenery under the feet from the high altitude, and combines the real scenery under the feet with the stimulating virtual effect to achieve an enhanced realistic effect.

The invention discloses a neural network-based time-period combination prediction method for bus passenger flow, which includes four steps: collecting basic relevant information, determining optimal input data, determining a combination prediction algorithm, and determining the performance of the prediction algorithm. This method fully considers the applicability of different algorithms in different situations, assigns the weight value of each time period to the two neural network algorithms in different time periods, and combines the prediction advantages of different single algorithms in different time periods to obtain the optimal prediction results in each time period. It makes up for the shortcomings of a single algorithm with large limitations, improves the prediction accuracy, prediction stability and generalization of the model, thereby improving the overall performance of the model, and making the bus passenger flow prediction more reliable and accurate. In addition, the present invention screens data through a single algorithm, and guarantees the prediction performance of the combined algorithm to the greatest extent under the condition that the amount of information in the input data is sufficient.