288 results about "Rate parameter" patented technology

A system and method has been disclosed for controlling the transmission flow rate of data bits in a data bit transfer session from a serving entity to a client, the session involving bit transfer over a wireless communications link, the method comprising: setting up the session, included establishing the addressing, by providing a radio control node to establish flow rate parameters relating to the wireless link, monitoring the wireless communication link; based on monitoring, sending new flow rate parameters so that the serving entity can update the transmission rate of the session in accordance with the new rate control parameters.

Adaptive bandwidth allocation for Non-Reserved traffic over high speed transmission links of a digital network is operated through regulation of data packet transfers over network nodes / ports including input / output adapters connected through a switching device. A network node is assigned with a Control Point computing devise (CP) storing a Topology Data Base containing an image of the network. This Data Base is periodically and at call set up updated by Topology Data Base Update messages (TDUs) including an Explicit Rate parameter for link l indicating the current available bandwidth on link l, and a parameter NNRl indicating the number of Non-Reserved connections on link l. This information are used within each Adapter to periodically regulate the transmission bandwidth assigned to each Non-Reserved traffic connection within the network. To that end, each adapter is provided with an Access Control Function device for each attached connection (data source) and a Connection Agent (CA) getting, on request, required current link informations from the attached Topology Data Base.

A method and system for the insertion of local signals, including digital media advertisements, into statistically multiplexed streams is presented. The rate control and timing information is computed and is used to specify the insertion time and rate parameters for digital advertisements. In one embodiment, a maximum bit rate over the advertisement duration is specified. The maximum bit rate may be constant or may vary in time, such that high bit rate portions of the advertisement are supported. High bit rate portions of the advertisements in different program streams may be staggered, such that the total bandwidth required does not exceed a maximum, but allowing for high bit rate portions of advertisements. Custom bit rate profiles for advertisements may also be defined, with the profiles being defined at a high granularity or a low granularity.

Methods and apparatuses for estimating periodic worst-case delay for a class of traffic. An aggregate (i.e., a class or label on which traffic is classified and queued) has an associated bandwidth (“negotiated rate”), which typically is a maximum average bandwidth that has been agreed upon by the traffic generator (“customer”) and service provider. Delay for an aggregate is obtained by summing all the delays of class queues of each router in the path. Traffic data for the aggregate is collected. A burst-rate profile is calculated for the traffic data, with the rate parameter set to the value of the negotiated rate. A periodic worst-case delay estimate associated with the burst-rate profile is calculated, using the output link capacity allotted to the aggregate (“allocated bandwidth”) as input.

An initial movement indicator for a vehicle comprises at least one warning light, and a controller structured and arranged to receive a signal indicative of vehicle movement along the ground and to process said signal to determine a rate parameter that is proportional to a rate of said vehicle movement and to determine whether the vehicle is accelerating, the controller being structured and arranged to cause the at least one warning light to be illuminated if and only if the rate parameter is greater than zero but less than a predetermined value and the vehicle is accelerating.

The invention relates to an energy management device and system. The energy management device comprises at least two energy sensors and a concentrator, wherein the energy sensors are connected with at least two energy terminals on a user side respectively and used for collecting energy data of the energy terminals according to a time series; the concentrator is connected with the energy sensors and used for conducting analysis through combination with rated parameters and service data of the energy terminals on the basis of the energy data so as to determine the working conditions of the energy terminals and transmitting scheduling instructions to the energy sensors according to the determined working conditions so as to enable the energy sensors to be capable of responding to the scheduling instructions and controlling the working conditions of the energy terminals. The energy data, collected by various types of energy sensors, of various types of energy terminals can be gathered and analyzed comprehensively through the concentrator, so that the energy use condition of the user side is analyzed more accurately, and the requirements of users for energy scheduling on the condition that various kinds of energy are used can be met.

A computer-readable storage medium storing a dataset sorting program is provided to sort records in a dataset into a plurality of destination groups according to a given key item specification. An item value extractor creates an item value list for every record. Then a frequent tree builder builds a frequent tree from the item value lists by finding patterns of item values that appear more often than a threshold specified by a given growth rate parameter. Each item value pattern is a leading part of an item value list with a variable length. A destination group mapper associates each node of the frequent tree with one of the plurality of destination groups. A record sorter traces the frequent tree according to the item value list of each given record, and upon reaching a particular node, puts the record into the destination group associated with that node.

The embodiment of the invention discloses a media code rate adaptive method, a device, a computer device and a storage medium, comprising the following steps: acquiring network quality information ofa user network; acquiring rate information corresponding to the network quality information according to a preset rate correspondence list; defining the rate represented by the rate information as a rate parameter of a target object. By obtaining network quality information of the user network, Then, the rate information is selected according to the network quality information, and the rate represented by the rate information is defined as the rate parameter of the target object, so that the target object matched with the rate parameter is selected for downloading, so as to avoid the situationthat the target object is subjected to crashing or frame dropping in the downloading process, and improve the user experience.

A family of novel multi-layer discrete-time neural net controllers is presented for the control of an multi-input multi-output (MIMO) dynamical system. No learning phase is needed. The structure of the neural net (NN) controller is derived using a filtered error / passivity approach. For guaranteed stability, the upper bound on the constant learning rate parameter for the delta rule employed in standard back propagation is shown to decrease with the number of hidden-layer neurons so that learning must slow down. This major drawback is shown to be easily overcome by using a projection algorithm in each layer. The notion of persistency of excitation for multilayer NN is defined and explored. New on-line improved tuning algorithms for discrete-time systems are derived, which are similar to e-modification for the case of continuous-time systems, that include a modification to the learning rate parameter plus a correction term. These algorithms guarantee tracking as well as bounded NN weights. An extension of these novel weight tuning updates to NN with an arbitrary number of hidden layers is discussed. The notions of discrete-time passive NN, dissipative NN, and robust NN are introduced.

The invention discloses a multiple-working-condition automatic testing experiment system of electric vehicle charging equipment, belonging to the field of a new energy vehicle. The system is formed by an industrial computer and software system, a voltage and frequency controlled AC power supply unit, a load unit, a high precision measurement unit, temperature and humidity adjusting equipment, and a protection and control unit. The system is used for testing an electric vehicle vehicle-borne charger and a non vehicle-borne charger. According to the system, grid voltage and frequency static deviation, voltage and frequency fluctuation, a power battery pack with different rated parameters and different temperature and humidity conditions can be simulated to test the communication performance, output stability, output accuracy, protection limit characteristic, temperature rise characteristic, current harmonic wave, voltage fluctuation and flicker, three-phase unbalance, power factor, efficiency and abnormality response ability of experiment charging equipment, certain data support is provided for the research of electric vehicle charging equipment technical specifications, the whole process automatic control of the testing is realized, and the testing efficiency is greatly improved.

The invention discloses a deformable convolutional neural network-based infrared image object identification method. The method comprises the steps of constructing a training set and a test set; establishing a convolutional neural network architecture; adding a softmax classifier to the last layer, and setting an objective function; performing sampling by adopting a convolution kernel of linear ornonlinear deformation; performing pooling operation in a pooling layer by adopting a rule block sampling-based ROI pooling method which is the best in the industry at present; setting learning rate parameters according to experience; and easily performing standard back propagation end-to-end training, thereby obtaining a deformable convolutional neural network. An experiment proves that the spatial geometric deformation learning capability is introduced in the convolutional neural network, so that an identification task of an image with spatial deformation is better finished; the geometric transformation modeling capability of the convolutional neural network and the effectiveness of target detection and visual task identification are improved; and dense geometric deformation in space issuccessfully learnt.

The invention relates to a method for monitoring thermal runaway of a lithium ion battery of an electric vehicle. A voltage changing rate parameter when thermal runaway occurs in the lithium ion battery is monitored, and a battery voltage can be reduced abruptly, is maintained at a low voltage for 40-60 seconds and then is gradually raised to an initial voltage when the thermal runway occurs in the lithium ion battery; the fact that thermal runaway occurs in the battery can be judged by taking the battery voltage changing rate parameter as a monitoring object and is a first judgment method; abattery temperature rising rate changing parameter is monitored, the fact that thermal runaway occurs in the battery also can be judged when the battery temperature rising rate is larger than or equalto 10 DEG C per minute and is a second judgment method; and a battery management system (BMS) is used for monitoring, analyzing and judging the battery voltage changing parameter and the battery temperature rising rate parameter, the two methods are used as an independent condition or a logic combination condition and used as a technological basis for judgment on thermal runaway of the battery, and an early-warning signal can be given out in advance when thermal runaway occurs. By the method, the battery thermal runaway judgment time is greatly increased, and enough handling time is providedfor a person to escape and timely take a measure to prevent battery thermal runaway from being generated, propagating and being expanded.

The invention provides a method for carrying out Bayes judgment on modeling for a video image based on color-texture dual characteristic vectors so as to realize moving target detection. The method comprises the following steps: 1. obtaining a video frame; 2. preprocessing an image; 3. background modeling and carrying out motion detection by utilizing Bayes judgment, wherein the background modeling comprises image color-texture dual characteristic vectors, respectively adopting different characteristic vectors according to the variation of interframe difference of a pixel point, expanding the original Bayes judgment to be two-dimensional due to the independence of the characteristic vectors, setting model parameters and updating control parameters so as to adapt to the change of the state of a moving object; 4. shadow detection, using a texture model to detect a shadow candidate point and eliminating the shadow; 5. processing after the detection, using horizontal projection and vertical projection to determine a foreground area and perfecting the detection result in a divided area through block analysis; and 6. updating a background image, respectively updating according to a detection classification result and setting self-adaptive updating rate parameters so as to adapt to illumination change.

The invention discloses a system and a method for improving audio speech quality comprises the following steps that: firstly, preemphasis and Fourier transform treatment of the current frame of time domain first audio signal output by an audio collecting device and the current frame of the time domain second audio signal output by an audio playing device are completed; secondly, according to the frequency domain error signal parameter and frequency domain learning rate parameter obtained by the previous frame of the current frame, a frequency domain second audio signal is corrected; thirdly, de-emphasis treatment and subtraction of the first audio signal and the second audio signal are completed, thereby eliminating the echo in the first audio signal; meanwhile, according to the corrected time domain second audio signal and frequency domain second audio signal and preemphasized time domain first audio signal, the frequency domain error signal parameter and frequency domain learning rate parameter of the current frame are calculated and used for the treatment of the next frame, thereby effectively eliminating speech echo; in addition, through estimating the residual echo and noise of the audio signal with the echo eliminated, the invention effectively eliminates the noise in the audio signal and greatly improves the speech quality.

A method is disclosed for increasing the communication capacity of a shared ad-hoc wireless channel by using multiuser detection (MUD) to distinguish overlapping information transmitted simultaneously by a plurality of nodes. The transmitting nodes simultaneously provide parameter-estimating signals over separate, unshared, low-rate parameter channels generated using orthogonal frequencies, spread spectrum technology, or time multiplexing. Receiving nodes use these separate, non-overlapping parameter-estimating signals to estimate MUD-required signal parameters such as amplitude, phase, and frequency offset, thereby enabling use of lower complexity MUD receivers, because the parameters are not estimated in the presence of other interference. Node ID, spreading code type, and / or other information can also be transmitted over the parameter channels. Limiting the number of parameter channels can limit the maximum number of transmitting nodes. Amplitudes of parameter channel transmissions can be greater than communication channel transmissions by a known ratio. Parameter channels can be frequency-hopped for jam-resistance.

The invention provides an air conditioner and an air conditioner control method. The method comprises the following steps: acquiring a dressing amount parameter and a metabolism rate parameter; collecting initial indoor temperature, initial relative humidity and initial wind speed; calculating based on the dressing amount parameter, the metabolism rate parameter, the initial room temperature, theinitial relative humidity, the initial wind speed and target environment requirements to obtain target operation temperature of the air conditioner; controlling the air conditioner to operate at the target temperature; and if new target environment requirements are input, calculating new target operation temperature of the air conditioner again until shutdown of the air conditioner. By using the air conditioner control method, the control complexity is reduced, thereby improving control instantaneity and user experience, and solving problems of long calculation cycle and complicated realization for air conditioner control methods in the prior art.

The present invention discloses a cluster system load balancing method and a system thereof. The method comprises a step of obtaining the connection number of each server node and obtaining the processing utilization rate and memory occupation rate of each server node, a step of calculating the load rate of each server node according to the connection number, the processing utilization rate and the memory occupation rate, and a step of distributing a received task request to the server node with a lowest load rate. According to the method and the system, a degenerative feedback mechanism is used, the processing utilization rate and memory occupation rate parameters of the server node are introduced to represent the load condition of the server node, the load condition of the server node is truly and accurately reflected, the disadvantage that the server load condition can not be accurately reflected by an existing load balancing policy is effectively overcome, the overall throughput of a cluster system can be improved, and the overall efficiency and balance of the cluster system can be improved.

Systems and methods for synchronizing a source and sink device are disclosed. A sink device can efficiently determine the source data rate even in cases where the sink device is not directly coupled to the source device. A method for transmitting a source data stream from a source device to a sink device includes, forming a logical channel from a source device to a sink device, where the logical channel is configured to carry the source data stream, and one or more rate parameters. The rate parameters relate a data rate of the source data stream to a data rate of the logical channel. A method for a sink device to recover a source data rate includes, detecting a logical channel in a received data stream where the logical channel includes the source data stream, recovering one or more rate parameters from the received data stream, determining a data rate of the logical channel, and determining the data rate of the source data stream based on the data rate of the logical channel and the one or more rate parameters. Corresponding systems and computer program products are also described.

The invention relates to a method for triggering a listen-before-talk (LBT) random backoff mechanism in long term evolution licensed-assisted access (LTE LAA). The method comprises the steps of evaluating a current channel to obtain a first parameter representing a congestion state of the current channel, comparing the first parameter with a first threshold to obtain a first comparison result, and adapting the size of a contention window according to the first comparison result. The method further comprises the steps of comparing the first parameter with a second threshold to obtain a second comparison result, and adapting the size of the contention window according to the second comparison result. The first parameter is a packet error rate parameter or a collision parameter. The method innovatively uses the comparison between the first parameter representing the congestion state of the current channel and the threshold to correspondingly increase or decrease the size of the contention window according the comparison result, so that the fairness with other systems, such as a WiFi system, is guaranteed, the utilization rate of communication resources and the performance of the whole wireless communication system are also improved correspondingly.

The method of detecting and controlling crane safety system includes the following steps: starting the crane and judging the normality of the system, judging whether the set crane condition is the same as the required one, detecting and displaying the current load parameters and comparing with the rated parameters of the crane, receiving the D / A converter signal through the bus, displaying the comparison result and warning, and stopping the crane in case of overload. The present invention is intuitive, accurate and intelligent, and can operate the crane in optimal state.

A method and apparatus is disclosed for machine learning of a pattern sequence which is derived from a plurality of inputs. The pattern sequence is predicted from learning rate parameters that are exponentially related to an incrementally calculated gain parameter for each input. The gain parameter are increased or decreased in real time in correlation with the accuracy of the learning process. The disclosed method and apparatus are advantageously utilized in signal processing, adaptive control systems, and pattern recognition.

The invention discloses a loss expenditure calculating method for an electric vehicle battery participating in power grid dispatching. An aggregator determines a work mode of an electronic vehicle in a current work period in real time through an intelligent control algorithm; the charge and discharge rate, discharge depth and the number of circulation times of the electronic vehicle battery are obtained; the attenuation rate of the number of battery circulation times and the attenuation rate of battery capacity generated due to temperature and the attenuation rate of the number of battery circulation times generated due to discharge depth are calculated; according to the rated number of circulation times of the battery, the number of circulation under V2G application is calculated; degradation cost of the battery is calculated according to the number of circulation times; finally, loss expenditure of the battery in the current work period is calculated according to attenuation rate parameters of the current work period. Influences of temperature and discharge depth on the number of circulation times of the battery are combined, battery temperature rise caused by changes of internal impedance of the battery is introduced, and loss estimation of the battery is more precise and accords with actual situations better.

The invention relates to a body-building program real-time adjusting system and a method thereof, wherein the purpose on adjusting the exercise intensity in real time by a body-building device according to personal body-building states of a user can be realized by the steps of: loading a proper body-building mode by analyzing the physiological information of a user, working out exercise intensity according to target time and target calorie which are input by the user, further generating a body-building program, adjusting the exercise intensity of a body-building device in real time when the body-building program is executed, and detecting the heat rate parameters of the user in real time during the execution of the body-building program so as to adjust the exercise intensity.

Artificial neural networks (ANNs) are a distributed computing model in which computation is accomplished using many simple processing units (called neurons) and the data embodied by the connections between neurons (called synapses) and the strength of these connections (called synaptic weights). An attractive implementation of ANNs uses the conductance of non-volatile memory (NVM) elements to code the synaptic weight. In this application, the non-idealities in the response of the NVM (such as nonlinearity, saturation, stochasticity and asymmetry in response to programming pulses) lead to reduced network performance compared to an ideal network implementation. Disclosed is a method that improves performance by implementing a learning rate parameter that is local to each synaptic connection, a method for tuning this local learning rate, and an implementation that does not compromise the ability to train many synaptic weights in parallel during learning.

The invention relates to a method and a system for confirming and optimizing the throughput rate of short range wireless network. The invention mainly obtains every time parameter of competing visit period according to the information transmission characteristic of the short range wireless network. Every time parameter is used for calculating and optimizing the throughput rate of network. The time parameter is confirmed based on non-persistent carrier wave interception multipoint access CSMA. So the invention uses non-persistent CSMA for analyzing short range wireless network (competing visit period), which simplifies the complex degree of the whole process of confirming network throughput rate and network optimization and analysis. So the throughput rate parameter of short range wireless network of normative network can be obtained with accuracy, which facilitates the optimization treatment to network performance. The performance of short range wireless network is effectively improved.

The invention belongs to the technical field of audio systems, and particularly relates to an audio system balance adjustment method and device. The position of the best balance point of an audio system is calculated according to coordinates of audience positions, and the distances between all voice boxes and the best balance point position are calculated according to the positions of all the voice boxes; the standard distance is calculated according to the distances between all the voice boxes and the best balance point position, and finally according to the distances between all the voice boxes and the best balance point position, the standard distance, volume change rate parameters based on the distances and delay change rate parameters based on the distances, the volume and delay time of all the voice boxes are adjusted. Due to the fact that sound channel balance of the audio system is automatically adjusted according to the positions of audiences and positions of all the voice boxes, the balance adjusting effect is effectively improved, manual adjustment is not needed, and operation is simple.

The system consists of training databank, question-making module, read-following broadcast module and pronunciation speed rate regulation module. The method includes the follows: parameter is initialized first, then training specimen is started to play broad cast task; in course of broadcasting, the user carrys on real-following and to record the real-following pronunciation data of the user; the training specimen and the read-following pronunciation data are broad casted separately and finally the pronunciation speed rate parameter is regulated according to times of training specimen playing till broadcast task of the training specimen is finished.

The invention relates to a machine learning-based server energy consumption prediction method and system. The method comprises the following steps: separately collecting system resource utilization rate and real-time energy consumption of a server under a zero load state, a low load state and a high load state, as a zero load training set, a low load training set and a high load training set; respectively, inputting the zero load training set, the low load training set and the high load training set into a machine learning model for training, and generating a zero load energy consumption model, a low load energy consumption model, and a high load energy consumption model; according to the to-be-predicted system resource utilization rate of the server, selecting the zero load energy consumption model, or the low load energy consumption model, or the high load energy consumption model to predict the energy consumption value of the server, wherein the system resource utilization rate comprises the memory utilization rate, the network bandwidth utilization rate, the disk utilization rate, and the CPU utilization rate, thereby improving the prediction accuracy and practicability by expanding the collected system resource utilization rate parameters and the segmentation training models.