462 results about "Adaptive optimization" patented technology

A intelligent backhaul radio have an advanced antenna system for use in PTP or PMP topologies. The antenna system provides a significant diversity benefit. Antenna configurations are disclosed that provide for increased transmitter to receiver isolation, adaptive polarization and MIMO transmission equalization. Adaptive optimization of transmission parameters based upon side information provided in the form of metric feedback from a far end receiver utilizing the antenna system is also disclosed.

A intelligent backhaul radio is disclosed, which can operate by zero division duplexing for use in PTP or PMP topologies, providing for significant spectrum usage benefits among other benefits. Specific system architectures and structures to enable active cancellation of multiple transmit signals at multiple receivers within a MIMO radio are disclosed. Further disclosed aspects include the adaptive optimization of cancellation parameters or coefficients.

A system and method of optimizing delivery of a radiation therapy treatment. The system optimizes treatment delivery in real-time to take into account a variety of factors, such as patient anatomical and physiological changes (e.g., respiration and other movement, etc.), and machine configuration changes (e.g., beam output factors, couch error, leaf error, etc.).

A process is performed periodically or in response to an error in order to dynamically and adaptively optimize read compare levels based on memory cell threshold voltage distribution. One embodiment of the process includes determining threshold voltage distribution data for a population of non-volatile storage elements, smoothing the threshold voltage distribution data using a weighting function to create an interim set of data, determining a derivative of the interim set of data, and identifying and storing negative to positive zero crossings of the derivative as read compare points.

The invention discloses a cloud computing resource scheduling method based on scheduling object space. According to the method, the concept of scheduling object space is introduced for setting a flexible standard framework, so that a user can explain the requirement on service resource scheduling, namely the user can select required scheduling attribute for optimization, wherein a multi object resource distribution scheduling mechanism can make adaptive resource scheduling decision under the driving of the expandable user scheduling object space. The method can dynamically deploy cloud computing resource according to the requirement of the user on resource scheduling, and realizes the adaptive optimal distribution of the cloud computing resource driven by the user requirement. Moreover, the scheduling strategy can avoid over turning-on of a cloud computing server as much as possible, thereby improving the utility ratio of resources and reducing the cost of a cloud service provider.

The present invention provides a high-performance adaptive digital receiver with adaptive background control that optimizes the performance in rapidly changing signal environments and provides 3.6 GH; instantaneous bandwidth, SFDR>90 dB, SNR=66 dB, with dynamic digital channelization. The receiver takes advantage of several levels of adaptivity that conventional approaches do not offer. In addition to a dynamic digital channelizer that is adaptively tuned based on detected signals, the present invention employs a powerful software reconfigurable digitizer that is adaptively optimized for the current signal environment to control important receiver parameters such as bandwidth, dynamic range, resolution, and sensitivity.

The invention discloses an adaptive access network selection device and an adaptive access network selection method, which belong to the technical field of communication. Own macro and micro characteristics of a terminal are sensed, a service sensitivity variable is determined according to a user application status and preference analysis, network application priorities and quality of service (QoS) sensitivity priorities are sequenced based on information such as the characteristics of the terminal, application needs, network statuses and the like, adaptive modeling is performed on different networks according to historical data, the QoS characteristics of a user terminal accessing the networks are acquired, an access network is judged according to the guidance information of an intelligent information management module and the actual output of an intelligent network simulation module and inter-network switching is performed. By the device and the method, heterogeneous networks are accessed adaptively optimally and selectively to effectively realize the user transparency orientation of heterogeneous properties of the access network and the terminal universality orientation of application service characteristics.

A system and method of optimizing delivery of a radiation therapy treatment. The system optimizes treatment delivery in real-time to take into account a variety of factors, such as patient anatomical and physiological changes (e.g., respiration and other movement, etc.), and machine configuration changes (e.g., beam output factors, couch error, leaf error, etc.).

The invention discloses a multi-modality autofluorescence molecular tomographic imaging instrument, comprising a signal gathering module, a signal preprocessing module, a system control module, and a signal post-processing module. The method of the invention comprises determining the feasible region of light source through X-ray imaging and autofluorescence tomographic imaging based on multi-stage adaptive finite element combined with digital mouse, reconstruction target area optical characteristic parameter, and modality fusion, and adaptive optimized factorization for partial texture according to the posterior error estimation to obtain the fluorescence light source in reconstruction target area. The morbidity problem of autofluorescence molecular tomographic image can be efficiently solved, and the precise reconstruction of the autofluorescence light source can be carried out in the complicated reconstruction target area by the multi-modality fusion imaging mode of the autofluorescence molecular tomographic imaging. The precise reconstruction of the autofluorescence light source can be finished by the liquid nitrogen cooling CCD probe, multi-angle fluorescence probe technology, and multi-modality fusion technology, and the autofluorescence molecular tomographic imaging algorithm based on multi-stage adaptive finite element with the non-uniformity characteristics of the reconstruction target area.

The invention discloses an automatic optimization method for an on-line self-adaption radiotherapy plan and a device. The method comprises the following steps: by reading an original radiotherapy plan and the information, such as acquired present gradation guide image and sketching target section, calculating a beam direction view of each frame angle, thereby acquiring the optimized newly generated sub-view and corresponding dose distribution, machine hop count and the like; finishing the on-line self-adaption automatic optimization of the radiotherapy plan; and meanwhile, performing secondary check on the dose distribution of the automatic radiotherapy plan through the calculation and assessment for a three-dimensional gamma index value; if failing to pass, performing normalization operation and updating the machine hop count of the new sub-view, thereby finishing the quality ensuring work. Furthermore, through the radiotherapy plan parameter transmission process check, the possibly present problem in a parameter transmitting and writing executing system is effectively avoided, and the safety monitoring is performed through real-time dosage detection during a plan executing process. The on-line self-adaption optimization of the radiotherapy plan, the quality ensuring and the full automation of parameter transmission check and real-time dosage detection are realized.

The invention discloses a robust mechanism research method of characteristic significance in image quality evaluation. The robust mechanism research method comprises the following steps: firstly, determining a target function of characteristic selection in the image quality evaluation, and initializing a model parameter; secondly, adding an optimal characteristic into a characteristic matrix, and removing a characteristic disturbance term; thirdly, calculating the significance of the characteristic selection in an image quality evaluation system; fourthly, judging whether the significance meets a system robust requirement or achieves an upper limit of a characteristic number; and finally, verifying a model classification effect. The characteristic significance is measured through an imported system characteristic signal to noise ratio, a constrained optimization problem of a smooth convex function in the image quality evaluation system is solved, interference on a classification face by non-significant characteristics is effectively lowered, the robustness of the image evaluation system is improved, and the self-adaptive optimization problem of characteristic attribute selection on the basis of an image quality evaluation network of a learning mechanism is solved.

An integrated autonomic innovation infrastructure including an autonomic management system, a motivational signature management system and/or an innovation signature management system manages all aspects of innovation activity for an organization including maintaining a record of submissions of innovation and development thereof including support of collaboration, matching problems with solutions and review and evaluation thereof, management of employee motivation through matching of incentives to innovation and supporting optimal deployment of individuals within an organizational structure to support creative and innovative activity as well as handling consideration and collaboration in regard to submissions in regard to improvements in the infrastructure, itself. numerous feedback paths in each system and between systems allow adaptive optimization of each of the systems and the integrated infrastructure.

The present invention discloses an adaptive cell coverage and capacity optimization method and device in a mobile communication network, aiming to improve adaptive optimization performance of cell capacity and coverage. The technical solutions includes: calculating spectral efficiency of a cell; if the spectral efficiency of the cell is less than a first threshold, starting vertical sectorization processing of the cell to divide the cell into an inner sector and an outer sector, and configuring relevant parameters of the inner sector and the outer sector to improve the spectral efficiency of the cell; calculating spectral efficiency of the outer sector; and if the spectral efficiency of the outer sector is less than a second threshold, starting inter-cell interference coordination to improve the spectral efficiency of the outer sector, and reconfiguring the relevant parameters of the inner sector and the outer sector to improve the spectral efficiency of the cell.

A method and system for performing network transfer adaptive optimization in a large-scale parallel computing system. The method of network transfer adaptive optimization includes forming a message to be transferred through the network based on obtained information related to a task executed by computing nodes, wherein the message includes identification information of the computing nodes to perform data transfer and corresponding sink nodes, and an amount of data the computing nodes to transfer to the corresponding sink nodes; transferring the message to a network layer; and forming a new data transfer pattern for data transfer between the computing nodes and the corresponding sink nodes in accordance with the received message.

The invention relates to a surface element correction and grid beforehand self-adaption calculation method. According to the method, segmented linearizing correction is carried out on a low-order surface element method with multiple groups of CFD aerodynamic force load data under different attack angles, and distribution of surface element calculation grids is optimized by adopting a self-adaption simulated annealing algorithm. By means of self-adaption optimization of the surface element grids, the accuracy of the surface element correction method is improved, and the defect that a traditional surface element correction method depends on grid distribution is overcome. When the optimized grids are applied to the surface element correction method, the advantage of high calculation efficiency of the surface element method is kept, the accuracy of overall wing stress close to the CFD data can be ensured, and the accuracy and efficiency in the aeroelasticity optimal iteration design process are effectively improved. The accuracy error between the obtained aerodynamic load data and CFD calculation results is within 2% and effectively extends to the non-linear segment in which aerodynamic loads change along with the attack angle, and the aerodynamic load calculation efficiency in the changing process of structural rigidity parameters is improved.

The invention provides a lead-acid power battery system fault diagnosis method. The method involves an off-line part and an on-line part. The method includes the specific steps that in the off-line state, data are collected through a simulation model, the data are preprocessed by using a normalization method, a data classification training set and a testing set of a power battery system of a support vector machine are obtained, parameter adaptive optimization is conducted through a GA algorithm, a one-to-one method is used for training to obtain a diagnostic model of the support vector machine, and SVM decision classification is conducted; in the on-line state, a fault generating device is used for simulating fault signals, the signals are collected through a collection module, the data are preprocessed by using the normalization method, the data are further input into an SVM module in off-line training, and fault online classification based on an SVM algorithm is conducted. According to the lead-acid power battery system fault diagnosis method, intelligent off-line and on-line diagnosis of faults of the battery system can be achieved, and meanwhile the fault diagnosis recognition rate is increased.

The invention discloses an intelligent detection and active inhibition device for fluttering of a high-speed milling electric spindle. According to the device, in the milling process, a vibration signal of a spindle tool handle is collected in real time through built-in acceleration sensors, built-in displacement sensors and an external data collection and analysis meter; the acceleration vibration signal is analyzed in real time through a modern signal processing method, so that intelligent detection of milling fluttering is achieved; and if fluttering occurs, the vibration information collected by the displacement sensors is transmitted into an FPGA controller, the controller carries out adaptive optimization, a driving signal is output, a piezoelectric actuator is driven by a power amplifier to operate, mutual offset of secondary vibration and machining vibration of the piezoelectric actuator is achieved, and intelligent and active inhibition of the fluttering is achieved. Visual displaying and monitoring of the state information, comprising vibration, displacement, temperature and the like, of the whole system can be achieved on a computer in real time in the machining process. The intelligent detection and active inhibition device for the fluttering of the high-speed milling electric spindle can effectively improve milling stability, milling quality and milling efficiency.

The invention belongs to the technical field of digital control of electromechanical integration, and relates to an intelligent control method with the three-stage processing self-optimization function. The intelligent control method comprises the following steps: firstly, optimizing and an executing processing program according to optimum processing parameters; secondly, detecting the current and the power supply voltage of a main motor in real time during processing, taking the current as a decision-making value, the voltage as an supplementary means and the variation delta af of the feeding speed af as a system adjustment value, and realizing closed-loop feedback fuzzy control of the processing process; and thirdly, taking a trigger signal of a three-dimensional measuring head as an input mark value after completing the procedure of detection of geometrical information of workpieces, reading the actual coordinate of a current measuring point after detecting the input mark value of the measuring head, estimating errors of processed workpieces after measuring all the measuring points, and performing secondary self-adaptive optimization and adjustment on the processing program. The intelligent control method can realize self-optimization of a digital control program; and a digital control system can acquire shape and position error information of the workpieces in time, so that parameters in subsequent technology are convenient to adjust.

The invention relates to a method for carrying out adaptive optimization switching on group services based on demand prediction and priority and belongs to the technical field of broad band wireless communication. The method can be applied to that various heterogeneous networks can synchronously exist in the same space, and a mobile terminal has multi-module functions and can run different services synchronously. The method comprises: positioning a switching object onto the services, setting two kinds of preswitching demand threshold, calling relative weight matrixes in an analytic hierarchy process weight matrix database according to the factors, such as service quality, terminals, service types, terminal modes, switching requirements and the like, and comparing the matrixes after multiplication and addition to acquire priority queue of the services to be preswitched. The preswitched services are divided into groups according to priority dynamic state at the same time, the analytic hierarchy process weight matrix is used, and the adaptive optimization switching is selected by considering network internal influence factors, service priority, network selectable priority and the like. The switching result has a feedback function for the network selectable weight used for the next switching selection, and the method improves the switching efficiency and network gain.

The invention relates to an adaptive optimization scheduling method for mobile terminal software based on deep reinforcement learning, which relates to a computing technology. The method comprises thesteps of: automatically optimizing the number of software processes unloaded on the mobile device, prioritizing the local task processes, and allocating CPU computing resources and memory resources to reduce the energy consumption of the mobile device and reduce the task processing delay. By measuring the scale of the real-time threading tasks of the software on the mobile device, the bandwidth of the dynamic wireless link between the mobile device and the edge device is estimated, and the feedback information such as the time delay and the energy loss of each process is evaluated by using the depth reinforcement learning algorithm, so as to obtain the optimized scheduling scheme of the software. There is no need to predict the wireless channel model from mobile device to edge device andthe CPU computing resource and memory resource occupancy model of mobile device system, which can reduce the processing delay and energy consumption of software tasks on mobile device and improve theuser experience.

The invention discloses a real-time adaptive optimization method of numerical control processing parameters. The method comprises the following steps: performing real-time online monitoring on a cutting force for numerical control processing of parts, and performing real-time optimization and adaptive adjustment control on cutting parameters based on the cutting force; taking a main shaft current signal as a cutting force representation signal, wherein the signal acquisition cannot influence a processing process; calculating a real-time cutting force through the main shaft current signal acquired in real time, optimizing according to the cutting force to obtain proper cutting parameters, and adjusting and controlling the feeding speed and the main shaft speed through a fuzzy controller. According to the method, the processing quality of the parts can be effectively improved, the processing efficiency of the parts is improved, the damage to cutters and a machine tool is reduced, and the production cost is reduced.

The present invention discloses a radar optimal waveform design method based on radio frequency stealth in a frequency spectrum shared environment, belonging to the radar design and manufacturing field. The method comprises: obtaining priori knowledge of detection area target frequency response of a radar and communication system and signals of a communication system, and assuming that the echo of the communication signals reflected through the target to the radar can be received and processed by the radar; and establishing an optimal waveform design mathematic model; and employing the Lagrange multiplier method to solve the optimal waveform design mathematic model and performing iterative computation to determine the optimal emission waveform of the radar system. The minimization of the total emission power of the radar is taken as a target, the emission waveform of the radar system is subjected to adaptive optimization design in the conditions of satisfying a certain target parameter estimation performance and the communication system channel capacity so as to ensure the channel capacity and the communication quality of the communication system and allow the radar system to ensure the optimal radio frequency stealth performance in the condition of satisfying a certain target parameter estimation performance.