183 results about "Local convergence" patented technology

A variably configurable fiber optic terminal as a local convergence point in a fiber optic network is disclosed. The fiber optic terminal has an enclosure having a base and a cover which define an interior space. A feeder cable having at least one optical fiber and a distribution cable having at least one optical fiber are received into the interior space through a feeder cable port and a distribution cable port, respectively. A movable chassis positions in the interior space and is movable between a first position, a second position and third position. The movable chassis has a splitter holder area, a cassette area and a parking area. A cassette movably positions in the cassette area. A splitter module holder having a splitter module movably positioned therein movably positions in the splitter holder area. The optical fiber of the feeder cable and the optical fiber of the distribution cable are optically connected through the cassette, which also may be through the splitter module. In such case, the optical fiber of the feeder cable optically connects to an input optical fiber to the slitter module, where the optical signal is split into a plurality of output optical fibers. One of the plurality of output optical fibers connects to the optical fiber of the distribution cable for distribution towards a subscriber premises. The interior space is variably configurable by changeably positioning the cassette and splitter modules in the movable chassis.

There is provided fiber optic hardware and hardware components adapted to provide a desired amount of fiber optic connectivity and/or functionality for a desired amount of volume, materials, etc. The fiber optic hardware components include, but are not limited to multiports, local convergence points (LCPs), particularly LCPs for multiple dwelling units or similar applications, network interface devices, equipment frames, and fiber distribution hubs. Certain fiber optic hardware components are adapted to accommodate microstructured optical fiber or other bend performance optical fiber.

There are provided fiber optic local convergence points (“LCPs”) adapted for use with multiple dwelling units (“MDUs”) that facilitate relatively easy installation and/or optical connectivity to a relatively large number of subscribers. The LCP includes a housing mounted to a surface, such as a wall, and a cable assembly with a connector end to be optically connected to a distribution cable and a splitter end to be located within the housing. The splitter end includes at least one splitter and a plurality of subscriber receptacles to which subscriber cables may be optically connected. The splitter end of the cable assembly of the LCP may also include a splice tray assembly and/or a fiber optic routing guide. Furthermore, a fiber distribution terminal (“FDT”) may be provided along the subscriber cable to facilitate installation of the fiber optic network within the MDU.

In the invention, when monitoring the status of ports in local convergence group at two ends of communication network; classifying the monitored state variation information into different types; according to the type of the status variation information, making relevant process. Said method utilizes link convergence control protocol and makes some improvement for it.

The invention provides a generation method of a vector quantization code book. In the method, a global optimization method based on a random relaxation technology is introduced; and while iteratively updating the code book every time, random disturbance is generated and added to a corresponding code word, thus local convergence is effectively avoided during the process of updating the code book. The method can further rationally optimize a code book structure and bit positions of the code word according to the inherent characteristic of channel statistical distribution of a wireless communication system and the requirement on orthogonality of user scheduling in a base station in an MIMO system on the selected users. In addition, a method for expanding the code book is also introduced in order to improve the robustness of the code book in a multi-channel statistical distribution environment, and the size of the code book can be flexibly adjusted according to specific conditions. As the antenna number of the base station and downlink user equipment is greatly increased based on the demand of the development of the future communication system, the method can generate reserved interfaces for the future code book, and can achieve higher quantization and system performance with lower complexity even though the method is used in high-dimensional vector quantization.

The invention belongs to a quantitative analysis method for a laser induced breakdown spectroscopy and particularly relates to the quantitative analysis method for the laser induced breakdown spectroscopy based on a gGenetic Neural Networkgenetic neural network. In an element quantitative detection technology based on the laser induced breakdown spectroscopy and the gGenetic Neural Networkgeneticneural network, laser-induced breakdown spectroscopy (LIBS) data are analyzed by combining an artificial neural network (ANN) with the genetic algorithm (GA for short) so as to realize the quantitative detection of elements. Compared with the traditional LIBS internal calibration method and the traditional BP(Back Propagation)-ANN method, the method has the advantages of little influence from the matrix effect, simple and quick process, avoidance of local convergence, high reliability of quantitative detection and the like.

The invention discloses a hybrid particle swarm optimization algorithm in combination with a genetic algorithm. The advantages of strong global search capability of a particle swarm optimization (PSO)algorithm and high local convergence speed of the genetic algorithm (GA) are integrated. Firstly, global search is performed by virtue of the characteristic of the strong global search capability ofthe PSO algorithm, and when iteration is performed for a specified algebra and is close to a globally optimal solution, at the moment, a whole population enters a neighborhood of the globally optimalsolution; and secondly, local quick search is performed in the neighborhood of the globally optimal solution by utilizing an improved genetic algorithm, so that the globally optimal solution is reached finally.

There is provided fiber optic local convergence points that provide preconnectorized functionality to eliminate all or most of the splicing associated with local convergence points in fiber optic distribution networks. The local convergence points provide a plurality of preconnectorized multi-fiber ports adapted to receive a preconnectorized end of a distribution cable within the enclosure of the local convergence point, on the enclosure itself, or outside the enclosure. For example, the local convergence point may provide preconnectorized multi-fiber ports outside the enclosure with an accumulator optically connected to the interior of the local convergence with an accumulator cable, wherein the accumulator comprises the plurality of preconnectorized multi-fiber ports.

The invention discloses a robust controller of a permanent magnet synchronous motor based on a fuzzy-neural network generalized inverse and a construction method thereof. The construction method of the invention comprises the following steps of: combining an internal model controller and a fuzzy-neural network generalized inverse to form a compound controlled object; serially connecting two linear transfer functions and one integrator with the fuzzy-neural network with determined parameters and weight coefficients to form the fuzzy-neural network generalized inverse, serially connecting the fuzzy-neural network generalized inverse and the compound controlled object to form a generalized pseudo-linear system, linearizing a PMSM (permanent magnet synchronous motor), and decoupling and equalizing the linearized PMSM into a second-order speed pseudo-linear subsystem and a first-order current pseudo-linear subsystem; and respectively introducing an internal-model control method in the two pseudo-linear subsystems to construct the internal model controller. The robust controller of the invention has the advantages of overcoming the dependence and local convergence of the optimal gradient method on initial values and solving the problems of randomness and probability caused by using the simple genetic algorithm, obtaining the high performance control, anti-disturbance performance and adaptability of the motor and simplifying the control difficulty, along with simple structure and high system robustness.

The invention provides a method of reconstructing a power distribution network containing a distributed power supply. A network topology adjusting module considering various load modes is established according to actual operation conditions of the power distribution network and has good practical value. Meanwhile, topological adjusting is performed by means of a binary particle swarm optimization algorithm according to features of network topology adjustment; a dynamic inertial weight adjusting mode is provided; better coordination with global convergence speed and local convergence precision is provided; optimizing effect is better; the topological adjustment results and analysis of an IEEE-33 node typical distribution system show that the method is effective and practical.

The present invention relates to design method of optimizing water quality model parameters in improved mixed genetic algorithm, and belongs to the field of water quality model parameter calibration optimizing method. The design method includes the following steps: preparing data for water quality model parameter calibration optimizing design; establishing and optimizing improved mixed genetic algorithm; improving adaptability degree function; mixing with simplex algorithm serially; running the algorithm program, inspecting the simulating result of different algorithms and completing the optimization after the error reaching the specified standard. The present invention can calibrate model parameters fast accurately, and raise the application efficiency of comprehensive water quality model in various water body researches.

The invention discloses a reentry vehicle trajectory optimization method based on a variable-centroid rolling control mode. The reentry vehicle trajectory optimization method based on the variable-centroid rolling control mode is used for solving the technical problem that the robustness of an existing reentry vehicle trajectory control method is poor. According to the technical scheme, by establishing a one-dimensional variable-centroid rolling control reentry vehicle mathematical model, a one-dimensional variable-centroid rolling control reentry vehicle trajectory optimization model is established, and the one-dimensional variable-centroid rolling control reentry vehicle trajectory optimization model is solved. According to the method, all kinds of constraints in the reentry process of a reentry vehicle are taken into consideration, by introducing control constraints and one-dimensional variable-centroid control ability constraints, the one-dimensional variable-centroid rolling control reentry vehicle trajectory optimization model with a guidance system matched with a control system is established, meanwhile, the trajectory optimization model is solved through a simulated annealing algorithm, and thus the local convergence defect of a classic optimization method is overcome, a nominal trajectory suitable for the one-dimensional variable-centroid rolling control mode is acquired while process constraint and boundary constraint conditions are met, and the robustness of the guidance and control systems is improved.

There are provided fiber optic local convergence points (“LCPs”) adapted for use with multiple dwelling units (“MDUs”) that facilitate relatively easy installation and/or optical connectivity to a relatively large number of subscribers. The LCP includes a housing comprising an access cover for separating the subscriber portion from the provider portions. A splitter module of the LCP may be joined to the interior side of the access cover or may be joined to the housing opposite the access cover. The LCP may also include a splice tray assembly and/or an input receptacle to facilitate convenient installation of the provider cable. The LCP further comprises a plurality of receptacles for selective optical connection of a plurality of subscriber drop fibers/cables.

The invention discloses a non-line-of-sight stable positioning method based on signal arrival time in a wireless network. The method comprises the following steps: transmission distance measured values from the time when measuring signals are emitted from an unknown target source to the time when each sensor receives the measuring signals is measured; then re-describing is performed on a distance measurement model corresponding to each sensor; next, according to the re-described distance measurement model, an initial stable least square problem is established; afterwards, an epigraph is obtained according to the stable least square problem; then a second-order cone planning problem is obtained by use of a second-order cone relaxation technology relaxation constraint condition; and finally, the second-order cone planning problem is solved by use of an interior point method technology to obtain an estimated value of the position of the unknown target source. The method provided by the invention has the following advantages: a description of the second-order cone planning problem is obtained through relaxing the description of the stable least square problem by use of the second-order cone relaxation technology, it can be ensured that a global optimal solution is obtained without being affected by local convergence, and the positioning precision is high; and since the number of solved unknown optimization variables is small, and thus the calculation complexity is quite low.

The invention discloses a low-carbon power generation dispatching method for a wind farm, which belongs to the field of operation and control of power systems, and includes the steps: 1) building a low-carbon power generation dispatching model for the wind farm; and 2) solving the low-carbon power generation dispatching model for the wind farm by means of a chaotic differential evolution algorithm. The low-carbon power generation dispatching method for the wind farm has the advantages that 1) actual conditions of the wind farm are more accurately reflected, so that a dispatching scheme formulated on the basis is more reliable; 2) taking CO2 emission restriction into consideration, so that the environment-friendly requirement is met; and 3) taking minimization of fuel cost of a fuel coal fossil power plant as the purpose, so that dispatching economy is guaranteed. The low-carbon power generation dispatching method for the wind frame based on the chaotic differential evolution algorithm can effectively solve the problems of high dimension, non-convexity, nonlinearity and multiple restrictions in power generation dispatching of the power systems, and can overcome the shortcomings of easiness in local convergence and prematurity of a standard differential evolution algorithm.

The invention discloses a genetic simulated annealing method for solving new words in Chinese segmentation. The method comprises the steps of firstly acquiring and intelligently searching Internet information by adopting a crawler program to complete data preparation; then performing Chinese segmentation on the acquired data by adopting a dedicated lexicon, namely discovering a public opinion; proposing a genetic simulated annealing algorithm using the characteristics of parallel operation and global convergence of a genetic algorithm in combination with local convergence of a simulated annealing algorithm, and performing relevant design and application on a public opinion monitoring system. By adopting the method, the automatic segmentation problem in the field of Chinese information processing is solved; by combining the solution strategies of the genetic algorithm and the simulated annealing algorithm for new words continuously appearing with the development of society and Internet, the segmentation accuracy is improved, the problems of disperse strings and segmentation errors in the automatic segmentation result are effectively solved, and the method plays an important role in observing, researching and analyzing dynamic changes of language phenomena, normalizing languages and characters and improving the overall effect of automatic Chinese segmentation.

The invention discloses an orthogonal wavelet blind equalization method based on simulated annealing genetic optimization. The method introduces the genetic algorithm into the orthogonal wavelet blind equalization method (WTCMA), utilizes the global search characteristic of the genetic algorithm, and calculates the equalizer weight vector Optimized to reduce the possibility of local convergence of WTCMA, reduce the steady-state error, and aim at the poor local search ability of the genetic algorithm, embedded the idea of ​​simulated annealing in the genetic algorithm, and invented the orthogonal wavelet blind based on simulated annealing genetic optimization The balanced method (SA-GA-WTCMA) corrects the premature phenomenon of the genetic algorithm, further reduces the steady-state error and accelerates the convergence speed. The simulation results of the underwater acoustic channel verify the effectiveness of the inventive method.

The invention relates to an edge computing task allocation method for real-time online monitoring service of electric power Internet of Things, and belongs to the technical field of real-time online monitoring of the electric power Internet of Things. The method comprises the following steps: firstly, establishing a real-time online monitoring service task allocation model based on edge calculation; secondly, for an edge node task queuing problem in a particle swarm algorithm, setting a hybrid priority based on an EDF to queue tasks. For the problems that the particle swarm optimization algorithm solves the defects of a discrete problem and is easy to locally converge, a task allocation solving method based on the improved discrete particle swarm optimization algorithm is provided; simulation results show that the algorithm provided by the invention can effectively allocate real-time online monitoring service edge calculation tasks, further reduce service time delay and effectively improve the comprehensive performance of a real-time online monitoring system.

The invention discloses an arrival time-based cooperative localization method applied to a wireless sensor network, which uses a second-order cone relaxation technique and a positive semi-definite relaxation technique to relax a minimization problem obtained by constructing a maximum likelihood function to obtain a description of the problem mixing positive half-definite and second-order cone programming; thus, a global optimal solution can be ensured to obtain without the influence of local convergence, and the localization accuracy is high; the influence of measurement noise error can be effectively suppressed; meanwhile, the prior art can be used for solving an estimate value of the coordinates of an unknown target source, thereby reducing the distribution density of the anchor nodes and reducing the cost.

The invention provides a lung tissue image segmentation method based on deep learning, and belongs to the technical field of medical image segmentation. The lung tissue image segmentation method comprises the steps that an X-ray chest radiograph image is input into a segmentation model, the segmentation model is obtained through training of multiple sets of training data, and each set of trainingdata in the multiple sets of training data comprises the X-ray chest radiograph image and a corresponding gold standard used for identifying lung tissue; and output information of the model is obtained, and the output information comprises a segmentation result of the lung tissue in the X-ray chest radiography image. According to the lung tissue image segmentation method, the segmentation of the lung tissue of the X-ray chest radiography is realized through an improved Deeplabv3+ deep learning method, and the problems of local convergence and false positive segmentation when the lung tissue issegmented by using a traditional method are solved; the lung tissue image segmentation method respectively obtains 95.3% of MIoU and 94.8% of MIoU on the public data set and the pneumoconiosis data set; and the false positive problem of the FCN network is solved, and the segmentation accuracy of ribs at the thoracic diaphragm angle and on the X-ray chest radiography in the SCAN network method isimproved.

The invention discloses a multiple BP neural network load prediction method based on grey correlation degree. The method comprises the following steps: 1) carrying out load sequence correlation analysis based on grey correlation degree; 2) carrying out clustering based on a shortest distance method to determine a member set of a multiple BP neural network; 3) determining multiple number of the multiple BP neural network based on effectiveness index; 4) improving the BP neural network by introducing a momentum factor and adopting a multiple-calculation averaging method to solve the problem of easy local convergence of the BP neural network, and improve anti-vibration capability thereof; and 5) carrying out short-term power load prediction through an established multiple BP neural network prediction model. The method solves the problem of easy local convergence of the BP neural network and improves anti-vibration capability thereof; and compared with a conventional BP neural network prediction model, the multiple BP neural network has a better prediction effect.