54 results about "Generic algorithms" patented technology

A non-intrusive device for automatically dosing at least one liquid laundry care composition to an automatic laundry washing machine. The device employs a Venturi tube mechanism or a pump to dose the laundry detergent care composition. The dosing is controlled by a machine-generic algorithm capable of determining the actual cycle at any duration of wash for various cycle designs from various washing machines, without the input of precise cycle design; and dose the correct products correctly and is capable of distinguishing between major water addition and a water pulse.

A Path Generator connects to a communication network and uses genetic algorithms to assign flows to paths. Genotypes encode flow to path assignments for working and protection paths. Genotype fitness functions are computed as a weighted sum of constraint fitness functions. Each constraint fitness function evaluates the degrees to which the genotype is a satisfactory solution. The system can be used for network modeling. It can also receive requests for on-demand assignment of flows and on-demand rerouting of flows.

A non-intrusive device for automatically dispensing at least one liquid laundry care composition to an automatic laundry washing machine. The device employs a Venturi tube mechanism or a pump to dose the laundry detergent care composition. The dosing is either manually controlled or controlled by a machine-generic algorithm capable of determining the actual cycle at any duration of wash for various cycle designs from various washing machines, without the input of precise cycle design; and dose the correct products correctly and is capable of distinguishing between major water addition and a water pulse.

A generic algorithm for simulating communications software in the network environment, based on the model of plural Abstract State Machines (ASMs) is discussed. Other properties and behaviors can supplement the generic case, for example, network topology, delaying behaviors, message transport reliability, and message priority. Plural executing agents, each a sequential abstract state machine, are combined to simulate the behavior of a dynamic network environment. Agent and message behavior is altered based on the proposed or selected network under simulation. The desired behaviors and constraints are coded, compiled, and executed using an executable abstract state machine language.

The invention discloses a combined optimization method algorithm to an irregularly-arranged sub-array four-dimensional antenna array. The definition of information entropy is introduced into a four-dimensional array, the originally-complicated optimization problem is divided into two sub-problems, optimization is performed according to two steps, in the first step, an information entropy-based genetic algorithm is employed, the array topology structure with maximum information entropy value is optimized according to a sub-array arrangement algorithm, in the second step, information such as a static excitation phase of each sub-array of the sub-arrays, the closing continuous time of a switch and the initial closing time of the switch is optimized by a differential evolutionary algorithm according to the requirement of low sideband and low side lobe, so that the whole optimization problem can be more efficiently solved. The maximum innovation lies in that the intrinsic characteristic ofthe original optimization problem is dug, optimization is performed by combining the information entropy-based generic algorithm and the differential evolutionary algorithm, the complexity of the original optimization problem is reduced, T/R modules are saved by half, and meanwhile, the characteristics of low sideband and low side lobe under large-angle scanning are ensured.

The invention discloses an intelligent bus scheduling method based on a hybrid heuristic algorithm. A simulated annealing algorithm and a generic algorithm are combined, and an elitism strategy and a fitness stretching function are added. Individuals with maximum fitness in a population of each generation are directly retained to a next generation, so that the individuals are protected from being damaged by crossover and mutation operation. Through the fitness stretching function, differences among the individuals are reduced at the initial stage of the algorithm, so that the diversity of the population is increased, and the generic algorithm is prevented from falling into a local optimal solution. At the later stage of the algorithm, the differences among the individuals are increased, so that the selection probability of excellent individuals is increased, and a convergence speed is increased. The intelligent bus scheduling method is high in speed; an optimized scheduling plan can be obtained within a short time under the condition of given departure time frequency; and the waiting time of passengers is shortened greatly. The departure frequency can be adjusted dynamically, so that the departure frequency is accordant with the change rule of a total passenger flow volume. The departure time intervals can be adjusted dynamically, so that the waiting time of the passengers is shortened greatly.

The invention relates to a method for applying a commercial on-spot programmable device to triple-modular redundancy anti-irradiation in an irradiation environment based on lookup table structure and a generic algorithm thereof, aiming at easing the problem that commercial FPGA chips are prone to cause overturning of programming point single particle in the irradiation environment due to irradiation energy energetic particles in the space. The method carries out classification according to user circuit logical type; the first type is input logic, the second type is non-feedback logic and the third type is feedback logic, the fourth type is output logic. In the method of the invention, different triple-modular redundancy anti-irradiation processing methods are proposed with regards to the 4 types of logic. The method of the invention is a universal method through which various user circuits are mapped to the commercial FPGA, features widespread representativeness, generality and practicability.

The invention provides an orthogonal frequency division multiplexing (OFDM) system subcarrier allocation method based on SGA in wireless transmission technology field. Firstly, the invention obtains channel status information of each user on different subcarriers, according to channel estimation; and then, transfers SGA for optimum subcarrier allocation; wherein the SGA adopts integer coding and cyclic shift scissor to improve search capability; after fitness function computation of species group with generic variation each time, continues to judge whether the algorithm should stop, according to the termination condition and obtains the subcarrier allocation proposal. Compared with prior art, the invention improves system performance greatly and the advanced SGA in the invention has faster velocity of convergence, compared with the traditional SGA.

Techniques are disclosed relating to generating generic labels, translating generic labels to image pipeline-specific labels, and automatically adjusting images. In one embodiment, generic labels may be generated. Generic algorithm parameters may be generated based on training a regression algorithm with the generic labels. The generic labels may be translated to pipeline-specific labels, which may be usable to automatically adjust an image.

The invention discloses a wind farm multi-type wind driven generator arrangement optimization method based on a genetic algorithm nested in a particle swarm optimization. The genetic algorithm is used for choosing wind driven generator position. The particle swarm optimization is used for obtaining the optimal solution of the wind driven generator position type selection and the optimal solution is adopted as fitness of the wind driven generator position. The use of the generic algorithm guarantees that a feasible solution can be obtained for solving the nonlinear strong coupling optimization problem. The use of the particle swarm optimization not only guarantees that when various types of the wind driven generators have lots of parameters, and the type selection solution is obtained rapidly but also guarantees that the two algorithm are used in a nest mode, and when iteration times are too many, the calculation time is not likely to be too long. The wind driven generator position coordinate is directly encoded rather than choosing the checkerboards after the wind farm area is divided into checkerboards, and continuous search is capable of being conducted in the wind farm scope. The wind farm multi-type wind driven generator arrangement optimization method based on the genetic algorithm nested in the particle swarm optimization doesn't need to divide the wind farm into square grids, and compared with the prior art, the performance index is better, the position scheme is more accurate and the practicability is stronger.

The invention belongs to the technical field of network communication, and discloses a network reliability model and a hybrid intelligent optimization method. According to the invention, the network reliability model adds related evaluation indexes of the network invulnerability, optimization design is performed from the two aspects of survivability and invulnerability, a node is allowed to lose effectiveness with a certain probability in the optimization design, optimization design is performed on the a network topology structure, and a defect of single limiting condition of an existing reliability model is overcome, thereby being more perfect and confirming to an actual application scenario. The optimization method disclosed by the invention provides a hybrid intelligent algorithm combining a generic algorithm and an ant colony algorithm in allusion to the characteristics that the generic algorithm is poor in local search ability but high in overall search ability and that the ant colony algorithm is poor in total search ability but high in local search ability, thereby making the best of the two algorithms, overcoming intrinsic defects of the single intelligent algorithm, and improving the solving quality of a solution.

Techniques for performing combined scaling, filtering, and/or scan conversion are disclosed that reduce the amount of line buffer space required in the overall design of a video processing system. In particular, coefficients from all or a sub-set of the scaling, filtering (smoothing/sharpening), and scan conversion filters are combined into one representative coefficient that can be applied in a single generic algorithm. Thus, implementation costs are reduced, particularly in a system-on-chip implementations.

The invention provides an automatic oil deposit history fitting system and method based on a genetic algorithm, belonging to the field of oil deposit numerical simulation. In the invention, automatic history fitting is realized by taking a database as a data storage and computation center based on a generic algorithm flow. The system comprises an algorithm management module, a simulator pre-processing module, a simulator calling module, a simulator post-processing module a user interface module. Due to the adoption of the system and the method, automatic oil deposit history fitting is realized by organically combining the generic algorithm with a data technology, the indexing and storing process technology of a database is fully utilized, efficient storage of data is realized, a fitting evaluation value is calculated easily and rapidly, and the efficiency of automatic oil deposit history fitting is effectively increased.

The invention discloses a spectrum construction method for an LED light source used for neonatal jaundice therapeutic equipment. The method comprises the steps of obtaining the absorption spectrum of a standard bilirubin solution through experiment and constructing the data array of the absorption spectrum; selecting multiple kinds of monochrome LEDs and constructing the spectroscopic data matrix of the multiple kinds of monochrome LEDs; setting the unknown proportionality coefficient matrix of the multiple kinds of monochrome LEDs so that the multiple kinds of monochrome LEDs can generate the absorption spectrum through simulation after being combined according to certain proportion, and constructing an over-determined equation set according to the spectroscopic data matrix and the unknown proportionality coefficient matrix of the multiple kinds of monochrome LEDs and the data array of the absorption spectrum; obtaining the non-negative least-square solution of the over-determined equation set with the simple generic algorithm to further obtain the optimal proportion combination of the multiple kinds of monochrome LEDs and the fitting spectrum of the multiple kinds of monochrome LEDs under the optimal proportioncombination. The method has the advantages of being easy to operate, high in efficiency, high in matching rate and the like.

The invention discloses a log monitoring method for behavior anomaly detection based on deep learning improved IFOREST. The method comprises the following steps: collecting and vectorizing behavior information of a user; carrying out dimension reduction on the input log user behavior vector by utilizing an Auto-Encoder pair algorithm training model; using a generic algorithm and a Gan network to improve IFOREST, repeatedly training a model according to updated information to improve the recognition accuracy, finally, performing high-efficiency and high-precision abnormal behavior detection ondaily behaviors of a user through a log monitoring method, and applying a deep learning method to the field of network security of abnormal behavior detection, so as to detect the abnormal operation performed by the user or an administrator. Dimension reduction is performed on the extracted user behavior information by using the self-encoder in deep learning, preprocessing of high-dimensional datais realized, and a model with higher and more stable training accuracy compared with IFOREST is provided.

The invention relates to a hierarchical link tree routing method based on region partition. The method comprises the following steps: 1, partitioning a wireless sensor network into a plurality of regions; 2, separately clustering each region, and forming a first link in each corresponding region by using nodes in each cluster through a generic algorithm according to a PEGASIS (Power-Efficient Gathering in Sensor Information Systems) protocol; 3, selecting a cluster head from each cluster according to an energy maximum principle; 4, forming a second link by using a communication link between each cluster head and a Sink node through a generic algorithm according to the PEGASIS protocol; 5, transforming the second link into a hierarchical link tree taking the Sink node as the center; 6, transmitting node data to the Sink node through data fusion along the hierarchical link tree, and skipping to the step 1 after preset communication time. By adopting the hierarchical link tree routing method, the energy consumption of long-distance communication caused by the adoption of a single-hop way among nodes in a LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm is lowered effectively.

Systems and methods of integrating weapons systems with the aircraft systems of an aircraft carrying the weapon so as to generate on the aircraft in flight a display indicative of the weapon successfully engaging a target are disclosed. The system can include a ground station for generating a database describing the weapon performance envelope, a generator for creating coefficients characteristic of that performance envelope using a generic algorithm and an uploader for uploading the coefficients to the aircraft, and a reconstructor on the aircraft containing the same generic algorithm and adapted to select the coefficients for the algorithm according to the aircraft and target conditions in order to generate the feasibility display, wherein the algorithm is generic to both air to ground and air to air weapons.