163 results about "Continuous optimization" patented technology

A method is provided for continuous optimization of allocation of computing resources for a horizontally scalable application which has a cyclical load pattern wherein each cycle may be subdivided into a number of time slots. A computing resource allocation application pre-allocates computing resources at the beginning of a time slot based on a predicted computing resource consumption during that slot. During the servicing of the workload, a measuring application measures actual consumption of computing resources. On completion of servicing, the measuring application updates the predicted computing resource consumption profile, allowing optimal allocation of resources. Un-needed computing resources may be released, or may be marked as releasable, for use upon request by other applications, including applications having the same or lower priority than the original application. Methods, computer systems, and computer programs available as a download or on a computer-readable medium for installation according to the invention are provided.

The invention provides a service oriented architecture (SOA)-based enterprise collaboration management system service flow design method. The method is characterized by comprising the following steps of: 1) construction an SOA integrated development system platform; 2) service flow modeling and analysis; 3) service flow simulation and optimization; 4) quick assembly and deployment of service flows; 5) service flow performance management; and 6) service innovation and continuous optimization. The invention also provides an enterprise collaboration management system service flow system for implementing the method. The service flow system is a Web Service technology-based Web service integrated application platform and provides a uniform user interface, an operating method and data expression for a user, and the user accesses the corresponding information resources of an enterprise through the interface; and when the request passing simple object access protocol (SOAP) message expression arrives, request agent cases of corresponding services are generated by resolving the message, and corresponding operations are called by dynamic binding through an agent to finish mutual calling and integrate with an enterprise database system.

The invention provides a method and system for continuous optimization of a data center. The method includes monitoring loads of storage modules, server modules and switch modules in the data center, detecting an overload condition upon a load exceeding a load threshold, combining server and storage virtualization to address storage overloads by planning allocation migration between the storage modules, to address server overloads by planning allocation migration between the server modules, to address switch overloads by planning allocation migration mix between server modules and storage modules for overload reduction, and orchestrating the planned allocation migration to reduce the overload condition in the data center.

The invention provides a method for optimizing energy usage in commercial buildings. Energy consumption data is used, along with occupant data, to determine appropriate adjustments in energy, and for ongoing monitoring and reporting of energy savings. According to the inventive method, the building of interest is characterized, including calculation of lag parameters—temperature lag, solar gain lag, solar strength lag, and, in some instances, humidity lag, which inform a thermal energy equation particular to the building of interest. Mechanical heating lag and mechanical cooling lag are used for on-going energy use optimization. An outside temperature index may also be used. The resulting accuracy of the thermal energy equation is over 90% for both heat and chilling input, once the building has been optimized according to the inventive method.

The present invention is to formulate a production plan 5 by means of an event-based simulator 4 simulating movement of products within a factory through use of a production process model 2 and a production rule 3. There are provided a time-interval-based simulator 6 for computing the statuses of production processes at given time intervals, and a rule generator 7 for automatically deriving the production rule 3 through use of the time-interval-based simulator 6. As a result of a production plan being repeatedly formulated at high speed through use of the time-interval-based simulator 6, the rule generator 7 can automatically, efficiently formulate the production rule 3 by application of machine learning based on a consecutive optimization method. An event-based simulator 4 devises a high-quality production plan 5 using the generated production rule 3.

A system and associated method for continuously optimizing data archive management scheduling. A job scheduler receives, from an archive management system, inputs of task information, replica placement data, infrastructure topology data, and resource performance data. The job scheduler models a flow network that represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices according to the received inputs. An optimal path in the modeled flow network is computed as an initial schedule, and the archive management system performs tasks according to the initial schedule. The operations of scheduled tasks are monitored and the job scheduler produces a new schedule based on feedbacks of the monitored operations and predefined heuristics.

A computer network system within a virtual environment, includes several computers which are networked to each other to function as a single unit, including at least one parent computer synchronizing and delegating tasks among the several computers; so that the unit scans and gathers data from the virtual environment, and processes and organizes the data into an implementable representation of the data. The unit preferably additionally includes at least one dedicated computer for performing a specific task, and at least one processing computer for providing processing power to the unit. The unit alternatively delivers the implementable representation of the data in the form of a report to a user computer terminal and awaits a decision from a human user.

The invention discloses a short-term wind power prediction method based on deep learning, which comprises the steps of: 1) inputting historical wind power data and related weather characteristic factors such as wind speed and direction by a computer, and preprocessing the acquired data; 2) using a CNN to extract and mine the features of the preprocessed data to form feature map. 3) using a gated recurrent unit (GRU) neural network to train and model the feature map, and establishing a nonlinear relationship between the feature map and the wind farm power by continuous optimization and parameter adjustment to form a short-term wind power prediction model; 4) using the trained model to predict the wind power of the wind farm for a period of time and generate a wind power prediction result ofthe wind farm; 5) and outputting the wind power prediction result by the computer. The method has significant improvement in prediction accuracy and prediction efficiency, provides a basis for rational scheduling of a power grid, and has an industrial application value.

The invention relates to a method for reconstructing a high-resolution three-dimensional human face model from some human face pictures in different angles, which comprises the following steps of: starting from two more front human face images and solving internal and external parameters of a camera and three-dimensional coordinates of an angle point by applying a motion reconstructing structure technology based on beam adjustment on the basis of the stereomatching of angle point detection (containing characteristic point); repeating the process for the human face images in adjacent angles toobtain camera parameters corresponding to all human face images; dividing the human face images based on the characteristic point and registering a human face template to the practical human face position according to the three-dimensional characteristic point; by solving the problem of nonlinear energy optimization for maintaining the consistency of human face model details and the images many times, carrying out grid deformation on the human face template to obtain the reconstructed high-resolution three-dimensional human face model; and finally, by generating a texture map, a weight map and an overall texture map corresponding to each image, carrying out texture mapping on the reconstructed three-dimensional human face model to generate a real human face.

Methods and systems of continuously optimizing data in WiFi positioning systems. For example, data is monitored to infer whether a WiFi access point has moved or is new. In this fashion, data is continuously optimized. Likewise, suspect data may be avoided when determining the position of the WiFi-enabled device using such a system.

A system, method and computer program product for automatically providing a dynamically updated optimized itinerary (continuous optimization in real-time) which allows one to complete a specified task and also to complete other tasks from a to-do list, providing the user both with a packing list of resources to carry and a list of tasks that must be completed before leaving. The system and method also tracks the time and user's location to provide user with a new itinerary in the event a given task cannot be completed successfully. The method also provides an alert in case no practicable itinerary exists.

The invention discloses a method for optimizing a brushless DC motor fuzzy controller based on an improved particle swarm algorithm. The steps include that the whole solution space is divided into seven areas; adaptation degree of each particle is calculated according to a target function; the individual extremum of the particles and the global extremum are updated according to adaptation degree; the updated individual extremum and the global extremum are assigned to quantifying factors Ka and Kb and scaling factors Kp, Ki and Kd; and input and output performance indicators are assessed, if the input and output performance indicators meet the target function, the process ends, and if the input and output performance indicators do not meet the target function, Pi and Pg are substituted in a quantum particle swarm formula by applying the improved particle swarm algorithm, continuous optimization of the particles is performed in the space areas until the particles meet the target function and new particle swarms are generated. The globally optimal solution can be found out at the highest speed based on the improved particle swarm algorithm, and a motor stably operates under the rated rotating speed and is rapid in response without overshoot basically so that the method has great follow-up performance and dynamic and static characteristics.

The invention provides aircraft route planning method and device on the basis of a pigeon colony algorithm and belongs to the technical field of unmanned aerial vehicle route planning and multi-attribute decision making. The aircraft route planning method includes the steps of: 1) firstly establishing a trace prediction model including uncertainty, and determining a to-be-optimized route in a specified region; 2) with the pigeon colony algorithm, performing iteration to obtain an optimized route through operations with a map and a compass and landmark operation, and finally outputting parameters of the acquired optimized route. Correspondingly, the invention provides an aircraft route planning device on the basis of pigeon colony algorithm, which includes: an acquirement module, a construction module, a determination module, an optimizing module and a memory module. The method and device can deduct and calculate the trace prediction model, so that a route acquired by the model has good stability and has robustness and availability; in addition, through the pigeon colony intelligent optimization method, a problem of complex continuous optimization is solved. The calculation and searching process has the characters of parallelism, availability and high robustness.

The present invention provides a processing method after configuration update failure and a network element device. The method comprises: after a configuration of a first network element device changes, the first network element device sending a configuration update message to all its adjacent network element devices; if the first network element device receives a configuration update failure message returned by any of its adjacent network element devices, and the configuration update failure message does not contain information instructing the first network element device to perform subsequent processing, the first network element device rolling its configuration back to the one prior to configuration update; and the first network element device resending the configuration update message to all its adjacent network element devices in the rolled-back configuration. The method and network element device ensure continuous optimization of the network and enhancing robustness and stability of the system.

The invention provides a method for optimizing energy usage in commercial buildings. Energy consumption data is used, along with occupant data, to determine appropriate adjustments in energy, and for ongoing monitoring and reporting of energy savings. According to the inventive method, the building of interest is characterized, including calculation of lag parameters—temperature lag, solar gain lag, solar strength lag, and, in some instances, humidity lag, which inform a thermal energy equation particular to the building of interest. Mechanical heating lag and mechanical cooling lag are used for on-going energy use optimization. An outside temperature index may also be used. The resulting accuracy of the thermal energy equation is over 90% for both heat and chilling input, once the building has been optimized according to the inventive method.

The invention discloses a multi-target searching-based two-colony multidirectional route planning method for a robot. The multi-target searching-based two-colony multidirectional route planning method comprises the following steps: reasonably introducing multi-target searching and roulette into an ant colony algorithm, and expanding the search range and the direction of individual ants, wherein by generation and storage methods of dynamic pheromone, the current optimal route can be kept at a relatively high pheromone level to attract part of the ants to move along the optimal route so as to continuously optimize the current optimal route. According to the multi-target searching-based two-colony multidirectional route planning method for the robot, by the comprehensive consideration that part of the ants are randomly divided in the whole population to complete global random searching and the consideration that a positive feedback strategy of the ant colony algorithm is used for completing continuous optimization of the current shortest route, the route searching efficiency and the possibility of finding the optimal route are improved.

Static arrays of optical traps can be used to sort microscopic objects with exponential sensitivity to size. Such optical fractionation relies on competition between an externally applied force and the moving objects' differing affinities for optical gradient traps. In a reverse fractionation method, objects that are more strongly influenced by the traps tend to become kinetically locked in to the array and are systematically deflected back into an input flow. In a thermal ratcheting method, patterns are spaced to allow particle diffusion, thus providing the opportunity for forward or reverse movement through the patterns. Unlike other sorting techniques, optical fractionation can operate continuously and can be continuously optimized. The exponential sensitivity arises quite generally from the particle size dependence of the potential wells' apparent widths.

The invention discloses a translation method based on intelligent hardware, and the method comprises the following steps: sS1, obtaining audio, image, video or text information, and translating the audio, image and video to obtain text contents; S; s2, translating the obtained character information or character content into second language characters through an online or offline translation engine; S; s3, carrying out knowledge base knowledge point automatic identification is carried out on text information or keywords or semantics of text content before and after translation, and a use sceneis intelligently prejudged; S; s4, automatically or manually selecting the tone of the phonetic bank and adjusting the speed tone through a pre-judged use scene; S; s5, translating result voice broadcast. Information is transmitted by using a wireless transmission technology, translation is completed by combining applications of new technologies such as a voice transfer technology, an image recognition character technology, a translation engine and the like, meanwhile, storage, playback and sharing functions are provided for a user, and scene extension of the user and continuous optimization of a product are also realized.

The invention provides a method and system for optimizing energy usage (where “energy” means electric, gas, and other energy sources) in commercial buildings. In one embodiment of the invention, historical energy consumption data is used, along with occupant data, to determine appropriate adjustments in energy usage. The invention further provides for ongoing monitoring and reporting of energy savings.A system according to the invention implements a process which consists of six stages of analysis: Usage / Energy Use Mapping and data collection, Occupant Comfort Analysis (aka Energy Zoning), Load Matching / Plant scheduling, Occupant Comfort Data Collection / Building Characterization, Design Iterative Building Energy Strategies and Energy Usage / Savings Reports / Detailed Advice Reports.

The invention provides an intelligent camera motion detection alarm system and method based on big data analysis and user feedback, belonging to the mobile monitoring technical field. The alarm system comprises an intelligent camera, a central server, a cloud server, an image analysis server, a push server and a mobile client. The intelligent camera detects motion, uploads videos to the cloud server for storage, and sends a warning message to the central server. The central server notifies the image analysis server to perform secondary detection, and makes decisions according to a secondary detection result and camera alarm. The alarm method utilizes the alarm system to perform motion detection and to give an alarm; when a user receives the alarm, obtains the videos from the cloud server for checking, feeds back alarm results, and updates motion detection algorithm parameters according to alarm detection results and uploaded videos. The system and method realize the customized analysis of a concrete application scene and continuous optimization of a motion detection algorithm, and meanwhile improve motion detection accuracy.

A system and method is provided for a continual updating of the optimization of multiple operational control-variables during the operation of a nuclear reactor over a plurality of fuel cycles. A networked computer system includes one or more hosts programmed to execute an optimization process to identify and make changes in quantitative values of operational control-variables that result in improved efficiency and operational flexibility. Optimization and updating of operational control-variables may proceed selectively under manual control for inputting specific optimization constraints and reactor state-point information or may proceed autonomously through a repetitive performing of the optimization process based upon a predetermined user-defined strategy stored on the network. Communications between users and networked processors is facilitated by use of a TCP / IP server connected to the Internet so that portions of the optimization process may be conducted contemporaneously at remote locations and / or the results made accessible to users via conventional browser enabled computers.

The invention provides an unmanned aerial vehicle target detection method in a 5G environment. The method belongs to the technical fields of 5G technologies, unmanned aerial vehicles, target detection and edge calculation. According to the invention, the 5G high-bandwidth high-speed network and the 5G base station edge intelligent service are effectively utilized. An image obtained by aerial photography of the unmanned aerial vehicle in real time is detected by combining the computing power of the terminal side of the unmanned aerial vehicle. An uncertain target is transmitted back to the 5G base station side through 5G for analysis. A result is fed back to the unmanned aerial vehicle in real time, and changing the advancing mode and angle of the unmanned aerial vehicle or adjusting sensing equipment to complete a target detection task. Besides, edge intelligent computing nodes are managed in a unified mode through the cloud center, intelligent model and service personalized distribution is conducted on the edge nodes according to the unmanned aerial vehicle target detection task, and meanwhile images collected by the unmanned aerial vehicle are collected and used for continuous optimization of the cloud target detection model.

This invention provides an immunochromatography detection apparatus exhibiting excellent convenience, sensitivity, and specificity, a detection method therefor, a kit using the same, and a method for producing such apparatus and kit, by constructing a solid-phase support, on which the conditions for the reaction wherein an analyte specifically binds to a labeled reagent containing a ligand that specifically binds to the analyte and those for the reaction wherein a capture reagent specifically binds to a complex of the analyte and the labeled reagent in the immunochromatography detection method are continuously optimized, and the pretreated specimens are optimized for such reactions. This chromatography detection apparatus comprises: a sheet-like solid-phase support; a specimen-supply site thereon at which a specimen deduced to contain at least an analyte or a mixture of such specimen and a labeled reagent containing a ligand that specifically binds to the analyte is supplied; and a capture reagent site to which a capture reagent capable of specifically binding to and capturing a complex of the analyte and the labeled reagent has been immobilized, wherein at least one functional site having a particular or multiple functions is provided between the specimen-supply site and the capture reagent site.

The invention provides a storage device configuration information continuous optimization backup system. The continuous optimization backup system can back up the storage device configuration information in the premise of greatly reducing the backup data occupied space, recover and roll back the storage device configuration information according to the backup data. The system comprises a basic data zone for storing original configuration information of the storage device, and further comprises a configuration backup zone, wherein the configuration backup zone is provided with a plurality of backup blocks; each backup block comprises a head description zone and a data zone; the head description zone stores timestamp and data zone length of the current backup point; the data zone stores the backup data corresponding to the head description zone; the backup data stored in the data zone is obtained by performing OR operation and compression to the storage device configuration information of the point in time corresponding to the backup block and the storage device configuration information corresponding to the adjacent last point in time. The invention further provides an application method of the system.

A system and associated method for continuously optimizing data archive management scheduling. A job scheduler receives, from an archive management system, inputs of task information, replica placement data, infrastructure topology data, and resource performance data. The job scheduler models a flow network that represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices according to the received inputs. An optimal path in the modeled flow network is computed as an initial schedule, and the archive management system performs tasks according to the initial schedule. The operations of scheduled tasks are monitored and the job scheduler produces a new schedule based on feedbacks of the monitored operations and predefined heuristics.

The invention discloses a coal slime flotation clean coal ash content prediction method based on deep learning. The method comprises the following steps: assembling an image acquisition hardware system; collecting a coal slime flotation froth image and corresponding ash content data; dividing the data set into nine types according to the + / -0.5 interval of the gray unit digit, and performing dataenhancement; adopting a resnet50 network to extract foam surface features, adopting a random gradient descent process to update parameters and softmax function classification, obtaining a high accuracy rate through multiple times of iterative training of a model, and finally making suggestions for on-site working conditions according to prediction results. Compared with manual subjective observation operation, the method has the following advantages: representative high-order abstract detail features can be automatically extracted along with continuous optimization of the model; and in addition, compared with a traditional method, the method has the following advantages: the modeling time is greatly shortened, high-order abstract features are extracted through a convolutional network, a training sample input into the model is more real, an obtained prediction result is more accurate, and the method has an important guiding effect on flotation field production.

The invention discloses a human body cell protein automatic identification method and system based on a convolutional neural network. The method belongs to the field of utilizing a computer vision scheme to automatically identify and classify medical microscope images. The technical problem to be solved by the invention is how to identify protein categories in cells in an image by using a convolutional neural network classifier in supervised learning. According to the adopted technical scheme, the method for automatically identifying the protein in the human cells based on the convolutional neural network comprises the following steps: S1, preprocessing a data set: making the data set according to the input format of a Protein ResNet 32 model, and setting a protein sequence to be determined; S2, model realization and training; and S3, model deployment and continuous optimization. And (2) a human body cell protein automatic identification system based on a convolutional neural network.The system comprises a data set preprocessing module, a model realization and training module and a model deployment and continuous optimization module.

The invention provides a spray chamber used for a sampling system of an inductively coupled plasma massspectrometry (ICP-MS), the sampling system using the spray chamber, and ICP-MS using the spray chamber. The 3D printing technology is used, ABS plastic is used as the material, and the whole consumption type spray chamber used for the sampling system of ICP-MS is designed. The spray chamber is prepared by sleeving two concentric cylinders, can be used for separating and detecting silver ionic with different concentration, and can promote separation and detection used for nano-particle and other metal ionic. The sampling efficiency of the spray chamber is better than that of the common commercial two-channel cylindric spray chamber, and the spray chamber has low cost, simple manufacture process, stable signal, high throughput, and high detection sensitivity, and has possible of continuous optimization.