448 results about "Dynamical optimization" patented technology

A system and method to automatically and dynamically optimize available resources to meet application data availability and business objectives. In one embodiment, a backup and data recovery system continually and dynamically adjust to the backup and recovery or restore process depending on the customer's environment, workload, and business objectives. Acceptable tolerance of downtime due to recovery and backup impacts the customer's business or system operation. From this high-level business requirement, the present system determines the backup and recovery plan details. The present system accepts application data availability policies based on business objectives, and devises, executes and refines a resource optimal backup and recovery strategy required to deliver the desired quality of service in the environments that have dynamically changing application workloads, business objectives, and hardware / software infrastructure technologies. In addition, the present system performs backups outside blocked windows to minimize the impact on the customer's system.

Execution of a restartable sub-tree of a query execution plan comprises determining whether use of parallel processes is a preferred or optimal mode of executing the sub-tree. The determination is based, at least in part, on how long it takes to restart the sub-tree using two or more parallel processes and / or how long it takes to probe the sub-tree, i.e., to fetch a row that meets one or more conditions or correlations associated with the sub-query, using the two or more parallel processes. Thus, a dynamic computational cost-based operation is described, which determines at query runtime whether to execute the restartable sub-tree using a single server process or multiple parallel server processes.

The technology described herein provides methods and systems for powertrain optimization and improved fuel economy including multiple displacement engine modeling and control optimization, automotive powertrain matching for fuel economy, cycle-based automotive shift and lock-up scheduling for fuel economy, and engine performance requirements based on vehicle attributes and drive cycle characteristics. Also provided is a reverse tractive road load demand simulation algorithm used to propagate a reverse tractive road load demand and a corresponding component torque and speed, derived from a vehicle speed trace, in a reverse direction through a powertrain system. Also provided is a dynamic optimization algorithm. The dynamic programming algorithm is applied to a matrix of fuel flow rates to find the optimal control path that maximizes the powertrain efficiency over a cycle.

Methods and apparatus for dynamically determining whether portions of code should be interpreted or compiled in order to optimize a software application during run-time are disclosed. According to one aspect of the present invention, computer-implemented method for run-time processing of a computer program which includes byte-codes arranged as a plurality of methods includes invoking a first method selected from the plurality of methods. Invoking the first selected method involves interpreting the first selected method. An invocation tracker which is arranged to track the number of invocations of the first selected method is updated, and a determination is made regarding when the invocation tracker indicates that the number of invocations of the first selected method exceeds a threshold value. The first selected method is compiled when it is determined that the invocation tracker indicates that the number of invocations of the first selected method exceeds a threshold value. This threshold value is periodically adjusted to keep the compilation and the interpretation overheads within acceptable ranges.

A method of dynamic model predictive control is presented in which both steady state optimization and dynamic moves calculation of the manipulated variables are determined together as part of one optimization solution (211). The method combines steady state optimization and dynamic move calculation such that the steady state optimal targets are determined consistent with the dynamic moves and the resulting dynamic response of the process so that the controlled variables do not violate their low / high limits in both steady state and dynamically. The method utilizes what is described as high limit dynamic violation variables and low limit dynamic violation variables corresponding to each of the controlled variables. The method offers a unique capability of mixing a part dynamic optimization with a part steady state optimization as it relates to application of a large model predictive control under varying situations, in particular at or near constraints violations that can be done dynamically in real time for improved and robust control performance without having to change the controller tuning.

System and method for chemical manufacture utilizing a dynamic optimizer for a chemical process including upstream and downstream processes. The dynamic optimizer includes a maximum feed calculator, operable to receive one or more local constraints on the downstream processes and one or more model offsets, and execute steady state models for the downstream processes in accordance with the local constraints and the offsets to determine maximum feed capacities of the downstream processes; and a feed coordinator, operable to receive the maximum feed capacities, and execute steady state models for the upstream processes in accordance with the maximum feed capacities and a specified objective function, subject to global constraints, to determine upstream production parameters for the upstream processes, which are usable to control the upstream processes to provide feeds to the downstream processes in accordance with the determined maximum feeds and the objective function subject to the global constraints.

Methods, systems, and computer program products for dynamically adjusting computer resources, as appropriate, in response to predictions of batch runtimes as well as for rendering costs of the computer resources actually utilized, which costs are consistent with customer demands.

A power converter uses a control method to optimize efficiency by dynamically optimizing a controlled parameter. A change in efficiency of the converter after changing at least one controlled parameter is determined. The direction of change in efficiency of the converter is compared to a direction of the change in the controlled parameter. The controlled parameter is changed in a positive direction when the direction in the change in the efficiency of the converter and the direction of the change in the controlled parameter are the same and changed in a negative direction when the direction in the change in the efficiency of the converter and the direction of the change in the controlled parameter are opposite. The controlled parameters may include dead time between turn-on and turn-off and between turn-off and turn-on of the primary and corresponding secondary side switches of the controller, drive voltage(s) for the switches, and intermediate bus voltages.

A method and system of designing the operations and controls of a gas turbine, includes generating an operations model for the gas turbine including at least one objective function and defining operations and control constraints for the operations model of the gas turbine. An online dynamic optimizer / controller dynamically optimizes and controls operation of the gas turbine using model based control based on the operations model and the operations and control constraints. The model based control may include model predictive control.

Provided is a self-optimizing computer system that can achieve ultimate optimization (improvement in the speed) by preparing a mechanism that can observe the behavior of the program execution in the self-optimizing computer system and optimize dynamically depending on the execution behavior of program. The self-optimizing computer system comprising multiple processing units, characterized in that each of the processing units operates as at least one of an operation processing unit for executing a program, an observation processing unit for observing the behavior of the program under execution, an optimization processing unit for performing an optimization process according to the observation result of the observation processing unit, and a resource management processing unit for performing a resource management process of whole of the system such as a change of the contents of execution.

The invention discloses an intellectualized microwave continuous drier and a control method thereof; the drier comprises a rack, a conveying belt, a conveying belt wheel, a driving motor, an instrument control box and a computer; two ends of the conveying belt are respectively provided with a feed hopper and a discharge hopper and the conveying belt runs through a plurality of microwave drying cavities with doors; each microwave drying cavity is internally provided with a microwave magnetron, a temperature sensor, a humidity sensor and a speed-regulating fan; the feed hopper and the discharge hopper are respectively provided with a moisture sensor, and the conveying belt is driven by a speed-regulating motor controlled by a frequency converter. A control system can automatically draw variation curves of the temperature and the humidity of the materials in each drying cavity and the water content ratio of the discharged materials in the drying process of the materials in shorter time according to the collected data of the temperature, the humidity and the water content ratio of the materials in the drying process of the materials, can optimize control parameters continuously according to the change of the temperature, the humidity and the water content ratio in the drying process of the materials, and can regulate the system parameters automatically and realize the dynamic optimization of the drying technique parameters.

The present invention dynamically optimizes computing resources allocated to a simulation task while it is running. It satisfies application-imposed constraints and enables the simulation application performing the simulation task to resolve inter-instance (including inter-server) dependencies inherent in executing the simulation task in a parallel processing or other HPC environment. An intermediary server platform, between the user of the simulation task and the hardware providers on which the simulation task is executed, includes a cluster service that provisions computing resources on hardware provider platforms, an application service that configures the simulation application in accordance with application-imposed constraints, an application monitoring service that monitors execution of the simulation task for computing resource change indicators (including computing resource utilization and application-specific information extracted from output files generated by the simulation application) as well as restart files, and a computing resource evaluation engine that determines when a change in computing resources is warranted.

The invention discloses an on-line dynamic optimization control method for a converter steelmaking process based on data driving. The method comprises the following steps: building an off-line prediction model database; building a liquid steel temperature prediction model and a carbon content prediction model by use of a data driving method to obtain a corresponding relationship between operating variables and the temperature and carbon content of liquid steel in the converter steelmaking process; selecting a melt data unit matched with the information of the current converter steelmaking production process and determining a control reference curve of the liquid steel temperature and carbon element content; building a real-time dynamic optimization model of the converter steelmaking process and determining an optimized set value unit of each operating variable; and selecting the set value of each operating variable from the optimized set value unit so as to carry out control operation. The method disclosed by the invention can be used for realizing real-time on-line control over the converter steelmaking process and providing convenience for an operator to set selection conditions according to actual working conditions, thus the production efficiency of a steelmaking plant is improved.

The invention belongs to the technical field of Internet of Things task unloading, and particularly relates to a computing unloading method based on mobile edge computing in the Internet of Things. The invention relates to a theoretical framework of Internet of Things (Internet-of-Things-of-Things, IoT), Mobile Edge Computing (MEC), mode selection and node matching, dynamic optimization and the like. According to the technical scheme, local unloading is considered; Direct cloud unloading, Device end offload, Four computing unloading modes of equipment relay forwarding unloading are adopted, the influence of the social relation between the equipment on the unloading service level and the long-term dynamic performance of the system are added and considered, a system long-term income functionrelated to time delay and energy consumption is constructed, and a computing unloading scheme based on edge computing in the Internet of Things is obtained through mode selection and node matching. The method has the beneficial effects that better balance is achieved in the aspects of time delay and energy consumption performance, and the reliability and the stability of the system are improved.

An improved wave energy converter for use in offshore and deep-sea locations. The wave energy converter is adapted for secure attachment to the bottom of a body of water (e.g., the ocean floor), preferably beyond the breaker zone. The wave energy converter is selectively adjustable in length. A hydraulic power generation system is used to convert the energy present in the waves into hydraulic power that can be use to generate electricity and for other purposes, such as desalinization. The system may include a hydraulic piston assembly, a floatation device that is connected to the piston assembly, high and low pressure hydraulic reservoirs, and a hydraulically driven power generator. The floatation device moves upward in response to rising waves, and downward under the force of gravity in response to falling waves. The system utilizes this downward gravitational force to discharge fluid from the piston assembly, which in turn, drives the power generator. A control system is used to detect water conditions and to selectively adjust the length of the support structure and the fluid flow characteristics to dynamically optimize power generation based on changing water conditions.

Embodiments of the present invention provide a method, system and computer program product for dynamic optimization of command sequencing in a computing cluster. In an embodiment of the invention, a method for dynamic optimization of command sequencing in a computing cluster can include selecting a set of nodes in the computing cluster to receive a single command and probing the nodes of the set to compute a computer communications latency for each of the nodes. Thereafter, the single command can be transmitted to the nodes in the set according to a sequence determined by the computed computer communications latency for each of the nodes.

The invention discloses a dynamic optimization method for structural parameters of mechanical equipment parts based on digital twin. The method comprises the following steps: realizing digital mirrorimages of equipment corresponding to physical space in virtual space by constructing a high-fidelity model, which is convenient for later structural parameters modification and super realistic simulation; dynamically and synchronously reflecting the state of an entity corresponding to the physical space in real time in the virtual space by performing super realistic simulation, and realizing realistic motion of the real condition of the physical equipment in the virtual space; and meanwhile, constructing a neural network structure by utilizing a deep learning theory, establishing a relation between the structural parameters and the fatigue life with the help of strong digital mining and mapping capabilities of the neural network structure, and realizing the dynamic optimization of the structural parameters by combining the high-fidelity model and the super realistic simulation environment. According to the invention, dynamic optimization and reverse guidance of the virtual space to thestructural parameters of the physical space are realized, and optimization efficiency and authenticity are improved.

A method, system, and computer readable medium for converting a series of computer executable instructions in control flow graph form into an intermediate representation, of a type similar to Static Single Assignment (SSA), used in the compiler arts. The indeterminate representation may facilitate compilation optimizations such as constant propagation, sparse conditional constant propagation, dead code elimination, global value numbering, partial redundancy elimination, strength reduction, and register allocation. The method, system, and computer readable medium are capable of operating on the control flow graph to construct an SSA representation in parallel, thus exploiting recent advances in multi-core processing and massively parallel computing systems. Other embodiments may be employed, and other embodiments are described and claimed.

A method for dynamic reservation of cloud and on premises resources for software execution is disclosed. The method may be comprised of: receiving the specification for the resources needed for the software, evaluating the availability of resources and reserving the resources for the software. The method may include a protocol to discover, evaluate availability, negotiate terms and create a reservation contract for software execution resources based on the required specification. This can be implemented using resource agent modules that represent available software execution resources (e.g.: CPU, memory, storage, network . . . ) and reservation agent modules that are used for communicating with the resource agent modules for reserving software execution resources. In addition, monitoring agent modules are used to monitor the actual software execution reservation contract.In another embodiment, a method for dynamic reservation of cloud and on premises resources for software execution may include a system for dynamic optimized reservation of software execution resources across cloud and on premises resources based on excess and demand which may include the above method with one or more contract aggregation modules that holds one or more reservation contracts for software execution resources and one or more re-negotiation reservation agent modules that continuously re-negotiates software reservation contracts based on up to date information. The contract aggregation module can also be a resource agent module representing reservation contracts that have not yet been fully fulfilled and thus can still be re-negotiated. The re-negotiation reservation agent module can re-negotiate a reservation contract for software execution resources that are required in the immediate timeframe or in the future. The re-negotiation reservation agent can use but is not limited to the following information when re negotiating software execution resources contracts: Software execution requirement, price, monitoring information, contract cancellation information, migration cost (in case the software is already running), scalability, security and compliance changes and availability of resources.

The invention provides a system and method of providing a message passing fabric in a modular processing system where a plurality of processing elements (VFBs), access other available processing elements to provide a message passing fabric where the fabric asynchronously establishes routes for synchronous messages from an origin processing element to a destination processing element to permit an operation to occur at the destination processing element in a flexible, efficient, self-routing and real-time dynamically optimized manner.

The technology described herein provides methods and systems for powertrain optimization and improved fuel economy including multiple displacement engine modeling and control optimization, automotive powertrain matching for fuel economy, cycle-based automotive shift and lock-up scheduling for fuel economy, and engine performance requirements based on vehicle attributes and drive cycle characteristics. Also provided is a reverse tractive road load demand simulation algorithm used to propagate a reverse tractive road load demand and a corresponding component torque and speed, derived from a vehicle speed trace, in a reverse direction through a powertrain system. Also provided is a dynamic optimization algorithm. The dynamic programming algorithm is applied to a matrix of fuel flow rates to find the optimal control path that maximizes the powertrain efficiency over a cycle.

A combination of light source types is used to form a dynamically adjusted backlighted display with the particular combination dependant upon the desired light output at a given point in time. In one embodiment, RGB diodes are used for low brightness situations and as the brightness requirement increases white light sources are added. In another embodiment, different light source types are used to produce the different color components and optical feedback is used to control the power levels of the various diode types.

The invention provides a multi-user dialogue audio role recognition dialogue audio method based on machine learning. The method specifically includes steps: training voice data with marks by employing a UBM-GMM algorithm to obtain a UBM-GMM model; performing secondary segmentation on to-be-recognized voice data, and clustering voice to obtain voice samples with clustering marks; then extracting a part of the voice samples with the clustering marks as samples to be put into the UBM-GMM model, and performing alignment training to obtain an alignment training model; performing identity recognition according to the alignment training model to obtain collected voice clips with the same identity, and classifying the voices with the same identity; and outputting the voice data of each person in a dialogue. According to the method, roles in the audio are intelligently sampled for the role recognition model training, the precision of voice segmentation and role restoration is greatly improved, automatic optimization of the model is realized, and problems of low segmentation and recognition precision and failure of dynamic optimization of the conventional method are solved.

The invention relates to the internet safety technology, and is designed to provide an intelligent detection method for Java script malicious code facing to the webpage. The method comprises three processes of optimization of sample, safety detecting and renewal of optimization. A classical N-gram statistical model and a KNN grader are efficiently fused by means of the efficient mixing of static detecting and dynamic detecting. The dynamic behavioural analysis of the code is realized by setting up N-gram characteristic of a Java Script machine code operation order. The efficiency of the static classification detection is largely improved by the optimization of the sample relayed on the KNN grader. The suggested intelligent detecting method is specially provided with operability, the step of optimization of sample can ensure that the classification effectiveness is not decreased when a training script is enlarged, and the step of the safety detecting can ensure to execute the high-efficient intelligent detecting based in the optimization of sample, and the step of the renewal of optimization can ensure thag the precision of the intelligent detecting is not be declined when new malicious script is increased. The method has the advantages of providing the ability of detecting the new malicious script and the dynamic optimization adjust ability for continuing work.

A technique for reducing non-local access, in dynamically generated code that resides in a code buffer of a NUMA computer system including multiple nodes, for improving overall performance of dynamic optimization systems. In one example embodiment, this is accomplished by partitioning the code buffer into multiple smaller code buffers and assigning each of the multiple smaller code buffers to one of the multiple nodes. Statically determining which methods in the generated code are executed by a thread and then to place those methods in associated one of the multiple smaller code buffers to reduce memory latencies introduced by non-local accesses.

Resource usage is monitored for a hybrid cloud computing environment having on-premises computing resources and off-premises computing resources, for at least a first time period. For the time period, the amount of the on-premises computing resources and off-premises computing resources are related to the demand for computing services from the environment. Future demand for computing services from the environment is dynamically predicted based on the relating step and a learned function of the demand and / or a projection of future business growth for a business utilizing the on-premises and off-premises computing resources. Based on the predicted future demand, the amount of the on-premises computing resources is dynamically optimized for a future time period, so as to minimize costs for the future time period; and new amounts of on-premises computing resources and off-premises computing resources are provisioned for the future time period, in accordance with the optimizing step.

A method for fault detection workflow management is presented. The method includes determining a working environment, a rule application attribute, or both, where the working environment and the rule application attribute are associated with input data. Furthermore, the method includes dynamically optimizing a sequence of operations based on the determined working environment, the determined rule application attribute, or both. Additionally, the method includes processing the input data based on the optimized sequence of operations. Systems and computer-readable medium that afford functionality of the type defined by this method is also contemplated in conjunction with the present technique.

A multi-model generalized predictive control system based on dynamic optimization comprises a dynamic optimization layer, an MPC layer and a basic control layer. The dynamic optimization layer is located in the upper layer and calculates the optimization value of a critical control variable as the optimal set value of the MPC layer; the MPC layer is located in the lower layer and adjusts a to-be-optimized variable based on a rolling optimization prediction algorithm on the condition that the to-be-optimized variable satisfies a model dynamic behavior, thereby tracking the optimal set value obtained in the S1; the basic control layer is located in the bottom layer and transmits the final optimization value of the to-be-optimized variable to an actuating mechanism. According to the present invention, the cost consumption of the system is reduced, the economic benefit of the system is improved, the transient performance and the model parameter jump adjusting capability of the system can be improved, at the same time, the interference of disturbance on the output of the system can be eliminated effectively.