109 results about "Adaptive management" patented technology

A network switch having a unified, adaptive management paradigm for wireless network devices is disclosed. The switch includes configurable ports for connecting devices. A software application running on the switch allows a network administrator to selectively configure each port to support either a wired device or wireless device. Configuration information and software images that are needed for operation of the wireless device are associated with the port. When a wireless device is first plugged into the switch port, it downloads its configuration directly from the switch port. By storing the configuration information and images at the switch and automatically downloading them to the wireless devices, the task of configuring the devices is greatly simplified for the network administrator. This is particularly advantageous in heterogeneous network environments that support both wired and wireless devices, and where wireless device are readily moved to different ports.

Management of the bandwidth capacity of tunnels through a network may be automated such that the network is adaptive to the stochastic nature of incoming traffic. An edge node in the network includes four main elements. Three of the elements, namely tunnel signaling, admission control and path selection, are derived from known technologies, generalized from their particular technologies and enhanced. With the addition of a fourth element, called capacity management, the four elements cooperate to accommodate the capacity needs of the traffic incoming to the network at the edge node. This accommodation is performed by estimating the traffic demand and dynamically adapting tunnels to the traffic demand.

Systems, methods, and devices related to a microgrid system for providing power to a facility. A self-contained power system provides power to a facility using a combination of power storage elements and renewable energy sources. A connection to an external power grid may also be provided. The system optimizes power flow to the facility using power from the storage elements and the renewable energy sources and, if necessary, the external power grid. The optimization process predicts future power usage by the facility using power usage data from a predetermined time window. The optimization process can also take into account predicted energy generation amounts by the renewable energy sources. To optimize economic effects, the optimization process can also determine whether to buy and when to buy power from the external power grid.

A method for providing adaptive video compression includes encoding a video stream in a first compressed format, storing the video stream in a storage device, retrieving the video stream from the storage device, decoding the video stream, encoding the video stream in a second compressed format, and storing the video stream in the storage device. Systems and other methods for providing adaptive video compression are also disclosed.

The invention provides an adaptive management method for the power consumption of a data center. The method comprises the following steps of: partitioning a computer room according to functions of each part, arranging N+1 precision air conditioners in each function area, and sorting by priority according to the importance of a refrigerating action; adjusting the power consumption of a server in real time by using monitoring software according to a service load in the computer room; accomplishing generation of an air conditioner adjustment strategy with the help of a computational fluid dynamics (CFD) simulation computed result; and controlling the air conditioners of the computer room by using a fuzzy neural network control strategy. By the method, an optimum value can be achieved under the conditions of good radiation effect and low energy consumption.

A framework for adaptively managing the life of components. A sensor provides non-destructive test data obtained from inspecting a component. The inspection data may be filtered using reference signatures and by subtracting a baseline. The filtered inspection data and other inspection data for the component is analyzed to locate flaws and estimate the current condition of the component. The current condition may then be used to predict the component's condition at a future time or to predict a future time at which the component's condition will have deteriorated to a certain level. A current condition may be input to a precomputed database to look up the future condition or time. The future condition or time is described by a probability distribution which may be used to assess the risk of component failure. The assessed risk may be used to determine whether the part should continue in service, be replaced or repaired. A hyperlattice database is used with a rapid searching method to estimate at least one material condition and one usage parameter, such as stress level for the component. The hyperlattice is also used to rapidly predict future condition, associated uncertainty and risk of failure.

A system and method for monitoring resource requirements in a computer system and for adding (or subtracting) such resources as required. In a preferred embodiment, the system automatically, without user intervention, makes the resource adjustment when calculations indicate a need for such an adjustment. The system stores historical and statistical data concerning a multitude of parameters and the action taken with respect to the resources reflects a comparison against such stored data.

A method and system for interactive modeling of high-level network performance with low-level link design provides a tool for optimizing networked computing systems and their link components simultaneously. The method models a fixed portion of a network and specifies operational performance levels and power constraints. A solution is chosen for a non-fixed network portion and the network is simulated to determine link requirements and synthesizes links in conformity with the link requirements. The links are analyzed to determine performance (e.g., bandwidth) and requirements (e.g., power) and network performance is recalculated. An iterative loop from the selection of the non-fixed topology through synthesis and recalculation of link performance can be implemented to optimize the link and network design. The links may be links that use an adaptive management policy that trades off power and bandwidth in multiple operating modes and the method may be used to optimize the operating modes and/or management rules.

Adaptive control techniques, such as power management techniques for use in managing power consumption in an electronic device, employ optimization analysis to generate power consumption/quality of service performance plots. The optimization analysis can include experiments that are performed for various settings of control factors that control the operation of the device. The performance plots relate power consumption in the device to quality of service levels for the device. During operation, aspects of the device are monitored. A control factor and a level for the control factor are automatically selected based on the monitored aspects, by using the performance plots to identify an experiment having the desired quality of service level and the corresponding control factor level. The control factor is then automatically set to that selected level to control the power consumption or quality of service of the device.

The invention provides an LED cloud street lamp intelligent control and decision making system based on Internet of Things. The LED cloud street lamp intelligent control and decision making system based on Internet of Things is characterized in that novel efficient energy saving light source LED lamps are used based on a current street lamp control system; an intelligent chip is arranged in each street lamp to name and identify the street lamp; the Internet of Things wireless sensing technology is used to perform data communication between lamps so as to connect the lamps to form a controllable street lamp network, and then the controllable street lamp network is connected with a cloud computing center through a stable Internet of Things communication technology, and automatically sets a optimal illumination scheme by means of background data processing and a control program and according to the factors, such as the local latitude and longitude, the climate, the light and the traffic, so that remote single lamp control, real-time control, adaptive adjusting, early warning of faults and the like can be realized; and the data, such as the environment data, acquired by the street lamps, can be transmitted to the cloud computing platform in the wireless mode, and then an optimal illumination adjusting scheme is generated through intelligent computing analysis and judgment of the acquired data, so that the Illumination As Required adaptive management function can be realized.

A software tool or information system that is User Driven and allows the user to imagine, dictate, iterate and control the place, means, format and kind of data and information to be viewed and analyzed. The system allows the User to begin with an imagined image or picture of what is required. The system is Interactive and allows the user to iterate, change, modify improve, edit, correct, consolidate, drill down and perform all other actions that are required by the user. The system is Adaptive and has a learning capability that will anticipate and prompt progressively more accurately what the User desires from the User's inputs and therefore Adapts to the User. The System is Adaptive, Learning with intelligent estimation of the User's request when examining the target database or data source. The system is also Database or Data Source Abstracted and Independent by automating the query, search and seek process and by automatically generating all required actions including the appropriate code generation, including all query code and the abstraction of the key elements of database parameters and characteristics supplemented by a target specific element. The system also incorporates Network and Connectivity Abstraction and Independence. The System is supported by automated and Intelligent Administrative functions.

Systems and methods for adaptive database management and monitoring are disclosed. According to various embodiments, the present invention comprises training a neural network of a classification engine with real time performance data of a database. Once the neural network has been trained, real time performance data for the database may be input to the classification engine. If the classification engine detects a deviation in performance, it may cause an alert to be sent to a database administrator. In addition, the classification engine may send results of its analysis to a host, which posts the results on a web page. Users may provide feedback on the results to a batch relearn entries database or file. The classification may read the batch relearn entries to use in a backpropogation algorithm to update/retrain the neural network of the classification engine.

The invention discloses a visual multi-target tracking method based on multiple single trackers. The visual multi-target tracking method comprises the steps that targets are detected based on a classifier and are tracked through the multiple visual single-target trackers, the targets and the trackers are corrected through different strategies during tracing, and thus the multiple targets are tracked continuously; specifically, firstly, the target classifier is utilized to detect images so as to obtain the targets, then one visual single-target tracker is assigned to each target, and the multiple visual single-target trackers are utilized to jointly complete the task of multi-target tracking; and in order to deal with the cumulative error of the trackers, targets escaping from the view during tracking and new targets, the update cycle for the multiple single-target trackers and the multi-tracker consistency judgment cycle are introduced, thus adaptive management such as update and consistency judgment is conducted cyclically on the multiple single-target trackers, and the multiple targets are effectively tracked. According to the visual multi-target tracking method, the tracking efficiency is greatly improved, and the requirement for multi-target real-time tracking is basically met.

The present invention relates to a reliability management-based uncertainty elimination scene perception system and a working method thereof. The system comprises a scene information collection module, a scene information processing module, a knowledge library module, a scene response module, a scene information application module, a scene information retrieval or subscription module, a scene information correction module, and a user feedback module. The scene processing module comprises a multi-algorithm incompleteness elimination unit, an algorithm result inconsistency elimination unit, a credibility management unit, a reliability management unit, an information source inconsistency elimination unit, a scene information fusion deduction unit, and an adaptive management unit. Through adoption of the system and method, more reliable scene information can be obtained, and the accuracy, reliability and self-adaptation of the scene perception system are improved remarkably.

Systems and methods for generating a graphical interface and scheduling administrative rounds are presented. The graphical interface may present to an administrator a prioritized list of care providers based on care provider performance, care provider satisfaction, patient satisfaction, and labor data associated with the care provider. A schedule of administrative rounds to counsel the care provider may be presented to the administrator and may be based on the prioritized list, administrator and patient location, and administrator and patient schedule.

The invention provides an adaptive management entity, a base station self-configuring method and a base station self-configuring system. The base station self-configuring method comprises the following steps that the adaptive management entity receives a connection request sent by a base station eNodeB and sends an information acquisition request to the eNodeB; and the adaptive management entity receives identification information sent by the eNodeB according to the information acquisition request, acquires address information of an element management system EMS corresponding to a manufacturer identifier according to the manufacturer identifier in the identification information, and sends the address information of the EMS to the eNodeB, so that the eNodeB acquires software and configuration parameters from the EMS. By the adaptive management entity, the base station self-configuring method and the base station self-configuring system, when eNodeBs and EMSs which are provided by multiple manufacturers are in a network, the eNodeB and the EMS which are provided by the same manufacturer are not required to be bonded together, the eNodeB can acquire the address information of the corresponding EMS through the adaptive management entity, and the self-configuring process of the eNodeB in a multi-manufacturer environment is realized.

The invention relates to a multi-sensor adaptive management method for multi-UAV cooperative detection. The method includes target cooperative recognition model based on Bayesian network reasoning technology. Obtaining The cooperative perception probability of multi-sensor feature level by off-line learning and on-line learning based on Bayesian network. Adopoting Perceptual information entropy tocharacterize the uncertainty of the target category. Based on perceptual information entropy, establishing the detection closed loop of perceptual-learning-decision-making-action from target cooperative recognition to sensor dynamic management i. Using perceptual information entropy increment as the judging criterion of sensor resource dynamic scheduling in multi-UAV cooperative detection network; proposing An intelligent optimization algorithm based on genetic algorithm and particle swarm optimization algorithm to improve the allocation efficiency of multi-UAV-multi-objective. From the viewpoint of multi-sensor and multi-target feature recognition, the unknown target is recognized by using the multi-features detected by multi-sensors.

The invention discloses an Internet of Things operating system prototype based on multi-agent. The architecture is characterized in that an information node senses the change of a pervasive environment, an information node monitor is responsible for monitoring the update of the information node and stored context information, a context searcher is responsible for indexing and searching the storedcontext information, an interpreter provides services for the information node monitor and the context searcher, when a change is searched in the context, a related main body is used for performing adaptive management, performing fusion processing on the information according to a parameter adjustment and data prediction model and deducing a high level context according to the related main body ina cloud knowledge base, a decision making agent and an execution agent manage and control the message transmission between the agents and provide the same to an effector, and a multi-agent communication system acts on the pervasive environment. The Internet of Things operating system prototype based on multi-agent is constructed.

Systems and methods for intelligent, adaptive management of energy storage packs are disclosed. A method comprises receiving a first current measurement of a first energy storage cell electrically connected to a first converter circuit. The first converter circuit controls the charge and discharge of the first energy storage cell. A first voltage measurement of the first energy storage cell is received. A first temperature measurement of the first energy storage cell is received. The first current measurement, the first voltage measurement, and the first temperature measurement are translated into a state of charge of the first energy storage cell.