2033 results about "Intelligent algorithms" patented technology

A FLASH controller is disclosed. The controller comprises a USB interface unit. The USB interface unit implements a USB standard that has a bus speed equal or greater than 12 Mb/s. The controller includes an internal bus coupled to the USB interface unit; and a FLASH interface unit coupled to the internal bus. The FLASH interface unit includes FLASH controller logic that allows the throughput for access to the FLASH memory to match the speed of the USB standard. Advantages of the FLASH controller in accordance with the present invention include (1) utilizing the higher speed USB interface such as the USB 2.0 standard, which substantially increases the serial throughput between USB host and FLASH controller; (2) utilizing more advanced FLASH control logic which is implemented to raise the throughput for the FLASH memory access; (3) utilizing an intelligent algorithm to detect and access the different FLASH types, which broadens the sourcing and the supply of FLASH memory; (4) by storing the software program along with data in FLASH memory which results in the cost of the controller being reduced, and also makes the software program field changeable and upgradeable; and (5) providing high integration, which substantially reduces the overall space needed and reduces the complexity and the cost of manufacturing.

According to various embodiments of the present invention, user performance and/or motivation for a computing system may be maximized by optimizing one or more target components of a user interface of the computing system. The target components may be aspects of the user interface that is perceived by the user. One or more input features and one or more output features may be identified, and data regarding these input and output features may be gathered. This data may be compared with the results generated by a set of candidate artificial intelligence algorithms to determine which of them provides the best fit with the data collected. Then, the selected artificial intelligence algorithm may be applied to the user interface to iteratively change the target components over time until the optimal settings for each user are discovered.

The invention discloses a virtual gift display method and a virtual gift display device based on live broadcasting. The method comprises the steps of receiving virtual gift presentation instructions sent by a plurality of user terminals, wherein the virtual gift presentation instructions comprise presentation object information, picture information and value information of virtual gifts and the identity information of the user terminals; generating animations and display logics of the virtual gifts according to the virtual gift presentation instructions, wherein the display logics are the display sequence of the virtual gifts at a presentation object terminal; and according to the animations and the display logic, displaying the virtual gifts to the presentation object. The virtual gift display method and device based on live broadcasting implemented according to the invention provide a more intelligent algorithm for interactive design and interactive experience design laying emphasis on user experience, and can acquire beneficial effect on stimulating and enhancing interactivity and gift presentation in a live broadcasting platform.

Method and system for improving the detection of security threats in a communication network, including security devices which generate security events. The present invention assigns a dynamic tag to each event according to the description of the event, and the tags related to the same security threat are clustering forming a data model pattern. An artificial intelligence algorithm, learning from known real information, analyzes said patterns and decides whether an alarm should be generated or not.

An artificial intelligence data analytics system, including: a data analytics algorithm accessible by an analyst operable for receiving data from one or more sources, applying one or more analytical tools to the data, and displaying summary information to the analyst; an artificial intelligence algorithm accessible by the analyst operable for applying a preferred artificial intelligence methodology to the data and automatically triggering and executing an action on behalf of the analyst; and an artificial intelligence learning algorithm accessible by the analyst operable for statistically scoring a plurality of potential artificial intelligence methodologies to be used by the artificial intelligence algorithm, receiving a corresponding analyst preference as to the preferred artificial intelligence methodology, and causing the artificial intelligence algorithm to utilize the preferred artificial intelligence methodology.

The invention discloses a reinforcement learning-based AUV behavior planning and motion control method, which belongs to the technical field of underwater robots and aims at solving the problems of limited training experience and difficult application in a real environment as complex task planning by the AUV depends much on manual experience and a control method designed based on an intelligent algorithm needs an accurate environment mode. AUV tunnel detection is defined as a general task; behaviors corresponding to task completion comprise target trending, wall tracking and obstacle avoidance; a control instruction generated by the robot under water to complete the planned behavior is defined as an action; and when the AUV executes a tunnel detection task, a deep reinforcement learning DQN algorithm is used for real-time behavior planning, a corresponding deep learning behavior network is constructed, and planning of the tunnel detection task is completed. The AUV action network is trained by the DDPG method, the AUV is regarded as an environment model, and force-to-state mapping is obtained, thereby realizing the action control of the AUV.

The invention discloses a multifunctional intelligent clothing care machine. The machine is characterized by comprising a box body, wherein an air exhaust port is formed in the top part of the box body; a water tank and a water draining box which intercommunicate are arranged at the bottom part of the box body; a recognizing module, a washing module, a drying ironing or hanging ironing module provided with an intelligent algorithm system, and a caring module are arranged in the box body and are connected to an information storing and control module. According to the multifunctional intelligent clothing care machine, proper clothing stain removing program, washing program, drying program, ironing or hanging ironing program and casing program are accurately set; the clothing information recognizing and the stain type recognizing are intelligently combined; the care efficiency can be increased based on washing protecting models and intelligently-matched caring program, and meanwhile, the damage to the clothing appearance and physical and chemical performances can be decreased to the maximum. Therefore, common clothing and high-level clothing can be handled, and moreover, some clothing to be subjected to dry cleaning can be handled. The machine is high in automation degree; after a use puts clothing into the machine, the machine can automatically perform all tasks such as washing, drying, ironing and caring.

Approaches for managing and maintaining a state of charge of an energy storage device by adjusting (biasing) responses to electrical grid operator commands to perform ancillary services are disclosed. In embodiments, methods and systems regulate a set point regulation in an energy system. In an embodiment, a method determines when the set point needs to be changed, calculates a new set point, and moves the output of the system from an old set point to the new set point at a defined ramp rate. The method then incorporates, as part of a set point algorithm, the capability to restore the energy storage device to a desirable state of charge (SOC). Embodiments implement Dynamic Bias, SOC and Signal Bias Range Maintaining, Operational Limits, and Fixed Signal Bias algorithms and perform Intelligent Algorithm Selection to manage and maintain the SOC of an energy storage device by biasing responses to grid operator commands.

The invention relates to an intelligent auto-dial outbound system, the system comprises a system dialing module, a client number pool module, a line resource pool module, a system auto-dial mode setting module, a conversation line resource real-time monitoring module, a call connection state statistics module, an agent state real-time monitoring module, an agent call duration statistics module, anagent work mode parameter setting module and an outbound quantity prediction calculation module. The invention also discloses an intelligent auto-dial outbound method, and the method comprises stepsof acquiring parameters, calculating dialing quantity, dialing by the system, answering by agents, and so on. In the system and the method provided by the invention, the quantity of dialing outbound is predicted according to real-time external parameters through an intelligent algorithm, and the system and the method have the characteristics of being efficient and capable of making full use of resources.

A computer-based system, computer-implemented method and computer program product facilitate determining energy cost savings in an energy-consuming facility, such as a commercial building, using an artificial intelligence model, for example a neural network model, that projects or estimates the amount of energy that would have been consumed by the facility but for the implementation of energy efficiency or conservation measures. Energy savings are represented by the difference between the estimate of energy that would have been consumed but for the measures and the actual amount of energy consumed by the facility under actual conditions during a time interval after the measures have been implemented.

The invention provides a virtual synchronous generator virtual inertia and virtual damping coefficient adaptive control method, which relates to the technical field of smart grid and intelligent algorithms. Firstly, the inverter based on the virtual synchronous generator is modeled, and the correlation between the output frequency of the inverter and the virtual inertia J and the virtual damping coefficient D is obtained. Then the fitness function of the adaptive control method of virtual inertia J and virtual damping coefficient D based on the improved particle swarm optimization algorithm isdetermined. Finally, the improved particle swarm optimization algorithm is applied to the active power. In the frequency control part, the real-time adaptive control of virtual inertia J and virtualdamping coefficient D is realized in order to minimize the frequency deviation and stabilize the system. The invention provides a virtual synchronous generator virtual inertia and virtual damping coefficient real-time adaptive control method, which fully utilizes the characteristics of the virtual inertia and introduces the virtual damping coefficient, so that the inverter is more stable and the frequency offset is smaller.

The invention discloses an operation method of a self-positioning robot-based multi-camera parameter automatic calibration system. A self-positioning robot carrying a marker cruises and carries out self-positioning in an offline built environment map to obtain a world coordinate, and a main control computer acquires a camera image and processes the image to obtain an image coordinate of the marker; and the main control computer can automatically carry out the parameter calibration of cameras by combining the dot pair data of more than eight world coordinates and corresponding image coordinates. The operation method provided by the invention has the greatest characteristics of automatically carrying out the parameter calibration on multiple distributed and fixed cameras, greatly reducing labor calibration cost and improving the standardization and accuracy of a calibration process. The upgrading can be realized very conveniently based on the existing monitoring system, and an advanced intelligent algorithm applied to a monitoring and visual system is based on the results of camera calibration, therefore, the operation method provided by the invention has a very broad application and popularization prospect in monitoring systems in the future road traffic, office building corridors, factories, public security and the like.

The invention relates to an evaluation and decision method for the operation efficiency of a power grid. The method comprises the following steps of: in terms of the safety and stability of the power grid, the utilization level of equipment, power supply reliability, the quality of electric energy, and asset management level and economic benefits, performing multi-layer, long-term, global and integral full-viewing-angle evaluation on the operation efficiency of the power grid by using a modern intelligent algorithm; and in terms of the safety and stability of the power grid, the utilization level of the equipment, the power supply reliability, the quality of the electric energy, promotion of coordinated development of the power grid, and the service of economic and social development, performing auxiliary decision on capital construction projects of the power grid in terms of project scores, unit investment scores and multi-target comprehensive optimization by using an optimization algorithm, and determining the sequence of the capital construction projects of the power grid, so that the operation efficiency of the power grid is improved, and decision support is provided for planning and construction of the power grid.

The invention discloses an intelligent resource allocation method in the Internet of Vehicles, which applies an artificial intelligence algorithm to solve the problem of resource allocation in a vehicle network so as to maximize the revenue of a network operator. Specifically, the method comprises: establishing a base station-roadside node combined vehicle edge calculation and cache resource scheduling framework to distribute requested resources for vehicles; establishing a network operator revenue function in combination with the operator revenue and the user experience quality to evaluate the resource distribution problem, and establishing a joint optimization problem to maximize the network operator revenue; solving the joint optimization problem through deep reinforcement learning, obtaining an intelligent task scheduling and resource distribution scheme, arranging the obtained scheme in an intelligent control system, and performing intelligent scheduling and arrangement on vehiclerequests and server resources. According to the intelligent resource distribution method in the Internet of Vehicles, the revenue of a network operator can be maximized while the user experience is considered, and a new idea and angle are provided for practical application of artificial intelligence.

The invention provides a method for determining an optimal route of the airway of an unmanned aerial vehicle. According to the method, the threat of an operation area is more sufficiently considered, more efficient global searching ability is achieved and a more accurate flying route is provided for the unmanned aerial vehicle. The method comprises the following steps: by adopting a quantum encoding mode, changing the state of a basic quantum bit by using a quantum rotating gate and a quantum not-gate, and further updating the position of a bat individual. Because of the diversity of the quantum state, a quantum bat algorithm (QBA) is relatively high in global searching ability and an available or even optimal route avoiding the threat and limiting conditions can be found for the unmanned aerial vehicle. The experiment result shows that the quantum bat algorithm is an effective and stable method for solving the airway route planning problem of the unmanned aerial vehicle, and the search performance of the quantum bat algorithm is superior to that of other swarm intelligence algorithms.

The invention provides an automobile multielement driving safety protection method and equipment special for the automobile multielement driving safety protection method. The method comprises the steps that the face information and pulse information of a driver are collected, an empirical mode decomposition scheduling algorithm based on information entropy is applied, characteristic quantities capable of effectively reflecting the angry feelings, the fatigue states and the identity information of a human body in the face information and the pulse information are extracted, and the characteristic parameters of various types are combined through an intelligent algorithm so that the angry feelings, the fatigue states and the identity information of the driver can be accurately recognized. The equipment special for the method is achieved based on a DSP, Bluetooth wireless communication and other technologies, and when the device detects the abnormal feeling or driving fatigue or identity changing and other states of the driver, the driving behavior of the driver is interfered through alarming prompts, background recording and other modes, and then the driving safety of the driver is protected from multiple aspects.

The invention, which belongs to the field of intelligent algorithm optimization, provides a reinforced learning robot path planning algorithm based on a potential field in a complex environment, thereby realizing robot path planning in a complex dynamic environment under the environmental condition that a large number of movable obstacles exist in a scene. The method comprises the following steps:modeling environment space by utilizing the traditional artificial potential field method; defining a state function, a reward function and an action function in a Markov decision-making process according to a potential field model, and training the state function, the reward function and the action function in a simulation environment by utilizing a reinforcement learning algorithm of a depth deterministic strategy gradient; and thus enabling a robot to have the decision-making capability of performing collision-free path planning in a complex obstacle environment. Experimental results showthat the method has advantages of short decision-making time, low system resource occupation, and certain robustness; and robot path planning under complex environmental conditions can be realized.

The invention relates to a service robot based on voice interaction, cloud technology and integrated intelligent home monitoring. The service robot comprises a service robot body, an intelligent cloud platform which communicates with the service robot body, a house facility, and a user instruction sending end. A resident interacts with the service robot through voice or operating a mobile terminal, the service robot judges the expected instruction of the resident through an intelligent algorithm, and executes corresponding audio and video feedback, limb feedback and related intelligent facility scheduling. The service robot can monitor the intelligent facility at home in real time and upload data information to the cloud platform, and the preferences and habits of the user are analyzed through intelligent cloud computing and can be used as the basis of judging the user expected instruction by the service robot. The resident who is not at home can inquiry various information at home through the cloud platform, the information comprises real-time monitoring information, alarm information, home appliance state information, environmental monitoring information and the like, and an instruction is issued to the service robot through the cloud platform. According to the service robot, the high quality artificial intelligence of the service robot in the real sense is realized.

The invention relates to a short-term load predicting method of a power grid. The method comprises the steps: step 1, acquiring historical data and pre-treating the data; step2, decomposing the historical load sample data into a plurality of different-frequency sub-sequences by using wavelet decomposition; step 3, performing single-branch reconstruction to each sub-sequence; step 4, dynamically choosing training samples and establishing a neural network predicting model optimized by a vertical and horizontal intersection algorithm; step 5, predicting each sub-sequence 24 hours in advance by using the optimal neural network predicting model; and step 6, superposing the predicted value of each sub-sequence to obtain a whole prediction result. The inherent defects of the neutral network can be overcome by optimizing BP neutral network parameters by a brand-new swarm intelligence algorithm, that is, the vertical and horizontal intersection algorithm instead of the traditional algorithm; the burr problem caused by the impact load processing is solved by the wavelet decomposition, the precision declining resulting from the removal of the effective load in the burr pre-treatment is solved and the predicted value of the hybrid algorithm is more approximate to the actual measured load value.