335 results about "Target–action" patented technology

Described herein are methods, network devices and machine-readable storage media for conducting simulated phishing attacks on an individual so as to educate the individual about the various ways in which phishing attacks may be disguised. Specifically described is a simulated phishing attack involving a sequence of messages. At least one of the messages has an associated target action that would ordinary, if the attack were an actual phishing attack, result in the individual's personal information and/or computing device becoming compromised. In the simulated phishing attack, no malicious action is actually performed. At least one of the other messages is designed to draw attention to the message with the target action.

Framework may be implemented for transferring knowledge from an external agent to a robotic controller. In an obstacle avoidance/target approach application, the controller may be configured to determine a teaching signal based on a sensory input, the teaching signal conveying information associated with target action consistent with the sensory input, the sensory input being indicative of the target/obstacle. The controller may be configured to determine a control signal based on the sensory input, the control signal conveying information associated with target approach/avoidance action. The controller may determine a predicted control signal based on the sensory input and the teaching signal, the predicted control conveying information associated with the target action. The control signal may be combined with the predicted control in order to cause the robotic apparatus to execute the target action.

An interactive television program guide system is provided in which targeted advertisements may be presented to a user and targeted actions taken in the program guide based on the user's interests. The program guide monitors the user's interactions with the program guide to determine the user's interests. Interactions that may be monitored include interactions that indicate the categories of programming that interest the user (e.g., movies, sports, children's programming, etc.), setting a reminder for a program, purchasing a program, requesting information on a program, browsing program listings for a particular time or channel, etc.

A system and method for assessing student performance. The method includes receiving student data, third party data, a set of predetermined thresholds, and an activity guide at a computer processor. The student data and third party data are linked into a collected data, at least a part of which is evaluated against the set of predetermined thresholds. The student's performance is classified based on a determination if the collected data meets the predetermined thresholds. The collected data and classifications are stored and displayed, and a performance score is provided. A targeted strategy is provided for the student by identifying a set of targeted actions corresponding to the student classifications, and displaying targeted actions to authorized users.

The invention relates to a human body skeleton-based action recognition method. The method is characterized by comprising the following basic steps that: step 1, a continuous skeleton data frame sequence of a person who is executing target actions is obtained from a somatosensory device; step 2, main joint point data which can characterize the actions are screened out from the skeleton data; step 3, action feature values are extracted from the main joint point data and are calculated, and a feature vector sequence of the actions is constructed; step 4, the feature vectors are preprocessed; step 4, the feature vector sequence of an action sample set is saved as an action sample template library; step 6, actions are acquired in real time, the distance value of the feature vector sequence of the actions and the feature vector sequence of all action samples in the template library is calculated by using a dynamic time warping algorithm; and step 7, the actions are classified and recognized. The method of the invention has the advantages of high real-time performance, high robustness, high accuracy and simple and reliable implementation, and is suitable for a real-time action recognition system.

The invention relates to the technical field of mode recognizing, in particular to a real-time human body action recognizing method and device based on depth image sequence. The method comprises the steps of S1, extracting target action sketch from a target depth image sequence and extracting a training action sketch from a training depth image set; S2, performing gesture clustering on the training action sketch and performing action calibrating on the clustered outcome; S3, computing the gesture characteristics of the target action sketch and training action sketch; S4, performing the gesture training based on a Gauss mixing model by combining the gesture characteristics of the training action sketch and constructing a gesture model; S5, computing the transferring probability among all gestures of the clustered outcome in each action and constructing an action image model; and S6, performing action recognizing on the target depth image sequence according to the gesture characteristics of the target action sketch, the gesture model and the action image model. The real-time human body action recognizing method disclosed by the invention has the advantages of improving the efficiency of action recognizing and the accuracy and the robustness of the action recognizing.

There is proposed a method that may be universally used for controlling a man-machine interface unit. A learning sample is used in order at least to derive and/or initialize a target action (t) to be carried out and to lead the user from an optional current status (e_c) to an optional desired target status (e_t) as the final status (e_f). This learning sample (l) is formed by a data triple made up by an initial status (e_i) before an optional action (a) carried out by the user, a final status (e_f) after the action taken place (a).

The invention is applicable to the electronic field and provides a target identification method and a device. The method comprises the following steps of: judging whether a starting action exists in ROI (region-of-interest) of a back depth map according to variation of depth value of a front depth map and the back depth map which are adjacent in a depth frame sequence; detecting regions which have the same colour frames according to a preset limb target model, and judging regions which accord with the limb target model to be limb target regions; storing characteristic set parameters of the limb target regions; tracking regions of the previous colour frame which are judged to be the limb target regions in a depth frame, and detecting the regions which are the same in the colour frame corresponding to the preset colour frame by utilizing the preset limb target model and characteristic set parameters of the previous limb target region which are stored, so as to obtain the limb target regions; acquiring coordinates of each limb target region, and identifying a target action according to the acquired coordinate values. In the target identification method and device provided by the embodiment of the invention, a depth image sequence and a colour image sequence are used for detecting the limb target regions, thus detection accuracy is effectively improved.

The invention discloses a control method and device for intelligent equipment, a storage medium and a processor. The method includes the steps of displaying a virtual assistant in a virtual reality scene, wherein the virtual assistant is used for representing a virtual role, and the virtual role is used for interacting with a user; receiving a voice instruction sent by the user, wherein the voiceinstruction is used for instructing the intelligent equipment to execute a preset command; sending the preset command to the intelligent equipment according to the voice instruction; controlling the virtual assistant to execute a target action in the virtual reality scene when an execution result generated after the intelligent equipment executes the preset command is received, wherein the targetaction is matched with the voice instruction. The method solves the technical problem that an existing control mode is relatively simplex.

The invention discloses a policy information determining method. The method comprises the steps of obtaining data to be processed, wherein the data to be processed comprises a first image and a secondimage, the first image includes scene thumbnail data of an interactive application, and the second image comprises scene interactive data of the interactive application; processing the first image through a policy information model to obtain target action data, wherein the policy information model is the relation between label sample data and sample action data; according to the second image, determining target state data of a target object; according to the target action data and the target state data, determining policy information corresponding to the target object, wherein the policy information is used for controlling target object actions. The invention further discloses a policy information determining device. Game operation automation can be achieved, for the complex game scene, the policy information obtained by means of the policy information model is closer to actions of a real player, and therefore the flexibility of the scheme is improved.

The invention discloses a brain-myoelectricity artificial limb control device and method based on scene steady-state visual evoking. The method is characterized by comprising the following steps: stimulating the brain of a subject by adopting a scene steady-state visual evoking unit to induce the generation of an operation intent; picking up the electroencephalogram and myoelectricity signals of an operator respectively by adopting a portable electroencephalogram acquisition unit, a myoelectricity acquisition unit and a wireless Bluetooth technology; transmitting the electroencephalogram and myoelectricity signals onto a microprocessor; performing target action identification through a method for combining the electroencephalogram and myoelectricity signals; and coding an identification result to drive an intelligent artificial limb to finish corresponding actions. The key points are that a certain action is decomposed into a plurality of different scenes; two inverse color pictures with sharp contrast between black and white are obtained respectively after each scene is subjected to gray-scale processing; two inverse color pictures of each scene flicker continuously and alternatively through pulse width modulation of different frequencies to stimulate the brain of the subject in order to induce the generation of the operation intent.

The invention discloses an interaction method for a mobile communication terminal, which comprises the following steps that: focus information of a visual focus is acquired; and the target action is executed according to the focus information. The invention also discloses a method for interacting a mobile communication terminal, the mobile communication terminal comprises a display screen and a camera, and the method comprises the following steps that: the focus information of the visual focus is acquired when an image to be shot appears on the display screen; the focus information is processed to generate control information; and the camera is automatically focused according to the control information. The invention also discloses a device used for the interaction of a mobile communication terminal and the mobile communication terminal. The method and the device and the mobile communication terminal can realize the non-near distance control to the mobile communication terminal, increase the convenience of operation and control of a user, and are convenient for the disabled to use.

The invention relates to a nanomicelle of magnetic tumor double-targeting polymer and a preparation method thereof. The nanomicelle of magnetic tumor double-targeting polymer is a nuclear shell structure; the surface of an outer shell is connected with targeted ligand, an inner core is enclosed with nano-particles of super paramagnetic ferriferrous oxide and anticarcinogen with hydrophobic property; in virtue of the physical effect of an external magnetic field and induction of the targeting ligand, the tumor double-targeting function of the nanomicelle is realized. The invention also provides the preparation method of the nanomicelle of magnetic tumor double-targeting polymer: polyethylene glycol with end capping by folacin, and poly Epsilon-caprolactone or sandwich copolymer of poly propiolactone are taken as raw materials and enclosed with the nano-particles of super paramagnetic ferriferrous oxide and the anticarcinogen with hydrophobic property, and then dialysed to obtain the nanomicelle of magnetic tumor double-targeting polymer. The drug carrier system of the nanomicelle reinforces the treatment effect of the anticarcinogen with hydrophobic property on tumor, prolongs the circulating time of the anticarcinogen in human body, intensifies the targeting action of drugs, improves the release efficiency and partial concentration of drugs, and reduces the dosage and toxic and side effect of drugs.

The invention discloses a rule-based data processing method and a rule engine apparatus. A rule engine performs matching on received data based on a triggering condition defined in a rule, wherein therule is represented with a statement in a text form; and if the matching succeeds, the rule engine executes a target action defined in the rule. The rule configuration is easy to understand and the storage is simple.

The invention discloses a robot, a mechanical arm and a control method and device of the mechanical arm. The control method comprises the steps that an object to be grabbed is recognized through an image of an environment where the mechanical arm is located; the target distance between the object to be grabbed and the mechanical arm is determined; and a target action executed by the mechanical armis determined according to the target distance, wherein the target action is used for indicating and commanding the mechanical arm to grab the object to be grabbed and moving the object to be grabbedto a target position. The technical problem that grabbing precision of a mechanical arm in the prior art is low is solved.

The invention relates to a voice recognition system and method. The system comprises an acquisition module, a conversion module, an extraction module, a coupling module, an execution module, and a voice database, wherein the acquisition module is used for collecting to-be-recognized voice information; the conversion module is used for converting the to-be-recognized voice information into first standard audio information capable of being recognized by the extraction module; the extraction module is used for analyzing the first standard audio information and extracting a keyword from the first standard audio information; the coupling module is used for calling a target command word and coupling the target command word with the keyword in the first standard audio information, and if the coupling succeeds, sending the corresponding target command word to the execution module; and the execution module is used for receiving the target command word and executing a corresponding target action. According to the voice recognition system and method, the recognition rate and recognition accuracy of a voice signal is improved; the corresponding target action is executed through the execution module; and automation of voice control is realized. Under the precondition of ensuring the quality of voice recognition, user's experience and operation efficiency is improved.

The invention relates to a virtual object control and model training method, a device, a storage medium and equipment, wherein the virtual object control method comprises the following steps: acquiring interactive scene data corresponding to a virtual object to be controlled; carrying out characteristic extraction on the interactive scene data to obtain characteristic data; inputting the characteristic data into a virtual object control prediction model to obtain a target action output by an action prediction layer of the virtual object control prediction model; selecting action parameters output by an action parameter prediction layer corresponding to the target action in a plurality of action parameter prediction layers included in the virtual object control prediction model; controllingthe virtual object to be controlled to execute the target action according to the selected action parameters. The virtual object control and model training method, the device, the storage medium andequipment provided by the invention can provide the control efficiency of the virtual object.

The embodiment of the invention discloses a method and device for controlling an electrical device, equipment, and a storage medium. The method comprises: voice information inputted by a user is collected and the voice information is transformed into text information; the text information matches an action instruction in a preset action instruction library; if matching is done successfully, a target action instruction corresponding to the text information is extracted in the preset action instruction library and the type of the target action instruction is identified; if the instruction type is an operation value instruction type, whether a target operation value corresponding to the operation value instruction exists in the text information is identified; and if so, a current electrical device is controlled to perform a corresponding action based on the target action instruction and the target operation value. Therefore, the interaction operation between the user and the electrical device is simplified; and the electrical device is controlled by voice information instead of the remote controller, so that the user experience is improved.

The invention relates to an information processing technology, and discloses a gesture recognition system based on RFID (radio frequency identification devices). The gesture recognition system comprises an RFID signal information acquisition module used for receiving a wireless signal from a reader by a transponder, and preprocessing an RFID signal; an anomaly detection and extraction module used for using an anomaly detection algorithm to recognize and extract abnormal changes of RFID signal state information; an action recognition module used for recognizing a gesture action by taking an abnormal segment caused by the gesture action as a target action class based on a matching algorithm; and a response recognition module used for executing a meaning that the gesture represents when recognizing a target gesture action. The gesture recognition method and system based on the RFID provided by the invention have the following beneficial effects that the universality is very high, and people subjected to detection do not need to carry any extra equipment, so that the inconvenience caused by carrying detection equipment by people subjected to detection can be avoided.

The invention discloses a virtual object action control method and device, computer equipment and a storage medium, and belongs to the technical field of computers. The method includes the steps: acquiring information of a target virtual object and environmental information within view range of the target virtual object; inputting the information of the target virtual object and the environmentalinformation into an action acquiring model, and outputting target action information of the target virtual object by the action acquiring model according to the information of the target virtual object and the environmental information; controlling the target virtual object to make actions in a virtual scene according to the target action information in a terminal interface. The method only acquires the information of the target virtual object and the environmental information within the view range of the target virtual object instead of information of all virtual objects in the virtual scene,fairness of electronic games can be improved, scenes controlled and operated by a player based on a display image of the terminal interface can be really simulated in the real scene.

An information providing device includes an agent ECU that sets a reward function through the use of history data on a response, from a driver, to an operation proposal for an in-vehicle component, and calculates a probability distribution of performance of each of actions constructing an action space in each of states constructing a state space, through reinforced learning based on the reward function. The agent ECU calculates a dispersion degree of the probability distribution. The agent ECU makes a trial-and-error operation proposal to select a target action from a plurality of candidates and output the target action when the dispersion degree of the probability distribution is equal to or larger than a threshold, and makes a definitive operation proposal to fix and output a target action when the value of the dispersion degree of the probability distribution is smaller than the threshold.

The invention aims to provide a method and equipment used for executing object operation on a touch terminal. Particularly, first operation, on a touch screen of the touch terminal, of a user is obtained; a corresponding target action zone is distinguished and displayed in a corresponding scene on the touch screen according to the first operation; second operation, on a touch button on the touch screen, of the user is obtained; the object operation corresponding to the second operation is executed in the target action zone. In comparison with the prior art, the user can operate the target action zone and skill release on the touch screen to execute operation on an object in a specific scene, which conforms to human engineering, the user is supported to execute the operation on the scene object conveniently, correspondingly, more complex man-machine interaction is supported, the man-machine interaction efficiency is improved, and equipment use experience of the user is promoted.

The embodiment of the invention provides a lawyer recommendation method and system, which belongs to the technical field of data processing. The method comprises the steps that target action cause information corresponding to a user is acquired; a target arbitration document meeting preset similarity is acquired from an arbitration document database stored in the storage unit of an electronic device based on the target action cause information; and finally at least one recommended lawyer recommended to the user is acquired from lawyers in the target arbitration document. The recommended lawyeris the most similar lawyer who processes the target action cause information, and the recommended lawyer has a higher odds ratio. The user has a higher odds ratio. At least one recommended lawyer recommended to the user is acquired from lawyers in the target arbitration document, which can effectively improve the accuracy and efficiency of lawyer recommendation.

The invention discloses an artificial hand control method and apparatus for driving EEG signals on the basis of facial expressions. The method is characterized by comprising the following steps: a subject, according to four basic actions of an artificial hand, making corresponding four simple facial expressions; extracting the EEG signals through an EEG acquisition module, transmitting the EEG signals to a portable signal processing module through a Bluetooth transmission module, performing characteristic extraction and mode identification on the received EEG signals, transmitting an identification result to a control driving module through a wireless communication module, and driving a motor to finish artificial hand target actions through simulating the control driving module in an artificial arm cylinder.

The invention provides a video action migration method. The video action migration method comprises the following steps: extracting action sequences of a source video and a target action video, and respectively generating a source posture and a target posture; receiving an image input of the source video; performing preliminary feature extraction on the foreground and the background; respectivelyfusing the preliminary features of the background and the foreground to generate a fusion feature of the background and a fusion feature of the foreground; synthesizing fusion features through the fusion features of the background to synthesize the background; synthesizing fusion features through the fusion features of the foreground to synthesize a foreground and a foreground mask, and further obtaining a frame model of the target video after action migration at the t moment; and adding a loss function to the frame model, wherein the loss function comprises a content loss function and a confrontation loss function, the content loss function comprises pixel-level error loss and perception error loss, and the confrontation loss function comprises spatial confrontation loss and multi-scale time domain confrontation loss. An overall assembly line model with universality and flexibility is constructed.