96results about How to "Able to learn" patented technology

The present invention discloses a method for extracting keywords based on the K-MEANS and the WORD2VEC. The method is characterized in that: the global semantics and the theme of each branch are integrated; a space vector is constructed by using the WORD2VEC algorithm; the K-means algorithm is used to remove the fuzzy words; the centroids distance is calculated; high quality keywords are obtained after the clustering evaluation; and by lifting the weight value, the dynamic optimization of the lexicon is realized, so that the keyword extraction has the learning evolution ability. According to the method disclosed by the present invention, the extracted keywords can reflect the internal classification theme of the document, and each keyword can well reflect the classification; and the method has the characteristics of high quality of the final keywords, wider adaptability, a more accurate result, and the like.

The invention discloses a sparse matrix and convolutional neural network-based picture compression method. The method comprises the following steps of converting an image into two-dimensional matrix data; assessing, optimizing and constructing a sparse matrix image by utilizing a convolutional neural network; optimizing a compression scheme through the convolutional neural network; performing compression processing on the sparse matrix image by using the selected optimal compression scheme; and finally obtaining a high-compression-ratio and low-distortion compressed image. According to the method, the problem of difficult use and identification of the image due to low compression ratio and excessively reduced quality of the compressed image in an existing compression technology is solved; and the optimization can be carried out according to picture contents, and the proper image construction and compression method can be self-selected, so that the picture feature extraction and compression method has self-optimization and learning capabilities.

The invention provides an equipment monitoring and diagnosing method, comprising the following steps of obtaining a vibration signal of the equipment under a fixed rotation speed; calculating the characteristic value complying with Gaussian distribution within the corresponding frequency width range according to the vibration signal so as to establish at least one benchmark line; subsequently judging the normality or abnormality of the equipment according to the benchmark line, the corresponding frequency width range and the characteristic value; therefore, if the vibration signal exceeds the benchmark line of safe range, the abnormality and failure type can be diagnosed and recognized; and furthermore, the aim of owning the automatic monitoring and diagnosing and learning capabilities can be realized. Furthermore, the equipment monitoring and diagnosing method does not need to consider all failure types that may occur in the whole service life of the equipment so as to greatly reduce the development scale and difficulty of the failure monitoring and diagnosing system, thus improving the visualization of the equipment condition and increasing the completion rate of the equipment.

A communication method of word information includes matching language type of receiving terminal to language used in received word information by service server and retransmitting word information to translation platform to let it be translated then sending it to receiving terminal if language used in received word information is not matched to language type of receiving terminal. The translation platform is worked as querying databank to know qualified translator then sending word information to said translator to let word information be translated at his terminal.

The invention provides a hearing aid system based on an intelligent speech recognition technology, and relates to the field of medical equipment. The hearing aid system is characterized in that the system comprises a recognition terminal, a mobile terminal, and a cloud terminal. The mobile terminal comprises a voice collection device, a signal conversion device, a decomposition filter, an enhancing system, a preprocessing computation device, a judgment system, a synthetic filter, a data sending device, a local database, and a data receiving device. The mobile terminal comprises a controller, a processor, a display, a speech recognition device, and a mobile data transmission device. The cloud terminal comprises a cloud data transmission device, a cloud database, and a cloud analytical processor. The system has the advantages of accurate Chinese speech recognition, self-learning ability, leading recognition algorithm, good later maintenance, and the like.

The invention discloses a railway wind disaster prevention prediction method in the technical fields of railway disaster prevention alarming and monitoring. The method comprises the following steps of: acquiring wind speed and wind direction data of a current moment measuring point; calculating the first-order difference, the second-order difference and the third-order difference of the wind speed and wind direction data of the current moment measuring point; retrieving the wind speed and wind direction data, the first-order difference, the second-order difference and the third-order difference of three continuous moments before the current moment and establishing an identification equation of a wind speed and wind direction model; establishing a quaternion first-order equation set according to the identification equation of the wind speed and wind direction model and acquiring the model parameters of the wind speed and the wind direction through a Gaussian elimination method; updating the model parameters according to an average sliding coefficient; and calculating the wind speed and wind direction data of the next moment according to the updated model parameters. The invention improves the prediction accuracy and the accuracy of a prediction result.

The present invention provides an automatic questions selecting method of an examination system on a network based on a layered genetic algorithm. A key point of the invention is introducing characteristics of the layered genetic algorithm into the automatic questions selection of the examination system on the network. The method can select questions which are best matched with a question requirement to generate examination paper rapidly and satisfy the rapid and high-efficiency question requirement by the high capacity item bank of the examination system according to the crucial attributes of investigation scope and difficulty of paper etc. with respect to users. The automatic questions selecting method based on the layered genetic algorithm can integrate situations such as questions difficulty of various levels, total difficulty of the paper and questions that have already been used in examines, so that a question selection efficiency of the examination system can be greatly enhanced, a matching degree between the paper and the question selection requirement can be improved, and a checking efficiency can be increased.

The invention relates to an artificial intelligent calligraphy robot. The artificial intelligent calligraphy robot comprises a base, a stepping motor A, a rotary disk, a stepping motor B, a stepping motor C, a big cantilever, a small cantilever, a stepping motor D, a paper rolling motor, Chinese art paper, a paper winding tube, a connection rod, a rotating shaft, a stepping motor E, a clamping plate, a stepping motor F, a self-learning camera, a synchronous belt, a pen holder, a fastening screw, an automatic ink outlet brush pen, an ink conveying hose, an ink conveying pump and an ink box. According to the artificial intelligent calligraphy robot, a folding neutral network and a sequence neutral network are combined to obtain output of a sequence, and the complex feedback from images to movement data is achieved; and after basic training is carried out, by means of self-repeated writing, self-learning and training are carried out, and the calligraphy level of the calligraphy robot canbe improved without human intervention; and the calligraphy robot has a learning ability, the calligraphy robot can be applied to the calligraphic art study, and after certain training, the calligraphy robot can try to imitate famous fonts and can also help an ordinary person to learn and understand calligraphy.

The invention relates to a garbage can for intelligently sorting medical waste and a sorting method thereof, and belongs to the technical field of auxiliary sorting and recognition. The garbage can comprises a box body assembly, a box cover assembly, an image recognition system and a power supply module, wherein the box body assembly comprises a box body housing and a plurality of built-in garbagecans, the box cover assembly comprises a connecting cover, a central motor and an inner rotating bucket, the image recognition system comprises a camera, an information storage module, an informationprocessor and a human-computer interaction module, the box cover assembly is located above the box body housing, the connecting cover is used for fixing the inner rotating bucket, the central motor and the image recognition system, and the power supply module is connected with power supplies of all devices to which power needs to be supplied in the garbage can for intelligently sorting medical waste. The garbage can for intelligently sorting medical waste has the function of automatically identifying and sorting the medical waste by a machine, the use is convenient, and the sorting accuracy of the medical waste is increased; in addition, according to the garbage can for intelligently sorting medical waste, the number of garbage inlets is reduced, so that the condition of environmental pollution caused by the exposure of the medical waste is avoided, and the effects of cleanness and sanitation are achieved.

The invention discloses an intelligent exit and entry system based on faces and vehicle-related characteristics. The intelligent exit and entry system comprises a front-end recognition module, an information positioning module, a data analysis module, an intelligent decision module and an output control module. The front-end recognition module comprises a biometric identification intelligent camera and a vehicle identification intelligent camera. The information positioning module comprises the functions of personnel information positioning and vehicle information positioning, and based on artificial intelligence analysis of big data, the data analysis module analyzes intelligent exit and entry relationship information through vehicle information and personnel information acquired by the positioning module and relevant entry and exit record information. The intelligent decision module makes intelligent probabilistic decisions on the intelligent exit and entry relationship information to obtain probability assessment of various release decisions. According to the intelligent exit and entry system based on the faces and the vehicle-related characteristics, face information of a driver and passengers on a vehicle are also identified while the vehicle is identified, whether the vehicle can enter and exit the field or not is automatically determined by an artificial intelligence analysis system mining relevant information of vehicles and people, the entry and exit experience and the traffic efficiency are greatly improved, and manual management work is reduced.

The invention provides a speed sensorless control method, comprising the following steps: a deviation between the given speed of the motor and the feedback value of the rotor angular speed is adjustedby the CMAC-PID intelligent controller and the torque current is output, and the deviation between torque current and torque current feedback is regulated by PI regulator, and the output voltage is uq. The deviation between the excitation current and the feedback value of the excitation current is regulated by the PI regulator and the output voltage ud is obtained. The voltage uq and the voltageud are inversely transformed to obtain two-phase control voltages. The injected high-frequency carrier signal is superposed on the two-phase control voltage, and the superposed voltage signal is usedas the input signal of SVPWM modulation. The output of SVPWM modulates six PWM pulse signals needed to control the on-off of the trinomial inverter switching devices, and the inverter outputs three-phase voltage to control the permanent magnet synchronous motor. Thus, the purpose of adjusting PID parameters on line is realized, and the motor has good dynamic and static performance in a wide rangeof speed.

The invention provides a convolutional neural network-based image geographic positioning system and method, and relates to the field of satellite positioning. The system is characterized by comprising a video uploading module, a picture uploading module, an image frame extraction module, a geographic mark detection module, a decomposition module, a feature extraction module, an activation function, a pooling module, a full connection module, a classifier analysis and judgement module, a learning module, a regional database, a pixel database and a result display module. The system and method provided by the invention have the advantages of rapid in image analysis and correct in image recognition, positioning video information and having learning ability.

The invention relates a device and method having infrared transponding and infrared detection functions. The device comprises a processor, a power module, a wireless transceiver module, a low-power-consumption control module, an infrared receiving module, an infrared transmission module and an infrared detection module, wherein the processor is connected with the infrared receiving module, the wireless transceiver module, the low-power-consumption control module, the infrared transmission module and the infrared detection module, respectively; the power module is connected with the low-power-consumption control module, the infrared transmission module, the infrared detection module and the processor, respectively; the wireless transceiver module is connected with an external gateway; the low-power-consumption control module is connected with the infrared receiving module; the infrared transmission module is connected with an external household appliance. Compared with the prior art, the device and the method are capable of realizing infrared transponding and infrared detection simultaneously; the device is capable of sensing a human body by use of a pyroelectric sensor, has the capability of learning, and further is low in power consumption.

The invention discloses an artificial neuron circuit with a plastic threshold, which comprises an accumulation module, a threshold issuing module and a threshold adjusting module, and is characterizedin that the accumulation module is connected with the threshold issuing module and is used for accumulating signals generated by synapses and sending the signals to the threshold issuing module; thethreshold value issuing module is used for receiving the signals output by the accumulation module and comparing the received signals with a preset threshold value, if the received signals are largerthan the preset threshold value, neurons are activated, and if the received signals are smaller than the preset threshold value, the neurons are not activated; the threshold value adjusting module isconnected with the threshold value issuing module and used for adjusting the size of a preset threshold value according to a learning algorithm. In the training process of the circuit, synapses and neurons are adjustable at the same time, so that the artificial neural network has stronger learning ability, and is richer and more flexible in function and more intelligent.

The invention relates to the field of medical equipment and provides an intelligent medical ultrasonic image processing equipment, a system and a method. The equipment is characterized by comprising an image acquisition device, an image receiving device, an image classifying device, a general image processing device, a first algorithm database, an analytical judgment device, a display device, an ultrasonic image processing device and a second database. The equipment, the system and the method have advantages of low cost, high intelligent level, objectivity and accuracy of results, clearness in image processing, learning ability and the like.

The invention discloses a semi-supervised community discovery method based on maximum clique, comprising the following steps: selecting an initial node based on node degree, looking for each independent clique structure, and forming a clique set in a network; selecting k biggest cliques as initial core communities, and marking the nodes in the initial core communities, wherein the nodes in the same community are marked in the same way, and the nodes in different communities are marked differently; respectively adding MustLink and CannotLink relations according to whether the nodes are marked in the same way or differently, and calculating the membership degree between neighbor nodes; and finally, carrying out reasoning based on the properties of the MustLink and CannotLink relations to get more MustLink and CannotLink relations, and determining the membership of the nodes based on the maximum membership degree between adjacent nodes, thus completing community division. The method can help analyze the structure of a network and find features hidden in the network, and is of important practical significance.

The invention discloses an image retrieval system and method, and relates to the field of image identification. The image retrieval system comprises an image reading module, an image noise elimination module, an image segmentation module, a characteristic quantity calculation module, a screening module, a retrieval module, and a feedback module. The image reading module is used for reading image information; the image noise elimination module is used for eliminating the image noise; the image segmentation module is used for segmenting the image; the characteristic quantity calculation module is used for calculating the characteristic quantity of an unit image; the screening module is used for screening the characteristic quantity; the retrieval module is used for retrieving similar images based on the characteristic quantity; and the feedback module is used for feeding back a retrieval result. Aiming at the problems such as poor accuracy, poor retrieval speed and poor learnability of an existing image retrieval system and method, an improved system is provided; the improved system provided by the invention not only enables the retrieval result to be more accurate and the efficiency to be higher, but also has a learning capability and can improve the retrieval quality continuously.

The invention discloses a working method for a safe man-robot cooperation assembly robot and relates to the technical field of intelligent manufacturing. Firstly, a worker set working parameters and instructions through a controller and a touch screen, and a first joint arm and a second joint arm conduct rotational adjustment in the horizontal direction under driving of an alternating current torque electromotor; under driving of a hollow shaft motor inside a connecting sleeve, a screw rod conducts vertical adjustment movement, meanwhile, a vacuum suction nozzle is started to suck air, and a screw is sucked into a locking claw head by a material supplying device through a feeding pipeline and positioned by the vacuum suction nozzle; when moving to a corresponding screw hole position, the screw rod is driven by an electric screwdriver to press rotationally, and the screw is driven by the screw rod to be continuously screwed in the hole position till totally locked in a screw hole; and after the screw is locked, the screw rod moves up to leave the screw hole position. According to the working method, safety and stability are good, highly difficult and fine actions are achieved, programming is easy, and the working efficiency is high.