241results about How to "Improve accuracy and stability" patented technology

A method of analysis including an intrinsically valid class of statistical methods for dealing with multivariate ordinal data. A decision support system that can (1) provide automated decision support in a transparent fashion (2) optionally be controlled by a decision maker, (3) provide for an evidence acquisition concept, including automatically increasing the content of an underlying database, and (4) provide a computationally efficient interactive distributed environment. The method is exemplified in the context of assisted diagnostic support.

A method of analysis including an intrinsically valid class of statistical methods for dealing with multivariate ordinal data. A decision support system that can (1) provide automated decision support in a transparent fashion (2) optionally be controlled by a decision maker, (3) provide for an evidence acquisition concept, including automatically increasing the content of an underlying database, and (4) provide a computationally efficient interactive distributed environment. The method is exemplified in the context of assisted diagnostic support.

The invention provides a method used for simultaneous detection of three food-borne pathogenic bacteria based on multicolor upconversion fluorescence labeling. According to the method, three upconversion materials with differentiable fluorescence spectrums are used for forming multicolor upconversion fluorescent nanoprobes via respective connection with aptamers of staphylococcus aureus, vibrio parahaemolyticus, and salmonella, and complementary oligonucleotide single chains of the aptamers are connected with magnetic nanoparticles so as to form nano-composites. When bacteria to be tested are in a detection system, double chain unwinding is realized because of specific binding of the pathogenic bacteria with corresponding aptamers; it is possible to realize simultaneous quantitative determination of staphylococcus aureus, vibrio parahaemolyticus, and salmonella by monitoring upconversion fluorescence signal strength at 477nm, 550nm, and 660nm, detection linear range ranges from 50 to 1000000cfu / ml, and detection limits are 25cfu / ml, 10cfu / ml, and 15cfu / ml respectively. The method is used for detection of pathogenic bacteria, is high in sensitivity, is rapid and convenient, and can be used for detection of the three pathogenic bacteria in food such as milk and shrimp meat; and results are accurate and reliable.

The invention discloses a mobile pollution source emission concentration prediction method based on deep learning, and provides a convolutional long-short-term memory neural network prediction methodbased on an attention mechanism according to regional space-time distribution characteristics of mobile pollution source pollutants. Firstly, a Granger causal relationship between stations is analyzedand a hyper-parameter Gaussian vector weight function is developed to determine a spatial autocorrelation variable as a part of an input feature; secondly, extracting time-space characteristics of data used by the LSTM network by using a convolutional neural network, and meanwhile, attention models are respectively used for weighting a characteristic graph and a channel so as to enhance the effectiveness of the characteristics; finally, a time series predictor based on deep LSTM is used to learn long-term and short-term dependency of the atmospheric pollutant concentration. According to the method, inherent useful characteristics are extracted from historical atmospheric pollutant data, and auxiliary data are incorporated into a proposed model to improve the performance, so that the concentration prediction method is realized.

The invention discloses a light field image depth estimation method based on a hybrid convolutional neural network. The light field image depth estimation method includes the construction of a training data set, the training of a convolutional neural network model, and the generation of a depth estimation map of a light field image. According to the method, the light field depth calculation problem is converted into the classification problem and the relationship between pixel depths in a local area is effectively utilized; and light field data is represented by a four-dimensional parameter. By utilizing the fact that the slope of a straight line in an EPI image of the light field image is proportional to the depth of the scene, the EPI image is used as a medium to map a four-dimensional light field image into a two-dimensional image. By extracting EPI block regions corresponding to pixels in a center-viewing angle image of the light field image, a novel light field image depth estimation training data set is constructed by using a mode of polar line graph block region pairs. According to the light field image depth estimation method, the advantages of deep learning in feature abstraction is utilized, and the accuracy and stability of depth estimation of the method are more advantageous than those of an ordinary conventional methods.

The invention provides a parameter estimating method and a parameter estimating device of an embedded permanent magnet synchronous motor, wherein the parameter estimating method comprises the steps of S1, searching a stator winding resistance change table along with temperatures according to a stator winding temperature, obtaining a corresponding stator winding resistance, searching a rotor permanent magnet flux linkage change table along with temperatures according to an estimated rotor temperature, and obtaining a corresponding rotor permanent magnet flux linkage; and S2, using a stator winding resistance and the rotor permanent magnet flux linkage as parameters of an embedded permanent magnet synchronous motor state equation, and identifying a d-axis inductance and a q-axis inductance by means of a recursion least square method. According to the parameter estimating method and the parameter estimating device, table searching results of the stator winding resistance and the rotor permanent magnetic flux linkage are used as given parameters of the recursion least square method, thereby realizing relatively high accuracy and relatively high stability according to a real-time working condition change, reducing algorithm execution time and realizing easy practical application.

A floating gate circuit has a level shift circuit. The floating gate circuit includes: a floating gate; a first and second tunnel device formed respectively between a first and second tunnel electrode; a first circuit coupled to the floating gate for generating an output voltage at an output terminal; a level shift circuit having a third tunnel device coupled between the output terminal and the first tunnel electrode; and a second circuit for causing a first current to flow through the first and second tunnel devices and for causing a second current to flow through the third tunnel device. The floating gate circuit then settles to a steady state condition during the set mode such that the first and second currents are approximately equal and the floating gate voltage and the output voltage are approximately equal.

The single cell sensor for ectocytic action potential measurement is developed on the basis of multiple light source cell micro physiological gauge, and has light addressing potential sensor without limitation in cell culture and light source moving addressing capable of tracking and positioning randomly cultured single cell for measurement. The present invention has also improved light source system with raised precision and stability for locating the measurement in single cell or even in single ion passage, so as to measure precisely the action and action target point of medicine on single cell. The single cell sensor can realize the fast real-time monitoring of outer action potential of nerve cell and the qualitative analysis of cell excitation and inhibition of different medicine in different concentration, and is used mainly in medicine screening, analysis and estimation.

The invention relates to a human body upper limb functional rehabilitation training implement method based on muscle tone signals. A piezoelectric sensor is arranged on the surface of muscle skins to collect human body physiological random muscle tone signal monoclonal gamma globulin (MMG), collected human body physiological random muscle tone signals contain muscle contraction motion signals which are amplified and filtered by a preprocessing circuit, analog digital conversion is performed on the muscle contraction motion signals through an analog / digital conversion circuit, then the muscle contraction motion signals are led into a personal computer (PC) machine to perform characteristic extraction and pattern recognition to output control signals on behalf of different motion modes to control three-dimensional human body upper limb model motion in virtual environment, subjects can perform different model view controller (MVC) % motion modes in real time on human body upper limb models according to view feedback in the virtual environment, and finally the purpose of functional rehabilitation training is achieved. The human body upper limb functional rehabilitation training implement method is high in accuracy and stability, can achieve the purpose of industrialization and can widen scope of applicable persons.

The invention provides a method for quantitatively determining the content of various types of heavy metal in fly ash by adopting an XRF (X-ray Fluorescence) instrument. The method comprises the following steps: 1) collecting the fly ash as a sample to be detected; 2) taking a fly ash standard sample and adding a substrate substance to prepare a series of fly ash standard test samples containing lead, chromium, copper, zinc, arsenic, cesium, cadmium and barium elements with different concentrations; 3) detecting the sample to be detected and the fly ash standard test samples by adopting an X-ray fluorescence spectrometer respectively; carrying out quantification by adopting a standard curve method to obtain the content of the lead, chromium, copper, zinc, arsenic, cesium, cadmium and barium elements in the sample to be detected. According to the method for quantitatively determining the content of various types of the heavy metal in the fly ash by adopting the XRF (X-ray Fluorescence)instrument, provided by the invention, the disadvantages of digestion pre-treatment and ICP-OES detection of an existing fly ash detection technology can be overcome; accurate and rapid quantitative determination of the content of various types of the heavy metal in the fly ash is realized.

A humidity measurement device comprises a capacitance measurement unit (101) and a microcontroller (102). The capacitance measurement unit (101) is used to output a frequency signal of a humidity-sensitive capacitor (C1) by the humidity-sensitive capacitor (C1) and a frequency signal of a reference capacitor (C2) by the reference capacitor (C2) respectively. The microcontroller (102) is used to obtain a frequency value of the humidity-sensitive capacitor (C1) and a frequency value of the reference capacitor (C2) based on the frequency signal of the humidity-sensitive capacitor (C1) and the frequency signal of the reference capacitor (C2), and obtain a current ambient humidity value by using a linear function relationship of a frequency difference between the humidity-sensitive capacitor (C1) and the reference capacitor (C2) and a humidity value. A humidity measurement method includes using the said humidity measurement device to measure a current ambient humidity value.

The invention provides a phishing website detection method and system based on an adaptive heterogeneous multi-classification model. The method is characterized by for a multiple-base classification algorithm, through linear addition, constructing the adaptive heterogeneous multi-classification model; training the multi-classification model, wherein a model input is the input of each base classification algorithm and an output is a sample label, and each base classification algorithm extracts a corresponding characteristic from a sample record and is taken as the input; and using a machine learning algorithm to solve a model parameter, adopting a test set to test and optimize, and finally acquiring the detection model of the type of a phishing website. The system comprises a domain name morpheme characteristic classifier, a subject index characteristic classifier, a content similarity characteristic classifier, a structural style characteristic classifier, a visual rule characteristicclassifier, a linear addition training module, an integrated classifier, a training data set management module, and a detection and alarm module. In the invention, the phishing website can be detectedin real time, and the accuracy and the stability of phishing website detection are increased.

The invention relates to a reference-free damage identification method for a composite material plate-shaped structure, belonging to the field of structure health monitoring. The method comprises the following steps of: uniformly distributing a sensor array formed by piezoelectric elements on the composite material plate-shaped structure; sequentially exciting each piezoelectric element through a low-frequency and wide-wave signal and collecting corresponding signals of the residual piezoelectric elements when each piezoelectric element is excited; then calculating a damage index on each exciting sensing path according to a signal difference on a similar exciting sensing path; and finally, imaging damages by utilizing a possibility imaging method. According to the reference-free damage identification method disclosed by the invention, online damage identification and imaging of the composite material plate-shaped structure can be conveniently realized, so as to guarantee the safety of the composite material structure in a use process; and the accuracy and the stability are high.

A flow control system able to realize stabilization of a flow rate of fluid at a high precision even in cases where pressure fluctuation of an outlet fluid of the flow control system occurs, that is, a flow control system arranged in a supply line of fluid running from a fluid supply part to a predetermined fluid usage part, provided with a first pressure control valve part arranged on the fluid supply part side and a second pressure control valve part arranged on the fluid usage part side through the first pressure control valve part and a pressure loss part, the first pressure control valve part provided with a first pressure control mechanism maintaining the outlet fluid at a predetermined pressure by a first valve part arranged within a first valve chamber moving back and forth with respect to a first valve seat in accordance with pressure fluctuation of the inlet fluid, the second pressure control valve part provided with a second pressure control mechanism maintaining the inlet fluid at a predetermined pressure by a second valve part arranged within a second valve chamber moving back and forth with respect to a second valve seat in accordance with pressure fluctuation of the outlet fluid.

The invention relates to a fashion garment image segmentation method based on depth learning. The fashion garment segmentation method based on depth learning comprises the following steps of the construction of a depth neural network garment model, the loss function design of reverse error propagation and a model training strategy, wherein the depth neural network garment model comprises a featureextraction module, a garment semantic information extraction module and a garment segmentation prediction module, the loss functions comprise a regression function of a key point position, a key visibility loss function, a cross entropy loss function of a garment prediction category with weights and a regression loss function of a garment position; and the model training strategy comprises a weight parameter initialization method, the data preprocessing, an optimization algorithm and a training step. The method has the advantages of being able to automatically segment and recognize the upperbody clothing, lower body clothing and whole body clothing collocation in complex images, and being conducive to the deep learning and network training for fashion clothing design.

The invention discloses a spam filtering method and system based on deep learning. The spam filtering method based on deep learning comprises the first step of processing mail samples to generate a first vector space model and establishing a deep belief network, the second step of processing test mails to generate a second vector space model, the third step of detecting the second vector space model through the established deep belief network, and the fourth step of outputting a detection result. According to the spam filtering method based on deep learning, as the mode that the deep belief network is established and the test mails are detected through the established belief network is adopted, the accuracy and stability of spam recognition are improved, and the time and labor for labeling a large number of samples are saved.

A control system which is capable of enhancing both the stability and the accuracy of control when the output of a controlled object is feedback-controlled by a plurality of control inputs. An ECU 2 of a control system 1 controls engine speed NE during idling by an ignition control input Usl_ig and an intake control input Usl_ar. The ECU 2 calculates a target engine speed NE_cmd according to an engine coolant temperature TW and the like (step 3), and determines the ignition control input Usl_ig and the intake control input Usl_ar with a plurality of predetermined target value filter-type two-degree-of-freedom sliding mode control algorithms [equations (1) to (12)] sharing one switching function σne therebetween, such that the engine speed NE converges to the target engine speed NE_cmd (steps 4 to 7 and 9).

The present invention provides a multispectral color imaging device, an automatic calibration method based on a reference reflective surface, and a method for eliminating background signals. A multispectral color imaging device including a light house module comprising a light source and a light intensity collection device surrounding the light source; an integrating sphere module including an integrating sphere, a light inlet on one side of the integrating sphere, a light outlet on the top of the integrating sphere, a sample holder gateway on the other side of the integrating sphere and a sample holder having access to the interior of the integrating sphere; and a filter wheel module including a camera, a filter wheel below the camera, and a lens below the filter wheel. The device and calibration methods of the present invention together improve the accuracy and stability of the measurement.

The invention discloses a power battery pack anti-reburning automatic fire extinguishing device and method. The power battery pack anti-reburning automatic fire extinguishing device is characterized in that an outer shell of the fire extinguishing device is divided into an inner shell body and an outer shell body, wherein a fire extinguishing module is arranged in a gap at the top of the part between the inner shell body and the outer shell body, the fire extinguishing module comprises a flame sensor and a fire extinguisher spray head, and the outer end of the fire extinguishing module is connected with the two high-pressure fire extinguishing agent tanks which are correspondingly provided with a compression cooling fire extinguishing agent and a flame retardant extinguishing agent. When the flame sensor of the fire extinguishing module detects a flame signal in the area, the flame signal is transmitted to a controller, the controller issues an instruction to sequentially open a valveof the flame-retardant fire extinguisher tank and a valve of the cooling fire extinguisher tank at intervals, and the fire extinguisher spray head in the fire extinguishing module of the fire area isstarted at the same time, so that the purposes of early rapid fire extinguishing and continuous cooling and extinguishing are achieved, and further diffusion of a fire accident of a battery pack is effectively controlled; and the shell and the battery pack are independent, so that the shell is not limited by the type of batteries, and the application range is wide.

The invention discloses an element recognition method. The method includes following steps: a to-be-matched target picture and at least two template pictures of a to-be-recognized element are read; template matching of the at least two template pictures and the to-be-matched target picture is performed, and at least two corresponding groups of matching values are obtained; operation of the at least two groups of matching values is performed according to a preset calculation rule, and a group of matching results are generated; and each matching result in the group of matching results is compared with a preset threshold, and the position of the to-be-recognized element on the target picture is recognized according to the relation of the matching results and the threshold. The invention also discloses an element recognition apparatus. According to the element recognition method and the apparatus, high recognition accuracy is guaranteed when the target picture is stained, incomplete, or deformed.

The invention provides a gas sensor probe based on a multi-point reflected spiral light path and a detecting device. A light source module, a spiral light path module and a detector module are included. The light source module is used for emitting parallel light signals. The spiral light path module comprises a plurality of reflection mirrors and a plurality of linear light paths. Each linear light path and the adjacent linear light path are connected in a head to tail mode at a certain angle and are spiral inwardly, and at least one reflection mirror is detachably fixed between the two adjacent linear light paths. The spiral light path module is used to increase a light path length of the light signals, and the light signals pass through the spiral light path module, then enter into the detector module and are converted into electrical signals. Through the design of the multi-point reflected spiral light path, a volume of a sensor probe absorption pool is reduced and a measurement light path is increased, and simultaneously the stability and the reliability of the light path are greatly improved. Debugging difficulty during production is reduced and production and processing costis decreased.

The present invention discloses a 3D model retrieval method and a 3D model retrieval apparatus based on slow increment features. The method comprises: carrying out slow increment feature extraction to a preprocessed view set by applying a supervised slow increment feature analysis method; acquiring a sorting result of the slow increment features according to the extracted slow increment features, screening the slow increment features according to the sorting result and generating a slow increment feature library of a 3D model; and carrying out retrieval matching on the slow increment feature library of the 3D model by using a nearest neighbor algorithm to acquire and output an object that is similar to a candidate model. The apparatus comprises: an extraction module, an acquisition module, a generation module and a matching and outputting module. According to the method and the apparatus, the feature extraction difficulty of a nonrigid model is reduced, the stability and accuracy of feature extraction are improved, a good condition is provided for the subsequent 3D model retrieval, and the retrieval result is guaranteed to be more efficient and accurate.

The invention relates to a laser feedback interferometer which comprises a microchip laser device, a spectroscope, a first frequency shifter, a second frequency shifter, a converging lens, at least two photoelectric detectors, an electric signal processing system and a data collecting and processing system, wherein the microchip laser device can output at least two parallel light beams in a single longitudinal and single transverse mode simultaneously, and the at least two parallel light beams can be used as measurement light for measuring information of a plurality of degrees of freedom of an objective to be measured.

The invention relates to the environment perception field and especially relates to a water surface target detection and tracking method based on a laser radar. The method comprises the following steps of (1) acquiring point cloud data of a t-th frame sea surface environment through the laser radar; (2) filtering the point cloud data; (3) segmenting the filtered point cloud data to obtain a point cloud cluster; (4) clustering the point cloud cluster to form a target, carrying out threshold screening on the target, and obtaining the target of the t-th frame sea surface environment; (5) repeating the steps (1) to (4) and processing a t+1th frame sea surface environment to obtain the target of the t+1th frame sea surface environment; (6) matching and associating the target of the t-th frame sea surface environmental with the target of the t+1th frame sea surface environmental to obtain a matched target pair; and (7) calculating a target moving state of the matched target pair and tracking the target moving state. By using the method, target detection and tracking accuracy is high, stability is good, and the accuracy and stability of target detection and tracking of an unmanned surface vehicle are increased.

A range resistor, for deployment with a low voltage AC measuring instrument as a voltage range multiplying attenuator, provides increased accuracy in the measurement of high AC voltages at frequencies up to 1 MHz, particularly when large attenuation ratios are needed. Passive guarding and neutralizing structure permits the use of higher value resistors for a given frequency response accuracy and an intrinsically lower input capacitance than is obtainable with conventional structure. Very low drift and voltage coefficients are achieved by a coordinated thermal design. Applications include high accuracy voltage measurement in the 10 to 1000 volt range with low input signal current when attenuation is resistively scaled to match the operating input level of the measuring device to be used, typically a thermocouple and / or digital or analog precision voltmeter.

In one aspect of the disclosure, a gas sensor is provided, comprising: a chamber for containing a gas sample in use, and a radiation source adapted to emit radiation within a first waveband. A photoluminescent material, upon absorption of radiation of a first wavelength within the first waveband, emits radiation of a second wavelength, the photoluminescent material being responsive to the presence of a target gas species in the gas sample. The gas sensor further comprises a luminescence detector assembly. The luminescence detector assembly is adapted to detect radiation of the second wavelength and output a corresponding measurement signal related to the concentration of the target gas species. An optics assembly is adapted to receive radiation emitted by the radiation source and to converge the radiation towards a location at which the luminescence detector assembly cannot receive radiation.

The invention discloses a machine learning-based vehicle abnormal trajectory real-time recognition method and belongs to the field of vehicle trajectory anomaly recognition. The method includes the following steps that: collected data are cleaned, so that complete, non-repetitive, abnormal value-free training data are obtained; an unsupervised isolated forest method and training data are used to perform model training, so that an anomaly detection model is obtained; the anomaly detection model is put into a flow calculation engine for real-time prediction, and a prediction result is sent to avehicle owner; and the model is automatically updated and corrected according to the feedback information of the vehicle owner, and the updated model is put into the flow calculation engine for real-time prediction, and a prediction result is sent to the vehicle owner. According to the method of the invention, the vehicle information is periodically collected; the unsupervised isolated forest algorithm is adopted; real-time prediction analysis is performed on vehicle trajectories in the flow calculation engine; the probability value of the abnormal behavior of a vehicle is rendered; the modelis periodically adjusted according to the feedback data given by the vehicle user; the dynamic update of the model is realized; and the recognition accuracy of the model is improved.

The invention relates to monitoring method and system for microblogging public opinion. The system comprises a data acquiring die for crawling real-time microblog data and writing into a database, a hot microblog detecting module for sequencing the real-time microblog data within the preset time according to the preset algorithm and screening the hot microblogs within the specific time quantum, an abnormal microblog monitoring device for vectoring microblog texts, clustering the vectored microblog texts and selecting the microblogs with the highest hot level from various microblogs to be used as the monitored abnormal microblogs, and a data display module for displaying the hot microblogs and the abnormal microblogs with corresponding quantity according to the preset threshold. With the adoption of the method and system, the microblogs within a period of time can be quickly analyzed, the hot microblogs can be extracted, and the hot microblog topic can be tracked; the real-time microblog within a period of time can be monitored, and the abnormal microblog within the latest period of time can be detected; the data can be automatically cleaned by the system; the mature clustering method is used as the algorithm to ensure relatively high stability and accuracy.

The invention discloses an electrified cleaning insulating maintenance robot device for high-voltage power equipment. The electrified cleaning insulating maintenance robot device comprises a traveling mechanism, a lifting mechanism and a cleaning insulating maintenance mechanical arm. The lifting mechanism comprises a motor linkage driving circuit, a motor, a balance monitoring device and two or more pneumatic insulating lifting rods. The motor linkage driving circuit is in signal connection with the motor. The balance monitoring device is in signal connection with the motor linkage driving circuit. The pneumatic insulating lifting rods are made of epoxy resin or glass fiber steel and installed on the traveling mechanism. The cleaning insulating maintenance mechanical arm is arranged on the lifting mechanism. According to the electrified cleaning insulating maintenance robot device for the high-voltage power equipment, the supporting stability is improved, the driving force of the lifting mechanism is reduced, and the phenomenon of creepage short circuits is effectively avoided; the degree of balance of the lifting mechanism can be adjusted automatically, the probability of tilting and turning is lowered, and the safety performance is improved.

An intelligent orthopedic surgical system comprises a switcher, a surgical location device, a surgical planning and monitoring device connected to the switcher, a C-arm X-ray apparatus and an orthopedic surgical robot. The orthopedic surgical robot comprises a robot body, a robot arm fixed to the robot body, an intelligent bone drill fixed to the robot arm, a communication module, a robot arm control module, an intelligent bone drill control module and a surgical robot electrical control module. The intelligent bone drill comprises a surgery electric drill, a guide mechanism for an electric drill head, a pushing mechanism, a visual identification system, a pressure sensor and a bone drill controller. The orthopedic surgical robot of the present system can perforate accurately under the navigation control and improve the accuracy and stability of the operation.