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239results about How to "Improve accuracy and stability" patented technology

Method used for simultaneous detection of three food-borne pathogenic bacteria based on multicolor upconversion fluorescence labeling

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.
Owner:JIANGNAN UNIV

Mobile pollution source emission concentration prediction method based on space-time deep learning

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.
Owner:HANGZHOU DIANZI UNIV

Light field image depth estimation method based on hybrid convolutional neural network

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.
Owner:ZHEJIANG GONGSHANG UNIVERSITY

Parameter estimating method and parameter estimating device of embedded permanent magnet synchronous motor

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.
Owner:GAC AION NEW ENERGY AUTOMOBILE CO LTD

Human body upper limb functional rehabilitation training implement method based on muscle tone signals

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.
Owner:UNIV OF SHANGHAI FOR SCI & TECH

Method for quantitatively determining content of various types of heavy metal in fly ash by adopting XRF (X-ray Fluorescence) instrument

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.
Owner:SHANGHAI ENVIRONMENTAL & SANITARY ENG DESIGN INST CO LTD +1

Phishing website detection method and system based on adaptive heterogeneous multi-classification model

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.
Owner:NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT +1

Power battery pack anti-reburning automatic fire extinguishing device and method

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.
Owner:SOUTH CHINA UNIV OF TECH

Machine learning-based vehicle abnormal trajectory real-time recognition method

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.
Owner:成都古河云科技有限公司

Monitoring method and system for microblogging public opinion

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.
Owner:北京牡丹电子集团有限责任公司数字科技中心

Electrified cleaning insulating maintenance robot device for high-voltage power equipment

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.
Owner:上海旷奇科技股份有限公司 +1
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