69results about How to "Avoid underfitting" patented technology

The invention discloses a pedestrian re-identification model, method and system for adaptive difficulty mining. The identification method comprises the steps of: randomly dividing sample pictures intoa training set used for each iteration, inputting the training set into a convolutional neural network, obtaining the probability that each sample pair belongs to a positive or negative sample pair by using a softmax function, and then obtaining the loss of each sample pair by using a multinomial logistic function; obtaining a difficult sample pair by using the loss of each sample pair; and training the convolutional neural network by using the difficult sample pair until the current number of iterations reaches the upper limit of the number of iterations, thus obtaining the pedestrian re-identification model. The pedestrian re-identification model is used to extract features of each picture in a picture set to be identified, and then a similarity order of the sample pairs in the pictureset to be identified is obtained. The pedestrian re-identification model, method and system avoid over-fitting and under-fitting, and have high recognition accuracy.

The invention provides an abnormal behavior detection method and an abnormal behavior detection device based fused characteristics. The method comprises steps that, according to a detection tracking processing result of a motion target in a to-be-detected video, a behavior type of the motion target is determined; multi-dimensional characteristics of the motion target are extracted, including a pixel point change degree, a pixel point arrangement tightness degree, an integral shape, a frame image similarity degree, motion characteristics, position characteristics and form characteristics; the multi-dimensional characteristics are analyzed and processed according to a characteristic fusion model corresponding to the behavior type, whether the motion target has abnormal behaviors is determined according to the processing result; according to innovative characteristics of the multiple abnormal behaviors, algorithm robustness and stability can be effectively improved, according to the characteristic fusion model acquired through learning and training large-scale abnormal behaviors, the multi-dimensional characteristics are analyzed and processed, problems of algorithm overfitting and insufficient fitting can be effectively avoided, the method is suitable for multiple types of complex application scenes, time cost and manpower cost are greatly saved, and the method has high popularization values.

The invention discloses a semi-supervised intrusion detection method combining the improved grey wolf algorithm, and belongs to the technical field of network information security. The method can effectively avoid the defect that the basic GWO algorithm is prone to premature convergence, and balance the global exploration and local development ability of the GWO algorithm; and the cloud GWO algorithm is used to optimize the K-means algorithm to mark data having similar features, and realization of generation of the large-scale accurate mark training data set is realized on the basis of manually marking the data in a small amount. The proportion of unmarked data and marked data is provided, the phenomenon of "under-fitting" and "over-fitting" of the model is avoided, and the detection accuracy of the model is ensured. The optimized semi-supervised learning method is combined with the cloud GWO algorithm to optimize parameters of a single-class support vector machine model. Compared withthe unilateral optimization, the invention achieves higher detection precision.

The invention discloses a convolutional neural network-based music signal multi-instrument identification method. The method comprises the following steps of S1, extracting two features from an inputaudio, wherein the two features comprise a pitch feature matrix and a constant Q transformation matrix based on tone; S2, carrying out classification according to musical instrument groups, includingtubes, strings and percussion music, inputting the constant Q transformation matrix into a primary convolutional neural network to obtain a classification matrix, and inputting the classification matrix into a classifier to obtain a coarse classification result, namely the musical instrument group type; and S3, on the basis of the classification matrix, inputting the classification matrix into a secondary convolutional neural network with an attention network in combination with a pitch matrix to obtain a subdivision result, namely a specific musical instrument, wherein the attention network allocates weights to different harmonic waves. The method is suitable for musical instrument identification tasks in music information retrieval and can be used for the musical instrument identification method in music automatic transcription.

The invention relates to a target recognition training method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring video stream data, wherein the video stream data comprises multiple frames of images; reading the image, detecting a corresponding target in the image, and generating a plurality of types of sample image sets by utilizing the target;training the sample image sets of multiple categories by using a recognition model to obtain recognition probabilities corresponding to the categories; adjusting samples in the corresponding sample image set according to the identification probability; and optimizing the identification model by using the adjusted sample image set, and training the optimized identification model through the adjusted sample image set. By adopting the method, the problem of poor model learning effect caused by unbalanced samples can be solved, so that the trained recognition model can perform accurate target recognition, and the problems of under-fitting and over-fitting of the training model are avoided.

The invention belongs to the field of wireless equipment identification, and discloses a wireless equipment fingerprint identification method, a wireless equipment fingerprint identification system, equipment and a readable storage medium. The wireless equipment fingerprint identification method comprises the following steps of: collecting a network data frame of wireless equipment in a communication process; extracting characteristic parameters of the wireless equipment capable of reflecting the characteristics of the wireless equipment from the network data frame; constructing a characteristic fingerprint of the wireless equipment according to the characteristic parameters of the wireless equipment; training the characteristic fingerprint of the wireless equipment through using a classifier and then determining classifier parameters to obtain a wireless equipment fingerprint identification model; and identifying the current wireless equipment through using the wireless equipment fingerprint identification model and a characteristic fingerprint preset in a fingerprint library. The wireless equipment fingerprint identification method overcomes the defect that a traditional equipment identification method based on a certain characteristic parameter of the wireless equipment is prone to be forged and tampered, accurate identification of the wireless equipment is achieved by implicitly collecting the network traffic of the wireless equipment, the whole identification process does not interfere with normal operation of the wireless equipment, participation of a user is not needed, and good user experience is achieved.

The invention discloses a welding device for industrial machinery production. The welding device comprises a base; an arc groove is formed in the outer wall of the top of the base; a first ring is mounted on one side of the outer wall of the top of the arc groove; the inner wall of the first ring is rotationally connected with a first fixed ring; a tooth groove is formed in one side of the peripheral outer wall of the first fixed ring; a fixed plate is mounted on the outer wall of the top of the first ring; a motor is mounted on the outer wall of the top of the fixed plate; a gear sleeves theperipheral outer wall of an output shaft of the motor; and the gear is engaged with the tooth groove on the first fixed ring. In the welding device, through arrangement of a lead screw, a slide blockand a servo motor, a second ring can be driven to move, and two round pipes to be welded can be tightly bonded to achieve welding; and through arrangement of the motor, the gear and the tooth groove on the first fixed ring, the round pipes on the second fixed ring and the first fixed ring can be driven to rotate for total welding of the peripheral outer walls thereof, so that the working efficiency is improved.

The invention discloses an agricultural product growth environment big data detection system which is composed of a soil environment parameter acquisition and evaluation platform and a farmland soil environment big data processing subsystem, and realizes farmland soil environment parameter detection and management and farmland parameter evaluation. The system effectively solves the problems that an existing farmland soil environment detection system does not influence the production yield of farmland plants according to nonlinearity and large lag of changes of farmland plant growth environment parameters, large and complex farmland soil environment areas and the like, and therefore farmland soil yield prediction and production management are greatly influenced.

The invention discloses a self-learning image super-resolution reconstruction method and system based on a convolutional neural network. The method comprises the steps of 1, obtaining a training sample of a to-be-reconstructed image; 2, constructing a convolutional neural network; the convolutional neural network comprises a feature extraction unit, a feature enhancement unit, a residual error unit and a reconstruction unit. 3, training the convolutional neural network constructed in the step 2 based on the training sample obtained in the step 1 to obtain a trained reconstructed convolutionalneural network; and 4, performing super-resolution reconstruction on a to-be-reconstructed image based on the reconstructed convolutional neural network trained in the step 3. According to the method,the problem of insufficient training samples of the self-learning algorithm can be effectively solved, and the over-fitting phenomenon of the network can be avoided; meanwhile, a high-resolution image with a higher peak signal-to-noise ratio and a better visual effect can be obtained.

The invention discloses an intrusion detection method, which comprises the following steps: classifying data in an obtained data set, with the category of the data comprising a large sample and a small sample; carrying out data expansion on the data of which the category is a small sample in the data set; dividing the expanded data set into a training set and a test set, training a preset networkmodel by using the training set, and performing performance evaluation on the trained preset network model by using the test set; and determining the preset network model with the optimal performanceas an intrusion detection model, and performing intrusion detection on the obtained perception data by using the intrusion detection model. Data of which the category is a small sample in the data setis subjected to data expansion, so that the underfitting phenomenon of a learner on the small sample is avoided, the learning efficiency and generalization ability of the model are improved, and theintrusion detection accuracy is further improved. The invention further provides an intrusion detection system and device and a readable storage medium which have the above beneficial effects.

The invention discloses a moisture detection system based on a cloud platform. The moisture detection system is composed of a granary environment parameter acquisition and control platform and a granary environment big data processing subsystem, wherein the granary environment parameter acquisition platform detects, adjusts and monitors granary environment parameters, and the granary environment parameter big data processing subsystem comprises a plurality of moisture detection models and a detection parameter fusion model, so that moisture of grains is predicted, and the granary environment production management efficiency and benefits are improved. According to the invention, the system effectively solves the problems that an existing granary environment parameter detection system does not consider influence of nonlinearity and large lag of granary environment parameter change, large and complex granary environment area and like on grain safety, and does not predict granary environment moisture and accurately adjust granary environment parameters so as to greatly affect granary safety and grain management.

The invention discloses an aerial handwriting inertia sensing signal generation method based on deep adversarial learning, which comprises the following steps: carrying out filtering, smoothing and noise reduction on an obtained aerial handwriting inertia sensing signal sequence to serve as a training sample set; designing a deep convolution condition confrontation generation network based on timesequence feature map position coding; using the training sample set to train the generation network; and inputting the formulated sample length, the sample category label and the random noise vectorinto the trained adversarial generation network, and taking the input of the generator as a generated air handwriting inertial sensing signal sequence. According to the invention, aerial handwriting inertial sensing signal samples with certain diversity and good quality can be generated, and the effective lengths and types of the generated samples are controllable.

The invention discloses a pipe network big data detection system which is composed of a pipe network parameter acquisition platform and a pipe network safety big data processing subsystem, the pipe network parameter acquisition platform detects pipe network parameters, the pipe network safety big data processing subsystem classifies pipe network safety, and pipe network operation benefits and reliability are improved; the method effectively solves the problems that an existing pipe network has no influence on pipe network safety according to nonlinearity and large lag of pipe network parameter changes, large pipe network area, complex parameter changes and the like, pipe network parameters are not predicted, and pipe network safety is not pre-warned, so that reliable operation and intelligent management of the pipe network are greatly influenced.

The invention relates to the field of automatic production, and discloses an air injection control system. An NARX neural network model 1 and an NARX neural network model 2 are used for controlling air flow and predicting an air flow value respectively, and an NARX neural network establishes a dynamic recursive network of the models by introducing a time delay module and outputting feedback, input and output vector delay feedback is introduced into network training to form a new input vector, good nonlinear mapping capability is achieved, the input of the NARX neural network not only comprises the error of original gas flow, the control quantity and the input data of actual gas flow, but also comprises the trained corresponding output data; the generalization ability of the network is improved, so that the system has better prediction precision and adaptive ability in prediction of corresponding parameters of the gas flow compared with a traditional static neural network, and the single-chip microcomputer controller improves the precision, robustness and reliability of the control system.

The invention discloses an extreme random tree furnace temperature prediction control method based on longicorn beard search, and belongs to the field of furnace temperature prediction and control inthe industrial combustion process. By establishing the regression relation between the key variables influencing the temperature and the temperature of the combustion chamber, the predicted value of the furnace temperature in the combustion chamber is obtained in real time, and the prediction precision of the furnace temperature reaches +/-1.5 DEG C. In consideration of low control precision of anoriginal system, a longicorn beard search algorithm is designed, a secondary performance index function commonly used in predictive control is selected as a fitness function of longicorn, and an optimal control quantity is searched through an olfaction search mechanism of the algorithm, so that the control effect of the whole system is better.

The invention provides a fan noise prediction method based on an optimal neural network. The fan noise prediction precision and the generalization ability of the neural network are improved mainly through joint control on the number of input neurons and the number of hidden layer neurons. Based on correlation analysis, the influencing importance of input parameters in fan samples towards output parameters is sorted, and according to the training precision and the prediction precision, an input layer neuron number range and the maximum input layer neuron number are determined. The correlation analysis is used to effectively reduce the input neuron number, and the building difficulty of the optimal neural network structure is reduced. The best hidden layer neuron number is used to determinethe optimal neural network structure, over fitting and under fitting can be effectively avoided, and while the training precision is improved, the prediction precision and the generalization ability are also improved.

The invention discloses a substance mixing intelligent system, which is characterized in that the system is composed of a parameter acquisition and control platform and a substance mixing adjustment subsystem, the system realizes the parameter detection of a mixed object and the adjustment of a substance mixing process, and reliability and accuracy of the process of adjusting the mixed substance are improved; according to the influence of problems of inaccuracy, poor robustness and the like of an existing gas or liquid mixing ratio control system on the performance of the gas or liquid mixing ratio system, problems of poor robustness, slow response speed and the like of the gas or liquid mixing ratio system are effectively solved.

The invention discloses an ICA sparse representation and SOM-based distorted image quality evaluation method. The method comprises the steps of 1) performing ICA sparse representation on a reference image and a to-be-tested image to obtain sparse representation information of the images; 2) calculating an SSIM value between the reference image and the to-be-tested image after the sparse representation; 3) drawing a scatter diagram of the SSIM value and a DMOS value; 4) clustering scatter diagram data by using an SOM algorithm: classifying the data distributed more intensively into one category, and classifying other data into the other category; 5) using a cross validation regression algorithm for each category of the data, and performing regression mapping on the SSIM value in the step 4)to obtain the DMOS value; 6) calculating an error value between the DMOS value of the two categories of the data and a DMOS value in an actual database; and 7) performing weighted average on the obtained error value to serve as an index value of final image quality evaluation.

The invention discloses a deep cluster neural network model construction method for aerodynamic data processing, and relates to the technical field of deep cluster neural network model construction in the aerodynamic data processing field. The method comprises the following steps: a, preparing and preprocessing a data set: firstly, obtaining an aerodynamic data set through a computational fluid dynamics method, and extracting main design parameters and response parameters in the aerodynamic data set; classifying the data, and dividing the data into a plurality of subsets; labelling each subset ; finally, dividing the pneumatic data set into a training set, a verification set and a test set; b, constructing a deep cluster neural network model; c, training a deep cluster neural network model; and d, verifying the deep cluster neural network model. When the cluster neural network obtained through training of the method is used for processing aerodynamic data, the defect of a traditional neural network model in the environment of insufficient sample quality caused by uneven sample sampling or uneven sample distribution can be overcome, and therefore the prediction precision of the model is improved.

The invention belongs to the field of battery design, and particularly relates to a laminated solar battery structure optimization method, which comprises the steps of taking structure information ofa to-be-optimized laminated solar battery as population information of a differential evolution algorithm, taking a battery performance index as an optimization target of the differential evolution algorithm, and initializing the structure information; controlling a differential evolution algorithm to perform iterative evolution on the initial structure information for multiple times by adaptivelyadjusting a scaling factor and a crossover probability required by each iteration, wherein each iterative evolution is to jointly adjust each layer of structure in the laminated solar cell to obtaina new population and predict a cell performance index according to the new population by adopting a pre-constructed cell performance prediction neural network, and finally optimal structure information is obtained. According to the self-adaptive differential evolution algorithm, the structures of all layers can be jointly adjusted, the problem of local optimization is avoided, the differential evolution algorithm is combined with the battery performance prediction neural network, the battery structure can be designed in a high-efficiency and time-saving self-adaptive reverse optimization mode,and the optimization efficiency is improved.