31results about How to "Reduce generalization error" patented technology

The invention relates to a vehicle flow predicting method based on an integrated LSTM neural network. On the basis of historical data obtained by vehicle flow detection, an integrated LSTM neural network vehicle flow prediction model is established to carry out vehicle flow prediction, so that the generalization error of the prediction model is reduced and the accuracy is improved. The method comprises the following steps that: data preprocessing is carried out; according to a preprocessed vehicle flow time sequence value, a vehicle flow matrix data set is constructed and the vehicle flow of an (n+1)th period of time is predicted by using first n periods of time, wherein each period of time is delta t expressing the time length and the unit is min; a plurality of different LSTM neural network models are constructed by using different initial weights; on the basis of a bagging integrated learning method, a training set and a verification set are constructed; a plurality of LSTM neural networks are trained to obtain an optimized module; a weighting coefficient of the single LSTM model is calculated by using the verification set; and inverse transformation and reverse normalization are carried out on a predicted vehicle flow value to obtain a predicted vehicle flow and integrated weighting is carried out to obtain a vehicle flow value predicted finally by the model.

The invention relates to the technical field of speech signal recognition and processing, and provides a speech separation and tracking method for public security criminal investigation and monitoring. The speech separation and tracking method includes the following steps that initial speech is imported according to timing sequence, the initial speech is subjected to framing and windowing processing, and a windowed speech signal is obtained; the windowed speech signal is time-frequency decomposed, and a time-frequency two-dimensional signal is obtained by the short-time Fourier transform; an endpoint of the time-frequency two-dimensional signal is detected in a frequency domain, and a corresponding speech signal segment of an empty language segment is filtered; a bidirectional long and short time memory network structure is used for performing speech separation of the two filtered dimensional time-frequency signal, and a great deal of speech waveform of a target speaker are output; anda target speaker model based on GMM-UBM is established and trained, the speech waveform of the target speaker are taken as models and input, a GMM model of the target speaker is acquired through an adaptive method and the speech waveform are recognized, a sequence number of the target speaker is outputted, that is, a speech tracking result.

The invention discloses a shale gas well staged fracturing effect evaluation and yield prediction method based on a random forest. The method comprises the steps of firstly, finding main fracturing and geological influence factors influencing the section yield through a Pearson correlation coefficient and a secondary dimension reduction strategy of a recursive feature elimination method; establishing a random forest model based on optimized influence factors, analyzing the gain degree of the main fracturing factors to the section yield by using the model, and finishing fracturing effect evaluation and yield prediction. The calculation method is simple, and the method is advanced. The microstructure of reservoir rock is very complex and irregular, large-scale fracturing is carried out, it is difficult for a traditional theory to combine complex and numerous fracturing parameters and geological parameters together to establish a nonlinear equation, and the historical fitting difficulty in a numerical simulation method is large. And important yield influence factors can be identified by adopting the secondary dimension reduction strategy and a random forest algorithm, and yield prediction can be well carried out.

The invention relates to the technical field of data mining, and particularly relates to an e-commerce discount coupon use probability prediction method based on an integrated model. According to thee-commerce discount coupon use probability prediction method based on the integrated model, the integrated model comprising a primary learner and a secondary learner is constructed, and in combinationwith the advantages of a specific single model in the prediction of discount coupon use probability, the defects of single model prediction are avoided. In the case of large sample data, the prediction accuracy is improved, and the generalization error of the prediction model is reduced. The e-commerce can build the integrated model through the technology to more accurately predict the probability value of a user to use the discount coupon and to decide whether to issue the discount coupon to the user according to the probability value, thereby reducing the operation cost of the merchant andbetter achieving a promotion effect desired by the merchant.

The invention relates to a method of soft measurement for concentration of reactant in an unsaturated polyester resin reacting kettle. The method uses a data acquisition module based on OPC (object linking and embedding for process control) technology, an abnormal data eliminating and pretreating module, a data normalization pretreating module, a digital filtering and wavelet treating module, a main factor analyzing module, and a self-adapting genetic algorithm optimized BP (back propagation) neural network soft measurement module. The method with high measurement precision for the soft measurement of the concentration of reactant in the reacting kettle during a batch process is provided, and the difficulty in measuring the reactant concentration in a batch reacting kettle is solved.

The invention relates to an intelligent tooth brushing process evaluation method, which comprises the following steps that: 1) tracking a tooth brushing process of a standard Bass tooth brushing method, and collecting a denoising signal output by a GY-521 gyroscope in an electric toothbrush; 2) checking a data interval preprocessed and labeled in S1), and if the interval time of data is shorter than t seconds, according to the time interval of t seconds, solving a mean value in the period of time; 3) training an evaluation model based on a random forest; and 4) preprocessing the data of a person who brushes teeth according to a sensor data processing method in S1), and using a trained random forest model to classify the toothbrush state of the person who brushes teeth at different moments.The method has the beneficial effects that a machine learning method is adopted to classify the tooth state of the toothbrush in each time slice in a user tooth brushing process, the tooth brushing state of the user at different moments in the tooth brushing process can be predicted, and the tooth cleaning state and strength of the user at different moments can be accurately judged.

The invention provides a human respiration state monitoring system and method based on flexible sensing and deep learning, and the system comprises a flexible tension sensor which is used for converting human respiration data into an analog signal for output; a microcontroller which is connected with the flexible tension sensor through an analog-to-digital converter and used for receiving an analog signal output by the flexible tension sensor, converting the analog signal into a digital signal and outputting the digital signal through a wireless transmission module; a terminal which is connected with the microcontroller through a wireless transmission module and is used for receiving and outputting the digital signal output by the microcontroller; and a cloud server which is used for receiving the digital signal output by the terminal and monitoring and analyzing the respiration state of the human body by using a deep learning network model. According to the system and the method provided by the invention, the monitoring accuracy is improved based on deep learning, the cost is relatively low, and the use requirements can be well met.

The invention relates to the technical field of face detection and recognition and aims to improve classification precision effectively and reduce generalization errors to realize effective classification screening on human face false detection result. The invention particularly relates to a semantic feature-based face false detection screening method, which comprises the following steps of performing face detection and alignment on an original image through a face detection and alignment algorithm by taking original image data as input of the stage, and zooming a detection alignment result to112 * 112 dimensions; performing pixel-level face semantic segmentation on the input face detection alignment result by adopting a BiSeNet-based real-time face semantic segmentation method to obtaina semantic segmentation result; processing the semantic segmentation result by adopting a feature engineering technology, and constructing and selecting a semantic feature with the highest representation capability; calculating the input semantic features by adopting a Stacking model integration framework to obtain a final human face false detection classification result and complete false detection screening; and realizing effective classification and screening of human face false detection results, thus the performance and robustness of the overall detection algorithm are improved.

According to the power load frequency domain prediction method and system based on the IRF and the ODBSCAN, the technical problem that an existing method is large in error can be solved. The inventionprovides an improved random forest IRF (Implanted Random Field) and ODBSCAN (Open Distributed Broadcast System Controller Area Network)-based method. The improved random forest IRF and ODBSCAN-basedmethod is based on an improved random forest IRF (Implanted Random Field) and an improved ODBSCAN (Open Distributed Broadcast System Controller Area Network). The invention relates to a frequency domain combination prediction method based on frequency domain combination prediction (ions width Noise). The method comprises the following steps of: firstly, decomposing a load by adopting EWT (EnhancedWavelet Transform) to obtain different intrinsic mode parts (IMFs); secondly, predicting by adopting a reasonable method according to the characteristics of each part; wherein IRF prediction is adopted for the low-frequency part and the intermediate-frequency part; the high-frequency parts have uncertainty, the ODBSCAN is used for clustering according to the temperature and humidity of meteorological factors, and then a processing method is selected according to the characteristics of each type of samples. And finally, superposing the prediction values of the parts to obtain a total prediction result. An experiment is carried out according to field load data of a city; the prediction results are compared with the prediction results of an EWT-IRF model, an EWT-RF (Random Forest) model andan EMD (Empirical Mode Decomposition)-IRF model respectively, so that a better prediction effect can be obtained, and the change rule of an actual load is reflected.

The invention designs an online learning input degree identification method and system based on deep learning, and the method comprises the steps: firstly, carrying out the student face detection through YOLOv4 in order to guarantee that an image is not affected by an irrelevant background; secondly, aiming at the problems that the VGG16 network is huge in parameter quantity, time-consuming in training and the like, an improved VGG16 model is provided, and meanwhile, in the model training process, a depth deterministic information bottleneck method DIB is adopted to make up for the deficiency of a traditional loss function so as to obtain relatively compact feature expression, reduce generalization errors and improve the universality and stability of the model, and achieving precise identification of the learning input degree in a complex online learning scene; and finally, through comparison and analysis with various methods such as traditional machine learning and other deep learning, the effectiveness of the method is verified.