122 results about "Decision fusion" patented technology

The invention discloses a Chinese song emotion classification method based on multi-modal fusion. The Chinese song emotion classification method comprises the steps: firstly obtaining a spectrogram from an audio signal, extracting audio low-level features, and then carrying out the audio feature learning based on an LLD-CRNN model, thereby obtaining the audio features of a Chinese song; for lyricsand comment information, firstly constructing a music emotion dictionary, then constructing emotion vectors based on emotion intensity and part-of-speech on the basis of the dictionary, so that textfeatures of Chinese songs are obtained; and finally, performing multi-modal fusion by using a decision fusion method and a feature fusion method to obtain emotion categories of the Chinese songs. TheChinese song emotion classification method is based on an LLD-CRNN music emotion classification model, and the model uses a spectrogram and audio low-level features as an input sequence. The LLD is concentrated in a time domain or a frequency domain, and for the audio signal with associated change of time and frequency characteristics, the spectrogram is a two-dimensional representation of the audio signal in frequency, and loss of information amount is less, so that information complementation of the LLD and the spectrogram can be realized.

The invention discloses a method and system for detecting network pornography videos in real time. The method comprises the following steps: according to the length of a network video, establishing a pre-extracted frame number queue KFN of key frames, wherein KFN={n1, n2,..., nN} with the condition of n1<n2<...<nN; according to the pre-extracted frame number queue of key frames, extracting a key frame in order and decoding the key frame; carrying out pornographic content detection on the decoded key frame and judging whether the key frame contains the pornographic content; according to the detection result of the pornographic content of the single key frame together with the detection results, which are obtained previously, of other key frames, carrying out decision fusion judgment; if judging the key frame contains the pornographic content, considering the video contains the pornographic content and completing detection; if judging the key frame does not contain the pornographic content, considering the video does not contain the pornographic content and completing detection; and if whether the key frame contains the pornographic content is uncertain, continuing detecting the single key frame.

The invention discloses an emotion recognition method and device based on LSTM (Long Short Term Memory) audio and video fusion and a storage medium. An LSTM model is adopted, a more detailed frame-level feature is used for training the model, and the obtained emotion recognition is accurate. Meanwhile, a method of combining decision fusion with later fusion is adopted, the recognition results of the two modes can be more effectively fused for the features of speech emotion recognition and the features of facial expression recognition, and a more accurate emotion recognition result is obtainedthrough calculation. According to the method provided by the invention, the emotional state of the prediction object can be obtained more accurately, and the accuracy and robustness of emotion recognition are improved.

Disclosed is a trust management system for decision fusion in a network. The trust management system includes a detection subsystem having a plurality of sensors, and a plurality of channels. Each sensor of the plurality of sensors detects one of an occurrence and a non-occurrence of an event in the network. The trust management system further includes a fusion subsystem communicably coupled to the detection subsystem through the plurality of channels for receiving a decision of the each sensor and iteratively assigning a pre-determined weightage. The fusion subsystem ascertains a summation of respective decisions of the plurality of sensors and compares the weighted summation with a corresponding decision of the each sensor. The fusion subsystem further updates the assigned pre-determined weightage and determines the presence of the each sensor being one of a compromised sensor and a non-compromised sensor. Further disclosed is a method for decision fusion in a network.

The invention discloses a multi-modal three-dimensional point cloud segmentation system and method. According to the invention, the good fusion of modal data can be realized; a priori mask is introduced, robustness of an obtained scene segmentation result is improved, and the high segmentation precision is obtained. For different scenes, such as toilets, meeting rooms and offices, a good prediction result can be obtained, and the model has good generalization. For an unused skeleton network used for extracting point cloud features, a feature and decision fusion module can be attempted to be utilized, and the precision is improved; if calculation conditions allow, more points can be tried, and a larger area can be utilized, for example, the number of used points and the size of a scene areaare increased by the same multiple, so that the receptive field of the whole model is improved, and the perceptual capacity of the model to the whole scene is improved.

The invention provides an object-based OBIA-SVM-CNN remote sensing image classification method, and relates to a remote sensing image classification method. The objective of the invention is to solvethe problem of low remote sensing classification and identification accuracy of an existing complex farmland area. The method comprises the following steps: 1, segmenting a remote sensing image basedon a multi-scale segmentation algorithm until all segmented objects are matched with farmland plaque boundaries on the remote sensing image through visual inspection; 2, trainning RBF-SVM model and CNN model to get trained RBF-SVM model and CNN model; 3, using the trained RBF-SVM model and the CNN model to classify and predict each segmented object in the first, and obtaining the prediction results of the CNN model and the prediction results of the SVM model.; and 4, performing decision fusion on the CNN model prediction result and the SVM model prediction result to obtain a final classification result. The method is applied to the field of remote sensing image classification.

The invention discloses a colorectal cancer digital pathological image discrimination method and system based on weak supervised learning. The colorectal cancer digital pathological image discrimination system based on weak supervised learning comprises an acquisition module used for collecting a colorectal cancer digital pathological image data set; a preprocessing module used for preprocessing data in the collected data set to obtain preprocessed data; a first classification module used for constructing a sampling block discrimination model based on a weak supervised learning algorithm, inputting the preprocessed data into the constructed sampling block discrimination model for processing, and obtaining a classification result of all pathological image blocks in a full-slice sampling packet; and a second classification module used for constructing a decision fusion model, inputting the obtained classification result of the pathological image blocks into the decision fusion model for fusion processing, and obtaining a classification result of the all-digital pathological image.

The invention discloses a multi-mode fusion gesture keyboard input method, device and system and a storage medium. The method comprises the steps: acquiring IMU sensor data, myoelectricity sensor dataand bending sensor data of user keys; shooting the hand area of the user to obtain hand image data; and inputting the four pieces of preprocessed data into corresponding classifiers for feature extraction, then performing decision fusion, and identifying the four pieces of preprocessed data as corresponding gesture key input signals. The device comprises a data glove or a head-mounted device; thesystem comprises a data glove and a head-mounted device. The storage medium comprises a processor and a memory. Interaction between gestures and virtual keyboard characters is achieved in the head-mounted device, character images are displayed in real time, and an interactive interface with more three-dimensional scenes, richer information and more natural and friendly environment is presented for keyboard input of people.

The invention relates to an unmanned aerial vehicle target detection method based on multi-sensor information fusion. The method comprises the steps: step 1, carrying out time and coordinate registration on radar and photoelectric equipment, monitoring a low-altitude protection area in real time to obtain feature information of a small target, and carrying out feature layer fusion on the feature information; step 2, collecting and expanding images of various unmanned aerial vehicle targets to serve as an unmanned aerial vehicle target detection data set, and introducing an SSD deep learning network for training to obtain an SSD deep learning prediction model; and step 3, performing target detection by using the SSD deep learning prediction model and image information acquired by the photoelectric equipment, performing decision fusion on multiple types of information of the same target by setting a threshold range, and finally performing fusion decision on different information prediction results and multiple judgment results to determine whether the target is an unmanned aerial vehicle. Through fusion of multi-sensor information, the target detection range of the unmanned aerial vehicle is expanded, the detection efficiency is improved, and certain environmental interference can be resisted.

The invention discloses a building detection method based on pixel and region segmentation decision fusion. The building detection method comprises the following steps: respectively constructing a residual structure introduced pixel segmentation model and a feature pyramid network introduced region-based double segmentation model; generating a training sample set and a test sample set from the optical remote sensing data set; preprocessing the images in the training set samples; training a pixel segmentation model by using the mixed supervision loss added with the Dice loss and the cross entropy loss; inputting the test sample set into the trained double-segmentation network, and respectively outputting prediction results of the test sample set; and fusing the prediction result of the double-segmentation network according to the decision scheme, outputting the final detection result of the test sample set, and completing the detection. The method pays attention to the spatial consistency of the large-scale building, reserves the multi-scale features of the small-scale building, guarantees the richness of the features of the building, and improves the building detection accuracy.

The invention provides an oil-immersed transformer fault diagnosis method based on a neural network and decision fusion. The method based on the neural network and the decision fusion comprises the steps of fault coding, construction and training of a neural network model, and calculation of a decision fusion matrix. The method comprises the following steps: after encoding fault low-temperature overheating, medium-temperature overheating, high-temperature overheating, partial discharge, low-energy discharge and high-energy discharge, training a plurality of neural networks by using the contentof dissolved gas in five kinds of transformer oil as identification features, and calculating a decision fusion matrix according to the test accuracy of the neural networks to realize decision fusionof the plurality of neural networks. According to the method, the weight of the specific fault in the whole model identification can be adjusted according to the identification performance of the single neural network for the specific fault, so that the accuracy of fault diagnosis is improved, and the method has important significance for timely processing of transformer faults and stable and reliable operation of a power system.

The invention provides an equipment fault diagnosis method and device based on multi-sensor data fusion. The method comprises the steps of constructing a fault identification framework of equipment according to a predetermined fault mode; acquiring equipment operation state data acquired by a plurality of sensors, and converting the equipment operation state data into digital signals; performing fault feature extraction on the digital signal by using a pre-constructed adaptive fault diagnosis model, and calculating a BPA value of each fault mode by using a basic probability assignment method according to an extracted fault feature matrix to obtain a primary diagnosis evidence body of the equipment; and comparing the DS theoretical conflict factor with a preset threshold, and selecting a corresponding fusion rule according to a comparison result to perform decision fusion on the primary diagnosis evidence body to obtain a fault diagnosis result of the equipment. According to the invention, multi-source fault signals can be fused to carry out equipment state monitoring and intelligent diagnosis, so that the equipment fault diagnosis efficiency and accuracy are effectively improved.

The invention provides a medical diagnosis auxiliary method, which belongs to the technical field of computer assistance, and comprises the following steps: classifying based on various medical data by adopting various classification models to obtain a plurality of classification decision values; and performing decision fusion on the plurality of classification decision values to obtain a classification decision value as a classification result to be output. The invention further provides a medical diagnosis auxiliary system. According to the invention, the problems of incomplete data sources,single model and the like can be solved to a great extent by respectively operating various medical data and various classification models and finally performing decision fusion, and the overall accuracy of the universal medical diagnosis auxiliary system is effectively improved from the aspect of a technical framework.

The invention belongs to the technical field of radar radiation source signal characteristic evaluation in electronic countermeasure, and discloses a radar radiation source signal intra-pulse characteristic comprehensive evaluation method and system. The method comprises the following steps of firstly, carrying out feature extraction on a received radar radiation source signal, and carrying out feature evaluation index measurement and normalization according to an established evaluation system; carrying out improved interval analytic hierarchy process by combining expert priori knowledge and an actual environment, and establishing a nonlinear equation optimization model by using an improved projection pursuit algorithm; and finally, performing final subjective and objective decision fusionby using a projection spectrum gradient algorithm. The radar radiation source signal intra-pulse characteristic evaluation method and system can reasonably and effectively realize various radar radiation source signal intra-pulse characteristic evaluations based on an actual environment, and scientific and effective evaluation is carried out to help to select characteristics capable of highlighting radar radiation source signals so as to facilitate the subsequent radar radiation source signal sorting and identification.

The method comprises the following steps: performing discretization preprocessing on data in a complete data set of a target stock in a target time period by adopting an equidistant discretization algorithm and a one-dimensional K-Means clustering discretization algorithm; carrying out attribute reduction of the technical indexes; adopting a naive Bayes classifier and a K-nearest neighbor classifier, and according to the complete data set subjected to attribute reduction, carrying out classification prediction on the increase and decrease amplitude of the target stock in the next trading day;and performing decision fusion on the classification prediction results of the future increase and decrease of the target stock obtained by the two classifiers by using a D-S evidence combination rule, and finally taking the decision fusion result as a final classification prediction result of the future increase and decrease of the target stock. According to the invention, the prediction accuracyof various stock trend prediction methods based on a neural network, an SVM and the like can be obviously improved. When the method is used for constructing a multi-factor stock selection model, thenonlinear relationship between various stock index data and stock income is more significant.

The invention discloses a depth decision fusion method based on an entropy evaluation method and a D-S evidence theory, and belongs to the field of information fusion. Firstly, a plurality of classifiers are designed to carry out training recognition on samples, then single-layer decision fusion is carried out on recognition results of the multiple classifiers through an entropy evaluation method and a D-S evidence theory method; finally, a deep decision fusion method is adopted, and the result recognized by the method with the high recognition rate in the two decision fusion methods is reserved in each recognition process; and respective advantages of the two methods are fully exerted for identification. Compared with single classifier identification and single method decision fusion, the deep decision fusion method of the invention has higher identification accuracy, robustness and fault tolerance. Therefore, the method can be applied to various pattern recognition fields such as human body behavior recognition.

The invention discloses a multi-sensor Vine Copula heterogeneous information decision fusion method, and belongs to the field of information fusion. According to the method, a heterogeneous sensor Vine copula decision fusion algorithm is provided, different types of sensor data are unified into the same event space through feature extraction and event driving, and an event set corresponding to a target is established based on data features and prior information of the target; and modeling is carried out on the correlation between sensor data feature events through Vine copula. According to themethod, the correlation between the heterogeneous information fusion feature events can be flexibly constructed, more accurate event joint probability distribution is generated, and the decision fusion performance of heterogeneous sensors is improved; numerical analysis shows that the new method provided by the invention has a better decision-making effect on a target classification and identification problem, and can fuse heterogeneous sensor information more scientifically and reasonably.

The invention relates to a bus-oriented system and method for recognizing violent behaviors of passengers to drivers. The system comprises a processor module, a video collection module and a voice collection module, the processor module comprises a video and voice data collection model, a video stream recognition model, a voice stream recognition model and a decision fusion model; the processor module is connected with the video collection module and the voice collection module. According to the system for recognizing the violent behavior of the bus passenger to the driver, multi-mode recognition is carried out on the scene through videos and voices, and the accuracy of scene recognition is improved.

The invention relates to the technical field of network security, and particularly discloses a vulnerability restoration method based on a knowledge graph, and the method is high in restoration efficiency, low in error rate and safe. The method comprises the following steps: S1, extracting features which can be identified and retrieved by a computer in a new vulnerability; S2, substituting the extracted new vulnerability features into a knowledge mapping vulnerability knowledge base for retrieval and identification; S3, when the new vulnerability feature is matched with any vulnerability in the vulnerability knowledge base, repairing the new vulnerability according to a preset repairing method corresponding to the vulnerability in the vulnerability knowledge base; S4, when the new vulnerability features are not matched with the vulnerabilities in the vulnerability knowledge base, summarizing associated vulnerability information in the matching process, and jointly performing decision fusion arrangement on the associated vulnerability information and the new vulnerability features extracted in the step S1 to output decision information; S5, reasoning the output decision information to generate a vulnerability solution; and S6, repairing a new vulnerability by using the vulnerability solution generated in the step S5, and updating the vulnerability solution in the step S5 to the vulnerability knowledge base.