34 results about "Training study" patented technology

The invention relates to a system for predicting the efficacy of a drug directed at a target to treat a disease, the system comprising a processor configured for: - receiving (602) biomedical documents (214) comprising an identifier of the target and / or of the disease; - specifying (604) an offset time (d) the offset time indicating a time interval ahead of the performing of the prediction; - specifying (606) a time window (706) ending at the begin of the offset time; - extracting (608) a plurality of features (222) selectively from the ones of the received documents published during said timewindow; - providing (610) a classifier (226.3) having been trained on training features (220) extracted from biomedical training documents published within a training time window ending at the beginof the offset time ahead of a moment (OC) the outcome of one or more training studies on training target-disease-pairs was disclosed; - executing (612) the classifier, thereby providing the extractedfeatures as input; - outputting (614) a classification result indicating whether the drug directed at the target can be used to treat the disease.

The invention provides a human body motion state recognition method and system based on an acceleration sensor, which is divided into an offline stage and an online stage; wherein, the offline stage adopts the K-Means clustering method to construct a human body motion state recognition model, based on the existing tagged The data is used for training and research, and a classification strategy is proposed; then in the online stage, a real-time system for human motion state recognition is designed based on Android mobile phones, and the design is carried out from five functions: data collection, data processing, motion recognition, model update, and data display; finally, it is proved by experiments The effectiveness of the clustering algorithm, the experimental results show that it is feasible to build a human motion recognition model based on the clustering method, and the model has the advantages of good real-time performance, light weight and easy adjustment.

The invention discloses a training method of PSO-BP neural network model for heavy metal content prediction, a storage medium and terminal equipment. The training method includes: measuring content ofheavy metal of sample soil to acquire heavy metal content data of the sample soil; performing spectrum reflectivity treatment on the sample soil to acquire a spectrum reflectivity curve of the samplesoil after being treated; according to the heavy metal content data of the sample soil and the spectrum reflectivity curve of the sample soil after being treated, performing feature waveband selection to acquire feature waveband of the sample soil; building the PSO-BP neural network model; inputting the feature waveband of the sample soil into the PSO-BP neural network model for training study until the PSO-BP neural network model converges. Accuracy of soil heavy metal content estimation and predication through the model trained by the method is improved greatly.

The invention relates to an electronic mobile training and test system of hygienic knowledge for public service staff. The electronic mobile training and test system of the hygienic knowledge for the public service staff comprises a main test room system and a mobile test system. The main test room system comprises a main server, a first network exchanger and a touch type computer or a desktop computer. The mobile test room system comprises a subordinate server, a second network exchanger and a tablet personal computer. After the mobile test system is applied, a test can be organized by only one person. Testing on main test room and mobile test rooms can be started simultaneously and do not interfere. All test data are automatically collected, organized and undergo statistical analysis. Working efficiency is improved and office resources are saved. Knowledge training study materials for the public service staff can be installed on the electronic mobile training and test system in advance. The mobile test rooms can be converted into mobile classrooms as required at any time and at any places so that the employed staff can be conveniently trained.

The invention discloses a gesture recognition system and a gesture recognition method. The gesture recognition system at least comprises an intelligent mobile terminal which is provided with accelerated speed, direction and magnetic field sensors, and used to record sensor data of user gestures and extract features, and recognizes the user gestures according to the features by using a training model obtained in training of a model training platform, and further comprises the model training platform which is used to mark and perform normalization processing on the gesture features of the various user gestures, collected by the intelligent mobile terminal, perform feature training study and obtain the training model. The gesture recognition system and the gesture recognition method can input information through gesture information so as to complete corresponding tasks under the circumstance that a user can not or does not want to input the information through a touch screen or a keyboard, and enables a smart phone user to complete the corresponding tasks by simply waving a hand.

The invention discloses a MALDI-TOF-MS-based identification method for slaughtered meat, comprising: preparing a sample set, the sample set including a training sample set, a test sample set and a sample set to be tested; applying MALDI-TOF-MS to the sample set Measure to obtain the corresponding mass spectrum; establish a neural network classifier based on the mass spectrum of the training sample set and the test sample set; use the neural network classifier to classify the samples in the test sample set to obtain the identification result. The invention also discloses a MALDI-TOF-MS-based identification system for slaughtered meat. The present invention finds the metabolomics and proteomics differences between dead slaughtered meat and live slaughtered meat at the molecular level by means of a mass spectrometer, and minimizes the interference of environmental factors and sample factors through the training, learning and inspection of neural network classifiers. The detection process has high stability, sensitivity and accuracy, and the detection result has good reproducibility. It is a scientific, accurate and efficient dead meat identification method and system.

The invention discloses a method for semi-automatic marking of a three-dimensional (3D) model based on fuzzy K-nearest neighbor. The method comprises the following steps of: firstly establishing a 3D model training base; then extracting characteristic vectors of the input model to be marked, and matching similarity of the model with the model in the training base to find k neighbor models; then classifying by using a fuzzy K-nearest neighbor classifier to obtain a fuzzy classification result; and finally calculating classification uncertainty, and marking the models which are uncertain in classification by a feedback and retraining studying method. The marked models are added into the 3D model training base to further expand the model base. In the method for semi-automatic marking of the 3D model, the fuzzy classification result comprising more information is used; classification labels of the models with uncertain classification are determined by aid of feedback so as to solve overlapping problem of feature clustering edges; and a mechanism for training study is used to further automatically mark.

The invention discloses an information hiding detection method based on local learning, which comprises: constructing a training sample database containing positive and negative samples; for any sample to be detected, searching for K positive and negative samples most similar to it in the sample database Yes, it constitutes a local training set; on the local training set, the training and learning of the classifier is carried out. During the learning process, the constraint of pairing positive and negative samples is added, and the optimal classifier is obtained by using the optimization algorithm; the obtained classifier is used to treat The detection samples are discriminated and classified to obtain a detection result of whether the samples to be detected have undergone information hiding. The present invention makes full use of local learning to better overcome the advantages of large intra-class changes, noise reduction and less need for prior knowledge, improves the effect of information hiding detection, and can be applied to an information hiding detection algorithm analysis system based on pattern recognition middle.

The invention discloses a multifunctional rehabilitation training device for brain-injured rats, which relates to the technical field of medical simulation experiments. The circular plane runway, the circular three-dimensional runway and the static training room are set up. The circular plane runway and the circular three-dimensional runway are connected with multiple dynamic training rooms evenly distributed along the circumferential direction. There is a dark space between the static training room and the circular plane runway. Light training pipeline; the training path is the circular plane track, the dynamic training room, the circular three-dimensional track, the static training room, the dark light training pipeline, and the circular plane track. The invention provides diversified training conditions that meet actual conditions for rat brain injury rehabilitation training, and provides basic data for brain injury rehabilitation training research after collecting data in the training process.

The present invention relates to an abnormal behavior recognition method based on feature extraction of video motion information and error backpropagation network (BP Adaboost) based on adaptive enhancement algorithm, comprising: firstly calculating optical flow according to adjacent image frames of the video, by horizontal direction and The optical flow in the vertical direction is used to calculate the optical flow direction, and the optical flow direction histogram is calculated with the intensity of the optical flow as the weight, and the histogram features are converted into feature attributes with probability attributes, and then training based on the normal and abnormal training samples. The classifier is obtained by adapting the error backpropagation network (BP Adaboost) of the boosting algorithm. In the test phase, before using the trained classification model, the optical flow direction histogram of the test sample is obtained according to the same calculation method as the optical flow histogram of the adjacent frame, and finally the abnormal behavior in the test sample is checked according to the classification model obtained through training and learning. identify. The invention has the characteristics of high recognition rate and low computational complexity, and can be widely used in the fields of abnormal behavior recognition and motion analysis.

A MapReduce parallel big data text classification method comprises the following steps: firstly, establishing a reference test data set for text classification, and performing data preprocessing, comprising word segmentation, unused word removal and word root recovery; randomly dividing the reference test data set into a train text and a test text, and adopting a vector space model to establish the reference test data set into a text representation model; secondly, according to the text representation model, adopting CDMT to perform characteristic selection on the reference test data set; thirdly, adopting a Bayes classifier to perform training study on the reference test data set to obtain a classification result. The invention provides the MapReduce parallel big data text classification method with good classification performance and higher differentiation.