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2113 results about "Study methods" patented technology

Method study is the process of subjecting work to systematic, critical scrutiny to make it more effective and/or more efficient.

Deep and reinforcement learning-based real-time online path planning method of

ActiveCN106970615AReasonable method designAccurate path planningPosition/course control in two dimensionsPlanning approachStudy methods
The present invention provides a deep and reinforcement learning-based real-time online path planning method. According to the method, the high-level semantic information of an image is obtained through using a deep learning method, the path planning of the end-to-end real-time scenes of an environment can be completed through using a reinforcement learning method. In a training process, image information collected in the environment is brought into a scene analysis network as a current state, so that an analytical result can be obtained; the analytical result is inputted into a designed deep cyclic neural network; and the decision-making action of each step of an intelligent body in a specific scene can be obtained through training, so that an optimal complete path can be obtained. In an actual application process, image information collected by a camera is inputted into a trained deep and reinforcement learning network, so that the direction information of the walking of the intelligent body can be obtained. With the method of the invention, obtained image information can be utilized to the greatest extent under a premise that the robustness of the method is ensured and the method slightly depends on the environment, and real-time scene walking information path planning can be realized.

Medical information extraction system and method based on depth learning and distributed semantic features

ActiveCN105894088AAvoid floating point overflow problemsHigh precisionNeural learning methodsNerve networkStudy methods
he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.
Owner:神州医疗科技股份有限公司 +1

Computer-aided group-learning methods and systems

InactiveUSRE38432E1High tendencyFocusIndoor gamesElectrical appliancesSmall group learningComputer-aided
Providing methods and systems for a computer-aided group-learning environment, where a number of users can interact and work on a subject together. The system and method can monitor and analyze users' inputs. The analysis process can identify a user's performance on the subject, and can understand some of the user's traits, such as confidence level and learning attitude. The system can include an interaction controller, which sets a duration of time for the users to communicate in a dialogue environment. Working on the subject in a group and working alone can be intertwined. For example, the users first work on the materials generated individually, and then solve the problem together in a dialogue environment. During the dialogue session, the interaction controller can provide hints to the users. The system can also include a user registry, which restricts the users who can use the embodiment to work on the subject. The registry can receive potential user's characteristics to determine whether such user may be allowed to join the existing users to work on the subject. The registry can also access a summarized profile of the existing users to help the potential user make joining decisions. The system can also include a notepad for a user to take notes. The interaction controller can also guide the user to take notes.
Owner:FAI HO CHI +1

Traffic signal self-adaptive control method based on deep reinforcement learning

InactiveCN106910351ARealize precise perceptionSolve the problem of inaccurate perception of traffic statusControlling traffic signalsNeural architecturesTraffic signalReturn function
The invention relates to the technical field of traffic control and artificial intelligence and provides a traffic signal self-adaptive control method based on deep reinforcement learning. The method includes the following steps that 1, a traffic signal control agent, a state space S, a motion space A and a return function r are defined; 2, a deep neutral network is pre-trained; 3, the neutral network is trained through a deep reinforcement learning method; 4, traffic signal control is carried out according to the trained deep neutral network. By preprocessing traffic data acquired by magnetic induction, video, an RFID, vehicle internet and the like, low-layer expression of the traffic state containing vehicle position information is obtained; then the traffic state is perceived through a multilayer perceptron of deep learning, and high-layer abstract features of the current traffic state are obtained; on the basis, a proper timing plan is selected according to the high-layer abstract features of the current traffic state through the decision making capacity of reinforcement learning, self-adaptive control of traffic signals is achieved, the vehicle travel time is shortened accordingly, and safe, smooth, orderly and efficient operation of traffic is guaranteed.

Deep learning-based short-term traffic flow prediction method

The present invention discloses a deep learning method-based short-term traffic flow prediction method. The influence of the traffic flow rate change of the neighbor points of a prediction point, the time characteristic of the prediction point and the influence of the periodic characteristic of the prediction point on the traffic flow rate of the prediction point are considered simultaneously. According to the deep learning method-based short-term traffic flow prediction method of the invention, a convolutional neural network and a long and short-term memory (LSTM) recurrent neural network are combined to construct a Conv-LSTM deep neural network model; a two-way LSTM model is used to analyze the traffic flow historical data of the point and extract the periodic characteristic of the point; and a traffic flow trend and a periodic characteristic which are obtained through analysis are fused, so that the prediction of traffic flow can be realized. With the method of the invention adopted, the defect of the incapability of an existing method to make full use of time and space characteristics can be eliminated, the time and space characteristics of the traffic flow are fully extracted, and the periodic characteristic of the data of the traffic flow is fused with the time and space characteristics, and therefore, the accuracy of short-term traffic flow prediction results can be improved.
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