How to Sequence model?

Multi-target random fuzzy dynamic optimal energy flow modeling and solving method for multi-energy coupling transmission and distribution network,Steering deep sequence model with prototypes,On-chip network hardware Trojan horse detection platform based on machine learning

Patents

Literature

Hiro is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.

Hiro

3 results about "Sequence model" patented technology

Filter

Efficacy Topic

Property

Owner

Technical Advancement

Application Domain

Technology Topic

Technology Field Word

Patent Country/Region

Patent Type

Patent Status

Application Year

Inventor

Multi-target random fuzzy dynamic optimal energy flow modeling and solving method for multi-energy coupling transmission and distribution network

ActiveCN105703369ARealize comprehensive coordination and optimization of schedulingAc networks with different sources same frequencyElectric power systemEnergy coupling

The invention relates to a multi-target random fuzzy dynamic optimal energy flow modeling and solving method for a multi-energy coupling transmission and distribution network and belongs to the field of day-ahead scheduling plan research of electric power systems in an energy interconnection environment. The method comprises the following steps: basic data in a system scheduling period are obtained,; random fuzzy space-time sequence models for large-scale wind power, distributed power source and multi-energy loads are obtained via historical data mining; power and voltages of a power transmission network and all active distribution networks at joint nodes are used as share variables; multi-target SoS dynamic optimal energy flow models characterized by high economic performance, low carbon emission, renewable energy absorption, loss reduction and the like are built within static state security constraints; multi-energy source charge forecast can be realized through random fuzzy simulation; a Pareto solution set, an optimal compromise solution and an energy flow result can be obtained via adoption of an improved SoS layered optimizetion algorithm based on approximate dynamic programming and NSGA-11. The method can adapt to a development trend of energy interconnection, and comprehensive coordination optimization of day-ahead scheduling of transmission and distribution parties can be realized on the premise that requirements for static state safety and stabilization of systems can be satisfied.

Multi-target random fuzzy dynamic optimal energy flow modeling and solving method for multi-energy coupling transmission and distribution network

View all

Owner:马瑞

Steering deep sequence model with prototypes

PendingCN112951420AMedical data miningHealth-index calculationAlgorithmEngineering

A deep sequence model with prototypes may be steered. A prototype overview is displayed, the prototype overview including a plurality of prototype sequences learned by a model through backpropagation, each of the prototype sequences including a series of events, wherein for each of the prototype sequences, statistical information is presented with respect to use of the prototype sequence by the model. Input is received adjusting one or more of the prototype sequences to fine-tune the model. The model is updated using the plurality of prototype sequences, as adjusted, to create an updated model. The model, as updated, is displayed in the prototype overview.

Owner:ROBERT BOSCH GMBH

On-chip network hardware Trojan horse detection platform based on machine learning

PendingCN114139158AEfficient detectionImprove accuracyInternal/peripheral component protectionPlatform integrity maintainanceTraffic characteristicAlgorithm

The invention discloses an on-chip network hardware Trojan horse detection platform based on machine learning, and belongs to the technical field of calculation, reckoning or counting. A machine learning-based network-on-chip hardware Trojan horse detection platform is established by constructing a security detection module comprising a traffic feature tracking extraction module, a feature extraction register module, a change point detection module and a random forest Trojan horse detection module. The flow characteristic tracking and extracting module is used for converting flow data in a network-on-chip into a network-on-chip flow time sequence model by analyzing network-on-chip flow characteristics. And the feature extraction result register module stores the feature data. The change point detection module extracts change points and abnormal points in the on-chip network traffic time sequence model through a Bayesian change point method, and is used for separating normal data and change point data. And the random forest detection module accurately identifies network-on-chip abnormal behaviors by learning complex and mutually associated network-on-chip traffic characteristics.

Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

3 results about "Sequence model" patented technology

Multi-target random fuzzy dynamic optimal energy flow modeling and solving method for multi-energy coupling transmission and distribution network

Steering deep sequence model with prototypes

On-chip network hardware Trojan horse detection platform based on machine learning

Popular searches