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508 results about "Central layer" patented technology

Rated voltage of 35kv or below shallow sea wind farm photoelectrical transmission composite cable

A rated voltage of 35kv or below shallow sea wind farm photoelectrical transmission composite cable comprises a cable core provided with a lead and a water-blocking filling piece, a water-blocking belting layer, a plastic-aluminum composite band layer, an internal sheath, a wire armoring layer and an external sheath, wherein the cable core are externally coated with the water-blocking belting layer, the plastic-aluminum composite band layer, the internal sheath, the wire armoring layer and the external sheath in turn, the cable core is internally provided with a fiber optic communication unit, a steel-tape armoring layer is arranged between the external sheath and the wire armoring layer. The structure of the fiber optic communication unit is as follows: a fiber loose tube is externally coated with an internal sheathing layer, an armoring layer and an external sheathing layer in turn. A conductor of the lead adopts two-tier structure: a central layer conductor is formed by twisting multiple stranded brass wires coated with water-blocking balm, and then coats a central layer semiconductive water-blocking band layer; an external layer conductor is coated outside the central layer semiconductive water-blocking band layer by multiple stranded brass wires coated with water-blocking balm, and then coats an external layer semiconductive water-blocking band layer. The invention has the complex function of optical communication and power transmission, one cable has two uses, thereby saving cost and facilitating laying; in addition, the cable has high strength, excellent wear resistance, corrosion resistance and waterproof function, and can adapt to the requirement for subsea use in long term.
Owner:JIANGSU HENGTONG POWER CABLE

High-nickel material coated with aluminum and lithium silicate on surface and doped with fluorine on surface layer and preparation method

The invention discloses a high-nickel material coated with aluminum and lithium silicate on the surface and doped with fluorine on a surface layer. The high-nickel material comprises an aluminum and lithium silicate coating layer and a high-nickel ternary material central layer, wherein the thickness of the coating layer is 1-200 nm, and the coating layer is doped with a fluorine element. In addition, the invention discloses a preparation method of the high-nickel material. The preparation method comprises the steps of mixing, drying and screening, lithium-adding sintering and fluorine-addingthermal treatment. The aluminum and lithium silicate fast-ion conductor material coating layer has good lithium-ion conducting performance, doped fluorine ions replace oxygen in the coating layer or the high-nickel material, accordingly the electronic conductivity of the material is improved, finally the surface of the high-nickel material has better lithium-ion and electronic conductivity properties, and playing of the rate capability of an anode material for lithium ion batteries is facilitated. The preparation method of the high-nickel material is low in cost, the process is simple, and industrialization is easy to achieve.
Owner:余姚市海泰贸易有限公司

Pattern recognition method based on self-adaptation correction neural network

The invention relates to the field of pattern recognition, in particular to a pattern recognition method based on a self-adaptation correction neural network. The method comprises the steps of classifying input training samples through a probabilistic neural network model so as to obtain samples accurate in classification and samples inaccurate in classification; adding an input layer, a central layer and an excitation layer on the basis of the probabilistic neural network model structure so as to construct a self-adaptation correction neural network model structure; for the samples inaccurate in classification in the probabilistic neural network model, using themself as central points, calculating the allowance radius between the the samples and samples of other classifications, clustering error samples of same category so as to realize batch correction of classification patterns and replanning of a judging interface and build the self-adaptation correction neural network; finally, conducting pattern recognition on input testing samples based on the self-adaptation correction neural network model. The pattern recognition method has the advantages of being high in accuracy in mode classification, strong in mode generalization ability, good in classification real-time performance, wide in application prospect, and the like.
Owner:NANJING NORTH OPTICAL ELECTRONICS
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