Nokia Solutions and Networks Oy

University of Science and Technology Beijing

North China University of Techonlogy

Yu Yan

Li Ni Zhang

Peter Skov

Qiang Li

Haibo Wang

Chun Hai Yao

Ruize Sun

Lijie Qiao

Mingxing Lu

Zhenfeng Yang

Yanjing Su

Yanzhi Guan

A machine learning-based method for designing a high-strength high-toughness steel, including: (S) obtaining data and filling in missing parts to form a data set; (S) selecting feature data in the data set to form a standard data set; (S) constructing two machine learning models of the high-strength high-toughness steel; (S) completing training after the two models are evaluated to be qualified; (S) finding frontier points, drawing a Pareto front, and distinguishing a known region and a feature space; (S) in the feature space, setting a step for the feature data, drawing a grid space, and performing multiple training predictions on each grid point by using the models, to obtain predicted Gaussian distributions of two objectives; and (S) searching for an expected improvement point through an efficient global optimization algorithm, and obtaining design parameter values of corresponding features.

Machine learning-based method for designing high-strength high-toughness steel

A flexible roll-die forming machine for variable cross-section parts includes: a feeding device and a flexible roll-die forming unit; wherein the flexible roll-die forming unit includes at least a set of flexible roll-die forming devices; when two or more sets of the flexible roll-die forming devices are provided, the flexible roll-die forming devices are connected in sequence; wherein the feeding device is arranged at a front end of the flexible roll-die forming unit and is connected to an adjacent flexible roll-die forming device. The present invention effectively solves problems that conventional flexible roll-die forming machines are not able to produce narrow part with variable cross-sections, and the forming accuracy is poor. The present invention also reduces the amount of shafts controlled by servo motors, so as to simplify the controlling system, improve system operation reliability and simplify maintenance.

Flexible roll-die forming machine for variable cross-section parts

Techniques are disclosed for ACK/NAK bit bundling in carrier aggregation scenarios. Such measures exemplarily comprise generating a set of acknowledgement bits confirming receipt of payload data of a radio frame in a carrier aggregation mode aggregating a primary and at least one secondary carrier, said radio frame being divided into a plurality of downlink subframes and uplink subframes, each of said downlink subframes comprising at least one codeword per carrier, each of said acknowledgement bits is allocated to one of said codewords of one of said downlink subframes, and applying spatial domain bundling and/or time domain bundling on said set of acknowledgement bits distinctive for each of said primary and said at least one secondary carrier, wherein said spatial domain bundling is an AND operation of all acknowledgement bits allocated to each codeword of one carrier of said primary and said at least one secondary carrier and one downlink subframe.

ACK/NAK bit bundling in carrier aggregation scenarios

The present invention provides a method, apparatus and a program relating to an enhanced TDD UL HARQ timeline for UL-DL coexistence scenario. The present invention includes transmitting data in a first transmission, receiving acknowledgement information for Hybrid Automatic Repeat-Request pursuant to uplink-downlink configurations 0 or 6 in Time-Division-Duplex mode for LTE, determining the timing of a second transmission associated with a process for HARQ and transmitting data in a second transmission in the subframe according to the determined timing, wherein a second transmission associated with a process for Hybrid Automatic Repeat-Request is performed in a protected subframe if the first transmission associated with said process is performed in a protected subframe.

Enhanced TDD UL HARQ timeline for UL-DL coexistence scenario

Growing community of inventors

Beijing, China

Yu Yan