Alibaba Group Holding Limited

Zhejiang University

Hangzhou Hikrobot Co., Ltd.

Xu Yang

Xiaoyan Jiang

Shaomeng Wang

Bin Dai

Feng Lin

Wei Zheng

Qin Zhang

Peng Huang

Wei Chu

Huaxing Jin

Jiong Feng

Shen Li

Yuan Qi

Zihao Fu

Chao Wang

Guofang Gong

Yakun Zhang

Weiqiang Wu

Lintao Wang

Tong Liu

Liwen Duan

Ning Cai

The present application discloses a coding pattern, coding and reading methods for the same, a calibration board, and a calibration method. In the present application, the coding pattern comprises four positioning blocks, wherein three of the positioning blocks are located at three corner portions of the coding pattern, and the remaining positioning block only contacts one edge of the coding pattern, thereby forming an asymmetrical distribution configuration of the four positioning blocks. The invention can replace a two-dimensional code standard in the related art, saving the licensing and manufacturing costs of using two-dimensional code generation software in the related art, and is not subject to usage restrictions of the two-dimensional code generation software in the related art. The invention enables accurate and fast positioning of the coding pattern, facilitates quick discovery of an initial coding block position in a coding region, and allows a customizable size of the coding pattern and a customizable size of a coding region in the coding pattern according to a data volume of an application scenario, thereby providing flexibility in configuring data information recorded in the coding pattern. In addition, coding of the coding region is simple, thereby improving efficiency.

Coding pattern, coding and reading methods for same, calibration board, and calibration method

Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M&#x3c;N and P&#x3c;M; merging (N&#x2212;M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N&#x2212;M+P) feature columns; and training the model based on the training data including the (N&#x2212;M+P) feature columns.

Method and system for training model by using training data

The present disclosure provides methods and an apparatuses for building a data identification model. One exemplary method for building a data identification model includes: performing logistic regression training using training samples to obtain a first model, the training samples comprising positive and negative samples; sampling the training samples proportionally to obtain a first training sample set; identifying the positive samples using the first model, and selecting a second training sample set from positive samples that have identification results after being identified using the first model; and performing Deep Neural Networks (DNN) training using the first training sample set and the second training sample set to obtain a final data identification model. The methods and the apparatuses of the present disclosure improve the stability of data identification models.

Methods and apparatuses for building data identification models

Methods and apparatuses for training model based on random forest are provided. The method includes: dividing worker nodes into one or more groups; performing random sampling, by worker nodes in each group, in the preset sample data to obtain the target sample data; and training, by the worker nodes in each group, one or more decision tree objects using the target sample data. Example embodiments of the present disclosure do not need to scan the complete sample data for once, thereby greatly reducing the amount of data to be read, the time cost, and further the iterative update time of the model. The efficiency of training is improved.

Method and apparatus for training model based on random forest

The present disclosure provides feature data processing methods and devices. One exemplary feature data processing method comprises: classifying features into an important feature set and an auxiliary feature set according to information attribute values of the features; converting features in the auxiliary feature set to hash features; and combining the hash features with features in the important feature set, and setting the combined features as fingerprint features. Training and prediction of to-be-processed data can be performed based on the fingerprint features. With the embodiments of the present disclosure, training dimensions can be more controllable and the amount training data amount can be reduced. Therefore, the efficiency of data processing can be improved.

Feature data processing method and device

A drive system with both fixed-displacement hydraulic motors and variable-displacement hydraulic motors for a cutter head of a boring machine and a control method thereof are provided. The drive system includes a variable-displacement hydraulic motor group, a fixed-displacement hydraulic motor group and a variable-displacement hydraulic pump group. The variable-displacement hydraulic motor group, the fixed-displacement hydraulic motor group, and the variable-displacement hydraulic pump group are all connected to a main oil circuit of a cutter head system of the boring machine. The variable-displacement hydraulic pump group inputs flow to the main oil circuit; and, the variable-displacement hydraulic motor group and the fixed-displacement hydraulic motor group acquire flow from the main oil circuit. Displacements of the fixed-displacement hydraulic motor group and the variable-displacement hydraulic motor group are set in a way of specific displacement combination. The present invention decreases an engineering cost, and improves system reliability and efficiency.

Drive system with both fixed-displacement hydraulic motors and variable-displacement hydraulic motors for cutter head of boring machine and control method thereof

There is provided an association analysis method and apparatus. An original database is divided into projection databases, each not contributing to a support count of a frequent item set of another. The projection databases are used for sequential-pattern association analysis performed respectively by nodes corresponding to the projection databases. Local frequent item sets and corresponding support counts obtained by the nodes are combined. Since an established projection database does not contribute a support count of a frequent item set of another projection database, different nodes can perform association mining, including pruning, on different projection databases respectively.

Association analysis method and apparatus

Ordering search results may include obtaining an exposed log file from a log system, computing a Bayesian posterior probability for relevancy between the log file and a search request, computing an expected value of the relevancy between the log file and the search request based on the Bayesian posterior probability, storing the search request and an identifier of the log file as a key and the expected value of the relevancy between the log file and the search request as a value into a search data structure, in response to receiving a search request submitted by a user, finding expected values of relevancy between the submitted search request and log files that are relevant to the submitted search request from the search data structure, and ordering the found log files in a descending order of the expected values.

Method and apparatus of ordering search results

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Hangzhou, China

Xu Yang