International Business Machines Corporation

Transport Planning and Research Institute Ministry of Transport

Jun Hao

Jian Ding

Zhan Peng Huo

Xuan Feng

Kuo-Liang Chou

Chen Wang

Chen Shen

Jun Huang

Yue Qi

Yun Feng

Yu Zui You

Yaping Mao

Hanbing Sun

Wentao Ding

Haiyuan Yao

Processing within a container-based computing environment is facilitated by standardizing container identification processing. The process includes, for a container being provided from a container image, extracting multiple layer files from the container image and using natural language processing to determine frequency of occurrence of software components in the multiple layer files of the container image. Further, the process includes generating a container name for the container using the determined frequency of occurrence of software components in the multiple layer files of the container image. The method further includes providing the container for the container-based computing environment using the container image and the generated container name. The generated container name facilitates processing within the container-based computing environment by being based, at least in part, on the frequency of occurrence of software components in the multiple layer files of the container image.

Container name identification processing

Computer-implemented methods for performing automated pre-processing of data for a machine-learning based prediction system are provided. Aspects include receiving a plurality of raw data sets, receiving a plurality of processed data sets, wherein each of the plurality of processed data sets corresponds to one of the plurality of raw data sets, and generating a plurality of pre-processing templates based on the plurality of raw data sets and the processed data set. Aspects also include receiving an input data set, generating, for each of the plurality of pre-processing templates, a matching score for the input data set, and selecting one of the plurality of pre-processing templates based on the matching score. Aspects further include pre-processing the input data set using the selected pre-processing template and providing the pre-processed input data set to the machine learning based prediction system.

Automated data pre-processing for machine learning

A method of extracting an image of a port wharf through multispectral interpretation includes: first, extracting a blurred coastline by assigning values to grayscale values; then, performing a smoothing and noise removal processing on a remote sensing image in a targeted area to extract edge information; sequentially, establishing a multispectral database of a targeted port wharf; and extracting a port wharf using a projected eigenvector, performing an MAF transformation on the regularized kernel function again, projecting multivariate observed values to original eigenvectors, and identifying a remote sensing image area corresponding to the original eigenvector smaller than a transformation variance as a port wharf to be extracted, and then carrying out a validation operation.

Method of extracting image of port wharf through multispectral interpretation

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Beijing, China

Jun Hao