- 著者
- Oyama Satoshi Baba Yukino Ohmukai Ikki Dokoshi Hiroaki Kashima Hisashi
- 出版者
- IEEE (Institute of Electrical and Electronics Engineers)
- 巻号頁・発行日
- pp.1-9, 2015
- 被引用文献数
- 2
Despite recent open data initiatives in many coun- tries, a significant percentage of the data provided is in non- machine-readable formats like image format rather than in a machine-readable electronic format, thereby restricting their usability. This paper describes the first unified framework for converting legacy open data in image format into a machine- readable and reusable format by using crowdsourcing. Crowd workers are asked not only to extract data from an image of a chart but also to reproduce the chart objects in spreadsheets. The properties of the reconstructed chart objects give their data structures including series names and values, which are useful for automatic processing of data by computer. Since results produced by crowdsourcing inherently contain errors, a quality control mechanism was developed that improves the accuracy of extracted tables by aggregating tables created by different workers for the same chart image and by utilizing the data structures obtained from the reproduced chart objects. Experimental results demonstrated that the proposed framework and mechanism are effective.
- 著者
- Yahagi Shuichi Kajiwara Itsuro
- 出版者
- IEEE (Institute of Electrical and Electronics Engineers)
- 雑誌
- IEEE Control Systems Letters (ISSN:24751456)
- 巻号頁・発行日
- vol.6, pp.2966-2971, 2022-06-08
- 被引用文献数
- 6
In industry, a feedback controller with a look-up table (LUT) is often used for nonlinear systems. Although this structure is easy to understand, tuning the LUT parameters is time-consuming due to the huge number of parameters. This paper presents a direct data-driven design method for a gain-scheduled feedback controller with a LUT to directly tune the LUT parameters from single-experiment data without a system model. Specifically, conventional virtual reference feedback tuning (VRFT), which is a data-driven method, is extended and the L-2 norm for adjacent LUT parameters is added to the VRFT cost function to avoid overlearning. The optimized parameters are analytically obtained by a generalized ridge regression. A simulation of a nonlinear system demonstrates that the proposed method can directly obtain the LUT parameters without knowledge of the controlled object's characteristics.