• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.113-121
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• 2018

Position encoding is a method of applying weights according to position of words that appear in a sentence. Pointer networks is a deep learning model that outputs corresponding index with an input sequence. This model can be applied to coreference resolution using attribute. However, the pointer networks has a problem in that its performance is degraded when the length of input sequence is long. To solve this problem, we proposed two contributions to resolve the coreference. First, we applied position encoding and dynamic position encoding to pointer networks. Second, we stack deeply layers of encoder to make high-level abstraction. As results, the position encoding based stacked pointer networks model proposed in this paper had a CoNLL F1 performance of 71.78%, which was improved by 6.01% compared to vanilla pointer networks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.840-848
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• 2009

This paper proposes a new multi-start scheme that generates guided random bits in selecting initial search points for global optimization with univariate dynamic encoding algorithm for searches (uDEAS). The proposed method counts the number of 1 in each bit position from all the previously generated initial search matrices and, based on this information, generates 0 in proportion with the probability of selecting 1. This rule is simple and effective for improving diversity of initial search points. The performance improvement of the proposed multi-start is validated through implementation in uDEAS and function optimization experiments.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.29-36
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• 2013

This paper presents a sensitivity optimization of a MEMS (microelectromechanical systems) gyroscope for a magnet-gyro system. The magnet-gyro system, which is a guidance system for a AGV (automatic or automated guided vehicle), uses a magnet positioning system and a yaw gyroscope. The magnet positioning system measures magnetism of a cylindrical magnet embedded on the floor, and AGV is guided by the motion direction angle calculated with the measured magnetism. If the magnet positioning system does not measure the magnetism, the AGV is guided by using angular velocity measured with the gyroscope. The gyroscope used for the magnet-gyro system is usually MEMS type. Because the MEMS gyroscope is made from the process technology in semiconductor device fabrication, it has small size, low-power and low price. However, the MEMS gyroscope has drift phenomenon caused by noise and calculation error. Precision ADC (analog to digital converter) and accurate sensitivity are needed to minimize the drift phenomenon. Therefore, this paper proposes the method of the sensitivity optimization of the MEMS gyroscope using DEAS (dynamic encoding algorithm for searches). For experiment, we used the AGV mounted with a laser navigation system which is able to measure accurate position of the AGV and compared result by the sensitivity value calculated by the proposed method with result by the sensitivity in specification of the MEMS gyroscope. In experimental results, we verified that the sensitivity value through the proposed method can calculate more accurate motion direction angle of the AGV.