• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.449-456
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• 2004

This paper proposes a novel recognition model, a recurrent fuzzy associative memory(RFAM), for recognizing time-series patterns contained an ambiguity. RFAM is basically extended from FAM(Fuzzy Associative memory) by adding a recurrent layer which can be used to deal with sequential input patterns and to characterize their temporal relations. RFAM provides a Hebbian-style learning method which establishes the degree of association between input and output. The error back-propagation algorithm is also adopted to train the weights of the recurrent layer of RFAM. To evaluate the performance of the proposed model, we applied it to a word boundary detection problem of speech signal.

• Current Optics and Photonics
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• v.5 no.3
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• pp.213-219
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• 2021

We describe a single-channel rubidium (Rb) radio-frequency atomic magnetometer (RFAM) as a receiver that takes magnetic signal resonating with Zeeman splitting of the ground state of Rb. We optimize the performance of the RFAM by recording the response signal and signal-to-noise ratio (SNR) in various parameters and obtain a noise level of 159 $fT{\sqrt{Hz}}$ around 30 kHz. When a resonant radiofrequency magnetic field with a peak amplitude of 8.0 nT is applied, the bandwidth and signal-to-noise ratio are about 650 Hz and 88 dB, respectively. It is a good agreement that RFAM using alkali atoms is suitable for receiving signals in the very low frequency (VLF) carrier band, ranging from 3 kHz to 30 kHz. This study shows the new capabilities of the RFAM in communications applications based on magnetic signals with the VLF carrier band. Such communication can be expected to expand the communication space by overcoming obstacles through the high magnetic sensitive RFAM.

• Journal of the Korea Institute of Building Construction
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• v.16 no.6
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• pp.479-485
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• 2016

This paper presents an experimental study conducted to investigate the effect of recycled fine aggregate (RFA) on the mechanical properties and chloride diffusion behavior of mortar. The test results revealed that the addition of RFA plays an important role in the mechanical properties and pore structures of the investigated mortar specimens as well as chloride diffusion behavior. The mechanical properties such as compressive strength and flexural strength of recycled fine aggregate mortar (RFAM) were gradually decreased as RFA replacement ratio increase. The pore structure of RFAM was examined by permeability tests. The RFAM showed a increment in the permeability according to replacement ratio increase of RFA. But the chloride diffusion coefficient of RFAM was almost same up to 50% replacement ratio of RFA due to a chloride binding phenomenon of RFAM which may compensate the higher permeability of RFAM.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1171-1179
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• 2004

We describe word boundary detection that extracts the boundary between speech and non-speech. The proposed method uses two features. One is the normalized root mean square of speech signal, which is insensitive to white noises and represents temporal information. The other is the normalized met-frequency band energy of voice signal, which is frequency information of the signal. Our method detects word boundaries using a recurrent fuzzy associative memory(RFAM) that extends FAM by adding recurrent nodes. Hebbian learning method is employed to establish the degree of association between an input and output. An error back-propagation algorithm is used for teaming the weights between the consequent layer and the recurrent layer. To confirm the effectiveness, we applied the suggested system to voice data obtained from KAIST.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.287-293
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• 2005

Predicting RNA secondary structure as accurately as possible is very important in functional analysis of RNA molecules. However, different prediction methods and related parameters including terminal GU pair of helices, minimum length of helices, and free energy systems often give different prediction results for the same RNA sequence. Then, which structure is more important than the others? i.e. which combinations of the methods and related parameters are the optimal? In order to investigate above problems, first, three prediction methods, namely, random stacking of helical regions (RS), helical regions distribution (HD), and Zuker's minimum free energy algorithm (ZMFE) were compared by taking 1139 tRNA sequences from Rfam database as the samples with different combinations of parameters. The optimal parameters are derived. Second, Zuker's dynamic programming method for prediction of RNA secondary structure was revised using the above optimal parameters and related software BJRNAFold was developed. Third, the effects of short-range interaction were studied. The results indicated that the prediction accuracy would be improved much if proper short-range factor were introduced. But the optimal short-range factor was difficult to determine. A user-adjustable parameter for short-range factor was introduced in BJRNAFold software.