• Nuclear Engineering and Technology
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• v.32 no.4
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• pp.316-327
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• 2000

The Emergency Core Cooling System in current nuclear power plants typically has a considerable number of complex functions and largely cumbersome operator interfaces. Functions for initiation, switch-over between various phases of operation, interlocks, monitoring, and alarming are usually performed by relays and analog comparator logic which are difficult to maintain and test. To improve problems of an analog based ECC (Emergency Core Cooling) System, the trip computer for ECCS based on Dynamic Safety System (DSS) is implemented. The DSS is a computer based reactor protection system that has fail-safe nature and performs a dynamic self-testing. The most important feature of the DSS is the introduction of test signal that send the system into a tripped state. The test signals are interleaved with the plant signals to produce an output which switches between a tripped and health state. The dynamic operation is a key feature of the failsafe design of the system. In this work, a possible implementation of the DSS using PLC is presented for a CANDU Reactor. ECC System of the CANDU Reactor is selected as the reference system.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1380-1390
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• 2021

The video face recognition (FR) is one of the most popular researches in the field of computer vision due to a variety of applications. In particular, research using the attention mechanism is being actively conducted. In video face recognition, attention represents where to focus on by using the input value of the whole or a specific region, or which frame to focus on when there are many frames. In this paper, we propose a novel attention based deep learning method. Main novelties of our method are (1) the use of combining two loss functions, namely weighted Softmax loss function and a Triplet loss function and (2) the feasibility of end-to-end learning which includes the feature embedding network and attention weight computation. The feature embedding network has a positive effect on the attention weight computation by using combined loss function and end-to-end learning. To demonstrate the effectiveness of our proposed method, extensive and comparative experiments have been carried out to evaluate our method on IJB-A dataset with their standard evaluation protocols. Our proposed method represented better or comparable recognition rate compared to other state-of-the-art video FR methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1755-1777
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• 2022

The development of wireless communication technology has led to the underutilization of radio spectra. To address this limitation, an intelligent cognitive radio network was developed. Specific emitter identification (SEI) is a key technology in this network. However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance. To overcome these challenges, a meta-learning-based open-set identification system is proposed for SEI. First, the received signals were pre-processed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network. This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes. The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization. Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training. The experimental results demonstrate that this approach outperforms other state-of-the-art SEI methods in open-set scenarios. In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signal-to-noise ratio (SNR) environments was demonstrated.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.642-645
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• 2002

This paper introduces a way to realize high-pass, band-stop and all-pass transfer functions using Operational Transconductance Amplifiers (OTAs) and grounded capacitors. The basic circuit configuration is constructed with five OTAs and two grounded capacitors. In the circuit with the proportional block, it is shown that the circuit parameters can be independently set and electronically tuned by the transconductance gains. Although the circuit configuration has been Down, it seems that the feature for realizing the high-pass, the band-pass and the all-pass transfer functions makes the structure more attractive and useful. An example is given together with simulated results by PSPICE.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.156-163
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.48-53
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• 1999

The paper presents an automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples and an application to the land-cover classification. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy subspaces are further iteratively partitioned if the user-specified classification performance has not been archived on the training set. Our classifier was trained and tested on patterns consisting of the DN of each band, (XS1, XS2, XS3), extracted from KITSAT-3 multispectral scene. The result represents that our classification method has higher generalization power.

• BMB Reports
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• v.41 no.2
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• pp.108-111
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• 2008

Some proteins of E. coli are stable at temperatures significantly higher than $49^{\circ}C$, the maximum temperature at which the organism can grow. The heat stability of such proteins would be a property which is inherent to their structures, or it might be acquired by evolution for their specialized functions. In this study, we describe the identification of 17 heat-stable proteins from E. coli. Approximately one-third of these proteins were recognized as having functions in the protection of other proteins against denaturation. These included chaperonin (GroEL and GroES), molecular chaperones (DnaK and FkpA) and peptidyl prolyl isomerases (trigger factor and FkpA). Another common feature was that five of these proteins (GroEL, GroES, Ahpc, RibH and ferritin) have been shown to form a macromolecular structure. These results indicated that the heat stability of certain proteins may have evolved for their specialized functions, allowing them to cope with harsh environments, including high temperatures.

• Speech Sciences
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• v.11 no.2
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• pp.151-164
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• 2004

This study examined the use of intonation in children with single word sentences and investigated the communicative functions of pitch range and pitch direction. Two children aged 13months were observed in interaction with their mothers for 10 months. The vocalizations were coded separately for communicative function and for prosodic feature. Results show that level tones are used most frequently, and pitch range is higher for request than declaration or answer and lower for answer than request or declaration. And trends in prosodic contours were observed in request, declaration, and answer respectively. For one child, rising tones were frequently associated with request whereas rising-falling tone with declaration. For the other child, rising-level tones were more frequently associated with request whereas falling-level appeared more often in conjunction with declaration. These trends appeared more distinct in proportion as they grow in months. This result indicate that the way to express communicative functions transfer gradually from differentiating pitch range to diversify pitch direction.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.298-309
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• 2004

In this paper, a new evolutionary scheme to design a TSK fuzzy model from relevant data is proposed. The identification of the antecedent rule parameters is performed via the evolutionary algorithm with the unique fitness function and the various evolutionary operators, while the identification of the consequent parameters is done using the least square method. The occurrence of the multiple overlapping membership functions, which is a typical feature of unconstrained optimization, is resolved with the help of the proposed fitness function. The proposed algorithm can generate a fuzzy model with transparent membership functions. Through simulations on various problems, the proposed algorithm found a TSK fuzzy model with better accuracy than those found in previous works with transparent partition of input space.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.156-156
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.