• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.699-704
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• 2001

ABSTRACT In this paper, a new hybrid modular recurrent TDNN (time-delay neural network)-HMM (hidden Markov model) architecture for speech recognition has been studied. In TDNN, the recognition rate could be increased if the signal window is extended. To obtain this effect in the neural network, a high-level memory generated through a feedback within the first hidden layer of the neural network unit has been used. To increase the ability to deal with the temporal structure of phonemic features, the input layer of the network has been divided into multiple states in time sequence and has feature detector for each states. To expand the network from small recognition task to the full speech recognition system, modular construction method has been also used. Furthermore, the neural network and HMM are integrated by feeding output vectors from the neural network to HMM, and a new parameter smoothing method which can be applied to this hybrid system has been suggested.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1305-1313
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• 2010

Last few years, wireless personal area network (WPAN) has been widely researched for various healthcare applications. Due to restriction of device hardware (e.g., energy and memory), we need to design a highly-versatile MAC layer protocol for WBAN (Wireless Body Area Network). In addition, when an emergency occurs to a patient, a priority mechanism is necessitated for a urgent message to get through to the final destination. This paper presents a priority mechanism referred to as hybrid priority MAC for WBAN. Through extensive simulation, we show the proposed MAC protocol can minimize the average packet latency for urgent data. Thus, when patients have an emergency situation, our MAC allows adequate assistance time and medical treatment for patients. The simulation based on NS-2 shows that our Hybrid Priority MAC has the good performance and usability.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.167-176
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• 2012

In this paper we design and implement a new Hybrid Equipment Data Acquisition System (HEDAS) for data collection of semiconductor and optoelectronic manufacturing equipments in the equipment engineering system(EES) framework. The amount of the data collected from equipments have increased rapidly in equipment engineering system. The proposed HEDAS efficiently handles a large amount of real-time equipment data generated from EES framework. It also can support the real-time ESS applications as well as non real-time ESS applications. For the real-time EES applications, it performs high-speed real-time processing that uses continuous query and filtering techniques based on memory buffers. The HEDAS can optionally store non real-time equipment data using a HEDAS-based database or a traditional DBMS-based database. In particular, The proposed HEDAS offers the compression indexing based on the timestamp of data and query processing technique saving the cost of disks storage against extremely increasing equipment data. The HEDAS is efficient system to collect huge real-time and non real-time equipment data and transmit the collected equipment data to several EES applications in EES framework.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.3
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• pp.171-178
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• 2020

In this study, we develop MPI-OpenMP hybrid parallelization for multibody peridynamic simulations. Peridynamics is suitable for analyzing complicated dynamic fractures and various discontinuities. However, compared with a conventional finite element method, nonlocal interactions in peridynamics cost more time and memory. In multibody peridynamic analysis, the costs increase due to the additional interactions that occur when computing the nonlocal contact and ghost interlayer models between adjacent bodies. The costs become excessive when further refinement and smaller time steps are required in cases of high-velocity impact fracturing or similar instances. Thus, high computational efficiency and performance can be achieved by parallelization and optimization of multibody peridynamic simulations. The analytical code is developed using an Intel Fortran MPI compiler and OpenMP in NURION of the KISTI HPC center and parallelized through MPI-OpenMP hybrid parallelization. Further parallelization is conducted by hybridizing with OpenMP threads in each MPI process. We also try to minimize communication operations by model-based decomposition of MPI processes. The numerical results for the impact fracturing of multiple bodies show that the computing performance improves significantly with MPI-OpenMP hybrid parallelization.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.160-160
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• 2009

In recent years, the merging of nanomaterials and nano-technology into electro-optic (EO) device technology such as liquid crystal displays (LCDs) has attracted much attention because of their unique electro- and magneto-optic properties and novel display applications. One example of hybrid LC-inorganic systems is semiconductor nanorods added to LC for their strong reorientation effect and tunable refractive index. Doping of nanoparticles in LC or polymers can lead to changes in performance characteristics such as electro-optical, dielectric, memory effect, phase behavior, etc. Due to the tunability of LCDs with mixed inorganic materials, low voltage operation of a LC system can also be achieved using the significant electro-optical effect achieved through suspension of ferroelectric nanoparticles in NLC.

• ETRI Journal
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• v.23 no.1
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• pp.16-22
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• 2001

Efficient quantization methods of the line spectrum pairs (LSP) which have good performances, low complexity and memory are proposed. The adaptive quantization range method utilizing the ordering property of LSP parameters is used in a scalar quantizer and a vector-scalar hybrid quantizer. As the maximum quantization range of each LSP parameter is varied adaptively on the quantized value of the previous order's LSP parameter, efficient quantization methods can be obtained. The proposed scalar quantization algorithm needs 31 bits/frame, which is 3 bits less per frame than in the conventional scalar quantization method with interframe prediction to maintain the transparent quality of speech. The improved vector-scalar quantizer achieves an average spectral distortion of 1 dB using 26 bits/frame. The performances of proposed quantization methods are also evaluated in the transmission errors.

• 한국생물공학회:학술대회논문집
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• 2003.04a
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• pp.630-633
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• 2003

Transfer of an electron from one site to another in a molecular or between molecules and/or electrodes is one of the most fundamental and ubiquitous processes in chemistry, biology and physics. In this study fusion proteins composed by green fluorescent protein(GFP) and cytochrome b562 were used in fabricating molecular array as an electron sensitizer and electron acceptor, Protein formation onto the substrate was performed by the self-assembly technique. The fusion protein film were analyzed using scanning probe microscope(SPM), Surface Plasmon Resornance(SPR) and hybrid STM/I-V. The results suggest that the proposed molecular photodiode can be used as a basic unit of the memory device.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.321-322
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• 2011

차세대 저장매체로 각광받고 있는 BPRAM을 이용해, NAND Flash memory의 결점을 보완하고자 하는 많은 연구들이 진행되고 있다. 본 논문에서는 BPRAM과 NAND Flash를 이용하여, 계층적 스토리지에 사용하는 하이브리드 파일 시스템인 Compressing Metadata File System(CMFS)을 개발하였다. CMFS는 Mount time에 필요한 메타데이터를 BPRAM에 저장하여 마운트 시간을 줄이고, 메타데이터를 Update하기 위한 Overhead를 줄이기 위해 부분 갱신 기법을 개발하였다. 또한 메타데이터의 저장을 위해 필요한 BPRAM의 용량를 줄이기 위해 메타데이터를 경량화 및 압축하고, 압축률을 높이기 위해 Hybrid Coding 압축기법을 개발하여 적용한다. Marvel PXA320(806MHz) 보드를 이용하여 CMFS의 성능을 측정하였으며, 타 압축 기법보다 높은 메타데이터의 압축률을 보였다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.21-24
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• 2011

최근 NAND 플래시 메모리는 가벼운 무게, 적은 전력소모, 온도 및 충격에 강한 내구성 때문에 하드디스크를 대체할 저장 매체로 주목 받고 있다. 하지만 NAND 플래시 메모리는 비대칭적인 읽기 쓰기 소거 연산 처리 속도와 제자리 갱신이 불가능한 물리적인 특징으로 인해 디스크 기반의 대표적인 인덱스 구조 중의 하나인 해시 인덱스 구조를 NAND 플래시 메모리 상에 구현하였을 때, 레코드가 빈번하게 삽입, 삭제, 갱신되면 대량의 제자리 갱신이 발생하여 플래시 메모리에서 느린 쓰기 연산과 소거 연산이 수행되어 성능이 저하된다. 본 논문에서는 이러한 성능 저하를 피하기 위하여 버켓 오버플로우 발생 시 분할 연산을 수행하지 않고, 최대한 지연시킴으로써 쓰기 연산을 줄이는 인덱스 구조를 제안한다. 또한, 각 버켓에 대한 오버플로우 버켓의 갱신 및 삭제 비율에 따라 적응적으로 오버플로우 버켓을 할당하여 추가적인 읽기 쓰기 연산을 줄인다. 본 논문은 기존의 해시 인덱스 구조를 예제 및 수식을 통하여 제안하는 인덱스 구조의 우수성을 보인다.