• Biomolecules & Therapeutics
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• 제4권1호
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• pp.103-109
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• 1996

To investigate the effect of the third intracellular loop (i3 loop) peptide of human $\beta$$_2$-adrenergic receptor on receptor agonist binding, we expressed third intracellular loop region of human $\beta$$_2$-adrenergic receptor as glutathione S-transferase fusion protein in E. coli. DNA fragment of the receptor gene which encodes amino acid 221-274 of human $\beta$$_2$-adrenergic receptor was amplified by polymerase chain reaction and subcloned into the bacterial fusion protein expression vector pGEX-CS and expressed as a form of glutathione-S-transferase (GST) fusion protein in E. coli DH5$\alpha$. The receptor fusion protein was identified by SDS-PAGE and Western blot using monoclonal anti-GST antibody. The fusion protein expressed in this study was purified to an apparent homogeneity by glutathione Sepharose CL-4B affinity chromatography. The purified i3 loop fusion proteins at a concentration of 10 $\mu\textrm{g}$/ι caused right shift of the isoproterenol competition curve of [$^3$H]Dihydroalprenolol binding to hamster lung $\beta$$_2$-adrenergic receptor indicating lowered affinity of isoproterenol to $\beta$$_2$-adrenergic receptor possibly due to the uncoupling of receptor and G protein in the presence of the fusion protein. The uncoupling of receptor and G protein suggests that i3 loop region plays a critical role on $\beta$$_2$-adrenergic receptor G protein coupling.

• 한국미생물·생명공학회지
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• 제20권2호
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• pp.122-128
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• 1992

Runaway replciation plasmid pSY35AT를 이용하여 이 plasmid가 가지고 있는 $cI_{857}$ repressor를 숙주 MC1065를 이용하여 $30^{\circ}C$에서 $37^{\circ}C$로 온도를 올려 shift-up 법으로 생산하였다. $cI_{857}$은 wild type cI repressor 정제법을 수정하여 사용하였으며 정제된 $cI_{857}$ repressor 농도는 0.11mg/ml이었다. 그리고 정제된 $cI_{857}$ repressor와 $^3H-CTP$로 labelinf한 PrOr과의 결합활성은 cell 파쇄액보다 약 23배 높았으며, 온도가 올라갈수록 결합활성은 떨어졌다.

• 천문학회보
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• 제36권2호
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• pp.89.1-89.1
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• 2011

We have investigated a coronal jet near the limb on 2010 June 27 by Hinode/X-Ray Telescope (XRT), EUV Imaging Spectrograph (EIS), Solar Optical Telescope (SOT), SDO/Atmospheric Imaging Assembly (AIA), and STEREO. From EUV (AIA and EIS) and soft X-ray (XRT) images we identify the erupting jet feature in cool and hot temperatures. It is noted that there was a small loop eruption in Ca II images of the SOT before the jet eruption. Using high temporal and multi wavelength AIA images, we found that the hot jet preceded its associated cool jet. The jet also shows helical-like structures during the rising period. According to the spectroscopic analysis, the jet structure changes from blue shift to red one with time, implying the helical structure of the jet. The STEREO observation, which enables us to observe this jet on the disk, shows that there was a dim loop associated with the jet. Comparing the observations from the AIA and STEREO, the dim loop corresponds to the jet structure which implies the heated loop. Considering that the structure of its associated active region seen in STEREO is similar to that in AIA observed 5 days before, we compared the jet morphology on the limb with the magnetic fields extrapolated from a HMI vector magnetogram observed on the disk. Interestingly, the comparison shows that the open field corresponds to the jet which is seen as the dim loop in STEREO. Our observations (XRT, SDO, SOT, and STEREO) are well consistent with the numerical simulation of the emerging flux reconnection model.

• Steel and Composite Structures
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• 제19권3호
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• pp.569-583
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• 2015

Recent years, explosive welding structures have been widely used in many engineering fields. The bonding state detection of explosive welding structures is significant to prevent unscheduled failures and even catastrophic accidents. However, this task still faces challenges due to the complexity of the bonding interface. In this paper, a new method called dual-tree complex wavelet transform based permutation entropy (DTCWT-PE) is proposed to detect bonding state of such structures. Benefiting from the complex analytical wavelet function, the dual-tree complex wavelet transform (DTCWT) has better shift invariance and reduced spectral aliasing compared with the traditional wavelet transform. All those characters are good for characterizing the vibration response signals. Furthermore, as a statistical measure, permutation entropy (PE) quantifies the complexity of non-stationary signals through phase space reconstruction, and thus it can be used as a viable tool to detect the change of bonding state. In order to more accurate identification and detection of bonding state, PE values derived from DTCWT coefficients are proposed to extract the state information from the vibration response signal of explosive welding structure, and then the extracted PE values serve as input vectors of support vector machine (SVM) to identify the bonding state of the structure. The experiments on bonding state detection of explosive welding pipes are presented to illustrate the feasibility and effectiveness of the proposed method.

• Bulletin of the Korean Chemical Society
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• 제25권3호
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• pp.382-388
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• 2004

The sizes and structures of micelles formed in aqueous solutions of cationic octadecyl trimethyl ammonium chloride (OTAC) and anionic ammonium dodecyl sulfate (ADS) surfactants were investigated using smallangle neutron scattering (SANS), self-diffusion coefficients by pulsed-gradient spin-echo (PGSE) NMR, and dynamic light scattering (DLS) methods. SANS and DLS data indicate that their structures are spherical at concentrations as high as 300 mM. As the total surfactant concentration increases, the peaks of SANS spectra shift to higher scattering vector and become sharper, indicating that the intermicellar distance decreases and its distribution becomes narrower. This is due to more compact packing of surfactant molecules at high concentrations. The intermicellar distance of around 100 ${\AA}$ above 200 mM corresponds approximately to the diameter of one micelle. The sizes of spherical micelles are 61 ${\AA}$ and 41 ${\AA}$ for 9 mM OTAC and 10 mM ADS, respectively. Also the self-diffusion coefficients by PGSE-NMR yield the apparent sizes 96 ${\AA}$ and 31 ${\AA}$ for micelles of 1 mM OTAC and 10 mM ADS, respectively. For ADS solutions of high concentrations (100-300 mM), DLS data show that the micelle size remains constant at $25{\pm}2{\AA}$. This indicates that the transition in micellar shape does not take place up to 300 mM, which is consistent with the SANS results.

• 한국통신학회논문지
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• 제34권9C호
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• pp.871-879
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• 2009

본 논문에서는 코드분할다중접속(CDMA)시스템의 기지국에 설치할 수 있는 시공간 배열 스마트안테나의 성능이 다중경로 환경의 다양한 파라미터의 변화에 어떻게 반응하는지 살펴보았다. 이 논문에서 사용된 스마트안테나는 공간분산구조로 적응배열안테나를, 시간분산구조로 다중경로의 숫자에 따른 finger 의 레이크 수신기로 구성되었으며 빔형성 방법으로 Decision Directed LMS 방법을 사용한다. 스마트안테나는 다중경로 신호의 입사각분산의 정도와 다중경로(finger)의 숫자에 민감하게 반응하는 한편 지연분산의 정도와는 거의 반응하지 않음이 컴퓨터 모의실험으로 증명되었다. 레이크 수신기의 핑거 숫자의 증가가 스마트안테나의 성능을 비선행적으로 개선시키는 효과를 수반함 역시 모의실험으로 보여졌다. 본 논문의 빔형성 과정에서 가중치벡터의 갱신은 PN상관기가 수신신호를 역확산시킨 후에 이루어진다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제1권1호
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• pp.11-20
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• 2012

스트림 데이터는 시간에 따라 연속적으로 변화하는 일련의 값들로 나타난다. 이러한 스트림 데이터의 특성상 다양한 시간 간격의 기준에 따라 계속적으로 그 동향이 달라질 수 있다. 이 때문에 스트림 데이터의 추세 예측은 간격이 갱신될 때 마다 연속적인 환경에서 여러 간격들을 기준으로 동시에 이루어지는 연속 다중 예측(Continuous Multiple Prediction, CMP)이 지원되어야 한다. 본 논문은 스트림 데이터의 연속 다중 예측을 효과적으로 지원하기 위하여, 신피질 학습 모델인 계층형 시간적 메모리(Hierarchical Temporal Memory, HTM) 모델을 확장하여 연속통합 HTM(Continuous Integrated HTM, CIHTM) 네트워크를 제안한다. 이를 위해 우리는 HTM 네트워크를 구성하는 기존 노드들 외에 새롭게 이동 벡터 파일 센서, 시공간 분류 노드, 다중 통합 노드를 고안하였다. 그리고 이들을 바탕으로 CIHTM 네트워크의 학습과 추론 알고리즘을 개발하였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권1호
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• pp.19-26
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• 2019

본 논문에서는 기타 타브 악보에서 추출한 프렛 번호를 대상으로 학습 알고리즘의 분류 성능을 비교한다. 타브 악보로부터 세그먼트를 통해 추출된 타브 숫자 데이터는 타브 선과 악보 기호가 포함하기 때문에 레이블링 기법과 비선형 필터를 이용하여 프렛 숫자를 추출한다. 추가적인 데이터 확보를 위해 전처리가 수행된 데이터에 대해 4 방향으로 이동 연산을 수행한다. 선택된 학습 모델은 베이지안 분류기, 지지벡터기기, 프로토타입 기반 학습, 다층 신경망 그리고 합성곱 신경망 모델 등이다. 실험 결과 베이지안 분류기는 85.0% 평균 정확도를 보였고 나머지 분류기는 99.0% 이상의 평균 정확도를 보였다. 일반화 성능과 전처리 단계를 고려 시 합성곱 신경망이 다른 학습 모델들보다 우수하다.

• 정보보호학회논문지
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• 제12권3호
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• pp.49-59
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• 2002

이동 통신에서의 보안은 전자 거래가 급증함에 따라 더욱 중요하게 되었다. 무엇보다도 이동 통신 환경에 적합한 인증 및 키 합의는 보안의 필수 조건이다. 이를 위하여 Diffie-Hellman, EIGamal 등의 공개키 암호 시스템을 기반으로 하는 프로토콜이 제안되었으며, 이들은 대수학의 기반 아래 이산 대수 문제 어려움을 바탕으로 이뤄지는데, 연산 속도가 느리고 키 길이가 길어 이동 통신 환경에 적용하기에는 많은 제약점이 있다. 본 논문에서는 이동 통신 환경의 제약점인 제한된 자원들, 제한된 계산력, 제한된 대역폭을 극복할 수 있는 NTRU 기반의 인증 및 키 합의 프로토콜을 제안한다. 이는 잘려진 다항식 환(truncated Polynomial ing)에서 작은 수의 덧셈과 쉬프트 연산만 행하기 때문에 속도가 빠르며 키 생성이 용이하고 쉽다. 또한 NTRU 래티스 상에서의 짧은 벡터 찾는 어려운 문제(SVP/CVP)로 인해 보안성이 강하여 안전하다.

• Journal of Radiation Protection and Research
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• 제46권4호
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.