• 화약ㆍ발파
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• 제24권1호
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• pp.9-19
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• 2006

본 연구는 일단고정-일단자유 단부 경계조건을 가진 콘크리트 매설관의 동적응답을 연구하고, 내진성능 평가 시 실용적이고 실무적용성이 높은 곡선 적합식의 개발을 목적으로 한다. 매설관의 동적응답을 연구하기 위해 기존 연구에서 개발된 수치해석 프로그램을 사용하여 일단고정-일단자유 단부 경계조건에 대한 최대변형 률 발생지점을 산정한 후, 산정된 지점에 대하여 5-1000(m) 의 파장${\lambda}$과 100-2000(m/see)의 전파속도 $(V_s)$를 적용하여 파장${\lambda}$의 변화와 전파속도 $(V_s)$의 변화에 따른 단위 (휨) 변형률 곡선식을 산출하였다. 적합성이 높은 곡선 적합식을 개발하기 위해 비선형 최소자승법을 이용하여 다양한 형태의 지수방정식을 검정한 후, 가장 좋은 결과를 나타내는 곡선 적합식과 필요한 계수 값을 제시하였다.

• Journal of Advanced Marine Engineering and Technology
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• 제34권8호
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• pp.1165-1171
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• 2010

OFDM(Orthogonal Frequency Division Multiplexing) 통신 시스템은 단일 반송파를 사용해 정보를 전송하는 대신, 주파수의 직교성을 활용하여 정보 전송률이 낮은 다수의 부반송파로 나누어 전송하므로 주파수 사용 효율과 고속의 데이터 전송에서 우수한 특성을 가지는 기술이다. 그러나 OFDM 신호는 단일 반송파 전송방식에 비하여 PAPR(Peak-to-Average Power Ratio)이 증가하는 문제점이 있다. PAPR이 증가하면 RF 증폭기가 비선형적으로 동작하게 되어 효율이 감소하게 된다. 따라서 OFDM에서는 PAPR을 감쇄시키기 위하여 다양한 기법들이 사용되고 있다. 본 논문에서는 PTS(Partial Transfer Sequence) 기법의 단점인 많은 수의 IFFT(Inverse Fast Fourier Transform) 로 인한 연산의 복잡도가 급격하게 증가하는 부분을 개선하기위해 기존의 PTS 기법을 개선하여 두 개의 임계 레벨을 가지는 PTS 기법을 제안하였다. PAPR 값을 비교 분석한 결과, 기존의 PTS 기법과 근사한 BER(Bit Error Rate) 특성을 유지하면서 연산량을 크게 개선시킬 수 있음을 확인 하였다.

• 한국항공우주학회지
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• 제48권11호
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• pp.881-888
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• 2020

본 논문에서는 관측자가 얻을 수 있는 시선각(LOS) 측정값을 사용하여 관심표적의 상태변수를 추정하는 연구를 수행하였다. 관심상태변수는 표적의 위치, 속도 및 가속도로 설정하였다. 시선각 측정값은 필터에 표적운동모델 적용을 어렵게 하는 비선형성이 강한 측정값이다. 이러한 문제해결을 위해 가측정치 공식(Pseudomeasurement equation)을 사용하여 시선각 측정값 수식을 변경한 후 3D 가변선회(3D Variable Turn) 표적운동모델을 적용하였다. 또한 필터의 성능을 위해 기구학적구속조건(Kinematic Constraint)을 적용하였다. 필터는 초기조건에 강건한 특성을 가진 Bias Compensation Pseudomeasurement Filter (BCPMF)를 사용하였다. 병렬 계산의 이점을 위해 Two Stage Kalman Filter 형태를 추가적으로 적용하였다. 이 기법들을 사용하여 TBCPMF 3DVT-KC 필터를 제안하였고 시뮬레이션을 통해 성능을 확인하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권10호
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• pp.1143-1155
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• 1999

셀룰러 방식의 이동통신 시스템에서 전파의 유효신호 도달범위를 예측하기 위해서는 전파전파 모델을 이용한 예측기법이 주로 사용된다. 그러나, 전파과정에서 주변 지형지물에 의해 발생하는 전파손실은 매우 복잡한 비선형적인 특성을 가지며 수식으로는 정확한 표현이 불가능하다. 본 논문에서는 신경회로망의 함수 근사화 능력을 이용하여 전파손실 예측모델을 생성하는 방법을 제안한다. 즉, 전파손실을 송수신 안테나간의 거리, 송신안테나의 특성, 장애물 투과영향, 회절특성, 도로, 수면에 의한 영향 등과 같은 전파환경 변수들의 함수로 가정하고, 신경회로망 학습을 통하여 함수를 근사화한다. 전파환경 변수들이 신경회로망 입력으로 사용되기 위해서는 3차원 지형도와 벡터지도를 이용하여 전파의 반사, 회절, 산란 등의 물리적인 특성이 고려된 특징 추출을 통해 정량적인 수치들을 계산한다. 이와 같이 얻어진 훈련데이타를 이용한 신경회로망 학습을 통해 전파손실 모델을 완성한다. 이 모델을 이용하여 서울 도심 지역의 실제 서비스 환경에 대한 타 모델과의 비교실험결과를 통해 제안하는 모델의 우수성을 보인다.Abstract In cellular mobile communication systems, wave propagation models are used in most cases to predict cell coverage. The amount of propagation loss induced by the obstacles in the propagation path, however, is a highly non-linear function, which cannot be easily represented mathematically. In this paper, we introduce the method of producing propagation loss prediction models by function approximation using neural networks. In this method, we assume the propagation loss is a function of the relevant parameters such as the distance from the base station antenna, the specification of the transmitter antenna, obstacle profile, diffraction effect, road, and water effect. The values of these parameters are produced from the field measurement data, 3D digital terrain maps, and vector maps as its inputs by a feature extraction process, which takes into account the physical characteristics of electromagnetic waves such as reflection, diffraction and scattering. The values produced are used as the input to the neural network, which are then trained to become the propagation loss prediction model. In the experimental study, we obtain a considerable amount of improvement over COST-231 model in the prediction accuracy using this model.

• Asian-Australasian Journal of Animal Sciences
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• 제18권7호
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• pp.922-926
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• 2005

The present study was aimed to investigate proteome affected by Panax ginseng extracts in chicken muscles. The whole muscle proteins from chicken fed boiled extracts of 0% (control), 1%, 3%, and 5% Panax ginseng in water were separated by two-dimensional electrophoresis (2-DE) gels using immobilized non-linear gradient (pH 3-10) strips. More than 300 protein spots were detected on silver staining gels. Among them, four protein spots were distinctively up-regulated by Panax ginseng treatments and further investigated by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). The obtained MS data were searched against SwissProt database using the Mascot search engine. The up-regulated proteins were finally identified as $\alpha$-tropomyosin (2 spots), triosephosphate isomerase, and one unknown protein. Based on the known functions of the identified proteins, they are highly related to muscle development and enhanced immunity in chickens. These proteins can give valuable information of biochemical roles for Panax ginseng in chicken meats.

• Journal of Microbiology and Biotechnology
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• 제30권5호
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• pp.662-667
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• 2020

Epidermal growth factor receptor (EGFR) mutations are not only genetic markers for diagnosis but also biomarkers of clinical-response against tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC). Among the EGFR mutations, the in-frame deletion mutation in EGFR exon 19 kinase domain (EGFR exon 19-del) is the most frequent mutation, accounting for about 45% of EGFR mutations in NSCLCs. Development of sensitive method for detecting the EGFR mutation is highly required to make a better screening for drug-response in the treatment of NSCLC patients. Here, we developed a fluorometric tandem gene amplification assay for sensitive detection of low-abundance EGFR exon 19-del mutant genomic DNA. The method consists of pre-amplification with PCR, thermal cycling of ligation by Taq ligase, and subsequent rolling circle amplification (RCA). PCR-amplified DNA from genomic DNA samples was used as splint DNA to conjugate both ends of linear padlock DNA, generating circular padlock DNA template for RCA. Long stretches of ssDNA harboring multiple copies of G-quadruplex structure was generated in RCA and detected by thioflavin T (ThT) fluorescence, which is specifically intercalated into the G-quadruplex, emitting strong fluorescence. Sensitivity of tandem gene amplification assay for detection of the EGFR exon 19-del from gDNA was as low as 3.6 pg, and mutant gDNA present in the pooled normal plasma was readily detected as low as 1% fraction. Hence, fluorometric detection of low-abundance EGFR exon 19 deletion mutation using tandem gene amplification may be applicable to clinical diagnosis of NSCLC patients with appropriate TKI treatment.

• 한국표면공학회:학술대회논문집
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• 한국표면공학회 2018년도 춘계학술대회 논문집
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• pp.52-52
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• 2018

Only approximately 30% of fossil fuel energy is used; therefore, it is desirable to utilize the huge amounts of waste energy. Thermoelectric (TE) materials that convert heat into electrical power are a promising energy technology. The TE materials can be formed either as thin films or as bulk semiconductors. Generally, thin-film TE materials have low energy conversion rates due to their thinness compared to that in bulk. However, an advantage of a thin-film TE material is that the efficiency can be smartly engineered by controlling the nanostructure and composition. Especially nanostructured TE thin films are useful for mitigating heating problems in highly integrated microelectronic devices by accurately controlling the temperature. Hence, there is a rising interest in thin-film TE devices. These devices have been extensively investigated. It is demonstrated that transparent amorphous oxide semiconductors (TAOS) can be excellent thermoelectric (TE) materials, since their thermal conductivity (${\kappa}$) through a randomly disordered structure is quite low, while their electrical conductivity and carrier mobility (${\mu}$) are high, compared to crystalline semiconductors through the first-principles calculations and the various measurements for the amorphous In-Zn-O (a-IZO) thin film. The calculated phonon dispersion in a-IZO shows non-linear phonon instability, which can prevent the transport of phonon. The a-IZO was measured to have poor ${\kappa}$ and high electrical conductivity compared to crystalline $In_2O_3:Sn$ (c-ITO). These properties show that the TAOS can be an excellent thin-film transparent TE material. It is suggested that the TAOS can be employed to mitigate the heating problem in the transparent display devices.

• 수산경영론집
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• 제33권2호
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• pp.1-29
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• 2002

The main objective of this research aims at analyzing efficiency of government financial transfers(GFTs) to the Korean fisheries sector, using the Linear Structural Relations model(i.e., LISERL model) and the field survey data. Most policies of GFTs tend to be implemented to protect industries with weak competitive advantages such as infant and/or primary industries. Specific policy instruments include income transfers, government loans with lower interest rates, taxes and the like. Fishing activities are made at a highly changeable natural environment of the ocean with a great amount of risk and uncertainty. Fishing households make their livelihood under the small-scale fisheries. Such fisheries and fishing households have also a relatively weak market power. Because of these fisheries characteristics most coastal states have adopted a variety of government support programs. However, despite such a huge government support, during the past several decades the world fishing communities have seen a tendency of continuous fishereis resource overexploitation. For this resason there have been hot debates over the government support policies for fisheries through OECD, FAO, WTO, and UNEP. In general, policy evaluations tend to be made on the basis of benefit-cost(B/C) analysis. However, the B/C analysis may produce results quite different from real ones primarily due to many unmeasurable effects. Thus, the authors composed simple questionaires and let fishermen, government officials and academic people answer the questions. The survery was made in several ways such as post-mail and personal/group interviews. In recent years, for analysis of policy performances and effectiveness, the LISREL model has often been used, which consists of structural and measurement eqquations. This model has a good advantage of transforming unobservable variables to observable ones so that it helps construct endogenous cause and effect relationships among relevant variables. The evaluation was done from the two aspects: policy results and policy effectiveness. The policy result evaluation showed that there is a need for improvement for policy problem perception and decision-making process, while the policy effect evaluation suggested that the policy goals were successfully achieved and social justice was improved from the perspective of the entire society as well. However, the research results showed that the GFT policies rendered little contrubtion to narrowing down the gap between GFT beneficiaries and non-beneficiaries incomes.

• Steel and Composite Structures
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• 제25권6호
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• pp.663-670
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• 2017

The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2016년도 학술발표회
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• pp.259-259
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• 2016

This study examined Artificial Neurons Networks model (ANNs) for forecast flash discharge at Southern part of Thailand by using rainfall data and discharge data. The Sungai Kolok River Basin has meant the border crossing between Thailand and Malaysia which watershed drains an area lies in Thailand 691.88 square kilometer from over all 2,175 square kilometer. The river originates in mountainous area of Waeng district then flow through Gulf of Thailand at Narathiwat Province, which the river length is approximately 103 kilometers. Almost every year, flooding seems to have increased in frequency and magnitude which is highly non-linear and complicated phenomena. The purpose of this study is to forecast runoff on Sungai Kolok at X.119A gauge station (Sungai Kolok district, Narathiwat province) for 3 days in advance by using Artificial Neural Networks model (ANNs). 3 daily rainfall stations and 2 daily runoff station have been measured by Royal Irrigation Department and Meteorological Department during flood period 2000-2014 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Coefficient of determination ($R^2$). The result of the first day gets the highest accuracy and then decreased in day 2 and day 3, consequently. $R^2$values for first day, second day and third day of runoff forecasting is 0.71, 0.62 and 0.49 respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and real-time operated. In conclusion, the ANNs model is suitable to runoff forecasting during flood incident of Sungai Kolok river because it is straightforward model and require with only a few parameters for simulation.