• Explosives and Blasting
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• v.24 no.1
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• pp.9-19
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• 2006

The objective of this study is to propose curve fitted equations that can facilitate calculations and improve a practical applicability when the seismic performance of buried pipelines needs to be evaluated. The curve fitted equations are derived based on the evaluation of the dynamic responses of concrete pipe with a boundary condition of fixed-free ends. To study the dynamic response of underground pipe, the numerical analysis program developed in the previous research has been used. The location of maximum strain has been determined through dynamic analyses for a boundary condition of fixed-free ends. Then $wavelength{\lambda}$ of 5-1000(m) and propagation velocity(Vs) of 100-2000(m/s) have been applied at the location of maximum strain and the unit srain curve with the changes of the $wavelength{\lambda}$ and propagation velocity(Vs) has been obtaind. Non-linear least-square regression has been used to develop highly applicable curve fitted equations and various types of exponential regression equations have been checked out. Thus curve fitted equations and necessary coefficients with best results are suggested.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.8
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• pp.1165-1171
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• 2010

The OFDM system has better characteristics in transmission rate, power efficiency, bandwidth efficiency, impulse-noise immunity, and narrow band interference immunity etc. in comparison with other conventional systems. However, high PAPR of an OFDM signals causes some serious non-linear processing of RF amplifier. And performance of the communication system gets worse. Therefore, various methods reducing PAPR of an OFDM skills such as the clipping method, block coding method, and phase rotation method etc. have been researched. In this paper, we propose a high-speed adaptive PTS method which eliminates high PAPR. And we compare the proposed method with other conventional methods. The proposed method has decreased quantity of calculation compare with an adaptive PTS method. Of course, The more its calculation amount is decreased, the more its BER characteristic is not better than an adaptive PTS method. However, keeping up satisfactory BER performance, we highly improved calculation amount of a PTS method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.11
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• pp.881-888
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• 2020

In this paper, research on estimation of states of a target of interest using Line Of Sight(LOS) angle measurement is performed. Target's position, velocity, and acceleration are chosen to be the states of interests. The LOS measurement is known to be highly non-linear, making target dynamic modeling hard to be implemented into a filter. To solve this issue, the Pseudomeasurement equation was applied to the LOS measurement equation. With the help of this equation, 3D variable turn target dynamic model is applied to the filter model. For better performance, Kinematic Constraint is also implemented into the filter model. As for the filter, Bias Compensation Pseudomeasurement Filter (BCPMF) is used which is known for its robustness to initial conditions. Moreover, Two-Stage Kalman Filter (TSKF) form was also implemented to benefit from the parallel computation. As a result, TBCPMF 3DVT-KC is proposed and simulated to assess performance.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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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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• v.18 no.7
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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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• v.30 no.5
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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.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2018.06a
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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.

• The Journal of Fisheries Business Administration
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• v.33 no.2
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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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• v.25 no.6
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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%.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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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.