• Journal of computational fluids engineering
• /
• v.19 no.2
• /
• pp.86-92
• /
• 2014

In this paper, prediction of separation trajectory for Two-stage-To-Orbit space launch vehicle has been numerically simulated by using an aerodynamic database based on steady state analysis. Aerodynamic database were obtained for matrix of longitudinal and vertical positions. The steady flow simulations around the launch vehicle have been made by using a 3-D RANS flow solver based on unstructured meshes. For this purpose, a vertex-centered finite-volume method was adopted to discretize inviscid and viscous fluxes. Roe's finite difference splitting was utilized to discretize the inviscid fluxes, and the viscous fluxes were computed based on central differencing. To validate this flow solver, calculations were made for the wind-tunnel experiment model of the LGBB TSTO vehicle configuration on steady state conditions. Aerodynamic database was constructed by using flow simulations based on test matrix from the wind-tunnel experiment. ANN(Artificial Neural Network) was applied to construct interpolation function among aerodynamic variables. Separation trajectory for TSTO launch vehicle was predicted from 6-DOF equation of motion based on the interpolated function. The result of present separation trajectory calculation was compared with the trajectory using experimental database. The predicted results for the separation trajectory shows fair agreement with reference[4] solution.

• Restorative Dentistry and Endodontics
• /
• v.24 no.3
• /
• pp.447-452
• /
• 1999

The role of bacteria in root canals and periapical infections is well known and established. In these bacteria, black-pigmented bacteria(BPH) play important role in endodontic infection. BPB are Gram negative anaerobic rods which are closely related 50 clinical symptoms such as pain, percussion, tenderness, foul odor, etc. In America and Europe, many studies on BPB have been done and are continued. But, relatively few studies have been done in Korea, especially its prevalence in Korean population is not yet studied. The purpose of this study is to establish prevalence of BPB in infected root canals and periapical abscesses in Korean people. Microbial samples were collected from the root canals of 34 intact tooth with periapical rarefactions of endodontic origin and 3 periapical abscesses. All samples were incubated in an anaerobic chamber(Coy, Model No. 77. Ann Arbor, Michigan, USA.). Identification of In microorganism was based on its growth in the anaerobic chamber, colonial pigmentation, colonial morphology, Gram stain, and Rapid ID32A(BioMericux SA/69280 Marcy-l'Etoile/France) results. In addition, the polyme ase chain reaction using specific primers for 16S rRNA genes were used differentiate Prevotella nigrescens for Prevotella intermedia. The results were as follows : 1. In this study, thirteen (35%) of thirty seven samples were positive for the growth of BPB. In thirteen samples, sixteen strains of BPR were found. 2. The most frequently identified BPB in root canals and abscesses in Korean were P. nigrescens 5/37(14%) and P. intermedia 5/37(14%). Porphyromonas gingivalis 3/37(8%), Porphyromonas endodontalis 2/37(5%) and Prevotella loecheii 1/37(3%) were also found. 3. In this study, no significant differences were found between the prevalence of BPB in Korean and that of American and European.

• Journal of Korean Physical Therapy Science
• /
• v.18 no.4
• /
• pp.47-58
• /
• 2011

The purpose of this study was to analyses the main factors of research papers for related with home physical therapy. This study was retrospective descriptive study, the period of data collection was from 1991 to 2011. The data was collected by the journal related in physical therapy, the dissertation of academic degree, National Assembly Library and the web-site for academic information. In the web-site, searched with the keyword 'home physical therapy' and 'after school voucher'. The results were as follows; 1. In the home physical therapy, visiting physical therapy, school physical therapy, there were different based on laws; home physical therapy was based on medical law, visiting physical therapy was based on law for community health and law for long term health insurance, school physical therapy was based on special education law. 2. The summary of research title/thema from 1991 to 2011 was as follows; for the home and visiting physical therapy 'the needs and necessity of home and visiting physical therapy' was 18 papers, 'the contents of service of home and visiting physical therapy' was 18 papers, 'program and skill development' was 16 papers, 'system developing and induction strategy of home and visiting physical therapy 'was 15 papers, 'costs of nome and visiting physical therapy' was 2 papers, 'perception and information of home and visiting physical therapy' was 9 papers, 'Recoding system and administration management' was 9 papers, 'the others 'was 14 papers, for the school physical therapy 'after school voucher system' was 9 papers, the others was 4 papers. The total papers was 114 papers. 3. Finally suggested 'Model of Educational Program for HomeVisiting and School Physical Therapy'.

• Journal of The Korean Association For Science Education
• /
• v.18 no.4
• /
• pp.617-626
• /
• 1998

Inquiry approach in teaching science has been widespread in Korea since the Third National Curriculum in 1970's. But the effect of the approach has never been evaluated systematically in Korea, so the science educators do not know if the inquiry approach really works and if any critical problems inhibit the effect of the approach. In this context, the NAEP of the Unied States and the APU of the United Kingdom were model programs for assessing the effect of the inquiry approach in teaching science. The purpose of this study is to develop an assessment system for evaluating the effect of inquiry teaching in elementary and secandary schools. For this purpose, an assessment framework and 240 test items were developed and tried with a sample of 8,906 students. The results say that the developed tests are reliable. The average Cronbach $\alpha$ reliability coefficient of the tests was 0.69. The discrimination index(point-biserial correlation coefficient) ranged from 0.39 to 0.54 with a mean of 0.49, which indiate they are excellent in discriminating students in terms of their inquiry achievement. The test items were also analyzed by "item response theory." The results also say that the items are successfully developed.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.24 no.3
• /
• pp.18-25
• /
• 2020

The unconfined compressive strength of lightweight treated soils strongly depends on mixing ratio. To characterize the relation between various LTS components and the unconfined compressive strength of LTS, extensive studies have been conducted, proposing normalized factor using regression models based on their experimental results. However, these results obtained from laboratory experiments do not expect consistent prediction accuracy due to complicated relation between materials and mix proportions. In this study, deep neural network model(Deep-LTS), which was based on experimental test results performed on various mixing conditions, was applied to predict the unconfined compressive strength. It was found that the unconfined compressive strength LTS at a given mixing ratio could be resonable estimated using proposed Deep-LTS.

• Journal of Intelligence and Information Systems
• /
• v.11 no.2
• /
• pp.175-195
• /
• 2005

사례기반추론(case-based reasoning)은 사례간 유사도를 평가하여 유사한 이웃사례를 찾아내고, 이웃사례의 결과를 이용하여 새로운 사례에 대한 예측결과를 생성하는 전통적인 인공지능기법 중 하나다. 이러한 사례기반추론이 최근 적용이 쉽고 간단하다는 장점과 모형의 갱신이 실시간으로 이루어진다는 점 등으로 인해, 온라인 환경에서의 고객관계관리를 위한 도구로 학계와 실무에서 주목을 받고 있다 하지만, 전통적인 사례기반추론의 경우, 타 인공지능기법에 비해 정확도가 상대적으로 크게 떨어진다는 점이 종종 문제점으로 제기되어 왔다. 이에, 본 연구에서는 사례기반추론의 성과를 획기적으로 개선하기 위한 방법으로 유전자 알고리즘을 활용한 사례기반추론의 동시 최적화 모형을 제안하고자 한다. 본 연구가 제안하는 모형에서는 기존 연구에서 사례기반추론의 성과에 중대한 영향을 미치는 요소들로 제시된 바 있는 사례 특징변수의 상대적 가중치 선정(feature weighting)과 참조사례 선정(instance selection)을 유전자 알고리즘을 이용해 최적화함으로서, 사례간 유사도를 보다 정밀하게 도출하는 동시에 추론의 결과를 왜곡할 수 있는 오류사례의 영향을 최소화하고자 하였다. 제안모형의 유용성을 검증하기 위해, 본 연구에서는 국내 한 전문 인터넷 쇼핑몰의 구매예측모형 구축사례에 제안모형을 적용하여 그 성과를 살펴보았다. 그 결과, 제안모형이 지금까지 기존 연구에서 제안된 다른 사례기반추론 개선모형들은 물론, 로지스틱 회귀분석(LOGIT), 다중판별분석(MDA), 인공신경망(ANN), SVM 등 다른 인공지능 기법들에 비해서도 상대적으로 우수한 성과를 도출할 수 있음을 확인할 수 있었다.

• Journal of Korean Society of Water and Wastewater
• /
• v.24 no.1
• /
• pp.85-92
• /
• 2010

In this study, odorous compounds emitted from various wastewater treatment were treated with using the non-thermal plasma reaction, and the effluent gas from the plasma reactor was introduced to a waste sludge reactor to achieve simultaneous sludge reduction. Hydrogen sulfide, the model odorous compound, was removed at 70% using the plasma reaction, and greater than 99% removal efficiency was observed when treated by the sludge reactor. In addition, the sludge reactor showed a high efficiency of ozone removal. As ozone reacted with sludge, oxidation with organic matters took place, and total COD decreased by 50~60% and soluble COD increased gradually. As a result, the integrated process consisting of the non-thermal plasma and the sludge reactor can be successfully applied for the simultaneous treatment of malodorous gas and waste sludge.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.45-45
• /
• 2018

오염물의 혼합거동을 해석하기 위해 물리기반 모델을 이용하는 경우 모델을 구축하고 운용하는데 많은 시간과 재정이 소요되며 현장검증을 통한 검증이 반드시 필요하다. 하지만 데이터 기반 모델의 경우 축적된 데이터만으로도 예측을 수행할 수 있으며 물리기반모델에 비해 결정해야할 입력인자가 적어 모델운용이 용이하다는 장점이 있다. 다양한 데이터 모델 중 인공신경망(ANN) 모델은 데이터가 가지는 불확실성 및 비정상성, 복잡한 상호관련성에 효과적으로 대응할 수 있는 모델로 수자원 및 환경 분야에서 자주 사용되고 있다. 본 연구에서는 인공신경망 모델을 이용하여 지천유입이 있는 대하천의 수질인자 (pH, 전기전도도, DO, chl-a)를 예측하였다. 다른 데이터기반 모델과 같이 인공신경망 모델 또한 수집된 데이터 질에 크게 영향을 받으며, 내부 입력인자의 선택이 모델의 예측 결과에 큰 영향을 미친다. 이러한 인공신경망 모델의 특성을 바탕으로 예측모형의 정확도를 향상하기 위해서는 크게 데이터 처리부분과 모델구축 부분에서의 접근이 필요하다. 본 연구에서는 데이터 처리 과정에서 연구대상지점의 각각의 수질인자가 가지는 분포 특성을 유지하기 위해 층화표츨추출법을 이용하여 데이터를 구성하였다. 모델의 구축 과정에서는 초기가중치 값의 영향을 줄이기 위해 앙상블기법을 사용하였으며, 좀 더 견고하고 정확한 결과를 예측하기 위해 탄력적 역전파알고리즘을 추가하였다. 추가적으로 합류 후 본류의 미 계측지역 수질 예측 정확도 향상을 위해 본류의 수질인자뿐만 아니라 지류의 수질인자를 입력자료로 사용하여 모의를 수행하였다. 또한 동일 구간에서 수행한 현장추적자실험 자료를 이용하여 수질인자의 분포특성을 비교, 검증하였다. 개발된 모델을 이용하여 낙동강과 금호강 합류부 하류의 수질인자를 예측한 결과 지류의 수질인자를 입력자료로 추가한 경우 예측의 정확도가 증가하였으며, 현장실험 자료를 통해 밝혀진 오염물의 거동현상을 인공신경망 모델로도 동일하게 재현하는 것으로 나타났다. 본 연구에서 제안한 인공신경모델을 이용한다면 물리기반 수치모델을 대체하여 지천으로 유입된 오염물의 거동을 정확하고 효율적으로 파악할 수 있을 것이다.

• Membrane and Water Treatment
• /
• v.10 no.1
• /
• pp.1-11
• /
• 2019

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) predicted that recent extreme hydrological events would affect water quality and aggravate various forms of water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed and sunlight) were established using the Representative Concentration Pathways (RCP) 8.5 climate change scenario suggested by the AR5 and calculated the future runoff for each target period (Reference:1989-2015; I: 2016-2040; II: 2041-2070; and III: 2071-2099) using the semi-distributed land use-based runoff processes (SLURP) model. Meteorological factors that affect water quality (precipitation, temperature and runoff) were inputted into the multiple linear regression analysis (MLRA) and artificial neural network (ANN) models to analyze water quality data, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N) and total phosphorus (T-P). Future water quality prediction of the Anseongcheon River basin shows that DO at Gongdo station in the river will drop by 35% in autumn by the end of the $21^{st}$ century and that BOD, COD and SS will increase by 36%, 20% and 42%, respectively. Analysis revealed that the oxygen demand at Dongyeongyo station will decrease by 17% in summer and BOD, COD and SS will increase by 30%, 12% and 17%, respectively. This study suggests that there is a need to continuously monitor the water quality of the Anseongcheon River basin for long-term management. A more reliable prediction of future water quality will be achieved if various social scenarios and climate data are taken into consideration.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.193-202
• /
• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.