• Nuclear Engineering and Technology
• /
• 제16권1호
• /
• pp.21-28
• /
• 1984

수치확산을 포함한 truncation오차의 줄임은 수치해석의 중요한 과제가 되어왔다. Stream line방법이 교차수치 확산과 비확산형의 truncation 오차를 제거하기 위하여 고안되었다. 또한, stream line방법과 유한 차분법이 합쳐진 2단계 stream line방법이 비압축성 난류유동의 지배 방정식을 풀기 위하여 고안되었다. 이 방법은 유한 차분법과 비교되었으며, 두 방법 모두 실험자료와 비교되었다. 그리고, 두 방법의 truncation 오차를 비교하기 위하여 truncation 오차 분석이 행해졌다

• 문화기술의 융합
• /
• 제9권6호
• /
• pp.251-256
• /
• 2023

국제 경유 가격은 산업, 교통 및 에너지 생산과 같은 여러 분야에서 중요한 역할을 수행하며, 세계 경제와 국제 무역에도 큰 영향을 미친다. 특히, 국제 경유 가격의 상승은 소비자에게 부담을 주고 인플레이션의 원인이 될 수있다. 그러나 기존 연구들은 주로 휘발유에 초점을 맞추어 진행되었다. 따라서 본 연구는 국제 경유 가격 예측 모델을 제안하고자 한다. 이를 위해 다양한 세계 경제 지표들을 활용하여 머신러닝 방법론 중 하나인 선형 회귀 모델로 학습한다. 해당 모델은 세계 경제 지표들과 국제 경유 가격 간의 관계를 명확하게 파악함과 동시에 높은 정확도로 예측한다. 이는 시장 변화를 비롯한 전반적인 경제 흐름 파악에 도움이 될 것으로 기대된다.

• Journal of information and communication convergence engineering
• /
• 제21권2호
• /
• pp.117-129
• /
• 2023

High-utility itemset mining (HIUM) has emerged as a key data-mining paradigm for object-of-interest identification and recommendation systems that serve as frequent itemset identification tools, product or service recommendation systems, etc. Recently, it has gained widespread attention owing to its increasing role in business intelligence, top-N recommendation, and other enterprise solutions. Despite the increasing significance and the inability to provide swift and more accurate predictions, most at-hand solutions, including frequent itemset mining, HUIM, and high average- and fast high-utility itemset mining, are limited to coping with real-time enterprise demands. Moreover, complex computations and high memory exhaustion limit their scalability as enterprise solutions. To address these limitations, this study proposes a model to extract high-utility frequent closed itemsets based on an improved cumulative summary list structure (CSLFC-HUIM) to reduce an optimal set of candidate items in the search space. Moreover, it employs the lift score as the minimum threshold, called the cumulative utility threshold, to prune the search space optimal set of itemsets in a nested-list structure that improves computational time, costs, and memory exhaustion. Simulations over different datasets revealed that the proposed CSLFC-HUIM model outperforms other existing methods, such as closed- and frequent closed-HUIM variants, in terms of execution time and memory consumption, making it suitable for different mined items and allied intelligence of business goals.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.150-155
• /
• 2003

We discussed a quantitative structure-activity relationships (QSAR) technique on incomplete data set. We proposed a new solver that used 2 kinds of multi-layer neural networks. One is to compensate the defect data, and another is to evaluate the QSAR. The solver can predict the defects in model QSAR data. By using them, we get very high precision QSAR. It is 5-10 times higher than that of a traditional method. However, in case of anti-cancer Carboquone, the prediction is not so complete. It was about O(3) wrong than the model calculation. The predicted values would have rather large error. It is caused by noisy observations of Carboquone. However, if we used the uncertain predictions, new data are included in QSAR. If not, they were omitted. The effect would not be little. Therefore, we evaluated the QSAR. The results are contrary to the expectation, are not so wrong. We believe that the wrong effect is suppressed by including information of new data.

• Imaging Science in Dentistry
• /
• 제53권4호
• /
• pp.283-289
• /
• 2023

The apnea-hypopnea index is widely regarded as a measure of the severity of obstructive sleep apnea (OSA), a condition characterized by recurrent episodes of apnea or hypopnea during sleep that induce airway collapse. OSA is a catastrophic problem due to the wide range of health issues it can cause, including cardiovascular disease and memory loss. This review was conducted to clarify the roles of various imaging modalities, particularly cone-beam computed tomography (CBCT), in the diagnosis of and preoperative planning for OSA. Unfortunately, 2-dimensional imaging techniques yield insufficient data for a comprehensive diagnosis, given the complex anatomy of the airway. Three-dimensional (3D) imaging is favored as it more accurately represents the patient's airway structure. Although computed tomography and magnetic resonance imaging can depict the actual 3D airway architecture, their use is limited by factors such as high radiation dose and noise associated with the scans. This review indicates that CBCT is a low-radiation imaging technique that can be used to incidentally identify patients with OSA, thereby facilitating early referral and ultimately enhancing the accuracy of surgical outcome predictions.

• Journal of Forest and Environmental Science
• /
• 제36권1호
• /
• pp.7-16
• /
• 2020

Tree size distribution modelling is an integral part of forest management. Most distribution yield systems rely on some flexible probability models. In this study, a simple finite mixture of two components two-parameter Weibull distribution was compared with complex four-parameter distributions in terms of their fitness to predict tree size distribution of teak (Tectona grandis Linn f) plantations. Also, a system of equation was developed using Seemingly Unrelated Regression wherein the size distributions of the stand were predicted. Generalized beta, Johnson's SB, Logit-Logistic and generalized Weibull distributions were the four-parameter distributions considered. The Kolmogorov-Smirnov test and negative log-likelihood value were used to assess the distributions. The results show that the simple finite mixture outperformed the four-parameter distributions especially in stands that are bimodal and heavily skewed. Twelve models were developed in the system of equation-one for predicting mean diameter, seven for predicting percentiles and four for predicting the parameters of the finite mixture distribution. Predictions from the system of equation are reasonable and compare well with observed distributions of the stand. This simplified mixture would allow for wider application in distribution modelling and can also be integrated as component model in stand density management diagram.

• Journal of Microbiology and Biotechnology
• /
• 제15권6호
• /
• pp.1323-1329
• /
• 2005

A mathematical model was developed for predicting the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) as a function of combined effects of temperature, pH, and NaCl. The TSB containing four different concentrations of NaCl (2, 4, 5, and $10\%$) was initially adjusted to six different pH levels (pH 5, 6, 7, 8, 9, and 10) and incubated at 4, 10, 25, or 37$^{circ}C$. In all experimental variables, the primary growth curves were well fitted ($r^{2}$=0.982 to 0.998) to a Gompertz equation to obtain the lag time (LT) and specific growth rate (SGR). Surface response models were identified as appropriate secondary models for LT and SGR on the basis of coefficient determination ($r^{2}$=0.907 for LT, 0.964 for SGR), mean square error (MSE=3.389 for LT, 0.018 for SGR), bias factor ($B_{1}$B,=0.706 for LT, 0.836 for SGR), and accuracy factor ($A_{f}$=1.567 for LT, 1.213 for SGR). Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on both LT and SGR for L. monocytogenes in TSB.

• Nuclear Engineering and Technology
• /
• 제51권2호
• /
• pp.384-393
• /
• 2019

An experimental validation of a nuclear forensics methodology for the source reactor-type discrimination of separated weapons-useable plutonium is presented. The methodology uses measured values of intra-element isotope ratios of plutonium and fission product contaminants. MCNP radiation transport codes were used for various reactor core modeling and fuel burnup simulations. A reactor-dependent library of intra-element isotope ratio values as a function of burnup and time since irradiation was created from the simulation results. The experimental validation of the methodology was achieved by performing two low-burnup experimental irradiations, resulting in distinct fuel samples containing sub-milligram quantities of weapons-useable plutonium. The irradiated samples were subjected to gamma and mass spectrometry to measure several intra-element isotope ratios. For each reactor in the library, a maximum likelihood calculation was utilized to compare the measured and simulated intra-element isotope ratio values, producing a likelihood value which is proportional to the probability of observing the measured ratio values, given a particular reactor in the library. The measured intra-element isotope ratio values of both irradiated samples and its comparison with the simulation predictions using maximum likelihood analyses are presented. The analyses validate the nuclear forensics methodology developed.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
• /
• pp.313-315
• /
• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• 한국환경과학회지
• /
• 제31권7호
• /
• pp.555-567
• /
• 2022

With the introduction of the tele-monitoring system (TMS) in South Korea, monitoring of the concentration of pollutants discharged from nationwide water quality TMS attachments is possible. In addition, the Ministry of Environment is implementing a smart sewage system program that combines ICT technology with wastewater treatment plants. Thus, many institutions are adopting the automatic operation technique which uses process operation factors and TMS data of sewage treatment plants. As a part of the preliminary study, a multilayer perceptron (MLP) analysis method was applied to TMS data to identify predictability degree. TMS data were designated as independent variables, and each pollutant was considered as an independent variables. To verify the validity of the prediction, root mean square error analysis was conducted. TMS data from two public sewage treatment plants in Chungnam were used. The values of RMSE in SS, T-N, and COD predictions (excluding T-P) in treatment plant A showed an error range of 10%, and in the case of treatment plant B, all items showed an error exceeding 20%. If the total amount of data used MLP analysis increases, the predictability of MLP analysis is expected to increase further.