• Bulletin of the Korean Chemical Society
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• 제23권3호
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• pp.447-453
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• 2002

In this paper we have presented the results of thermodynamic, structural, and dynamic properties of model systems for liquid benzene, toluene and p-xylene in an isobaric-isothermal (NpT) ensemble at 283.15, 303.15, 323.15, and 343.15 K using molecular dynamics (MD) simulation. This work is initiated to compensate for our previous canonical (NVT) ensemble MD simulations [Bull. Kor. Chem. Soc. 2001, 23, 441] for the same systems in which the calculated pressures were too low. The calculated pressures in the NpT ensemble MD simulations are close to 1 atm and the volume of each system increases with increasing temperature. The first and second peaks in the center of mass g(r) diminish gradually and the minima increase as usual for the three liquids as the temperature increases. The three peaks of the site-site gC-C(r) at 283.15 K support the perpendicular structure of nearest neighbors in liquid benzene. Two self-diffusion coefficients of liquid benzene via the Einstein equation and via the Green-Kubo relation are in excellent agreement with the experimental measures. The self-diffusion coefficients of liquid toluene and p-xylene are in accord with the trend that the self-diffusion coefficient decreases with increasing number of methyl group. The friction constants calculated from the force auto-correlation (FAC) function with the assumption that the fast random force correlation ends at time which the FAC has the first negative value give a correct qualitative trends: decrease with increase of temperature and increase with the number of methyl group. The friction constants calculated from the FAC's are always less than those obtained from the friction-diffusion relation which reflects that the random FAC decays slower than the total FAC as described by Kubo [Rep. Prog. Phys. 1966, 29, 255].

• Bulletin of the Korean Chemical Society
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• 제23권3호
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• pp.441-446
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• 2002

We have presented the results of thermodynamic, structural and dynamic properties of liquid benzene, toluene, and p-xylene in canonical (NVT) ensemble at 293.15 K by molecular dynamics (MD) simulations. The molecular model adopted for these molecules is a combination of the rigid body treatment for the benzene ring and an atomistically detailed model for the methyl hydrogen atoms. The calculated pressures are too low in the NVT ensemble MD simulations. The various thermodynamic properties reflect that the intermolecular interactions become stronger as the number of methyl group attached into the benzene ring increases. The pronounced nearest neighbor peak in the center of mass g(r) of liquid benzene at 293.15 K, provides the interpretation that nearest neighbors tend to be perpendicular. Two self-diffusion coefficients of liquid benzene at 293.15 K calculated from MSD and VAC function are in excellent agreement with the experimental measures. The self-diffusion coefficients of liquid toluene also agree well with the experimental ones for toluene in benzene and for toluene in cyclohexane.

• ETRI Journal
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• 제41권3호
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• pp.371-382
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• 2019

Machine reading comprehension is the task of understanding a given context and finding the correct response in that context. A simple recurrent unit (SRU) is a model that solves the vanishing gradient problem in a recurrent neural network (RNN) using a neural gate, such as a gated recurrent unit (GRU) and long short-term memory (LSTM); moreover, it removes the previous hidden state from the input gate to improve the speed compared to GRU and LSTM. A self-matching network, used in R-Net, can have a similar effect to coreference resolution because the self-matching network can obtain context information of a similar meaning by calculating the attention weight for its own RNN sequence. In this paper, we construct a dataset for Korean machine reading comprehension and propose an $S^2-Net$ model that adds a self-matching layer to an encoder RNN using multilayer SRU. The experimental results show that the proposed $S^2-Net$ model has performance of single 68.82% EM and 81.25% F1, and ensemble 70.81% EM, 82.48% F1 in the Korean machine reading comprehension test dataset, and has single 71.30% EM and 80.37% F1 and ensemble 73.29% EM and 81.54% F1 performance in the SQuAD dev dataset.

• E2M - 전기 전자와 첨단 소재
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• 제10권10호
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• pp.1000-1004
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• 1997

In this paper the current-voltage characteristics of a submicron GaAs MESFET is simulated by using the self-consistent ensemble Monte Carlo method. The numerical algorithm employed in solving the two-dimensional Poisson equation is the successive over-relaxation(SOR) method. The total number of employed superparticles is about 1000 and the field adjusting time is 10fs. To obtain the steady-state results the simulation is performed for 10ps at each bias condition. The simulation results show the average electron velocity is modified by the gate voltage.

• 스마트미디어저널
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• 제13권4호
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• pp.9-15
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• 2024

Tree-based algorithms have been the dominant methods used build a prediction model for tabular data. This also includes personal credit data. However, they are limited to compatibility with categorical and numerical data only, and also do not capture information of the relationship between other features. In this work, we proposed an ensemble model using the Transformer architecture that includes text features and harness the self-attention mechanism to tackle the feature relationships limitation. We describe a text formatter module, that converts the original tabular data into sentence data that is fed into FinBERT along with other text features. Furthermore, we employed FT-Transformer that train with the original tabular data. We evaluate this multi-modal approach with two popular tree-based algorithms known as, Random Forest and Extreme Gradient Boosting, XGBoost and TabTransformer. Our proposed method shows superior Default Recall, F1 score and AUC results across two public data sets. Our results are significant for financial institutions to reduce the risk of financial loss regarding defaulters.

• Computers and Concrete
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• 제34권3호
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• pp.279-296
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• 2024

In this study, ensemble machine learning (ML) models are employed to estimate the hardened properties of Self-Compacting Geopolymer Concrete (SCGC). The input variables affecting model development include the content of the SCGC such as the binder material, the age of the specimen, and the ratio of alkaline solution. On the other hand, the output parameters examined includes compressive strength, flexural strength, and split tensile strength. The ensemble machine learning models are trained and validated using a database comprising 396 records compiled from 132 unique mix trials performed in the laboratory. Diverse machine learning techniques, notably K-nearest neighbours (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost), have been employed to construct the models coupled with Bayesian optimisation (BO) for the purpose of hyperparameter tuning. Furthermore, the application of nested cross-validation has been employed in order to mitigate the risk of overfitting. The findings of this study reveal that the BO-XGBoost hybrid model confirms better predictive accuracy in comparison to other models. The R² values for compressive strength, flexural strength, and split tensile strength are 0.9974, 0.9978, and 0.9937, respectively. Additionally, the BO-XGBoost hybrid model exhibits the lowest RMSE values of 0.8712, 0.0773, and 0.0799 for compressive strength, flexural strength, and split tensile strength, respectively. Furthermore, a SHAP dependency analysis was conducted to ascertain the significance of each parameter. It is observed from this study that GGBS, Flyash, and the age of specimens exhibit a substantial level of influence when predicting the strengths of geopolymers.

• Smart Structures and Systems
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• 제32권3호
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• pp.167-177
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• 2023

A probabilistic micromechanical framework is proposed to quantify numerically the self-healing capabilities of polymers containing microcapsules. A two-step self-healing process is designed in this study: A probabilistic micromechanical framework based on the ensemble volume-averaging method is derived for the polymers, and a hitting probability model combined with a crack nucleation model is then utilized for encountering microcapsules and microcracks. Using this framework, a series of parametric investigations are performed to examine the influence of various model parameters (e.g., the volume fraction of microcapsules, microcapsule radius, radius ratio of microcracks to microcapsules, microcrack aspect ratio, and scale parameter) on the self-healing capabilities of the polymers. The proposed framework is also implemented into a finite element code to solve the self-healing behavior of tapered double cantilever beam specimens.

• 지구물리와물리탐사
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• 제26권4호
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• pp.211-228
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• 2023

방조제의 모니터링에는 지구물리학적 비파괴 검사인 GPR (Ground Penetrating Radar) 탐사가 주로 이용된다. GPR 반응은 상황에 따라 복잡한 양상을 보이므로 자료의 처리와 해석은 전문가의 주관적 판단에 의존하며, 이는 오 탐지의 가능성을 불러옴과 동시에 시간이 오래 걸린다는 단점이 있다. 따라서 딥 러닝을 이용하여 GPR 탐사자료의 공동을 탐지하는 다양한 연구들이 수행되고 있다. 딥 러닝 기반 방법은 데이터 기반 방법으로써 풍부한 자료가 필요하나 GPR 탐사의 경우 비용 등의 이유로 학습에 이용할 현장 자료가 부족하다. 따라서 본 논문에서는 데이터 증강 전략을 이용하여 딥 러닝 기반 방조제 GPR 탐사자료 공동 탐지 모델을 개발하였다. 다년간 동일한 방조제에서 탐사 자료를 사용하여 데이터 세트를 구축하였으며, 컴퓨터 비전 분야의 객체 탐지 모델 중 YOLO (You Look Only Once) 모델을 이용하였다. 데이터 증강 전략을 비교 및 분석함으로써 최적의 데이터 증강 전략을 도출하였고, 초기 모델 개발 후 앵커 박스 클러스터링, 전이 학습, 자체 앙상블, 모델 앙상블 기법을 단계적으로 적용하여 최종 모델 도출 후 성능을 평가하였다.

• Bulletin of the Korean Chemical Society
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• 제34권12호
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• pp.3800-3804
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• 2013

In this study, molecular dynamics simulations of SPC/E (extended simple point charge) model have been carried out in the canonical NVT ensemble over the range of temperatures 300 to 550 K with and without Ewald summation. The quaternion method was used for the rotational motion of the rigid water molecule. Radial distribution functions $g_{OO}(r)$, $g_{OH}(r)$, and $g_{HH}(r)$ and self-diffusion coefficients D for SPC/E water were determined at 300-550 K and compared to experimental data. The temperature dependence on the structural and diffusion properties of SPC/E water was discussed.

• Bulletin of the Korean Chemical Society
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• 제24권11호
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• pp.1590-1598
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• 2003

In this paper we have presented the results for viscosity and self-diffusion constants of model systems for four liquid n-alkanes ($C_{12}, C_{20}, C_{32}, and C_{44}$) in a canonical ensemble at several temperatures using molecular dynamics (MD) simulations. The small chains of these n-alkanes are clearly $<{R_{ee}}^2>/6<{R_g}^2>>1$, which leads to the conclusion that the liquid n-alkanes over the whole temperatures considered are far away from the Rouse regime. Calculated viscosity ${\eta}$ and self-diffusion constants D are comparable with experimental results and the temperature dependence of both ${\eta}$ and D is suitably described by the Arrhenius plot. The behavior of both activation energies, $E_{\eta}$ and $E_D$, with increasing chain length indicates that the activation energies approach asymptotic values as n increases to the higher value, which is experimentally observed. Two calculated monomeric friction constants ${\zeta}$ and ${\zeta}_D$ give a correct qualitative trend: decrease with increasing temperature and increase with increasing chain length n. Comparison of the time auto-correlation functions of the end-to-end vector calculated from the Rouse model for n-dodecane ($C_{12}$) at 273 K and for n-tetratetracontane ($C_{44}$) at 473 K with those extracted directly from our MD simulations confirms that the short chain n-alkanes considered in this study are far away from the Rouse regime.