• YAKHAK HOEJI
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• v.54 no.5
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• pp.328-333
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• 2010

Comparative molecular field analyses (CoMFA) on the antiproliferative activity of N-substituted (R) 2-amino-5-(2,4-dihydroxyphenyl)-1,3,4-thiadiazole analogues (ADTs: 1-17) against bladder cancer (HCV29T) and rectal cancer (SW707) cells were studied quantitatively. The statistical predictability and fitness of CoMFA A1 model for bladder cancer cells were better than those of CoMFA F1 model for rectal cancer cells and the antiproliferative activity of ADTs depends on steric field (HCV29T: 93.1% & SW707: 83.8%). Also, from the contour maps of optimized CoMFA models, the activity for bladder cancer cells had predicted to increase when sterically favored groups were substituted on meta- and para-position, and sterically disfavored groups were substituted on one ortho-position of phenyl ring. The activity for rectal cancer cells had predicted to increase when sterically favored groups were substituted on para-position, and sterically disfavored groups were substituted on two ortho-position of phenyl ring as R-group.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.704-710
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• 2017

Our research team is developing a 6-DOF manipulator that is adequate for the narrow workspace of press forming processes. This paper addresses the task sequence optimization methods for the manipulator to minimize the task-finishing time. First, a kinematic model of the manipulator is presented, and the anticipated times for moving among the task locations are computed. Then, a mathematical model of the task sequence optimization problem is presented, followed by a comparison of three meta-heuristic methods to solve the optimization problem: an ant colony system, simulated annealing, and a genetic algorithm. The simulation shows that the genetic algorithm is robust to the parameter settings and has the best performance in both minimizing the task-finishing time and the computing time compared to the other methods. Finally, the algorithms were implemented and validated through a simulation using Mathworks' Matlab and Coppelia Robotics' V-REP (virtual robot experimentation platform).

• Korean Journal of Veterinary Research
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• v.35 no.3
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• pp.471-480
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• 1995

The purpose of this experiment was to develop a simple and reliable HPLC method for the detection of ciprofloxacin in chicken serum and to provide a basic data on pharmacokinetic parameters after oral and intramuscular administration. The results obtained were as follows: 1. 0.2% meta-phosphoric acid: acetonitrile(7:3, v/v) solution had a high and regular recovery rates and was selected as an extraction solution. 2. The recovery rates of ciprofloxacin were 83-97% with the selected solution in chicken serum and the detection limit was 50ng/ml in serum. 3. Ka(abosorption rate constant) were 3.652 1/h in fasted group and 0.880 1/h in non-fasted group, and Ke (elimination rate constant) were 0.061 1/h and 0.133 1/h, respectively. 4. The highest concentration in serum after intramuscular injection was 840ng/ml within 15-30min and 160-324ng/ml in 1.1-3.2 hours after oral administration. 5. The time course of blood concentration fits well into a 2 compartment model. 6. On oral administration of ciprofloxacin with feed, ciprofloxacin was absorbed more slowly and the amount of absorbed was smaller than that of in fasted chickens. 7. Blood concentration of ciprofloxacin increased in a dose-dependent manner after intramusclular and oral administraiton.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.247-249
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• 2014

Ubiquitous Sensor Network (USN) environments, sensors and sensor nodes, and coming from heterogeneous sensor networks consist of one another, the characteristics of each component are also very diverse. Thus the sensor and the sensor nodes to interoperability between metadata for a single definition, management is very important. For this, the standard language for modeling sensor SensorML (Sensor Model Language) has. In this paper, sensor devices, sensor nodes and sensor networks for information technology in the application stage XMDR-DAI -based metadata to define the USN. The proposed XMDR-DAI USN based store and retrieve metadata for a method for effectively agent technology. Metadata of the proposed sensor is based SensorML USN environment by maintaining interoperability 50-200 USN middleware or a metadata management system for managing metadata in applications can be utilized directly.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.35-48
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• 2022

In this study, it propose a proof theory method for expressing and reasoning knowledge in a multiagent environment. Since this method determines logical results in a mechanical way, it has developed as a core field from early AI research. However, since the proposition cannot always be proved in any set of closed sentences, in order for the logical result to be determinable, the range of expression is limited to the sentence in the form of a clause. In addition, the resolution principle, a simple and strong reasoning rule applicable only to clause-type sentences, is applied. Also, since the proof theory can be expressed as a meta predicate, it can be extended to the metalogic of the proof theory. Metalogic can be superior in terms of practicality and efficiency based on improved expressive power over epistemic logic of model theory. To prove this, the semantic method of epistemic logic and the metalogic method of proof theory are applied to the Muddy Children problem, respectively. As a result, it prove that the method of expressing and reasoning knowledge and common knowledge using metalogic in a cooperative multiagent environment is more efficient.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.75-77
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• 2021

A variety of search engines are currently in use. The search engine supports the retrieval of data required by users through three stages: crawling, index generation, and output of search results based on meta-tag information. However, a large number of documents obtained by searching for keywords are often unrelated or scarce. Because of these problems, it takes time and effort to grasp the content from the search results and classify the accuracy. The index of search engines is updated periodically, but the criteria for weighted values and update periods are different from one search engine to another. Therefore, this paper uses the LSTM model, which extracts the relationship between keywords entered by the user and documents instead of the existing search engine, and improves the relationship between keywords and documents by entering keywords that the user wants to find.

• Biomolecules & Therapeutics
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• v.31 no.4
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• pp.402-410
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• 2023

Long-term administration of levodopa (L-DOPA) to patients with Parkinson's disease (PD) commonly results in involuntary dyskinetic movements, as is known for L-DOPA-induced dyskinesia (LID). 5-Hydroxytryptophan (5-HTP) has recently been shown to alleviate LID; however, no biochemical alterations to aberrant excitatory conditions have been revealed yet. In the present study, we aimed to confirm its anti-dyskinetic effect and to discover the unknown molecular mechanisms of action of 5-HTP in LID. We made an LID-induced mouse model through chronic L-DOPA treatment to 6-hydroxydopamine-induced hemi-parkinsonian mice and then administered 5-HTP 60 mg/kg for 15 days orally to LID-induced mice. In addition, we performed behavioral tests and analyzed the histological alterations in the lesioned part of the striatum (ST). Our results showed that 5-HTP significantly suppressed all types of dyskinetic movements (axial, limb, orolingual and locomotive) and its effects were similar to those of amantadine, the only approved drug by Food and Drug Administration. Moreover, 5-HTP did not affect the efficacy of L-DOPA on PD motor manifestations. From a molecular perspective, 5-HTP treatment significantly decreased phosphorylated CREB and ΔFosB expression, commonly known as downstream factors, increased in LID conditions. Furthermore, we found that the effects of 5-HTP were not mediated by dopamine1 receptor (D1)/DARPP32/ERK signaling, but regulated by AKT/mTOR/S6K signaling, which showed different mechanisms with amantadine in the denervated ST. Taken together, 5-HTP alleviates LID by regulating the hyperactivated striatal AKT/mTOR/S6K and CREB/ΔFosB signaling.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.91-91
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• 2020

IPCC (Intergovernmental Panel on Climate Change) 5차 평가보고서에 의하면 최근 배출 온실가스의 양은 관측 이래 최고 수준이며 온실가스로 인한 기후변화는 인간계와 자연계에 광범위한 영향을 주고 있다고 보고하였다. 기후변화의 영향은 국제적으로 빙하 감소, 사막화, 해수면 상승 등 뚜렷하게 나타나고 있다. 이러한 기후변화에 대응하기 위해 온실가스 완화 정책과 동시에 새로운 기후변화 환경에 적응하는 것이 필요하다. 기후변화 적응이란 현재 나타나고 있거나 미래에 나타날 것으로 예상되는 기후변화의 파급효과와 영향에 대응할 수 있도록 하는 모든 행동이며 이를 위해서는 기후변화 영향분석이 수반되어야 한다. MOTIVE 연구단에서는 기후변화 적응대책 수립의 지원을 목표로 7개 부문(건강, 물관리, 농업, 산림, 생태, 해양, 수산)에서 "한국형 통합평가 모형"을 개발하고 있다. 각 부문에서 개발하는 프로세스 모델은 시스템에 대한 지식을 가진 상황에서 사용하면 신뢰할 수 있는 예측 결과를 얻을 수 있지만, 부문별 통합을 통한 영향 분석 시 타 분야에 대한 지식이 수반되어야 하는 어려움을 가진다. 이를 위해 본 연구에서는 시스템 내의 물리적 프로세스에 대한 요구 없이 입출력 데이터만을 이용하여 결과를 신속하게 추정하는 데이터 모델링(기계학습)을 이용하였다. 데이터 모델링을 위한 데이터는 다양한 자연 현상에 대한 BANPOL(수질 프로세스 모델) 분석을 통한 자료를 이용하여 학습 자료를 구축하였다. 즉, 데이터 모델링은 BANPOL 모델을 대리하는 메타모델이며, 낙동강 표준유역에 대한 유량 및 수질을 높은 상관성으로 추정하였다. 원 모델보다 정확도는 낮을 수 있으나 메타모델의 개발을 통한 웹 시스템을 개발하여 비전문가의 구동 및 신속한 기후 시나리오를 적용할 수 있는 환경을 개발하였다.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.