• 한국문헌정보학회지
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• 제50권2호
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• pp.309-336
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• 2016

본 논문에서는 지지벡터기계(Support Vector Machines, SVM) 기반의 기계 학습 모듈을 활용하여 특정 문장 내에서의 두 개체 간의 관계를 자동으로 식별하고 분류하는 바이오 분야 관계 추출 시스템을 제안한다. 제안된 시스템의 특징은 개체를 포함하고 있는 문장 내에서 풍부한 언어 자질을 추출하여 학습에 활용함으로써 그 성능을 극대화할 수 있는 다양한 기능들을 포함하고 있다는 점이다. 제안된 시스템의 성능 측정을 위해서 전 세계적으로 많이 활용되고 있는 바이오 분야 관계 추출 표준 컬렉션 3가지를 활용하여 심층적인 실험을 수행한 결과 모든 컬렉션에서 높은 성능을 획득하여 그 우수성을 입증하였다. 결론적으로, 본 논문에서 수행한 바이오 분야 관계 추출에 대한 광범위하고 심층적인 실험 연구가 향후 기계학습 기반의 바이오 분야 텍스트 분석 연구에 많은 시사점을 제공할 것으로 보인다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.652-655
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• 1997

An exploration for an intelligence paradigm has been delineated. Artificial intelligence and artificial life paradigms seem to fail to show the whole picture of human intelligence. We may understand the human intelligence better by adding the emotional part of human intelligence to the intellectual part of human intelligence. Emotional intelligence is investigated in terms of composing machine as a modern abstract art. Various algorithmic composition and performance concepts are currently being investigated and implemented. Intelligent mapping algorithms restructure the traditional predetermined composition algorithms. Music based on fractals and neural networks is being composed. Also, emotional intelligence and aesthetic aspects of Korean traditional music are investigated in terms of fractal relationship. As a result, this exploration will greatly broaden the potentials of the intelligence research. The exploration of art in the view of intelligence, information and structure will restore the balanced sense, of art and science which seeks happiness in life. The investigations of emotional intelligence will establish the foundations of intelligence, information and control technologies.

• 한국지반공학회지:지반
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• 제10권3호
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• pp.5-16
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• 1994

본 논문은 향후 현장에서 유용하게 사용될 수 있는 사질토층에 있어서 정방형 얕은 기초의 지지력 향상을 위한 새로운 Geogrid 보강토 방법을 제시하고자 하였다. 기계 기초, 철로 제방, 그리고 지진 예상 지역의 구조물 기초 등에 대한 지반은 Geogrid로 보강하는 것이 필수적이다. 보강되지 않은 사질토층과 보강된 사질토층에 대한 지지력을 비교하였으며 또한 극한 지지력을 증대시키기 위해 보강재의 길이, 설치 간격 및 폭을 평가하였다. 모형 실험 결과 Geogrid보강재는 중간 정도의 밀도를 가진 사질토층에서 극한 지지력이 상당량 증가됨을 알 수 있었다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제28권1호
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• pp.1-21
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• 2024

This review comprehensively explores the evolution, theoretical underpinnings, variations, and applications of diffusion models. Originating as a generative framework, diffusion models have rapidly ascended to the forefront of machine learning research, owing to their exceptional capability, stability, and versatility. We dissect the core principles driving diffusion processes, elucidating their mathematical foundations and the mechanisms by which they iteratively refine noise into structured data. We highlight pivotal advancements and the integration of auxiliary techniques that have significantly enhanced their efficiency and stability. Variants such as bridges that broaden the applicability of diffusion models to wider domains are introduced. We put special emphasis on the ability of diffusion models as a crucial foundation model, with modalities ranging from image, 3D assets, and video. The role of diffusion models as a general foundation model leads to its versatility in many of the downstream tasks such as solving inverse problems and image editing. Through this review, we aim to provide a thorough and accessible compendium for both newcomers and seasoned researchers in the field.

• Geomechanics and Engineering
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• 제31권2호
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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.

• 한국지반공학회논문집
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• 제36권11호
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• pp.157-165
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• 2020

현재 국내외에서는 진동기계기초에 진동으로 발생하는 추가 연직 동하중을 산정하고 설계함에 있어서 명확하게 제시된 기준이나 이론이 정립되어 있지 않아 국내의 경우, 심각한 진동조건이 아님에도 불구하고 진동에 의한 추가 동하중을 정적하중의 최대 100%로 간주하는 극히 보수적인 설계가 이루어지고 있다. 본 연구의 목적은 연직 기계진동으로 인하여 정하중외에 추가적으로 발생하는 동적하중의 정량적 크기를 평가하기 위한 모형실험 방안을 제시하는 것이다. 실내 모형실험의 기초실험으로, 제한된 크기의 모형 토조 내에서 발생할 수 있는 진동 반사파의 영향을 분석 및 보완하였고, 제작한 모형 진동기계기초의 고유진동수를 산정하여 실험 시 공진영향을 최소화하였다. 제안된 기법을 적용한 본 모형실험을 수행하여, 중간 조밀도의 모래 기초지반에 놓인 기계진동 얕은 기초에 기계진동에 의해 발생하는 추가적인 동하중의 정량적 크기를 평가해 보았다. 모형실험결과로부터, 현재 국내외에서 제시하고 있는 설계기법의 적합성을 논의해 보았다.

• Earthquakes and Structures
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• 제11권1호
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• pp.83-107
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• 2016

In this study, the response and behavior of machine foundations resting on dry and saturated sand was investigated experimentally. A physical model was manufactured to simulate steady state harmonic load applied on a footing resting on sandy soil at different operating frequencies. Total of (84) physical models were performed. The parameters that were taken into consideration include loading frequency, size of footing and different soil conditions. The footing parameters are related to the size of the rectangular footing and depth of embedment. Two sizes of rectangular steel model footing were used. The footings were tested by changing all parameters at the surface and at 50 mm depth below model surface. Meanwhile, the investigated parameters of the soil condition include dry and saturated sand for two relative densities; 30 % and 80 %. The dynamic loading was applied at different operating frequencies. The response of the footing was elaborated by measuring the amplitude of displacement using the vibration meter. The response of the soil to dynamic loading includes measuring the stresses inside soil media by using piezoelectric sensors. It was concluded that the final settlement (St) of the foundation increases with increasing the amplitude of dynamic force, operating frequency and degree of saturation. Meanwhile, it decreases with increasing the relative density of sand, modulus of elasticity and embedding inside soils. The maximum displacement amplitude exhibits its maximum value at the resonance frequency, which is found to be about 33.34 to 41.67 Hz. In general, embedment of footing in sandy soils leads to a beneficial reduction in dynamic response (displacement and excess pore water pressure) for all soil types in different percentages accompanied by an increase in soil strength.

• 지식경영연구
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• 제18권1호
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• pp.67-81
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• 2017

As the important breakthrough continues in the field of machine learning and artificial intelligence recently, there has been a growing interest in the analysis and the utilization of the big data which constitutes a foundation for the field. In this background, while the economic value of the data held by the corporates and public institutions is well recognized, the research on the evaluation of its economic value is still insufficient. Therefore, in this study, as a part of the economic value evaluation of the data, we have conducted the economic value measurement of the data generated through the small business guarantee program of Korean Federation of Credit Guarantee Foundations (KOREG). To this end, by examining the previous research related to the economic value measurement of the data and intangible assets at home and abroad, we established the evaluation methods and conducted the empirical analysis. For the data value measurements in this paper, we used 'cost-based approach', 'revenue-based approach', and 'market-based approach'. In order to secure the reliability of the measured result of economic values generated through each approach, we conducted expert verification with the employees. Also, we derived the major considerations and issues in regards to the economic value measurement of the data. These will be able to contribute to the empirical methods for economic value measurement of the data in the future.

• Geomechanics and Engineering
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• 제32권3호
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• pp.271-291
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• 2023

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S_m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated the S_m, with an R² of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulated S_m. The model's results outperformed those of ANFIS - PSO, and COM - RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommended COM - RF was the outperformed approach in the forecasting process of S_m of shallow foundation, while other techniques were also reliable.

• Geomechanics and Engineering
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• 재33권6호
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• pp.611-624
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• 2023

Soil properties make it attractive as a building material due to its mechanical strength, aesthetically appearance, plasticity, and low cost. However, it is frequently necessary to improve and stabilize the soil mechanical properties with binders. Soil-cement is applied for purposes ranging from housing to dams, roads and foundations. Unconfined compression strength (UCS) and split tensile strength (CD) are essential mechanical parameters for ascertaining the aptitude of soil-cement for a given application. However, quantifying these parameters requires specimen preparation, testing, and several weeks. Methodologies that allowed accurate estimation of mechanical parameters in shorter time would represent an important advance in order to ensure shorter deliverable timeline and reduce the amount of laboratory work. In this work, an extensive campaign of UCS and CD tests was carried out in a sandy soil from the Leiria region (Portugal). Then, using the machine learning tool Neural Pattern Recognition of the MATLAB software, a prediction of these two parameters based on six input parameters was made. The results, especially those obtained with resource to a Bayesian regularization-backpropagation algorithm, are frankly positive, with a forecast success percentage over 90% and very low root mean square error (RMSE).