• 한국정보통신학회논문지
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• 제26권6호
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• pp.842-849
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• 2022

딥러닝 기술의 발달로 무인 자동차, 드론, 로봇 등의 임베디드 시스템 분야에서 DNN을 활용하는 사례가 많아지고 있다. 대표적으로 자율주행 시스템의 경우 정확도가 높고 연산량이 큰 몇 개의 DNN들을 동시에 수행하는 것이 필수적이다. 하지만 상대적으로 낮은 성능을 갖는 임베디드 환경에서 다수의 DNN을 동시에 수행하면 추론에 걸리는 시간이 길어진다. 이러한 현상은 추론 결과에 따른 동작이 제때 이루어지지 않아 비정상적인 기능을 수행하는 문제를 발생시킬 수 있다. 이를 해결하기 위하여 본 논문에서 제안한 솔루션은 먼저 연산량이 큰 DNN에 터커 분해 기법을 적용하여 연산량을 감소시킨다. 그다음으로 DNN 모델들을 GPU 내부에서 은닉층 단위로 최대한 병렬적으로 수행될 수 있게 한다. 실험 결과 DNN의 추론 시간이 제안된 기법을 적용하기 전 대비 최대 75.6% 감소하였다.

• 말소리와 음성과학
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• 제13권3호
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• pp.71-78
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• 2021

인공신경망에 기반한 대부분의 음성 합성 모델은 고음질의 자연스러운 발화를 생성하기 위해 보코더 모델을 사용한다. 보코더 모델은 멜 스펙트로그램 예측 모델과 결합하여 멜 스펙트로그램을 음성으로 변환한다. 그러나 보코더 모델을 사용할 경우에는 많은 양의 컴퓨터 메모리와 훈련 시간이 필요하며, GPU가 제공되지 않는 실제 서비스 환경에서 음성 합성이 오래 걸린다는 단점이 있다. 기존의 선형 스펙트로그램 예측 모델에서는 보코더 모델을 사용하지 않으므로 이 문제가 발생하지 않지만, 대신에 고품질의 음성을 생성하지 못한다. 본 논문은 뉴럴넷 기반 보코더를 사용하지 않으면서도 양질의 음성을 생성하는 Tacotron 2 & Transformer 기반의 선형 스펙트로그램 예측 모델을 제시한다. 본 모델의 성능과 속도 측정 실험을 진행한 결과, 보코더 기반 모델에 비해 성능과 속도 면에서 조금 더 우세한 점을 보였으며, 따라서 고품질의 음성을 빠른 속도로 생성하는 음성 합성 모델 연구의 발판 역할을 할 것으로 기대한다.

• Computers and Concrete
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• 제32권2호
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.1-8
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• 2024

실시간 처리 및 프라이버시 강화를 위해 인공지능 모델을 엣지에서 동작시킬 수 있는 온디바이스 AI 기술이 각광받고 있다. 지능형 사물인터넷 기술이 다양한 산업에 적용되면서 온디바이스 AI 기술을 활용한 서비스가 크게 증가하고 있다. 그러나 일반적인 딥러닝 모델은 추론 및 학습을 위해 많은 연산 자원을 요구하고 있다. 따라서 엣지에 적용되는 경량 기기에서 딥러닝 모델을 동작시키기 위해 양자화나 가지치기와 같은 다양한 경량화 기법들이 적용되어야 한다. 본 논문에서는 다양한 경량화 기법 중 가지치기 기술을 중심으로 엣지 컴퓨팅 기기에서 딥러닝 모델을 경량화하여 적용할 수 있는 방안을 분석한다. 특히, 동적 및 정적 가지치기 기법을 적용하여 경량화된 비전 모델의 추론 속도, 정확도 그리고 메모리 사용량을 시험한다. 논문에서 분석된 내용은 실시간 특성이 중요한 지능형 영상 관제 시스템이나 자율 이동체의 영상 보안 시스템에 적용될 수 있다. 또한 사물인터넷 기술이 적용되는 다양한 서비스와 산업에 더욱 효과적으로 활용될 수 있을 것으로 기대된다.

• Structural Engineering and Mechanics
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• 제56권5호
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• pp.787-796
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• 2015

When the temperature of a structure varies, there is a tendency to produce changes in the shape of the structure. The resulting actions may be of considerable importance in the analysis of the structures having non-prismatic members. The computation of design forces for the non-prismatic beams having symmetrical parabolic haunches (NBSPH) is fairly difficult because of the parabolic change of the cross section. Due to their non-prismatic geometrical configuration, their assessment, particularly the computation of fixed-end horizontal forces and fixed-end moments becomes a complex problem. In this study, the efficiency of the Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) in predicting the design forces and the design moments of the NBSPH due to temperature changes was investigated. Previously obtained finite element analyses results in the literature were used to train and test the ANN and ANFIS models. The performances of the different models were evaluated by comparing the corresponding values of mean squared errors (MSE) and decisive coefficients ($R^2$). In addition to this, the comparison of ANN and ANFIS with traditional methods was made by setting up Linear-regression (LR) model.

• Steel and Composite Structures
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• 제48권5호
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• pp.531-545
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• 2023

Composite dowels are implemented as a powerful alternative to headed studs for the efficient combination of Ultra High-Performance Concrete (UHPC) with high-strength steel in novel composite structures. They are required to provide sufficient shear resistance and ensure the transmission of tensile forces in the composite connection in order to prevent lifting of the concrete slab. In this paper, the load bearing capacity of puzzle-shaped and clothoidal-shaped dowels encased in UHPC specimen were investigated based on validated experimental test data. Considering the influence of the embedment depth and the spacing width of shear dowels, the characteristics of UHPC square plate on the load bearing capacity of composite structure, 240 numeric models have been constructed and analyzed. Three artificial intelligence approaches have been implemented to learn the discipline from collected experimental data and then make prediction, which includes Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Extreme Learning Machine (ELM). Among the factors, the embedment depth of composite dowel is proved to be the most influential parameter on the load bearing capacity. Furthermore, the results of the prediction models reveal that ELM is capable to achieve more accurate prediction.

• Geomechanics and Engineering
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• 제31권3호
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• pp.291-304
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• 2022

Maximum shear modulus (G_max or G₀) is an important soil property useful for many engineering applications, such as the analysis of soil-structure interactions, soil stability, liquefaction evaluation, ground deformation and performance of seismic design. In the current study, bender element (BE) tests are used to evaluate the effect of the void ratio, effective confining pressure, grading characteristics (D₅₀, C_u and C_c), anisotropic consolidation and initial fabric anisotropy produced during specimen preparation on the G_max of sand-gravel mixtures. Based on the tests results, an empirical equation is proposed to predict G_max in granular soils, evaluated by the experimental data. The artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were also applied. Coefficient of determination (R²) and Root Mean Square Error (RMSE) between predicted and measured values of G_max were calculated for the empirical equation, ANN and ANFIS. The results indicate that all methods accuracy is high; however, ANFIS achieves the highest accuracy amongst the presented methods.

• 상하수도학회지
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• 제19권4호
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• 응용통계연구
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• 제21권5호
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• pp.835-843
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• 2008

In this paper, we investigate a Bayesian inference for software reliability models based on mean value functions which take the form of the mixture of beta distribution functions. The posterior simulation via the Markov chain Monte Carlo approach is used to produce estimates of posterior properties. Its applicability is illustrated with two real data sets. We compute the predictive distribution and the marginal likelihood of various models to compare the performance of them. The model comparison results show that the model based on the beta-mixture performs better than other models.

• 로봇학회논문지
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• 제14권2호
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• pp.114-121
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• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.