• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.10
• /
• pp.995-999
• /
• 2010

Every year, traffic accidents and traffic congestion have been rapidly increasing, Although the roadway design and signal system have been improved to relieve traffic accidents, traffic casualties and property damage do not decrease. This paper develops a real-time traffic accident detection and analysis system (RTADAS): In the proposed system, we aim to precisely detect traffic accidents at different design and flow of intersections, However, because the data collected from intersections have uncertainty and complicated causal dependency between them, we construct probability-based networks for correct accident detection.

• Journal of Internet Computing and Services
• /
• v.19 no.6
• /
• pp.101-112
• /
• 2018

A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

• Steel and Composite Structures
• /
• v.44 no.1
• /
• pp.119-139
• /
• 2022

This paper aims to use the artificial intelligence approach to develop a new model for predicting the ultimate axial strength of the circular concrete-filled steel tubular (CFST) stub columns. For this, the results of 314 experimentally tested circular CFST stub columns were employed in the generation of the design model. Since the influence of the column diameter, steel tube thickness, concrete compressive strength, steel tube yield strength, and column length on the ultimate axial strengths of columns were investigated in these experimental studies, here, in the development of the design model, these variables were taken into account as input parameters. The model was developed using the backpropagation algorithm named Bayesian Regularization. The accuracy, reliability, and consistency of the developed model were evaluated statistically, and also the design formulae given in the codes (EC4, ACI, AS, AIJ, and AISC) and the previous empirical formulations proposed by other researchers were used for the validation and comparison purposes. Based on this evaluation, it can be expressed that the developed design model has a strong and reliable prediction performance with a considerably high coefficient of determination (R-squared) value of 0.9994 and a low average percent error of 4.61. Besides, the sensitivity of the developed model was also monitored in terms of dimensional properties of columns and mechanical characteristics of materials. As a consequence, it can be stated that for the design of the ultimate axial capacity of the circular CFST stub columns, a novel artificial intelligence-based design model with a good and robust prediction performance was proposed herein.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.373-373
• /
• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.292-292
• /
• 2020

방조제는 국토 확장, 수자원 확보 및 배수 개선에 사용되는 구조물로, 재난 발생 시 자산, 국가산업 및 환경에 큰 영향을 끼칠 위험이 있다. 따라서, 파도월류, 지진, 투수, 액상화와 같은 다양한 피해 원인에 대비하여 구조적 사용성과 안정성을 확보하기 위해 신중한 검토 및 분석이 수행된다. 그러나 변화하는 환경조건에서 방조제는 다양한 외력의 변동성과 불확실성에 노출되며, 설계 시 고려된 손상 요인이 개별적으로 발생하기보다는 여러 요인이 복합적으로 반응하고 그 영향이 전달되어 피해의 발생과 전파 과정이 복잡한 양상을 나타낸다. 따라서 방조제에 대한 사고 예방 및 안정적인 유지관리를 위해서는 발생 가능한 위험을 종합적으로 고려한 위험도 평가가 중요하게 요구된다. 본 연구에서는 방조제 손상 원인 중 큰 비중을 차지하는 제체 내부 침식 위험에 대하여 위험인자 간 상호작용을 고려할 수 있는 확률통계학적 접근으로 Bayesian network 기법을 도입하였다. 위험인자에 대한 파괴 메커니즘을 조사하여 분류 후, 설계값과 측정자료를 기반으로 위험변수의 통계적 특성을 반영하기 위해 Monte Carlo 시뮬레이션을 수행하여 파괴 매커니즘의 위험도를 계산하였다. 위험도는 연간기대피해액으로 제공되었으며, 이는 방조제 손상으로 인한 피해에 대비하여 예방할 수 있는 솔루션을 제공할 것으로 기대된다.

• Steel and Composite Structures
• /
• v.47 no.6
• /
• pp.759-779
• /
• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.1
• /
• pp.11-16
• /
• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.5
• /
• pp.227-235
• /
• 2024

Since nuclear power plants (NPPs) increasingly employ digital I&C systems, reliability evaluation for NPP software has become crucial for NPP probabilistic risk assessment. Several methods for estimating software reliability have been proposed, but there is no available tool support for those methods. To support NPP software manufacturers, we propose a reliability measurement tool for NPP software. We designed our tool to provide reliability estimation depending on available qualitative and quantitative information that users can offer. We applied the proposed tool to an industrial reactor protection system to evaluate the functionality of this tool. This tool can considerably facilitate the reliability assessment of NPP software.

• Asia pacific journal of information systems
• /
• v.29 no.4
• /
• pp.771-788
• /
• 2019

Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) show superior performance in text classification than traditional approaches such as Support Vector Machines (SVMs) and Naïve Bayesian approaches. When using CNNs for text classification tasks, word embedding or character embedding is a step to transform words or characters to fixed size vectors before feeding them into convolutional layers. In this paper, we propose a parallel word-level and character-level embedding approach in CNNs for text classification. The proposed approach can capture word-level and character-level patterns concurrently in CNNs. To show the usefulness of proposed approach, we perform experiments with two English and three Korean text datasets. The experimental results show that character-level embedding works better in Korean and word-level embedding performs well in English. Also the experimental results reveal that the proposed approach provides better performance than traditional CNNs with word-level embedding or character-level embedding in both Korean and English documents. From more detail investigation, we find that the proposed approach tends to perform better when there is relatively small amount of data comparing to the traditional embedding approaches.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.5
• /
• pp.923-932
• /
• 2017

In this paper, we propose a GRNN(: Generalized Regression Neural Network) algorithms for new eyes and face recognition identification system to solve the points that need corrective action in accordance with the existing problems of facial movements gaze upon it difficult to identify the user and. Using a Kalman filter structural information elements of a face feature to determine the authenticity of the face was estimated future location using the location information of the current head and the treatment time is relatively fast horizontal and vertical elements of the face using a histogram analysis the detected. And the light obtained by configuring the infrared illuminator pupil effects in real-time detection of the pupil, the pupil tracking was to extract the text print vector. The abstract is to be in fully-justified italicized text as it is here, below the author information.