• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.38 no.2
• /
• pp.205-210
• /
• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Korean Chemical Engineering Research
• /
• v.61 no.1
• /
• pp.123-141
• /
• 2023

A sufficient amount of data with quality is needed for training artificial neural networks (ANNs). However, developing ANN models with a small amount of data often appears in engineering fields. This paper presented an ANN model to improve prediction performance of the ammonia emission amount with 83 data. The ammonia emission rate included eleven inputs and two outputs (maximum ammonia loss, N_max and time to reach half of N_max, K_m). Categorical input variables were transformed into multi-dimensional equal-distance variables, and 13 data were added into 66 training data using a generative adversarial network. Hyperparameters (number of layers, number of neurons, and activation function) of ANN were optimized using Gaussian process. Using 17 test data, the previous ANN model (Lim et al., 2007) showed the mean absolute error (MAE) of K_m and N_max to 0.0668 and 0.1860, respectively. The present ANN outperformed the previous model, reducing MAE by 38% and 56%.

• Journal of Korea Water Resources Association
• /
• v.46 no.7
• /
• pp.697-706
• /
• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.79-99
• /
• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.301-306
• /
• 2006

본 연구에서는 강우의 시 공간적 분포의 불규칙한 변동성을 고려한 강우-유출예측을 위해 인공신경망(Artificial Neural Networks: ANNs)의 기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론과 역전파 학습 알고리즘(Back Propagation Algorithm: BPA) 이론을 복합적으로 이용하였다. 기존의 인공신경망 연구에서 야기된 저..갈수기의 유출량에 대한 과대평가, 홍수기의 유출량에 대한 과소평가, 예측값이 선행 유출량의 지속성을 갖는 Persistence 현상을 해결하기 위하여 패턴분류 성능을 지닌 SOM 이론을 도입하여 예측모형의 전처리 과정으로 이용하였다. 이는 기존의 인공신경망 모형이 하나의 모형을 구성하여 유출량의 전 범위에 해당하는 자료를 예측하는 방법을 개선한 것으로 SOM에 의해 패턴이 분류된 강우-유출관계의 각 패턴별 예측모형을 통해 분류된 자료들의 예측을 수행하는 방법이다. 이와 같이 SOM을 강우-유출예측모형의 전처리과정으로 이용함으로서 기존의 인공신경망 연구에서 야기된 현상들을 해결할 수 있었고, 예측력 또한 기존의 인공신경망 모형의 결과에 비해 우수하였다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.2066-2070
• /
• 2008

본 연구는 인공신경망(Artificial Neural Networks: ANNs)기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론을 이용한다. 자기조직화 특성을 이용하여 스스로 학습이 가능하고, 구조상 수행이 빨라 학습 단계에 소요되는 시간을 줄 일 수 있는 장점을 가진 자기조직화 이론을 도입하고, 수질자료 중 전체 유기물의 양을 나타내며 난분해성 물질에 대한 해석이 가능하고 재현성이 탁월한 TOC 와 강우-유출량 자료의 분포적 양상과 특징을 분석하여 예측을 위한 모형화 과정에 기여하고자 한다. 최적의 Map Size와 Map Array 결정을 위해 수집된 강우와 유출량자료 및 TOC 자료에 대해 Garcia의 경험식을 이용하여 Map을 구성하는 단위구조의 총 수(M)를 산정하여 M값에 따른 종방향 및 횡방향 크기를 결정하는 다수의 Map 크기를 검토하고, 또한 Map 배열은 2차원 배열의 사각형배열(Rectangular array)과 육각형배열(Hexagonal array)에 대해서도 복합적으로 검토하여 최적의 특성조건을 결정하여 강우-유출 및 TOC 관계의 분할특성을 분석한다.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.509-515
• /
• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.31-40
• /
• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• Fourth Industrial Review
• /
• v.3 no.1
• /
• pp.13-20
• /
• 2023

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.

• Structural Engineering and Mechanics
• /
• v.85 no.3
• /
• pp.315-335
• /
• 2023

Limited studies are available on the mathematical estimates of the compressive strength (CS) of glass fiber-embedded polymer (glass-FRP) compressive elements. The present study has endeavored to estimate the CS of glass-FRP normal strength concrete (NSTC) compression elements (glass-FRP-NSTC) employing two various methodologies; mathematical modeling and artificial neural networks (ANNs). The dataset of 288 glass-FRP-NSTC compression elements was constructed from the various testing investigations available in the literature. Diverse equations for CS of glass-FRP-NSTC compression elements suggested in the previous research studies were evaluated employing the constructed dataset to examine their correctness. A new mathematical equation for the CS of glass-FRP-NSTC compression elements was put forwarded employing the procedures of curve-fitting and general regression in MATLAB. The newly suggested ANN equation was calibrated for various hidden layers and neurons to secure the optimized estimates. The suggested equations reported a good correlation among themselves and presented precise estimates compared with the estimates of the equations available in the literature with R²= 0.769, and R² =0.9702 for the mathematical and ANN equations, respectively. The statistical comparison of diverse factors for the estimates of the projected equations also authenticated their high correctness for apprehending the CS of glass-FRP-NSTC compression elements. A broad parametric examination employing the projected ANN equation was also performed to examine the effect of diverse factors of the glass-FRP-NSTC compression elements.