• Smart Media Journal
• /
• v.13 no.1
• /
• pp.24-35
• /
• 2024

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.9
• /
• pp.2232-2245
• /
• 2013

The present study proposes efficient procedural modeling methods for enabling the growth and creation of various trees with minimal user control. Growth volume algorithms are utilized in order to easily and effectively calculate many parameters that determine tree growth, including branch propagation. Procedural methods are designed so that users' interactive control structures can be applied to these algorithms to create unique tree models efficiently. First, through a two-line-based interactive growth volume control method, the growth information that determines the overall shape of the tree is intuitively adjusted. Thereafter, independent branch control methods designed to control individual branches are added to the growth deformation in order to enable the growth of unique trees. Whether the growth processes of desired trees can be easily and intuitively controlled by the proposed method is verified through experiments. Methods that can apply the proposed methods are also verified.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.50-56
• /
• 2003

In the development of complex software system, it is important to use hierarchical use case model due to the complex scope of development procedure. The use case model is core factor of the OMG (Object Management Group)'s UML (Unified Modeling Language) diagrams. In this paper, we propose a novel method to check syntactic consistency automatically in use case models at the different level of abstraction. This method is a rule-based approach which utilizes actor tree, use case tree and use case description. The proposed method is simulated on ITS (Intelligent Transportation System) architecture for the verification.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.1
• /
• pp.23-31
• /
• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• Proceedings of the IEEK Conference
• /
• summer
• /
• pp.382-386
• /
• 2004

Recently, with the machine learning trend, most of the machine translation systems on over the world use two syntax tree sets of two relevant languages to learn syntactic tree transfer rules. However, for the English-Vietnamese language pair, this approach is impossible because until now we have not had a Vietnamese syntactic tree set which is correspondent to English one. Building of a very large correspondent Vietnamese syntactic tree set (thousands of trees) requires so much work and take the investment of specialists in linguistics. To take advantage from our available English-Vietnamese Corpus (EVC) which was tagged in word alignment, we choose the SITG (Stochastic Inversion Transduction Grammar) model to construct English- Vietnamese syntactic tree sets automatically. This model is used to parse two languages at the same time and then carry out the syntactic tree transfer. This English-Vietnamese bilingual syntactic tree set is the basic training data to carry out transferring automatically from English syntactic trees to Vietnamese ones by machine learning models. We tested the syntax analysis by comparing over 10,000 sentences in the amount of 500,000 sentences of our English-Vietnamese bilingual corpus and first stage got encouraging result $(analyzed\;about\;80\%)[5].$ We have made use the TBL algorithm (Transformation Based Learning) to carry out automatic transformations from English syntactic trees to Vietnamese ones based on that parallel syntactic tree transfer set[6].

• Information Systems Review
• /
• v.26 no.2
• /
• pp.83-93
• /
• 2024

This study proposes a preprocessing methodology based on Dynamic Time Warping (DTW) and k-Nearest Neighbors (kNN) to effectively represent time series data for predicting the completion quality of electroplating baths. The proposed DTW-based kNN preprocessing approach was applied to various regression models and compared. The results demonstrated a performance improvement of up to 43% in maximum RMSE and 24% in MAE compared to traditional decision tree models. Notably, when integrated with neural network-based regression models, the performance improvements were pronounced. The combined structure of the proposed preprocessing method and regression models appears suitable for situations with long time series data and limited data samples, reducing the risk of overfitting and enabling reasonable predictions even with scarce data. However, as the number of data samples increases, the computational load of the DTW and kNN algorithms also increases, indicating a need for future research to improve computational efficiency.

• The KIPS Transactions:PartD
• /
• v.12D no.2 s.98
• /
• pp.211-218
• /
• 2005

This study presents an integrated data mining model for the credit evaluation of the customers of a capital company. Based on customer information and financing processes in capital market, we derived individual models from multi-layered perceptrons(MLP), multivariate discrimination analysis(MDA), and decision tree. Further, the results from the existing models were compared with the results from the integrated model using genetic algorithm. The integrated model presented by this study turned out to be superior to the existing models. This study contributes not only to verifying the existing individual models but also to overcoming the limitations of the existing approaches.

• Korean Journal of Computational Design and Engineering
• /
• v.7 no.4
• /
• pp.240-247
• /
• 2002

Most commercial CAD systems provide parametric modeling functions, and by using these capabilities designers can edit a CAD model in order to create design variants. It is necessary to transfer parametric information during a CAD model exchange to modify the model inside the receiving system. However, it is not possible to exchange parametric information of CAD models based on the cur-rent version of STEP. The designer intents which are contained in the parametric information can be lost during the STEP transfer of CAD models. This paper introduces a hybrid CAB model translator, which also uses the feature tree of commercial CAD systems in addition to the macro file to allow transfer of parametric information. The macro-parametric approach is to exchange CAD models by using the macro file, which contains the history of user commands. To exchange CAD models using the macro-parametric approach, the modeling commands of several commercial CAD systems are analyzed. Those commands are classified and a set of standard modeling commands has been defined. As a neutral fie format, a set of standard modeling commands has been defined. Mapping relations between the standard modeling commands set and the native modeling commands set of commercial CAD systems are defined. The scope of the current version is limited to parts modeling and assemblies are excluded.

• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.4
• /
• pp.259-275
• /
• 2021

This study explored the usefulness and implications of the Bayesian hyperparameter optimization in developing species distribution models (SDMs). A variety of machine learning (ML) algorithms, namely, support vector machine (SVM), random forest (RF), boosted regression tree (BRT), XGBoost (XGB), and Multilayer perceptron (MLP) were used for predicting the occurrence of four benthic macroinvertebrate species. The Bayesian optimization method successfully tuned model hyperparameters, with all ML models resulting an area under the curve (AUC) > 0.7. Also, hyperparameter search ranges that generally clustered around the optimal values suggest the efficiency of the Bayesian optimization in finding optimal sets of hyperparameters. Tree based ensemble algorithms (BRT, RF, and XGB) tended to show higher performances than SVM and MLP. Important hyperparameters and optimal values differed by species and ML model, indicating the necessity of hyperparameter tuning for improving individual model performances. The optimization results demonstrate that for all macroinvertebrate species SVM and RF required fewer numbers of trials until obtaining optimal hyperparameter sets, leading to reduced computational cost compared to other ML algorithms. The results of this study suggest that the Bayesian optimization is an efficient method for hyperparameter optimization of machine learning algorithms.

• Advances in nano research
• /
• v.13 no.5
• /
• pp.499-512
• /
• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.