• The KIPS Transactions:PartC
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• v.9C no.5
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• pp.725-732
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• 2002

Presented is a Genetic Algorithm (GA) for dynamic partitioning an ATM LANE(LAN Emulation) network. LANE proves to be one of the best solutions to provide guaranteed Quality of Service (QoS) for mid-size campus or enterprise networks with minor modification of legacy LAN facilities. However, there are few researches on the efficient LANE network operations to deal with scalability issues arising from broadcast traffic delivery. To cope with this scalability issue, proposed is a decision model named LANE Partitioning Problem (LPP) which aims at partitioning the entire LANE network into multiple Emulated LANs (ELANS), each of which works as an independent virtual LAN.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.111-118
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• 2023

For the implementation of a smart factory, it is necessary to collect data by connecting various sensors and devices in the manufacturing environment and to diagnose or predict failures in production facilities through data analysis. In this paper, to predict the residual useful lifetime of milling insert used for machining products in CNC machine, weight k-NN algorithm, Decision Tree, SVR, XGBoost, Random forest, 1D-CNN, and frequency spectrum based on vibration signal are investigated. As the results of the paper, the frequency spectrum does not provide a reliable criterion for an accurate prediction of the residual useful lifetime of an insert. And the weighted k-nearest neighbor algorithm performed best with an MAE of 0.0013, MSE of 0.004, and RMSE of 0.0192. This is an error of 0.001 seconds of the remaining useful lifetime of the insert predicted by the weighted-nearest neighbor algorithm, and it is considered to be a level that can be applied to actual industrial sites.

• Journal of Platform Technology
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• v.11 no.3
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• pp.45-55
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• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.

• Journal of IKEEE
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• v.24 no.1
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• pp.302-310
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• 2020

In this paper, we have implemented the living waste analysis system based on IoT and AI(Artificial Intelligence), and proposed effective waste process and management method. The Jeju location have the strong point to devise a stratagem and estimate waste quantization, rather than others. Especially, we can recognized the amount variation of waste to the residence people compare to the sightseer number, and the good example a specific waste duty. Thus this paper have developed the IoT device for interconnecting the existed CCTV camera, and use the AI algorithm to analysis the waste image. By using these decision of image analysis, we can inform their deal commend and a decided information to the map of the waste cars. In order to evaluate the performance of IoT, we have experimented the electromagnetic compatibility under a national official authorization KN-32, KN61000-4-2~6, and obtained the stable experimental results. In the further experimental results, we can applicable for an data structure for precise definition command by using the simulated several waste image with artificial intelligence algorithm.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.83-91
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• 2011

The objective of VMS(Variable Message Sign) is to provide the traffic information downstream to drivers upstream so that they can choose their routes or expect the travel time to arrive the destination. Because there is not enough time and space to show the message, VMS message should be selected carefully. However, the message of VMS has been simply selected among the pre-designed message sets based on the priority rule of events. If the traffic demand between origin and destination is identified along the freeway, message can be selected to provide the information of a route that more drivers will use. In this study, a time sliced OD(Origin/Destination) estimation method will be developed using the detector information of the on-ramp, exit ramp, and the main lanes. And the strategy of a priority rule of message was planned.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.373-382
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• 2016

Carbon dioxide Capture and Storage/Sequestration (CCS) technology has attracted attention as an ideal method for most carbon dioxide reduction needs. When the collected carbon dioxide is transported to storage via pipelines, the direct transport is made if the storage is close, otherwise it can also be transported via an intermediate storage hub. Determining the number and the location of the intermediate storage hubs is an important problem. A decision-making algorithm using a mathematical model for solving the problem requires considerably more variables and constraints to describe the multi-objective decision, but the computational complexity of the problem increases and it also does not guarantee the optimality. This research proposes an algorithm to determine the location and the number of the intermediate storage hub and develop a simulator for the connection network of the carbon dioxide emission site. The simulator also provides the course of transportation of the carbon dioxide. As a case study, this model is applied to Korea.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.167-174
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• 2012

Data mining technique can be adapted to analysing Employment information in order to discover valuable information out of large data. As the issue employment such as jobless of college graduate, recruitment for women, recruitment for elders etc. became social problem, there are many efforts of various public employment services and studies. The factors that affects the college graduate's employment type (regular, temporary, daily) can be used to guide employment and to prepare employment for college students. In analyzing large number of attributes and the huge amount of data elements, regular statistical methods faces their limitation; therefore, data-mining technique is more suitable for the dataset of about 170 attributes and 20,000 elements. We divide the factors that may affect the employment type into personal factor, school factor, company factor, and experience factor; decision tree algorithm is used to find out the interesting relationship between the attributes of the factors and employment type. Personal factors such as the income of parents and marital status were the most affective factors to the employment type. The learned decision tree was able to classify the employment type with 87% of accuracy. We also assume the level of the school affects the employment type of the graduates.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of the Korean Society of Radiology
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• v.13 no.7
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• pp.901-907
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• 2019

The aim of this study is to evaluate the ability of target localizations of Leksell GammaPlan(LGP) in Gamma Knife Subthalamotomy(or Pallidotomy, Thalamotomy) of functional diseases. To evaluate the accuracy of LGP's location settings, the difference Δr of the target coordinates calculated by LGP (or LSP) and author's algorithm was reviewed for 10 patients who underwent Deep Brain Stimulation(DBS) surgery. Δr ranged from 0.0244663 mm to 0.107961 mm. The average of Δr was 0.054398 mm. Transformation matrix between stereotactic space and brain atlas space was calculated using PseudoInverse or Singular Value Decomposition of Mathematica to determine the positional relationship between two coordinate systems. Despite the precise frame positioning, the misalignment of yaw from -3.44739 degree to 1.82243 degree, pitch from -4.57212 degree to 0.692063 degree, and rolls from -6.38239 degree to 7.21426 degree appeared. In conclusion, a simple in-house algorithm was used to test the accuracy for location settings of LGP(or LSP) in Gamma Knife platform and the possibility for Gamma Knife Subthalamotomy. The functional diseases can be treated with Gamma Knife Radiosurgery with safety and efficacy. In the future, the proposed algorithm for target localizations' QA will be a great contributor to movement disorders' treatment of several Gamma Knife Centers.