• Asia pacific journal of information systems
• /
• v.16 no.2
• /
• pp.69-84
• /
• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.6
• /
• pp.2310-2332
• /
• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.4
• /
• pp.221-227
• /
• 2000

This paper combines the constrained model predictive control with the feedback linearization to solve a nonlinear system control problem with input constraints. The combined approach consists of two steps: Firstly, the nonlinear model is linearized by the feedback linearization. Secondly, based on the linearized model, the constrained model predictive controller is designed taking input constraints into consideration. The proposed controller is applied to two link robot system, and tracking performances of the controller are investigated via some simulations, where the comparisons are done for the cases of unconstrained, constrained input in feedback linearization.

• Communications for Statistical Applications and Methods
• /
• v.24 no.1
• /
• pp.67-80
• /
• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.6
• /
• pp.588-598
• /
• 2015

In this paper, a simulation model for assessing the impact of alternative field service engineer replenishment policies is introduced. The end-to-end supply chain simulation model is created using a discrete-event and agent-based simulation model, which enables accurate description of key individual entities in the investigated supply chain, such as field service engineers. Once the model is validated with the historical data, it is used to assess the impacts of field service engineer replenishment policies for a major printing equipment manufacturing firm.In the case study, newly proposed replenishment policies for post-sale distribution supply chain are assessed for the level of service improvement to end customers.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.1454-1458
• /
• 2007

The determination of feasible design flood is the most important to control flood damage in river management. Model parameters should be calibrated using observed discharge but due to deficiency of observed data the parameters have been adopted by engineer's empirical sense. Storage constant in the Clark unit hydrograph method mainly affects magnitude of peak flood. This study is to estimate the storage constant based on the observed rainfall-runoff data at the three stage stations in the Imjin river basin and the three stage stations in the Ansung river basin. In this study four methods have been proposed to estimate the storage constant from observed rainfall-runoff data. The HEC-HMS model has been adopted to execute the sensitivity of storage constant. A criteria has been proposed to determine storage constant based on the results of the observed hydrograph and the HEC-HMS model.

• The Journal of Korea Robotics Society
• /
• v.11 no.4
• /
• pp.226-234
• /
• 2016

In this study, a model-referenced underwater navigation algorithm is proposed for high-precise underwater navigation using monocular vision near underwater structures. The main idea of this navigation algorithm is that a 3D model-based pose estimation is combined with the inertial navigation using an extended Kalman filter (EKF). The spatial information obtained from the navigation algorithm is utilized for enabling the underwater robot to navigate near underwater structures whose geometric models are known a priori. For investigating the performance of the proposed approach the model-referenced navigation algorithm was applied to an underwater robot and a set of experiments was carried out in a water tank.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.9
• /
• pp.4355-4374
• /
• 2018

Crowd escape event detection has become one of the hottest problems in intelligent surveillance filed. When the 'escape event' occurs, pedestrians will escape in a disordered way with different velocities and directions. Based on these characteristics, this paper proposes a Direction-Collectiveness Model to detect escape event in crowd scenes. First, we extract a set of trajectories from video sequences by using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Second, a Direction-Collectiveness Model is built based on the randomness of velocity and orientation calculated from the trajectories to express the movement of the crowd. This model can describe the movement of the crowd adequately. To obtain a generalized crowd escape event detector, we adopt an adaptive threshold according to the Direction-Collectiveness index. Experiments conducted on two widely used datasets demonstrate that the proposed model can detect the escape events more effectively from dense crowd.

• International Journal of Fluid Machinery and Systems
• /
• v.5 no.4
• /
• pp.143-151
• /
• 2012

Predicting the future condition of machine and assessing the remaining useful life are the center of prognostics. This paper contributes a new prognostic method based on grey model and survival probability. The first step of the method is building a normal condition model then determining the error indicator. In the second step, the survival probability value is obtained based on the error indicator. Finally, grey model coupled with one-step-ahead forecasting technique are employed in the last step. This work has developed a modified grey model in order to improve the accuracy of prediction. For evaluating the proposed method, real trending data of low methane compressor acquired from condition monitoring routine were employed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.5
• /
• pp.481-486
• /
• 2002

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-5) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for the indirect adaptive fuzzy control. Based on the derived design method, the parameter estimation for controllable canonical T-S fuzzy model is also Presented.