• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1106-1116
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• 2011

LINS (Laser Inertial Navigation System) consists of RLG (Ring Laser Gyroscopes)/accelerometers and provides real-time navigation information to the target system. Therefore it is very important to make a decision in the real time whether LINS is in the normal operation or not. That is called a fault detection method. In this paper, we propose the fault detection method of LINS based on the overlapping model. We also show that the fault detection probability is increased through overlapping the hardware model and the software model. It is verified through the long-term operation and RAM (Reliability Availability Maintainability) analysis of LINS that the fault detection method proposed in this paper is able to detect about 97% of probable system failures.

• Journal of Korean Institute of Industrial Engineers
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• v.2 no.1
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• pp.79-83
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• 1976

This paper deals with a computer simulation for the stochastic inventory system in which the decision rules are associated with the problem of forecasting uncertain demand, lead time, and amount of shortages. The model consists of mainly three parts; part I$\cdots$the model calculates the expected demand during lead time through the built-in subrou tine program for random number generator and the probability distribution of the demand, part II$\cdots$the model calculates all the possible expected shortages per lead time period, part III$\cdots$finally the model calculates all the possible total inventory cost over the simulation period. These total inventory costs are compared for searching the optimal inventory cost with the best ordering quantity and reorder point. An application example of the simulation program is given.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.799-811
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• 2006

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.

• IE interfaces
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• v.24 no.4
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• pp.274-280
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• 2011

Delayed access to surgery may lead to deterioration in the patient condition, poor clinical outcomes, increase in the probability of emergency admission, or even death. The purpose of this work is to decide the number of patients selected from a waiting list and to schedule them in accordance with the operating room capacity in the next period. We formulate the problem as an infinite horizon Markov Decision Process (MDP), which attempts to strike a balance between the patient waiting times and overtime works. Structural properties of the proposed model are investigated to facilitate the solution procedure. The proposed procedure modifies the conventional value iteration method along with the binary search technique. An example of the optimal policy is provided, and computational results are given to show that the proposed procedure improves computational efficiency.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.1-8
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• 2003

The operation and management of a plant require proper accounting for the constraints coming from reliability requirements as well as from budget and resource considerations. Most of the mathematical methods to decide the inspection time interval for plant maintenance by reliability theory are too complicated to be solved. Moreover, the mathematical and theoretical models are not usually cases in the practical applications. In order to overcome these problems, we propose a new the decision-making method of optimal inspection interval to minimize the maintenance cost by reliability theory and genetic algorithm (GA). The most merit of the proposed method is to decide the inspection interval for a plant machine of which failure rate $\lambda$(t) conforms to any probability distribution. Therefore, this method is more practical. The efficiency of the proposed method is verified by comparing the results obtained by GA-based method with the inspection model haying regular time interval.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.635-641
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• 2008

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.

• Research in Plant Disease
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• v.22 no.3
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• pp.168-177
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• 2016

A forecast model for estimating the infection risk of bacterial canker caused by Pseudomonas syringae pv. actinidiae on kiwifruit leaves in Korea was developed using the generic infection model of Magarey et al. (2005). Two-way contingency table analysis was carried out to evaluate accuracy of forecast models including the model developed in this study for estimating the infection of bacterial canker on kiwifruit using the weather and disease data collected from three kiwifruit orchards at Seogwipo in 2015. All the tested models had more than 80% of probability of detection indicating that all the tested models could be effective to manage the disease. The model developed in this study showed the highest values in proportion of correct (51.1%), probability of detection (90.9%), and critical success index (47.6%). It indicated that the model developed in this study would be the best model for estimating the infection of bacterial wilt on kiwifruit leaves in Korea. The model developed in this study could be used for a part of decision support system for managing bacterial wilt on kiwifruit leaves and help growers to reduce the loss caused by the disease in Korea.

• Journal of the military operations research society of Korea
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• v.29 no.2
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• pp.100-110
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• 2003

Equipment replacement policy may not be defined with certainty, because physical states of any technological system may not be determined with foresight. This paper presents Markov Decision Process(MDP) model for army equipment which is subject to the uncertainty of deterioration and ultimately to failure. The components of the MDP model is defined as follows: ⅰ) state is identified as the age of the equipment, ⅱ) actions are classified as 'keep' and 'replace', ⅲ) cost is defined as the expected cost per unit time associated with 'keep' and 'replace' actions, ⅳ) transition probability is derived from Weibull distribution. Using the MDP model, we can determine the optimal replacement policy for an army equipment replacement problem.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.586-598
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• 2021

Nowadays, users' reviews and feedback on e-commerce sites stored in text create a huge source of information for analyzing customers' experience with goods and services provided by a business. In other words, collecting and analyzing this information is necessary to better understand customer needs. In this study, we first collected a corpus with 99,322 customers' comments and opinions in English. From this corpus we chose the best number of topics (K) using Perplexity and Coherence Score measurements as the input parameters for the model. Finally, we conducted an experiment using the latent Dirichlet allocation (LDA) topic model with K coefficients to explore the topic. The model results found hidden topics and keyword sets with high probability that are interesting to users. The application of empirical results from the model will support decision-making to help businesses improve products and services as well as business management and development in the field of hotel services.

• Journal of the Korean Institute of Gas
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• v.22 no.5
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• pp.107-113
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• 2018

While accident data are used to show alertness to accidents or to review similar cases, the analysis of nature of accident data its association with surrounding environment is very insufficient. Therefore, it is very necessary to demonstrate the possibility of an accident for a particular region by developing analysis techniques with the related accident data. The purpose of this study is to develop an analysis model and implement a system that produces regional accident probability based on historical weather information data and accident and reporting data. In other words, the system is designed and developed to create models by k-NN and decision tree algorithms with optional user-environment variables based on the probability between weather and accidents about many particular region of Korea. In the future, the models developed in this study are intended to be used to analyze and calculate the risk of a more narrow area.