• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.555-562
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• 2010

After starting digital terrestrial broadcasting, there have been a number oftrials to provide new services like data broadcasting on a spare bandwidth of a DTV channel. Recently, the Push-VOD service, which provides A/V contents on that bandwidth, gets more attention and is being standardized as NRT(Non-Real-Time) by ATSC. However, it is highly probable that the contents transmitted in this way contain many errors due to the DTV receiving environment. Thus, in order to improve the reliability of transmission, the contents should be transmitted repeatedly several times, considering the unidirectional property of DTV terrestrial network. In this paper, we propose a method to calculate the optimal number of repetitions to transmit each contents in a way that minimizes the number of errors occured, when trying to transmit several contents to the receiver in a restricted time, using Markov-chain modeling and dynamic programming approach.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.98-104
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• 2018

Macroscopic accident analyses have been conducted to incorporate transportation safety into long-term transportation planning. In macro-level accident prediction model, exposure variable(e.g. a settled population) have been used as fundamental explanatory variable under the concept that each trip will be subjected to a probable risk of accident. However, a settled population may be embedded error by exclusion of active population concept. The objective of this research study is to develop macro-level accident prediction model using floating population variable(concept of including a settled population and active population) collected from mobile phone data. The concept of accident prediction models is introduced utilizing exposure variable as explanatory variable in a generalized linear regression with assumption of a negative binomial error structure. The goodness of fit of model using floating population variable is compared with that of the each models using population and the number of household variables. Also, log transformation models are additionally developed to improve the goodness of fit. The results show that the log transformation model using floating population variable is useful for capturing the relationships between accident and exposure variable and generally perform better than the models using other existing exposure variables. The developed model using floating population variable can be used to guide transportation safety policy decision makers to allocate resources more efficiently for the regions(or zones) with higher risk and improve urban transportation safety in transportation planning step.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.4
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• pp.329-334
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• 2017

As the motion response of heave for floating bodies on the water surface is relatively large near the natural frequency, it is necessary to predict its value accurately from the stage of initial design. Bodies accelerating in fluid experience force acted upon by the fluid, and this force is quantified by using the concept of added mass. For predicting the natural frequency of heave we need to know the added mass, which is given as a function of frequency, and hence the natural frequency can be obtained through only by iteration process, as was pointed out by Lee (2008). His method was applied to circular cylinders, and two dimensional cylinders of Lewis form by making use of the Ursell-Tasai method in the previous works, Lee and Lee (2013), Kim and Lee (2013), and Song and Lee (2015). In this work, a similar algorithm employing the concept of strip method is adopted for predicting the heave natural frequency of KCS(KRISO Container Ship), and the obtained computational result was compared with other existing experimental data, and the agreement seems reasonable. Furthermore, through the error analysis, it is shown that why the frequency corresponding to the local minimum of the added mass and the natural frequency are very close. And it seems probable that we can predict the heave natural frequency if we know only the local minimum of added mass and the corresponding frequency under a condition, which holds for ship-like bodies in general.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.228-242
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• 2005

The projected clustering seeks to find clusters in different subspaces within a high dimensional dataset. We propose an algorithm to discover near optimal projected clusters without user specified parameters such as the number of output clusters and the average cardinality of subspaces of projected clusters. The objective function of the algorithm computes projected energy, quality, and the number of outliers in each process of clustering. In order to minimize the projected energy and to maximize the quality in clustering, we start to find best subspace of each cluster on the density of input points by comparing standard deviations of the full dimension. The weighting factor for each dimension of the subspace is used to get id of probable error in measuring projected distances. Our extensive experiments show that our algorithm discovers projected clusters accurately and it is scalable to large volume of data sets.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.32-38
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• 2017

In this study, we introduce a new way to improve the efficiency of Gait Analysis and promote its usage. This program can be shown in different points of view such as an individual or group patient data. The 'Gate Data Visualization Program' is improving its efficiency by minimizing the margin of error during research and promoting the easy interpretation. In addition, this program is designed to have an easy access, and can be used to develop the most basic medical equipment program to predict a probable disease for patient by collaborating with physicians specializing in neurosurgery.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.103-109
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• 2018

This paper presents an idea and framework to automate a full cycle business process management and re-engineering by integrating traditional business process management systems, process mining, data mining, machine learning, and simulation. We build our framework on the cloud-based platform such that various data sources can be incorporated. We design our systems to be extensible so that not only beneficial for practitioners of BPM, but also for researchers. Our framework can be used as a test bed for researchers without the complication of system integration. The automation of redesigning phase and selecting a baseline process model for deployment are the two main contributions of this study. In the redesigning phase, we deal with both the analysis of the existing process model and what-if analysis on how to improve the process at the same time, Additionally, improving a business process can be applied in a case by case basis that needs a lot of trial and error and huge data. In selecting the baseline process model, we need to compare many probable routes of business execution and calculate the most efficient one in respect to production cost and execution time. We also discuss the challenges and limitation of the framework, including the systems adoptability, technical difficulties and human factors.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.508-524
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• 2006

We present our experience of combining, in a realistic setting, a static analyzer with a statistical analysis. This combination is in order to reduce the inevitable false alarms from a domain-unaware static analyzer. Our analyzer named Airac(Array Index Range Analyzer for C) collects all the true buffer-overrun points in ANSI C programs. The soundness is maintained, and the analysis' cost-accuracy improvement is achieved by techniques that static analysis community has long accumulated. For still inevitable false alarms (e.g. Airac raised 970 buffer-overrun alarms in commercial C programs of 5.3 million lines and 737 among the 970 alarms were false), which are always apt for particular C programs, we use a statistical post analysis. The statistical analysis, given the analysis results (alarms), sifts out probable false alarms and prioritizes true alarms. It estimates the probability of each alarm being true. The probabilities are used in two ways: 1) only the alarms that have true-alarm probabilities higher than a threshold are reported to the user; 2) the alarms are sorted by the probability before reporting, so that the user can check highly probable errors first. In our experiments with Linux kernel sources, if we set the risk of missing true error is about 3 times greater than false alarming, 74.83% of false alarms could be filtered; only 15.17% of false alarms were mixed up until the user observes 50% of the true alarms.

• Journal of the Korea Concrete Institute
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• v.20 no.3
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• pp.375-381
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• 2008

Various problems such as durability degradation may happen when extra water is added to concrete. Because of these reasons, the change of water content is managed by using rapid evaluation method of unit water content such as electric capacity method, heat drying method making use of micro wave, unit capacity mass method among various methods. Especially, in Japan, guidance for the change of water content ($\pm$ 10, 15, 20 kg/$m^3$ etc.) were regulated and used. However, it is the real situation that the guidance which were regulated in South Korea evaluate suitability only considering production and measurement error under the circumstances which are not considering the degree of durability degradation. Therefore, this study tries to investigate the influence of addition of extra water in the concrete on the durability degradation of concrete when it was added by artificial manipulation or by management error. From the test results, a guideline of the contents of extra water for the quality control is suggested with the consideration of the degree of durability degradation and the probable error resulted from the addition of extra water. The contents of extra water for tests are set as 0, 15, 25, 35 kg/$m^3$. To examine the durability degradation of concrete, freezing and thawing, carbonation, chloride penetration and compressive strength are tested.

• Asian Journal of Atmospheric Environment
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• v.10 no.2
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• pp.67-79
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• 2016

The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the greatest concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of $PM_{2.5}$ was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, $NO_2$, $NO_x$, CO, $SO_2$ and $PM_{10}$ were used as inputs to the artificial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting $PM_{2.5}$ concentrations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coefficient of determination ($R^2$), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused $R^2$ to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of $PM_{2.5}$ pollution is predictable using a Markov chain method.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.149-153
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• 2006

Precipitation is the most important component and critical to the study of water and energy cycle. This study investigates the propagation of precipitation retrieval uncertainty in the simulation of hydrologic variables for varying spatial resolution on two different vegetation cover. We explore two remotely sensed rain retrievals (space-borne IR-only and radar rainfall) and three spatial grid resolutions. An offline Community Land Model (CLM) was forced with in situ meteorological data In turn, radar rainfall is replaced by the satellite rain estimates at coarser resolution $(0.25^{\circ},\;0.5^{\circ}\;and\;1^{\circ})$ to determine their probable impact on model predictions. Results show how uncertainty of precipitation measurement affects the spatial variability of model output in various modelling scales. The study provides some intuition on the uncertainty of hydrologic prediction via interaction between the land surface and near atmosphere fluxes in the modelling approach.