• Journal of Korean Society for Atmospheric Environment
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• v.24 no.4
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• pp.439-454
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• 2008

The purpose of this study was to extensively identify $PM_{10}$ sources and to estimate their contributions to the study area, based on the analysis of the $PM_{10}$ mass concentration and the associated inorganic elements, ions, and total carbon. The contribution of $PM_{10}$ sources was estimated by applying a receptor method because identifying air emission sources were effective way to control the ambient air quality. $PM_{10}$ particles were collected from May to November 2007 in the Yongin-Suwon bordering area. $PM_{10}$ samples were collected on quartz filters by a $PM_{10}$ high-volume air sampler. The inorganic elements (Al, Mn, V, Cr, Fe, Ni, Cu, Zn, Cd, Pb, Si, Ba, Ti and Ag) were analyzed by an ICP-AES after proper pre-treatments of each sample. The ionic components of these $PM_{10}$ samples ($Cl^_$, $NO_3^-$, $SO_4^{2-}$, $Na^+$, $NH_4^+$, $K^+$, $Ca^{2+}$, and $Mg^{2+}$) were analyzed by an IC. The carbon components (OC1, OC2, OC3, OC4, OP, EC1, EC2 and EC3) were also analyzed by DRI/OGC analyzer. Source apportionment of $PM_{10}$ was performed using a positive matrix factorization (PMF) model. After performing PMF modeling, a total of 8 sources were identified and their contribution were estimated. Contributions from each emission source were as follows: 13.8% from oil combustion and industrial related source, 25.4% from soil source, 22.1% from secondary sulfate, 12.3% from secondary nitrate, 17.7% from auto emission including diesel (12.1%) and gasoline (5.6%), 3.1% from waste incineration and 5.6% from Na-rich source. This study provides information on the major sources affecting air quality in the receptor site, and therefore it will help us maintain and manage the ambient air quality in the Yongin-Suwon bordering area by establishing reliable control strategies for the related sources.

• The Journal of the Korea Contents Association
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• v.14 no.7
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• pp.395-406
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• 2014

This research observes how an adolescent's usage of social network services is related with media use time, and also tries to verify how a parent's participation in acting to limit usage time serves as a mediating effect. For this analysis, data from The Korean Information Society Development Institute [Korea Media Panel Research] was used, being a survey conducted on 1,572 students from primary to secondary and high schools nationwide. The subjects' gender in the final analysis was 818 male students and 754 female students. In addition, primary school students consisted of 672 individuals, 416 were from secondary and 484 from high schools. To verify the appropriateness of the model proposed in this research, analyzed through the structural equation, using social network services(SNS) was not shown to have an effect on media usage time. However, there was an indirect effect from the use of alternative activities. In addition, the parent's limitation of usage showed no effect on media use time. This research attempts to identify the reasons why parents' limiting the usage time, along with alternative activities, do not have any significant effect in reducing media usage time in this era of smart media evolution. Further, this research hopes to suggest meaningful and political implications for the nation and society to understand and resolve media addiction.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.179-188
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• 2008

An important issue in multiagent reinforcement learning is how an agent should team its optimal policy through trial-and-error interactions in a dynamic environment where there exist other agents able to influence its own performance. Most previous works for multiagent reinforcement teaming tend to apply single-agent reinforcement learning techniques without any extensions or are based upon some unrealistic assumptions even though they build and use explicit models of other agents. In this paper, basic concepts that constitute the common foundation of multiagent reinforcement learning techniques are first formulated, and then, based on these concepts, previous works are compared in terms of characteristics and limitations. After that, a policy model of the opponent agent and a new multiagent reinforcement learning method using this model are introduced. Unlike previous works, the proposed multiagent reinforcement learning method utilize a policy model instead of the Q function model of the opponent agent. Moreover, this learning method can improve learning efficiency by using a simpler one than other richer but time-consuming policy models such as Finite State Machines(FSM) and Markov chains. In this paper. the Cat and Mouse game is introduced as an adversarial multiagent environment. And effectiveness of the proposed multiagent reinforcement learning method is analyzed through experiments using this game as testbed.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.282-296
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• 2010

OWL is an ontology language for the Semantic Web, and suited to modelling the knowledge of a specific domain in the real-world. Ontology also can infer new implicit knowledge from the explicit knowledge. However, the modeled knowledge cannot be complete as the whole of the common-sense of the human cannot be represented totally. Ontology do not concern handling nonmonotonic reasoning to detect incomplete modeling such as the integrity constraints and exceptions. A default rule can handle the exception about a specific class in ontology. Integrity constraint can be clear that restrictions on class define which and how many relationships the instances of that class must hold. In this paper, we propose a practical reasoning system for open and closed-world reasoning that supports a novel hybrid integration of ontology based on open world assumption (OWA) and non-monotonic rule based on closed-world assumption (CWA). The system utilizes a method to solve the problem which occurs when dealing with the incomplete knowledge under the OWA. The method uses the answer set programming (ASP) to find a solution. ASP is a logic-program, which can be seen as the computational embodiment of non-monotonic reasoning, and enables a query based on CWA to knowledge base (KB) of description logic. Our system not only finds practical cases from examples by the Protege, which require non-monotonic reasoning, but also estimates novel reasoning results for the cases based on KB which realizes a transparent integration of rules and ontologies supported by some well-known projects.

• Nutrition Research and Practice
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• v.14 no.3
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• pp.218-229
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• 2020

BACKGROUND/OBJECTIVES: Type 2 Diabetes mellitus (T2DM) is a hereditary disease that is also strongly dependent on environmental factors, lifestyles, and dietary habits. This study explored the relationship between lifestyle habits and glycosylated hemoglobin management in T2DM patients to provide empirical outcomes to improve T2DM management and patient health literacy. SUBJECTS/METHODS: This study enrolled 349 diabetic patients with more than 5 care visits to a Diabetes Mellitus care network under the Health Management Plan led by Taiwan Department of Health (DOH). Based on relevant literature, an Outpatient Record Form of Diabetes Mellitus Care was designed and lipid profile tests were conducted for data collection and analysis. RESULTS: When modeling the data, the results showed that the odds for HbA1c > 7.5% in T2DM patients duration over 10 years was 3.785 (P = 0.002) times that in patients with disease duration of fewer than 3 years. The odds of HbA1c > 7.5% in illiterate patients was 3.128 (P = 0.039) times that in patients with senior high school education or above. The odds of HbA1c > 7.5% in patients with other chronic illness was 2.207 (P = 0.019) times that in participants without chronic illness. Among 5 beneficial lifestyle habits, the odds of HbA1c > 7.5% in patients with 2 or 3 good habits were 3.243 (P = 0.003) and 3.424 (P = 0.001) times that in patients with more than 3 good habits, respectively. CONCLUSION: This empirical outcome shows that maintaining a good lifestyle improves T2DM management and patients' knowledge, motivation, and ability to use health information. Patients with longer disease duration, education, or good lifestyle habits had optimal HbA1c management than those in patients who did not. Thus, effective selfmanagement and precaution in daily life and improved health literacy of diabetic patients are necessary to increase the quality of T2DM care.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.335-344
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• 2018

The power model is needed to handle the power consumption of mobile AMOLED display at the software level. However, the existing studies to generate the power model have required the experimental environment and equipment for the power measurement activity. In addition, the combination of RGB values used for modeling was imprudent and small, so it was difficult to reflect the mutual influence between the RGB values into the model. To solve these problems, we propose an RGB color space partitioning method, which is used to prudently sample the combinations of the RGB values based on the color or the power. We also propose a process for generating a mapping table composed of . We analyzed the characteristics of the samples generated according to the proposed partitioning methods, taking into account the color and the power, and generated the mapping table for the AMOLED display. Furthermore, we confirmed the reusability of the mapping table by utilizing one mapping table multiple times in evaluating different power models. These mapping tables are provided to researchers and can be used to generate and evaluate power models without power measurement activities.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.155-163
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• 2010

The past researches on flood inundation simulation mainly focused on development of numerical models based on unstructured mesh networks to improve model performances. However, despite the accurate simulation results, such models are not suitable for real-time flood inundation forecasting due to a huge computational burden in terms of geographic data processing. In addition, even though various types of vector and raster data are available to be compatible with flood inundation models for post-processes such as flood hazard mapping and flood inundation risk analysis, the unstructured mesh-based models are not effective to fully use such information due to data incommensurability. Therefore, this study aims to develop a raster-based two-dimensional inundation model; it guarantees computational efficiency because of direct application of DEM for flood inundation modeling and also has a good compatibility with various types of raster data, compared to a commercial model such as FLUMEN. We applied the model to simulate the BaekSan levee break in the Nam river during a flood period from August 10 to 13, 2002. The simulation results showed a good agreement with the field-surveyed inundation area and were also very similar with results from the FLUMEN. Moreover, the model provided physically-acceptable velocity vectors with respect to inundating and returning flows due to the difference of water level between channel and lowland.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4331-4338
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• 2014

$N^1$-(2, 5-dimethoxyphenyl)-$N^8$-hydroxyoctanediamide (N25) is a novel SAHA cap derivative of HDACi, with a patent (No. CN 103159646). This invention is a hydroxamic acid compound with a structural formula of $RNHCO(CH_2)6CONHOH$ (wherein R=2, 5dimethoxyaniline), a pharmaceutically acceptable salt which is soluble. In the present study, we investigated the effects of N25 with regard to drug distribution and molecular docking, and anti-proliferation, apoptosis, cell cycling, and $LD_{50}$. First, we designed a molecular approach for modeling selected SAHA derivatives based on available structural information regarding human HDAC8 in complex with SAHA (PDB code 1T69). N25 was found to be stabilized by direct interaction with the HDAC8. Anti-proliferative activity was observed in human glioma U251, U87, T98G cells and human lung cancer H460, A549, H1299 cells at moderate concentrations ($0.5-30{\mu}M$). Compared with SAHA, N25 displayed an increased antitumor activity in U251 and H460 cells. We further analyzed cell death mechanisms activated by N25 in U251 and H460 cells. N25 significantly increased acetylation of Histone 3 and inhibited HDAC4. On RT-PCR analysis, N25 increased the mRNA levels of p21, however, decreased the levels of p53. These resulted in promotion of apoptosis, inducing G0/G1 arrest in U251 cells and G2/M arrest in H460 cells in a time-dependent and dose-dependent manner. In addition, N25 was able to distribute to brain tissue through the blood-brain barrier of mice ($LD_{50}$: 240.840mg/kg). In conclusion, our findings demonstrate that N25 will provide an invaluable tool to investigate the molecular mechanism with potential chemotherapeutic value in several malignancies, especially human glioma.

• Journal of the Korean Geographical Society
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• v.39 no.4
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• pp.495-513
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• 2004

The shape of land surface work as a cradle for various environmental processes and human activities. As spatially distributed process modelings become increasing important in current research communities, a classification system that delineates land surface into characteristic geomorphological units is a pre-requisite for sustainable land use planning and management. Existing classification systems are either morphometric or generic, which have limitations to characterize continuous ecological processes over the landscape. A new classification system was developed to delineate the land surface into different geomorphological units from Digital Elevation Models(DEMs). This model assumes that there are pedo-geomorphological units in which distinct sets of hydrological, pedological, and consequent ecological processes occur. The classification system first divides the whole landsurface into eight soil-landscape units. Possible energy and material nows over the land surface were interpreted using a continuity equation of mass flow along the hillslope, and subsequently implemented in terrain analysis procedures. The developed models were tested at a 12$\textrm{km}^2$ area in Yangpyeong-gun, Kyeongi-do, Korea. The method proposed effectively delineates land surface into distinct pedo-geomorphological units, which identify the geomorphological characteristics over a large area at a low cost. The delineated landscape units mal provide a basic information for natural resource survey and environmental modeling practices.