• Journal of Korean Society of Water and Wastewater
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• v.30 no.1
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• pp.9-17
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• 2016

Disinfectant/oxidation process is a crucial process in water treatment for supplying safe drinking water. Chlorination is still widely used for water treatment area due to its effectiveness on microbial inactivation and economic feasibility. Recently, disinfection concern in marine environment is increasing, for example, movement of hazardous marine organism due to ballast water, marine environmental degradation due to power plant cooling water discharge, and increase of the amount of disinfectant in the offshore plant. It is needed to conduct the assessment of disinfectant behavior and the development of disinfectant prediction model in seawater. The appropriate prediction model for disinfectant behavior is not yet provided. The objective of the study is to develop chlorine decay model in seawater. Various model types were applied to develop the seawater chlorine decay model, such as first order decay model, EPA model, and two-phase model. The model simulation indicated that chlorine decay in seawater is influenced by both organic and inorganic matter in seawater. While inorganic matter has a negative correlation with the chlorine decay, organic matter has a positive correlation with the chlorine decay.

• Atmosphere
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• v.26 no.1
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• pp.47-58
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• 2016

On 10 July 2014, tornado outbreak occurred over Goyang province in Korea. This was the first supercell tornado ever reported or documented in Korea. The characteristics of the supercell tornado were investigated using an X-band polarimetric radar, surface meteorological observation, wind profiler, and operational numerical weather prediction (Regional Data Assimilation and Prediction System, RDAPS). The supercell tornado developed along a preexisting dryline that was contributed to surface wind shear. The radar analyses examined here show that the supercell tornado indicated a hook echo with mesocyclone. The decending reflectivity core as well was detected before tornadogenesis and prior to intensification of supercell. The supercell tornado exhibited characteristics similar to typical supercell tornado over the Great Plains of the United States, such as hook echo, bounded weak echo region, and slower movement speed relative to the mean wind. Compared to the typical supercell tornado over U.S., this tornado showed horizontal scale of the mesocyclone was relatively smaller and left-mover.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.465-474
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• 2014

Wireless sensor network (WSN) is considered to be one of the most important research fields for ubiquitous healthcare (u-healthcare) applications. Healthcare systems combined with WSNs have only been introduced by several pioneering researchers. However, most researchers collect physiological data from medical nodes located at static locations and transmit them within a limited communication range between a base station and the medical nodes. In these healthcare systems, the network link can be easily broken owing to the movement of the object nodes. To overcome this issue, in this study, the fast link exchange minimum cost forwarding (FLE-MCF) routing protocol is proposed. This protocol allows real-time multi-hop communication in a healthcare system based on WSN. The protocol is designed for a multi-hop sensor network to rapidly restore the network link when it is broken. The performance of the proposed FLE-MCF protocol is compared with that of a modified minimum cost forwarding (MMCF) protocol. The FLE-MCF protocol shows a good packet delivery rate from/to a fast moving object in a WSN. The designed wearable platform utilizes an adaptive linear prediction filter to reduce the motion artifacts in the original electrocardiogram (ECG) signal. Two filter algorithms used for baseline drift removal are evaluated to check whether real-time execution is possible on our wearable platform. The experiment results shows that the ECG signal filtered by adaptive linear prediction filter recovers from the distorted ECG signal efficiently.

• Proceedings of the ESK Conference
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• 1994.04a
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• pp.115-123
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• 1994

A man model can be used as an effective tool to design ergomonically sound products and workplaces, and subsequently evaluate them properly. For a man model to be truly useful, it must be integrated with a posture prediction model which should be capable of representing the human arm reach posture in the context of equipments and workspaces. Since the human movement possesses redundant degrees of freedom, accurate representation or prediction of human movemtn was known to be a difficult problem. To solve this redundancy problem, the psychophysical cost function can predict the arm reach posture accurately. But the joint discomfort that human feels at the joint can not be predicted since the effects of external factors on the joint discomfort is not known. In this study a psychophysical experi- ment using the magnitude estimation technique was performed to evaluate the effects of external factors such as joint, joint angle and Perceived Exertion Ratio on the joint discomfort. Results showed that the joint discomfort increased as the Perceived Exertion Ratio increased, but the relation is not linear and was affected not only by the joint but also by the joint angle for the same Perceived Exertion Ratio. The interaction effect of the joint and the joint angle was also significant. From the results it is needed to develope the cost function which can predict the joint discomfort considering the joint, joint angle and external load.

• Journal of the Korea Computer Industry Society
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• v.2 no.7
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• pp.997-1014
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• 2001

In this paper, we propose a method for dynamic selection of unrestricted motion vector(UMV) or default prediction mode(DPM) in H.263 bit stream. For this, we use the error of compensated image and the magnitude of motion vector. In the proposed strategy, the UMV mode is dynamically applied in a frame according to average magnitude of motion vector and error of compensated image. This scheme has improved the quality of image compared to the fixed mode UMV or DPM only. Number of searching points are greatly reduced when comparing to UMV. The Proposed method is more profitable to long video sequences having camera movement locally.

• Journal of The Geomorphological Association of Korea
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• v.23 no.3
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• pp.67-78
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• 2016

Soil carbon(C) is an essential property for characterizing soil quality. Understanding spatial patterns of soil C is particularly limited for mountain areas. This study aims to predict the spatial pattern of soil C using terrain analysis in a steep mountainous area. Specifically, model performances and prediction uncertainties were investigated based on the number of resampling repetitions. Further, important predictors for soil C were also identified. Finally, the spatial distribution of uncertainty was analyzed. A total of 91 soil samples were collected via conditioned latin hypercube sampling and a digital soil C map was developed using support vector regression which is one of the powerful machine learning methods. Results showed that there were no distinct differences of model performances depending on the number of repetitions except for 10-fold cross validation. For soil C, elevation and surface curvature were selected as important predictors by recursive feature elimination. Soil C showed higher values in higher elevation and concave slopes. The spatial pattern of soil C might possibly reflect lateral movement of water and materials along the surface configuration of the study area. The higher values of uncertainty in higher elevation and concave slopes might be related to geomorphological characteristics of the research area and the sampling design. This study is believed to provide a better understanding of the relationship between geomorphology and soil C in the mountainous ecosystem.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.11a
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• pp.199-202
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• 2008

This paper presents a mathematical-physical model to predict the performance of a gas pusher used as a separation system powered by a gas generator. The empirical coefficients of heat loss and friction were determined from experiments. Based on the grain configuration of the gas generator, the analytical approach of combustion, flow and movement of a piston inside the chamber of a gas generator and a gas pusher was simulated by numerical method. The prediction method developed can be usefully applied to the design of separation mechanism systems.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• The korean journal of orthodontics
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• v.27 no.1
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• pp.65-78
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• 1997

This study was carried out in order to determine soft tissue response to incisor movement and mandibular repositioning and to determine feasibility of predicting vertical and horizontal changes in soft tissue with hard tissue movement. For this study, cephalometric records of 41 orthodontically treated adult females who had Angle's Class II division 1 malocclusion were selected and stepwise multiple regression analysis was employed. Following conclusions were obtained by analysing the changes of soft tissue and hard tissue before and after treatment. 1. Hard tissue measurements that showed significant changes before and after treatment were horizontal and angular changes of maxillary incisor, horizontal,vertical and angular changes of mandibular incisor, overjet, overbite, interincisal angle, mandibular repositioning, A,B, skeletal convexity and soft tissue measurements that showed significant changes were horizontal, thickness and angular changes of upper lip, horizontal and angular changes of lower lip, interlabial angle, nasolabial angle labiomental angle, Sri, Ss, Si and soft tissue convexity(P<0.05). 2. All Soft tissue measurements changed significantly before and after treatment had between one and four hard tissue independent variables at statistically significant level, indicating that all soft tissue changes were direct relationship with hard tissue changes 3. Ova jet, horizontal change of maxillary incisor, horizontal change of maxillary root apex and horizontal change of pogonion entered into prediction equations most frequentely indicating that they were more significant variables in prediction of vertical and horizontal changes in the soft tissue with treatment, but vertical changes of mandibular incisor not entered any prediction equations, indicating that it was not considered a good predictor for soft tissue changes with maxillary incisor retraction. 4. Horizontal and vertical changes in subnasale were found to have most independent variables, significant at the 0.05 level in prediction-equations(${\Delta}$Sn(H):Ur, Is(H), Pg(H), UIA,${\Delta}$Sn(V): Is(H), Pg(H), overjet, A), indicating that subnasale changes are influenced by complex hard tissue interaction. 5. Multiple correlation coefficient($R^2$) of the soft tissue prediction equations ranges from 0.2-0.6.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.53-59
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• 2020

Initial response is important in marine oil spills, such as the Hebei Spirit oil spill, but it is very difficult to predict the movement of oil out of the ocean, where there are many variables. In order to solve this problem, the forecasting of oil spill has been carried out by expanding the particle prediction, which is an existing study that studies the movement of floats on the sea using the data of the float. In the ocean data format HDF5, the current and wind velocity data at a specific location were extracted using bilinear interpolation, and then the movement of numerous points was predicted by particles and the results were visualized using polygons and heat maps. In addition, we propose a spill oil particle matching algorithm to compensate for the lack of data and the difference between the spilled oil and movement. The spilled oil particle matching algorithm is an algorithm that tracks the movement of particles by granulating the appearance of surface oil spilled oil. The problem was segmented using principal component analysis and matched using genetic algorithm to the point where the variance of travel distance of effluent oil is minimized. As a result of verifying the effluent oil visualization data, it was confirmed that the particle matching algorithm using principal component analysis and genetic algorithm showed the best performance, and the mean data error was 3.2%.