• International Commerce and Information Review
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• v.9 no.1
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• pp.403-425
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• 2007

The sudden emergence of China as a superpower in world politics and economics is apparently a big surprise. This, however, is not an unusual incident. As far as the Chinese are concerned, they say that China is simply running almost the same track that its neighboring countries. In the recent years, a number of experts and scholars have expected that the emergence of China as a great economic power will be argued as a major issue in world politics and economics. So its economic progress will require experts and scholars to watch carefully how China is going to change. It certainly has created an atmosphere that most of the world leaders, experts and scholars are very concerned about China's remarkable performance in its economics and then willing to accept China's rapid growth as an urgent matter. Many experts and scholars began to analyze carefully the factors that have contributed to the rapid growth. Foreign direct investment (FDI), import-export, and economic reform were then listed as the most important factors. As a result, philosophy of economics, analytical economics, and economics are immediately needed for China who is at the moment very anxious to sustain the stable and continuity of rapid economic growth. But unfortunately China does not even recognize the reason why they need to adopt these economic concepts and methods.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.117-124
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• 2013

In this paper, we present a new method which efficiently estimates a face direction from a sequences of input video images in real time fashion. For this work, the proposed method performs detecting the facial region and major facial features such as both eyes, nose and mouth by using the Haar-like feature, which is relatively not sensitive against light variation, from the detected facial area. Then, it becomes able to track the feature points from every frame using optical flow in real time fashion, and determine the direction of the face based on the feature points tracked. Further, in order to prevent the erroneously recognizing the false positions of the facial features when if the coordinates of the features are lost during the tracking by using optical flow, the proposed method determines the validity of locations of the facial features using the template matching of detected facial features in real time. Depending on the correlation rate of re-considering the detection of the features by the template matching, the face direction estimation process is divided into detecting the facial features again or tracking features while determining the direction of the face. The template matching initially saves the location information of 4 facial features such as the left and right eye, the end of nose and mouse in facial feature detection phase and reevaluated these information when the similarity measure between the stored information and the traced facial information by optical flow is exceed a certain level of threshold by detecting the new facial features from the input image. The proposed approach automatically combines the phase of detecting facial features and the phase of tracking features reciprocally and enables to estimate face pose stably in a real-time fashion. From the experiment, we can prove that the proposed method efficiently estimates face direction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.139-145
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• 2016

Dumbbell is the most basic topology that can be used in almost all kind of network experiment within it or just by little expansion. While Transmission Control Protocol TCP is the basic protocol that is used for the connectivity among networks and stations. TCP major and basic goal is to provide path and services to different applications for communication. For that reason TCP has to transfer a lot of data through a communication medium that cause serious congestion problem. To calculate the congestion problem, different kind of pre-cure solutions are developer which are Loss Based Variant and Delay Based Variant. While LBV keep track of the data that is going to be passed through TCP protocol, if the data packets start dropping that means congestion occurrence which notify as a symptom, TCP CUBIC use LBV for notifying the loss. Similarly the DBV work with the acknowledgment procedure that is used in when data ACK get late with respect to its set data rate time, TCP COMPOUND/VAGAS are examples of DBV. Many algorithms have been purposed to control the congestion in different TCP variants but the loss of data packets did not completely controlled. In this paper, the congestion control algorithms are implemented and corresponding results are analyzed in Dumbbell topology, it is typically used to analyze the TCP traffic flows. Fairness of throughput is evaluated for different TCP variants using network simulator (NS-2).

• Korean Journal of Childcare and Education
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• v.8 no.4
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• pp.5-31
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• 2012

The purpose of this study was to examine major journals of early childhood education and childcare in an effort to determine research trends in early childhood education and childcare policies between 1995 and 2011. One hundred twenty-six papers included in journals that registered with Korea Research Foundation were analyzed to keep track of research trends in early childhood education and childcare policies. A research trend in another area that early childhood education and childcare policies were linked to each other in a broad sense was explored as well. Specifically, the year of the papers, their research themes, purposes, data gathering methods, form of research, number of researchers and financial assistance were analyzed. As a result, it was found that a lot of papers were presented in and after the mid-2000s, and that the most dynamic research efforts were channeled into childcare policies. As for research theme, a wide variety of themes were covered every year. The most dominant type of research was literature review, and the most common purpose of the studies was to examine the state of national policies. Literature analysis was the most prevailing data collection methods, and the most dominant form of research was case studies. In terms of the number of researchers and financial aid, the largest number of the papers was conducted by individuals and without any financial assistance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.727-737
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• 2011

The emergency restoration scenarios for efficient railway accident management and restoration were developed. The emergency restoration procedures defined by the worst case of emergency restoration and the important events was proposed based on questionnaires from specialists and the result of survey. Via these studies, the railway accident in the tunnel could be the worst case among all railway accident types. Therefore, educations for a restoration team in confined area condition should be planned and performed to recover the worst case accident. In order for the emergency restoration, when a railway accident is occurred, the restoration should be performed in orders of handing collapse of facilities, burying track, and derailment of vehicle in tunnel based on the statistical analysis. The result of priorities were established by the period of restoration. The standard operation system for efficient railway accident management was developed by synthesizing the worst case for rapid emergency restoration, and important events for the standard operation procedures according to each emergency restoration type. Through this study, the restoration operation system of railway accident are recommended. This paper suggests to develop emergency restoration scenarios for the efficient railway accident management and recovery system. The study results will contribute not only for insuring punctuality, but also for minimizing delays from accidents. Therefore, emergency restoration scenarios will play a major role in the SOP for the damage limitation and the prevention of accident spread.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.357-368
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• 2020

This study identifies the aspects and characteristics of 'Digital Sexual Crimes' that changed rapidly in recent years. It has identified the so-called "Telegram sexual harassment and exploitation" incident on the front page. We also want to analyze this and draw up policy suggestions that can help prepare social measures. In the wake of the Telegram sexual exploitation scandal, The National Assembly is quickly proposing related bills. However, the reality is that even a clear concept and definition of "Digital sexual Crimes" have not been made yet. The effective support system for victims is also insufficient. Therefore, this paper examines the definition and concept of child sexual exploitation and harassment. We will look at the features, causes, and conditions. In addition, it will examine the current status of Digital Sexual Crimes distribution and deletion of domestic, foreign platforms. Major foreign countries, including the U. S. A. refer to cases in which big data and artificial intelligence technologies are actively used to protect victims and track perpetrators.

• Korean Journal of Applied Biomechanics
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• v.15 no.3
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• pp.143-152
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• 2005

The purpose of this study was to predict the failure or success of the Snatch-lifting trial as a consequence of the stand-up phase simulated in Kane's equation of motion that was effective for the dynamic analysis of multi-segment. This experiment was a case study in which one male athlete (age: 23yrs, height: 154.4cm, weight: 64.5kg) from K University was selected The system of a simulation included a multi-segment system that had one degree of freedom and one generalized coordinate for the shank segment angle. The reference frame was fixed by the Nonlinear Trans formation (NLT) method in order to set up a fixed Cartesian coordinate system in space. A weightlifter lifted a 90kg-barbell that was 75% of subject's maximum lifting capability (120kg). For this study, six cameras (Qualisys Proreflex MCU240s) and two force-plates (Kistler 9286AAs) were used for collecting data. The motion tracks of 11 land markers were attached on the major joints of the body and barbell. The sampling rates of cameras and force-plates were set up 100Hz and 1000Hz, respectively. Data were processed via the Qualisys Track manager (QTM) software. Landmark positions and force-plate amplitudes were simultaneously integrated by Qualisys system The coordinate data were filtered using a fourth-order Butterworth low pass filtering with an estimated optimum cut-off frequency of 9Hz calculated with Andrew & Yu's formula. The input data of the model were derived from experimental data processed in Matlab6.5 and the solution of a model made in Kane's method was solved in Matematica5.0. The conclusions were as follows; 1. The torque motor of the shank with 246Nm from this experiment could lift a maximum barbell weight (158.98kg) which was about 246 times as much as subject's body weight (64.5kg). 2. The torque motor with 166.5 Nm, simulated by angular displacement of the shank matched to the experimental result, could lift a maximum barbell weight (90kg) which was about 1.4 times as much as subject's body weight (64.5kg). 3. Comparing subject's maximum barbell weight (120kg) with a modeling maximum barbell weight (155.51kg) and with an experimental maximum barbell weight (90kg), the differences between these were about +35.7kg and -30kg. These results strongly suggest that if the maximum barbell weight is decided, coaches will be able to provide further knowledge and information to weightlifters for the performance improvement and then prevent injuries from training of weightlifters. It hopes to apply Kane's method to other sports skill as well as weightlifting to simulate its motion in the future study.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.63-72
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• 2018

The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.365-379
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• 2021

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.