• International journal of advanced smart convergence
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• v.9 no.4
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• pp.106-114
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• 2020

As information and communication technology develops, it brings various benefits to our lives. However, information and communication technology has had various side effects in our lives. Representative side effects include internet addiction, smartphone addiction, copyright violation, personal information infringement, cyber bullying and hacking. Recently, smart phone addiction rate is increasing with the spread of smart devices in Korea. In this study, we analyze the correlation between age group and smartphone addiction. In order to obtain fair and objective results, statistical analysis was performed based on the national statistical data of the National Information Society Agency. The results showed that the infant group and the adult group were correlated with the smartphone addiction rate. In this study, we analyzed the causes of smartphone addiction for different age groups. We also discuss dangers of smartphone addiction for different age groups. In additions, we proposed various ways to prevent and cure smartphone addiction for infants, adults, and senior citizen group. The results of this study are expected to be widely used as a remedy for smartphone addiction and future smartphone addiction research works.

• ETRI Journal
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• v.29 no.6
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• pp.823-825
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• 2007

In this letter, we propose a novel algorithm to predict MPEG-coded real-time variable bit rate (VBR) video traffic. From the frame size measurement, the algorithm extracts the statistical property of video traffic and utilizes it for the prediction of the next frame for I-, P-, and B- frames. The simulation results conducted with real-world MPEG-4 VBR video traces show that the proposed algorithm is capable of providing more accurate prediction than those in the research literature.

• The Journal of Economics, Marketing and Management
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• v.12 no.2
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• pp.1-10
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• 2024

Purpose: This study conducted an empirical study to estimate the loss aversion rate of individual investors in the Seoul condominium market. Research design, data and methodology: A survey was conducted with Seoul residents ranging from 30's to 60's with various backgrounds. Descriptive statistical analysis and a paired sample t-test were conducted using SPSS 27.0 statistical package. Results: The results of the t-test showed that Seoul residents are indeed more sensitive to loss than gains, as pointed out in various researches related to behavioral economics. Also, the loss aversion rate associated with KRW 50 million risk was found to be 2.14. Finally, the same question was asked with KRW 100 million risk, doubled associated risk of previous question, using the same scenario, and it's been verified that the loss aversion rate increases as the associated risk or stake increases. The loss aversion rate with double risk is 2.26 which is about 5% higher than the one with KRW 50 million risk. Conclusions: This study can help many groups of people in society who need to establish rewards and punishment policies within any organization. In particular, incorporating human cognitive biases, such as loss aversion can help the South Korean government shape more effective reward and punishment policies when building rewards and punishments using taxes.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.98.5-98
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• 2001

An efficient adaptive nonlinear compensator, based on a wavelet transform domain adaptive Volterra filter along with a modified escalator algorithm, is proposed to speed up the convergence rate of an adaptive LMS algorithm. In particular, it is well known that the e.g., slow convergence speed of an adaptive LMS algorithm depends on the statistical characteristics (e.g., large eigenvalue spread) of the corresponding auto-correlation matrix of the input vector. To solve such a convergence problem, the proposed approach utilizes a modified escalator algorithm and a wavelet transform domain adaptive LMS Volterra filtering technique, which leads to diagonalization of the auto-correlation matrix of the ...

• Journal of the Korea Convergence Society
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• v.7 no.1
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• pp.155-160
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• 2016

The convergence process utilized both leaching and ion exchange techniques has been investigated for the heavy metals removal in the abandoned mine of Chungyang Province, Korea. The contaminated soil samples by heavy metals from Samkwang mine were analysed by statistical analyses. The highly contaminated soils was initially separated by the flotation process. The selectivity indices were increased with increasing flotation reagents. The selectivity of separation was then improved by the use of both leaching and ion exchange processes in order to extract the heavy metals. The results of this study showed that the higher the sulfuric acid concentration, the leaching rate of heavy metals was increased. The lecheate then was removed by the ion exchange method. The anticipating results might imply that convergence process utilized both leaching and ion exchange techniques would somehow apply for the removal of heavy metals in the abandoned mine.

• Journal of Convergence Korean Medicine
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• v.6 no.1
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• pp.37-42
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• 2024

Objectives: This study aims to evaluate the clinical validity of the system by conducting a clinical study to assess the diagnostic agreement between the system and Korean medical doctors. Methods: This study was conducted from September 7, 2023, to December 7, 2023, across five Korean medicine institutions, involving 100 adult participants aged 20-64 who consented to participate. Participants first entered their symptoms into a web-based program, which utilized an AI-based algorithm to diagnose 36 types of pattern differentiation. Subsequently, Korean medical doctors conducted face-to-face diagnoses using the same 36 types. The diagnostic agreement between the system and the doctors' diagnoses was analyzed using descriptive statistical analysis, and the results were expressed as a percentage agreement. Results: Analysis of the diagnostic data from 100 participants revealed that the web-based diagnosis support system identified an average of 7.76±0.79 patterns per patient, while Korean medical doctors identified an average of 7.99±0.10 patterns per patient. The diagnostic agreement between the system and the doctors showed an average of 7.08±1.08 patterns per patient, with an overall diagnostic agreement rate of 88.57±13.31%. Conclusion: This study developed a web-based diagnosis support system for traditional Korean medicine and evaluated its clinical validity by assessing diagnostic agreement. Comparing the diagnoses of the system with those of Korean medical doctors for 100 patients, the system showed an approximately 89% agreement rate with the clinical diagnoses. The system holds potential for aiding Korean medical doctors in pattern differentiation diagnosis in clinical practice.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.49-54
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• 2002

For arbitrary random variables {$X_{n},n{\geq}1$}, the order of growth of the series. $S_{n}\;=\;{\sum}_{j=1}^n\;X_{j}$ is studied in this paper. More specifically, when the series S_{n}$ diverges almost surely, the strong law of large numbers $S_{n}/g_{n}^{-1}$($A_{n}{\psi}(A_{n}))\;{\rightarrow}\;0$ a.s. is constructed by extending the results of Petrov (1973). On the other hand, if the series $S_{n}$ converges almost surely to a random variable S, then the tail series $T_{n}\;=\;S\;-\;S_{n-1}\;=\;{\sum}_{j=n}^{\infty}\;X_{j}$ is a well-defined sequence of random variables and converges to 0 almost surely. For the almost surely convergent series $S_{n}$, a tail series strong law of large numbers $T_{n}/g_{n}^{-1}(B_{n}{\psi}^{\ast}(B_{n}^{-1}))\;{\rightarrow}\;0$ a.s., which generalizes the result of Klesov (1984), is also established by investigating the duality between the limiting behavior of partial sums and that of tail series. In particular, an example is provided showing that the current work can prevail despite the fact that previous tail series strong law of large numbers does not work.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.657-668
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• 2023

It's important to note that air compressors in the industrial sector are major energy consumers, accounting for a significant portion of total energy costs in manufacturing plants, ranging from 12% to 40%. To address this issue, researchers have compared forecasting models that can predict the power consumption of air compressors. The forecasting models were designed to incorporate variables such as flow rate, pressure, temperature, humidity, and dew point, utilizing statistical methods, machine learning, and deep learning techniques. The model performance was compared using measures such as RMSE, MAE and SMAPE. Out of the 21 models tested, the Elastic Net, a statistical method, proved to be the most effective in power comsumption forecasting.

• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.40-44
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• 2010

opportunistic spectrum access (OSA) allows unlicensed users to share licensed spectrum in space and time with no or little interference to primary users, with bring new research challenges in MAC design. We propose a cognitive MAC protocol using statistical channel information and selecting appropriate idle channel for transmission. The protocol based on the CSMA/CA, exploits statistics of spectrum usage for decision making on channel access. Idle channel availability, spectrum hole sufficiency and available channel condition will be included in algorithm statistical information. The model include the control channel and data channel, the transmitter negotiates with receiver on transmission parameters through control channel, statistical decision results (successful rate of transmission) from exchanged transmission parameters of control channel should pass the threshold and decide the data transmission with spectrum hole on data channel. A dynamical sensing range as a important parameter introduced to maintain the our protocol performance. The proposed protocol's simulation will show that proposed protocol does improve the throughput performance via traditional opportunistic spectrum access MAC protocol.