• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.75-94
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• 2023

The growing trend of cyber risk has put forward the importance of cyber risk management. Cyber risk is defined as an accidental or intentional risk related to information and technology assets. Although cyber risk is a subset of operational risk, it is reported to be handled differently from operational risk due to its different features of the loss distribution. In this study, we aim to detect the characteristics of cyber loss and find a suitable model by measuring value at risk (VaR). We use the loss distribution approach (LDA) and the time series model to describe cyber losses of financial and non-financial business sectors, provided in SAS^® OpRisk Global Data. Peaks over threshold (POT) method is also incorporated to improve the risk measurement. For the financial sector, the LDA and GARCH model with POT perform better than those without POT, respectively. The same result is obtained for the non-financial sector, although the differences are not significant. We also build a two-dimensional model reflecting the dependence structure between financial and non-financial sectors through a bivariate copula and check the model adequacy through VaR.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.217-223
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• 2009

Vocabulary recognition system of Mobile terminal is executed statistical method for vocabulary recognition and used statistical grammar recognition system using N-gram. If limit arithmetic processing capacity in memory of vocabulary to grow then vocabulary recognition algorithm complicated and need a large scale search space and many processing time on account of impossible to process. This study suggest vocabulary recognition optimize using MLHF System. MLHF separate acoustic search and lexical search system using FLaVoR. Acoustic search feature vector of speech signal extract using HMM, lexical search recognition execution using Levenshtein distance algorithm. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%, represent recognition speed of 1.61 second.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.28.1-28.1
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• 2021

We present a statistical analysis of dark matter halos with interacting neighbors using a set of cosmological simulations. We classify the neighbors into two groups based on the total energy (E₁₂) of the target-neighbor system; flybying neighbors (E₁₂ ≥ 0) and merging ones (E₁₂ < 0). First, we find a different trend between the flyby and merger fractions in terms of the halo mass and large-scale density. The flyby fraction highly depends on the halo mass and environment, while the merger fraction show little dependence. Second, we measure the spin-orbit alignment, which is the angular alignment between the spin of a target halo (${\vec{S}}$ ) and the orbital angular momentum of its neighbor (${\vec{L}}$). In the spin-orbit angle distribution, the flybying neighbors show a weaker prograde alignment with their target halos than the merging neighbors do. With respect to the nearest filament, the flybying neighbor has a behavior different from that of the merging neighbor. Finally, we discuss the physical origin of two interaction types.

• Korean Journal of Biological Psychiatry
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• v.19 no.2
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• pp.106-113
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• 2012

Objectives : The purpose of this study is to investigate the relationship between the triallelic serotonin transporter gene and stressful life events to determine their effect on depression with alcohol dependence. Methods : Ninety-five hospitalized patients with alcohol dependence (73 male, 22 female) were enrolled in this study. Thirty-two (33.7%) of the total patients were diagnosed with major depressive disorder and dysthymic disorder by Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV. The characteristics of stress were evaluated using the stressful life events scale, and depressive symptoms were assessed using the depression scale (Beck Depression Inventory, BDI). Alcoholism with depression (n = 32) and alcoholism without depression (n = 63) were genotyped for the triallelic serotonin transporter gene ($L_A$ : higher expressing allele, $L_G$/S : lower expressing allele). Results : There was no significant difference in the allele frequency between the depression group and the non-depression group (${\chi}^2$ = 0.345, p = 0.619). $L_G$/S alleles had more comorbid depression in the higher score of stressful life events scale [Mental-Haenszel (MH)-${\chi}^2$ = 4.477, p = 0.034]. But there was no significant difference in the comorbidity according to the scores from the stressful life event scale in the $L_A$ alleles (MH-${\chi}^2$ = 0.741, p = 0.399). In the results, alcohol-dependent individuals with $L_G$/S alleles had more comorbid depression than those with $L_A$ alleles when they had experienced severe stressful life events (MH-odds ratio = 2.699, p = 0.028). Conclusions : These results suggest that there is no direct relationship between triallelic serotonin transporter gene and depression in the alcohol dependent patients. But alcohol dependent individuals with the lower expressing alleles of the serotonin transporter gene were more susceptible to depression than those with the higher expressing alleles in response to stressful life events.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.149-157
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• 2014

As the prediction of geomagnetic storms is becoming an important and practical problem, conditions in the Earth's magnetosphere have been studied rigorously in terms of those in the interplanetary space. Another approach to space weather forecast is to deal with it as a probabilistic geomagnetic storm forecasting problem. In this study, we carry out detailed statistical analysis of solar wind parameters and geomagnetic indices examining the dependence of the distribution on the solar cycle and annual variations. Our main findings are as follows: (1) The distribution of parameters obtained via the superimposed epoch method follows the Gaussian distribution. (2) When solar activity is at its maximum the mean value of the distribution is shifted to the direction indicating the intense environment. Furthermore, the width of the distribution becomes wider at its maximum than at its minimum so that more extreme case can be expected. (3) The distribution of some certain heliospheric parameters is less sensitive to the phase of the solar cycle and annual variations. (4) The distribution of the eastward component of the interplanetary electric field BV and the solar wind driving function BV2, however, appears to be all dependent on the solar maximum/minimum, the descending/ascending phases of the solar cycle and the equinoxes/solstices. (5) The distribution of the AE index and the Dst index shares statistical features closely with BV and $BV^2$ compared with other heliospheric parameters. In this sense, BV and $BV^2$ are more robust proxies of the geomagnetic storm. We conclude by pointing out that our results allow us to step forward in providing the occurrence probability of geomagnetic storms for space weather and physical modeling.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.79-87
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• 2011

A spatial time series model was used for analyzing the method of spatial time series (not the ARIMA model that is popular for analyzing spatial time series) by using chicken pox data which is a highly contagious disease and grid data due to ARIMA not reflecting the spatial processes. Time series model contains a weighting matrix, because that spatial time series model influences the time variation as well as the spatial location. The weighting matrix reflects that the more geographically contiguous region has the higher spatial dependence. It is hypothesized that the weighting matrix gives neighboring areas the same influence in the study of the spatial time series model. Therefore, we try to present the conclusion with a weighting matrix in a way that gives the same weight to existing neighboring areas in the study of the suitability of the STARMA model, spatial time series model and STBL model, in the comparative study of the predictive power for statistical inference, and the results. Furthermore, through the Kalman-Filter method we try to show the superiority of the Kalman-Filter method through a parameter assumption and the processes of prediction.

• Korean Journal of Materials Research
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• v.11 no.6
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• pp.492-497
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• 2001

We report the characteristics and the dependence of sputter-deposited Ta films on the process parameters. The properties of as-deposited Ta films such as deposition rate, resistivity, Rs uniformity, reflectivity, and stress were investigated and analyzed as a function of process parameter using a statistical experimental method. The functional relationships between the independent and dependent variables were predicted by surface response. The optimal deposition condition of DC magnetron sputtered Ta films was obtained at the chamber pressure of 2 mTorr, power density of 8 W/$\textrm{cm}^2$, and substrate temperature of 2$0^{\circ}C$ by means of resistivity and Rs uniformity. The fitness value for quadratic model as evaluated by the R- square was 0.85~ 0.9 without pooling. The as-deposited Ta films exhibited the resistivity of ~180$\mu$$\Omega$cm with Rs uniformity of ~2%. The transmission electron microscopy and x-ray diffractometry identified that the phase of as-deposited film was $\beta$-Ta having the grain size of 100~200.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.57-68
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• 2021

Successfully adapting to digital and customer-oriented transformation, the concept of lean manufacturing professes the philosophy of creating greater benefit while minimizing losses. These losses are operations that do not add value in the production process to ensure the efficiency, flexibility, and profitability of projects. In the context of broad automation and digitalization of all sectors of the economy, mechanisms for combining automation technologies and lean production are becoming available. Moreover, when it comes to the efficient use of financial, human, or material resources, it is clear that the use of Industry 4.0 technologies can be an effective tool for achieving the goals of lean production, as many of them pursue the same goal. In this context, this article aims to study the effectiveness of the implementation of project management concepts at the global level and identify the main factors influencing its effectiveness to ensure the achievement of lean production through LEAN technologies and Industry 4.0 technologies. To achieve this goal, several statistical indicators were selected and several statistical methods of analysis were used: pairwise correlation, regression analysis, methods of comparison, synthesis, and generalization. Statistical analysis was conducted according to a survey conducted by the Project Management Institute (PMI) in 2020. An economic-mathematical model of dependence of project effectiveness in different regions of the world on the level of implementation of project management approaches is built, which shows that the increase in project effectiveness by 85% is due to financial losses, technical training, and consumer orientation. These results allow project managers to develop appropriate strategies to improve project management approaches at all levels. It is established that LEAN technologies and technologies of Industry 4.0 have several tools that have a positive effect on minimizing losses following the concept of lean production. Besides, given that the technology of Industry 4.0 is focused on the automation of Lean Production technology, a mechanism for the introduction of lean production using these technologies and methods.