• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1307-1329
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• 2022

This paper proposes an improved Pseudorandom Sequence Generator (PRSG) based on the concept of modular arithmetic systems with non-integral numbers. The generated random sequence use in various cryptographic applications due to its unpredictability. Here the mathematical model is designed to solve the problem of the non-uniform distribution of the sequences. In addition, PRSG has passed the standard statistical and empirical tests, which shows that the proposed generator has good statistical characteristics. Finally, image encryption has been performed based on the sort-index method and diffusion processing to obtain the encrypted image. After a thorough evaluation of encryption performance, there has been no direct association between the original and encrypted images. The results show that the proposed PRSG has good statistical characteristics and security performance in cryptographic applications.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.301-317
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• 2022

In this paper, we propose a change point detection procedure based on the modified information criterion in a generalized lambda distribution (GLD) model. Simulations are conducted to obtain empirical critical values of the proposed test statistic. We have also conducted simulations to evaluate the performance of the proposed methods comparing to the log-likelihood method in terms of power, coverage probability, and confidence sets. Our results indicate that, under various conditions, the proposed method modified information criterion (MIC) approach shows good finite sample properties. Furthermore, we propose a new goodness-of-fit testing procedure based on the energy distance to evaluate the asymptotic null distribution of our test statistic. Two real data applications are provided to illustrate the use of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.130-138
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• 1996

In general, the scattler is observed in fatigue test data due to the nonhomogeneity of a material. Consequently. It is necessary to use the statistical method to describe the fatigue crack growth process precisely. Bogdanoff and Kozin suggested and developed the B-model which is the probabilistic models of cumulative damage using the Markov process in order to describe the damage process. But the B-model uses only constant probability ratior(r), so it is not consistent with the actual damage process. In this study, the r-decreasing model using a monotonic decreasing function is introduced to improve the B-model. To verify the model, thest data of fatigue crack growth of A12024-T351 and A17075-T651 are used. Compared with the empirical distribution of test data, the distribution from the r-decreasing model is satisfactory and damage process is well described from the probabilistic and physical viewpoint.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.147-153
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• 1997

Fault diagnosis technique in machining system which is one of engineering techniques absolutely necessary to automation of manufacturing system has been proposed. As a whole, diagnosis process is explained by two steps: sensor data acquisition and reasoning current state of system with the given sensor data. Flexible disk grinding process implemented in milling machine was employed in order to obtain empirical manufacturing process information. Resistance force data during machining were acquired using tool dynamometer known as sensor which is comparably accurate and reliable in operation. Tool status during the process was analyzed using influnece diagram assigning probability from the statistical analysis procedure.

• Pediatric Infection and Vaccine
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• v.18 no.2
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• pp.109-116
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• 2011

Purpose : Pneumococcus is one of the most important causes of invasive infection through the childhood period. In January 2008, the Clinical and Laboratory Standards Institute (CLSI) published revised penicillin breakpoints for Streptococcus pneumoniae and penicillin susceptibility rates of S. pneumoniae increased in Korea. This study was performed to determine the probability of oral amoxicillin for the empirical treatment achieving bactericidal exposure against pneumococcus using pharmacodynamics model. Methods : Twenty-three isolates of pneumococci were subjected to determine minimum inhibitory concentration (MIC) for ${\beta}$-lactams and macrolide. For the ${\beta}$-lactams, exposure of fT >MIC (time that free drug concentrations remain above the MIC) for 50% of the administration interval have determined the probability of target attainment (PTA), and regimens that had a PTA >90% were considered optimal. An analysis was performed by applying MIC of 23 isolates to a 5000-patient Monte Carlo simulation model. Results : Among 23 isolates from healthy children, 7 (30.4%) isolates were MIC ${\leq}$1.0 ${\mu}g$/mL and 19 (82.6%) were MIC ${\leq}$2 ${\mu}g$/mL for amoxicillin. Amoxicillin 40 mg/kg/day achieved PTA >90% at MIC ${\leq}$1.0 ${\mu}g$/mL but PTA decreased to 52% at MIC 2 ${\mu}g$/mL, whereas amoxicillin 90 mg/kg/day can predict 97% of PTA at MIC 2 ${\mu}g$/mL. Overall, oral amoxicillin 90 mg/ kg/day for the empirical treatment against pneumococcus can expect more successful response in Korean children. Conclusion : Considering the resistantce pattern of pneumococci in Korean children, we estimate that oral amoxicillin 90 mg/kg/day will provide a pharmacodynamic advantage for the empirical treatment against pneumococcus. And low dose amoxicillin or macrolide are expected to have higher chance of treatment failure than high dose oral amoxicillin.

• Journal of the Korea Concrete Institute
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• v.21 no.2
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• pp.187-197
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• 2009

This paper presents a systematic approach for estimating fragility curves and damage probability matrices for different frequencies. Fragility curves and damage probability indicate the probabilities that a structure will sustain different degrees of damage at different ground motion levels. The seismic damages are to achieved by probabilistic evaluation because of uncertainty of earthquakes. In contrast to previous approaches, this paper presents a method that is based on nonlinear dynamic analysis of the structure using empirical data. This paper presents the probability of damage as a function of peak ground acceleration and estimates the probability of five damage levels for prestressed concrete (PSC) bridge pier subjected to given ground acceleration. At each level, 100 artificial earthquake motions were generated in terms of soil conditions, and nonlinear time domain analyses was performed for the damage states of PSC bridge pier structures. These damage states are described by displacement ductility resulting from seismic performance based on existing research results. Using the damage states and ground motion parameters, five fragility curves for PSC bridge pier with five types of dominant frequencies were constructed assuming a log-normal distribution. The effect of dominant frequences was found to be significant on fragility curves.

• Journal of Distribution Science
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• v.15 no.6
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• pp.77-96
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• 2017

Purpose - This study aims to develop correspondence strategies to the environment change in domestic retail store types. Recently, new types of retails have emerged in retail industries. Therefore, trade area platform has developed focusing on the speed of data, no longer trade area from district border. Besides, 'trade area smart' brings about change in retail types with the development of giga internet. Thus, context shopping is changing the way of consumers' purchase pattern through data capture, technology capability, and algorithm development. For these reasons, the sales estimation model has been shown to be flawed using the notion of former scale and time, and it is necessary to construct a new model. Research design, data, and methodology - This study focuses on measuring retail change in large multi-shopping mall for the outlook for retail industry and competition for trade area with the theoretical background understanding of retail store types and overall domestic retail conditions. The competition among retail store types are strong, whereas the borders among them are fading. There is a greater need to analyze on a new model because sales expectation can be hard to get with business area competition. For comprehensive research, therefore, the research method based on the statistical analysis was excluded, and field survey and literature investigation method were used to identify problems and propose an alternative. In research material, research fidelity has improved with complementing research data related with retail specialists' as well as department stores. Results - This study analyzed trade area survival and its pattern through sales estimation and empirical studies on trade areas. The sales estimation, based on Huff model system, counts the number of households shopping absorption expectation from trade areas. Based on the results, this paper estimated sales scale, and then deducted modified probability model. Conclusions - In times of retail store chain destruction and off-line store reorganization, modified Huff model has problems in estimating sales. Transformation probability model, supplemented by the existing problems, was analyzed to be more effective in competitiveness business condition. This study offers a viable alternative to figure out related trade areas' sale estimation by reconstructing new-modified probability model. As a result, the future task is to enlarge the borders from IT infrastructure with data and evidence based business into DT infrastructure.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• The Journal of Information Systems
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• v.27 no.1
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• pp.171-191
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• 2018

Purpose Corporate technology leakage is not only monetary loss, but also has a negative impact on the corporate image and further deteriorates sustainable growth. In particular, since SMEs are highly dependent on core technologies compared to large corporations, loss of technology leakage threatens corporate survival. Therefore, it is important for SMEs to "prevent and protect technology leakage". With the recent development of data analysis technology and the opening of public data, it has become possible to discover and proactively detect companies with a high probability of technology leakage based on actual company data. In this study, we try to construct profiles of enterprises with and without technology leakage experience through profiling analysis using data mining techniques. Furthermore, based on this, we propose a classification model that distinguishes companies that are likely to leak technology. Design/methodology/approach This study tries to develop the empirical model for prevention and protection of technology leakage through profiling method which analyzes each SME from the viewpoint of individual. Based on the previous research, we tried to classify many characteristics of SMEs into six categories and to identify the factors influencing the technology leakage of SMEs from the enterprise point of view. Specifically, we divided the 29 SME characteristics into the following six categories: 'firm characteristics', 'organizational characteristics', 'technical characteristics', 'relational characteristics', 'financial characteristics', and 'enterprise core competencies'. Each characteristic was extracted from the questionnaire data of 'Survey of Small and Medium Enterprises Technology' carried out annually by the Government of the Republic of Korea. Since the number of SMEs with experience of technology leakage in questionnaire data was significantly smaller than the other, we made a 1: 1 correspondence with each sample through mixed sampling. We conducted profiling of companies with and without technology leakage experience using decision-tree technique for research data, and derived meaningful variables that can distinguish the two. Then, empirical model for prevention and protection of technology leakage was developed through discriminant analysis and logistic regression analysis. Findings Profiling analysis shows that technology novelty, enterprise technology group, number of intellectual property registrations, product life cycle, technology development infrastructure level(absence of dedicated organization), enterprise core competency(design) and enterprise core competency(process design) help us find SME's technology leakage. We developed the two empirical model for prevention and protection of technology leakage in SMEs using discriminant analysis and logistic regression analysis, and each hit ratio is 65%(discriminant analysis) and 67%(logistic regression analysis).

• Health Policy and Management
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• v.19 no.3
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• pp.109-124
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• 2009

Background: This study aimed to examine whether cases of punishing false claimants threat general physicians to check their medical cost claims with care to avoid being suspected, and identify empirically general deterrence effects of the on-site investigation system in the Korean National Health Insurance. Methods: 800 clinics were selected among a total of 15,443 clinics that had no experience of on-site investigation until June 2007 using a stratified proportional systematic sampling method. We conducted logistic multiple regression to examine the association between factors related to provider's perception of on-site investigation and high level of perceived deterrence referring to fear of punishment after adjusting provider's service experiences and general characteristics. Results: The probability of high perceived deterrence was higher 1.7 times (CI: 1.13-2.56), 2.73 times (CI: 1.68-4.45) each among clinics exchanging the information once or more per year or once or more for 2-3 months than among clinics no exchanging the information about on-site investigation. Also, the probability of high perceived deterrence was higher 2.27 times (CI: 1.28-4.45) among clinics that knows more than 3 health care institutions having experienced an on-site investigation than among clinics knowing no case. Conclusion: A clinic knowing more punishment cases by onsite investigation and exchanging more frequently information about on-site investigation is likely to present high perceived deterrence. This result will provide important information to enlarge preventive effects of on-site investigation on fraud and abuse claims.