• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.179-186
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• 2017

Technology forecasting is predict future trend and state of technology by analyzing the results so far of developing technology. In general, a patent has novel information about the result of developed technology, because the exclusive right of technology included in patent is protected for a time period by patent law. So many studies on the technology forecasting using patent data analysis has been performed. The patent keyword data widely used in patent analysis consist of occurred frequency of the keyword. In most previous researches, the continuous data analyses such as regression or Box-Jenkins Models were applied to the patent keyword data. But, we have to apply the analytical methods of discrete data for patent keyword analysis because the keyword data is discrete. To solve this problem, we propose a patent analysis methodology using Bayesian Poisson discrete model. To verify the performance of our research, we carry out a case study by analyzing the patent documents applied by Apple until now.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.7-14
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• 2020

This study analyzed the effect of the number of deaths of circulatory system diseases according to 12-day short-term exposure of carbon monoxide from January 2010 to December 2018, and predicted the future treatment cost of circulatory system diseases according to increased carbon monoxide concentration. Data were extracted from Air Korea of Korea Environment Corporation and Korea Statistical Office, and analyzed using Poisson regression analysis and ARIMA intervention model. For statistical processing, SPSS Ver. 21.0 program was used. The results of the study are as follows. First, as a result of analyzing the relationship between the impact of short-term carbon monoxide exposure on death of circulatory system diseases from the day to the previous 11 days, it was found that the previous 11 days had the highest impact. Second, with the increase in carbon monoxide concentration, the future circulatory system disease treatment cost was estimated at 10,123 billion won in 2019, higher than the observed value of 9,443 billion won at the end of December 2018. In addition, when summarized by month, it can be seen that the cost of treatment for circulatory diseases increases from January to December, reflecting seasonal fluctuations. Through such research, the future for a healthy life for all citizens can be realized by distributing various devices and equipment utilizing IoT to preemptively respond to the increase in air pollutants such as carbon monoxide.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.

• Health Policy and Management
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• v.34 no.1
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• pp.26-37
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• 2024

Background: The purpose of this study was to compare and analyze the differences in charges and length of stay per case between spine specialty hospitals and non-specialty hospitals, and to identify the factors that influenced them. Methods: This study used claims data from the Health Insurance Review and Assessment Service, including inpatient visits from January 2021 to December 2022. The healthcare facility status data was used to identify the characteristics of study hospitals. Multilevel analysis was conducted to identify factors associated with the charges and Poisson regression analysis was conducted to analyze the length of stay between spine specialty hospitals and non-specialty hospitals. There were 32,015 cases of spine specialty hospitals and 17,555 cases of non-specialty hospitals. Results: For four of five common spinal surgeries, specialty hospitals shaped longer length of stay than those of non-specialty hospitals. Multilevel and Poisson regression analysis revealed that regardless of the type of surgery, higher age and higher Charlson comorbidity index scores were significantly associated with an increase in both the charges per case and length of stay (p<0.05). However, when hospitals were located in metropolitan areas, there was a significant decrease (p<0.05). Conclusion: This study found that specialty hospital had higher inpatient charges and loner length of stay contrary to the previous study results. Further studies will be needed to find which contribute to the differences.

• Journal of the Korea Concrete Institute
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• v.24 no.4
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• pp.423-433
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• 2012

The axial load on the concrete-filled steel tube (CFT) column produces confinement stress, which enhances strength of the core concrete. The amount of strength increase in concrete depends on the magnitude of produced confinement stress. From nonlinear analyses, the ultimate resisting capacity of the CFT columns subjected to axial loads was calculated. Nonlinear material properties such as Poisson's ratio and stress-strain relation were considered in the suggested model, and the maximum confining stress was obtained by multi axial yield criteria of the steel tube. This proposed model was verified by comparing the analytical results with experimental results. Then, regression analyses were conducted to predict the maximum confining stress according to D/t ratio and material properties without rigorous structural analysis. To ensure the validity of the suggested regression formula, various empirical formulas and Eurocode4 design code were compared.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.485-494
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• 2011

When the observations can take only the non-negative integer values, it is called the count data such as the numbers of car accidents, earthquakes, or insurance coverage. In general, the Poisson regression model has been used to model these count data; however, this model has a weakness in that it is restricted by the equality of the mean and the variance. On the other hand, the count data often tend to be too dispersed to allow the use of the Poisson model in practice because the variance of data is significantly larger than its mean due to heterogeneity within groups. When overdispersion is not taken into account, it is expected that the resulting parameter estimates or standard errors will be inefficient. Since coverage is the main issue for insurance, some accidents may not be covered by insurance, and the number covered by insurance may be zero. This paper considers the zero-inflated model for the count data including many zeros. The performance of this model has been investigated by using of real data with overdispersion and many zeros. The results indicate that the Zero-Inflated Negative Binomial Regression Model performs the best for model evaluation.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.187-196
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• 2015

The team's performances were analyzed by evaluating the scores gained by their offense and the scores allowed by their defense. To evaluate the team's attacking and defending abilities, we also considered the factors that contributed the team's gained points or the opposing team's gained points? In order to analyze the outcome of the games, three prediction models were used such as decision trees, logistic regression, and discriminant analysis. As a result, the factors associated with the defense showed a decisive influence in determining the game results. We analyzed the offense and defense by using the response variable. This showed that the major factors predicting the offense were non-stop pass and attack speed and the major factor predicting the defense were the distance between right and left players and the distance between front line attackers and rearmost defenders during the game.

• International Journal of Highway Engineering
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• v.12 no.2
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• pp.43-49
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• 2010

This study deals with the traffic accident of the Cheongju arterial link sections. The purpose of the study is to develop the traffic accident model. In pursuing the above, this study gives particular attentions to developing the ZAM(zero-altered model) model using the accident data of arterial roads devided by 322 small link sections. The main results analyzed by ZIP(zero inflated Poisson model) and ZINB(zero inflated negative binomial model) which are the methods of ZAM, are as follows. First, the evaluation of various developed models by the Vuong statistic and t statistic for overdispersion parameter ${\alpha}$ shows that ZINB is analyzed to be optimal among Poisson, NB, ZIP(zero-inflated Poisson) and ZINB regression models. Second, ZINB is evaluated to be statistically significant in view of t, ${\rho}$ and ${\rho}^2$ (0.63) values compared to other models. Finally, the accident factors of ZINB models are developed to be the traffic volume(ADT), number of entry/exit and length of median. The traffic volume(ADT) and the number of entry/exit are evaluated to be the '+' factors and the length of median to be '-' factor of the accident.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.47-56
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• 2008

Generally, accident exposure at intersections is relatively higher than that at roadway segments due to more possibility of merging, diverging, turning, crossing, and weaving maneuver. Furthermore, the traffic accident rate at intersections has been rapidly increasing since 1990's. Since there is more opportunity of conflict at unsignalized intersection, frequency and severity of traffic accident are more severe than signalized intersections. The purpose of the study is to analyze factors causing vehicle crashes and provide intersection design guidelines to improve intersection safety. For this study, vehicle to vehicle crash data of 116 rural 3 legs unsignalized were collected and field surveys were conducted for traffic and geometric conditions. Ordered probit models were developed to analyze the severity of crashes. It was found that weather, obstacles in minor roadsides, presence of major exclusive right lane, presence of major road crosswalk, difference between posted speed of major road and minor road, land-use around intersections, shoulder width of major road, ADT of major road are significant factors for intersection safety.