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| 1 | A constrained regulation approach to robust corner detection显示文摘 | Sohn Kwanghoon Alexander Winser E Kim Jung H | 1994 | IEEE Transactions on System Man and Cybernetics1994，24，5： | 1 |
| 2 | Visual Comfort Enhancement for Stereoscopic Video Based on Binocular Fusion Characteristics 显示文摘 | Donghyun Kim Sunghwan Choi Kwanghoon Sohn | 2013 | Circuits and Systems for Video Technology2013，23，3： | 1 |
| 3 | A Mean Field Anealing Approach to Robust Comer Detection显示文摘 | Kwanghoon S Kim J H Alexander W E | 1998 | IEEE Trans on Systems Man and Cybernetics Part B1998，28，1： | 1 |
| 4 | An eLeaming Activity Control Model for SCORM' s Sequencing Prerequisites显示文摘 | NaraePark HyunaKim Kwanghoon Kim Hwajung Park Junchul Chun Yeiseon Hwang | 2008 | Semantics Knowledge and Grid2008，，8： | 1 |
| 5 | Visual fatigue prediction for stereoscopic image显示文摘 | Donghyun Kim Kwanghoon Sohn | | 0，，： | 1 |
| 6 | Comparison of Missing Data Imputation Methods in Time Series Forecasting显示文摘Time series forecasting has become an important aspect of data analysis and has many real-world applications.However,undesirable missing values are often encountered,which may adversely affect many forecasting tasks.In this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time series.Our approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom.In an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation method.Subsequently,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models.The results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods. | Hyun Ahn Kyunghee Sun Kwanghoon Pio Kim | 2022 | Computers, Materials & Continua2022，，1： | 1 |
| 7 | A model-driven workflow fragmentation framework for collaborative workflow architectures and systems显示文摘 | Kwanghoon Kim | 2012 | Journal of Network and Computer Applications2012，35，1： | 1 |
| 8 | Formal Approach to Workow Application Fragmentations Over Cloud Deployment Models显示文摘Workow management technologies have been dramatically improving their deployment architectures and systems along with the evolution and proliferation of cloud distributed computing environments.Especially,such cloud computing environments ought to be providing a suitable distributed computing paradigm to deploy very large-scale workow processes and applications with scalable on-demand services.In this paper,we focus on the distribution paradigm and its deployment formalism for such very large-scale workow applications being deployed and enacted across the multiple and heterogeneous cloud computing environments.We propose a formal approach to vertically as well as horizontally fragment very large-scale workow processes and their applications and to deploy the workow process and application fragments over three types of cloud deployment models and architectures.To concretize the formal approach,we rstly devise a series of operational situations fragmenting into cloud workow process and application components and deploying onto three different types of cloud deployment models and architectures.These concrete approaches are called the deployment-driven fragmentation mechanism to be applied to such very large-scale workow process and applications as an implementing component for cloud workow management systems.Finally,we strongly believe that our approach with the fragmentation formalisms becomes a theoretical basis of designing and implementing very large-scale and maximally distributed workow processes and applications to be deployed on cloud deployment models and architectural computing environments as well. | Hyun Ahn Kwanghoon Pio Kim | 2021 | Computers, Materials & Continua2021，，6： | 0 |
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