• Journal of Korean Ophthalmic Optics Society
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• v.8 no.1
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• pp.11-15
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• 2003

Purpose : The purpose of this study is to investigate the corneal topography changes as a result of near task in myopes and emmetropes. Methods : Thirty university students, aged from 18 to 24 years old, were randomly selected. They were divided into two refractive groups of fifteen each : myopia and emmetropia. The corneal topography of each subject was measured with the Eye-Sys Videokeratography system. Measurements were taken : before and after 30 minutes of near task (copy N10 text at 20 cm working distance). Both simulated keratometry and semi-meridian keratometric map program were used in the data analysis. Results : Our results reveal no significant changes in both simulated keratometry and semi-meridian presentation as a result of near task for both myopes and emmetropes, except a significant change (p<0.05) found at the flattest meridian of the central (3 mm) portion of the corneal topography after near task for emmetropes only. Conclusions : We conclude that the corneal topography, does not change significantly as a result of near task in both myopes and emmetropes.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.301-303
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• 2003

The extraction of 3D road network from high-resolution aerial images is still one of the current challenges in digital photogrammetry and computer vision. For many years, there are many researcher groups working for this task, but unt il now, there are no papers for doing this with TLS (Three linear scanner), which has been developed for the past several years, and has very high-resolution (about 3 cm in ground resolution). In this paper, we present a methodology of road extraction from high-resolution digital imagery taken over urban areas using this modern photogrammetry’s scanner (TLS). The key features of the approach are: (1) Because of high resolution of TLS image, our extraction method is especially designed for constructing 3D road map for next -generation digital navigation map; (2) for extracting road, we use the global context of the intensity variations associated with different features of road (i.e. zebra line and center line), prior to any local edge. So extraction can become comparatively easy, because we can use different special edge detector according different features. The results achieved with our approach show that it is possible and economic to extract 3D road data from Three Linear Scanner to construct next -generation digital navigation road map.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.732-737
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• 2009

Due to various project delivery methods and the complexity of construction projects in the construction industry, developing the framework of construction management for critical, highly complex projects in the construction industry has become problematic. Currently, a set of construction manuals play a pivotal role in planning and managing construction projects as subcontractors try to complete their scope of work according to the instructions of a general contractor. It is challenging for general contractors to write a construction management procedure manual to cover various types of project delivery methods and construction projects. In construction, the construction procedure manuals describe specific actions to be taken through the project. In reality a few contactors own such manuals and their construction schedules include more construction operation activities. Thus, it is hard to estimate the workload and productivity of construction managers because the manual and the schedule do not present the amount of management efforts required to complete a project. This paper proposes a framework to present construction management tasks according to project delivery methods which can be applied to various construction projects. Actions for management tasks were mapped and were integrated with construction activities throughout the project life cycle. The framework can then be used to give specific instructions to project participants, collect management actions, and replicate management actions throughout the project life cycle. The framework can also be can used to visualize complete construction project to analyze and manage construction management activities in each phase of a project in order to enhance productivity and efficiency. The studies of existing construction manuals were carried out to identify construction managers' responsibilities. An artificial intelligence program, CLIPS (C-Language Integrated Production System) was used to search for appropriate actions for impending tasks from a set of predefined actions to be performed for a given situation. The framework would significantly help construction managers to understand interrelations among management tasks or actions within a project. Furthermore, the framework can be embedded into Building Information Modeling (BIM) or Facility Management Systems (FMS) so that designers and constructors would execute constructability review before construction begins.

• Progress in Medical Physics
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• v.13 no.4
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• pp.181-186
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• 2002

The IMRT planning depends on the algorithm of each planning system and MLC performance of each Linac system. Yonsei Cancer Center introduced an IMRT System at the beginning of February, 2002. The system consists of CORVUS (Nomos, U.S.A.) treatment planning system, LANTIS, PRIMEVIEW and PRIMART (Siemens, U.S.A) linac system. The optimization of CORVUS planning system with PRIMART is an important task to make a desirable quality treatment plan. Our Step ＆ Shoot IMRT system uses Finite Size Pencil Beams (FSPB) dose model, simulated annealing optimization algorithm and IMFAST segmentation algorithm. We constructed treatment plans for four different patient cases with two basic beamlet sizes, 1.0$\times$1.0 $\textrm{cm}^2$ and 0.5$\times$1.0 $\textrm{cm}^2$, and four intensity steps, 5%, 10%, 20%, 33%. Each case's plan was evaluated with the dose volume histograms of target volumes and delivery efficiencies. The patient case of small target volume is sensitive at the change of intensity map's segmentation and it highlighted an effective treatment plan at marrow intensity step and small basic projection beamlet.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.205-217
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• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.