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TransMpeg : 인터넷 환경하에서의 교통동영상 전송 시스템
이원하,최종욱,유세근 한국전문가시스템학회 1997 학술대회 Vol.1 No.1
With the rapid development of communication technology and widespread uses of Internet service, Internet-based traffic information systems which transmits moving pictures of traffic scene are in use. The Internet-based traffic information service provides richer information than conventional services such as touch-tone telephone, personal computers, pagers, personal communication devices, kiosks, or voice synthesizers, because $quot;a picture is worth a thousand words$quot;. However, the systems passively transmits moving pictures of the traffic scenes to the user, and thus the system cannot intelligently adjust itself to provide better service. As the tradeoff exists between transmission speed and quality of the image, there is a need that moving pictures of the traffic scene be analyzed to adjust the transmission speed and quality of the image. In other words, when very little difference between consecutive images are detected, the system can increase the size of the image, enhancing the quality of image. In contrast, the system should increase the number of image to send more pieces of images, sacrificing the quality of the images, when a significant difference is detected. In this paper an adaptive filtering technique, called Transfuge, is introduced which adjusts the quality of image and transmission speed according to the traffic situation on the road.
이원하,석경호,이상민,김원정 대한면역학회 2010 Immune Network Vol.10 No.5
APRIL, originally known as a cytokine involved in B cell survival, is now known to regulate the inflammatory activation of macrophages. Although the signal initiated from APRIL has been demonstrated, its role in cellular activation is still not clear due to the presence of BAFF, a closely related member of TNF superfamily, which share same receptors (TACI and BCMA) with APRIL. Methods: Through transfection of siRNA, BAFF-deficient THP-1 cells (human macrophage-like cells) were generated and APRIL-mediated inflammatory activities were tested. The expression patterns of APRIL were also tested in vivo. Results: BAFF-deficient THP-1 cells responded to APRIL-stimulating agents such as monoclonal antibody against APRIL and soluble form of TACI or BCMA. Furthermore, co-incubation of the siBAFF-deficient THP-1 cells with a human B cell line (Ramos) resulted in an activation of THP-1 cells which was dependent on interactions between APRIL and TACI/BCMA. Immunohistochemical analysis of human pathologic samples detected the expression of both APRIL and TACI in macrophage-rich areas. Additionally, human macrophage primary culture expressed APRIL on the cell surface. Conclusion: These observations indicate that APRIL, which is expressed on macrophages in pathologic tissues with chronic inflammation, may mediate activation signals through its interaction with its counterparts via cell-to-cell interaction.
시계열 데이터의 성격과 예측 모델의 예측력에 관한 연구
이원하,최종욱 한국지능정보시스템학회 1998 지능정보연구 Vol.4 No.1
Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deteministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or AR1MA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministieally chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model appropriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.