• Journal of Korea Society of Industrial Information Systems
• /
• v.11 no.2
• /
• pp.1-8
• /
• 2006

In this paper, we propose a distance-based location registration scheme and evaluate it's performance in a mobility model. We compare performance of the distance-based registration scheme to that of zone-based registration scheme at the mobility model. Numerical results show that the registration load of the distance-based registration with call arrival is similar to that of the zone-based registration, and is equally distributed to all cells in a location area. So the proposed scheme can be effectively used in the limited radio resources.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.15 no.2
• /
• pp.60-64
• /
• 2011

Purpose: The brain perfusion SPECT is the examination which is able to know adversity information related brain disorder. But brain perfusion SPECT has also high failure rates by patient's motions. In this case, we have to use two days method and patients put up with many disadvantages. We think that we don't use two days method in brain perfusion SPECT, if we can use registration method. So this study has led to look over registration method applications in brain perfusion SPECT. Materials and Methods: Jaszczak, Hoffman and cylindrical phantoms were used for acquiring SPECT image data on varying degree in x, y, z axes. The phantoms were filled with $^{99m}Tc$ solution that consisted of a radioactive concentration of 111 MBq/mL. Phantom images were acquired through scanning for 5 sec long per frame by using Triad XLT9 triple head gamma camera (TRIONIX, USA). We painted the ROI of registration image in brain data. So we calculated the ROIratio which was different original image counts and registration image counts. Results: When carring out the experiments under the same condition, total counts differential was from 3.5% to 5.7% (mean counts was from 3.4% to 6.8%) in phantom and patients data. In addition, we also run the experiments in the double activity condition. Total counts differential was from 2.6% to 4.9% (mean counts was from 4.1% to 4.9%) in phantom and patients data. Conclusion: We can know that original and registration data are little different in image analysis. If we use the image registration method, we can improve disadvantage of two days method in brain perfusion SPECT. But we must consider image registration about the distance differences in x, y, z axes.

• Journal of the Korean Institute of Telematics and Electronics T
• /
• v.35T no.2
• /
• pp.81-85
• /
• 1998

Location Registration and deregistration is necessary to mobility management in a Mobile Communication networks when a mobile phone moves between location registration areas. After considering several deregistration schemes, a sample scheme that eliminate network traffic is chosen. However, this scheme may delete valid registration record, so the size of location registration database must be sufficiently large to ensure low probability that a valid records are deleted. This paper describes an analytic model to determine the size of location registration database for a location registration area in this simple scheme. The simulation results of analytic model show that the size of database must 3-5 times than the expected number of mobile phones in a location registration area.(Mobile, Communications, Location, Deregistration, Database)

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.8
• /
• pp.4209-4214
• /
• 2012

The current situation of cancer registration in China was systematically reviewed. So far, cancer registration in China has been making a great progress in the following aspects: the number of cancer registries and covered population have increased dramatically; a registration network has been established and completed gradually; regulations and rules improved remarkably; more attention is being paid by every level of government; a lot of registration software has been created and financial support ensured. However, we are still facing some problems and challenges, such as no stable groups of registrars, shortage of training opportunities, poor data quality, insufficient utilization and lack of multidisciplinary mechanisms, so that the cancer registration system still needs to be enhanced and improved. Along with the development of economy, science and information technology, methods and patterns of cancer registration is changing. It is to be expected that cancer registration will be automatic, nationwide and integrated with community healthcare in the near future.

• Journal of Mechanical Science and Technology
• /
• v.19 no.12
• /
• pp.2213-2223
• /
• 2005

In order to reconstruct a full 3D human model in reverse engineering (RE), a 3D scanner needs to be placed arbitrarily around the target model to capture all part of the scanned surface. Then, acquired multiple scans must be registered and merged since each scanned data set taken from different position is just given in its own local co-ordinate system. The goal of the registration is to create a single model by aligning all individual scans. It usually consists of two sub-steps: rough and fine registration. The fine registration process can only be performed after an initial position is approximated through the rough registration. Hence an automated rough registration process is crucial to realize a completely automatic RE system. In this paper an automated rough registration method for aligning multiple scans of complex human face is presented. The proposed method automatically aligns the meshes of different scans with the information of features that are extracted from the estimated principal curvatures of triangular meshes of the human face. Then the roughly aligned scanned data sets are further precisely enhanced with a fine registration step with the recently popular Iterative Closest Point (ICP) algorithm. Some typical examples are presented and discussed to validate the proposed system.

• Environmental Analysis Health and Toxicology
• /
• v.30
• /
• pp.17.1-17.6
• /
• 2015

Objectives In this study, the possibility of using existing test data provided in Korea and elsewhere for the registration of chemical substances was examined. Data on 510 chemical substances that are among the first subject to registration under the "Act on the Registration and Evaluation, etc. of Chemical Substances (K-REACH)" were analyzed. Methods The possibility of using existing data from 16 reference databases was examined for 510 chemical substances notified in July 2015 as being subject to registration. Results Test data with the reliability required for the registration of chemical substances under the K-REACH constituted 48.4% of the required physicochemical characteristics, 6.5% of the required health hazards, and 9.4% of the required environmental hazards. Conclusions Some existing test data were not within the scope of this research, including data used for registration in the European Union (EU). Thus, considering that 350 of these 510 species are registered in EU Registration, Evaluation, Authorisation & Restriction of Chemicals, more test data may exist that can be utilized in addition to the data identified in this study. Furthermore, the K-REACH states that non-testing data (test results predicted through Read Across, Quantitative Structure-Activity Relationships) and the weight of evidence (test results predicted based on test data with low reliability) can also be utilized for registration data. Therefore, if methods for using such data were actively reviewed, it would be possible to reduce the cost of securing test data required for the registration of chemical substances.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.13
• /
• pp.5549-5556
• /
• 2015

Background: Cancer has important social consequences with cancer registration as the basis of moving towards prevention. The present study aimed to estimate the completeness of registration of the ten most common cancers in patients referred to selected hospitals in Shiraz, Iran by using capture-recapture method. Materials and Methods: This cross-sectional analytical study was performed in 2014 based on the data of 2009, on a total of 4,388 registered cancer patients. After cleaning data from two sources, using capture-recapture common findings were identified. Then, the percentage of the completeness of cancer registration was estimated using Chapman and Chao methods. Finally, the effects of demographic and treatment variables on the completeness of cancer registration were investigated. Results: The results showed that the percentages of completeness of cancer registration in the selected hospitals of Shiraz were 58.6% and 58.4%, and influenced by different variables. The age group between 40-49 years old was the highest represented and for the age group under 20 years old was the lowest for cancer registration. Breast cancer had the highest registration level and after that, thyroid and lung cancers, while colorectal cancer had the lowest registration level. Conclusions: According to the results, the number of cancers registered was very few and it seems that factors like inadequate knowledge of some doctors, imprecise diagnosis about the types of cancer, incorrectly filled out medical documents, and lack of sufficient accuracy in recording data on the computer cause errors and defects in cancer registration. This suggests a necessity to educate and teach doctors and other medical workers about the methods of documenting information related to cancer and also conduct additional measures to improve the cancer registration system.

• Journal of Biomedical Engineering Research
• /
• v.37 no.2
• /
• pp.68-74
• /
• 2016

In this study, we investigated the rotational characteristics which were comprised of directionality and linearity of target registration error (TRE) as a study in advance to enhance the accuracy of contour-based registration in neuronavigation. For the experiment, two rigid head phantoms that have different faces with specially designed target frame fixed inside of the phantoms were used. Three-dimensional coordinates of facial surface point cloud and target point of the phantoms were acquired using computed tomography (CT) and 3D scanner. Iterative closest point (ICP) method was used for registration of two different point cloud and the directionality and linearity of TRE in overall head were calculated by using 3D position of targets after registration. As a result, it was represented that TRE had consistent direction in overall head region and was increased in linear fashion as distance from facial surface, but did not show high linearity. These results indicated that it is possible for decrease TRE by controlling orientation of facial surface point cloud acquired from scanner, and the prediction of TRE from surface registration error can decrease the registration accuracy in lesion. In the further studies, we have to develop the contour-based registration method for improvement of accuracy by considering rotational characteristics of TRE.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.3
• /
• pp.125-132
• /
• 2017

Even after applying an image registration, Very High Resolution (VHR) multi-temporal images acquired from different optical satellite sensors such as IKONOS, QuickBird, and Kompsat-2 show a local misalignment due to dissimilarities in sensor properties and acquisition conditions. As the local misalignment, also referred to as Registration Noise (RN), is likely to have a negative impact on multi-temporal information extraction, detecting and reducing the RN can improve the multi-temporal image processing performance. In this paper, an approach to fine registration between VHR multi-temporal images by considering local distribution of RN is proposed. Since the dominant RN mainly exists along boundaries of objects, we use edge information in high frequency regions to identify it. In order to validate the proposed approach, datasets are built from VHR multi-temporal images acquired by optical satellite sensors. Both qualitative and quantitative assessments confirm the effectiveness of the proposed RN-based fine registration approach compared to the manual registration.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_3
• /
• pp.1707-1720
• /
• 2023

With the increase in satellite time series data, the utility of remote sensing data is growing. In the analysis of time series data, the relative positional accuracy between images has a significant impact on the results, making image registration essential for correction. In recent years, research on image registration has been increasing by applying deep learning, which outperforms existing image registration algorithms. To train deep learning-based registration models, a large number of image pairs are required. Additionally, creating a correlation map between the data of existing deep learning models and applying additional computations to extract registration points is inefficient. To overcome these drawbacks, this study developed a data augmentation technique for training image registration models and applied it to OffsetNet, a registration model that predicts the offset amount itself, to perform image registration for KOMSAT-2, -3, and -3A. The results of the model training showed that OffsetNet accurately predicted the offset amount for the test data, enabling effective registration of the master and slave images.