• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.57-64
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• 2018

In this paper, we propose the automatic detection method for individual region separation using panorama image. Finding areas that contain individual teeth is one of the most important tasks in automating 3D models through individual tooth separation. In the conventional method, the maxillary and mandibular teeth regions are separated using a straight line or a specific CT slide, and the tooth regions are separated using a straight line in the vertical direction. In the conventional method, since the teeth are arranged in a curved shape, there is a problem that each tooth region is incorrectly detected in order to generate an accurate tooth region. This is a major obstacle to automating the creation of individual tooth models. In this study, we propose a method to find the correct tooth curve by using the jawbone curve which is very similar to the tooth curve in order to overcome the problem of finding the area containing the existing tooth. We have proposed a new method to accurately set individual tooth regions using the feature that individual teeth are arranged in a direction similar to the normal direction of the tooth alignment curve. In the proposed method, the maxillary and mandibular teeth can be more precisely separated than the conventional method, and the area including the individual teeth can be accurately set. Experiments using real dental CT images demonstrate the superiority of the proposed method.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.281-287
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• 2003

This paper proposed an improvement scheme for effective usage of radio-frequency management system(RFMS), which has been operated to facilitate national spectrum management and monitoring in Korea. Based on the wave propagation models, interference analysis algorithms, and sharing criteria recommended by ITU-R, we derived criteria for the automated selection of the channel interference analysis algorithms and sharing conditions adequate to the environment to be analysed. Then using the obtained criteria, computer and program has been made and shown to select the most appropriate propagation models, interference analysis algorithms, and sharing criteria from the ones provided in RFMS, with the illustrative example.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.631-634
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• 2005

Traditional database systems have been used models supported for the operations and relationships based on simple interval. video data models are required in order to provide supporting temporal paradigm, various object operations and temporal operations, efficient retrieval and browsing in video model. As video model is based on object-oriented paradigm, I present entire model structure for video data through the design of metadata which is used of logical schema of video, attribute and operation of object, and inheritance and annotation. by using temporal paradigm through the definition of time point and time interval in object-oriented based model, we can use video information more efficiently by time variation.

• Journal of Radiation Protection and Research
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• v.8 no.2
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• pp.41-46
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• 1983

Annual collective dose within 50 miles radius of Ko-ri I reactor site due to normal airborne effluent discharges in 1979 has been estimated by AIRDOS-EPA computer code. Gaussian plume equation is used for estimation of both horizontal and vertical dispersion of radionuclide release into the atmosphere. Also, radionuclide concentrations in meat, milk, and fresh produce consumed by near-by population are estimated by coupling the output of the atmospheric transport models with the USNRC terrestrial food chain models. Annual collective doses are found to be $3.348{\times}10^{-1}$ whole body manrem and 84.95 thyroid manrem. Whole body manrem calculated by AIRDOS-EPA computer code do not differ greatly from that calculated by GASPAR computer code, but value for thyroid manrem have been estimated lower than that calculated by GASPAR computer code.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.305-319
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• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.409-415
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• 2022

In computer vision, an image of a measurement target is acquired using a camera. And feature values, vectors, and regions are detected by applying algorithms and library functions. The detected data is calculated and analyzed in various forms depending on the purpose of use. Computer vision is being used in various places, especially in the field of automatically recognizing automobile parts or measuring the quality. Computer vision is being used as the term machine vision in the industrial field, and it is connected with artificial intelligence to judge product quality or predict results. In this study, a vision system for judging the quality of automobile parts was built, and the results were compared by applying five machine learning classification models to the produced data.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.143-152
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• 2022

The ongoing adoption of 5G will increase the data traffic, throughput, multimedia services, and power consumption for future wireless applications and services, including sensor and mobile networks. Multipath fading on wireless channels also reduces the system performance and increases energy consumption. To address these issues, device-to-device (D2D) and cooperative communications have been proposed. In this study, we propose two inter-clustering models using the relay selection method to improve system performance and increase energy efficiency in cooperative D2D networks. We develop two inter-clustering models and present their respective algorithms. Subsequently, we run a computer simulation to evaluate each model's outage probability (OP) performance, throughput, and energy efficiency. The simulation results show that inter-clustering model II has the lowest OP, highest throughput, and highest energy efficiency compared with inter-clustering model I and the conventional inter-clustering-based multirelay method. These results demonstrate that inter-clustering model II is well-suited for use in 5G overlay D2D and cellular communications.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.161-164
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• 2022

Homomorphic Encryption (HE) schemes have been recently growing as a reliable solution to preserve users' information owe to maintaining and operating the user data in the encrypted state. In addition to that, several Neural Networks models merged with HE schemes have been developed as a prospective tool for privacy-preserving machine learning. Those mentioned works demonstrated that it is possible to match the accuracy of non-encrypted models but there is always a trade-off in the computation time. In this work, we evaluate the implementation of CKKS HE scheme operations over the layers of a LeNet5 convolutional inference model, however, owing to the limitations of the evaluation environment, the scope of this work is not to develop a complete LeNet5 encrypted model. The evaluation was performed using the MNIST dataset with Microsoft SEAL (MSEAL) open-source homomorphic encryption library ported version on Python (PyFhel). The behavior of the encrypted model, the limitations faced and a small description of related and future work is also provided.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.