• Progress in Medical Physics
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• v.23 no.4
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• pp.309-316
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• 2012

Multimodal-imaging technique has been rapidly developed for improvement of diagnosis and evaluation of therapeutic effects. In despite of integrated hardware, registration accuracy was decreased due to a discrepancy between multimodal image and insufficiency of count in accordance with different acquisition method of each modality. The purpose of this study was to improve the PET image by event data resampling through analysis of data format, noise and statistical properties of small animal PET list data. Inveon PET listmode data was acquired as static data for 10 min after 60 min of 37 MBq/0.1 ml $^{18}F$-FDG injection via tail vein. Listmode data format was consist of packet containing 48 bit in which divided 8 bit header and 40 bit payload space. Realigned sinogram was generated from resampled event data of original listmode by using adjustment of LOR location, simple event magnification and nonparametric bootstrap. Sinogram was reconstructed for imaging using OSEM 2D algorithm with 16 subset and 4 iterations. Prompt coincidence was 13,940,707 count measured from PET data header and 13,936,687 count measured from analysis of list-event data. In simple event magnification of PET data, maximum was improved from 1.336 to 1.743, but noise was also increased. Resampling efficiency of PET data was assessed from de-noised and improved image by shift operation of payload value of sequential packet. Bootstrap resampling technique provides the PET image which noise and statistical properties was improved. List-event data resampling method would be aid to improve registration accuracy and early diagnosis efficiency.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.5
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• pp.285-291
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• 2014

The statistical information of the coastal water temperature monitoring data can be biased because of outliers and missing intervals. Though a number of outlier detection methods have been developed, their applications are very limited to the in-situ monitoring data because of the assumptions of the a prior information of the outliers and no-missing condition, and the excessive computational time for some methods. In this study, the practical robust method is developed that can be efficiently and effectively detect the outliers in case of the big-data. This model is composed of these two parts, one part is the construction part of the approximate components of the monitoring data using the robust smoothing and data re-sampling method, and the other part is the main iterative outlier detection part using the detailed components of the data estimated by the approximate components. This model is tested using the two-years 5-minute interval water temperature data in Lake Saemangeum. It can be estimated that the outlier proportion of the data is about 1.6-3.7%. It shows that most of the outliers in the data are detected and removed with satisfaction by the model. In order to effectively detect and remove the outliers, the outlier detection using the long-span smoothing should be applied earlier than that using the short-span smoothing.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.673-679
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• 2017

In this paper a method for estimating the 'service life' and 'residual life' of a water pipe based on the Water Pipe Network Performance Evaluation(WPNPE) results of Water Supply Technical Diagnosis was developed for efficient maintenance of water pipes. The residual life of a pipe was defined as the difference between the service life and elapsed time since installation. The service life was defined as the time when a pipe reaches the reference score for determining deteriorated pipes that was used in the WPNPE. The pipe evaluation criteria and deterioration scores used in the WPNPE for the case study area were considered as independent variables in the multiple regression model for estimating the service life and residual life of the pipes in the area. To estimate the service life for the pipes the reference scores for determining deteriorated pipes were used as the values of the variables that represent the deterioration scores in the constructed regression models. Subsequently, the statistics of the service life and residual life of the pipes in the case study area were presented and analyzed in comparison with the service life defined by the Local Public Enterprizes Act.

• Journal of the Korean Society of Radiology
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• v.9 no.4
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• pp.249-255
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• 2015

In this study, through the additional information of the PET / CT images by utilizing the basic data of TPS clinical application on the basis of the image re-forming synthetic function, the True-D technology and MIM software for continued research and development in combination, based on the combination-work between the respective images, reducing the time and cost of useful reading in clinical wide use of image width, efficient, effective tool for tumor targeting at diagnosis and radiation therapy by use as, by using the precise therapeutic effect determination, the time taken to read in the clinical, unnecessary and expect to a can reduce the additional examination by the creation of tumor patients read reports and PACS such asWe expect to be utilized for compatibility development with other software to evaluate the performance of PET / CT equipment.

• Journal of Advanced Navigation Technology
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• v.8 no.1
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• pp.26-37
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• 2004

Consistent and realistic error covariance information is important for position estimation, error analysis, fault detection, and integer ambiguity resolution for differential GNSS. In designing a position domain carrier-smoothed-code filter where incremental carrier phases are used for time-propagation, formulation of consistent error covariance information is not easy due to being bounded and temporal correlation of propagation noises. To provide consistent and correct error covariance information, this paper proposes two recursive filter algorithms based on carrier-smoothed-code techniques: (a) the stepwise optimal position projection filter and (b) the stepwise unbiased position projection filter. A Monte-Carlo simulation result shows that the proposed filter algorithms actually generate consistent error covariance information and the neglection of carrier phase noise induces optimistic error covariance information. It is also shown that the stepwise unbiased position projection filter is attractive since its performance is good and its computational burden is moderate.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.90-98
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• 2011

The recognition and classification of paddy rice disease are of major importance to the technical and economical aspect of agricultural industry over the world. Computer vision techniques are used to diagnose rice diseases and to efficiently manage crops. Segmentation of lesions is the most important technique to detect paddy rice disease early and accurately. A new Gaussian Mean (GM) method was proposed to segment paddy rice diseases in various color spaces. Different color spaces produced different results in segmenting paddy diseases. Thus, this empirical study was conducted with the motivation to determine which color space is best for segmentation of rice disease. It included five color spaces; NTSC, CIE, YCbCr, HSV and the normalized RGB(NRGB). The results showed that YCbCr was the best color space for optimal segmentation of the disease lesions with 98.0% of accuracy. Furthermore, the proposed method demonstrated that diseases lesions of paddy rice can be segmented automatically and robustly.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.1
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• pp.104-109
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• 2005

The aircraft gas turbine engines with the Electronic Engine Controls(EEC) had been developed to save fuel and enhance their performance in the early days, and had employed the health monitoring function in the Full Authority Digital Engine Controls(FADEC) to improve their reliability. This has led to an increasing demand for the certification technology of these controls. The design and certification issues of power supply, aircraft supplied data, failure modes, software verification/validation, and lightning requirements need to be addressed. This paper presents the design considerations and the certification techniques applied to the electronic engine controls. And it is believed that this paper will be basis to establish a requirement in Korean Airworthiness Standard.

• Journal of Aerospace System Engineering
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• v.14 no.spc
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• pp.31-38
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• 2020

The aircraft system safety assessment, which is emphasized in the development and certification of aircraft, is a systematic and comprehensive evaluation process to determine that all relevant failure conditions have been identified and that all significant combinations of failures cannot result in unacceptable hazards. As the aircraft systems become more complex and require integrated function and performance, proper safety objectives must be established and appropriate assessments are need to be accompanied. This paper has prepared to propose the efficient probabilistic analysis of failure data to evaluate the risk level over the entire aircraft lifecycle through the safety assessment and to review the considerations for aircraft certification and safety improvement.