• Geophysics and Geophysical Exploration
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• v.5 no.3
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• pp.157-168
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• 2002

Improved procedures were implemented in the production of the lithospheric magnetic anomaly map from Magsat satellite magnetometer data of East Asia between $90^{\circ}E-150^{\circ}E$ and $10^{\circ}S-50^{\circ}N$. Procedures included more effective selection of the do·it and dawn tracks, ring current correction, and separation of core field and external field effects. External field reductions included an ionospheric correction and pass-by-pass correlation analysis. Track-line noise effects were reduced by spectral reconstruction of the dusk and dawn data sets. The total field magnetic anomalies were differentially-reduced-to-the-pole to minimize distortion s between satellite magnetic anomalies and their geological sources caused by corefield variations over the study area. Aeromagnetic anomalies were correlated with Magsat magnetic anomalies at the satellite altitude to test the lithospheric veracity of anomalies in these two data sets. The aeromagnetic anomalies were low-pass filtered to eliminate high frequency components that may not be shown at the satellite altitude. Although the two maps have a low CC of 0.243, there are many features that are directly correlated (peak-to-peak and trough-to-trough). The low CC between the two maps was generated by the combination of directly- and inversely-correlative anomaly features between them. It is very difficult to discriminate directly, inversely, and nully correlative features in these two anomaly maps because features are complicatedly correlated due to the depth and superposition of the anomaly sources. In general, the lithospheric magnetic components were recovered successfully from satellite magnetometer observations and correlated well with aeromagnetic anomalies in the study area.

• Journal of Magnetics
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• v.22 no.1
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• pp.146-149
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• 2017

In this study, we compared the standardized value of each signal intensity, the apparent diffusion coefficient (ADC) that digitizes the diffusion of water molecules, and the signal to noise ratio (SNR) using b value 0 400, 1400 ($s/mm^2$). From March 2013 to December 2013, patients with suspicion of simple compound fracture and metastatic spine cancer were included in the MR readout. We used a 1.5 Tesla Achieva MRI system and a Syn-Spine Coil. Sequence is a DWI SE-EPI sagittal (diffusion weighted imaging spin echo-echo planar imaging sagittal) image with b-factor ($s/mm^2$) 0, 400, 1400 were used. Data analysis showed ROI (Region of Interest) in diseased area with high SI (signal intensity) in diffusion-weighted image b value 0 ($s/mm^2$) Using the MRIcro program, each SI was calculated with images of b-value 0, 400, and 1400 ($s/mm^2$), ADC map was obtained using Metlab Software with each image of b-value, The ADC is obtained by applying the ROI to the same position. The standardized values ($SI_{400}/SI_0$, $SI_{400}/SI_0$) of simple compression fractures were $0.47{\pm}0.04$ and $0.23{\pm}0.03$ and the standardized values ($SI_{400}/SI_0$, $SI_{400}/SI_0$) of the metastatic spine were $0.57{\pm}0.07$ and $0.32{\pm}0.08$ And the standardized values of the two diseases were statistically significant (p < 0.05). The ADC ($mm^2/s$) for b value 400 ($s/mm^2$) and 1400 ($s/mm^2$) of the simple compression fracture disease site were $1.70{\pm}0.16$ and $0.93{\pm}0.28$ and $1.24{\pm}0.21$ and $0.80{\pm}0.15$ for the metastatic spine. The ADC ($mm^2/s$) for b value 400($s/mm^2$) was statistically significant (p < 0.05) but the ADC ($mm^2/s$) for b value 1400 (p > 0.05). In conclusion, multi - b value recognition of signal changes in diffusion - weighted imaging is very important for the diagnosis of various spinal diseases.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.646-654
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• 2007

In recent years, the use of text inserted into TV contents has grown to provide viewers with better visual understanding. In this paper, video text is defined as superimposed text region located of the bottom of video. Video text extraction is the first step for video information retrieval and video indexing. Most of video text detection and extraction methods in the previous work are based on text color, contrast between text and background, edge, character filter, and so on. However, the video text extraction has big problems due to low resolution of video and complex background. To solve these problems, we propose a method to extract text from videos using the Harris corner detector. The proposed algorithm consists of four steps: corer map generation using the Harris corner detector, extraction of text candidates considering density of comers, text region determination using labeling, and post-processing. The proposed algorithm is language independent and can be applied to texts with various colors. Text region update between frames is also exploited to reduce the processing time. Experiments are performed on diverse videos to confirm the efficiency of the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.162-167
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• 2015

This paper describes the reason and the problem solving for the double-layer pattern of a Daejeon correlator operated in Korea-Japan Correlation Center. When the electric power of an input signal in the correlator is charged small enough to be buried in the noise, it is hard to see a signal with a specific pattern in the input signal, but when the electric power is large, a specific one is reported to be seen. By comparing data from observation with one from software correlator, it was confirmed from the analysis using the AIPS software that the amplitude gain of a source signal was affected about 3%. Therefore, in order to solve the problem of double-layer patterns, we found that a problem in the memory management module responsible for both the data input and the data serialization of the correlator is a cause for the double-layer pattern detected periodically. In other words, while data is serialized and read repeatedly in the memory area assigned to serialize the data from the serialization module, redundant last data is generated and an overlap for the memory allocation is occurred. Therefore, by modifying the program of the FPGA memory sections on serialization module to correct the problem, we confirmed that double-layer pattern is disappeared and correlation results are normally acquired.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.716-723
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• 2008

With rapid development of science and technology and recent widening of mankind's range of activities, development of coastal waters and the environment have emerged as global issues. In relation to this, to allow more extensive analyses, the use of satellite images has been on the increase. This study aims at utilizing hyperspectral satellite images in determining the depth of coastal waters more efficiently. For this purpose, a partial image of the research subject was first extracted from an EO-1 Hyperion satellite image, and atmospheric and geometric corrections were made. Minimum noise fraction (MNF) transformation was then performed to compress the bands, and the band most suitable for analyzing the characteristics of the water body was selected. Within the chosen band, the diffuse attenuation coefficient Kd was determined. By deciding the end-member of pixels with pure spectral properties and conducting mapping based on the linear spectral unmixing method, the depth of water at the coastal area in question was ultimately determined. The research findings showed the calculated depth of water differed by an average of 1.2 m from that given on the digital sea map; the errors grew larger when the water to be measured was deeper. If accuracy in atmospheric correction, end-member determination, and Kd calculation is enhanced in the future, it will likely be possible to determine water depths more economically and efficiently.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.261-274
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• 2004

The feature extraction using remotely sensed imagery has been recognized one of the important tasks in remote sensing applications. As the high-resolution imagery are widely used to the engineering purposes, need of more accurate feature information also is increasing. Especially, in case of the automatic extraction of linear feature such as road using mid or low-resolution imagery, several techniques was developed and applied in the mean time. But quantitatively comparative analysis of techniques and case studies for high-resolution imagery is rare. In this study, we implemented a computer program to perform and compare GDPA (Gradient Direction Profile Analysis) algorithm and Hough transformation. Also the results of applying two techniques to some images were compared with road centerline layers and boundary layers of digital map and presented. For quantitative comparison, the ranking method using commission error and omission error was used. As results, Hough transform had high accuracy over 20% on the average. As for execution speed, GDPA shows main advantage over Hough transform. But the accuracy was not remarkable difference between GDPA and Hough transform, when the noise removal was app]ied to the result of GDPA. In conclusion, it is expected that GDPA have more advantage than Hough transform in the application side.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.82-88
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• 2014

Purpose: The purpose of study was to evaluate radiation dose for pediatric patients by changing tube voltage (kVp) and tube current (mA) at minimum conditions. By evaluating radiation dose, we want to provide dose reduction for pediatric patients and maintain good quality of SPECT/CT images. Materials and Methods: Discovery NM/CT 670 Scanne was used as SPECT/CT. Tube voltages are 80 and 100 kvP. Tube currents are 10, 15, 20, 25 mA. Using PMMA (Polymethyl methacrylate) Phantom, radiation dose which were calculated at center and peripheral dose and SNRD (Signal to Noise Ratio Dose) were evaluated. Using the CT performance phantom, spatial resolution was evaluated as the MTF (Modulation Transfer Function) graph. Jaszczak phantom was used for SPECT image evaluation by CNR (Contrast to Noise to Ratio). Results: Radiation dose using the PMMA phantom was higher peripheral dose than center dose about 7%. SNRD were 7.8, 8.2, 8.3, 8.8, 8.8, 9.9, 9.8, 9.6 for 80 kVp 10, 15, 20, 25 mA, 100 kVp 10, 15, 20, 25 mA. We can distinguish 35, 45, 70, 71, 52, 58, 90, 110 linepair for 80 kVp 10, 15, 20, 25 mA, 100 kVp 10, 15, 20, 25 mA at resolution with MTF. CNR of SPECT images using CT attenuation map were 57.8, 57.7, 57.1, 56.7, 56.6, 56.7, 56.7, 56.7% for 80 kVp 10, 15, 20, 25 mA, 100 kVp 10, 15, 20, 25 mA. Conclusion: In this study, radiation dose for pediatric patients showed decreased low dose condition. And SNRD value was similar in all condition. Resolution showed higher value at 100kVp than 80kVp. for CNR, there was no significant difference. we should take additional study to prove better quality and dose reduction.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.157-168
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• 2020

Advancements in segmentation methodology has made automatic segmentation of brain structures using structural images accurate and consistent. One method of automatic segmentation, which involves registering atlas information from template space to subject space, requires a high quality atlas with accurate boundaries for consistent segmentation. The Allen Mouse Brain Atlas, which has been widely accepted as a high quality reference of the mouse brain, has been used in various segmentations and can provide accurate coordinates and boundaries of mouse brain structures for tractography. Through probabilistic tractography, diffusion tensor images can be used to map comprehensive neuronal network of white matter pathways of the brain. Comparisons between neural networks of mouse and human brains showed that various clinical tests on mouse models were able to simulate disease pathology of human brains, increasing the importance of clinical mouse brain studies. However, differences between brain size of human and mouse brain has made it difficult to achieve the necessary image quality for analysis and the conditions for sufficient image quality such as a long scan time makes using live samples unrealistic. In order to secure a mouse brain image with a sufficient scan time, an Ex-vivo experiment of a mouse brain was conducted for this study. Using FSL, a tool for analyzing tensor images, we proposed a semi-automated segmentation and tractography analysis pipeline of the mouse brain and applied it to various mouse models. Also, in order to determine the useful signal-to-noise ratio of the diffusion tensor image acquired for the tractography analysis, images with various excitation numbers were compared.

• The Korean Journal of Nuclear Medicine
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• v.39 no.1
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• pp.57-68
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• 2005

Purpose: The objective of this study was to assess attenuation correction algorithms with the $^{137}Cs$ point source for the brain positron omission tomography (PET) imaging process. Materials & Methods: Four different types of phantoms were used in this study for testing various types of the attenuation correction techniques. Transmission data of a $^{137}Cs$ point source were acquired after infusing the emission source into phantoms and then the emission data were subsequently acquired in 3D acquisition mode. Scatter corrections were performed with a background tail-fitting algorithm. Emission data were then reconstructed using iterative reconstruction method with a measured (MAC), elliptical (ELAC), segmented (SAC) and remapping (RAC) attenuation correction, respectively. Reconstructed images were then both qualitatively and quantitatively assessed. In addition, reconstructed images of a normal subject were assessed by nuclear medicine physicians. Subtracted images were also compared. Results: ELEC, SAC, and RAC provided a uniform phantom image with less noise for a cylindrical phantom. In contrast, a decrease in intensity at the central portion of the attenuation map was noticed at the result of the MAC. Reconstructed images of Jaszack and Hoffan phantoms presented better quality with RAC and SAC. The attenuation of a skull on images of the normal subject was clearly noticed and the attenuation correction without considering the attenuation of the skull resulted in artificial defects on images of the brain. Conclusion: the complicated and improved attenuation correction methods were needed to obtain the better accuracy of the quantitative brain PET images.