• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.185-193
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• 2007

This review focuses on the use of $^{18}F-FDG$ PET/CT to evaluate second primary cancers. The emergence of a second primary cancer is an important prognostic factor in cancer patients. The early detection of a second primary cancer and the appropriate treatment are essential for reducing the morbidity and mortality associated with these tumors. Integrated $^{18}F-FDG$ PET/CT, which can provide both the metabolic and anatomic information of a cancer, has been shown to have a better accuracy in oncology than either CT or conventional PET. The whole body coverage and high sensitivity of $^{18}F-FDG$ PET/CT along with its ability to provide both metabolic and anatomic information of a cancer make it suitable for evaluating a second primary cancer in oncology. Whole body $^{18}F-FDG$ PET/CT is useful for screening second primary cancers with a high sensitivity and good positive predictive value. In order to rule out the presence of a second primary cancer or an unexpected metastasis, further diagnostic work-up is essential when abnormal findings indicative of a second primary cancer are found on the PET/CT images. PET/CT is better in detecting a second primary tumor than conventional PET.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.245-265
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• 2017

Recently, studies on cognitive control revealed that the optimal level of control is determined on the basis of reward and cost. The value of reward can be subjective and therefore, the optimal control strength can vary accordingly. The inconsistent effect of aging on cognitive control can be the result of flexible adjustment of control signal strength made by the older subjects. In other words, the elderly people maintains the ability to set the optimal level of control, which is known as the function of the dorsal anterior cingulate cortex. On the other hand, the age-related decline in cognitive control is obvious in rule maintenance and inhibition, which has to do with the function of lateral prefrontal cortex. In this study, we had young and old adults perform go-no go task and compared the behavioral and neural results for different reward conditions. Both age groups showed the best performance and the largest ERN amplitude when the reward was most appealing to them. And there was no age effect in ERN amplitude even though older adults' d' and accuracy was inferior to younger participants. These findings suggest that the effect aging on different cognitive control processes can be asymmetric.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.283-290
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• 2016

Even though existing methods for orthophoto production in traditional photogrammetry are effective in large areas, they are inefficient when dealing with change detection of geometric features and image production for short time periods in small areas. In recent years, the UAV (Unmanned Aerial Vehicle), equipped with various sensors, is rapidly developing and has been implemented in various ways throughout the geospatial information field. The data and imagery of specific areas can be quickly acquired by UAVs at low costs and with frequent updates. Furthermore, the redundancy of geospatial information data can be minimized in the UAV-based orthophoto generation. In this paper, the orthophoto and DEM (Digital Elevation Model) are generated using a standard low-end UAV in small sloped areas which have a rather low accuracy compared to flat areas. The RMSE of the check points is σ_H = ±0.12 m on a horizontal plane and σ_V = ±0.09 m on a vertical plane. As a result, the maximum and mean RMSE are in accordance with the working rule agreement for the airborne laser scanning surveying of the NGII (National Geographic Information Institute) on a 1/500 scale digital map. Through this study, we verify the possibilities of the orthophoto generation in small slope areas using general-purpose low specification UAV rather than a high cost surveying UAV.

• Journal of KIISE:Information Networking
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• v.36 no.6
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• pp.514-527
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• 2009

Wireless sensor networks (WSNs) can be used in various applications for military or environmental purpose. Recently, there are lots of on-going researches for detecting and tracking the spread of continuous objects or phenomena such as poisonous gas, wildfires, earthquakes, and so on. Some previous work has proposed techniques to detect edge nodes of such a continuous object based on the information of all the 1-hop neighbor nodes. In those techniques, however, a number of nodes are redundantly selected as edge nodes, and thus, the boundary of the continuous object cannot be presented accurately. In this paper, we propose a new edge detection method in which edge nodes of the continuous object are detected based on the information of the neighbor nodes obtained via the Localized Delaunay Triangulation so that a minimum number of nodes are selected as edge nodes. We also define the sensor behavior rule for tracking continuous objects energy-efficiently. Our simulation results show that the proposed edge detection method provides enhanced performance compared with previous 1-hop neighbor node based methods. On the average, the accuracy is improved by 29.95% while the number of edge nodes, the amount of communication messages and energy consumption are reduced by 54.43%, 79.36% and 72.34%, respectively. Moreover, the number of edge nodes decreases by 48.38% on the average in our field test with MICAz motes.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.95-105
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• 2020

In order to promote efficient coastal management, it is important to continuously monitor the characteristics of the terrain, which are changed by various factors. In this study, time series UAV images were taken of Gyeokpo beach. And the standard deviation of ±11cm(X), ±10cm(Y), and ±15cm(Z) was obtained as a result of comparing with the VRS measurement performance for UAV position accuracy evaluation. Therefore, it was confirmed that the tolerance of the digital map work rule was satisfied. In addition, as a result of monitoring the erosion and sedimentation changes using the DSM(digital surface model) constructed through UAV images, an average of 0.01 m deposition occurred between June 2018 and December 2018, and in December 2018 and June 2019. It was analyzed that 0.03m of erosion occurred. Therefore, 0.02m of erosion occurred between June 2018 and June 2019. From the topographical change analysis results, the area of erosion and sediment height was analyzed, and the area of erosion and sedimentation was widely distributed in the ±0.5m section. If we continuously monitor the topographical changes in the coastal regions by using the 3D terrain modeling results using the time series UAV images presented in this study, we can support the coastal management tasks such as supplement or dredging of sand.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.38-48
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• 2010

Adverse weather (e.g. strong winds, snow and ice) will probably appear as a more serious and frequent threat to road traffic than in clear climate. Another consequence of climate change with a natural disastrous on road traffic is respond to traffic accident more the large and high-rise bridge zone, tunnel zone, inclined plane zone and de-icing zone than any other zone, which in turn calls for continuous adaption of monitoring procedures. Accident mitigating measures against this accident category may consist of intense winter maintenance, the use of road weather information systems for data collection and early warnings, road surveillance and traffic control. While hazard from reduced road friction due to snow and ice may be eliminated by snow removal and de-icing measures, the effect of strong winds on road traffic are not easily avoided. The purpose of the study described here, was to design of amber information the relationship between traffic safety, weather, user information on road weather and driving conditions in local-scale Geographic. The most applications are the optimization of the amber information definition, improvements to road surveillance, road weather monitoring and improved accuracy of user information delivery. Also, statistics on wind gust, surface condition, vehicle category and other relevant parameters for wind induced accidents provide basis for traffic control, early warning policies and driver education for improved road safety at bad weather-exposed locations.

• Clinical and Experimental Pediatrics
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• v.61 no.9
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• pp.285-290
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• 2018

Purpose: To study the usefulness of the procalcitonin (PCT) test in young febrile infants between 1 and 3 months of age. Methods: We evaluated the medical records of 336 febrile infants between 1 and 3 months of age who visited the Emergency Department or outpatient department of Samsung Changwon Hospital from May 2015 to February 2017, and analyzed the clinical characteristics between infants in the serious bacterial infection (SBI) group and non-SBI group. Results: Among the 336 infants, 38 (11.3%) had definitive SBI (bacteremia, n=3; meningitis, n=1; urinary tract infection, n=34). The mean PCT ($6.4{\pm}11.9ng/mL$) and C-reactive protein (CRP) level ($3.8{\pm}2.6mg/dL$), and the absolute neutrophil count (ANC) ($6,984{\pm}4,675$) for patients in the SBI group were significantly higher than those for patients in the non-SBI group (PCT, $0.3{\pm}1.2ng/mL$; CRP, $1.3{\pm}1.6mg/dL$; ANC, $4,888{\pm}3,661$). PCT had lower sensitivity (43.6%), but higher specificity (92.6%) and accuracy (86.9%) than CRP (92.3%, 25.3%, and 33.0%) for identifying SBI. The area under the receiver operating characteristic curves (AUCs) for definitive SBI were PCT 77.0%, CRP 80.8%, WBC 56.8%, ANC 67.8%, and PLT 48.1%. The AUCs for definitive SBI were PCT+CRP 85.4%, PCT+WBC 77.2%, PCT+ANC 81.3%, CRP+WBC 80.1%, and CRP+ANC 81.6%. Conclusion: Our results suggest that the PCT test or a combination of PCT and CRP tests is a more accurate and specific biomarker to detect and rule out SBIs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.10 no.2
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• pp.63-71
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• 1992

The digital map database is often produced for multiple purposes, including mapping at multiple scales; it is increasingly rare that a base map is digitized for mapping at a single scale. The most important problems in process of line simplification in map generalization and multiple representation is that tolerance value selected for simplifying base map information must be modified as feature geometry varies within the digital file to ensure both accuracy and recognizability of graphic details on a generalized map. In this study, we explored various algorithms regarding to line simplication at many scales from a single digital file, and presents a rule by which to determine those scale at which line feature geometry might be expected to change in map representation. By applying two measured of displacement between a digitized line and its simplification, five algorithms have been evaluated. The results indicate that, of the five, the Douglas-Peucker routine produced less displacement between a line and its simplification. The research has proved to automating map simplification, incorporating numeric guidelines into digital environment about what magnitude and variation in geometric detail should be preserved as the digital data is simplified for representation at reduced map scales.