• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2213-2220
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• 2022

The maximum dose delivery at the end of the beam range provides the main advantage of using proton therapy. The range of the proton beam, however, is subject to uncertainties, which limit the clinical benefits of proton therapy and, therefore, accurate in vivo verification of the beam range is desirable. For the beam range verification in spot scanning proton therapy, a prompt gamma detection system, called as gamma electron vertex imaging (GEVI) system, is under development and, in the present study, the performance of the GEVI system in spot scanning proton therapy was predicted with Geant4 Monte Carlo simulations in terms of shift detection sensitivity, accuracy and precision. The simulation results indicated that the GEVI system can detect the interfractional range shifts down to 1 mm shift for the cases considered in the present study. The results also showed that both the evaluated accuracy and precision were less than 1-2 mm, except for the scenarios where we consider all spots in the energy layer for a local shifting. It was very encouraging results that the accuracy and precision satisfied the smallest distal safety margin of the investigated beam energy (i.e., 4.88 mm for 134.9 MeV).

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.175-175
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• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.516-518
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• 2022

In this paper, we introduce a dependence of number of nodes of hidden-layer in fall detection system using Long Short-Term Memory that can detect falls. Its training is carried out using the parameter theta(θ), which indicates the angle formed by the x, y, and z-axis data for the direction of gravity using a 3-axis acceleration sensor. In its learning, validation is performed and divided into training data and test data in a ratio of 8:2, and training is performed by changing the number of nodes in the hidden layer to increase efficiency. When the number of nodes is 128, the best accuracy is shown with Accuracy = 99.82%, Specificity = 99.58%, and Sensitivity = 100%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.1
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• pp.39-49
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• 2002

In this study, limits of detection (LOD), accuracy and precision of four sampling/ analytical methods were evaluated and compared for the determination of airborne hexavalent chromium, Cr (VI). The methods include : (1) a combination of the National Institute for Occupational Safety and Health (NIOSH) Method 7600/U. S. Environmental Protection Agency (EPA) Method 218.6 (NIOSH/EPA Method) proposed by Shin and Paik, 2) two impinger methods using 2% NaOH/3% Na$_2$CO$_3$. (3) same as (2) but with 0.02 N NaHCO$_3$absorbing solution, and (4) the Occupational Safety and Health (OSHA) Method ID-215. An ion chromatograph/visible absorbance detector was used for the analysis of Cr (VI) in sample solution. Limit of detection (LOD) , analytical accuracy, and precision were also tested using Cr (VI) spike samples. Recoveries (as index of accuracy) and coefficient of variation (CV) (as a index of precision) were determined. Two-way ANOVA and Turkey's test were performed to test the significance in differences among recoveries and CVs of the methods. In all the methods, the peaks of Cr (VI) were separated sharply on chromatograms and exhibited a strong linearity with Cr (VI) concentrations in solution. The correlation coefficients of calibration curves typically ranged from 0.9997 to 0.9999, and the analytical LODs from 0.025 to 0.1$\mu\textrm{g}$/sample. All the method had good sensitivities and linearities between Cr (VI) levels and peak areas. The accuracies (% mean recoveries) of the methods ranged from 80.1 to 104.2%, while the precisions (pooled coefficient of variation) ranged from 3.16 to 4.43%. The impinger methods showed higher recoveries ( > 95%) than those of the PVC filter methods (the OSHA Method and the NIOSH/EPA Method). It was assumed that Cr (VI) on PVC filter was exposed to air and reduced to trivalent chromium, Cr (III), whereas it was stabilized in alkali solution contained in impinger. Thus, a special treatment of Cr (VI) samples collected on PVC filters may be required.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.177-181
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• 1999

This paper presents an algorithm for building detection from aerial image using segment measure function and line relation. In the detection algorithm proposed, edge detection, linear approximation and line linking are used and then line measure function is applied to each line segment in order to improve the accuracy of linear approximation. Parallelisms, orthogonalities are applied to the extracted liner segments to extract building. The algorithm was applied to aerial image and the buildings were accurately detected.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.560-565
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• 2010

In this study, the existence of chaotic characteristics in snoring signals obtained in the form of time series data was checked through quantitative and qualitative analysis methods, and a snoring signal detection system was designed applied with detection algorithms considering diverse parameters of occurring signals in order to enhance the accuracy and reliability of detections and the performance of the system was checked. The system was tested with certain snoring patients and thereby the results as follows could be obtained.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.40-42
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• 2009

This paper presents an algorithm, which is based on Otsu's method, for accurate and robust endpoint detection for speech recognition under noisy environments. The features are extracted in time domain, and then an optimal threshold is selected by minimizing the discriminant criterion, so as to maximize the separability of the speech part and environment part. The simulation results show that the method play a good performance in detection accuracy.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.61-64
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• 2003

In this paper, we implement PAMD(Positive Answering Machine Detection) for discrimination human and ARS. We are used Grunt detection, Glitch Noise detection and Tone detection for PAMD. It distinguishes voice signals from ring-back tone and glitch noise respectively. And as a second step, it judges whether human responses or ARS responses after integrating pattern changes like initial response period, the number of voice data, each time of voice data period and glitch noise. The accuracy is about 9375 in ASR and about 98％ in Mobile phone.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.3
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• pp.129-138
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• 2019

In this work, we propose detection method for ionosphere storm that occurs locally using widespread GNSS reference stations. For ionosphere storm detection, we compare ionosphere condition with other reference stations and estimate direction of movement based on ionosphere time variation. The method use carrier phase measurement of dual frequency, for accuracy and precision of test statistics, are evaluated with multiple GNSS reference stations data.