• Journal of the Korean institute of surface engineering
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• v.57 no.1
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• pp.38-48
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• 2024

We fabricated In₂O₃ zig-zag nanocolumns(ZZNCs) by oblique angle deposition method based on e-beam evaporator for highly sensitive and selective CH₃COCH₃ sensor. Our results indicate that as the ZZNCs layer stacks, the gas response also increases. In comparison to thin films, ZZNCs at 5 layer show a 117-fold enhancement in gas response and a rapid response time (~2 s). When measured with various gases, it showed a high selectivity towards acetone. Under conditions of 80% R.H., exposure to CH₃COCH₃ gas theoretically indicated a detection limit of 1.2 part-per-billion(ppb). These results suggest the potential of In₂O₃ ZZNCs as a breath analyzer for the diagnosis of diabetes.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.173-181
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• 2007

In this paper, we propose a moving target localization algorithm using acoustic spectrograms. A time-versus-frequency spectrogram provide a information of trajectory of the moving target in underwater. For a source at sufficiently long range from a receiver, broadband striation patterns seen in spectrogram represents the mutual interference between modes which reflected by surface and bottom. The slope of the maximum intensity striation is influenced by waveguide invariant parameter ${\beta}$ and distance between target and sensor. When more than two sensors are applied to measure the moving ship-radited noise, the slope and frequency of the maximum intensity striation are depend on distance between target and receiver. We assumed two sensors to fixed point then form a circle of apollonios which set of all points whose distances from two fixed points are in a constant ratio. In case of three sensors are applied, two circle form an intersection point so coordinates of this point can be estimated as a position of target. To evaluates a performance of the proposed localization algorithm, simulation is performed using acoustic propagation program.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.63-74
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• 2024

In agriculture, frost can be devastating, which is why observation and forecasting are so important. According to a recent report analyzing frost observation data from the Korea Meteorological Administration, despite global warming due to climate change, the late frost date in spring has not been accelerated, and the frequency of frost has not decreased. Therefore, it is important to automate and continuously operate frost observation in risk areas to prevent agricultural frost damage. In the existing frost observation using leaf wetness sensors, there is a problem that the reference voltage value fluctuates over a long period of time due to contamination of the observation sensor or changes in the humidity of the surrounding environment. In this study, a datalogger program was implemented to automatically solve these problems. The established frost observation system can stably and automatically accumulate time-resolved observation data over a long period of time. This data can be utilized in the future for the development of frost diagnosis models using machine learning methods and the production of frost occurrence prediction information for surrounding areas.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.33-39
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• 2023

This study is a performance evaluation of a blood sugar monitoring system that combines a PPG sensor, which is an evaluation device for blood glucose monitoring, and a DNN algorithm when monitoring capillary blood glucose. The study is a researcher-led clinical trial conducted on participants from September 2023 to November 2023. PPG-BGMS compared predicted blood sugar levels for evaluation using 1-minute heart rate and heart rate variability information and the DNN prediction algorithm with capillary blood glucose levels measured with a blood glucose meter of the standard personal blood sugar management system. Of the 100 participants, 50 had type 2 diabetes (T2DM), and the average age was 67 years (range, 28 to 89 years). It was found that 100% of the predicted blood sugar level of PPG-BGMS was distributed in the A+B area of the Clarke error grid and Parker(Consensus) error grid. The MARD value of PPG-BGMS predicted blood glucose is 5.3 ± 4.0%. Consequentially, the non-blood-based PPG-BGMS was found to be non-inferior to the instantaneous blood sugar level of the clinical standard blood-based personal blood glucose measurement system.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.81-86
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• 2024

The Image Signal Processor (ISP) converts RAW images captured by the camera sensor into user-preferred sRGB images. While RAW images contain more meaningful information for image processing than sRGB images, RAW images are rarely shared due to their large sizes. Moreover, the actual ISP process of a camera is not disclosed, making it difficult to model the inverse process. Consequently, research on learning the conversion between sRGB and RAW has been conducted. Recently, the ParamISP[1] model, which directly incorporates camera parameters (exposure time, sensitivity, aperture size, and focal length) to mimic the operations of a real camera ISP, has been proposed by advancing the simple network structures. However, existing studies, including ParamISP[1], have limitations in modeling the camera ISP as they do not consider the degradation caused by lens shading, optical aberration, and lens distortion, which limits the restoration performance. This study introduces Positional Encoding to enable the camera ISP neural network to better handle degradations caused by lens. The proposed positional encoding method is suitable for camera ISP neural networks that learn by dividing the image into patches. By reflecting the spatial context of the image, it allows for more precise image restoration compared to existing models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.263-270
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• 2024

As unmanned aerial vehicles(UAVs) and sensors have been developed in a variety of ways, it has become possible to update information on the ground faster than existing aerial photography or remote sensing. However, acquisition and input of ground control points(GCPs) UAV photogrammetry takes a lot of time, and geometric distortion occurs if measurement and input of GCPs are incorrect. In this study, RGB-based orthophotos were generated to reduce GCPs measurment and input time, and comparison and evaluation were performed by applying feature point algorithms to target orthophotos from various sensors. Four feature point extraction algorithms were applied to the two study sites, and as a result, speeded up robust features(SURF) was the best in terms of the ratio of matching pairs to feature points. When compared overall, the accelerated-KAZE(AKAZE) method extracted the most feature points and matching pairs, and the binary robust invariant scalable keypoints(BRISK) method extracted the fewest feature points and matching pairs. Through these results, it was confirmed that the AKAZE method is superior when performing geometric correction of the objective orthophoto for each sensor.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.253-263
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• 2013

Frequency Scanning Interferometry(FSI) system, one of the most promising optical surface measurement techniques, generally results in superior optical performance comparing with other 3-dimensional measuring methods as its hardware structure is fixed in operation and only the light frequency is scanned in a specific spectral band without vertical scanning of the target surface or the objective lens. FSI system collects a set of images of interference fringe by changing the frequency of light source. After that, it transforms intensity data of acquired image into frequency information, and calculates the height profile of target objects with the help of frequency analysis based on Fast Fourier Transform(FFT). However, it still suffers from optical noise on target surfaces and relatively long processing time due to the number of images acquired in frequency scanning phase. 1) a Polarization-based Frequency Scanning Interferometry(PFSI) is proposed for optical noise robustness. It consists of tunable laser for light source, ${\lambda}/4$ plate in front of reference mirror, ${\lambda}/4$ plate in front of target object, polarizing beam splitter, polarizer in front of image sensor, polarizer in front of the fiber coupled light source, ${\lambda}/2$ plate between PBS and polarizer of the light source. Using the proposed system, we can solve the problem of fringe image with low contrast by using polarization technique. Also, we can control light distribution of object beam and reference beam. 2) the signal processing acceleration method is proposed for PFSI, based on parallel processing architecture, which consists of parallel processing hardware and software such as Graphic Processing Unit(GPU) and Compute Unified Device Architecture(CUDA). As a result, the processing time reaches into tact time level of real-time processing. Finally, the proposed system is evaluated in terms of accuracy and processing speed through a series of experiment and the obtained results show the effectiveness of the proposed system and method.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.45-55
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• 2013

Thereby using smartphone and mobile device be more popular the more people utilize mobile device in many area such as education, news, financial. In January, 2007 Apple release i-phone it touch off rapid increasing in user of smartphone and it create new market and these broaden its utilization area. Smartphone use WiFi or 3G mobile radio communication network and it has a feature that can access to internet whenever and anywhere. Also using smartphone application people can search arrival time of public transportation in real time and application is used in mobile banking and stock trading. Computer's function is replaced by smartphone so it involves important user's information such as financial and personal pictures, videos. Present smartphone security systems are not only too simple but the unlocking methods are spreading out covertly. I-phone is secured by using combination of number and character but USA's IT magazine Engadget reveal that it is easily unlocked by using combination with some part of number pad and buttons Android operation system is using pattern system and it is known as using 9 point dot so user can utilize various variable but according to Jonathan smith professor of University of Pennsylvania Android security system is easily unlocked by tracing fingerprint which remains on the smartphone screen. So both of Android and I-phone OS are vulnerable at security threat. Compared with problem of password and pattern finger recognition has advantage in security and possibility of loss. The reason why current using finger recognition smart phone, and device are not so popular is that there are many problem: not providing reasonable price, breaching human rights. In addition, finger recognition sensor is not providing reasonable price to customers but through continuous development of the smartphone and device, it will be more miniaturized and its price will fall. So once utilization of finger recognition is actively used in smartphone and if its utilization area broaden to financial transaction. Utilization of biometrics in smart device will be debated briskly. So in this thesis we will propose fingerprint numbering system which is combined fingerprint and password to fortify existing fingerprint recognition. Consisted by 4 number of password has this kind of problem so we will replace existing 4number password and pattern system and consolidate with fingerprint recognition and password reinforce security. In original fingerprint recognition system there is only 10 numbers of cases but if numbering to fingerprint we can consist of a password as a new method. Using proposed method user enter fingerprint as invested number to the finger. So attacker will have difficulty to collect all kind of fingerprint to forge and infer user's password. After fingerprint numbering, system can use the method of recognization of entering several fingerprint at the same time or enter fingerprint in regular sequence. In this thesis we adapt entering fingerprint in regular sequence and if in this system allow duplication when entering fingerprint. In case of allowing duplication a number of possible combinations is $\sum_{I=1}^{10}\;{_{10}P_i}$ and its total cases of number is 9,864,100. So by this method user retain security the other hand attacker will have a number of difficulties to conjecture and it is needed to obtain user's fingerprint thus this system will enhance user's security. This system is method not accept only one fingerprint but accept multiple finger in regular sequence. In this thesis we introduce the method in the environment of smartphone by using multiple numbered fingerprint enter to authorize user. Present smartphone authorization using pattern and password and fingerprint are exposed to high risk so if proposed system overcome delay time when user enter their finger to recognition device and relate to other biometric method it will have more concrete security. The problem should be solved after this research is reducing fingerprint's numbering time and hardware development should be preceded. If in the future using fingerprint public certification becomes popular. The fingerprint recognition in the smartphone will become important security issue so this thesis will utilize to fortify fingerprint recognition research.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.