• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.349-354
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• 2011

Passive millimeter wave imaging has the capability of detecting concealed objects under clothing. Also, passive millimeter imaging can obtain interpretable images under low visibility conditions like rain, fog, smoke, and dust. However, the image quality is often degraded due to low spatial resolution, low signal level, and low temperature resolution. This paper addresses image registration and fusion between passive millimeter images and visual images. The goal of this study is to combine and visualize two different types of information together: human subject's identity and concealed objects. The image registration process is composed of body boundary detection and an affine transform maximizing cross-correlation coefficients of two edge images. The image fusion process comprises three stages: discrete wavelet transform for image decomposition, a fusion rule for merging the coefficients, and the inverse transform for image synthesis. In the experiments, various types of metallic and non-metallic objects such as a knife, gel or liquid type beauty aids and a phone are detected by passive millimeter wave imaging. The registration and fusion process can visualize the meaningful information from two different types of sensors.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of Pharmaceutical Investigation
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• v.39 no.1
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• pp.65-71
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• 2009

The aim of the present study was to evaluate the bioequivalence of two domperidone maleate tablets, Motilium-$M^{(R)}$ Tablet (Janssen Korea Ltd., reference product) and $Toriem^{(R)}$ Tablet (Daewon Pharm. Co., Ltd., test product). Domperidone was extracted by liquid-liquid extraction using tert-butyl methyl ether and separated in less than 3 min on $C_{18}$ reverse-phase column using an isocratic elution. A tandem mass spectrometer, as detector, was used for quantitative analysis in positive mode by a multiple reaction monitoring mode to monitor the m/z $426.1{\rightarrow}119.1$ and the m/z $837.4{\rightarrow}158.2$ transitions for domperidone and the internal standard (roxithromycin), respectively. Calibration curves, from $0.05{\sim}50$ ng/mL of domperidone, showed correlation coefficients (r) higher than 0.9941. Intra day and inter day precision (C.V. %) for quality control were ranged from 10.04 to 16.09% and from 10.87 to 18.69%, respectively. The lower limit of quantification (LLOQ) of domperidone was 0.05 ng/mL. The method described is precise and sensitive and has been successfully applied to the study of bioequivalence of domperidone in 24 healthy Korean volunteers. Twenty-four healthy male Korean volunteers received a single dose of each medicine ($2{\times}12.72\;mg$ domperidone maleate) in a $2{\times}2$ crossover study. There was a one-week washout period between the doses. Plasma concentrations of domperidone were monitored for over a period of 24 hr after the administration. $AUC_{0-t}$ (the area under the plasma concentration-time curve) was calculated by the linear trapezoidal rule. $C_{max}$ (maximum plasma drug concentration) and $T_{max}$ (time to reach $C_{max}$) were compiled from the plasma concentration-time data. The 90% confidence intervals for the log transformed data were within acceptable range of log 0.8 to log 1.25 (e.g., $log\;0.92{\sim}log\;1.05$ for $AUC_{0-t}$, $log\;0.81{\sim}log\;1.05$ for $C_{max}$). The major parameters, $AUC_{0-t}$ and $C_{max}$ met the criteria of KFDA for bioequivalence indicating that $Toriem^{(R)}$ tablet is bioequivalent to Motilium-$M^{(R)}$ tablet.

• Structural Engineering and Mechanics
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• v.48 no.4
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• pp.501-518
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• 2013

Apart from thinning of cortical layers, the local bone curvature, varying along bone periphery, modulates ultrasound waves as well, which is however often underestimated or overlooked in clinical quantitative ultrasound (QUS). A dedicated three-dimensional finite element modelling technique for cortical bones was established, for quantitatively exploring and calibrating the effect of local curvature of cortical bone on ultrasound. Using a correlation-based mode extraction technique, high-velocity group (HVG) and low-velocity group (LVG) wave modes in a human radius were examined. Experimental verification using acrylic cylinders and in vitro testing using a porcine femur were accomplished. Results coherently unravelled the cortical curvature exerts evident influence on bone-guided ultrasound when RoC/${\lambda}$ <1 for HVG mode and RoC/${\lambda}$ <2 for LVG mode (RoC/${\lambda}$: the ratio of local bone curvature radius to wavelength); the sensitivity of LVG mode to bone curvature is higher than HVG mode. It has also been demonstrated the local group velocity of an HVG or LVG mode at a particular skeletal site is equivalent to the velocity when propagating in a uniform cylinder having an outer radius identical to the radius of curvature at that site. This study provides a rule of thumb to compensate for the effect of bone curvature in QUS.

• Parasites, Hosts and Diseases
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• v.52 no.2
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• pp.193-196
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• 2014

Fascioliasis is a zoonotic infection caused by Fasciola hepatica or Fasciola gigantica. We report an 87-year-old Korean male patient with postprandial abdominal pain and discomfort due to F. hepatica infection who was diagnosed and managed by endoscopic retrograde cholangiopancreatography (ERCP) with extraction of 2 worms. At his first visit to the hospital, a gallbladder stone was suspected. CT and magnetic retrograde cholangiopancreatography (MRCP) showed an intraductal mass in the common bile duct (CBD) without proximal duct dilatation. Based on radiological findings, the presumed diagnosis was intraductal cholangiocarcinoma. However, in ERCP which was performed for biliary decompression and tissue diagnosis, movable materials were detected in the CBD. Using a basket, 2 living leaf-like parasites were removed. The worms were morphologically compatible with F. hepatica. To rule out the possibility of the worms to be another morphologically close species, in particular F. gigantica, 1 specimen was processed for genetic analysis of its ITS-1 region. The results showed that the present worms were genetically identical (100%) with F. hepatica but different from F. gigantica.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.872-877
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• 2012

We proposes the method to detect the Hangul character region from natural image using topological structural feature of Hangul grapheme. First, we transform a natural image to a gray-scale image. Second, feature extraction performed with edge and connected component based method, Edge-based method use a Canny-edge detector and connected component based method applied the local range filtering. Next, if features are not corresponding to the heuristic rule of Hangul character, extracted features filtered out and select candidates of character region. Next, candidates of Hangul character region are merged into one Hangul character using Hangul character merging algorithm. Finally, we detect the final character region by Hangul character class decision algorithm. Experimental result, proposed method could detect a character region effectively in images that contains a complex background and various environments. As a result of the performance evaluation, A proposed method showed advanced results about detection of Hangul character region from mobile image.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.65-74
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• 2012

Cloud services are used for improving business. Moreover, customer relationship management(CRM) approaches use social networking as tools to enhance services to customers. However, most cloud systems do not support the semantic structures, and because of this, vital information from social network sites is still hard to process and use for business strategy. This paper proposes a collaboration framework based on social semantic web for cloud system. The proposed framework consists of components to support social semantic web to provide an efficient collaboration system for cloud consumers and service providers. The knowledge acquisition module extracts rules from data gathered by social agents and these rules are used for collaboration and business strategy. This paper showed the implementations of processing of social network site data in the proposed semantic model and pattern extraction which was used for the virtual grouping of cloud service providers for efficient collaboration.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.37-44
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• 2001

This paper describes the modeling of work environment for the extraction of abstract operation rules for cooperative work with multiple agent. We propose the modeling method using a potential field. In the method, it is applied to a box pushing problem, which is to move a box from a start to a goal b multiple agent. The agents follow the potential value when they move and work in the work environment. The work environment is represented as the grid space. The potential field is generated by Genetic Algorithm(GA) for each agent. GA explores the positions of a potential peak value in the grid space, and then the potential value stretching in the grid space is spread by a potential diffusion function in each grid. However it is difficult to explore suitable setting using hand coding of the position of peak potential value. Thus, we use an evlolutionary computation way because it is possible to explore the large search space. So we make experiments the environment modeling using the proposed method and verify the performance of the exploration by GA. And we classify some types from acquired the environment model and extract the abstract operation rule, As results, we find out some types of the environment models and operation rules by the observation, and the performance of GA exploration is almost same as the hand coding set because these are nearly same performance on the evaluation of the consumption of agent's energy and the work step from point to the goal point.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.715-720
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• 2005

This paper presents a hybrid recognition method of first moment of face image and principal component analysis(PCA). First moment is applied to reduce the dimension by shifting to the centroid of image, which is to exclude the needless backgrounds in the face recognitions. PCA is implemented by single layer neural network which has a teaming rule of Foldiak algorithm. It has been used as an alternative method for numerical PCA. PCA is to derive an orthonormal basis which directly leads to dimensionality reduction and possibly to feature extraction of face image. The proposed method has been applied to the problems for recognizing the 48 face images(12 Persons $\ast$ 4 scenes) of 64$\ast$64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.717-722
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• 2004

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer using neural network with weighted fuzzy membership functions (NNWFM). NNWFM is capable of self-adapting weighted membership functions to enhance accuracy in prediction from the given clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from the enhanced bounded sums of n set of weighted fuzzy membership functions. Two number of prediction rules extracted from NNWFM outperforms to the current published results in number of rules and accuracy with 99.41%.