• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.67-75
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• 2012

Purpose : We studied enhanced method to view the vessels in the brain using Magnetic Resonance Angiography (MRA). Noticing that Maximum Intensity Projection (MIP) image is often used to evaluate the arteries of the neck and brain, we propose a new method for view brain vessels to stereo image in 3D space with more superior and more correct compared with conventional method. Materials and Methods: We use 3T Siemens Tim Trio MRI scanner with 4 channel head coil and get a 3D MRA brain data by fixing volunteers head and radiating Phase Contrast pulse sequence. MRA brain data is 3D rotated according to the view angle of each eyes. Optimal view angle (projection angle) is determined by the distance between eye and center of the data. Newly acquired MRA data are projected along with the projection line and display only the highest values. Each left and right view MIP image is integrated through anaglyph imaging method and optimal stereoscopic MIP image is acquired. Results: Result image shows that proposed method let enable to view MIP image at any direction of MRA data that is impossible to the conventional method. Moreover, considering disparity and distance from viewer to center of MRA data at spherical coordinates, we can get more realistic stereo image. In conclusion, we can get optimal stereoscopic images according to the position that viewers want to see and distance between viewer and MRA data. Conclusion: Proposed method overcome problems of conventional method that shows only specific projected image (z-axis projection) and give optimal depth information by converting mono MIP image to stereoscopic image considering viewers position. And can display any view of MRA data at spherical coordinates. If the optimization algorithm and parallel processing is applied, it may give useful medical information for diagnosis and treatment planning in real-time.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

• Progress in Medical Physics
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• v.14 no.1
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• pp.34-42
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• 2003

In medical imaging, three-dimensional (3D) display using Virtual Reality Modeling Language (VRML) as a portable file format can give intuitive information more efficiently on the World Wide Web (WWW). The web-based 3D visualization of functional images combined with anatomical images has not studied much in systematic ways. The goal of this study was to achieve a simultaneous observation of 3D anatomic and functional models with planar images on the WWW, providing their locational information in 3D space with a measuring implement using VRML. MRI and ictal-interictal SPECT images were obtained from one epileptic patient. Subtraction ictal SPECT co-registered to MRI (SISCOM) was performed to improve identification of a seizure focus. SISCOM image volumes were held by thresholds above one standard deviation (1-SD) and two standard deviations (2-SD). SISCOM foci and boundaries of gray matter, white matter, and cerebrospinal fluid (CSF) in the MRI volume were segmented and rendered to VRML polygonal surfaces by marching cube algorithm. Line profiles of x and y-axis that represent real lengths on an image were acquired and their maximum lengths were the same as 211.67 mm. The real size vs. the rendered VRML surface size was approximately the ratio of 1 to 605.9. A VRML measuring tool was made and merged with previous VRML surfaces. User interface tools were embedded with Java Script routines to display MRI planar images as cross sections of 3D surface models and to set transparencies of 3D surface models. When transparencies of 3D surface models were properly controlled, a fused display of the brain geometry with 3D distributions of focal activated regions provided intuitively spatial correlations among three 3D surface models. The epileptic seizure focus was in the right temporal lobe of the brain. The real position of the seizure focus could be verified by the VRML measuring tool and the anatomy corresponding to the seizure focus could be confirmed by MRI planar images crossing 3D surface models. The VRML application developed in this study may have several advantages. Firstly, 3D fused display and control of anatomic and functional image were achieved on the m. Secondly, the vector analysis of a 3D surface model was defined by the VRML measuring tool based on the real size. Finally, the anatomy corresponding to the seizure focus was intuitively detected by correlations with MRI images. Our web based visualization of 3-D fusion image and its localization will be a help to online research and education in diagnostic radiology, therapeutic radiology, and surgery applications.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.379-399
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• 2020

The detection of illegal ship is one of the key factors in building a marine surveillance system. Effective marine surveillance requires the means for continuous monitoring over a wide area. In this study, the possibility of ship detection monitoring based on satellite SAR, HF radar, UAV and AIS integration was investigated. Considering the characteristics of time and spatial resolution for each platform, the ship monitoring scenario consisted of a regular surveillance system using HFR data and AIS data, and an event monitoring system using satellites and UAVs. The regular surveillance system still has limitations in detecting a small ship and accuracy due to the low spatial resolution of HF radar data. However, the event monitoring system using satellite SAR data effectively detects illegal ships using AIS data, and the ship speed and heading direction estimated from SAR images or ship tracking information using HF radar data can be used as the main information for the transition to UAV monitoring. For the validation of monitoring scenario, a comprehensive field experiment was conducted from June 25 to June 26, 2019, at the west side of Hongwon Port in Seocheon. KOMPSAT-5 SAR images, UAV data, HF radar data and AIS data were successfully collected and analyzed by applying each developed algorithm. The developed system will be the basis for the regular and event ship monitoring scenarios as well as the visualization of data and analysis results collected from multiple platforms.

• International Journal of Vascular Biomedical Engineering
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• v.4 no.2
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• pp.19-24
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• 2006

Background: Pulse wave velocity (PWV), which is inversely related to the distensibility of an arterial wall, offers a simple and potentially useful approach for an evaluation of cardiovascular diseases. In spite of the clinical importance and widespread use of PWV, there exist no standard either for pulse sensors or for system requirements for accurate pulse wave measurement. Objective of this study was to assess the reproducibility of PWV values using a newly developed PWV measurement system in healthy subjects prior to a large-scale clinical study. Methods: System used for the study was the PP-1000 (Hanbyul Meditech Co., Korea), which provides regional PWV values based on the measurements of electrocardiography (ECG), phonocardiography (PCG), and pulse waves from four different sites of arteries (carotid, femoral, radial, and dorsalis pedis) simultaneously. Seventeen healthy male subjects with a mean age of 33 years (ranges 22 to 52 years) without any cardiovascular disease were participated for the experiment. Two observers (observer A and B) performed two consecutive measurements from the same subject in a random order. For an evaluation of system reproducibility, two analyses (within-observer and between-observer) were performed, and expressed in terms of mean difference ${\pm}2SD$, as described by Bland and Altman plots. Results: Mean and SD of PWVs for aorta, arm, and leg were $7.07{\pm}1.48m/sec,\;8.43{\pm}1.14m/sec,\;and\;8.09{\pm}0.98m/sec$ measured from observer A and $6.76{\pm}1.00m/sec,\;7.97{\pm}0.80m/sec,\;and\;\7.97{\pm}0.72m/sec$ from observer B, respectively. Between-observer differences ($mean{\pm}2SD$) for aorta, arm, and leg were $0.14{\pm\}0.62m/sec,\;0.18{\pm\}0.84m/sec,\;and\;0.07{\pm}0.86m/sec$, and the correlation coefficients were high especially 0.93 for aortic PWV. Within-observer differences ($mean{\pm}2SD$) for aorta, arm, and leg were $0.01{\pm}0.26m/sec,\;0.02{\pm}0.26m/sec,\;and\;0.08{\pm}0.32m/sec$ from observer A and $0.01{\pm}0.24m/sec,\;0.04{\pm}0.28m/sec,\;and\;0.01{\pm}0.20m/sec$ from observer B, respectively. All the measurements showed significantly high correlation coefficients ranges from 0.94 to 0.99. Conclusion: PWV measurement system used for the study offers comfortable and simple operation and provides accurate analysis results with high reproducibility. Since the reproducibility of the measurement is critical for the diagnosis in clinical use, it is necessary to provide an accurate algorithm for the detection of additional features such as flow wave, reflection wave, and dicrotic notch from a pulse waveform. This study will be extended for the comparison of PWV values from patients with various vascular risks for clinical application. Data acquired from the study could be used for the determination of the appropriate sample size for further studies relating various types of arteriosclerosis-related vascular disease.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.77-96
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• 2011

An exhibition is defined as market events for specific duration to present exhibitors' main products to business or private visitors, and it plays a key role as effective marketing channels. As the importance of exhibition is getting more and more, domestic exhibition industry has achieved such a great quantitative growth. But, In contrast to the quantitative growth of domestic exhibition industry, the qualitative growth of Exhibition has not achieved competent growth. In order to improve the quality of exhibition, we need to understand the preference or behavior characteristics of visitors and to increase the level of visitors' attention and satisfaction through the understanding of visitors. So, in this paper, we used the observation survey method which is a kind of field research to understand visitors and collect the real data for the analysis of behavior pattern. And this research proposed the following methodology framework consisting of three steps. First step is to select a suitable exhibition to apply for our method. Second step is to implement the observation survey method. And we collect the real data for further analysis. In this paper, we conducted the observation survey method to obtain the real data of the KIDS & EDU EXPO for Children in SETEC. Our methodology was conducted on 160 visitors and 78 booths from November 4th to 6th in 2010. And, the last step is to analyze the record data through observation. In this step, we analyze the feature of exhibition using Demographic Characteristics collected by observation survey method at first. And then we analyze the individual booth features by the records of visited booth. Through the analysis of individual booth features, we can figure out what kind of events attract the attention of visitors and what kind of marketing activities affect the behavior pattern of visitors. But, since previous research considered only individual features influenced by exhibition, the research about the correlation among features is not performed much. So, in this research, additional analysis is carried out to supplement the existing research with data mining techniques. And we analyze the relation among booths using data mining techniques to know behavior patterns of visitors. Among data mining techniques, we make use of two data mining techniques, such as clustering analysis and ARM(Association Rule Mining) analysis. In clustering analysis, we use K-means algorithm to figure out the correlation among booths. Through data mining techniques, we figure out that there are two important features to affect visitors' behavior patterns in exhibition. One is the geographical features of booths. The other is the exhibit contents of booths. Those features are considered when the organizer of exhibition plans next exhibition. Therefore, the results of our analysis are expected to provide guideline to understanding visitors and some valuable insights for the exhibition from the earlier phases of exhibition planning. Also, this research would be a good way to increase the quality of visitor satisfaction. Visitors' movement paths, booth location, and distances between each booth are considered to plan next exhibition in advance. This research was conducted at the KIDS & EDU EXPO for Children in SETEC(Seoul Trade Exhibition & Convention), but it has some constraints to be applied directly to other exhibitions. Also, the results were derived from a limited number of data samples. In order to obtain more accurate and reliable results, it is necessary to conduct more experiments based on larger data samples and exhibitions on a variety of genres.

• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.589-597
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• 2016

In this study, we investigated the use of satellite-derived flow (SDF) signals and a data-based model for the estimation of outflow for the river reach where in situ measurements are either completely unavailable or are difficult to access for hydraulic and hydrology analysis such as the upper basin of Imjin River. It has been demonstrated by many studies that the SDF signals can be used as the river width estimates and the correlation between SDF signals and river width is related to the shape of cross sections. To extract the nonlinear relationship between SDF signals and river outflow, Artificial Neural Network (ANN) model with SDF signals as its inputs were applied for the computation of flow discharge at Imjin Bridge located in Imjin River. 15 pixels were considered to extract SDF signals and Partial Mutual Information (PMI) algorithm was applied to identify the most relevant input variables among 150 candidate SDF signals (including 0~10 day lagged observations). The estimated discharges by ANN model were compared with the measured ones at Imjin Bridge gauging station and correlation coefficients of the training and validation were 0.86 and 0.72, respectively. It was found that if the 1 day previous discharge at Imjin bridge is considered as an input variable for ANN model, the correlation coefficients were improved to 0.90 and 0.83, respectively. Based on the results in this study, SDF signals along with some local measured data can play an useful role in river flow estimation and especially in flood forecasting for data-scarce regions as it can simulate the peak discharge and peak time of flood events with satisfactory accuracy.