• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.256-264
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• 2020

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.185-194
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• 2014

One in every 10 persons suffer from chronic gastritis in Korea. Endoscopy is most commonly used to diagnose the chronic gastritis. Endoscopic diagnosis is precise but it is accompanied with pain and high cost. According to pulse diagnosis in Traditional East Asian Medicine, health problems in stomach can be diagnosed with radial pulse signals in 'Guan' location in the right wrist, which are non-invasive and cost-effective. In this study, we developed a classification model of chronic gastritis using pulse signals in right 'Guan' location. We used both linear discrimination method and logistic regression model with respect to pulse features obtained with a peak-valley detection algorithm and a Gaussian model. As a result, we obtained sensitivity ranged between 77%~89% and specificity ranged between 72%~83% depending on classification models and feature extraction methods, and the average classification rates were approximately 80%, irrespective of the models. Specifically, the Gaussian model were featured by superior sensitivities (89.1% and 87.5%) while the peak-valley detection method showed superior specificities (82.8% and 81.3%), and the average classification rate (sensitivity + specificity) of the Gaussian model was 80.9% which was 1.2% ahead of the peak-valley method. In conclusion, we obtained a reliable classification model for the chronic gastritis based on the radial pulse feature extraction algorithms, where the Gaussian model was featured by outperformed sensitivity and the peak-valley method was featured by outperformed specificity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.31-40
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• 2016

In vehicular ad-hoc networks (VANETs), vehicles sense information on emergency incidents (e.g., accidents, unexpected road conditions, etc.) and propagate this information to following vehicles and a server to share the information. However, this process of emergency message propagation is based on multiple broadcast messages and can lead to broadcast storms. To address this issue, in this work, we use a novel approach to detect the vehicles that are farthest away but within communication range of the transmitting vehicle. Specifically, we discuss a signal-to-noise ratio (SNR)-based linear back-off (SLB) scheme where vehicles implicitly detect their relative locations to the transmitter with respect to the SNR of the received packets. Once the relative locations are detected, nodes that are farther away will set a relatively shorter back-off to prioritize its forwarding process so that other vehicles can suppress their transmissions based on packet overhearing. We evaluate SLB using a realistic simulation environment which consists of a NS-3 VANET simulation environment, a software-based WiFi-IP gateway, and an ITS server operating on a separate machine. Comparisons with other broadcasting-based schemes indicate that SLB successfully propagates emergency messages with latencies and hop counts that is close to the experimental optimal while reducing the number of transmissions by as much as 1/20.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.31-43
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• 2015

This study had been aimed to conduct the camera calibration on VLBI antenna in the Space Geodetic Observation Center of Sejong City with a low-cost digital camera, which embedded in a mobile phone to determine the three-dimension position coordinates of the VLBI antenna, based on stereo images. The initial values for the camera calibration have been obtained by utilizing the Direct Linear Transformation algorithm and the commercial digital photogrammetry system, PhotoModeler $Scanner^{(R)}$ ver. 6.0, respectively. The accuracy of camera calibration results was compared with that the camera calibration results, acquired by a bundle adjustment with nonlinear collinearity condition equation. Although two methods showed significant differences in the initial value, the final calibration demonstrated the consistent results whichever methods had been performed for obtaining the initial value. Furthermore, those three-dimensional coordinates of feature points of the VLBI antenna were respectively calculated using the camera calibration by the two methods to be compared with the reference coordinates obtained from a total station. In fact, both methods have resulted out a same standard deviation of $X=0.004{\pm}0.010m$, $Y=0.001{\pm}0.015m$, $Z=0.009{\pm}0.017m$, that of showing a high degree of accuracy in centimeters. From the result, we can conclude that a mobile phone camera opens up the way for a variety of image processing studies, such as 3D reconstruction from images captured.

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.256-267
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• 2015

The intelligent transportation systems allow us to have valuable opportunities for collecting wide-area coverage traffic data. The significant efforts have been made in many countries to provide the reliable traffic conditions information such as travel time. This study analyzes the impacts of the fog weather conditions on the traffic stream. Also, a strategy for predicting the long-term traffic speeds is developed under foggy weather conditions. The results show that the average of speed reductions are 2.92kph and 5.36kph under the slight and heavy fog respectively. The best prediction performance is achieved when the previous 45 pattern cases data is used, and the 14.11% of mean absolute percentage error(MAPE) is obtained. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• Korean Journal of Ecology and Environment
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• v.39 no.1 s.115
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• pp.52-61
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• 2006

The aim of this study was to analyze the seasonal patterns of zooplankton community dynamics in the lower Nakdong River (Mulgum, RK; river kilometer; 27 km from the estuarine barrage), with a Self-Organizing Map (SOM) based on weekly sampled data collected over ten years(1994 ${\sim}$ 2003). It is well known that zooplankton groups had important role in the food web of freshwater ecosystems, however, less attention has been paid to this group compared with other community constituents. A non-linear patterning algorithm of the SOM was applied to discover the relationship among river environments and zooplankton community dynamics. Limnological variables (water temperature, dissolved oxygen, pH , Secchi transparency, turbidity, chlorophyll a, discharge, etc.) were taken into account to implement patterning seasonal changes of zooplankton community structures (consisting of rotifers, cladocerans and copepods). The trained SOM model allocated zooplankton on the map plane with limnological parameters. Three zooplankton groups had high similarities to one another in their changing seasonal patterns, Among the limnological variables, water temporature was highly related to the zooplankton community dynamics (especially for cladocerans). The SOM model illustrated the suppression of zooplankton due to the increased river discharge, particularly in summer. Chlorophyll a concentrations were separated from zooplankton data set on the map plane, which would intimate the herbivorous activity of dominant grazers. This study introduces the zooplankton dynamics associated with limnological parameters using a nonlinear method, and the information will be useful for managing the river ecosystem, with respect to the food web interactions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2393-2399
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• 2013

In this paper, we proposed a method to screening of Alzheimer's disease (AD) from Raman spectra of platelet with synthesis of basis spectra using singular value decomposition (SVD). Raman spectra of platelet from AD transgenic mice are preprocessed with denoising, removal background and normalization method. The column vectors of each data matrix consist of Raman spectrum of AD and normal (NR). The matrix is factorized using SVD algorithm and then the basis spectra of AD and NR are determined by 12 column vectors of each matrix. The classification process is completed by select the class that minimized the root-mean-square error between the validation spectrum and the linear synthesized spectrum of the basis spectra. According to the experiments involving 278 Raman spectra, the proposed method gave about 97.6% classification rate, which is better performance about 6.1% than multi-layer perceptron (MLP) with extracted features using principle components analysis (PCA). The results show that the basis spectra using SVD is well suited for the diagnosis of AD by Raman spectra from platelet.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.459-470
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• 2003

It is required to research on high-dimensional index structures for efficiently retrieving high-dimensional data because an attribute vector in data warehousing and a feature vector in multimedia database have a characteristic of high-dimensional data. For this, many high-dimensional index structures have been proposed, but they have so called ‘dimensional curse’ problem that retrieval performance is extremely decreased as the dimensionality is increased. To solve the problem, the cell-based filtering (CBF) scheme has been proposed. But the CBF scheme show a linear decreasing on performance as the dimensionality. To cope with the problem, it is necessary to make use of parallel processing techniques. In this paper, we propose a parallel CBF scheme which uses a horizontally-partitioned technique as declustering. In order to maximize the retrieval performance of the proposed parallel CBF scheme, we construct our parallel CBF scheme under a SN (Shared Nothing) cluster architecture. In addition, we present a data insertion algorithm, a rage query processing one, and a k-NN query processing one which are suitable for the SN cluster architecture. Finally, we show that our parallel CBF scheme achieves better retrieval performance in proportion to the number of servers in the SN cluster architecture, compared with the conventional CBF scheme.

• Progress in Medical Physics
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• v.6 no.2
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• pp.83-92
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• 1995

The T1 mapping of an human anatomy may give a characteristic contrast among the various tissues and the normal/abnormal tissues. Here, the methodology of constructing T1 map out of several images with different TRs, will be described using non-linear curve fitting. The general curve fitting algorithm requires the initial trial values T1t and Mot for the variables to be fitted. Three different methods of suppling the trial T1t and Mot are suggested and compared for the efficiency and the accuracy. The curve-fitting routine was written in ANSI C and excuted on a SUN workstation. Several distilled-water phantoms with various concentrations of Gd-DTPA were prepared to examine the accuracy of the curve-fitting program. An MR image was used as the true proton density image without any random noise, and several images with different TRs were generated with the theoretical T1 relaxation times 250, 500, and 1000msec. The random noise of 1, 5, and 10％ were embedded into the simulated images. These images were used to generate the T1 map, and the resultant T1 maps for each T1 were analyzed to study the effect of the random noise on the T1 map.

• Journal of Preventive Medicine and Public Health
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• v.43 no.6
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• pp.479-485
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• 2010

Objectives: The Korean Genome and Epidemiology Study (KoGES), a multicenter-based multi-cohort study, has collected information on body composition using two different bioelectrical impedence analysis (BIA) machines. The aim of the study was to evaluate the possibility of whether the test values measured from different BIA machines can be integrated through statistical adjustment algorithm under excellent inter-rater reliability. Methods: We selected two centers to measure inter-rater reliability of the two BIA machines. We set up the two machines side by side and measured subjects' body compositions between October and December 2007. Duplicated test values of 848 subjects were collected. Pearson and intra-class correlation coefficients for inter-rater reliability were estimated using results from the two machines. To detect the feasibility for data integration, we constructed statistical compensation models using linear regression models with residual analysis and R-square values. Results: All correlation coefficients indicated excellent reliability except mineral mass. However, models using only duplicated body composition values for data integration were not feasible due to relatively low $R^2$ values of 0.8 for mineral mass and target weight. To integrate body composition data, models adjusted for four empirical variables that were age, sex, weight and height were most ideal (all $R^2$ > 0.9). Conclusions: The test values measured with the two BIA machines in the KoGES have excellent reliability for the nine body composition values. Based on reliability, values can be integrated through algorithmic statistical adjustment using regression equations that includes age, sex, weight, and height.