• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.636-645
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• 2008

Most of routing schemes that protect the source's location from a malicious attacker usually make use of a path of a long length per message for the sake of lengthening the safety period. The biggest problem to such approaches is taking a very long latency in transferring messages to the destination. In this paper we show the problem to find the least-cost single path that is enough to keep the source-location always secure from the attacker, provided that it is used for the delivery of a set of messages given in priori, is NP-complete. Consequently we propose a routing protocol GSLP-w(GPSR-based Source-Location Privacy with crew size co) that is a trade-off between two extreme approaches. The advantage of GSLP-co lies in its enhanced safety period for the source and its lowered delivery latency in messaging. We consider NSP(Normalized Sefety Period) and NDL(Normalized Delivery Latency), measured in terms of the least number of hops to the destination, to achieve tangible interpretation of the results. We ran a simulation to confirm our claim by generating 100 topologies of 50,000 nodes with the average number of neighbors being 8. The results show that GSLP-$\omega$ provides more enhanced NSP compared to other protocols GSLP, an earlier version of GSLP-$\omega$, and PR-SP(Phantom Routing - Single Path), the most notable existing protocol for the source-location privacy, and less NDL than that of GSLP but more than that of PR-SP.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.135-150
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• 2002

The basic assumption of analytical Dynamic Traffic Assignment models is that traffic demand and network conditions are known as a priori and unchanging during the whole planning horizon. This assumption may not be realistic in the practical traffic situation because traffic demand and network conditions nay vary from time to time. The rolling horizon implementation recognizes a fact : The Prediction of origin-destination(OD) matrices and network conditions is usually more accurate in a short period of time, while further into the whole horizon there exists a substantial uncertainty. In the rolling horizon implementation, therefore, rather than assuming time-dependent OD matrices and network conditions are known at the beginning of the horizon, it is assumed that the deterministic information of OD and traffic conditions for a short period are possessed, whereas information beyond this short period will not be available until the time rolls forward. This paper introduces rolling horizon implementation to enable a multi-class analytical DTA model to respond operationally to dynamic variations of both traffic demand and network conditions. In the paper, implementation procedure is discussed in detail, and practical solutions for some raised issues of 1) unfinished trips and 2) rerouting strategy of these trips, are proposed. Computational examples and results are presented and analyzed.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.85-93
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• 2011

The Vertex Coloring Problem hasn't been solved in polynomial time, so this problem has been known as NP-complete. This paper suggests linear time algorithm for Vertex Coloring Problem (VCP). The proposed algorithm is based on assumption that we can't know a priori the minimum chromatic number ${\chi}(G)$=k for graph G=(V,E) This algorithm divides Vertices V of graph into two parts as independent sets $\overline{C}$ and cover set C, then assigns the color to $\overline{C}$. The element of independent sets $\overline{C}$ is a vertex ${\upsilon}$ that has minimum degree ${\delta}(G)$ and the elements of cover set C are the vertices ${\upsilon}$ that is adjacent to ${\upsilon}$. The reduced graph is divided into independent sets $\overline{C}$ and cover set C again until no edge is in a cover set C. As a result of experiments, this algorithm finds the ${\chi}(G)$=k perfectly for 26 Graphs that shows the number of selecting ${\upsilon}$ is less than the number of vertices n.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1827-1840
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• 2001

In digital data transmission/storage systems, the compensation for channel distortion is conducted normally using a training sequence that is known a priori to both the sender and receiver. The use of the training sequences results in inefficient utilization of channel bandwidth. Sometimes, it is also impossible to send training sequences such as in the burst-mode communication. As such, a great deal of attention has been given to the approach requiring no training sequences, which has been called the blind equalization technique. On the other hand, to utilize the limited bandwidth effectively, the concept of partial response (PR) signaling has widely been adopted in both the high-speed transmission and high-density recording/playback systems such as digital microwave, digital subscriber loops, hard disk drives, digital VCRs and digital versatile recordable disks and so on. This paper is concerned with blind adaptive equalization of partial response channels whose transfer function zeros are located on the unit circle, thereby causing some problems in performance. Specifically we study how the problems of blind channel equalization associated with the PR channels can be improved. In doing so, we first discuss the existing methods and then propose new structures for blind PR channel equalization. Our structures have been extensively tested by computer simulation and found out to be encouraging in performance. The results seem very promising as well in terms of the implementation complexity compared to the previous approach reported in literature.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.45-66
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• 2018

Crowdfunding has become more popular than angel funding for fundraising by venture companies. Identification of success factors may be useful for fundraisers and investors to make decisions related to crowdfunding projects and predict a priori whether they will be successful or not. Recent studies have suggested several numeric factors, such as project goals and the number of associated SNS, studying how these affect the success of crowdfunding campaigns. However, prediction of the success of crowdfunding campaigns via non-numeric and unstructured data is not yet possible, especially through analysis of structural characteristics of documents introducing projects in need of funding. Analysis of these documents is promising because they are open and inexpensive to obtain. We propose a novel method to predict the success of a crowdfunding project based on the introductory text. To test the performance of the proposed method, in our study, texts related to 1,980 actual crowdfunding projects were collected and empirically analyzed. From the text data set, the following details about the projects were collected: category, number of replies, funding goal, fundraising method, reward, number of SNS followers, number of images and videos, and miscellaneous numeric data. These factors were identified as significant input features to be used in classification algorithms. The results suggest that the proposed method outperforms other recently proposed, non-text-based methods in terms of accuracy, F-score, and elapsed time.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.111-121
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• 2011

Intelligent video analysis systems require techniques which can predict accidents and provide alarms to the monitoring personnel. In this paper, we present an abnormal behavior analysis technique based on adaptive background generation. More specifically, abnormal behaviors include fence climbing, abandoned objects, fainting persons, and loitering persons. The proposed video analysis system consists of (i) background generation and (ii) abnormal behavior analysis modules. For robust background generation, the proposed system updates static regions by detecting motion changes at each frame. In addition, noise and shadow removal steps are also were added to improve the accuracy of the object detection. The abnormal behavior analysis module extracts object information, such as centroid, silhouette, size, and trajectory. As the result of the behavior analysis function objects' behavior is configured and analyzed based on the a priori specified scenarios, such as fence climbing, abandoning objects, fainting, and loitering. In the experimental results, the proposed system was able to detect the moving object and analyze the abnormal behavior in complex environments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.102-109
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• 2008

In this paper, the robust hybrid modulation type classifier which use both HOS and WT key features and can recognize 10 digitally modulated signals without a priori information in multipath fading channel conditions is proposed. The proposed classifier developed using data taken field measurements in various propagation model (i,e., rural area, small town and urban area) for real world scenarios. The 9 channel data are used for supervised training and the 6 channel data are used for testing among total 15 channel data(i.e., holdout-like method). The Proposed classifier is based on HOS key features because they are relatively robust to signal distortion in AWGN and multipath environments, and combined WT key features for classifying MQAM(M=16, 64, 256) signals which are difficult to classify without equalization scheme such as AMA(Alphabet Matched Algorithm) or MMA(Multi-modulus Algorithm. To investigate the performance of proposed classifier, these selected key features are applied in SVM(Support Vector Machine) which is known to having good capability of classifying because of mapping input space to hyperspace for margin maximization. The Pcc(Probability of correct classification) of the proposed classifier shows higher than those of classifiers using only HOS or WT key features in both training channels and testing channels. Especially, the Pccs of MQAM 3re almost perfect in various SNR levels.

• Journal of vocational education research
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• v.36 no.2
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• pp.23-50
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• 2017

The purpose of this study was to determine the hierarchical linear relationship among receptivity to organizational change, individual variables of professors and organizational variables in junior colleges. The population for this study was 12,920 professors in 139 junior colleges. Using random sampling method considering subject, 800 professors in 40 colleges were sampled for this study. The data were collected from May 26 to June 13. A total of 445 out of 800 questionnaires were returned of which 441 of 40 junior colleges were used for analysis after data cleaning. These data were analyzed by both descriptive statistics and One-way ANOVA with Random Effects, Ranmdom-Coefficients Regression Model, and Intercepts-and Slopes-as-Outcomes Model of hierarchical linear model(HLM). All data analysis was accomplished using the SPSS 20.0 for windows program and the HLM 6.0 for windows program. An alpha level of 0.05 was established priori for determining the significance. The findings of the study were as follows: First, the level of receptivity to organizational change of professions in junior college was 3.94. Second, 56.5% of total variance in receptivity to organizational change was individual level variance. 43.5% of total variance in receptivity to organizational change was organizational level variance. Third, personal valence about organizational change, psychological ownership, experience of assignment, years of service and job security had positive effects on receptivity to organizational change while years of service had negative effects on receptivity to organizational change. The effect of personal valence about organizational change was highest, and the effect of job security was lowest. Fourth, degree of organizational change, participative decision-making, group culture and accessibility of information related to organizational change had positive effects on receptivity to organizational change. The effect of degree of organizational change was highest, and the effect of accessibility of information related to organizational change was lowest.

• Geophysics and Geophysical Exploration
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• v.15 no.2
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• pp.66-74
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• 2012

This paper presents development of a three-dimensional marine controlled-source electromagnetic (mCSEM) modeling algorithm and its application to a salt and reservoir model to examine detectability of mCSEM for a reservoir under complex subsurface structures. The algorithm is based on the finite difference method, and employs the secondary field formulation for an accurate and fast calculation of modeling responses. The algorithm is verified for a two-layer model by comparing solutions not only with analytic solutions but also with those from other 3D modeling algorithm. We calculate and analyze electric and magnetic fields and their normalized responses for a salt and reservoir model due to three sources located at boundaries between a salt, a reservoir, and background. Numbers and positions of resistive anomalies are informed by normalized responses for three sources, and types of resistive anomalies can be informed when there is a priori information about a salt by seismic exploration.