• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.660-666
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• 2014

In this paper, we consider a classification criteria of sensor nodes based on equipment function, and propose a routing search algorithm between node when an IP-USN is applied inside a ship. whereas a tree-type routing algorithm is applied to the limited mobile enviroment, such as engine room or machine room, a mesh-type routing alogrithm is to free mobile enviroment, such as passager corridor liviing quarters or restanrats areas. For mesh-type routing, it is necessary to maintain a seamless route path between a sink node and sensor nodes for which we consider a novel message exchange periodically. We proposed a new message, RDES message, which is issued periodically to update the topology of sensor node and check a connectivity between nodes.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.24-29
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• 2014

Digital mammography is the most common technique for the early detection of breast cancer. To diagnose the breast cancer in early stages and treat efficiently, many image enhancement methods have been developed. This paper presents a multi-scale contrast enhancement method in the Laplacian pyramid for the digital mammogram. The proposed method decomposes the image into the contrast measures by the Gaussian and Laplacian pyramid, and the pyramid coefficients of decomposed multi-resolution image are defined as the frequency limited local contrast measures by the ratio of high frequency components and low frequency components. The decomposed pyramid coefficients are modified by the contrast measure for enhancing the contrast, and the final enhanced image is obtained by the composition process of the pyramid using the modified coefficients. The proposed method is compared with other existing methods, and demonstrated to have quantitatively good performance in the contrast measure algorithm.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.325-331
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• 2016

The purpose of this study is to analyze the effect of the glass ceiling induction factors on the improvement of the job Commitment on the glass ceiling perception and to analyze the effect of the organizational Commitment on the influence of the local medical institute and private medical institute employees. As a research method, structural equation model analysis was carried out to investigate the influence relationship of each factor. In particular, multiple group analysis was performed to analyze the difference of influence relations between public and private medical personnel, respectively. Result: First, empirical studies on the effect of the glass ceiling inducing factors on job Commitment showed that job Commitment was influenced by stereotype and organizational culture, and the magnitude of the influence was different. Second, the employees of the room medical center were influenced by perceived promotion, job placement, education and training, reward, and job satisfaction. Third, in the hypothesis that job Commitment will be affected by the perception of glass ceiling, only the workers of the private medical center showed significant results. Based on the results of this study, it will be necessary to plan policies to improve the perception of the glass ceiling phenomenon and improve its status in order to improve the personnel and system with which women workers in the medical field can enter more senior management positions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1B
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• pp.1-10
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• 2003

An EPON which is going on standardization in IEEE 802.3ah, is tree topology consists of a OLT and multiple ONU using passive optical components, so this network is susceptible to variable security threats - eavesdropping, masquerading, denial of service and so on. In this paper, we design a security protocol supporting authentication and confidentiality services in MAC layer in order to prevent these security threats and to guarantee secure data exchange The designed security protocol introduce public-key based authentication and key management protocols for efficient key management, and choose Rijndael algorithm, which is recent standard of AES, to provide the confidentiality of EPON Proposed authentication and key management protocols perform authentication and public-key exchange at a time, and are secure protocols using derived common cipher key by exchanging public random number To implement the designed security protocol, we propose the procedures of authentication and public-key exchange, session key update, key recovery. This proposed protocol is verified using unknown session key, forward secrecy, unknown key-share, key-compromise impersonation.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.37-50
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• 2009

The SCADA(Supervisory Control And Data Acquisition) system is a control system for infrastructure of nation. In the past, the SCADA system was designed without security function because of its closed operating environment. However, the security of the SCADA system has become an issue with connection to the open network caused by improved technology. In this paper we review the constraints and security requirements for SCADA system and propose advanced key management architecture for secure SCADA communications. The contributions of the present work are that our scheme support both message broadcasting and secure communications, while the existing key management schemes for SCADA system don't support message broadcasting. Moreover, by evenly spreading much of the total amount of computation across high power nodes (MTU or SUB-MTU), our protocol avoids any potential performance bottleneck of the system while keeping the burden on low power (RTU) nodes at minimal.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.383-390
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• 2015

It is appropriate to use the NETCONF to monitor and manage today's complex networks that are composed of variety links and heterogeneous equipment. Since the first standard of the NETCONF are launched, there have been several revisions, but disadvantages of each layer capabilities is still present and the most typical disadvantage is XML document processing efficiency of the Content layer. In this paper, we perform data modeling by constructing a sub-tree based on the dependencies between Content layer data, and suggest method of extract efficiently data from XML by applying the extended VTD-XML technique for the XPath query. We performs experiment to compare NETCONF in proposed method to NETCONF in previous studies and NETCONF standard. we validate superiority of improved NETCONF in the paper. As experimental results, we verify that improved NETCONF is better than the other two NETCONF each 4% and 10% in terms of query processing rate, and faster than each 3.9 seconds and 10.4 seconds in terms of query processing speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.155-159
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• 2008

In this thesis, in order to develop a new classification model of Sasang Constitutional medical types, which is helpful for improving the accuracy of diagnosis of medical types. various data-mining classification models such as discriminant analysis. decision trees analysis, neural networks analysis, logistics regression analysis, clustering analysis which are main classification methods were applied to the questionnaires of medical type classification. In this manner, a model which scientifically classifies constitutional medical types in the field of Sasang Constitutional Medicine, one of a traditional Korean medicine, has been developed. Also, the above-mentioned analysis models were systematically compared and analyzed. In this study, a classification of Sasang constitutional medical types was developed based on the discriminate analysis model and decision trees analysis model of which accuracy is relatively high, of which analysis procedure is easy to understand and to explain and which are easy to implement. Also, a diagnosis system of Sasang constitution was implemented applying the two analysis models.

• Neurospine
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• v.15 no.4
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• Knowledge Management Research
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• v.20 no.3
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• pp.155-171
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• 2019

In the entertainment industry which has great uncertainty, it is essential to predict public preference first. Thanks to various mass media channels such as cable TV and internet-based streaming services, the reality audition program has been getting big attention every day and it is being used as a new window to new entertainers' debut. This phenomenon means that it is changing from a closed selection process to an open selection process, which delegates selection rights to the public. This is characterized by the popularity of the public being reflected in the selection process. Therefore, this study aims to implement a machine learning model which predicts the winner of , which has recently been popular in South Korea. By doing so, this study is to extend the research method in the cultural industry and to suggest practical implications. We collected the data of winners from the 1st, 2nd, and 3rd seasons of the Produce 101 and implemented the predictive model through the machine learning method with the accumulated data. We tried to develop the best predictive model that can predict winners of by using four machine learning methods such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network. This study found that the audience voting and the amount of internet news articles on each participant were the main variables for predicting the winner and extended the discussion by analyzing the precision of prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.480-502
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• 2022

This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.