• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.11-18
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• 2016

Accessing unauthorized rogue APs in WiFi environments is a very dangerous behavior which may lead WiFi users to be exposed to the various cyber attacks such as sniffing, phishing, and pharming attacks. Therefore, prompt and precise detection of rogue APs and properly alarming to the corresponding users has become one of most essential requirements for the WiFi security. This paper proposes a new rogue AP detection method which is mainly using the installation information of authorized APs and the DHCP snooping information of the corresponding switches. The proposed method detects rogue APs promptly and precisely, and notify in realtime to the corresponding users. Since the proposed method is simple and does not require any special devices, it is very cost-effective comparing to the wireless intrusion prevention systems which are normally based on a number of detection sensors and servers. And it is highly precise and prompt in rogue AP detection and flexible in deployment comparing to the existing rogue AP detection methods based on the timing information, location information, and white list information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.6
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• pp.466-475
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• 2014

The IVEF service is the draft standard designed for exchange of information on sea traffic between the vessel traffic systems and between the vessels. Standardization of this service is under way as a part of the next-generation navigation system, called e-Navigation. The International Association of Lighthouse Authorities (IALA) suggests, on its recommendation V-145, the IVEF service model and the protocol for provisioning of this service. But the detailed configuration of this service must be designed by the users. This study suggests, based on the basic service model and protocol provided in the recommendation V-145, the implementation of the J-VTS middleware which will facilitate exchange of information on sea traffic. The J-VTS middleware consists of various components for providing the IVEF service and for processing the IVEF message protocols. The vessel traffic systems and the vessels corresponding to upper-layer applications may use the IVEF service with the functions provided by the J-VTS middleware, and the services are designed to be accessed according to the security level of users.

• Journal of Preventive Medicine and Public Health
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• v.27 no.3 s.47
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• pp.487-504
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• 1994

A cross-sectional study was performed to evaluate the effects of chronic low-dose solvent on neurobehavioral performance of 118 male car painters. A control group of 113 workers matched for age was selected from different sections of the factory. The mean age and the mean duration of employment were 33 years and 6.7 years in both groups. Mean years of education were 11.4 years in car painters and 11.8 years in controls. Each worker completed a medical and occupational questionnaire and four tests of Swedish performance evaluation system. These included simple reaction time, symbol digit, digit span and finger tapping speed. Althougth the mean duration of employment was 6.7 years, comparison of mean performance showed a significantly poorer performance on simple reaction time (p<0.05), symbol digit(p<0.01) and digit span(p<0.05) in car painters. In univariate analysis, age and educational level contributed to poorer performance on symbol digit and digit span. Smoking appeared to slow finger tapping speed in car painters. Performance of four tests of car painters exposed to high level of solvent was poorer than that of car painters exposed to low level. In multiple regression models, controlling for age, alcohol, smoking and shift work, solvent exposure was found to be associated with performance of simple reaction time, symbol digit and digit span and exposure to high level of solvent was related to poorer performance of symbol digit and digit span.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.606-619
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• 1998

In this paper, we have tfesigned and ihlplemented ACE(Application sharing Collaboration Engine) which is pessible an application sharing to collaborate among peoples who are geographically dispersed. The application sharing is a technology whereby two or more users collaborate to share the output of single application running on one computer system to the other users, and to provide input to the applications. We defined ASO(Applicatio!1 Sharing Object) object and its behavior to share applications in real time and ACE processes a sharing using ASO object among the distributed systems. ASO is classified into activateASO, updateASO, inputASO, and controlASO. The each ASO's behavior involves both events which occur at specific moments such as keystrokes and mouse clicks and more persistent status which can be observed at any time such as the image on the screen. The implemented ACE can be applied to the data conferencing, distance education, and project collaboration for engineer in distributed environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.47-54
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• 2019

An improved method of spectrum detection algorithm for mass spectrum analysis system is proposed. In the conventional spectrum detection algorithm that utilizes the results of the linear approximation and quadratic curve fitting on the ion signal block of each mass index, it is possible to reduce the detection error in the mass spectrum detection by further improving the condition of eliminating the invalid ion signals. Also, the proposed method can reduce the estimation error of the peak value of the mass spectrum by using the result of quadratic curve fitting for the effective ion signal block in which the peak position error is corrected. To evaluate the effectiveness of the proposed method, computer simulations were carried out step by step using the measured ion signal. Also, by comparing the rate of false detection for several inputs, the proposed method showed better detection performance than the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.942-948
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• 2020

In this paper, we propose a clustered algorithm that possible more efficient user distinction within clustering using context-aware attribute information. In typically, the data provided to classify interrelationships within cluster information in the process of clustering data will be as a degrade factor if new or newly processing information is treated as contaminated information in comparative information. In this paper, we have developed a clustering algorithm that can extract user's recognition information to solve this problem in using K-means algorithm. The proposed algorithm analyzes the user's clustering attributed parameters from user clusters using accumulated information and clustering according to their attributes. The results of the simulation with the proposed algorithm showed that the user management system was more adaptable in terms of classifying and maintaining multiple users in clusters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.755-762
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• 2022

This paper propose a clustered algorithm that possible more efficient COVID-19 disease learning prediction within clustering using context-aware attribute information. In typically, clustering of COVID-19 diseases provides to classify interrelationships within disease cluster information in the clustering process. The clustering data will be as a degrade factor if new or newly processing information during treated as contaminated factors in comparative interrelationships information. In this paper, we have shown the solving the problems and developed a clustering algorithm that can extracting disease correlation information in using K-means algorithm. According to their attributes from disease clusters using accumulated information and interrelationships clustering, the proposed algorithm analyzes the disease correlation clustering possible and centering points. The proposed algorithm showed improved adaptability to prediction accuracy of the classification management system in terms of learning as a group of multiple disease attribute information of COVID-19 through the applied simulation results.

• Journal of Advanced Technology Convergence
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• v.1 no.2
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• pp.01-09
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• 2022

Recently, safety issues in companies and public institutions are no longer a task that can be postponed, and when a major safety accident occurs, not only direct financial loss, but also indirect loss of social trust in the company and public institution is greatly increased. In particular, in the case of a fatal accident, the damage is even more serious. Accordingly, as companies and public institutions expand their investments in industrial safety education and prevention, open AI learning model creation technology that enables safety management services without being affected by user behavior in industrial sites where high-risk situations exist, edge terminals System development using inter-AI collaboration technology, cloud-edge terminal linkage technology, multi-modal risk situation determination technology, and AI model learning support technology is underway. In particular, with the development and spread of artificial intelligence technology, research to apply the technology to safety issues is becoming active. Therefore, in this paper, an open cloud platform design method that can support AI model learning for high-risk site safety management is presented.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.749-755
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• 2022

Electricity is essential energy in modern society, such as being used in various industries. However, the rate of fires occurring on electric wiring to deal with it is very high. In this work, we implemented a system to predict the temperature change of an electric circuit through analysis using various regression models. To do so, we collected the temperature data of 27 types of magnetic switches which control electric circuits as well as trained the regression models by using the collected temperature data. In our experiments, we confirmed that the regression models can be trained at a sufficiently usable level since the difference between the actual temperature and predicted temperature is about 4℃. The results of our work will be useful to predict the temperature of electric circuits and preventing fires on them.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.245-253
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• 2019

To evaluate the achievements of the Solar School Project that has been implemented in twelve African countries since 2013, a case study was implemented in Kenya and in Uganda to investigate networking activities, student accessibility to computers, the frequency of student computer use, the extent to which teaching quality was improved by the enhanced accessibility to ICT-based teaching and learning practices. The results showed the followings. First, Solar Schools have significantly improved the rates of enrollment, transferring, and school attendance. Second, Solar Schools have organized local and invitational training programs to build the capacities of teachers. Third, Solar Schools have facilitated change in neighboring schools and local communities. Fourth, the participants are required to have a clear vision, take ownership of the project, and make a commitment to continuing their individual efforts toward empowerment.