• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.90-92
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• 2017

Due to the popularity of social networks and the development of technology in mobile networking, the mobile social networks (MSNs) provide opportunities for the spread of information between mobile devices. As a result, understanding the information diffusion in the emerging MSNs is a critical issue. Many research studies have addressed diffusion minimization, which is a problem of how to find the proper initial k users who can effectively propagate as widely as possible in the minimum amount of time, similar to influence maximization. We address a study on association analysis among nodes in information diffusion and mobility pattern for mobile social networks. Experiments in our study were conducted in the Opportunistic Network Environment (ONE) simulator using GPS trace of mobile node, to show that the study results in MSNs. We also demonstrate that our experiments outperform other existing algorithms with various communication range and ratio of k influential nodes.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.159-164
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• 2019

Detecting wildfire using only color in image information is a very difficult issue. This paper proposes an algorithm to detect forest fire area by analyzing color and motion of the area in the video including forest fire. The proposed algorithm removes the background region using the Gaussian Mixture based background segmentation algorithm, which does not depend on the lighting conditions. In addition, the RGB channel is changed to an HSV channel to extract flame candidates based on color. The extracted flame candidates judge that it is not a flame if the area moves while labeling and tracking. If the flame candidate areas extracted in this way are in the same position for more than 2 minutes, it is regarded as flame. Experimental results using the implemented algorithm confirmed the validity.

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.133-141
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• 2009

A tag collision problem occurs when many tags are placed in a interrogation zone in RFID system. A tag collision problem is one of core issues and various protocols have been proposed to solve the collision problems. Generally tree-based protocols generate unique prefixes and identify tags with them as quick as possible. In this paper, we propose the QT-BCS protocol which decreases the identification time by reducing the number of query-response. The QT-BCS protocol makes a prefixes using time slot and bit change sensing unit. This protocol compares the current bit of tags until the current bit is differ from the previous one. When this occurs, all of the bits scanned so far are transferred to slot-0 and slot-1 depending on the first bit value in Reader. Consequently, this method can reduce the number of queries by tracing prefixes easily. Simulation result shows QT-BCS is more efficient in identifying tags than Query Tree and 4-ary Query Tree protocol.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.203-208
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• 2021

At the manufacturing site, workers have been operating by inputting materials into the manufacturing process and leaving input records according to the work instructions, but product LOT tracking has been not possible due to many omissions. Recently, it is being carried out as a system to automatically input materials using RFID-Tag. In particular, the initial automatic recognition rate was good at 97 percent by automatically generating input information through RACK (TAG) ID and RACK input time analysis, but the automatic recognition rate continues to decrease due to multi-material RACK, TAG loss, and new product input issues. It is expected that it will contribute to increasing speed and yield (normal product ratio) in the overall production process by improving automatic recognition rate and real-time monitoring through the establishment of artificial intelligent smart factory datasets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.97-102
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• 2019

Disaster accidents at industrial sites have been increasing every year. In shipyards there are countless enclosed spaces causing issues like harmful-toxic gases stuck in those sealed areas. And due to such special and complicated structures of the working places with many layers of walls separating each other, there exist more issues of communication with workers trapped inside when accidents happen. Under this circumstance there must be a huge difficulty to evacuate or rescue the workers in case of any disaster. Therefore, in this paper, We would like to introduce the "integrated safety system" to more effectively deal with the problems and prevent such disasters in tough working environments. The suggested integrated safety system can prevent accidents in advance because it can control the data on the location of the workers in real time and the numerical values such as gas, oxygen, and carbon dioxide generated in the workplace in real time.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.145-155
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• 2019

The Healthcare supply chain management has recently attracted attention as a critical tool to improve service quality and reduce healthcare operational cost. Improving large hospital supply chain performance has become increasingly important as healthcare organizations strive to improve the service quality, while reducing the ever-increasing healthcare cost. This paper explores the strategic areas where the traditional supply chain management may enhance the overall performance of the large hospitals. Based on the literature review and relevant case analysis, this paper argues that the visibility, information sharing and standardization are the critical factors for deploying the supply chain principles, and also proposes the supply chain framework for efficient planning and execution, the use of RFID-enabled system for the end-to-end traceability of medical products, and cross-docking system for minimizing the inventory level in the hospital supply chain. Implications of the study findings are discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.91-98
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• 2021

In most hacking attacks, hackers tend to access target systems in a variety of circumvent connection methods to hide their original IP. Therefore, finding the attacker's IP(Internet Protocol) from the defender's point of view is one of important issue to recognize hackers. If an attacker uses a proxy, original IP can be obtained through a program other than web browser in attacker's computer. Unfortunately, this method has no effect on the connection through VPN(Virtual Private Network), because VPN affects all applications. In an academic domain, various IP traceback methods using network equipments such as routers have been studied, but it is very difficult to be realized due to various problems including standardization and privacy. To overcome this limitation, this paper proposes a practical way to use client's network configuration temporarily until it can detect original IP. The proposed method does not only restrict usage of network, but also does not violate any privacy. We implemented and verified the proposed method in real internet with various VPN tools.

• Convergence Security Journal
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• v.22 no.3
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• pp.101-110
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• 2022

As the cases of using artificial intelligence in various fields increase, attempts to solve various issues through artificial intelligence in the intrusion detection field are also increasing. However, the black box basis, which cannot explain or trace the reasons for the predicted results through machine learning, presents difficulties for security professionals who must use it. To solve this problem, research on explainable AI(XAI), which helps interpret and understand decisions in machine learning, is increasing in various fields. Therefore, in this paper, we propose an explanatory AI to enhance the reliability of machine learning-based intrusion detection prediction results. First, the intrusion detection model is implemented through XGBoost, and the description of the model is implemented using SHAP. And it provides reliability for security experts to make decisions by comparing and analyzing the existing feature importance and the results using SHAP. For this experiment, PKDD2007 dataset was used, and the association between existing feature importance and SHAP Value was analyzed, and it was verified that SHAP-based explainable AI was valid to give security experts the reliability of the prediction results of intrusion detection models.

• The Korean Journal of Air & Space Law and Policy
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• v.30 no.1
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• pp.207-244
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• 2015

Aviation safety is the stage in which the risk of harm to persons or of property damage is reduced to, and maintained at or below, an acceptable level through a continuing process of hazard identification and risk management. Many accidents and incidents have been taking place since 2014, while there had been relatively safer skies before 2014. International civil aviation community has been exerting great efforts to deal with these emerging issues, thus enhancing and ensuring safety throughout the world over the years. The Preamble of the Chicago Convention emphasizes safety and order of international air transport, and so many Articles in the Convention are related to the safety. Furthermore, most of the Annexes to the Convention are International Standards and Recommended Practices pertaining to the safety. In particular, Annex 19, which was promulgated in Nov. 2013, dealing with safety management system. ICAO, as law-making body, has Air Navigation Commission, Council, Assembly to deliberate and make decisions regarding safety issues. It is also implementing USOAP and USAP to supervise safety functions of member States. After MH 370 disappeared in 2014, ICAO is developing Global Tracking System whereby there should be no loophole in tracking the location of aircraft anywhere in world with the information provided by many stakeholders concerned. MH 17 accident drove ICAO to install web-based repository where information relating to the operation in conflict zones is provided and shared. In addition, ICAO has been initiating various solutions to emerging issues such as ebola outbreak and operation under extreme meteorological conditions. Considering the necessity of protection and sharing of safety data and information to enhance safety level, ICAO is now suggesting enhanced provisions to do so, and getting feedback from member States. It has been observed that ICAO has been approaching issues towards problem-solving from four different dimensions. First regarding time, it analyses past experiences and best practices, and make solutions in short, mid and long terms. Second, from space perspective, ICAO covers States, region and the world as a whole. Third, regarding stakeholders it consults with and hear from as many entities as it could, including airlines, airports, community, consumers, manufacturers, air traffic control centers, air navigation service providers, industry and insurers. Last not but least, in terms of regulatory changes, it identifies best practices, guidance materials and provisions which could become standards and recommended practices.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.