• Journal of Advanced Navigation Technology
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• v.14 no.3
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• pp.432-438
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• 2010

In this paper, we propose a system which can perceive status of semiconductor equipment and evaluate its performance. The proposed system acquires the information such acceleration, pressure, temperature and gas sensors in the surrounding semiconductor equipment. After acquiring information, it is sent to server through multi hop transmission. The transmitted data generates 3 steps alarm using context-aware algorithm of unit or multiple event. From the experiment's result of the proposed system, we confirm that the reliability and efficiency of information is more improved about 80% than a system that doesn't use context-aware algorithm. Moreover, this system can be effective status monitoring of semiconductor equipment because lots of client nodes acquire surrounding information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.547-549
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• 2010

In this paper, we propose context-aware system for semiconductor equipment that acquires information from multiple sensors in indoor environment. This proposed system acquires information from acceleration, pressure, temperature and gas sensors then the acquired information send to server. The data transmitted to server generates an alarm via context-aware algorithm of unit event and multi event. From that result, high-quality real-time monitoring is possible because of the reduced unnecessary alarms, and the efficient management is possible because the surrounding information is recognized at once.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1803-1810
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• 2018

Recently, many researches related to IoT (Internet of Things) have been actively conducted. In order to improve the context aware function of smart wearable devices using the IoT, we proposed a noise reduction method for the event data of the sensor part. In thisstudy, the adoption of the low - pass filter induces the attenuation of the abnormally measured value, and the benefit was obtained from the situation recognition using the event data of the sensor. As a result, we have validated attenuation for abnormal or excessive noise using event data detected and reported by 3-axis acceleration sensors on some devices, such as smartphones and smart watches. In addition, various pattern data necessary for real - time context aware were obtained through noise pattern analysis.

• Journal of Korea Game Society
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• v.21 no.3
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• pp.53-66
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• 2021

This study explored how the network structure in terms of social interactions among users via a mobile game has changed longitudinally. The main findings are summarized as follows. First, in-game events are likely to increase the social interactions of users. Second, some power users played key roles in the game users' network and tend to make a large sub-group for interacting among users. Third, it is suggested that the analysis of network structure among users can be a useful tool for monitoring which in-game events are effective and how they are mutually interconnected for collaboration.

• Journal of Advanced Navigation Technology
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• v.25 no.3
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• pp.248-255
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• 2021

The main causes of the July 2013 OZ 214 accident were poorly performed approach and the failure to recognize the autothrottle in the HOLD position which the automated speed control was not provided. The pilots late decision for go-around was also a critical factor leading to the accident. The B777 POM restricts the use of FLCH mode beyond the FAF. This research utilized the QAR data of an airline's B777 fleet in the period of two years where 44 cases were found. In many cases, the FLCH mode was used for rapid descent from an higher than normal situation. In addition, in the base turn, continuous use of FLCH mode even when the path was below the glide path were observed. Airports with elevation above 500 ft MSL had a higher rate of occurrence. In this research, the proper descent planning and vertical path monitoring, and the adherence to the limitation set in the manuals and the stabilized approach criteria were re-emphasized as mitigation to reduce event occurences.

• Journal of KIISE
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• v.44 no.3
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• pp.287-294
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• 2017

With the personal-media development that has emerged through various means such as UCC and SNS, many media studies have been completed for the purposes of analysis and recognition, thereby improving the object-recognition level. The focus of these studies is a classification of media that is based on a recognition of the corresponding objects, rather than the use of the title, tag, and scripter information. The media-classification task, however, is intensive in terms of the consumption of time and energy because human experts need to model the underlying media ontology. This paper therefore proposes an automated approach for the modeling of the media-classification ontology schema; here, the OWL-DL Axiom that is based on the frequency of the recognized media-based objects is considered, and the automation of the ontology modeling is described. The authors conducted media-classification experiments across 15 YouTube-video categories, and the media-classification accuracy was measured through the application of the automated ontology-modeling approach. The promising experiment results show that 1500 actions were successfully classified from 15 media events with an 86 % accuracy.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.641-649
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• 2017

As Internet of Things devices become pervasive, services improve to better assess the context and to alert other users to deal with emergencies. Such services use Internet of Things devices to detect the context around the user and promptly notify public institutions, hospitals or other parental users in emergencies. Most of these systems analyze an event when the value of the device is unchanged for a period of time or if it detects an abnormal value. However, just monitoring sensor values makes it difficult to accurately understand the context surrounding a user. Also if the device is inactive, it can not identify the context or provide services again. However, understanding the user requirements, services provided through other devices, information sent to other users lets, appropriate actions be taken. This paper, proposes a device search method and system based on a context-aware model that includes user demands. The proposed system analyzes the user's context and demands by using data collected from the internet of things devices. If user devices are inactive, they can recognize other devices by searching for other devices and providing services to users again. Through the proposed method, the user-centric services are provided. This method also analyzes and responds to requirements in various emergencies.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.197-202
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• 2016

This study suggests a novel way of safety cognition with single sensor. It is beneficial to make the most of the 3-axis acceleration sensor for context inference. It has remarkable advantages that size is too small and less malfunction or error. This study shows the calculation of the common life safety through the events data from the 3-axis acceleration sensor only. That includes the analyzation of the volume of energy and converting quantitative numerical value.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.297-304
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• 2015

Importance and necessity is enlarged due to globalization. So, English education is diverse and recently applies AR(Augmented Reality) technology to offer immersion and interest. Contents in this paper are Mobile AR contents for training infant English cognition. Our system displays teaching aid objects into 3D models, lets infants listen to English conversation and teaches them everyday English like a play. Considering infants that like games, this system adds an English game about each object of teach aids. To attract infants and parents interest persistingly, We give them immersion and interest by suggesting a different event at each step.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1197-1213
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• 2014

Due to the increase in size of the computer network, the network security systems such as a firewall, IDS, IPS generate much more vast amount of information related to network security. So detecting signs of hidden security threats has become more difficult. Security personnels' 'Network Security Situational Awareness(NSSA)' is effectively determining the security situation of overall computer network on the basis of the relation between the security events that occur in the several views. The process of situational awareness is divided into three stages of the 'identification,' 'understanding' and 'prediction'. And 'identification' and 'understanding' are prerequisites for 'predicting' and the following appropriate responses. But 'identification' and 'understanding' in the vast amount of information became more difficult. In this paper, we propose Honeycomb security situational awareness visualization system that is designed to help NSSA in large-scale networks by using visualization techniques known effective to the 'identification' and 'understanding' stages. And we identified the empirical effects of this system on the basis of the 'VAST Challenge 2012' data.