• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.255-260
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• 2017

In Korea, traffic accidents between pedestrians and vehicles in 2015 account for 38.8% of all accidents. This study proposes a system design that can measure the risk of conflict between a vehicle and a pedestrian. Firstly the systemdetect and estimate the position, speed, and directional data of the vehicle and the pedestrian. And then it estimate the conflict point between a vehicle and a pedestrian. The risk of conflict is quantified by estimating the pedestrian safety margin (PSM), which is the time difference between the arrival of the pedestrian at the crossing point to the point of conflict and the vehicle approaching the point. In this system each data is acquired through an external monitoring based evaluation module and an individual wearing module. In the future, such a system can be used for decision making such as the design of road hazard improvement facilities and the designation of the elderly protection area.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.690-695
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• 2006

This study was carried out to develop a system for forest resources inventory using PDA. The Forest Inventory Software running on PDA was developed based on a Forest Resources Inventory Method by Korea Forestry Service and 4th National Forest Inventory Method. The forest inventory data stored in PDA contains sea level and GPS positioning data. Forest inventory input items are 34 tree species, 18 diameter class by each tree species, number of trees, tree height per each diameter class, thickness of bark, and annual growth of tree. Application test of the software with the forest workers shorted that, hours of forest works were remarkably reduced. It is possible to do forest inventory effectively using Forest Inventory Software developed by this study. Therefore, investigation and management expenses can be reduced and labor productivity will be improved.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.1-10
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• 2020

The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.

• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• Journal of the Korean Institute of Gas
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• v.3 no.3 s.8
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• pp.9-16
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• 1999

Gas energy facilities which are located in urban areas have been shown as terrifying objects to the people who live nearby, because of increasing recent disastrous accidents. So, it is essential to develop a integrated safety management system including quantitative risk assessment in order to predict and to reduce the effect of gas accidents. In this study, the framework of synthesized QRA(Quantitative Risk Assessment) considering the recent situation and condition was established in order to provide proper models for analysing gas accidents. The deviation of LPG storage facilities was described and then supposed incident scenarios were provided. Procedures which could assess the risk of facilities according to incident scenarios were showed and the practical application of individual risk was suggested in order to represent the quantified risk. And, a user-friendly computer program was developed to implement these methods at the same time.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.111-121
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• 2021

As the main carrier of information, digital image is becoming more and more important. However, with the popularity of image acquisition equipment and the rapid development of image editing software, in recent years, digital image counterfeiting incidents have emerged one after another, which not only reduces the credibility of images, but also brings great negative impacts to society and individuals. Image copy-paste tampering is one of the most common types of image tampering, which is easy to operate and effective, and is often used to change the semantic information of digital images. In this paper, a method to protect the authenticity and integrity of image content by studying the tamper detection method of image copy and paste was proposed. In view of the excellent learning and analysis ability of deep learning, two tamper detection methods based on deep learning were proposed, which use the traces left by image processing operations to distinguish the tampered area from the original area in the image. A series of experimental results verified the rationality of the theoretical basis, the accuracy of tampering detection, location and classification.

• Journal of Convergence for Information Technology
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• v.9 no.2
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• pp.108-114
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• 2019

In this paper, we propose an intelligent vocabulary recommendation agent that automatically provides vocabulary corresponding to game-based learners' needs and requirements in the mobile education augmented reality game environment. The proposed agent reflects the characteristics of mobile technology and augmented reality technology as much as possible. In addition, this agent includes a vocabulary reasoning module, a single game vocabulary recommendation module, a battle game vocabulary recommendation module, a learning vocabulary list Module, and a thesaurus module. As a result, game-based learners' are generally satisfied. The precision of context vocabulary reasoning and thesaurus is 4.01 and 4.11, respectively, which shows that vocabulary related to situation of game-based learner is extracted. However, In the case of satisfaction, battle game vocabulary(3.86) is relatively low compared to single game vocabulary(3.94) because it recommends vocabulary that can be used jointly among recommendation vocabulary of individual learners.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.443-452
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• 2021

One of the key technologies in edge computing is that it always provides services close to the user by moving data between edge servers according to the user's movements. As such, the movement of data between edge servers is frequent. As IoT technology advances and usage areas expand, the data generated also increases, requiring technology to accurately track and process each data to properly manage the data present in the edge computing environment. Currently, cloud systems do not have data disposal technology based on tracking technology for data movement and distribution in their environment, so users cannot see where it is now, whether it is properly removed or not left in the cloud system if users request it to be deleted. In this paper, we propose a tracking data server to create and manage the movement and distribution of data for each edge server and data stored in the central cloud in an edge computing environment.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.57-64
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• 2021

In this paper, we propose and test the efficacy of an easy-to-use user location certification system for public places that relies on frequencies outside the audible range for humans. The inaudible frequencies come in signal frequency between 18-20 kHz and are generated by general audio speaker. After an individual's smart device detects the signal frequency, it sends the frequency value, user's personal ID, and user's location to a system server that certifies the user's visit location currently. The system server then saves a user visit record and categorizes it by individual. To show the usefulness of this proposed system, we developed a user visit certification application for smart devices linked to a system server. We then conducted a user visit certification experiment using the proposed system, with the result showing 99.6% accuracy. For a comparison, we then held a user visit certification experiment using a QR code, which confirmed that our proposed system performs better than QR code location certification. This proposed system can thus provide restaurants and other facilities reliable user contact tracing and electronic visitor access lists in the age of COVID-19.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.133-142
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• 2021

Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.