• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.298-303
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• 2008

User adaptive services have been important features in many applications. To provide such services, various techniques with various kinds of data are being used. In this paper, we propose a method to analyze user's past moving paths for predicting the goal position and the path to the goal by observing the user's current moving path. We develop a spatio-temporal similarity measure between paths. We choose a past path which is the most similar to the current path using the similarity. Based on the chosen path, user's spatio-temporal position is estimated. Through experiments we confirm this method is useful and effective.

• ETRI Journal
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• v.15 no.3
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• pp.27-34
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• 1994

There have been traditional approaches to model radio propagation path loss mechanism both theoretically ad empirically. Theoretical approach is simple to explain and effective in certain cases. Empirical approach accommodates the terrain configuration and distance between base station and mobile unit along the propagation path only. In other words, it does not accommodate natural terrain configuration over a specific area. In this paper, we propose a spatial prediction technique for the mobile radio propagation path loss accommodating complete natural terrain configuration over a specific area. Statistical uncertainty analysis is also considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4665-4683
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• 2019

Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.191-194
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• 2004

This study Is an measurement and prediction of transmission loss through dash panel with multi-path in a vehicle. Measurement results of transmission loss are decided by sound power measured using the sound intensity method under locating a sound source in the anechoic room and reverberant room, respectively. Prediction one is decided by multi-path analysis of dash panel composed by a various part of materials and complicated shape. Finally, two results show a great agreement between measured and predicted transmission loss.

• Journal of the Earthquake Engineering Society of Korea
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• v.24 no.1
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• pp.9-17
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• 2020

The stochastic point-source model has been widely used in generating artificial ground motions, which can be used to develop a ground motion prediction equation and to evaluate the seismic risk of structures. This model mainly consists of three different functions representing source, path, and site effects. The path effect is used to emulate decay in ground motion in accordance with distance from the source. In the stochastic point-source model, the path attenuation effect is taken into account by using the geometrical attenuation effect and the inelastic attenuation effect. The aim of this study is to develop accurate equations of ground motion attenuation in the Korean peninsula. In this study, attenuation was estimated and validated by using a stochastic point source model and observed ground motion recordings for the Korean peninsula.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.105-112
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• 1999

In this paper we deal with tool interference problem in the case of compound surface machining. A new tool interference detection and correction method based on the envelope of the tool path is suggested to identify and correct the tool interference - not only within the local path of tool movement, but also outside of the tool path. Therefore, the developed strategy can be used to check the possible interference in any region of the surface. In order to analyze quantitatively the milled surface error produced by the tool interference, improved surface prediction model is also suggested in cutting process by general cutters. The effectiveness of the proposed method is demonstrated through simulation study.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.568-574
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• 2006

User adaptive services have been important features in many applications. To provide such services, various techniques with various kinds of data are being used. In this paper, we propose a method to analyze user's past moving paths and predict the goal position and the path to the goal by observing the user's current moving path. We develop a spatiotemporal similarity measure between paths. We choose a past path which is the most similar to the current path using the similarity. Based on the chosen path, user's spatiotemporal position is estimated. Through experiments we confirm this method is useful and effective.

• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.14-19
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• 2019

In this paper, we propose a system for controlling the brightness of street lights by predicting pedestrian paths, identifying the position of pedestrians with motion sensing sensors and obtaining motion vectors based on past walking directions, then predicting pedestrian paths through the route prediction smart street lighting system. In addition, by using motion vector data, the pre-treatment process using linear interpolation method and the fuzzy system and neural network system were designed in parallel structure to increase efficiency and the rough set was used to correct errors. It is expected that the system proposed in this paper will be effective in securing the safety of pedestrians and reducing light pollution and energy by predicting the path of pedestrians in the detection of movement of pedestrians and in conjunction with smart street lightings.