• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.397-402
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• 2013

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.

• Journal of Engineering Education Research
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• v.21 no.1
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• pp.66-76
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• 2018

In order to facilitate the process of designing a fusion curriculum, this study used the learning outcomes presented in KEC2015 to design a fusion goal dimension. The three dimensions of fusion education in the fusion curriculum framework are fusion goals, fusion approach and fusion context. As a result of examining the curriculum learning activities in order to confirm that the developed curriculum meets these three dimensions, it can be confirmed that it contributes relatively evenly to the fusion goal, fusion approach and fusion context dimension. In addition, it provides a procedure to develop the fusion curriculum through the concrete procedures and examples from the design to implementation of the developed fusion curriculum, and to confirm the result of the fusion dimension.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.37-45
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• 2013

Context dependent fusion (CDF) is a fusion algorithm that combines multiple outputs from different classifiers to achieve better performance. CDF tries to divide the problem context into several homogeneous sub-contexts and to fuse data locally with respect to each sub-context. CDF showed better performance than existing methods, however, it is sensitive to noise due to the large number of parameters optimized and the innate linearity limits the application of CDF. In this paper, a variant of CDF using support vector machines (SVMs) for fusion and kernel principal component analysis (K-PCA) for context extraction is proposed to solve the problems in CDF, named CDF-SVM. Kernel PCA can shape irregular clusters including elliptical ones through the non-linear kernel transformation and SVM can draw a non-linear decision boundary. Regularization terms is also included in the objective function of CDF-SVM to mitigate the noise sensitivity in CDF. CDF-SVM showed better performance than CDF and its variants, which is demonstrated through the experiments with a landmine data set.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.397-402
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• 2003

This study is aimed to improve the quality of image fusion results through shadow effects correction. For this, shadow effects correction algorithm is proposed and visual comparisons have been made to estimate the quality of image fusion results. The following four steps have been performed to improve the image fusion qualify First, the shadow regions of satellite image are precisely located. Subsequently, segmentation of context regions is manually implemented for accurate correction. Next step, to calculate correction factor we compared the context region with the same non-shadow context region. Finally, image fusion is implemented using collected images. The result presented here helps to accurately extract and interpret geo-spatial information from satellite imagery.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.1
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• pp.1-6
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• 2013

Variety of smart devices including smart phone have become and essential item in user's daily life. This means that smart devices are good mediators to get collecting user's behavior by sensors mounted on the devices. The information from smart devices is important clues to identify by analyzing the user's preferences and needs. Through this, the intelligent service which is fitted to the user is possible. This paper propose a smart service recommendation model based on user scenario using fusion context-awareness. The information for recommendation services is collected to make the scenario depending on time, location, action based on the Fusion process. The scenarios can help predict a user's situation and provide the services in advance. Also, content categories as well as the content types are determined depending on the scenario. The scenario is a method for providing the best service as well as a basis for the user's situation. Using this method, proposing a smart service model with the fusion context-awareness based on the hybrid sensing is the goal of this paper.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.465-470
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• 2013

Dempster-Shafer Evidence Theory is available for multi-sensor data fusion. Basic Probability Assignment is essential for multi-sensor data fusion using Dempster-Shafer Theory. In this paper, we proposed a novel method of BPA calculation with signal assessment. We took notice of the signal that reported from the sensor mote at the time slot. We assessed the variation rate of the reported signal from the terminal. The trend of variation implies significant component of the context. We calculated the variation rate of signal for reveal the relation of the variation and the context. We could reach context inference with BPA that calculated with the variation rate of signal.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.409-418
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• 2015

In order to delivery of the correct information in IoT environment, it is important to deduce collected information according to a user's situation and to create a new information. In this paper, we propose a control access scheme of information through context-aware to protect sensitive information in IoT environment. It focuses on the access rights management to grant access in consideration of the user's situation, and constrains(access control policy) the access of the data stored in network of unauthorized users. To this end, after analysis of the existing research 'CP-ABE-based on context information access control scheme', then include dynamic conditions in the range of status information, finally we propose a access control policy reflecting the extended multi-dimensional context attribute. Proposed in this paper, access control policy considering the dynamic conditions is designed to suit for IoT sensor fusion environment. Therefore, comparing the existing studies, there are advantages it make a possible to ensure the variety and accuracy of data, and to extend the existing context properties.

• International Journal of Contents
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• v.18 no.3
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• pp.11-20
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• 2022

Human Emotion Recognition is an exciting topic that has been attracting many researchers for a lengthy time. In recent years, there has been an increasing interest in exploiting contextual information on emotion recognition. Some previous explorations in psychology show that emotional perception is impacted by facial expressions, as well as contextual information from the scene, such as human activities, interactions, and body poses. Those explorations initialize a trend in computer vision in exploring the critical role of contexts, by considering them as modalities to infer predicted emotion along with facial expressions. However, the contextual information has not been fully exploited. The scene emotion created by the surrounding environment, can shape how people perceive emotion. Besides, additive fusion in multimodal training fashion is not practical, because the contributions of each modality are not equal to the final prediction. The purpose of this paper was to contribute to this growing area of research, by exploring the effectiveness of the emotional scene gist in the input image, to infer the emotional state of the primary target. The emotional scene gist includes emotion, emotional feelings, and actions or events that directly trigger emotional reactions in the input image. We also present an attention-based fusion network, to combine multimodal features based on their impacts on the target emotional state. We demonstrate the effectiveness of the method, through a significant improvement on the EMOTIC dataset.

• Journal of Korea Entertainment Industry Association
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• v.4 no.2
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• pp.58-63
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• 2010

The varieties of situated cognition applications provide various information to a user in a ubiquitous computing environment. In this paper, We propose a system that provides an optimized output using a fusion of temporal data and spatial data from sensing devices.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.960-965
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• 2006

Privacy and security are latent problems in pervasive healthcare system. For the sake of protecting health monitoring information, it is necessary to classify and categorize the various contexts in terms of obfuscation. In this paper, we propose the physiological context categorization and specification methodology by exploiting data fusion network for automatic context alignment. In addition, we introduce the methodologies for making various level of physiological context on the context aware application model, which is wear-UCAM. This physiological context has several layers of context according to the level of abstraction such as user-friendly level or parametric level. This mechanism facilitates a user to restrict access to his/her monitoring results based on the level of details in context.