• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.28-36
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• 2010

Some context is characterized by a single event in computing environment, but many other contexts are determined by a lot of things which occur with a space and a time. The Realtime Sensor Network context-awareness service that interacts with the physical space can have property such as time. A methodology that is specified the relationship between the contexts and the service needs to be developed to Realtime context-awareness deal with spatio-temporal. In this paper, we propose an approach which should include spatio-temporal property in the context model, and verify its effectiveness using enhanced Petri-Net. The context-awareness service modeling of Realtime Sensor Network is discussed the properties of model such as basic Petri-Net, patterned Petri-Net, or Spatio-temporal Petri-Net. The proposed methodology demonstrated using an example that is SAEMANGUEM warming watching system. The use of Spatio-temporal Petri-Net will contribute not only to develop the application but also to model the spatio-temporal context awareness.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.382-389
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• 2006

Developing context awareness service In these day, It demands high security in context awareness service. So GEO-RBAC that provide user assignment of spatial role, assignment of permission, role schema, role instance and spatial role hierarchy to context awareness service is access control model to perfect in context awareness service. But GEO-RBAC is not considering temporal constraints that have to need context awareness environment. Consequently this paper improves the flexibleness of GEO-RBAC to consider time and period constraints notion and the time of GTRBAC that presents effective access control model. also we propose GEO-RBAC to consider temporal constraints for effective access control despite a various case.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.36-45
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• 2006

In this paper, we introduce visual contexts in terms of types and utilization methods for robust object recognition with intelligent mobile robots. One of the core technologies for intelligent robots is visual object recognition. Robust techniques are strongly required since there are many sources of visual variations such as geometric, photometric, and noise. For such requirements, we define spatial context, hierarchical context, and temporal context. According to object recognition domain, we can select such visual contexts. We also propose a unified framework which can utilize the whole contexts and validates it in real working environment. Finally, we also discuss the future research directions of object recognition technologies for intelligent robots.

• The Journal of the Korea Contents Association
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• v.11 no.1
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• pp.16-25
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• 2011

Some context is characterized by a single event in computing environment, but many other contexts are determined by a lot of things which occur with a space and a time. The Realtime Embedded System context-awareness service that interacts with the physical space can have property such as time. The exceptional behaviors of the system that interact with physical space can result in critical damage and cause danger to the operation of an embedded system. we propose an approach which should include spatio-temporal property and exceptional management in the context model, and verify its effectiveness using an expanded Petri-Net. The context-awareness service modeling of an embedded system is discussed the properties of model such as basic Petri-Net, patterned Petri-Net, or Spatio-temporal Petri-Net for the exceptional behaviors of the system. The proposed methodology demonstrated using an example that is emergency medical service. The use of expanded Petri-Net will contribute not only to develop the application but also to model the spatio-temporal context awareness for the exceptional handling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.405-412
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• 2010

Previous background generation techniques showed bad performance in complex environments since they used only temporal contexts. To overcome this problem, in this paper, we propose a new background generation method which incorporates spatial as well as temporal contexts of the image. This enabled us to obtain 'clean' background image with no moving objects. In our proposed method, first we divided the sampled frame into m*n blocks in the video sequence and classified each block as either static or non-static. For blocks which are classified as non-static, we used MRF framework to model them in temporal and spatial contexts. MRF framework provides a convenient and consistent way of modeling context-dependent entities such as image pixels and correlated features. Experimental results show that our proposed method is more efficient than the traditional one.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.141-145
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• 2009

In this paper, we propose an efficient lossless compression algorithm using spatial and temporal information. The proposed method obtains higher lossless compression of images than other lossless compression techniques. It is divided into two parts, a motion adaptation based predictor part and a residual error coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors. The predictor decides the proper selection of the spatial and temporal prediction values according to each past prediction error. The reduced error is coded by existing context coding method. Experimental results show that the proposed algorithm has better performance than those of existing context modeling methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.219-234
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• 2009

This paper presents a novel approach for extracting simultaneously human daily activity patterns and discovering the temporal relations of these activity patterns. It is necessary to resolve the services conflict and to satisfy a user who wants to use multiple services. To extract the simultaneous activity patterns, context has been collected from physical sensors and electronic devices. In addition, a context model is organized by the proposed incremental statistical method to determine conflicts and to infer user intentions through analyzing the daily human activity patterns. The context model is represented by the sets of the simultaneous activity patterns and the temporal relations between the sets. To evaluate the method, experiments are carried out on a test-bed called the Ubiquitous Smart Space. Furthermore, the user-intention simulator based on the simultaneous activity patterns and the temporal relations from the results of the inferred intention is demonstrated.