• Journal of Digital Convergence
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• v.10 no.6
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• pp.253-258
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• 2012

In this thesis, I categorize expression of context data required for context data inference according to five Ws and one H(5W1H) in Ubiquitous computing environment and infer superordinate context by combining context data of 4W1H with inferred context of why. This thesis suggests that we categorize specific context and service according to 6W2H added Whom(specific data or service) and How much (accuracy), and determine proper services for specific contexts by introducing the concept of rough set for expression and inference of categorized contexts and inaccurate knowledge. Since there is an limitation of the set of 0 and 1 when concerned with accuracy of services, I introduce the concept of fuzzy set. To provide users with the most appropriate service by ridding of unnecessary properties through the process of reduction, I also use the concept of rough set.

• The KIPS Transactions:PartA
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• v.16A no.2
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• pp.135-142
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• 2009

With the development of ubiquitous computing technology, the number of HCI applications is increasing, where they utilize various contexts to provide adaptive services to users according to the change of contexts, and also, technologies for collecting various sensor data and generating integrated contexts get more important. However, the research on the collection and integration of multi-sensor data is not sufficient when we consider the various utilization areas of the integrated contexts. In particular, they have some problems to be solved such as duplication of the context data and the high system load. In this paper, we propose an integrated context generation scheme based on Ant Colony System. Proposed scheme generates the context data as a form of XML and avoids the generation of unnecessary context information by detecting the repeated sensor information based on the ant colony system. As a result of detections, we reduce wasted resources and repositories when the integrated context is created. We also reduce the overhead for reasoning.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.1-10
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• 2012

Recently, Social Network services(SNS) is gaining popularity as Facebook and Twitter. Popularity of SNS leads to active service and social data is to be increased. Thus, social search is remarkable that provide more meaningful information to users. but previous studies using social network structure, network distance is calculated using only familiarity. It is familiar as distance on network, has been demonstrated through several experiments. If taking advantage of social context data that users are using SNS to produce, then familiarity will be helpful to evaluate further. In this paper, reflect user's attention through comments and tags, Facebook context is determined using familiarity between friends in SNS. Facebook context is advantageous finding a friend who has a similar propensity users in context of profiles and interests. As a result, we provide a blog post that interest with a close friend. We also assist in the retrieval facilities using Near Field Communication(NFC) technology. By the experiment, we show the proposed soicial search method is more effective than only tag.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.312-320
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• 2012

In this study, we introduce a new design methodology of a granular-oriented self-organizing polynomial neural networks (GoSOPNNs) that is based on multi-layer perceptron with Context-based Polynomial Neurons (CPNs) or Polynomial Neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a methodological design strategy of GoSOPNNs as follows : (a) The 1st layer of the proposed network consists of Context-based Polynomial Neuron (CPN). In here, CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Context-based Fuzzy C-Means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data while the build of the clusters is guided by a collection of some predefined fuzzy sets (so-called contexts) defined in the output space. (b) The proposed design procedure being applied at each layer of GoSOPNN leads to the selection of preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of performance of the proposed GoSOPNN network, we describe a detailed characteristic of the proposed model using a well-known learning machine data(Automobile Miles Per Gallon Data, Boston Housing Data, Medical Image System Data).

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.105-114
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• 2007

In sensor network processing environments, current location tracking methods have problems in accuracy on receiving the transmitted data and pinpointing the exact locations depending on the applied methods, and also have limitations on decision making and monitoring the situations because of the lack of considering context-awareness. In order to overcome such limitations, we proposed a method which utilized context-awareness in a data processing module which tracks a location of the magnetic object(Magnetic Line Tracer) and controlled introspection data based on magnetic sensor. Also, in order to prove its effectiveness we have built a wireless sensor network test-bed and conducted various location tracking experiments of line tracer using the data and resulted in processing of context-aware data. Using the new data, we have analyzed the effectiveness of the proposed method for locating the information database entries and for controlling the route of line tracer depending on context-awareness.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.73-88
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• 2022

This exploratory study aims to understand the potential of log data analysis and expand its utilization in user research methods. Transaction log data are records of electronic interactions that have occurred between users and web services, reflecting information-seeking behavior in the context of digital libraries where users interact with the service system during the search for information. Two ways were used to analyze South Korea's National Digital Science Library (NDSL) log data for three days, including 150,000 data: a log pattern analysis, and log context analysis using statistics. First, a pattern-based analysis examined the general paths of usage by logged and unlogged users. The correlation between paths was analyzed through a χ² analysis. The subsequent log context analysis assessed 30 identified users' data using basic statistics and visualized the individual user information-seeking behavior while accessing NDSL. The visualization shows included 30 diverse paths for 30 cases. Log analysis provided insight into general and individual user information-seeking behavior. The results of log analysis can enhance the understanding of user actions. Therefore, it can be utilized as the basic data to improve the design of services and systems in the digital library to meet users' needs.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.61-72
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• 2006

Ubiquitous computing technologies become mature enough to be applied in acceptable ubiquitous services. In particular, in u-shopping area, personalized recommender systems which automatically collect the nomadic user-related context data and then provide them with products or shops in a flexible manner. However, legacy cooperative queries and context-aware queries so far do not come up with dynamically changing situations and ambiguous query commands, respectively. Hence, The purpose of this paper is to propose a personalized context-aware cooperative query that supports a multi-level data abstraction hierarchy and conceptual distance metric among node instances, while considering the user's context data. To show the feasibility of the methodology proposed in this paper, we have implemented a prototype system, CACO, in the area of site search in a large-scale shopping mall.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.675-682
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• 2021

In an environment in which a limited type and number of sensors are used, a demand for acquiring various context information may appear. In this study, a new method for acquiring various context information than before was proposed in an environment in which a limited number of sensors are required. To this end, a clue was obtained from the Exon-Intron theory, which is gaining great interest in the field of biology, and a method for acquiring various context information was proposed based on this. By applying Exon-Intron's selective cutting and combining method, events of each sensor were efficiently cut and each event data was combined and utilized, thereby realizing the diversification of the acquired context information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4228-4236
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• 2015

Due to the rapid advance in web and mobile computing technologies, normal users have become to produce, provide, and share a varied form of spatial data and information. In the domain of spatial information, numerous researches on GIS have been conducted to provide spatial information services based on a geo-sensor network and a data integration and processing technology. However, to provide user-oriented information, a context information model is necessary to associate GIS data with web and sensor data. Context awareness services is designed to provide specific information, minimizing users' interference. For which, the context information model expresses the relationship of various data from sensor networks and mobile applications and provides a user-specific information considering location and area of interest. Thus, this research aims to develops a context information model based air-pollution information system that obtains and analyses air pollution data and reflects the analysis results on an air-pollution policy. Also, this system aims to raise citizens' awareness on air-pollution and to promote citizens' participatory to improve city's air quality.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.389-395
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• 2015

According to much research on cognitive science, the impact of the scene-context on human visual search in real-world images could be as important as the saliency. Therefore, this study proposed a method of Adaptive Control of Thought-Rational (ACT-R) modeling of visual search in real-world images, based on saliency and scene-context. The modeling method was developed by using the utility system of ACT-R to describe influences of saliency and scene-context in real-world images. Then, the validation of the model was performed, by comparing the data of the model and eye-tracking data from experiments in simple task in which subjects search some targets in indoor bedroom images. Results show that model data was quite well fit with eye-tracking data. In conclusion, the method of modeling human visual search proposed in this study should be used, in order to provide an accurate model of human performance in visual search tasks in real-world images.