• Journal of the Institute of Convergence Signal Processing
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• v.10 no.4
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• pp.225-232
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• 2009

Context-aware technologies for ubiquitous computing are necessary to study the representation of gathered context-information appropriately, the understanding of user's intention using context-information, and the offer of pertinent services for users. In this paper, we propose the WS-CAM(Web Services based Context-Aware Middleware) framework for context-aware computing. WS-CAM provides ample power of expression and inference mechanisms to various context-information using an ontology-based context model. We also consider that WS-CAM is the middleware-independent structure to adopt web services with characteristic of loosely coupling as a matter of communication of context-information. In this paper, we describe a scenario for lecture services based on the ubiquitous computing e e e e e e to verify the utilization of WS-CAM We also show an example of middleware-independent system expansion to display the merits of web-based services. WS-CAM for lecture services represented context-information itodomaits as OWL-based ontology model effectively, and we confirmed the information is inferred to high level context-information by user-defined rules. We also confirmed the context-information is transferred to application services middleware-independently using various web methods provided by web services.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.201-208
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• 2000

In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.393-401
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• 2019

In this study, based on the general reasoning that the higher the resolution of the HMD, the greater the presence and immersion of the audience and the greater the presence experience, the difference in the presence experience according to the resolution of the virtual reality device is verified empirically Presence effect. To do this, 300 college students were included in the population, and VR simulations of the Oculus store were conducted using HMD and mobile devices with different resolutions (HD, FHD, WQHD) as stimuli. As a result, there was a significant difference in the presence experience of the audience according to the resolution of the mobile device attached to the virtual reality HMD, and the presence effect had a significant influence on the awakening. On the other hand, no significant influence was found in the effect of emotion. Therefore, we can prove a general reasoning hypothesis that the higher the resolution of the virtual reality device, the greater the presence experience of the audience. However, since the confounding effect and the impression are contradictory in the presence effect side, additional experiments research is required.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.85-110
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• 2018

A chatbot is an interactive assistant that utilizes many communication modes: voice, images, video, or text. It is an artificial intelligence-based application that responds to users' needs or solves problems during user-friendly conversation. However, the current version of the chatbot is focused on understanding and performing tasks requested by the user; its ability to generate personalized conversation suitable for relationship-building is limited. Recognizing the need to build a relationship and making suitable conversation is more important for social chatbots who require social skills similar to those of problem-solving chatbots like the intelligent personal assistant. The purpose of this study is to propose a text analysis method that evaluates relationships between chatbots and users based on content input by the user and adapted to the communication situation, enabling the chatbot to conduct suitable conversations. To evaluate the performance of this method, we examined learning and verified the results using actual SNS conversation records. The results of the analysis will aid in implementation of the social chatbot, as this method yields excellent results even when the private profile information of the user is excluded for privacy reasons.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.305-313
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• 2021

The upcoming warfare will be network-centric warfare with the acquiring and sharing of information on the battlefield through the connection of the entire weapon system. Therefore, the amount of information generated increases, but the technology of evaluating the information is insufficient. Threat assessment is a technology that supports a quick decision, but the information has many uncertainties and is difficult to apply to an advanced battlefield. This paper proposes a threat assessment based on artificial intelligence while removing the target uncertainty. The artificial intelligence system used was a fuzzy inference system and a multi-layer perceptron. The target was classified by inputting the unique characteristics of the target into the fuzzy inference system, and the classified target information was input into the multi-layer perceptron to calculate the appropriate threat value. The validity of the proposed technique was verified with the threat value calculated by inputting the uncertain target to the trained artificial neural network.

• Asia Marketing Journal
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• v.9 no.2
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• pp.1-21
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• 2007

This research investigates the role of causal inferences about the endorser's motivation - specifically, dispositional versus situational attributions - and their impact on persuasion of noncelebrity testimonial advertisements. Based on the correspondent inference theory and the relevant literature, it is posited that consumers will generate predictable patterns of attributional responses to testimonial messages, which in turn will influence ad and brand evaluations. An experiment with 335 consumer panelists, after a pilot experiment with the college student sample, has been conducted. Results suggest the greater impact of dispositional attributions than situational attributions on persuasion of noncelebrity testimonial messages and general evocations of situational attributions regardless of the levels of endorser credibility and dispositional attributions. On the basis of the findings from this study, theoretical and practical implications are discussed, as are directions for future research.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.135-144
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• 2007

Feature models are widely used in domain engineering phase of software product lines development to model the common and variable concepts among products. From the feature model, the feature configurations are generated by selecting the features to be included in target product. The feature configuration represents the requirements for the specific product to be implemented. Although there are a lot of researches on how to build and use the feature models and feature configurations, the researches on the formal semantics and reasoning of them are rather inactive. This paper proposes the feature configuration verification approach based on JESS, java-based rule-base system. The Graph Product Line, a standard problem for evaluating the software product line technologies, is used throughout the paper to illustrate this approach. The approach in this paper has advantage of presenting the exact reason causing inconsistency in the feature configuration. In addition, this approach should be easily applied into other software product lines development environments because JESS system can be easily integrated with Java language.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.267-275
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• 2012

In this study, crack growth in a center-cracked plate is predicted under mode I variable amplitude loading, and the result is validated by experiment. Huang's model is employed to describe crack growth with acceleration and retardation due to the variable loading effect. Experiment is conducted with Al6016-T6 plate, in which the load is applied, and crack length is measured periodically. Particle Filter algorithm, which is based on the Bayesian approach, is used to estimate model parameters from the experimental data, and predict the crack growth of the future in the probabilistic way. The prediction is validated by the run-to-failure results, from which it is observed that the method predicts well the unique behavior of crack retardation and the more data are used, the closer prediction we get to the actual run-to-failure data.

• Knowledge Management Research
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• v.22 no.1
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• pp.155-181
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• 2021

The purpose of this study is to verify the correlation between leader's machiavellianism and turnover intention. To this end, we tried to investigate the overall mechanism of the research model through the mediating effect of hindsight bias. To verify the hypothesis, surveys were conducted twice with 335 employees working at companies with more than 300 employees in various occupations. As a result of the study, first, it was found that the machiavellianism of the leader had a positive significant effect on the employee turnover intention. Second, it was found that hindsight bias had a positive significant mediating effect between the leader's machiavellianism and employee turnover intention. It can be inferred that the higher the machiavellianism tendency of the leader, the higher the hindsight bias is experienced and the negative impact on the effectiveness of the organization, the higher the employee turnover intention. Therefore, this study in-depth verifies the mechanism between the leader's machiavellianism, hindsight bias, and employee turnover intentions, suggesting new implications from a perspective different from the existing research flow, and suggesting future research tasks and limitations on the role of leaders.