• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.154-159
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• 2015

Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.

• Journal of KIISE:Software and Applications
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• v.29 no.6
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• pp.377-385
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• 2002

An authentic simulation platform for events situated in spatio-temporal space is presented. The authenticity, i.e., logical fidelity to the reality, of this cyberspace is realized by maximizing the diversity and unpredictability of events occurring therein. The knowledge components and associated schemes required for the simulation of events situated in spatio-temporal space encompass the environmental factors, the objects, the events, and their interrelations. We deviled event activation, triggering mechanism, and cognitive function related to event to realize an authentic simulation of discretionary events. The agents in this simulation environments are autonomous in that they have their own existence and capability of event planning. We focused on identifying basic constructs relevant to authentic simulation of discretionary events whose initiation depends on human intention. Several key ideas are implemented in a typical spatio-temporal situation to demonstrate the viability of our simulation mechanism.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.201-211
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• 2017

Social scientists have studied interaction between human beings, while computer scientists have expanded the research domain from human-human to human-machine, human-agent, or machine-machine. The reason why an adoption of Smart Work is failed is an anxiety about ICT usage which middle managers have. It is important to explore the concept both to reduce an anxiety on an application and to increase continuance to use it. Therefore this study takes "empathy" as a key factor to play a leading role both to relieve the anxiety about the application and to improve the intention to use it. The data is gathered from a survey of undergraduate who have experience to use MS-Access. The findings show that application empathy decrease the application anxiety, but the empathy increase the continuance mediated by cognitive and affective attitude.

• Journal of Microbiology and Biotechnology
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• v.22 no.5
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• pp.708-720
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• 2012

In this study, we assessed the effects of ginsenoside Re (GRe) administration on repeated immobilization stress-induced behavioral alterations using the forced swimming test (FST), the elevated plus maze (EPM), and the active avoidance conditioning test (AAT). Additionally, we examined the effect of GRe on the central adrenergic system by observing changes in neuronal tyrosine hydroxylase (TH) immunoreactivity and brain-derived neurotrophic factor (BDNF) mRNA expression in the rat brain. Male rats received 10, 20, or 50 mg/kg GRe (i.p.) 30 min before daily exposures to repeated immobilization stress (2 h/day) for 10 days. Activation of the hypothalamic-pituitary-adrenal (HPA) axis in response to repeated immobilization was confirmed by measuring serum levels of corticosterone (CORT) and the expression of corticotrophin-releasing factor (CRF) in the hypothalamus. Repeated immobilization stress increased immobility in the FST and reduced open-arm exploration in the EPM test. It also increased the probability of escape failures in the AAT test, indicating a reduced avoidance response. Daily administration of GRe during the repeated immobilization stress period significantly inhibited the stress-induced behavioral deficits in these behavioral tests. Administration of GRe also significantly blocked the increase in TH expression in the locus coeruleus (LC) and the decrease in BDNF mRNA expression in the hippocampus. Taken together, these findings indicate that administration of GRe prior to immobilization stress significantly improved helpless behaviors and cognitive impairment, possibly through modulating the central noradrenergic system in rats. These findings suggest that GRe may be a useful agent for treating complex symptoms of depression, anxiety, and cognitive impairment.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.92-101
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• 2018

As the interactive AI speaker becomes popular, voice recognition is regarded as an important vehicle-driver interaction method in case of autonomous driving situation. The purpose of this study is to confirm whether multimodal interaction in which feedback is transmitted by auditory and visual mode of AI characters on screen is more effective in user experience optimization than auditory mode only. We performed the interaction tasks for the music selection and adjustment through the AI speaker while driving to the experiment participant and measured the information and system quality, presence, the perceived usefulness and ease of use, and the continuance intention. As a result of analysis, the multimodal effect of visual characters was not shown in most user experience factors, and the effect was not shown in the intention of continuous use. Rather, it was found that auditory single mode was more effective than multimodal in information quality factor. In the semi-autonomous driving stage, which requires driver 's cognitive effort, multimodal interaction is not effective in optimizing user experience as compared to single mode interaction.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.49-58
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• 2011

Augmented Reality(AR) assists human's cognitive ability through the information visualization by substantiating information about virtual situation. This technology is studied in a variety of ways including education, design, industry, and so on, by various supply of information devices equipped with cameras and display monitors. Since the most of the AR system depends on limited interaction that responds to the order from user, it can not reflect diverse real world situation. In this study, we suggest a knowledge based augmented reality system, which is composed of context awareness agent that provides recognized context information, along with knowledge based component that provides intelligent capability by utilizing domain knowledges. With this capability, the augmented object can generate dynamic model intelligently by reflecting context information, and can make the interaction possible among the multiple objects. We developed rule based context awareness system along with 3D model generation, and tested interaction among the augmented objects. And we suggest a framework that can provide a convenient way of developing augmented reality system for user.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.112-123
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• 2005

The latest E-commerce sites provide personalized services to maximize user satisfaction for Internet user The collaborative filtering is an algorithm for personalized item real-time recommendation. Various supplementary methods are provided for improving the accuracy of prediction and performance. It is important to consider these two things simultaneously to implement a useful recommendation system. However, established studies on collaborative filtering technique deal only with the matter of accuracy improvement and overlook the matter of performance. This study considers representative attribute-neighborhood, recommendation textile set, and similarity grouping that are expected to improve performance to the recommendation agent system. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommendation Agent System (FDRAS ).

• Korean Journal of Cognitive Science
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• v.26 no.2
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• pp.167-207
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• 2015

This study focused on the awareness and coping methods of psychological trauma and human suffering in the contemporary era after the development of posttraumatic stress disorder(PTSD) including the situations in the Korean society, and proposed principles for an alternative paradigm of trauma recovery. Trauma is defined as an 'external' stress causing chronic suffering mediated by memory, and the American Psychiatric Association approved PTSD in the Diagnostic and Statistical Manual of Mental Disorders in 1980. The development of PTSD empowered moral legitimacy to the victims, opened a successful way to treatment, and accomplished explosive amount of research in the area of neurobiology and cognitive neuroscience. However, this also narrowed the understanding of human suffering, and the importance of an alternative coping method which overcomes the limitations of technical intervention became overlooked. Moreover, the Korean society has an underlying mechanism of replacing the matter of trauma to a problem of an individual. This is shown among the historical context of splitting and denial, and among medicalized bureaucracy. Trauma should be acknowledged as a social suffering, and searching for an alternative paradigm is in need. This study suggested the following principles; seeking for truth and justice, survivor as the agent of recovery emphasizing the responsibility of the community, ecological adaptations of recent bio-psychological achievements, and finally putting emphasis on continuous discussions about the definition of recovery.

• Journal of the Korean Geographical Society
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• v.41 no.5 s.116
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• pp.582-599
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• 2006

Due to the growing popularity of mobile information technology, more people, especially in the general public, have access to computerized geospatial information systems for wayfinding tasks or urban navigation. One of the problems with the current services is that, whether the users are exploring or navigating, whether they are travelers who are totally new to a region or long-term residents who have a fair amount of regional knowledge, the same method is applied and the direction are given in the same way. However, spatial knowledge for a given urban region expands in proportion to residency. Urban navigation is highly dependent on cognitive mental images, which is developed through spatial experience and social communication. Thus, the wayfinding service for a regional community can be highly supported, using well-known regional places. This research is to develop the framework for urban navigation within a regional community. The concept of communal ontology is proposed to aid in urban regional navigation. The experimental work was implemented with case study to collect regional landmarks, develop the ontological model and represent it with formal structure. The final product of this study will provide the geographical information of a region to the other agent and be the fundamental information structure for cognitive urban regional navigation.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.27-40
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• 2016

Predicting stealthy behaviors plays an important role in designing stealth games. It is, however, difficult to automate this task because human players interact with dynamic environments in real time. In this paper, we present a reinforcement learning (RL) method for simulating stealthy movements in dynamic environments, in which an integrated model of Q-learning with Artificial Neural Networks (ANN) is exploited as an action classifier. Experiment results show that our simulation agent responds sensitively to dynamic situations and thus is useful for game level designer to determine various parameters for game.