• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2005.05a
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• pp.9-13
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• 2005

A comprehension-based computational model of pilot action planning called ADAPT is presented to model pilot performance in a flight simulation context. Individual pilots were asked to execute a series of flight maneuvers using a flight simulator, and their eye-scanning, control movements, and flight performance were recorded in a time-synched database. Computational models of each of the 25 individual pilots were constructed, and the individual models simulated execution of the same flight maneuvers performed by human pilots. The time-synched eye-scanning, control movements, and flight performance of individual pilots and their respective models were compared to test ADAPT's predictive validity.

• Proceedings of the Korea Society for Simulation Conference
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• 2002.05a
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• pp.163-168
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment was designed in within-subject design with two factors; vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods; the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.

• Journal of the Ergonomics Society of Korea
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• v.21 no.3
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• pp.25-34
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment the cognition time of the road sign in dynamic environment. This experiment was designed in with-subject design with two factors: vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods: the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.

• ETRI Journal
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• v.44 no.3
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• pp.400-412
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• 2022

In cognitive radio networks (CRNs), the computational complexity of resource allocation algorithms is a significant problem that must be addressed. However, the high computational complexity of the optimal solution for tackling resource allocation in CRNs makes it inappropriate for use in practical applications. Therefore, this study proposes a power-based pricing algorithm (PPA) primarily to reduce the computational complexity in downlink CRN scenarios while restricting the interference to primary users to permissible levels. A two-stage approach reduces the computational complexity of the proposed mathematical model. Stage 1 assigns subcarriers to the CRN's users, while the utility function in Stage 2 incorporates a pricing method to provide a power algorithm with enhanced reliability. The PPA's performance is simulated and tested for orthogonal frequency-division multiplexing-based CRNs. The results confirm that the proposed algorithm's performance is close to that of the optimal algorithm, albeit with lower computational complexity of O(M log(M)).

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2010.05a
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• pp.60-65
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• 2010

Language learning involves linguistic environments around the learner. So the variation in training input to which the learner is exposed has been linked to their language learning. We explore how linguistic experiences can cause differences in learning linguistic structural features, as investigate in a probabilistic graphical model. We manipulate the amounts of training input, composed of natural linguistic data from animation videos for children, from holistic (one-word expression) to compositional (two- to six-word one) gradually. The recognition and generation of sentences are a "probabilistic" constraint satisfaction process which is based on massively parallel DNA chemistry. Random sentence generation tasks succeed when networks begin with limited sentential lengths and vocabulary sizes and gradually expand with larger ones, like children's cognitive development in learning. This model supports the suggestion that variations in early linguistic environments with developmental steps may be useful for facilitating language acquisition.

• Korean Journal of Cognitive Science
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• v.33 no.3
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• pp.155-168
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• 2022

In this paper, we introduce the SNUDM-G, a diffusion model analysis tool with improved computational speed. Although the diffusion model has been applied to explain various cognitive tasks, its use was limited due to computational difficulties. In particular, SNUDM(Koh et al., 2020), one of the diffusion model analysis tools, has a disadvantage in terms of processing speed because it sequentially generates 20,000 data when approximating the diffusion process. To overcome this limitation, we propose to use graphic processing units(GPU) in the process of approximating the diffusion process with a random walk process. Since 20,000 data can be generated in parallel using the graphic processing units, the estimation speed can be increased compared to generating data through sequential processing. As a result of analyzing the data of Experiment 1 by Ratcliff et al. (2004) and recovering the parameters with SNUDM-G using GPU and SNUDM using CPU, SNUDM-G estimated slightly higher values for certain parameters than SNUDM. However, in term of computational speed, SNUDM-G estimated the parameters much faster than SNUDM. This result shows that a more efficient diffusion model analysis for various cognitive tasks is possible using this tool and further suggests that the processing speed of various cognitive models can be improved by using graphic processing units in the future.

• Korean Journal of Cognitive Science
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• v.14 no.3
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• pp.23-36
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• 2003

A comprehension-based approach to learning assumes that incoming information and background knowledge are integrated to form a mental representation which is subsequently used to incorporate new knowledge. It is demonstrated that this approach can be validated by comparing human and computational model performance in the prompt learning context. A computational model (ADAPT-UNIX) based on the construction-integration theory of comprehension (Kintsch, 1988; 1998) predicted how users learn from help prompts which are designed to assist UNIX composite command production. In addition, the comparison also revealed high similarity in composite production task performance between model and human. Educational implications of present research are discussed on the basis of the fact that prompt instructions have differential effect on learning and application as background knowledge varies.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.606-611
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• 2014

The understanding of another's behavior is a fundamental cognitive ability for primates including humans. Recent neuro-physiological studies suggested that there is a direct matching algorithm from visual observation onto an individual's own motor repertories for interpreting cognitive ability. The mirror neurons are known as core regions and are handled as a functionality of intent recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper, we addressed previous works used to model the function and mechanisms of mirror neurons and proposed a computational model of a mirror neuron system which can be used in human-robot interaction environments. The major focus of the computation model is the reproduction of an individual's motor repertory with different embodiments. The model's aim is the design of a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and execution. We also propose a biologically inspired plausible equation model.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1295-1302
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• 2011

We present an analytic model of an unslotted opportunistic spectrum access (OSA) network and evaluate its performance such as interruption probability, service completion time, and throughput of secondary users. Numerical examples are given to show the performance of secondary users in cognitive networks. The developed modeling and analysis method can be used to evaluate the performance of various OSA networks.