• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2350-2364
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• 2014

This paper addresses the problem of resource allocation in amplify-and-forward (AF) relayed OFDM based cognitive radio networks (CRNs). The purpose of resource allocation is to maximize the overall throughput, while satisfying the constraints on the individual power and the interference induced to the primary users (PUs). Additionally, different from the conventional resource allocation problem, the rate-guarantee constraints of the subcarriers are considered. We formulate the problem as a mixed integer programming task and adopt the dual decomposition technique to obtain an asymptotically optimal power allocation, subcarrier pairing and relay selection. Moreover, we further design a suboptimal algorithm that sacrifices little on performance but could significantly reduce computational complexity. Numerical simulation results confirm the optimality of the proposed algorithms and demonstrate the impact of the different constraints.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.59-69
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• 2020

The purpose of this study is to design the framework of evaluation on learner's cognitive skill for artificial intelligence(AI) education through computational thinking. To design the rubric and framework for evaluating the change of leaner's intrinsic thinking, the evaluation process was consisted of a sequential stage with a) agency that cognitive learning assistance for data collection, b) abstraction that recognizes the pattern of data and performs the categorization process by decomposing the characteristics of collected data, and c) modeling that constructing algorithms based on refined data through abstraction. The evaluating framework was designed for not only the cognitive domain of learners' perceptions, learning, behaviors, and outcomes but also the areas of knowledge, competencies, and attitudes about the problem-solving process and results of learners to evaluate the changes of inherent cognitive learning about AI education. The results of the research are meaningful in that the evaluating framework for AI education was developed for the development of individualized evaluation tools according to the context of teaching and learning, and it could be used as a standard in various areas of AI education in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2B
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• pp.178-187
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• 2009

Cognitive radio (CR) technology is to maximize the spectrum utilization by allocating the unused spectrums to the unlicensed users. In CR environment, it is strictly required for the unlicensed users not to interference with the licensed users. Thus, it is essential to rely on the scheduling algorithm to avoid the interference when utilizing spectrum holes that are changing in time and frequency. However, the existing scheduling algorithms that are applied for the wireless communication environment considering the licensed users only is not appropriate for CR environment. In this paper, we propose downlink scheduling algorithm based on probability of incumbent user presence for cognitive radio networks. With computer simulations, it is shown that the proposed scheduling algorithm outperforms the conventional scheduling algorithm.

• Anxiety and mood
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• v.4 no.1
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• pp.28-33
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• 2008

Objective : A working group of psychiatrists from the Korean Academy of Anxiety Disorders was established to determine the appropriate medication algorithm for treating patients with panic disorder. In this article, we discussed the consensus among psychiatrists regarding the use of cognitive behavior therapy (CBT) in the development of a treatment algorithm for panic disorder in Korea. Methods : Based on the guidelines or algorithms published by the American Psychiatric Association, National Institute for Clinical Excellence, and Canadian Psychiatric Association, we constructed questionnaires regarding the core components and contents of CBT for patients with panic disorder. Fifty-four experts in panic disorder completed the questionnaires. Results : There was statistically significant consensus among the experts in the belief that cognitive reconstruction and psychological education are the core components of CBT for the treatment of patients with panic disorder. However, there was some inconsistency between the opinions of some experts regarding the content and frequency of CBT and the results of studies published outside of Korea. Conclusions : CBT, especially the psychological education and cognitive reconstruction components, should be considered when treating patients with panic disorder. However, further consideration needs to be put into the design of a more detailed treatment guideline for the use of CBT in the treatment of patients with panic disorder.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.631-640
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• 2023

Short-text similarity calculation is one of the hot issues in natural language processing research. The conventional keyword-overlap similarity algorithms merely consider the lexical item information and neglect the effect of the word order. And some of its optimized algorithms combine the word order, but the weights are hard to be determined. In the paper, viewing the keyword-overlap similarity algorithm, the short English text similarity algorithm based on lexical chunk theory (LC-SETSA) is proposed, which introduces the lexical chunk theory existing in cognitive psychology category into the short English text similarity calculation for the first time. The lexical chunks are applied to segment short English texts, and the segmentation results demonstrate the semantic connotation and the fixed word order of the lexical chunks, and then the overlap similarity of the lexical chunks is calculated accordingly. Finally, the comparative experiments are carried out, and the experimental results prove that the proposed algorithm of the paper is feasible, stable, and effective to a large extent.

• 한국정보교육학회:학술대회논문집
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• 2010.08a
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• pp.235-241
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• 2010

This paper has been studied about algorithm teaching methods for improving the problem-solving skills and creativity in rapidly changing information society. Especially the algorithms for teaching about computer is very important. Because it is effective learning content for finding the best solution to solve a problem and improve the students' logical thinking. However, teaching algorithms can be monotonous to children on account of using only computer and languages. So It needs to consider about the cognitive structure and level of elementary school students. Therefore, this study has the purpose to acquaint students with the principle of algorithm and improve problem-solving and creativity using games, not computer.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.211-221
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• 1998

It is becoming apparent that stereo matching algorithms need much information from high level cognitive processes. Otherwise, conventional algorithms based on bottom-up control alone are susceptible to local minima. We introduce a system that consists of two levels. A lower level, using a usual matching method, is based upon the local neighborhood and a second level, that can integrate the partial information, is aimed at contextual matching. Conceptually, the introduction of bottom-up and top-down feedback loop to the usual matching algorithm improves the overall performance. For this purpose, we model the image attributes using a Markov random field (MRF) and thereupon derive a maximum a posteriori (MAP) estimate. The energy equation, corresponding to the estimate, efficiently represents the natural constraints such as occlusion and the partial informations from the other levels. In addition to recognition, we derive a training method that can determine the system informations from the other levels. In addition to recognition, we derive a training method that can determine the system parameters automatically. As an experiment, we test the algorithms using random dot stereograms (RDS) as well as natural scenes. It is proven that the overall recognition error is drastically reduced by the introduction of contextual matching.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.3-8
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• 2002

This paper introduces the basic concept of computational modeling of perception processes for multimedia data. Such processes are modeled as hierarchical inter- and intra- relationships amongst information in physical, physiological, psychological and cognitive layers in perception. Based on our framework, this paper gives the algorithms for content-based retrieval for multimedia database systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5A
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• pp.391-400
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• 2009

Recently, cellular communication networks are migrating from 2G to 3G. Spectrum utilization tends to be inefficient during the transition. Cognitive radio (CR) technology can be a key solution to increase spectrum efficiency by allowing secondary networks to utilize frequency resource of primary networks. However, conventional CR approaches which do not utilize the frequency reuse factor of primary networks may incur degradation of whole network performance. In this paper, we propose a mechanism that a secondary network senses pilot signals of a primary network and select optimum frequency bands. In order to maximize whole network performance, we formulate an optimization problem subject to interference constraints for a primary network and present algorithms. Simulation results compare the proposed method with the conventional method. Our proposed method shows performance gain over the conventional method if channel variation of a primary network is dynamic and the frequency reuse factor of a primary network is high.