• Korean Journal of Biological Psychiatry
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• v.25 no.1
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• pp.9-15
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• 2018

Objectives This study intended to identify the deficits of cognitive control among patients with bipolar I disorder and their first-degree relatives, and identify the possibility of cognitive control as an endophenotype of bipolar disorder. Methods The study included three groups: euthymic states patients with bipolar I disorder (n = 55), unaffected first-degree relatives of probands with bipolar I disorder (n = 30), and a healthy control group (n = 51), that was matched on age, sex, and years of education. The AX version of the continuous performance test (CPT) was used to examine cognitive control. Error rate, correct response times of each subsets (AX, BX, AY, BY), and d' as an indication of accuracy sensitivity index were calculated. Psychopathology, intelligence, and psychomotor speed were also assessed. Results Patients with bipolar I disorder showed significantly worse error rates in the AX (p = 0.01) and BX (p = 0.02) subsets and d' (p = 0.05) than the others. They also showed more delayed correct response times than the healthy control group and first-degree relatives in all subsets (p < 0.01). But first-degree relatives showed neither high error rates nor delayed correct response times than healthy control group. Conclusions These findings suggest that cognitive control is impaired in bipolar I disorder but less likely to be an endophynotype of bipolar I disorder.

• Journal of Korean society of Dental Hygiene
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• v.14 no.5
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• pp.667-672
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• 2014

Objectives : The purpose of this study was to examine classifying the level and accuracy of the meta-cognitive level of students and dental hygiene, and to understand the impact on the process of problem solving and accordingly, it is intended to provide a basis for learning strategies. Methods : A self-reported questionnaire was filled out by 328 dental hygiene students in 3 colleges in Gyeonggi-do and Chungnam. Data were analyzed by the frequency analysis, one-way ANOVA, Scheffe's post-hoc test, Pearson's correlation coefficient using SPSS 12.0. Results : Meta-cognitive level of the subject was on average 4.43 points and problem solving level was lower at 2.82 points. Showed a significant difference in satisfaction with the major motives meta-cognitive level in accordance with the general characteristics of the subjects(p<0.05). Results of this study showed that no statistically significant differences in both the sub-areas of the level of problem solving according to the general characteristics of the subject(p>0.05). There was no correlation between the ability to solve problems and meta-cognitive level of the subjects(p>0.05). Conclusions : The finding of the study showed that meta-perception of dental hygiene students are lower the level of problem-solving that is compared to meta-cognition. It is suggested that development of a variety of learning methods for improving meta-cognitive thinking and problem-solving skills required in dental hygiene school curriculum.

• Journal of Korean Academy of Nursing
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• v.50 no.2
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• pp.191-199
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• 2020

Purpose: The purpose of this study was to compare sociodemographic characteristics of a normal cognitive group and mild cognitive impairment group, and establish prediction models of Mild Cognitive Impairment (MCI). Methods: This study was a secondary data analysis research using data from "the 4th Korea Longitudinal Study of Ageing" of the Korea Employment Information Service. A total of 6,405 individuals, including 1,329 individuals with MCI and 5,076 individuals with normal cognitive abilities, were part of the study. Based on the panel survey items, the research used 28 variables. The methods of analysis included a χ²-test, logistic regression analysis, decision tree analysis, predicted error rate, and an ROC curve calculated using SPSS 23.0 and SAS 13.2. Results: In the MCI group, the mean age was 71.4 and 65.8% of the participants was women. There were statistically significant differences in gender, age, and education in both groups. Predictors of MCI determined by using a logistic regression analysis were gender, age, education, instrumental activity of daily living (IADL), perceived health status, participation group, cultural activities, and life satisfaction. Decision tree analysis of predictors of MCI identified education, age, life satisfaction, and IADL as predictors. Conclusion: The accuracy of logistic regression model for MCI is slightly higher than that of decision tree model. The implementation of the prediction model for MCI established in this study may be utilized to identify middle-aged and elderly people with risks of MCI. Therefore, this study may contribute to the prevention and reduction of dementia.

• Korean Journal of Forensic Psychology
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• v.11 no.2
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• pp.155-171
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• 2020

The purpose of this study is to examine the effects of emotional arousal and cognitive load on pupil diameter during a lie detection interview. The guilty group (n = 30) committed a mock crime (i.e., stealing cash) and the innocent group (n = 30) performed a mission (i.e., sending a message) in the research assistant's office. After that, their pupil size was measured using a wearable eye-tracker during the interview. The interview questions were classified with the three cognitive load, three emotional arousal, and three neutral questions. The results indicate that the main effects of group and time were not significant, but the interaction between group and time was significant. It means that when answering cognitive load questions, the guilty group showed larger increase in pupil diameter than the innocent group. The present study suggests that inducing cognitive load is more effective than inducing emotional arousal during an interview when using pupil diameter as an index of deception, and it is expected to improve the accuracy of lie detection.

• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.217-233
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• 2011

In this paper, an automatic word spacing system is proposed, which assumes sentences with no spaces between the words and segments them into proper words. Segmentation is regarded as a labeling problem in that segmentation can be done by attaching appropriate labels to each syllables of the given sentences. The system is based on Conditional Random Fields, which were reported to show excellent performance in labeling problems. The system is trained with a corpus of 1.12 million syllables, and evaluated with 2,114 sentences, 93 thousand syllables. The best results obtained are 98.84% of syllable-based accuracy and 95.99% of word-based accuracy.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.42-42
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• 2002

An adverb has been generally defined as what modifies verbs or adjectives in Korean, but we can find that some adverbs can modify nouns. These kinds of adverbs lead a structural analysis complicated; therefore, they should be exceptionally processed by a syntactic parser. In this paper, we categorize a noun-modifying adverb and characterize that from a syntactic analysis standpoint. And also, we propose a method to handle noun-modifying adverbs for improving the accuracy of syntactic analysis. By using this proposed method, we can show that the parser increases it′s accuracy from 81.9 to 83.6% on testing corpus.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.43-53
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• 2002

An adverb has been generally defined as what modifies verbs or adjectives in Korean, but we can find that some adverbs can modify nouns. These kinds of adverbs lead a structural analysis complicated; therefore, they should be exceptionally processed by a syntactic parser. In this paper, we categorize a noun-modifying adverb and characterize that from a syntactic analysis standpoint. And also, we propose a method to handle noun-modifying adverbs for improving the accuracy of syntactic analysis. By using this proposed method, we can show that the parser increases it's accuracy from 81.9 to 83.6％ on testing corpus.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.604-615
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• 2019

Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase. When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED) is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR with low complexity. The local sensing node transmits the perceived results through the control channel to the fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is effectively saved. Simulation results show that the proposed scheme can effectively improve the system detection probability, shorten the average sensing time, and has better robustness without largely increasing the costs of sensing system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4664-4681
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• 2020

Modulation recognition (MR) plays a key role in cognitive radar, cognitive radio, and some other civilian and military fields. While existing methods can identify the signal modulation type by extracting the signal characteristics, the quality of feature extraction has a serious impact on the recognition results. In this paper, an end-to-end MR method based on long short-term memory (LSTM) and the gated recurrent unit (GRU) is put forward, which can directly predict the modulation type from a sampled signal. Additionally, the sliding window method is applied to fast-changing mixed-modulation signals for which the signal modulation type changes over time. The recognition accuracy on training datasets in different SNR ranges and the proportion of each modulation method in misclassified samples are analyzed, and it is found to be reasonable to select the evenly-distributed and full range of SNR data as the training data. With the improvement of the SNR, the recognition accuracy increases rapidly. When the length of the training dataset increases, the neural network recognition effect is better. The loss function value of the neural network decreases with the increase of the training dataset length, and then tends to be stable. Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%. As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.69-80
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• 2024

Currently rebar spacing inspection is carried out by human inspectors who heavily rely on their individual experience, lacking a guarantee of objectivity and accuracy in the inspection process. In addition, if incorrectly placed rebars are identified, the inspector need to correct them. Recently, laser scanning and AR technologies have been widely used because of their merits of measurement accuracy and visualization. This study proposes a technology for rebar spacing inspection and fixing by combining laser scanning and AR technology. First, scan data acquisition of rebar layers is performed and the raw scan data is processed. Second, AR-based visualization and fixing are performed by comparing the design model with the model generated from the scan data. To verify the developed technique, performance comparison test is conducted by comparing with existing drawing-based method in terms of inspection time, error detection rate, cognitive load, and situational awareness ability. It is found from the result of the experiment that the AR-based rebar inspection and fixing technology is faster than the drawing-based method, but there was no significant difference between the two groups in error identification rate, cognitive load, and situational awareness ability. Based on the experimental results, the proposed AR-based rebar spacing inspection and fixing technology is expected to be highly useful throughout the construction industry.