• Science of Emotion and Sensibility
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• v.14 no.3
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• pp.371-384
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• 2011

Three experiments were carried out to examine how LED lighting hues influence to the rating and recognition of affective stimuli. In Experiment 1 and 2, IAPS affective pictures were used and an affective rating(valence and arousal) task and a recognition memory task were conducted under red, green, blue, and white hue LED lightings in Experiment 1 and cyan, magenta, yellow, and white ones in Experiment 2, respectively. In Experiment 3, affective words were used and the same two tasks were conducted under red, green, blue, and white hue LED lightings. According to the results of affective rating tasks, when primary hues(RGB) were used, red LED lighting elicited an excitement at the arousal dimension and green LED lighting evoked pleasantness at the valence one. When secondary hues(CMY) were used, magenta and cyan showed the similar but weaker patterns of responses comparing to red and green. The results of recognition memory task showed that the responses to the picture stimuli presented at green and cyan hue lightings tended to be a bit faster comparing to the stimuli presented at the other conditions but the difference was insignificant. In Experiment 3, however, recognition memory responses to the affective words presented at green hue lighting were faster significantly. These results indicate that warm colors like red and magenta elicit unpleasantness or excitement while cool colors like green and cyan evoke pleasantness or relaxation, and the primary hues provoke more positive or negative affectivity than secondary ones do. Particularly, the result of recognition memory task in Experiment 3 suggests that green hue LED lighting might be advantageous at the memory performance of language stimuli rather than visual ones.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.2
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• pp.251-254
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• 2016

An energy-efficient object matching accelerator is proposed for mobile object recognition based on matching prediction scheme. Conventionally, vocabulary tree has been used to save the external memory bandwidth in object matching process but involved massive internal memory transactions to examine each object in a database. In this paper, a novel object matching accelerator is proposed based on matching predictions to reduce unnecessary internal memory transactions by mitigating non-target object examinations, thereby improving the energy-efficiency. Experimental results show a 26% reduction in power-delay product compared to the prior art.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.6
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• pp.61-69
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• 1994

For distortion-invariant recognition of Korean characters, a holographic implementation of an optical associative memory system is proposed. The structure of the proposed system is a single-layer neural network employing interconneclion matrix, thresholding and feedback. To provide the interconnection matrix, we use two CGII's which are placed on intermcdiate plane of cascaded Vander Lugt corrclators to form an optical memory loop. The holographic correlator stores reference images in a hologram and retrives them in a coherently illuminated feedback loop. An input image which maybe noisy or incomplete, is applicd to the system and simultaneously correlated optically with all of the stord images. These correlations are throsholed and fed back to the input, where the strongest correlation reinforces the input image. The enhanced image passes arround the loop repeatedly, approaching the stored image more closely on each pass until the system stabilizes on the desired image. The computer simulation results show that the proposed Korean Character recognition algorithm has high discrimination capability and noise immunity.

• ETRI Journal
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• v.44 no.3
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.567-571
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• 2009

This study was conducted in order to investigate the role of working memory in word recognition. As a preliminary step in tackling this topic, word frequency and working memory load were manipulated in a naming task. The results showed that word frequency is significantly involved with the working memory load. The effects of working memory load were greater in low-frequency word processing than in high-frequency word processing. These results indicat that working memory is involved more in the processing of low-frequency words. The implications for the teaching of children at the early reading acquisition stage are discussed in this paper.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.101-111
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• 2019

This study was to examine association between working memory and social cognitive ability, and the influence of story-use on social cognitive ability. To this end, this study measured working memory(via n-back), and randomly assigned 82 participants into three groups(5th level intentionality, 3rd-level intentionality, and exposition conditions), and then compared the accuracy of perspective taking and emotion recognition(RMET: Reading Minds in the Eyes Test) as social cognitive ability. The results suggested that perspective taking accuracy was significantly associated with working memory capacity, whereas emotion recognition accuracy was not. Contrary to the hypothesis, perspective taking in the 5th-level intentionality story group were significantly lower than those in the 3rd-level intentionality story group. Emotions recognition accuracy was not significantly different among the three groups. Overall, this study produced inconsistent results, which has been discussed in terms of theory and methods.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.17-27
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• 2005

In this study, it proposes the neural network system for character recognition and restoration. Proposes system composed by recognition part and restoration part. In the recognition part. it proposes model of effective pattern recognition to improve ART Neural Network's performance by restricting the unnecessary top-down frame generation and transition. Also the location feature extraction algorithm which applies with Hangeul's structural feature can apply the recognition. In the restoration part, it composes model of inputted image's restoration by Hopfield neural network. We make part experiments to check system's performance, respectively. As a result of experiment, we see improve of recognition rate and possibility of restoration.

• Korean Journal of Cognitive Science
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• v.19 no.2
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• pp.125-142
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• 2008

This study investigated the effects of space- and object-based attention on spatial and visual working memory, by measuring recognition of working memory on the spatial Stroop task including two modalities of attention resource. The similarity condition of stimulus arrangement between working memory task and spatial stroop task was manipulated in order to examine the effects of space-based attention on spatial rehearsal during working memory task, while Stroop rendition was manipulated in order to examine the effects of object-based attention on object rehearsal during working memory task. The results showed that in a condition that stimulus arrangement was highly similar for the spatial working memory task and the spatial Stroop task, recognition accuracy of the spatial working memory was high, but it was not significantly different with the Stroop conditions. In contrast, the recognition accuracy of visual working memory in the incongruent Stroop condition was lower than that in the congruent Stroop condition, but it was not significantly different with the similarity conditions (25% vs. 75%). The results indicated that selective attention has effects on working memory only when resource modality of working memory is the same as that of selective attention.

• Toxicological Research
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• v.24 no.3
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• pp.183-188
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• 2008

The behavioral phenotypes of out-bred ICR mice were compared with those of in-bred C57BL/6 and BALB/c mice. In particular, this study examined the locomotor activity and two forms of hippocampus-dependent learning paradigms, passive avoidance and object recognition memory. The basal open-field activity of the ICR strain was greater than that of the C57BL/6 and BALB/c strains. In the passive avoidance task, all the mice showed a significant increase in the cross-over latency when tested 24 hours after training. The strength of memory retention in the ICR mice was relatively weak and measurable, as indicated by the shorter cross-over latency than the C57BL/6 and BALB/c mice. In the object recognition memory test, all strains had a significant preference for the novel object during testing. The index for the preference of a novel object was lower for the ICR and BALB/c mice. Nevertheless, the variance and the standard deviation in these strains were comparable. Overall, these results confirm the strain differences on locomotor activity and hippocampus-dependent learning and memory in mice.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.67-73
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• 2020

In this paper, we present a CNN-based gesture recognition approach which reduces the memory burden of input data. Most of the neural network-based gesture recognition methods have used a sequence of frame images as input data, which cause a memory burden problem. We use a motion history image in order to define a meaningful gesture. The motion history image is a grayscale image into which the temporal motion information is collapsed by synthesizing silhouette images of a user during the period of one meaningful gesture. In this paper, we first summarize the previous traditional approaches and neural network-based approaches for gesture recognition. Then we explain the data preprocessing procedure for making the motion history image and the neural network architecture with three convolution layers for recognizing the meaningful gestures. In the experiments, we trained five types of gestures, namely those for charging power, shooting left, shooting right, kicking left, and kicking right. The accuracy of gesture recognition was measured by adjusting the number of filters in each layer in the proposed network. We use a grayscale image with 240 × 320 resolution which defines one meaningful gesture and achieved a gesture recognition accuracy of 98.24%.