• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.115.3-115
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• 2001

In this paper, discrete-time cellular neural networks are designed in order to function as associative memories by using Hebbian learning rule and non-cloning template. The proposed method has a very simple structure to design and to learn. Weights are updated by the connection between the neuron and its neighborhood. In the simulation, the proposed method is applied to the classification of a traffic sign pattern.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.45-50
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• 2020

A Language model with neural networks commonly trained with likelihood loss. Such that the model can learn the sequence of human text. State-of-the-art results achieved in various language generation tasks, e.g., text summarization, dialogue response generation, and text generation, by utilizing the language model's next token output probabilities. Monotonous and boring outputs are a well-known problem of this model, yet only a few solutions proposed to address this problem. Several decoding techniques proposed to suppress repetitive tokens. Unlikelihood training approached this problem by penalizing candidate tokens probabilities if the tokens already seen in previous steps. While the method successfully showed a less repetitive generated token, the method has a large memory consumption because of the training need a big vocabulary size. We effectively reduced memory footprint by encoding words as sequences of subword units. Finally, we report competitive results with token level unlikelihood training in several automatic evaluations compared to the previous work.

• Korean Journal of Biological Psychiatry
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• v.19 no.1
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• pp.1-8
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• 2012

The mechanism of psychotherapy is explained by the recent developments in neuroscience and neuroimaging. The purpose of this study is to understand the nature of psychotherapy and to discuss the future of psychotherapy improvement with the help of advances of the neurobiological findings in psychotherapy. For this study, we investigated a wide range of materials. We searched for various researches on psychotherapy, brain, and neurobiology. In addition to the conventional psychodynamic psychotherapy, we investigated research findings on cognitive behavioral therapy, interpersonal psychotherapy and eye movement desensitization and reprocessing (EMDR). Moreover, based on the actual experiences of treating patients, we speculated the neurobiological mechanisms of the process and results of psychotherapy. With the development of neuroscience, we are now able to understand the personal consciousness, unconsciousness and developmental process. Also subdividing the disease is made possible. Personalized treatment has become available, and we are able to predict the prognosis of patients. Our memories are composed by implicit memory and explicit memory. By psychotherapy, we can consciously remember explicit memory, and it becomes easier to explore implicit memory through free association. Through psychotherapy, we will also be able to learn the effect of acquired environment and experience. Psychotherapy is able to correct human behaviors by modifying the memories. Through the regulation of emotions, it becomes possible to modify the memories and correct the behaviors. In this process, doctor-patient relationship is the main factor which cause positive treatment effects. Furthermore imagination therapy or unconscious, non-verbal stimuli could bring about positive treatment effects. Now psychotherapy could be explained and studied by neuroscientific researches. In this sense, we could provide the direction of future advances in neuroscience by the neurobiological understanding of psychotherapy.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.2
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• pp.45-68
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• 2012

Archives have directly and indirectly served for memory. What is collected in archives, how it is presented to users, and how users understand and use the documents affects how a given society remembers its past. Some archival scholars see that how users interpret documents from their perspectives and by social interests may play a central role in constructing social memory because memories are often triggered by individual and social concerns of the present time. Therefore, knowing what causes users to seek for a certain materials, how they use those materials and why can offer a clue to learn how archives serve for social memory. In the Web space, the interaction between users and archives/archival materials can be easily observed. Beyond making access simple for users and promoting archival documents using Web technology, archives can serve the broader purpose of memory by skillfully exploiting the characteristics of Web 2.0 and digital cultures in a way to observe how users engage in and contribute to archival contents available on the Web. This study examines the discourses on memory in the archival context, and in particular, how archives can serve as platforms for memory within the new environment of Web 2.0 technologies. It surveys discussions on memory in relation to archives, history, and evidence, focusing on the user and use context as it is represented in the archival literature. This paper discusses how that technology provides features that allow us to see collective memory being constructed in the archives, and presents examples of how the Web 2.0 technology can structure the way users share their memories in building a larger narrative around the archive.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.1-9
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• 2003

In implicit learning tasks, human participants learn grammatical letter strings better than random letter strings. After learning grammatical letter strings, participants were able to judge the grammaticality of new letter strings that they have never seen before. EPAM (Elementary Perceiver and Memorizer) IV, a rote learner without any rule abstraction mechanism, was used to simulate these results. The results showed that EPAM IV with a within-item chunking function was able to learn grammatical letter strings better than random letter strings and discriminate grammatical letter strings from non-grammatical letter strings. The success of EPAM IV in simulating human performance strongly indicated that recognition memory based on chunking plays a critical role in implicit learning.

• Journal of Physiology & Pathology in Korean Medicine
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• v.17 no.1
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• pp.57-63
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• 2003

Background: Rehmanniae radix preparata is a medicinal herb widely used in Asian countries, to replenish Yin and Vital Essence through strengthen the kidney function. In Oriental Medicine the brain has physiological relationship with the Vital Essence. And central nerve system (CNS) is also refered to as 'Sea of the Vital Essence'. So if the Vital Essence is strong it will nourish the brain and memory will all be keen. The purpose of this study was to investigate the effect of 7 to 14 days treatment with Rehmanniae radix preparata (3.4 g /100 g, per os) on retention performance of rats in a passive avoidance situation. Methods: Male Sprague-Dawley rats were trained on a one-trial passive avoidance task using a two-way shuttle box by giving a foot shock. They were tested for retention of 6h, 24h, 72h, 168h, and 336h after training. Results: Rehmanniae radix preparata treated rats showed a increased performance in retention test as compared to saline at 6hr (P<0.05) and 24hr (P<0.05). Conclusion: These data suggest that Rehmanniae radix preparata treatment can improve memory in the rat.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.43-54
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• 2019

Currently, a majority of artificial intelligence is used to secure big data; however, it is concentrated in a few of major companies. Therefore, automatic data augmentation and efficient learning algorithms for small-scale data will become key elements in future artificial intelligence competitiveness. In addition, it is necessary to develop a technique to learn meanings, correlations, and time-related associations of complex modal knowledge similar to that in humans and expand and transfer semantic prediction/knowledge inference about unknown data. To this end, a neural memory model, which imitates how knowledge in the human brain is processed, needs to be developed to enable knowledge expansion through modality cooperative learning. Moreover, declarative and procedural knowledge in the memory model must also be self-developed through human interaction. In this paper, we reviewed this essential methodology and briefly described achievements that have been made so far.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• Cross-Cultural Studies
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• v.38
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• pp.351-384
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• 2015

This paper is concerned with the effectiveness of songs in the acquisition of formulaic sequences in the college EFL classroom. The existing research mentions the use of songs in terms of the power of their melodies (Fonseca-Mora, 2000), linguistic features in song lyrics (Abbott, 2002), and the emotional basis of memory (LI & Brand, 2009). Learners' opinions about the use of songs has been ignored, however. In this paper, seven subjects with English ability ranging from advanced (one) intermediate-high (three), intermediate-middle (two), and intermediate-low (one) studied five different pop songs. The results showed that they did not agree with the existing research findings. Rather, they were negative about using songs in the classroom. Their complaints were the burden of using too many hours to memorize lyrics, few language expressions to learn, and too much emphasis on expressions about love and feelings. Students at all levels expressed similar negativity about the use of songs. When their complaints were discussed during interviews, however, their attitude changed from negative to positive. The case study in this paper was on a small-scale but it is suggested that through further research the use of songs could be activated in the EFL classroom. Considering college language learners disregard most existing EFL materials, it appears to be worthwhile to continue further with this kind of research.