• Journal of Oriental Neuropsychiatry
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• v.22 no.3
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• pp.1-11
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• 2011

Objectives : As the number of patient with dementia increases, interest in mild cognitive impairment (MCI), which is a pre-dementia stage, has been expanding. In this study, we investigated the effects from selected clinical research articles to evaluate the effectiveness of non-pharmacological interventions. Methods : We searched MCI related articles on MEDLINE and the Web of Science using keywords related to MCI. We selected 26 articles, and 13 evaluated efficiency using the Jadad score. Results : Physical exercise and cognitive remediation techniques were effective for improving MCI. Transcutaneous electrical nerve stimulation, taichi, and music belonged to "perhaps" effectiveness group. Many of the 13 articles that evaluated MCI using the Jadad score evaluated them as "good" or "poor", and only three articles evaluated MCI as "excellent". Conclusions : The present evidence suggests that cognitive remediation techniques to improve memory and physical exercise were effective for people with MCI. However, further studies are needed to identify the physical exercise effects.

• Journal of Korea Multimedia Society
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• v.5 no.1
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• pp.38-46
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• 2002

In this paper, we present an adaptive contrast controller for improving the Quality of motion-picture in the video signals on the display. Using a median of image signals, we can improve the contrast according to the middle brightness, adaptively. In addition, the proposed method is useful for real-time image processing and can be composed of simpler hardware structure than other conventional methods because it does not require field and frame memory for computed data. The proposed method can be applied for video signals as well as the still image, while existing methods are confined to only the static image Also, we designed the algorithm through the VHDL, and implemented it through the FPGA. From the testing results, we see that the proposed method can effectively improve the image contrast.

• Cross-Cultural Studies
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• v.37
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• pp.267-286
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• 2014

The purpose of this study is to improve the writing skills through activities to read and summarize the internet children newspaper article. The subjects of study are the college students of A2-B1 level in the French writing classes. The range of study was as follows: - As the previous activity of writing, activities of teaching and learning of vocabularies to comprehend the internet children newspaper article. - learn about the rules of summary - writing the summary The children's newspaper used in this study has the advantage that can increase the learning motivation of learners as having a topicality by itself and a level of easy language. The summary activities can be called a comprehensive activities of teaching and learning that combine the critical reading ability that can distinguish important information and secondary one with the creative writing ablility that can reconstruct one's own style from the selected content. In addition, the summary assists the understanding of a text and is a help to its memory. It is the strategy of reading comprehension and also is simultaneously the strategy of writing that can write with one's own vocabulary by newly structuring the text. The results of this study will provide a vitality for the education environment and field of study of French language that have been neglected the writing ability. Moreover it will be the motivation to propose a way of a balanced French language communication to our French language learners weighted on oral communication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4522-4536
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• 2020

Nowadays, cloud is the fastest emerging technology in the IT industry. We can store and retrieve data from the cloud. The most frequently occurring problems in the cloud are security and privacy preservation of data. For improving its security, secret information must be protected from various illegal accesses. Numerous traditional cryptography algorithms have been used to increase the privacy in preserving cloud data. Still, there are some problems in privacy protection because of its reduced security. Thus, this article proposes an ElGamal Elliptic Curve (EGEC) Homomorphic encryption scheme for safeguarding the confidentiality of data stored in a cloud. The Users who hold a data can encipher the input data using the proposed EGEC encryption scheme. The homomorphic operations are computed on encrypted data. Whenever user sends data access permission requests to the cloud data storage. The Cloud Service Provider (CSP) validates the user access policy and provides the encrypted data to the user. ElGamal Elliptic Curve (EGEC) decryption was used to generate an original input data. The proposed EGEC homomorphic encryption scheme can be tested using different performance metrics such as execution time, encryption time, decryption time, memory usage, encryption throughput, and decryption throughput. However, efficacy of the ElGamal Elliptic Curve (EGEC) Homomorphic Encryption approach is explained by the comparison study of conventional approaches.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1552-1559
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• 2021

Convolution neural networks (CNNs) show notable performance in image processing and are used as representative core models. CNNs extract and learn features from large amounts of train dataset. In general, it has a structure in which a convolution layer and a fully connected layer are stacked. The core of CNN is the convolution layer. The size of the kernel used for feature extraction and the number that affect the depth of the feature map determine the amount of weight parameters of the CNN that can be learned. These parameters are the main causes of increasing the computational complexity and memory usage of the entire neural network. The most computationally expensive components in CNNs are fully connected and spatial convolution computations. In this paper, we propose a Fourier Convolution Neural Network that performs the operation of the convolution layer in the Fourier domain. We work on modifying and improving the amount of computation by applying the fast fourier transform method. Using the MNIST dataset, the performance was similar to that of the general CNN in terms of accuracy. In terms of operation speed, 7.2% faster operation speed was achieved. An average of 19% faster speed was achieved in experiments using 1024x1024 images and various sizes of kernels.

• ETRI Journal
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• v.44 no.2
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• pp.300-312
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• 2022

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.415-415
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• 2023

홍수는 일반적으로 많은 피해와 인명 손실을 초래하는 자연재해 중 하나로, 홍수위 예측은 이를 방지하고 대처하는 데 중요한 역할을 한다. 최근 기계학습 기술을 이용하여 홍수위 예측 모델을 개발하고자 하는 연구가 많이 진행되고 있다. 특히, LSTM(long short-term memory) 모형은 시계열 예측에 대해 검증된 모형으로 홍수위 예측 연구에도 활발하게 적용되고 있다. 하지만 기계학습 모델의 학습 성능은 하이퍼파라미터의 값에 영향을 크게 받을 수 있으며, 특히 집중호우로 인해 수위가 급변하는 경우에는 과거 시계열 자료에 영향을 받는 LSTM 모형의 예측 성능이 오히려 낮게 나타날 수 있다. 따라서 본 연구에서는 홍수위 예측시 LSTM 모형의 예측 성능을 향상시킬 수 있는 세부 하이퍼파라미터 값을 분석하여 최적의 하이퍼파라미터 조합을 제안하고자 한다. 이를 위해 하이퍼파라미터 조정을 위한 자동화 도구인 W&B(Weights&Bias)의 Sweep 기능을 적용하고자 한다. 본 연구를 통해 LSTM 모형을 적용한 홍수위 예측의 정확도를 향상시키는 데에 기여할 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.226-226
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• 2023

하천유량, 댐유입량 등을 예측하기 위해 다양한 Long Short-Term Memory (LSTM) 방법들이 활발하게 적용 및 개발되고 있다. 최근 연구들은 s2s (sequence-to-sequence), Attention 기법 등을 통해 LSTM의 성능을 개선할 수 있음을 제시하고 있다. 이에 따라 본 연구에서는 LSTM-s2s와 LSTM-s2s에 attention까지 첨가한 모델을 구축하고, 시간 단위 자료를 사용하여 유입량 예측을 수행하여, 이의 실제 댐 운영에 모델들의 활용 가능성을 확인하고자 하였다. 소양강댐 유역을 대상으로 2013년부터 2020년까지의 유입량 시자료와 종관기상관측기온 및 강수량 데이터를 학습, 검증, 평가로 나누어 훈련한 후, 모델의 성능 평가를 진행하였다. 최적 시퀀스 길이를 결정하기 위해 R², RRMSE, CC, NSE, 그리고 PBIAS을 사용하였다. 분석 결과, LSTM-s2s 모델보다 attention까지 첨가한 모델이 전반적으로 성능이 우수했으며, attention 첨가 모델이 첨두값 예측에서도 높은 정확도를 보였다. 두 모델 모두 첨두값 발생 동안 유량 패턴을 잘 반영하였지만 세밀한 시간 단위 변화량 패턴 모의에는 한계가 있었다. 시간 단위 예측의 한계에도 불구하고, LSTM-s2s에 attention까지 추가한 모델은 향후 댐유입량 예측에 활용될 수 있을 것으로 판단한다.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.159-166
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• 2023

Due to the aging workforce in the construction industry in South Korea, the accident rate has been increasing. The cognitive abilities of older workers are closely related to both safety incidents and labor productivity. Therefore, there is a need to improve cognitive abilities through personalized training based on cognitive assessment results, using cognitive training content, in order to enable safe performance in labor-intensive environments. The provided cognitive training content includes concentration, memory, oreintation, attention, and executive functions. Difficulty levels were applied to each content to enhance user engagement and interest. To stimulate interest and encourage active participation of the participants, the difficulty level was automatically adjusted based on feedback from the MMSE-DS results and content measurement data. Based on the accumulated data, individual training scenarios have been set differently to intensively improve insufficient cognitive skills, and cognitive training programs will be developed to reduce safety accidents at construction sites through measured data and research. Through such simple cognitive training, it is expected that the reduction of accidents in the aging construction workforce can lead to a decrease in the social costs associated with prolonged construction periods caused by accidents.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.860-863
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• 2008

플래시 메모리는 우리 생활에 널리 사용되고 있는 휴대용 저장장치 중의 하나이다. 빠른 입출력 속도와 저전력, 무소음, 작은 크기 등의 장점을 가지나 덮어쓰기가 불가능하고 읽기/쓰기의 속도에 비해 소거 연산의 속도가 매우 느리다는 단점이 있다. 이를 보완하기 위해, 호스트와 플래시 메모리 사이에 버퍼 캐시를 두어 사용하고 있으며, 버퍼 캐시에 사용되는 교체 정책에 따라 플래시 메모리 장치의 성능이 크게 영향을 받는다. 본 논문에서는 블록 단위의 LRU 기법의 단점을 개선한 HPLRU 기법을 제안한다. HPLRU 기법은 최근에 자주 참조되었던 페이지인 핫 페이지 들을 모아 리스트를 만들어 관리하고, 이를 통해 페이지 적중률을 향상시키고 다른 페이지들로 인해 핫 페이지들이 소거되는 현상을 개선하였다. 이 알고리즘은 임의 데이터 패턴에 좋은 성능을 보이며 쓰기 발생 횟수를 많이 감소시키는 결과를 보였다.