• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2007년도 하계학술대회 논문집 Vol.8
• /
• pp.171-171
• /
• 2007

Dysprosium-doped bismuth titanate (BDT) thin films were successfully deposited on Pt(111)/Ti/$SiO_2$/Si(100) substrates by liquid delivery MOCVD process and their structural and ferroelectric properties were characterized. Fabricated BDT thin films were found to be random orientations, which were confirmed by X-ray diffraction experiment and scanning electron microscope analysis. The crystallinity of the BDT films was improved and the average grain size increased as the crystallization temperature increased from 600 to $720^{\circ}C$ at an interval of $40^{\circ}C$. The BDT thin film annealed at $720^{\circ}C$ showed a large remanent polarization (2Pr) of $52.27\;{\mu}C/cm^2$ at an applied voltage of 5V. The BDT thin film exhibits a good fatigue resistance up to $1.0{\times}10^{11}$ switching cycles at a frequency of 1 MHz with applied pulse of ${\pm}5\;V$. These results indicate that the randomly oriented BDT thin film is a promising candidate among ferroelectric materials useti비 in lead-free nonvolatile ferroelectric random access memory applications.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2012년도 추계학술발표대회
• /
• pp.750-751
• /
• 2012

스마트폰의 급속한 확산과 함께 스마트폰 애플리케이션 시장이 빠르게 성장하고 있다. 이러한 성장세에 따라 많은 애플리케이션 개발자들이 생겨났으며, 다양한 콘텐츠와 수많은 애플리케이션이 개발되어지고 있다. 여기서 우리는 모바일 기기들의 제한적인 요소를 간과해서는 안 된다. 제한적인 모바일기기에서 유저가 만족할 만할 애플리케이션을 개발하기 위해서는 효율적인 자원 활용과 함께 효율적인 프로그래밍을 해야 할 필요가 있다. 본 논문은 안드로이드 NDK 및 SDK를 기반으로 Native C와 Java를 이용해 애플리케이션을 설계하고, 각 애플리케이션간의 알고리즘 수행속도, 프로세서 점유율측면에서 성능측정 실험을 수행했다. 실험 결과를 통해 보다 우수한 성능의 안드로이드 애플리케이션 개발 방법에 관해 연구했다. 성능측정 항목으로는 JNI delay, Integer, Floating point, Memory access algorithm, String이며, 실험은 삼성 갤럭시 S1에서 수행하였다.

• Journal of Ginseng Research
• /
• 제45권5호
• /
• pp.599-609
• /
• 2021

Ginseng has long been considered as an herbal medicine. Recent data suggest that ginseng has antiinflammatory properties and can improve learning- and memory-related function in the central nervous system (CNS) following the development of CNS neuroinflammatory diseases such as Alzheimer's disease, cerebral ischemia, and other neurological disorders. In this review, we discuss the role of ginseng in the neurovascular unit, which is composed of endothelial cells surrounded by astrocytes, pericytes, microglia, neural stem cells, oligodendrocytes, and neurons, especially their blood-brain barrier maintenance, anti-inflammatory effects and regenerative functions. In addition, cell-cell communication enhanced by ginseng may be attributed to regeneration via induction of neurogenesis and angiogenesis in CNS diseases. Thus, ginseng may have therapeutic potential to exert cognitive improvement in neuroinflammatory diseases such as stroke, traumatic brain injury, multiple sclerosis, Parkinson's disease, and Alzheimer's disease.

• 전자통신동향분석
• /
• 제33권6호
• /
• pp.129-138
• /
• 2018

As recent applications are requiring more CPUs for their performance, manycore systems have evolved. Since existing operating systems do not provide performance scalability in manycore systems, Azalea, a multi-kernel based system, has been developed for supporting performance scalability. Unikernel is a new operating system technology starting with the concept of a library OS. Applying unikernel to Azalea enables an improvement in performance. In this paper, we first analyze the current technology trends of unikernel, and then discuss the applications and effects of unikernel to Azalea. Azalea-unikernel was built in a single image consisting of libOS, runtime libraries, and an application, and executed with the desired number of cores and memory size in bare-metal. In particular, it supports source and binary compatibility such that existing linux binaries can be rebuilt and executed in Azalea-unikernel, and already built binaries can be run immediately without modification with a better performance. It not only achieves a performance enhancement, it is also a more secure OS for manycore systems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권12호
• /
• pp.4664-4681
• /
• 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%.

• 인터넷정보학회논문지
• /
• 제22권3호
• /
• pp.9-16
• /
• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• 중환자간호학회지
• /
• 제14권1호
• /
• pp.50-62
• /
• 2021

Purpose : This study examined the effects of an integrated management program on physical function, cognitive function, and depression in patients with subacute stroke. Methods : A nonequivalent control group design was adopted. The participants were assigned to either the experimental group (n=20) or control group (n=23). The experimental group received an 8-week integrated management program and standard rehabilitation service (i.e., physical therapy and occupational therapy), while the control group received the standard rehabilitation service only. Physical function was measured as gait speed and balance ability using the Berg Balance Scale (BBS). Cognitive function was measured with neuro-behavioral cognitive status examination (NCSE), and depression was measured using the Beck Depression Inventory-II (BDI-II). Repeated measure ANOVA was used to determine changes in physical function, cognitive function, and depression over 8-weeks. Results : The interaction between group and time was significant, indicating that the experimental group showed improvement in gait speed, balance ability, cognitive function (linguistic ability, linguistic memory, reasoning), and a decrease in depression compared to the control group. Conclusion : These results indicate that the integrated management program developed herein was beneficial in restoring physical function, cognitive function, and depression in subacute stroke patients.

• 한국게임학회 논문지
• /
• 제20권6호
• /
• pp.13-22
• /
• 2020

RIO는 윈도우의 최신 네트워크 API로 낮은 부하와 지연을 통해 높은 IO 성능을 발휘하도록 설계되었으며. 고성능의 네트워크 IO를 요구하는 대규모 동시접속(MMO) 게임 서버에 적합할 것으로 기대된다. 또한 HTM은 기존의 멀티스레드 동기화 방식보다 생산성과 성능이 우수하여 MMO 게임 서버에 적용 시 성능향상이 예상된다. 본 논문에서는 MMO 게임 서버에 RIO를 적용함과 동시에 RIO의 성능을 최대한 끌어내도록 구조를 개선하고, 기존의 시야 처리 알고리즘을 HTM 방식으로 변경하여 서버의 성능을 향상시켰다. 결과적으로 동시 접속자 수를 19%가량 증가시켰으며, 벤치마킹 프로그램을 사용하여 이를 검증하였다.

• 대기
• /
• 제31권1호
• /
• pp.73-83
• /
• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• 한국임상보건과학회지
• /
• 제10권2호
• /
• pp.1600-1606
• /
• 2022

Objective: This study was to compares and analyzes programs applied to improve cognitive function in patients with mild cognitive impairment and early dementia in the community to find out their effectiveness. Methods: In this study, 12 papers were finalized by searching for "elderly", "cognitive", "community", and "program" using the database of the Research Information System (RISS), National Assembly Library, and Korean Studies Information (KISS). Results: Programs for cognitive function were in the order of cognitive stimulation program, arts and crafts, and exercise program. In the program, rather than applying the cognitive stimulation program alone, the program was operated by combining leisure or exercise, music, art, and handicraft. The time was shown to be 30 minutes. The most frequently used evaluation tool was MMSE, followed by GDS and BBS. By cognitive domain, cognitive stimulation program and memory, satisfaction in psychology, and balance ability in exercise were evaluated the most. In the cognitive area, various cognitive stimulation areas were included, and in the exercise area, basic exercise, muscle strength exercise, joint exercise, and balance exercise were applied. Conclusion: Therefore, developing a program to improve cognitive function for mild cognitive impairment, it will be possible to prepare guidelines to establish and development.