• Nutrition Research and Practice
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• 제12권3호
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• pp.191-198
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• 2018

BACKGROUD/OBJECTIVES: Neuroinflammation plays critical role in neurodegenerative disorders, such as Alzheimer's disease (AD). We investigated the effect of three licorice varieties, Glycyrhiza uralensis, G. glabra, and Shinwongam (SW) on a mouse model of inflammation-induced memory and cognitive deficit. MATERIALS/METHODS: C57BL/6 mice were injected with lipopolysaccharide (LPS; 2.5 mg/kg, intraperitoneally) and orally administrated G. uralensis, G. glabra, and SW extract (150 mg/kg/day). SW, a new species of licorice in Korea, was combined with G. uralensis and G. glabra. Behavioral tests, including the T-maze, novel object recognition and Morris water maze, were carried out to assess learning and memory. In addition, the expressions of inflammation-related proteins in brain tissue were measured by western blotting. RESULTS: There was a significant decrease in spatial and objective recognition memory in LPS-induced cognitive impairment group, as measured by the T-maze and novel object recognition test; however, the administration of licorice ameliorated these deficits. In addition, licorice-treated groups exhibited improved learning and memory ability in the Morris water maze. Furthermore, LPS-injected mice had up-regulated pro-inflammatory proteins, such as inducible nitric oxide synthase (iNOS), cyclooxygenase-2, interleukin-6, via activation of toll like receptor 4 (TLR4) and nuclear factor-kappa B ($NF{\kappa}B$) pathways in the brain. However, these were attenuated by following administration of the three licorice varieties. Interestingly, the SW-administered group showed greater inhibition of iNOS and TLR4 when compared with the other licorice varieties. Furthermore, there was a significant increase in the expression of brain-derived neurotrophic factor (BDNF) in the brain of LPS-induced cognitively impaired mice that were administered licorice, with the greatest effect following SW treatment. CONCLUSIONS: The three licorice varieties ameliorated the inflammation-induced cognitive dysfunction by down-regulating inflammatory proteins and up-regulating BDNF. These results suggest that licorice, in particular SW, could be potential therapeutic agents against cognitive impairment.

• 디지털융복합연구
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• 제15권4호
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• pp.413-419
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• 2017

본 연구는 영화 미디어의 프레즌스 관련 실증 연구로써, 빠른 속도로 보급된 스마트폰의 무분별한 사용으로 인한 영화 관객들의 주목도 저하가 프레즌스 경험, 감동의 정도, 기억의 정도에 어떤 영향을 미치는지 실험을 통해 살펴본 연구이다. 본 연구를 위해 대학생 피험자 83명을 대상으로 주목도를 조작하지 않은 집단과 스마트폰의 의도적인 사용으로 주목도를 조작한 집단 등 2 집단에게 동일한 영화를 감상하게 한 후, 자기 보고식 설문조사에 의한 측정 방법으로 프레즌스 경험의 횟수, 감동의 정도, 기억의 정도 등을 측정, 분석하였다. 분석 결과, 주목도를 조작하지 않은 피험자들이 주목도를 낮게 조작한 피험자들 보다 더 많은 프레즌스 경험과 더 큰 감동의 정도를 보여주었다. 그러나 이들 두 집단 간의 재인기억에는 유의미한 차이가 발견되지 않았다. 그동안의 프레즌스 관련연구는 HDTV, 3D 영상, 스테레오 음향 등이 프레즌스 경험과, 관객의 각성과 즐거움에 미치는 효과를 주로 다루었으며, 영화 관객의 주목도와 프레즌스, 감동, 기억 간의 관계를 탐구한 연구는 찾아보기 어렵다. 따라서 본 연구에서 영화 관객의 주목도가 프레즌스 경험과 감동 정도에 영향을 준다는 사실을 밝혀냈다는 점에서 연구의 의의를 찾을 수 있을 것이다.

• Journal of Electrical Engineering and Technology
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• 제4권3호
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• pp.330-336
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• 2009

This paper proposes an on-line voltage management and control solution for a distribution system which can improve the efficiency and accuracy of existing off-line work by collecting customer voltage on-line as well as the voltage compensation capability of the existing ULTC (Under Load Tap Changer) operation and control strategy by controlling the ULTC tap based on pattern clustering and recognition. The proposed solution consists of an ADVMD (Advanced Digital Voltage Management Device), a VMS (Voltage Management Solution) and an OLDUC (On-Line Digital ULTC Controller). An on-line voltage management emulator based on multi-thread programming and the shared memory method is developed to emulate on-line voltage management and digital ULTC control methodology based on the on-line collection of the customer's voltage. In addition, using this emulator, the effectiveness of the proposed pattern clustering and recognition based ULTC control strategy is proven for the worst voltage environments for three days.

• 음성과학
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• 제14권4호
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• pp.157-167
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• 2007

In this paper, we implemented a single-chip speech recognizer using the TMS320C2000 DSPs. For this implementation, we had developed very small-sized speaker-dependent recognition engine based on dynamic time warping, which is especially suited for embedded systems where the system resources are severely limited. We carried out some optimizations including speed optimization by programming time-critical functions in assembly language, and code size optimization and effective memory allocation. For the TMS320F2801 DSP which has 12Kbyte SRAM and 32Kbyte flash ROM, the recognizer developed can recognize 10 commands. For the TMS320F2808 DSP which has 36Kbyte SRAM and 128Kbyte flash ROM, it has additional capability of outputting the speech sound corresponding to the recognition result. The speech sounds for response, which are captured when the user trains commands, are encoded using ADPCM and saved on flash ROM. The single-chip recognizer needs few parts except for a DSP itself and an OP amp for amplifying microphone output and anti-aliasing. Therefore, this recognizer may play a similar role to dedicated speech recognition chips.

• 전자공학회논문지C
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• 제34C권9호
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• pp.77-83
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• 1997

Based on the curve segment grouping, an efficient recognition of round objects form partially occuluded round boundaries is proposed. Curve segments are extracted from an image using a criterion based on the intra-segment curvature and local contrast. During the curve segment extraction the boundaries of pratially occluding and occuluded objects are segmented to different curve segments. The extracted segments of constant intra-segment curvature are grouped to different curve segments. The extracted segments of constant intra-segment curvature are grouped nto a round boundary by the proposed grouping algorithm using inter-segment curvature which gives the relatinships among the curve segments of the same round boundary. The 1st and the 2nd order moments are used for the parameter estimation of the best fitted ellipse with round boundary, and then recognition is perfomed based on the estimated parameters. The proposed scheme processes in segment unit and is more efficient in computational complexity and memory requirements those that of the conventional scheme which processed in pixel units. Experimental results show that the proposed technique is very efficient in recognizing the round object sfrom the real images with apples and pumpkins.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 춘계 학술대회 발표논문집
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• pp.87-90
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• 2005

Today's state-of the-art speech recognition systems typically use continuous distribution hidden Markov models with the mixtures of Gaussian distributions. To obtain higher recognition accuracy, the hidden Markov models typically require huge number of Gaussian distributions. Such speech recognition systems have problems that they require too much memory to run, and are too slow for large applications. Many approaches are proposed for the design of compact acoustic models. One of those models is subspace distribution clustering hidden Markov model. Subspace distribution clustering hidden Markov model can represent original full-space distributions as some combinations of a small number of subspace distribution codebooks. Therefore, how to make the codebook is an important issue in this approach. In this paper, we report some experimental results on various quantization methods to make more accurate models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.328-330
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• 2007

Emotion is getting one of the important elements of the intelligent service robots. Emotional communication can make more comfortable relation between humans and robots. We developed emotional head robot system using 3D character. We designed emotional engine for making emotion of the robot. The results of face recognition and hand recognition is used for the input data of emotional engine. 3D character expresses nine emotions and speaks about own emotional status. The head robot has memory of a degree of attraction. It can be chaIU!ed by input data. We tested the head robot and conform its functions.

• 한국통신학회논문지
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• 제15권9호
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• pp.738-744
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• 1990

본 논문은 음성신호의 실시간 저장법과 기존 표본 데이터에서 개선된 표본 데이터 방법을 제안하여, 신경회로망의 역전파 학습 알고리즘을 이용한 고립 단어 음성인식 시스템에 대하여 연구하였다. 각 층의 노드 수 변화에 의한 기존 표본 데이터방식과 새로운 표본 데이터 방식에서의 인식률과 에러율 변화를 비교하였다. 본 연구 결과, 인식률은 95.1%를 얻었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.29-30
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• 2007

In this paper, we propose Group Separation(GS) algorithm for RFID tag recognition. In GS algorithm, reader calculates tag ID by collision point, stores memory with the collision table. And reader classifies according to total number of tag ID's 1, requests each group. If tag comes into collision with the other tag, reader searches tag ID in collision table. As a result, we observes that transmitted data rate, the recognition time is decreased.

• ETRI Journal
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• 제46권2호
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• pp.205-217
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• 2024

We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short-term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine-tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root-mean-square error of only 2.12%.