• Nutrition Research and Practice
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• v.9 no.5
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• pp.480-488
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• 2015

BACKGROUND/OBJECTIVES: Alzheimer's disease (AD) is characterized by deficits in memory and cognitive functions. The accumulation of amyloid beta peptide ($A{\beta}$) and oxidative stress in the brain are the most common causes of AD. MATERIALS/METHODS: Caffeic acid (CA) is an active phenolic compound that has a variety of pharmacological actions. We studied the protective abilities of CA in an $A{\beta}_{25-35}$-injected AD mouse model. CA was administered at an oral dose of 10 or 50 mg/kg/day for 2 weeks. Behavioral tests including T-maze, object recognition, and Morris water maze were carried out to assess cognitive abilities. In addition, lipid peroxidation and nitric oxide (NO) production in the brain were measured to investigate the protective effect of CA in oxidative stress. RESULTS: In the T-maze and object recognition tests, novel route awareness and novel object recognition were improved by oral administration of CA compared with the $A{\beta}_{25-35}$-injected control group. These results indicate that administration of CA improved spatial cognitive and memory functions. The Morris water maze test showed that memory function was enhanced by administration of CA. In addition, CA inhibited lipid peroxidation and NO formation in the liver, kidney, and brain compared with the $A{\beta}_{25-35}$-injected control group. In particular, CA 50 mg/kg/day showed the stronger protective effect from cognitive impairment than CA 10 mg/kg/day. CONCLUSIONS: The present results suggest that CA improves $A{\beta}_{25-35}$-induced memory deficits and cognitive impairment through inhibition of lipid peroxidation and NO production.

• Korean Journal of Pharmacognosy
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• v.51 no.4
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• pp.310-316
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• 2020

Kyung-Ok-Go (KOK) is a traditional prescription used for debilitating natural aging and post-illness debilitation. KOK has been used in a variety of ways because it strengthens immunity, prevents illness, and helps recovery in case of illness. In particular, recent research has revealed that KOK helps improve memory and cognition. Therefore, in this study, we investigated whether KOK was effective in improving memory decline and depression-state observed during menopause. In the present study, we employed ovariectomized mouse as an animal model for measuring menopausal syndrome. The administration of KOK for 8 weeks, the object recognition memory and working memory were improved in novel object recognition test and Y-maze test. And in the forced swimming test, the immobility time were decreased. Additionally, the expression level of mature brain derived neurotropic factor (mBDNF) was increased by KOK administration in ovariectomized mouse hippocampus. These results suggested that KOK could improve cognitive decline and depression during menopausal period, and it might be come from enhancing expression level of mBDNF in hippocampus.

• Journal of Ginseng Research
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• v.33 no.2
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• pp.132-138
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• 2009

Previous studies suggest that even low-dose irradiation can lead to progressive cognitive decline and memory deficits, which implicates, in part, hippocampal dysfunction in both humans and experimental animals. In this study, whether red ginseng (RG) could attenuate memory impairment was investigated through a passive-avoidance and object recognition memory test, as well as the suppression of hippocampal neurogenesis, using the TUNEL assay and immunohistochemical detection with markers of neurogenesis (Ki-67 and doublecortin (DCX)) in adult mice treated with a relatively low-dose exposure to gamma radiation (0.5 or 2.0 Gy). RG was administered intraperitonially at a dosage of 50 mg/kg of body weight, at 36 and 12 h pre-irradiation and at 30 minutes post-irradiation, or orally at a dosage of 250 mg! kg of body weight/day for seven days before autopsy. In the passive-avoidance and object recognition memory test, the mice that were trained for one day after acute irradiation (2 Gy) showed significant memory deficits compared with the sham controls. The number of TUNEL-positive apoptotic nuclei in the dentate gyrus (DG) was increased 12 h after irradiation. In addition, the number of Ki-67- and DCX-positive cells was significantly decreased. RG treatment prior to irradiation attenuated the memory defect and blocked apoptotic death as well as a decrease in the Ki-67- and DCX-positive cells. RG may attenuate memory defect in a relatively low-dose exposure to radiation in adult mice, possibly by inhibiting the detrimental effect of irradiation on hippocampal neurogenesis.

• 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.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.149-154
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• 2004

In this research, we selected the speech recognition to implement the electric wheelchair system as a method to control it by only using the speech and used DTW (Dynamic Time Warping), which is speaker-dependent and has a relatively high recognition rate among the speech recognitions. However, it has to have small memory and fast process speed performance under consideration of real-time. Thus, we introduced VQ (Vector Quantization) which is widely used as a compression algorithm of speaker-independent recognition, to secure fast recognition and small memory. However, we found that the recognition rate decreased after using VQ. To improve the recognition rate, we applied ART2 (Adaptive Reason Theory 2) algorithm as a post-process algorithm to obtain about 5% recognition rate improvement. To utilize ART2, we have to apply an error range. In case that the subtraction of the first distance from the second distance for each distance obtained to apply DTW is 20 or more, the error range is applied. Likewise, ART2 was applied and we could obtain fast process and high recognition rate. Moreover, since this system is a moving object, the system should be implemented as an embedded one. Thus, we selected TMS320C32 chip, which can process significantly many calculations relatively fast, to implement the embedded system. Considering that the memory is speech, we used 128kbyte-RAM and 64kbyte ROM to save large amount of data. In case of speech input, we used 16-bit stereo audio codec, securing relatively accurate data through high resolution capacity.

• Laboraroty Animal Research
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• v.34 no.4
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• pp.239-247
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• 2018

Bacopa monnieri is a medicinal plant with a long history of use in Ayurveda, especially in the treatment of poor memory and cognitive deficits. In the present study, we hypothesized that Bacopa monnieri extract (BME) can improve memory via increased cell proliferation and neuroblast differentiation in the dentate gyrus. BME was administered to 7-week-old mice once a day for 4 weeks and a novel object recognition memory test was performed. Thereafter, the mice were euthanized followed by immunohistochemistry analysis for Ki67, doublecortin (DCX), and phosphorylated cAMP response element-binding protein (CREB), and western blot analysis of brain-derived neurotrophic factor (BDNF). BME-treated mice showed moderate increases in the exploration of new objects when compared with that of familiar objects, leading to a significant higher discrimination index compared with vehicle-treated mice. Ki67 and DCX immunohistochemistry showed a facilitation of cell proliferation and neuroblast differentiation following the administration of BME in the dentate gyrus. In addition, administration of BME significantly elevated the BDNF protein expression in the hippocampal dentate gyrus, and increased CREB phosphorylation in the dentate gyrus. These data suggest that BME improves novel object recognition by increasing the cell proliferation and neuroblast differentiation in the dentate gyrus, and this may be closely related to elevated levels of BDNF and CREB phosphorylation in the dentate gyrus.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.77-80
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• 2013

SURF algorithm is an algorithm to extract feature points and to generate descriptors from input images. It is robust to change of environment such as scale, rotation, illumination and view points. Because of these features, it is used for many image processing applications such as object recognition, constructing panorama pictures and 3D image restoration. But there is disadvantage for real time operation because many recognition algorithms such as SURF algorithm requires a lot of calculations. In this paper, we propose a design of feature extractor and descriptor generator based on SURF for high memory efficiency. The proposed design reduced a memory access and memory usage to operate in real time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.215-220
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• 2002

This paper proposes a method to apply the Backpropagation(BP) algorithm of MVL(Multi-Valued Logic) Neural Network to pattern recognition. It extracts the property of an object density about an original pattern necessary for pattern processing and makes the property of the object density mapped to MVL. In addition, because it team the pattern by using multiple valued logic, it can reduce time f3r pattern and space fer memory to a minimum. There is, however, a demerit that existed MVL cannot adapt the change of circumstance. Through changing input into MVL function, not direct input of an existed Multiple pattern, and making it each variable loam by neural network after calculating each variable into liter function. Error has been reduced and convergence speed has become fast.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.2
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• pp.206-216
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• 1986

In this paper, the moving distance and velocity of a single moving object are measured by sampling three frames in a two-dimensional line sequence image. The brightness of each frame is analyzed, and the bit data of their pixel are rearranged so that the difference image may be extracted. The parameters for recognition of the object are the gray level of the object, the number of vertex points and the distance between the vertex points. The moving distance obtained from the coordinate which is constructed by the bit processing of the data in the memory map of a microcomputer, and the moving velocity is obtained from the moving distance and the time interval between the first and second sampled frames.