• Wind and Structures
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• 제36권6호
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• pp.379-392
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• 2023

Structural health monitoring is used to ensure the well-being of civil structures by detecting damage and estimating deterioration. Wind flow applies external loads to high-rise buildings, with the horizontal force component of the wind causing structural displacements in high-rise buildings. This study proposes a deep learning-based predictive model for measuring lateral displacement response in high-rise buildings. The proposed long short-term memory model functions as a sequence generator to generate displacements on building floors depending on the displacement statistics collected on the top floor. The model was trained with wind-induced displacement data for the top floor of a high-rise building as input. The outcomes demonstrate that the model can forecast wind-induced displacement on the remaining floors of a building. Further, displacement was predicted for each floor of the high-rise buildings at wind flow angles of 0° and 45°. The proposed model accurately predicted a high-rise building model's story drift and lateral displacement. The outcomes of this proposed work are anticipated to serve as a guide for assessing the overall lateral displacement of high-rise buildings.

• 동의생리병리학회지
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• 제25권1호
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• pp.37-47
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• 2011

This study was aimed to investigate the memory enhancing effect of Jeongjihwan against scopolamine-induced amnesia in C57BL/6 mice. To determine the effect of Jeongjihwan on the memory and cognitive function, we have injected scopolamine (1 mg/kg, i.p.) into C57BL/6 mice 30 min before beginning of behavior tests. We have conducted Y-maze, Morris water-maze, passive avoidance and fear conditioning tests to compare learning and memory functions. Scopolamine-induced behavior changes of memory impairment were significantly restored by oral administration of Jeongjihwan (100 or 200 mg/kg/day). To elucidate the molecular mechanism underlying the memory enhancing effect of Jeongjihwan, we have examined the antioxidant defense system and neurotrophic factors. Jeongjihwan treatment attenuated intracellular accumulation of reactive oxygen species and up-regulated mRNA and protein expression of antioxidant enzymes as assessed by RT-PCR and western blot analysis, respectively. Jeongjihwan also increased protein levels of brain-derived neurotrophic factor (BDNF) compared with those in the scopolamine-treated group. Furthermore, as an upstream regulator, the activation of cAMP response element-binding protein (CREB) via phosphorylation was assessed by Western blot analysis. Jeongjihwan elevated the phosphorylation of CREB (p-CREB), which seemed to be mediated partly by extracellular signal-regulated kinase1/2 (ERK1/2) and protein kinase B/Akt. These findings suggest that Jeongjihwan may have preventive and therapeutic potential in the management of amnesia.

• Journal of Ginseng Research
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• 제43권4호
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• pp.499-507
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• 2019

Background: Ginsenoside Rb1 (Rb1), a dominant component from the extract of Panax ginseng root, exhibits neuroprotective functions in many neurological diseases. This study was intended to investigate whether Rb1 can attenuate cisplatin-induced memory impairments and explore the potential mechanisms. Methods: Cisplatin was injected intraperitoneally with a dose of 5 mg/kg/wk, and Rb1 was administered in drinking water at the dose of 2 mg/kg/d to rats for 5 consecutive wk. The novel objects recognition task and Morris water maze were used to detect the memory of rats. Nissl staining was used to examine the neuron numbers in the hippocampus. The activities of superoxide dismutase, glutathione peroxidase, cholineacetyltransferase, acetylcholinesterase, and the levels of malondialdehyde, reactive oxygen species, acetylcholine, tumor necrosis factor-${\alpha}$, interleukin-$1{\beta}$, and interleukin-10 were measured by ELISA to assay the oxidative stress, cholinergic function, and neuroinflammation in the hippocampus. Results: Rb1 administration effectively ameliorates the memory impairments caused by cisplatin in both novel objects recognition task and Morris water maze task. Rb1 also attenuates the neuronal loss induced by cisplatin in the different regions (CA1, CA3, and dentate gyrus) of the hippocampus. Meanwhile, Rb1 is able to rescue the cholinergic neuron function, inhibit the oxidative stress and neuroinflammation in cisplatin-induced rat brain. Conclusion: Rb1 rescues the cisplatin-induced memory impairment via restoring the neuronal loss by reducing oxidative stress and neuroinflammation and recovering the cholinergic neuron functions.

• 생약학회지
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• 제51권1호
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• pp.70-77
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• 2020

Mentha arvensis is used traditional medicine to treat various disorders. In the present study, M. arvensis were extracted by the solid-phase microextraction (SPME) method and analyzed by gas chromatograph-mass spectrometry (GC-MS). We investigated the protective effects and mechanisms of a M. arvensis extract on scopolamine-induced cognitive and memory impairment. Mice were orally pretreated with a M. arvensis extract or normal saline, and then behavior tests were conducted 30 min after scopolamine injection. The antioxidant capacities were analyzed by free radical scavenging (DPPH and ABTS). Acetylcholinesterase (AChE) activity were also measured using Ellman's method ex vivo test. In behavior tests, percent of spontaneous alteration, escape latency and swimming time in target quadrant were improved by the administration of the M. arvensis extract, which suggests that the M. arvensis extract improves memory function in the scopolamine-treated mice model. In addition, M. arvensis extract showed inhibition of the free radical and AChE activity. The results of the present study suggest that the M. arvensis extract ameliorates scopolamine-induced cognitive and memory deficits through the inhibition of free radicals and AChE activity. Therefore, M. arvensis may be a promising neuroprotective agent for management of learning and memory improvements in human dementia patients.

• Industrial Engineering and Management Systems
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• 제12권4호
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• pp.368-379
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• 2013

Efficiency in work training is a perennial issue due to high-diversity low-volume production, particularly for manufacturers producing office machines which are manually assembled by workers. To reduce the training cost, parts used in training are usually reused; a trainer disassembles a product assembled by a worker in training. This paper proposes a training method that employs disassembly usually performed by a trainer. This method assigns both assembly and disassembly to a worker in training, in contrast to the conventional method. The effectiveness of the proposed method is experimentally discussed in terms of learning assembly motions and work procedure at each learning stage, namely, "undergoing learning," "immediately after learning," and "seven days after learning." The effectiveness of the training method is confirmed. The method improves the stability of work procedure recollection immediately after training. Furthermore, at seven days after training, it improves retention of the assembly motions and work procedure, and also promotes and maintains memory related to product structure.

• 전자공학회논문지B
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• 제33B권2호
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• pp.149-158
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• 1996

In this paper we describe designing and implementing a digital neural chip and a parallel neural machine for simulating large scale neural netsorks. The chip is a single-chip multiprocessor which has four digiral neural processors (DNP-II) of the same architecture. Each DNP-II has program memory and data memory, and the chip operates in MIMD (multi-instruction, multi-data) parallel processor. The DNP-II has the instruction set tailored to neural computation. Which can be sed to effectively simulate various neural network models including on-chip learning. The DNP-II facilitates four-way data-driven communication supporting the extensibility of parallel systems. The parallel neural machine consists of a host computer, processor boards, a buffer board and an interface board. Each processor board consists of 8*8 array of DNP-II(equivalently 2*2 neural chips). Each processor board acn be built including linear array, 2-D mesh and 2-D torus. This flexibility supports efficiency of mapping from neural network models into parallel strucgure. The neural system accomplishes the performance of maximum 40 GCPS(giga connection per second) with 16 processor boards.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.818-833
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• 2021

To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequency-inverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model's F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.

• 전자통신동향분석
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• 제34권4호
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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.

• 대한원격탐사학회지
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• 제37권4호
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• 한국수자원학회논문집
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• 제53권2호
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• pp.131-140
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• 2020

본 연구는 심층학습 기법인 Long Short Term Memory (LSTM)를 이용하여 지하수위를 예측 후 표준지하수위지수(Standardized Groundwater level Index, SGI)를 산정함으로써 미래 가뭄 발생 가능성의 분석을 목적으로 하고 있다. LSTM 모형을 이용하여 금호강 유역의 지하수위를 미래 3년에 대해 예측을 하였으며, 예측시 최근 3년을 제외한 관측 자료로 학습 후 RMSE를 통해 검증하였다. 예측 자료와 관측 자료를 이용하여 시간적 SGI를 산정하였다. 산정된 SGI는 연구 지역 내 보간을 하였고, 보간된 SGI는 소유역별 평균값으로 공간적 SGI를 산정하였다. 산정된 시공간적 SGI를 이용하여 시공간적 가뭄 발생 가능성에 대해 분석하였다. 시공간별로 가뭄 발생 가능성에서 차이가 발생하는 것을 확인하였다. 향후 심층학습 모형의 개선 및 검증 방법의 다양화를 통해 신뢰성이 더욱 높은 예측 결과를 도출할 수 있고, 연구 적용 지역의 확대를 통해 전국적인 가뭄 대응 정책에 활용이 될 수 있으며, 더 나아가 미래 수자원 관리 차원에서 중요한 정보를 제공할 수 있을 것이다.