• 산업공학
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• 제14권2호
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• pp.111-119
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• 2001

A tandem AGV system is based on partitioning all the stations into non-overlapping single vehicle closed loops with additional stations provided as an interface between adjacent loops. For an efficient use of this configuration, it is required to solve the load routing problem(LRP), which is primarily based on the fact that a load may be handled by several vehicles and moved through several loops before it reaches its destination. In this paper, a heuristic based on genetic algorithm(GA) is first developed to solve LRP. The first model obtains the optimal route of each job and the optimal direction of each loop when the vehicle in each loop travels unidirectionally. The second GA model obtaines the optimal polling sequence of the empty vehicle in each loop, when the vehicle can move bidirectionally.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.449-452
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• 2023

The rapid development of the Internet has led to a significant increase in multimedia content in social networks. How to better analyze and improve video classification models has become an important task. Deep learning models have typical "black box" characteristics. The model requires explainable analysis. This article uses two classification models: ConvLSTM and VGG16+LSTM models. And combined with the explainable method of LRP, generate visualized explainable results. Finally, based on the experimental results, the accuracy of the classification model is: ConvLSTM: 75.94%, VGG16+LSTM: 92.50%. We conducted explainable analysis on the VGG16+LSTM model combined with the LRP method. We found VGG16+LSTM classification model tends to use the frames biased towards the latter half of the video and the last frame as the basis for classification.

• Biomolecules & Therapeutics
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• 제30권2호
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• pp.212-212
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• 2022

• Molecules and Cells
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• 제37권11호
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• pp.767-776
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• 2014

Alzheimer's disease (AD) is clinically characterized with progressive memory loss and cognitive decline. Synaptic dysfunction is an early pathological feature that occurs prior to neurodegeneration and memory dysfunction. Mounting evidence suggests that aggregation of amyloid-${\alpha}$ ($A{\alpha}$) and hyperphosphorylated tau leads to synaptic deficits and neurodegeneration, thereby to memory loss. Among the established genetic risk factors for AD, the ${\varepsilon}4$ allele of apolipoprotein E (APOE) is the strongest genetic risk factor. We and others previously demonstrated that apoE regulates $A{\alpha}$ aggregation and clearance in an isoform-dependent manner. While the effect of apoE on $A{\alpha}$ may explain how apoE isoforms differentially affect AD pathogenesis, there are also other underexplored pathogenic mechanisms. They include differential effects of apoE on cerebral energy metabolism, neuroinflammation, neurovascular function, neurogenesis, and synaptic plasticity. ApoE is a major carrier of cholesterols that are required for neuronal activity and injury repair in the brain. Although there are a few conflicting findings and the underlying mechanism is still unclear, several lines of studies demonstrated that apoE4 leads to synaptic deficits and impairment in long-term potentiation, memory and cognition. In this review, we summarize current understanding of apoE function in the brain, with a particular emphasis on its role in synaptic plasticity and the underlying cellular and molecular mechanisms, involving low-density lipoprotein receptor-related protein 1 (LRP1), syndecan, and LRP8/ApoER2.

• 동의생리병리학회지
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• 제30권5호
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• pp.347-354
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• 2016

This study evaluated the effects of Fructus Corni Officinalis water extract (FCE) on congnitive impairment and Aβ clearance induced by beta amyloid Aβ (1-42) injection in the hippocampus of rat. Aβ (1-42) was injected into the hippocampus using a Hamilton syringe and micropump (5 ㎍/5 ㎕, 1 ㎕/min, each hippocampus bilaterally). FCE was administered orally once a day (100, 250, 500 mg/kg) for 4 weeks after the Aβ (1-42) injection. The acquisition of learning and retention of memory were tested using the Morris water maze. Aβ accumulation and Aβ clearance in the hippocampus were observed using immunostaining. Aβ (1-42) level in plasma was confirmed using enzyme-linked immunosorbent assay (ELISA). FCE significantly shortened the escape latencies during acquisition training trials. FCE significantly increased the number of target heading to the platform site and significantly shortened the time for the 1^sttargetheadingduringtheretentiontesttrial.FCEsignificantlyattenuatedtheAβ accumulation in the hippocampus produced by Aβ (1-42) injection. FCE significantly increased LRP-1 expression around vessels in the hippocampus and Aβ (1-42) levels in plasma. The results suggest that FCE improved cognitive impairment by ameliorate Aβ clearance and Aβ accumulation in the hippocampus. FCE may be a beneficial herbal formulation in treating cognitive impairment including Alzheimer's disease.

• 산업공학
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• 제22권2호
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• pp.153-164
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• 2009

An effective management for reverse flows of products such as reuse, repair and disposal, has become an important issue for every aspect of business. In this paper, we study the Location-Routing Problem (LRP) in the multi-stage closed-loop supply chain network. The closed-loop supply chain in this study integrated both forward and reverse flows. In forward flow, a factory, Distribution Center (DC) and retailer are considered as usual. Additionally in reverse flow, we consider the Central Returns collection Center (CRC) and disposal facility. We propose a mixed integer programming model for the design of closed-loop supply chain integrating both forward and reverse flows. Since the LRP belongs to an NP-hard problem, we suggest a heuristic algorithm based on genetic algorithm. For some test problems, we found the optimal locations and routes by changing the numbers of retailers and facility candidates. Furthermore, we compare the efficiencies between open-loop and closed-loop supply chain networks. The results show that the closed-loop design is better than the open one in respect to the total routing distance and cost. This phenomenon enlarges the cut down effect on cost as an experimental space become larger.

• 한국정보시스템학회지:정보시스템연구
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• 제30권4호
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• 한국산학기술학회논문지
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• 제14권1호
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• pp.336-343
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• 2013

본 연구의 목적은 요추 추간판 탈출증 환자에게 치료적 모달리티와 SpineMT(mobilization & traction)를 이용한 척추 감압치료 효과를 확인하는 것이다. 요추 추간판 탈출증 환자 15명(나이 36.62, 범위 20-50, 남자 7명과 여자 8명)을 대상으로 4주간 적용하였다. 치료적 모달리티와 척추 감압치료를 첫 2주 동안 주 6일, 12회 적용하였으며, 마지막 2주간은 주 4일 8회 적용하였다. 모든 실험대상자에게 4주 동안 20회를 적용하였다. 측정은 오스웨스트리 요통장애지수, 근력, 하지 직거상 검사는 실험 전, 치료 10회 후, 치료 20회 후 변화의 차이를 일요인 반복측정을 이용하였으며, 추간판 탈출지수는 실험 전, 치료 20회 후 변화 차이를 대응표본 t-검정을 이용하여 측정하였다. 치료적 중재 기간에 따라 치료 전, 2주 후, 4주 후 측정결과 오스웨스트리 요통장애 지수, 하지 직거상 검사, 그리고 근력은 치료 10회 후, 치료 20회 후가 치료 전에 비하여 통계학적 유의한 변화가 있었다(p<0.05). 그러나 추간판 탈출 지수는 치료 전에 비하여 감소함을 나타냈으나 통계학적으로 유의한 변화는 없었다(p>0.05). 결론적으로 요추 추간판 탈출증 환자에게 치료적 모달리티와 척추 감압치료가 요통장애지수, 하지 직거상 검사, 근력 개선에 효과적이란 결론을 얻었다. 이것은 척추 감압치료의 안전성과 효과의 확인, 그리고 요추 추간판 탈출증 환자에게 비수술적 치료법으로서의 근거를 제시하였다.

• 대한화학회지
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• 제63권6호
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• pp.505-510
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• 2019

• 전자통신동향분석
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• 제15권5호통권65호
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• pp.60-72
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• 2000

데스크탑 시스템과 내장형 시스템의 하드웨어 특성과 요구사항은 서로 다르기 때문에 데스크탑 기반의 Unix 운영체제로 널리 쓰이고 있는 GNU/Linux를 내장형 시스템의 운영체제로 사용하기 위해서는 여러 가지 구성 요소들에 대한 최적화가 뒤따라야 한다. GNU/Linux의 최적화를 위해 고려해 볼 수 있는 다섯 가지 방법을 설명하고, 실례로서 ZDISK와 LRP에서 만든 결과를 분석해 보기로 한다.