• 한국지구과학회지
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• 제30권1호
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• pp.127-140
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• 2009

본 연구에서는 대표적인 자율선택학습 환경인 자연사박물관에서 일어나는 또래 아동간의 상호작용적 학습에 대해 Vygotsky의 근접발달영역 개념을 토대로 살펴보았다 초등학교 3, 4학년 아동들 13쌍의 자연사박물관 관람을 녹화하여 영상물과 전사본을 바탕으로 대화를 분석하였으며 이들의 대화를 근접발달영역 발달수준에 근거하여 분석한 결과 아동들이 자신이 이미 도달해 있는 수준 내에서 대화를 나누는 실제적 발달수준 내의 대화가 압도적으로 높은 빈도를 차지하고 있었다. 각 유형별로 대표적인 대화 사례를 세 가지 근접발달영역 체계화 요소를 토대로 해석한 결과 아동들은 언어 기호를 매개로 상호작용을 하여 상호주관성을 형성하고 동일한 상황정의를 이루어 가는 과정을 통해 학습적으로 유의미한 대화를 나누고 있었다. 가장 인상적인 연구 참여쌍의 사례를 통해 아동들의 대화 내용을 과학 개념 측면에서 보다 자세히 들여다본 결과 아동들이 자신의 기존 지식과 연결짓거나 확장하고 정교화 하는 등의 모습들을 볼 수 있었다. 본 연구의 결과는 자연사박물관에서 일어나는 아동들의 학습의 본질을 이해하는 데 공헌할 수 있을 것으로 생각된다.

• 대한산업공학회지
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• 제32권1호
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• pp.61-73
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• 2006

This paper deals with traveling salesman problem(TSP) with the stochastic travel time. Practically, the travel time between demand points changes according to day and time zone because of traffic interference and jam. Since the almost pervious studies focus on TSP with the deterministic travel time, it is difficult to apply those results to logistics problem directly. But many logistics problems are strongly related with stochastic situation such as stochastic travel time. We need to develop the efficient solution method for the TSP with stochastic travel time. From the previous researches, we know that Q-learning technique gives us to deal with stochastic environment and neural network also enables us to calculate the Q-value of Q-learning algorithm. In this paper, we suggest an algorithm for TSP with the stochastic travel time integrating Q-learning and neural network. And we evaluate the validity of the algorithm through computational experiments. From the simulation results, we conclude that a new route obtained from the suggested algorithm gives relatively more reliable travel time in the logistics situation with stochastic travel time.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

• 한국정보통신학회논문지
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• 제26권8호
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• pp.1248-1254
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• 2022

스쿨존에서 교통사고를 사전에 예방하려고 노력하고 있다. 하지만, 스쿨존 내 교통사고는 계속 발생하고 있다. 운전자가 어린이보호구역 내 상황 정보를 미리 알 수 있으면, 사고를 줄일 수 있다. 본 논문에서는 스쿨존 내 사각지대를 없애는 카메라, 사전 교통정보를 수집할 수 있는 번호인식 카메라 시스템을 설계한다. 차량속도 및 보행자를 인식하는 LIDAR 시스템을 개선하여 설계한다. 카메라 및 LIDAR에서 인식된 보행자 및 차량 영상 정보를 수집하고 가공하여, 인공지능 시계열 분석 및 인공지능 알고리즘을 적용한다. 본 논문에서 제안한 딥러닝으로 학습된 인공지능 교통사고 예방 시스템은, 스쿨존 진입 전 차량 내 모바일 장치에 스쿨존의 정보를 운전자에게 전달하는 강제 푸시서비스를 한다. 그리고 LED 안내판에 스쿨존 교통정보를 알람으로 제공한다.

• Clinical and Experimental Pediatrics
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• 제63권3호
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• pp.88-95
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• 2020

Accurate localization of the seizure onset zone is important for better seizure outcomes and preventing deficits following epilepsy surgery. Recent advances in neuroimaging techniques have increased our understanding of the underlying etiology and improved our ability to noninvasively identify the seizure onset zone. Using epilepsy-specific magnetic resonance imaging (MRI) protocols, structural MRI allows better detection of the seizure onset zone, particularly when it is interpreted by experienced neuroradiologists. Ultra-high-field imaging and postprocessing analysis with automated machine learning algorithms can detect subtle structural abnormalities in MRI-negative patients. Tractography derived from diffusion tensor imaging can delineate white matter connections associated with epilepsy or eloquent function, thus, preventing deficits after epilepsy surgery. Arterial spin-labeling perfusion MRI, simultaneous electroencephalography (EEG)-functional MRI (fMRI), and magnetoencephalography (MEG) are noinvasive imaging modalities that can be used to localize the epileptogenic foci and assist in planning epilepsy surgery with positron emission tomography, ictal single-photon emission computed tomography, and intracranial EEG monitoring. MEG and fMRI can localize and lateralize the area of the cortex that is essential for language, motor, and memory function and identify its relationship with planned surgical resection sites to reduce the risk of neurological impairments. These advanced structural and functional imaging modalities can be combined with postprocessing methods to better understand the epileptic network and obtain valuable clinical information for predicting long-term outcomes in pediatric epilepsy.

• Nuclear Engineering and Technology
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• 제49권4호
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• pp.817-824
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• 2017

Residual stress is a critical element in determining the integrity of parts and the lifetime of welded structures. It is necessary to estimate the residual stress of a welding zone because residual stress is a major reason for the generation of primary water stress corrosion cracking in nuclear power plants. That is, it is necessary to estimate the distribution of the residual stress in welding of dissimilar metals under manifold welding conditions. In this study, a cascaded support vector regression (CSVR) model was presented to estimate the residual stress of a welding zone. The CSVR model was serially and consecutively structured in terms of SVR modules. Using numerical data obtained from finite element analysis by a subtractive clustering method, learning data that explained the characteristic behavior of the residual stress of a welding zone were selected to optimize the proposed model. The results suggest that the CSVR model yielded a better estimation performance when compared with a classic SVR model.

• 대한지리학회지
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• 제36권2호
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• pp.161-176
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• 2001

인지발달심리학에서 이루어지고 있는 두 가지 관점을 중심으로 지리개념 발달과 학습에 보다 효과적인 이론적 토대를 모색하였다. 연구 결과 Piaget의 인지발달계론은 공간개념 발달을 설명하는데 실증적으로 적용.검증되어 왔으며 지리교육심리의 기초를 제공했다는 점에서 긍정적이지만, 학습자의 발달단계에 따라 개념학습이 이루어 질 수 있다는 제한적인 관점이었다. 이에 비해 고등정신기능발달과 근접발달영역으로 설명되는 Vygotsky의 이론은, 교수-학습에 의해 개념 발달이 이루어진다는 관점으로 지리개념 발달에 보다 효과적인 교수-학습의 이론적 토대로 제시된다.

• 한국BIM학회 논문집
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• 제12권2호
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• pp.1-11
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• 2022

Road traffic accidents (RTAs) are the leading cause of accidental death among children. RTA reduction is becoming an increasingly important social issue among children. Municipalities aim to resolve this issue by introducing "Smart Pedestrian Crosswalks" that help prevent traffic accidents near children's facilities. Nonetheless such facilities tend to be installed in relatively limited number of areas, such as the school zone. In order for budget allocation to be efficient and policy effects maximized, optimal location selection based on machine learning is needed. In this paper, we employ machine learning models to select the optimal locations for smart pedestrian crosswalks to reduce the RTAs of children. This study develops an optimal location index using variable importance measures. By using k-means clustering method, the authors classified the crosswalks into three types after the optimal location selection. This study has broadened the scope of research in relation to smart crosswalks and traffic safety. Also, the study serves as a unique contribution by integrating policy design decisions based on public and open data.

• 대한본초학회지
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• 제24권4호
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• pp.165-171
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• 2009

Objectives : In the present study, we assessed the effects of the ethanolic extract of Crataegus pinnatifida Bunge on the learning and memory impairments induced by scopolamine using the passive avoidance and the Morris water maze tasks in mice. Methods : The cognition-enhancing effect of C. pinnatifida was investigated using a passive avoidance test, the Morris water maze test and Y-maze test in mice. Drug-induced amnesia was induced by treating animals with scopolamine (1 mg/kg, i.p.). Results : The ethanolic extract of C. pinnatifida (100, and 200 mg/kg) significantly reversed the scopolamine-induced cognitive impairments in the passive avoidance test (p < 0.05). Moreover, C. pinnatifida (200 mg/kg) also improved escape latencies in training trials and increased swimming times and distances within the target zone of the Morris water maze (p < 0.05). On the Y-maze test, C. pinnatifida (100, and 200 mg/kg) also significantly reversed scopolamine- induced cognitive impairments in mice (p < 0.05). Conclusions : The ethanolic extract of Crataegus pinnatifida dramatically possesses the anti-amnestic and cognitive-enhancing activities related to the memory processes, and these activities were parallel to treatment duration and dependent on the learning models.

• 해양환경안전학회지
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• 제7권2호
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• pp.67-87
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• 2001

ICZM(integrated coastal zone management) is recognized globally as an optimal solution on coastal issues and problems because it aims integration of all the different programs and stakeholders on coastal interests. Evaluation of ICZM program is also recognized globally for the success of ICZM because we can get many useful experiences and learning through evaluation. There are many issues and problems in coastal zone in Korea such as coastal development, wetland loss, deterioration of water quality, decreasing of fisheries stocks, limit of public access, etc. Recently highly centralized ICfM has been introduced in Korea but there are so high barrier between ICZM program and other relevant sectors. This paper tries to fond factors to evaluate and further evolution of ICZM in Korea which is very centralized system.