• 부동산연구
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• 제27권3호
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• pp.71-86
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• 2017

본 연구의 목적은 딥 러닝 방법을 부동산가격지수 예측에 적용해보고, 기존의 시계열분석 방법과의 비교를 통해 부동산 시장 예측의 새로운 방법으로서 활용가능성을 확인하는 것이다. 딥 러닝(deep learning)방법인 DNN(Deep Neural Networks)모형 및 LSTM(Long Shot Term Memory networks)모형과 시계열분석 방법인 ARIMA(autoregressive integrated moving average)모형을 이용하여 여러 가지 부동산가격지수에 대한 예측을 시도하였다. 연구결과 첫째, 딥 러닝 방법의 예측력이 시계열분석 방법보다 우수한 것으로 나타났다. 둘째, 딥 러닝 방법 중에서는 DNN모형의 예측력이 LSTM모형의 예측력보다 우수하나 그 정도는 미미한 수준인 것으로 나타났다. 셋째, 딥 러닝 방법과 ARIMA모형은 부동산 가격지수(real estate price index) 중 아파트 실거래가격지수(housing sales price index)에 대한 예측력이 가장 부족한 것으로 나타났다. 향후 딥 러닝 방법을 활용함으로써 부동산 시장에 대한 예측의 정확성을 제고할 수 있을 것으로 기대된다.

• 한국인공지능학회지
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• 제11권4호
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• pp.21-27
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• 2023

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

• Journal of Information Processing Systems
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• 제10권2호
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.730-734
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• 2001

In this study in order to confirm the algorithms that are suggested from paper (I) as the experimental result, as the applied results of the hydraulic servo system are very strong a non-linearity of the fluid in the computer simulation, the real-time adaptive learning control algorithms is validated. The evolutionary strategy has characteristics that are automatically. adjusted in search regions with natural competition among many individuals. The error that is generated from the dynamic system is applied to the mutation equation. Competitive individuals are reduced with automatic adjustments of the search region in accord with the error. In this paper, the individual parents and offspring can be reduced in order to apply evolutionary algorithms in real-time as the description of the paper (I). The possibility of a new approaching algorithm that is suggested from the computer simulation of the paper (I) would be proved as the verification of a real-time test and the consideration its influence from the actual experiment.

• 공학교육연구
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• 제12권4호
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• pp.150-158
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• 2009

본 연구의 목적은 비실시간 온라인 토론에서 학습자의 자기조절학습전략이 토론 만족도와 참여 메시지 유형에 미치는 효과를 살펴보는 것이다. 본 연구의 결과 분석을 통해 도출된 결론은 다음과 같다. 우선, 비실시간 온라인 토론에서 자기조절학습전략이 토론 만족도에 영향을 주는 것으로 나타났다. 다음으로, 자기조절학습전략이 인지적 차원의 참여 유형 형태에 영향을 주는 것으로 나타났다. 이상의 연구결과를 통해, 비실시간 온라인 토론에서 학습자의 인지적 차원의 참여와 토론 만족 수준의 향상을 위해 자기조절학습전략을 활용할 것이 제안되었다.

• 한국멀티미디어학회논문지
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• 제22권4호
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• pp.424-431
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• 2019

In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제12권2호
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• pp.53-60
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• 2023

최근 실시간 스트리밍 플랫폼을 기반으로 한 다양한 멀티미디어 컨텐츠의 수요량과 트래픽 양이 급격히 증가하고 있는 추세이다. 본 논문에서는 실시간 스트리밍 서비스의 품질을 향상시키기 위해서 실시간 스트리밍 트래픽을 예측한다. 네트워크 트래픽을 예측하기 위해 통계적 모형을 활용하였으나, 실시간 스트리밍 트래픽은 매우 동적으로 변화함에 따라 통계적 모형보다는 순환 신경망 기반 딥러닝 모델이 적합하다. 따라서, 실시간 스트리밍 트래픽을 수집, 정제 후 Vanilla RNN, LSTM, GRU, Bi-LSTM, Bi-GRU 모델을 활용하여 예측하며, 각 모델의 학습 시간, 정확도를 측정하여 비교한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.716-719
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• 1999

In this paper, a real-time control system for robot manipulator is implemented using real-time operating system with capabilities of multitasking, intertask communication and synchronization, event-driven, priority-driven scheduling, real-time clock control, etc. The hardware system with VME bus and related devices is developed and applied to implement a dynamic learning control scheme for robot manipulator. Real-time performance of the proposed dynamic learning controller is tested for tasks of tracking moving objects and compared with the conventional servo controller.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.355-360
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• 2021

코로나19는 학교 교육에 커다란 변화를 가지고 왔다. 원격수업을 통해 학생들의 학습권을 보장하고자 하였으나 대면수업에 비해 원격수업이 가지는 제한점은 뚜렷했다. 그럼에도 불구하고 원격수업이라는 방식은 분리된 시공간을 고려할 수 있으며, 학습자의 개별적이고 자율적인 학습이 가능하다는 측면에서 지속적으로 발전시켜 나가야 할 학습 방법임에는 틀림없다. 이에 본 연구에서는 초등학교 단계에서 실시간 원격수업 사례를 분석하고, 이에 대한 교사 면담을 통하여 초등학교 실시간 원격수업에서의 문제를 발견하고 이를 개선하고자 하였다. 사례를 통해 살펴본 초등학교 단위차시 실시간 원격수업에서의 문제점은 첫째, 원격수업이라는 낯선 환경의 불안감으로 인해 교사 활동 비중이 높은 수업이 된다는 점, 둘째, 실시간 쌍방향 수업임에도 불구하고 학생들의 개별 활동 점검이나 원활한 피드백 제공이 불가능하다는 점이었다. 이에 대한 해결방안으로는 기본적인 수업의 단계(도입-전개-정리)을 고려할 필요가 있다는 것과 적절한 소통과 피드백 제공을 위한 수업 도구의 활용을 제시하였다.

• International journal of advanced smart convergence
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• 제9권1호
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• pp.113-120
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• 2020

Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.