• Computers and Concrete
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• 제30권3호
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• pp.209-224
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• 2022

In this paper, the artificial neural network (ANN) is employed to predict the flexural behavior of reinforced concrete (RC) beams retrofitted with carbon fiber/epoxy composites modified by carbon nanotubes (CNTs). Multiple techniques are used to improve the accuracy of the ANN prediction, as the data represents a multivalued function. These techniques include static ANN modeling, ANN modeling with load history, and ANN modeling with double load history. The developed ANN models are used to predict the load-displacement profiles of beams retrofitted with either CFRP or CNTs modified CFRP, flexural capacity, and maximum displacement of the beams. The results demonstrate that the ANN is able to predict the flexural behavior of the retrofitted RC beams as well as the effect of each parameter including the type of the used epoxy and the presence of the CNTs.

• 한국멀티미디어학회논문지
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• 제12권3호
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• pp.436-444
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• 2009

최근 들어 센서 네트워크에 대한 관심이 증가됨에 따라서 다양한 분야에서 이를 활용한 연구가 이루어지고 있다. 본 논문에서는 센서 네트워크 기반 강의실 제어 시스템을 제안하고 구현한다. 강사의 강의 이력 정보를 센서 네트워크를 통하여 수집하고, 이 정보를 활용하여 강사가 강의실에 들어갈 때 강의실 내 PC, 빔 프로젝터, 전등 등을 강의에 적합한 형태로 제어해 주면 효과적인 강의 진행이 가능하다. 본 논문에서 제안한 강의실 제어 시스템이 효과적으로 동작할 수 있도록 센서 네트워크를 구축하고 성능 실험을 한다. 이를 통하여 강의실에 설치한 센서 노드의 메시지 발생 주기와, 센서 노드 간 통신이 원활하게 이루어질 수 있도록 강의실 내 배치된 센서 노드의 수를 최적화할 수 있는 값을 구하였다.

• 방송공학회논문지
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• 제23권1호
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• pp.86-92
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• 2018

본 논문에서는 디지털 음성 신호의 부호화 이력을 검출하는 방법을 제안한다. 음성 신호를 디지털 방식으로 전송 또는 저장할 때 데이터양을 줄이기 위해 부호화한다. 따라서 음성 신호 파형이 주어질 때, 해당 신호가 원본인지 부호화된 신호인지 판단하고, 만일 부호화 되었다면 부호화 횟수를 검출하는 부호화 이력 검출 과정이 필요하다. 본 논문에서는 12.2kbps 비트율의 AMR 부호화기에 대하여 원본, 단일 부호화, 이중 부호화 여부를 판단하는 부호화 이력 검출 방법을 제안한다. 제안한 방법은 입력 음성 신호에서 음성 고유의 특성 벡터를 추출하고, 해당 특성 벡터를 심층 신경망으로 모델링 하는 방법을 사용한다. 본 논문에서 제안하는 특성 벡터가 일반적인 스펙트로그램으로부터 추출한 특성 벡터보다 우수한 부호화 이력 검출 성능을 제공하는 것을 확인하였다.

• Asian review of World Histories
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• 제4권1호
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• pp.3-34
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• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.

• 디지털융복합연구
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• 제11권1호
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• pp.423-430
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• 2013

정보통신기술의 발달은 다양한 디지털매체와 소셜네트워크(SNS) 출연을 가져왔으며, 정보와 지식을 전달하는 방법에 있어서 '객관적지식'에서 '경험적지식'을 실시간 이야기하고 감성을 소통가능하게 하였다. 본 연구에서는 가족의 역사, 개인의 인물사등 선형적인 서사 장르의 이야기를 가지고 다양한 형태로 이야기를 생성할 수 있는 인터렉티브 스토리텔링 시스템을 연구하고자 한다. CEO이야기나 특정 조직이야기, 가족이야기등 다양한 콘텐츠 가운데 가족이야기(familyHistory)를 본 연구의 사례연구 관점에서 내러티브인터뷰, 직접관찰, 문서 및 영상자료수집 등을 통해 수집 분석 한 후 장르별 DB와 키워드DB 설계를 하여 분류 저장하였다. 저장된 자료는 인터렉티브 스토리텔링 서술구조를 통하여 사용자가 가족의 역사와 이야기에 대한 스토리텔링을 재구성하여 사용함으로써 이야기의 가치와 활용도를 높이고자 하였다. 본 연구를 통하여 각 세대간의 소통의 도구가 부족한 한국의 현황에서 1세대와 3세대 간의 텍스트, 이미지, 영상등 다양한 형태의 디지털 매체를 사용하는 저작시스템의 데이터모델을 작성하였으며, 더 나아가 DB화된 다양한 장르의 가족이야기를 가지고 인터렉티브 스토리텔링 창작 도구로 연동 가능한 시스템 설계를 연구하였다.

• 생물정신의학
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• 제4권2호
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• pp.194-197
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• 1997

Neural network models, also known as connectionist models or PDP models, simulate some functions of the brain and may promise to give insight in understanding the cognitive brain functions. The models composed of neuron-like elements that are linked into circuits can learn and adapt to its environment in a trial and error fashion. In this article, the history and principles of the neural network modeling are briefly reviewed, and its applications to psychiatry are discussed.

• Smart Structures and Systems
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• 제29권1호
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2007년도 학술대회
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• pp.121-124
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• 2007

This article describes a function and structure of access network equipment under BcN environment. Access network until now have constructed separately to offer voice, data service. However, simplifies network structure, function that can do traffic concentration, subscriber certification, individual charging, QoS according to service and routing is required in BcN. In this paper, compare method offering by separate system with existing access network and method that offer integrating function inside system for structure of suitable access network to BcN and search structure of access network equipment for desirable access network of hereafter. Composition of this paper is as following. In Chapter 2, establishment history and structure of access network until present. In Chaprte 3, define suitable requirement and functions to BcN. And compare structure for access net work that is new with present. Last Chapter 4, suggests direction of structure of BcN access network and concludes conclusion.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.271-274
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• 2005

Under competitive power markets, various long-term and short-term contracts based on spot price are used by producers and consumers. So an accurate forecasting for spot price allow market participants to develop bidding strategies in order to maximize their benefit. Artificial Neural Network is a powerful method in forecasting problem. In this paper we used Radial Basis Function(RBF) network to forecast spot price. To learn ANN, in addition to price history, we used some other effective inputs such as load level, fuel price, generation and transmission facilities situation. Results indicate that this forecasting method is accurate and useful.

• 건축역사연구
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• 제20권6호
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• pp.169-184
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• 2011

The following research of the road network of Jeju-Eupseong during Daehan Empire period has a twofold purpose: to study some characteristics of the road network at that time; and, to restore it to the original form of that period before a newly constructed road, called Shinjakro, has been established. As an attempt to trace the old shape of Jeju-Eupseong, this study analyzed some historical factors based on the first land cadastral map which was made in 1914, including outskirts of Jeju-Eupseong; such as castle itself, castle gate, road, bridge, lots of land, etc. Then this study also tried to restore the old road network of Jeju-Eupseong, through finding the original land-lot shape in the land cadastral map. There was five Shinjakro made between 1914 and 1917. The road network before then was composed of the double east-west axes and the single north-south axis. These axes was connected to some important place of the inside of Jeju-Eupseong; such as castle gates, fountains, Gaek-sa, etc. There were many branch lines between these main axes at about 80-120m intervals. Also there was an outer road along the outer wall of castle, connected with each castle gates. Especially, the north-west axis was the baseline which divided into two large parts, a government office area and non-government area (housing and commercial street for the people). Finally, this paper examines that the road network of Jeju-Eupseong was the true result for the efficient function of the city, especially considering natural geographical conditions and environment of living of that time.