• 전기학회논문지
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• 제60권3호
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• pp.465-470
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• 2011

Recently fault currents are increasing in a network. It is caused by increase in electric demand and high penetration of distributed generation with renewable energy sources. Moreover, distribution network has become more and more complex as mesh network to improve the distribution system reliability and increase the flexibility and agility of network operation. Accordingly, the fault current will exceed capacity of circuit breakers soon and all the various rational solutions to solve this problem are taken into account. Under these circumstances, superconducting fault current limiter(SFCL) is a new alternative in the viewpoint of technical and economic aspects. This study presents operation processes for a resistive-type of SFCL, and it proposes reliability model for the SFCL. When a SFCL is installed into a network, the contribution of decreased fault currents to failure for distribution equipments can be quantified. As a result, it is expected that a SFCL makes the reliability of adjacent equipments on existing network improve and these changes are analyzed. We propose a methodology to evaluate the reliability in the distribution network where a SFCL is installed considering a reliability model for resistive-type of SFCL and reliability changes for adjacent equipments which are proposed in this paper.

• 한국항행학회논문지
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• 제22권3호
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• pp.233-239
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• 2018

태양광 발전은 기상 상태에 따라 간헐적이기 때문에 태양광 발전의 효율과 경제성 향상을 위해 정확한 발전량 예측이 요구된다. 본 연구는 목포 기상대에서 예보하는 기상 데이터와 영암 태양광 발전소의 발전량 데이터를 이용하여 태양광 발전량 단기 딥러닝 예측모델을 제안하였다. 기상청은 기온, 강수량, 풍향, 풍속, 습도, 운량 등의 기상요소를 3일간 예보한다. 그러나 태양광 발전량 예측에 가장 중요한 기상요소인 일조 및 일사 일사량 예보하지 않는다. 제안 모델은 예보 기상요소를 이용하여, 일조 및 일사 일사량을 예측 하였다. 또한 발전량은 기상요소에 예측된 일조 및 일사 기상요소를 추가하여 예측하였다. 제안 모델의 발전량 예측 결과 DNN의 평균 RMSE와 MAE는 0.177과 0.095이며, RNN은 0.116과 0.067이다. 또한, LSTM은 가장 좋은 결과인 0.100과 0.054이다. 향후 본 연구는 다양한 입력요소의 결합으로 보다 향상된 예측결과를 도출할 수 있을 것으로 기대된다.

• 한국통신학회논문지
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• 제41권7호
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• pp.754-764
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• 2016

최근 들어 다양한 SNS(Social Network Service)에 대한 이해와 분석을 위해 가장 중요한 두 종류의 망인 small-world와 scale-free망에 대한 많은 연구가 수행되고 있다. 본 연구에서는 두 개의 입력 파라미터를 적절히 조정함으로서 small-world 망, scale-free 망 혹은 두 개의 성질을 동시에 모두 갖는 망을 생성 할 수 있는 보다 일반화된 망 생성 방법을 제안하였다. 두개의 입력 파라미터중 하나는 small-world 성질을 나태내주는 파라미터고 다른 하나는 scale-free와 small-world 성질 모두를 나타내주는 파라미터다. Small-world와 scale-free를 나타내주는 망의 성질로 군집계수, 평균 최단거리 그리고 power-law 상수를 이용하였다. 본 연구에서 제안한 방법을 사용하면 small-world 망과 scale-free 망의 성질과 관계에 대한 보다 명확한 이해를 할 수 있다. 다양한 여러 예제들을 통하여 두 개의 입력 파라미터들이 군집계수, 평균 최단거리 그리고 power-law 상수에 미치는 영향을 검증하였다. 이를 통해 어떠한 입력 파라미터들의 조합이 small-world 망, scale-free 망 혹은 두 개의 성질을 모두 갖는 망을 생성 할 수 있는지를 조사하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1794-1806
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• 2023

This study presents a method for capturing photographs of users as input and converting them into 2D character animation sprites using a generative adversarial network-based artificial intelligence network. Traditionally, 2D character animations have been created by manually creating an entire sequence of sprite images, which incurs high development costs. To address this issue, this study proposes a technique that combines motion videos and sample 2D images. In the 2D sprite generation process that uses the proposed technique, a sequence of images is extracted from real-life images captured by the user, and these are combined with character images from within the game. Our research aims to leverage cutting-edge deep learning-based image manipulation techniques, such as the GAN-based motion transfer network (impersonator) and background noise removal (U² -Net), to generate a sequence of animation sprites from a single image. The proposed technique enables the creation of diverse animations and motions just one image. By utilizing these advancements, we focus on enhancing productivity in the game and animation industry through improved efficiency and streamlined production processes. By employing state-of-the-art techniques, our research enables the generation of 2D sprite images with various motions, offering significant potential for boosting productivity and creativity in the industry.

• 산업공학
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• 제23권2호
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• pp.193-202
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• 2010

The quality of service(QoS) model should be presented with the optimal network design to effectively provide the multimedia data services between users and converged services with mobile or TV in the next-generation Internet. In specific, the method to provide differentiated services for each user is needed in the given Internet node to offer the previously negotiated QoS with the user. In this paper, the performance of the QoS enabling technologies in the differentiated services(DiffServ) network domain is analyzed. The QoS offering model and QoS metrics are presented to analyze the performance of the major scheduling algorithms. Under the real network topology and virtual service scenarios in the university, the NS-2 network simulation based on the discrete-event is performed. The results show that the ratio-based scheduling method is more effective rather than the bandwidth-assignment method.

• 한국IT서비스학회:학술대회논문집
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• 한국IT서비스학회 2006년도 추계학술대회
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• pp.366-371
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• 2006

To compensate the loss of leased-line subscribers and the excessive increase of residential xDSL (Digital Subscriber Line) ones of KT (Korea Telecom), the paper proposes the service model by which it can reinstate the subscription ratio status through employing next generation OSS (Operations Support System) and highquality MPLS (Multiprotocol Label Switching) VPN (Virtual Private Network). It also describes diverse modules comprising NeOSS (New OSS) of KT, followed by detailed accounts regarding the service delivery process of KT VPN. Shortly visited are the primary constituents as well as configuration parameters of MPLS VPN. Finally the network topology along with a feasible service model case is presented.

• 한국정밀공학회지
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• 제19권11호
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• 제어로봇시스템학회논문지
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• 제9권6호
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

• 한국정밀공학회지
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• 제20권5호
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.