• 대한수학교육학회지:수학교육학연구
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• 제8권2호
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• pp.605-620
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• 1998

This paper suggested a geometry learning model which relates an exploratory learning model with GSP applications, Such a model adopts GSP's capability of visualizing dynamic geometric figures and exploratory learning method's advantages of discovering properties and relations of geometric problem proving and concepts associated with geometric inferencing of students. The research was conducted for 3 middle school students by applying the proposed model for 6times at computer laboratory. The overall procedure was videotaped so that the collected data was later analyzed by qualitative methodology. The analysis indicated that the students with less than van Hiele 4 level took advantages of adoption our proposed model to gain concrete understandings of geometric principles and concepts with GSP. One of the lessons learned from this study suggested that the roles of students and a teacher who want to employ the proposed model need to change their roles respectively.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 Asia Navigation Conference
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• pp.176-183
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• 2006

A novel intelligent anti-collision decision-making supporting system is addressed in this paper. To obtain precise anti-collision information capability, an innovative neurofuzzy network is proposed and applied. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to train the parameters of the Fuzzy Inference System (FIS). The learning process is based on a hybrid learning algorithm and off-line training data. The training data are obtained by trial manoeuvre. This neurofuzzy network can be considered to be a self-learning system with the ability to learn new information adaptively without forgetting old knowledge. This supporting system can decrease ship operators' burden to deal with bridge data and help them to make a precise anti-collision decision.

• Journal of information and communication convergence engineering
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• 제2권1호
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• pp.36-39
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• 2004

In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• 대한임베디드공학회논문지
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• 제17권2호
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.

• 한국정밀공학회지
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• 제10권3호
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• pp.175-181
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• 1993

In this paper, a practical method to generate task strategies applicable to chamferless and high-precision assembly, is proposed. The difficulties in devising reliable assembly strategies result from various forms of uncertainty such as imperfect knowledge on the parts being assembled and functional limitations of the assembly devices. In approach to cope with these problems, the robot is provided with the capability of learning the corrective motion in response to the force signal trrough iterative task execution. The strategy is realized by adopting a learning algorithm and represented in a binary tree type database. To verify the effectiveness of the proposed algorithm, a series of simulations and experiments are carried out under assimilated real production environments. The results show that the sensory signal-to-robot action mapping can be acquired effectively and, consequently, the chamferless assembly can be performed successfully.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.127-136
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• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• 대한전기학회논문지:전력기술부문A
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• 제53권12호
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• pp.637-644
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• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• 기술혁신연구
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• 제29권2호
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• pp.1-30
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• 2021

급변하는 환경에서 지속 가능한 경쟁우위를 확보하기 위해 기업들은 끊임없이 새로운 지식을 획득해야 한다. 인수합병이나, 지식재산권의 구매 등이 이를 위한 대표적인 수단이나, 합작회사의 설립도 보완자산을 내재화하기 위한 효과적인 지식 획득 방법으로 주목받고 있다. 그러나 모든 합작회사가 새로운 지식을 성공적으로 획득하는 것은 아닌데, 이는 기업들이 획득하고자 하는 지식의 특성에 맞는 적절한 학습전략 및 조직 구조를 갖추지 못했기 때문인 경우가 많다. 본 연구는 이 같은 문제의식 하에 상황이론 관점에서 지식의 특성을 다차원 적으로 구분하고 지식 특성에 맞는 학습전략과 조직구조의 필요성을 자동차 부품분야의 국제 합작회사 사례를 통해 살펴보았다. 하드웨어 기술에 최적화된 사례 회사(국제합작회사)는 다른 성격의 지식인 소프트웨어 기술을 습득하기 위해 차별화된 학습 전략과 조직구조를 갖추지 못했고, 이러한 미스매치로 인해 합작회사를 통한 새로운 지식(엔진제어시스템 기술)의 흡수에 실패하였다. 본 연구는 기업이 성공적 지식흡수를 위해 고려해야 할 요소들을 이론적 프레임워크를 통해 제시하고 실증분석을 통해 이를 검증함으로써 합작회사 설립, 인수합병 등 조직 변화를 통해 동태적 역량을 확보하고자 하는 기업들에게 실무적 시사점을 제공하고 있다.

• 한국콘텐츠학회논문지
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• 제22권2호
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• pp.281-293
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• 2022

지능형 정부를 구성하기 위해 인공지능(AI)의 도입의 필요성이 대두되기 시작하였다. 이에따라 본 연구는 중앙정부 조직의 AI 도입에 영향을 미치는 조직의 특성으로서 '조직 민첩성'과 '활용적 & 탐색적 조직학습', '전자정부의 역량'을 조작화 하여 독립변수로 설정하였다. 이후 정책 기획을 주로 실시하는 중앙정부 조직 '부'와 정책 집행을 주로 수행하는 '청'조직의 AI 도입여부를 종속변수로 구축한 후, 각각의 두 모형에 대해 로지스틱 회귀분석을 실시하였다. 분석 결과, 부 조직은 조직 민첩성이 증가할수록 AI를 도입하였으며, 청 조직은 전자정부 역량이 증가할수록 AI를 도입하는 것이 도출되었다. 이때, 청 조직의 조직학습 수준과 전자정부 역량 변수간의 상호작용항을 파악한 결과, 활용적 조직학습은 전자정부 역량에 따른 AI 도입의 영향력을 상쇄시켰으며, 탐색적 조직학습은 이를 촉진하였다. 본 연구는 AI 도입을 위한 전략 수립 시 중앙정부 조직의 특성에 따라 주목할 핵심 개념을 제시하였다는 점에서 그 의의가 있다.