• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• 대한기계학회논문집A
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• 제23권11호
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• pp.1929-1939
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• 1999

This paper presents a neural network approach, which was named PRONET, to 3D object recognition and pose calculation. 3D objects are represented using a set of centroidal profile patterns that describe the boundary of the 2D views taken from evenly distributed view points. PRONET consists of the training stage and the execution stage. In the training stage, a three-layer feed-forward neural network is trained with the centroidal profile patterns using an error back-propagation method. In the execution stage, by matching a centroidal profile pattern of the given image with the best fitting centroidal profile pattern using the neural network, the identity and approximate orientation of the real object, such as a workpiece in arbitrary pose, are obtained. In the matching procedure, line-to-line correspondence between image features and 3D CAD features are also obtained. An iterative model posing method then calculates the more exact pose of the object based on initial orientation and correspondence.

• International Journal of Naval Architecture and Ocean Engineering
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• 제7권2호
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• pp.426-434
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• 2015

In this paper we proposed a dada-aided Doppler estimation method for underwater acoustic communication. The training sequence is non-dedicate, hence it can be designed for Doppler estimation as well as channel equalization. We assume the channel has been equalized and consider only flat-fading channel. First, based on the training symbols the theoretical received sequence is composed. Next the least square principle is applied to build the objective function, which minimizes the error between the composed and the actual received signal. Then an iterative approach is applied to solve the least square problem. The proposed approach involves an outer loop and inner loop, which resolve the channel gain and Doppler coefficient, respectively. The theoretical performance bound, i.e. the Cramer-Rao Lower Bound (CRLB) of estimation is also derived. Computer simulations results show that the proposed algorithm achieves the CRLB in medium to high SNR cases.

• Journal of Information Science Theory and Practice
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• 제11권1호
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• pp.61-78
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• 2023

Ontological knowledge modelling of epic texts, though being an established research arena backed by concrete multilingual and multicultural works, still suffers from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most often combining existing general purpose ontology development methodologies. Secondly, none of the ad-hoc methodologies consider the potential reuse of existing epic ontological models for enrichment, if available. This paper presents, as a unified solution to the above shortcomings, the design and development of GOMME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GOMME is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms, and cognitive generative questions. It is also the first methodology (in epic modelling but also in general) to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch. The feasibility of GOMME is validated via a first brief implementation of ontological modelling of the Indian epic Mahabharata by reusing an existing ontology. The preliminary results are promising, with the GOMME-produced model being both ontologically thorough and competent performance-wise.

• ETRI Journal
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• 제45권6호
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• pp.963-973
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• 2023

Non-orthogonal multiple access (NOMA) is considered a key candidate technology for next-generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple-input-multiple-output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy-efficient (EE) subchannel assignment framework for MIMO-NOMA systems under the quality-of-service and interference constraints. This framework handles an energy-efficient co-training-based semi-supervised learning (EE-CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE-CSL, initial assignment is performed by a many-to-one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE-CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.

• 한국측량학회지
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• 제33권4호
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• pp.267-275
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• 2015

토지피복지도는 환경, 군사, 의사결정 등 다양한 분야에서 널리 사용되고 있다. 본 연구에서는 단일 위성영상과 환경부에서 제공하는 국가토지피복도를 이용하여 훈련자료를 자동으로 추출하고, 이를 활용하여 피복을 분류하는 방법을 제안하였다. 이를 위하여 초기 훈련자료는 무감독분류인 ISODATA와 기존 토지피복도를 이용하였으며, 무감독 분류 사용시 각 클래스별 분류 선정과 클래스 명명, 감독분류에서 훈련자료 선정 등의 문제점을 해결하기 위하여 기존 토지피복도의 클래스 정보를 활용하여 자동으로 클래스를 분류하고 명명하였다. 추출된 초기 훈련자료는 대상 위성영상의 토지피복분류를 위하여 MLC의 훈련자료를 활용하였고, 피복분류의 정확도 향상을 위하여 반복방법을 적용하여 훈련자료를 갱신하였으며 최종적으로 토지피복지도를 추출하였다. 또한, 화소분류방법에서 발생하는 salt and pepper를 감소시키기 위하여 각 반복단계별 MRF를 적용하여 분류정확도를 향상시켰다. 본 연구에서 제안된 방법을 대상지역에 적용한 결과 효과적으로 토지피복지도를 생성할 수 있음을 정량적, 시각적으로 확인하였다.

• 디지털융복합연구
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• 제14권5호
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• pp.1-10
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• 2016

새로운 제도나 경영전략 등이 성공적으로 정착되어 조직의 성과제고로 연계되기 위해서는 내재화과정이 반드시 필요하다. 성공적인 내재화를 위해서 많은 기관에서 가장 많이 활용하는 방법은 교육방법 및 커뮤니케이션 전략이라고 할 수 있다. 그러나 교육이나 커뮤니케이션을 원활하게 진행하기 위해서는 조직구성원들간 신뢰와 협력지향적인 문화 등 다양한 측면에서의 융합전략이 뒷받침되어야만 한다. 본 연구에서는 특정 공공기관의 직원들을 대상으로 새로이 도입한 조직비전, 핵심가치 등에 대한 내재화 정도 및 조직문화 등에 대해 조사해보고, 성공적인 경영전략 내재화 방안을 제시하고자 하였다. 우선, 경영전략 강화 교육을 위해서는 반복학습을 통해 자주 노출시키고 자주 대화하는 방법이 효과적이다. 이를 위해 집합교육과 이러닝 등 집중적인 교육방법을 병행해야 한다. 둘째, 의사소통의 활성화를 위해서는 기관장이 직원들과의 내재화를 위한 의사소통에 관심을 기울여야 하며, 다양한 방법의 의사소통 전략을 세워야 할 것이다. 끝으로, 조직문화의 변화를 위해서는 동료들간 개방적이고 협조적인 문화구축을 위한 비공식적인 의사소통의 장을 자주 마련하고 인적네트워크의 양적, 질적 확대를 지원해주어야 한다.

• 대한원격탐사학회지
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• 제27권3호
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• pp.315-328
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• 2011

Landsat data incorporated with additional bands-normalized difference vegetation index (NDVI) and band ratios were used to assess the extent and rate of deforestation in the Gunung Mutis Nature Reserve (GMNR), a mountainous tropical forest in Eastern of Indonesia. Hybrid classification was chosen as the classification approach. In this approach, the unsupervised classification-iterative self-organizing data analysis (ISODATA) was used to create signature files and training data set. A statistical separability measurement-transformed divergence (TD) was used to identify the combination of bands that showed the highest distinction between the land cover classes in training data set. Supervised classification-maximum likelihood classification (MLC) was performed using selected bands and the training data set. Post-classification smoothing and accuracy assessment were applied to classified image. Post-classification comparison was used to assess the extent of deforestation, of which the rate of deforestation was calculated by the formula suggested by Food Agriculture Organization (FAO). The results of two periods of deforestation assessment showed that the extent of deforestation during 1989-1999 was 720.72 ha, 0.80% of annual rate of deforestation, and its extent of deforestation during 1999-2009 was 1,059.12 ha, 1.31% of annual rate of deforestation. Such results are important for the GMNR authority to establish strategies, plans and actions for combating deforestation.

• Journal of Power Electronics
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• 제17권4호
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• pp.1014-1026
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• 2017

The traditional fault diagnosis method for photovoltaic (PV) inverters has a difficult time meeting the requirements of the current complex systems. Its main weakness lies in the study of nonlinear systems. In addition, its diagnosis time is long and its accuracy is low. To solve these problems, a hidden Markov model (HMM) is used that has unique advantages in terms of its training model and its recognition for diagnosing faults. However, the initial value of the HMM has a great influence on the model, and it is possible to achieve a local minimum in the training process. Therefore, a genetic algorithm is used to optimize the initial value and to achieve global optimization. In this paper, the HMM is combined with a genetic algorithm (GHMM) for PV inverter fault diagnosis. First Matlab is used to implement the genetic algorithm and to determine the optimal HMM initial value. Then a Baum-Welch algorithm is used for iterative training. Finally, a Viterbi algorithm is used for fault identification. Experimental results show that the correct PV inverter fault recognition rate by the HMM is about 10% higher than that of traditional methods. Using the GHMM, the correct recognition rate is further increased by approximately 13%, and the diagnosis time is greatly reduced. Therefore, the GHMM is faster and more accurate in diagnosing PV inverter faults.

• 한국가스학회지
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• 제28권2호
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• pp.84-92
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• 2024

본 연구는 특정 사용자들이 효과적으로 사용할 수 있는 VR 안전교육콘텐츠를 개발하기 위해 사용자 참여 디자인 방법론(UCD)을 적용하였다. 이에 따라 UCD 기반의 설계를 통해 얻은 인사이트를 VR 매체에 특화시키고, 효율적인 설계 활동이 가능하도록 방법론을 개발하였다. 개발된 UCD는 다음과 같은 주요 활동으로 구성된다: 첫째, 사용자 니즈와 관련된 다양한 자료를 활용하여 설계 방향을 수립하는 '컨셉 도출'; 둘째, VR 특성을 내용과 구현적 측면으로 나누어 설계하는 '디자인 설계'; 셋째, 프로토타입을 구현하는 '개발'; 넷째, 사용자 집단을 포함한 전문가와 일반인 집단으로 종합적인 검토를 진행하는 '평가'; 마지막으로 최종 콘텐츠를 완성하는 '완료'이다. 본 연구에서 제안하는 프로세스는 기존 UCD와 달리 콘텐츠 개발 과정이 설계 내용 개선에 집중될 수 있도록 다음과 같이 탄력적으로 운영할 수 있다. '① 디자인 설계 → 개발 → 평가 → 완료', '② 디자인 설계 → 개발 → 완료', '③ 디자인 설계 → 개발 → 평가 → 보완 → 디자인 설계 → 개발 → 평가 → 완료' 등으로, 이를 통해, VR 안전교육콘텐츠의 품질과 사용자의 만족도를 높일 수 있다.