• 대한원격탐사학회지
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• 제37권1호
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• pp.57-67
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• 2021

An earthquake occurred on 17 November 2017 in Pohang, South Korea with a strength of 5.4 Mw. This is the second strongest earthquake recorded by local authorities since the equipment was first installed. In order to improve understanding of earthquakes and surface deformation, many studies have been conducted according to these phenomena. In this research, we will estimate the surface deformation using the Okada model equation. The SAR images of three satellites with different wavelengths (ALOS-2, Cosmo SkyMed and Sentinel-1) were used to produce the interferogram pairs. The interferogram is used as a reference for surface deformation changes by using Okada to determine the source of surface deformation that occurs during an earthquake. The Non-linear optimization (Levemberg-Marquadrt algorithm) and Monte Carlo restart was applied to optimize the fault parameter on modeling process. Based on the modeling results of each satellite data, the fault geometry is ~6 km length, ~2 km width and ~5 km depth. The root mean square error values in the surface deformation model results for Sentinel, CSK and ALOS are 0.37 cm, 0.79 cm and 1.47 cm, respectively. Furthermore, the results of this modeling can be used as learning material in understanding about seismic activity to minimize the impacts that arise in the future.

• Current Optics and Photonics
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• 제8권2호
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• pp.127-137
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• 2024

The distribution of tags is an important factor that affects the performance of radio-frequency identification (RFID). To study RFID performance, it is necessary to obtain RFID tags' coordinates. However, the positioning method of RFID technology has large errors, and is easily affected by the environment. Therefore, a new method using optical measurement is proposed to achieve RFID performance analysis. First, due to the possibility of blurring during image acquisition, the paper derives a new image prior to removing blurring. A nonlocal means-based method for image deconvolution is proposed. Experimental results show that the PSNR and SSIM indicators of our algorithm are better than those of a learning deep convolutional neural network and fast total variation. Second, an RFID dynamic testing system based on photoelectric sensing technology is designed. The reading distance of RFID and the three-dimensional coordinates of the tags are obtained. Finally, deep learning is used to model the RFID reading distance and tag distribution. The error is 3.02%, which is better than other algorithms such as a particle-swarm optimization back-propagation neural network, an extreme learning machine, and a deep neural network. The paper proposes the use of optical methods to measure and collect RFID data, and to analyze and predict RFID performance. This provides a new method for testing RFID performance.

• 정보처리학회논문지B
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• 제16B권5호
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• pp.359-366
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• 2009

구조적 생물 정보학 분야는 단백질의 3차원 구조를 대상으로 단백질을 연구하는 분야이며, 본 논문에서는 구조적 생물 정보학 분야의 핵심 연구 주제중의 하나인 Flexible 단백질 구조 정렬에 관한 새로운 알고리즘을 제시한다. Flexible 단백질 구조 정렬을 위하여, 단백질의 3차원 구조의 지역적인 유사성을 이용하여 두 단백질의 유사한 부분 구조를 추출해 내고, 이 추출된 유사 구조간에 연결 가능성을 검색하여 정렬이 가능한 모든 유사 구조를 찾고, 이 유사 구조에 꺽임점을 도입하여 Flexible 단백질 구조 정렬을 수행하였다. 이 과정에서 단백질의 지역적 유사성을 정확히 비교하기 위하여 RDA를 이용한 방법을 제안하였고, Flexible 단백질 구조 정렬시 신뢰성 있는 꺽임점 위치 선정 방법과 그래프를 이용한 최적화 방법을 제안하였다. 성능 평가를 위하여 다양한 방법으로 Flexible 단백질 구조 정렬의 성능 평가를 수행하였고, 기존의 방법인 DALI, CE, FATCAT 보다 성능의 우수함을 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4268-4290
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• 2015

Future mobile communications face enormous challenges as traditional voice services are replaced with increasing mobile multimedia and data services. To address the vast data traffic volume and the requirement of user Quality of Experience (QoE) in the next generation mobile networks, it is imperative to develop efficient content distribution technique, aiming at significantly reducing redundant data transmissions and improving content delivery performance. On the other hand, in recent years cloud computing as a promising new content-centric paradigm is exploited to fulfil the multimedia requirements by provisioning data and computing resources on demand. In this paper, we propose a cooperative caching framework which implements State based Content Distribution (SCD) algorithm for future mobile networks. In our proposed framework, cloud service providers deploy a plurality of cloudlets in the network forming a Distributed Cloud Service Network (DCSN), and pre-allocate content services in local cloudlets to avoid redundant content transmissions. We use content popularity and content state which is determined by content requests, editorial updates and new arrivals to formulate a content distribution optimization model. Data contents are deployed in local cloudlets according to the optimal solution to achieve the lowest average content delivery latency. We use simulation experiments to validate the effectiveness of our proposed framework. Numerical results show that the proposed framework can significantly improve content cache hit rate, reduce content delivery latency and outbound traffic volume in comparison with known existing caching strategies.

• 지능정보연구
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• 제22권2호
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• pp.127-142
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• 2016

딥러닝은 인공신경망(neural network)이라는 인공지능분야의 모형이 발전된 형태로서, 계층구조로 이루어진 인공신경망의 내부계층(hidden layer)이 여러 단계로 이루어진 구조이다. 딥러닝에서의 주요 모형은 합성곱신경망(convolutional neural network), 순환신경망(recurrent neural network), 그리고 심층신뢰신경망(deep belief network)의 세가지라고 할 수 있다. 그 중에서 현재 흥미로운 연구가 많이 발표되어서 관심이 집중되고 있는 모형은 지도학습(supervised learning)모형인 처음 두 개의 모형이다. 따라서 본 논문에서는 지도학습모형의 가중치를 최적화하는 기본적인 방법인 오류역전파 알고리즘을 살펴본 뒤에 합성곱신경망과 순환신경망의 구조와 응용사례 등을 살펴보고자 한다. 본문에서 다루지 않은 모형인 심층신뢰신경망은 아직까지는 합성곱신경망 이나 순환신경망보다는 상대적으로 주목을 덜 받고 있다. 그러나 심층신뢰신경망은 CNN이나 RNN과는 달리 비지도학습(unsupervised learning)모형이며, 사람이나 동물은 관찰을 통해서 스스로 학습한다는 점에서 궁극적으로는 비지도학습모형이 더 많이 연구되어야 할 주제가 될 것이다.

• 전기학회논문지
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• 제60권3호
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• 전기학회논문지
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• 제66권9호
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• pp.1392-1401
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• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• 대한원격탐사학회지
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• 제33권4호
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• pp.359-375
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• 2017

We developed fog detection algorithm (KNU_FDA) based on the optical and textural properties of fog using satellite (COMS) and ground observation data. The optical properties are dual channel difference (DCD: BT3.7 - BT11) and albedo, and the textural properties are normalized local standard deviation of IR1 and visible channels. Temperature difference between air temperature and BT11 is applied to discriminate the fog from other clouds. Fog detection is performed according to the solar zenith angle of pixel because of the different availability of satellite data: day, night and dawn/dusk. Post-processing is also performed to increase the probability of detection (POD), in particular, at the edge of main fog area. The fog probability is calculated by the weighted sum of threshold tests. The initial threshold and weighting values are optimized using sensitivity tests for the varying threshold values using receiver operating characteristic analysis. The validation results with ground visibility data for the validation cases showed that the performance of KNU_FDA show relatively consistent detection skills but it clearly depends on the fog types and time of day. The average POD and FAR (False Alarm Ratio) for the training and validation cases are ranged from 0.76 to 0.90 and from 0.41 to 0.63, respectively. In general, the performance is relatively good for the fog without high cloud and strong fog but that is significantly decreased for the weak fog. In order to improve the detection skills and stability, optimization of threshold and weighting values are needed through the various training cases.

• 한국통신학회논문지
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• 제37B권12호
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• pp.1102-1112
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• 2012

스마트폰의 급격한 보급에 따른 무선 접속망의 과부하 문제가 네트워크에서 중요한 문제로 부각되고 있다. 이 논문에서는 매크로 셀, 펨토 셀, 와이파이 접속망으로 다양하게 구성되어 있는 현재 이종 네트워크에서 접속망 과부하 문제를 해결하기 위한 최적의 셀 선정 기법과 리소스 할당 기법을 제안한다. 주어진 현재 서비스 부하 상태에서 네트워크가 동시에 추가 수용할 수 있는 사용자 수를 최대화할 수 있는 사용자-셀 간의 선정 기법을 제공한다. 이를 위해 이종 무선 접속망에서의 셀 선정 문제를 이진 정수계획 모형으로 최적화 문제를 수립하고, 이를 최적화 해법 도구를 이용하여 접속망 과부하를 억제할 수 있는 최적의 셀 선정 기법을 도출한다. 네트워크 레벨 시뮬레이션을 통해 이 논문에서 제안된 기법이 현재 무선 접속망에서 주로 사용되고 있는 국소적 셀 선정기법에 비해, 과부하가 걸린 무선 접속망에서 주어진 여러 셀들을 최대한 균등하게 효율적으로 활용함으로써 현저하게 네트워크 접속 장애율을 감소시킬 수 있음을 보인다. 또한 논문에서 사용된 이진 정수계획 모형의 최적화 문제를 푸는 데 소요되는 계산 복잡도에 대한 실험을 통해 제안된 알고리즘의 실용 가능성에 대해서 검증한다.

• 산업경영시스템학회지
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• 제40권4호
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• pp.203-210
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• 2017

Data clustering is one of the most difficult and challenging problems and can be formally considered as a particular kind of NP-hard grouping problems. The K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, it has high possibility to trap in local optimum and high variation of solutions with different initials for the large data set. Therefore, we need study efficient computational intelligence method to find the global optimal solution in data clustering problem within limited computational time. The objective of this paper is to propose a combined artificial bee colony (CABC) with K-means for initialization and finalization to find optimal solution that is effective on data clustering optimization problem. The artificial bee colony (ABC) is an algorithm motivated by the intelligent behavior exhibited by honeybees when searching for food. The performance of ABC is better than or similar to other population-based algorithms with the added advantage of employing fewer control parameters. Our proposed CABC method is able to provide near optimal solution within reasonable time to balance the converged and diversified searches. In this paper, the experiment and analysis of clustering problems demonstrate that CABC is a competitive approach comparing to previous partitioning approaches in satisfactory results with respect to solution quality. We validate the performance of CABC using Iris, Wine, Glass, Vowel, and Cloud UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KABCK (K-means+ABC+K-means) is better than ABCK (ABC+K-means), KABC (K-means+ABC), ABC, and K-means in our simulations.