• 한국공간정보시스템학회:학술대회논문집
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• 한국공간정보시스템학회 2002년도 춘계학술대회 논문집
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• pp.45-52
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• 2002

Artificial neural networks (ANN) have been successfully used for classifying remotely sensed imagery. However, ANN still is not the preferable choice for classification over the conventional classification methodology such as the maximum likelihood classifier commonly used in the industry production environment. This can be attributed to the ANN characteristic built-in stochastic process that creates difficulties in dealing with unequally represented training classes, and its training performance speed. In this paper we examined some practical aspects of training classes when using a back propagation neural network model for remotely sensed imagery. During the classification process of remotely sensed imagery, representative training patterns for each class are collected by polygons or by using a region-growing methodology over the imagery. The number of collected training patterns for each class may vary from several pixels to thousands. This unequally populated training data may cause the significant problems some neural network empirical models such as back-propagation have experienced. We investigate the effects of training over- or under- represented training patterns in classes and propose the pattern repopulation algorithm, and an adaptive alpha adjustment (AAA) algorithm to handle unequally represented classes. We also show the performance improvement when input patterns are presented in random fashion during the back-propagation training.

• 디지털융복합연구
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• 제12권3호
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• pp.249-254
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• 2014

무선 랜 시스템은 고속의 데이터를 전송하기 위하여 끊임없이 진화 중이며 통신성능을 향상시키기 위해서는 더욱 정밀한 채널추정이 필수적으로 요구된다. 본 논문에서는 IEEE 802.11 기반 무선 모뎀의 PLCP 구조에서 기존의 긴 훈련심볼만을 이용하여 채널을 추정하는 LS 알고리즘의 성능을 개선하고자 하였다. 48개의 부반송파 중에서 12개의 위치에 짧은 훈련 심볼을 전송하고 있다는 사실을 이용하여 2개의 긴 훈련심볼 뿐만 아니라 하나의 짧은심볼도 함께 사용하여 채널을 추정하는 새로운 LS 추정 알고리즘을 제안하였다. 두 개의 긴 훈련심볼 뿐만 아니라 짧은 훈련 심볼을 이용함으로써 보다 향상된 채널 추정을 제공할 수 있음을 보였으며 제안된 채널 추정알고리즘은 IEEE 802.11p WAVE 차량통신 시스템에도 적용이 가능하리라 생각된다. 또한 학부 및 대학원의 OFDM 관련 채널 추정 교육 시 본 논문의 내용이 유용하게 사용될 수 있을 것이다.

• Journal of information and communication convergence engineering
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• 제13권2호
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.997-1002
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• 1994

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. Those models can effectively normalize the temporal and spatial variability of speech signals. But those models suffer from poor discrimination between acoustically similar words. In this paper, we propose a discriminative training algorithm for predictive neural network models based on a generalized probabilistic descent (GPD) algorithm and minimum classification error formulation (MCEF). The Evaluation of our training algorithm on ten Korean digits shows its effectiveness by 40% reduction of recognition error.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.298-300
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• 2021

국방부에서는 "국방개혁 2.0"의 과학화 훈련체계 확대에 따라 가상현실·증강현실 기반의 실감형 전투 모의 훈련체계를 구축하기로 하였다. 실감형 전투 모의 훈련체계는 훈련자간 교전을 통해 실전과 같은 긴장감 조성과 훈련효과를 극대화 할 수 있어야 한다. 또한, 엄폐훈련을 통해 실전과 유사한 사격훈련과 동시에 생존훈련 효과 배가가 가능해야 한다. 선행 연구들은 훈련자의 사격 정밀도를 향상 시키기에는 적합한 기술이지만 실전과 같이 쌍방 교전을 연습하기는 어려우며 특히 엄폐물을 활용한 전투사격 훈련을 하기에는 부족한 점이 있다. 따라서 본 논문에서는 가상 사격 훈련자의 스크린에 상대 훈련자의 사격 자세를 인식하여 가상의 아바타를 생성하기 위해 Depth센서를 통해 취득된 깊이 정보를 토대로 훈련자와 엄폐물을 인지하고 훈련자의 자세를 추정할 수 있는 S/W 알고리즘을 제시한다.

• 전기학회논문지
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• 제60권3호
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• pp.654-661
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• 2011

This paper is about a personal identification algorithm using an ECG that has been studied by a few researchers recently. Previously published algorithm can be classified as two methods. One is the method that analyzes of ECG features and the other is the morphological analysis of ECG. The main characteristic of proposed algorithm can be classified the method of analysis ECG features. Proposed algorithm adopts DSTW(Down Slope Trace Wave) for extracting ECG features, and applies SVM(Support Vector Machine) to training and testing as a classifier algorithm. We choose 18 ECG files from MIT-BIH Normal Sinus Rhythm Database for estimating of algorithm performance. The algorithm extracts 100 heartbeats from each ECG file, and use 40 heartbeats for training and 60 heartbeats for testing. The proposed algorithm shows clearly superior performance in all ECG data, amounting to 93.89% heartbeat recognition rate and 100% ECG recognition rate.

• 한국컴퓨터산업학회논문지
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• 제2권4호
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• pp.537-546
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• 2001

DSSS(Direct-Sequence Spread-Spectrum) CDMA 시스템에서 MAI(Multiple Access Interference)와 원근 문제를 해결할 수 있는 단일-사용자 검파에 적합한 알고리즘으로 Griffiths’알고리즘과 LCCMA(Linearly Constrained Constant Modulus Algorithm)에 제안되었으며 MMSE 검파기에 적합한 다중-사용자 알고리즘인 MOE 알고리즘 또한 제안되었다. 본 논문은 training sequence의 요구 없이 시스템의 성능을 향상시킬 수 있는 이 세 가지 Blind 적합 알고리즘을 가지고 간섭 사용자의 수나 원하는 사용자의 데이터 업데이트율에 따라 각각의 알고리즘별 성능을 비교 분석하였다. 시뮬레이션 결과 간섭 사용자수와 원하는 사용자의 업데이트율의 변화에 따라 모두 LCCMA 알고리즘이 뛰어난 성능을 보았다. Blind 적용은 하나의 training sequence의 필요성을 없앰으로써 더욱 융통성 있는 네트웍디자인을 가능케 했다.

• ETRI Journal
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• 제34권5호
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• pp.696-705
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• 2012

Accurate synthesis models based on artificial neural networks (ANNs) are proposed to directly obtain the physical dimensions of an asymmetric coplanar waveguide with conductor backing and substrate overlaying (ACPWCBSO). First, the ACPWCBSO is analyzed with the conformal mapping technique (CMT) to obtain the training data. Then, a modified genetic-algorithm-Levenberg-Marquardt (GA-LM) algorithm is adopted to train ANNs. In the algorithm, the maximal relative error (MRE) is used as the fitness function of the chromosomes to guarantee that the MRE is small, while the mean square error is used as the error function in LM training to ensure that the average relative error is small. The MRE of ANNs trained with the modified GA-LM algorithm is less than 8.1%, which is smaller than those trained with the existing GA-LM algorithm and the LM algorithm (greater than 15%). Lastly, the ANN synthesis models are validated by the CMT analysis, electromagnetic simulation, and measurements.

• 한국소음진동공학회논문집
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• 제12권10호
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.52-59
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• 2014

Operator training simulator, within a training environment designed to understand the principles and behavior of the railway system with respect to operator's entries and predefined scenario, can provide a very strong benefit in facilitating operators' handling undesired operations. This simulator consists of computer system and applications, and the purpose of applications is to generate the power and voltage and analyze the AC substation and DC railway, respectively. This paper describes a novel approach to the new techniques for AC/DC hybrid simulation for the operator training simulator in the railway system. We first propose the structure the database of railway system. Then, topology processing and power flow using a linked-list method based on the proposed database, full or decoupled newton-rapshon methods are presented. Finally, the interface between the analysis for AC substation using a newton-rapshon method and the analysis for DC railway system using a time-interval power flow method is described. We have verified and tested the developed algorithm through the extensive testing for the proposed test system. To demonstrate the validity of the developed algorithm, comparative simulations between the proposed algorithm and PSS/E for the test system were conducted.