• 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.

• 한국정보통신학회논문지
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• 제23권12호
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• pp.1638-1643
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• 2019

본 논문에서는 MIMO (Multiple-Input Multiple Output, 다중입출력) 시스템을 위한 적응형 섭동을 이용한 기회적 프리코딩(Adaptive Perturbation-aided Opportunistic Precoding) 방식을 제안한다. 제안 프리코딩 방식에서는 MIMO 시스템을 위한 프리코딩 행렬을 생성할 때 랜덤한 섭동 뿐 아니라 사용자로부터 받은 전송률 정보에 의해 결정되는 적응적 변화값을 함께 이용한다. 이전 시간의 랜덤 섭동이 전송속도를 상승시켰을 경우 적응형 섭동을 이전 랜덤 섭동과 동일하게 하고, 그렇지 않을 경우 이전 랜덤한 섭동 값의 음의 값에 해당하는 값을 적용시킨다. 또한 전송 속도 최적화를 위해 스케줄링에서 현재 생성된 프리코딩 행렬 뿐 아니라 메모리에 저장된 최근 프리코딩 행렬 정보도 함께 이용한다. 모의실험 결과에서 기존 프리코딩 방식에 비해 제안한 섭동 기반 기회적 프리코딩 방식이 높은 전송속도를 얻으며, 특히 사용자의 수가 적은 환경에서 전송 속도 이득이 큰 것을 확인할 수 있다.

• 한국전자파학회논문지
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• 제28권10호
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• pp.825-831
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• 2017

실제 레이다가 운용되는 환경에서 발생되는 클러터는 비균질성(heterogeneous)의 특성을 갖는 동시에 바이스태틱 레이다나 모노스태틱 non-sidelooking 레이다 구조인 경우는 클러터의 비정상성(nonstationary) 특성도 갖는다. 이러한 특성에 의해서 클러터 신호를 추정하는데 필요한 IID(Independent Identically Distributed) secondary 데이터 개수에 제약이 따르므로 클러터 억제 성능이 저하된다. 본 논문에서는 바이스태틱 레이다 환경에서 Cell under test에 대한 사전정보만을 이용하여 클러터 신호를 추정함으로써 secondary 데이터 없이 클러터를 억제하는 알고리즘을 제시한다. 바이스태틱 클러터의 angle-Doppler 스펙트럼 상에서 구조 분석을 통해 사전정보로 부터 클러터를 추정하는 것이 가능함을 보이고, 고유치 해석에 의해 클러터 억제 과정을 제시한다. 마지막으로 시뮬레이션을 통해 제시하는 클러터 억제 알고리즘의 성능을 보인다.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.1133-1138
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• 2000

This paper describes a signal recognition method for diagnosing the rotating machinery using wavelet-aided Self-Organizing Feature Map(SOFM). The SOFM specialized from neural network is a new and effective algorithm for interpreting large and complex data sets. It converts high-dimensional data items into simple order relationships with low dimension. Additionally the Learning Vector Quantization(LVQ) is used for reducing the error from SOFM. Multi-resolution and wavelet transform are used to extract salient features from the primary vibration signals. Since it decomposes the raw timebase signal into two respective parts in the time space and frequency domain, it does not lose either information unlike Fourier transform. This paper is focused on the development of advanced signal classifier in order to automatize vibration signal pattern recognition. This method is verified by the experiment and several abnormal vibrations such as unbalance and rubbing are classified with high flexibility and reliability by the proposed methods.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 춘계학술대회 논문집 방전 플라즈마 유기절연재료 초전도 자성체연구회
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• pp.100-103
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• 2004

In this paper, BP learning algorithm is studied to apply as a PD source classification in GIS specimens. For occurred partial discharge, three defected models are made; floating particle, surface discharge of spacer, needle to plane. And PD data for discrimination were acquired from PD detector. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And also these parameter is applied to classify PD sources by neural networks. Neural Networks has good recognition rate for three PD sources.

• 대한의용생체공학회:의공학회지
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• 제27권5호
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• pp.291-299
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• 2006

A Computer-Aided Diagnosis system has been examined to reduce the effort of radiologist. In this paper, we propose the algorithm using Support Vector Machine(SVM) classifier to discriminate whether microcalcifications are malignant or benign tumors. The proposed method to detect microcalcifications is composed of two detection steps each of which uses SVM classifier. The coarse detection step finds out pixels considered high contrasts comparing with neighboring pixels. Then, Region of Interest(ROI) is generated based on microcalcification characteristics. The fine detection step determines whether the found ROIs are microcalcifications or not by merging potential regions using obtained ROIs and SVM classifier. The proposed method is specified on Korean mammogram database. The experimental result of the proposed algorithm presents robustness in detecting microcalcifications than the previous method using Artificial Neural Network as classifier even when using small training data.

• 드라이브 ㆍ 컨트롤
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• 제15권4호
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• pp.30-38
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• 2018

Electronic control units (ECUs) are currently popular, and have evolved further towards the high-end application of autonomous vehicles in the automotive industry. Such digital technologies have also become widespread, in agriculture and construction equipment. Likewise, transmission control of high-speed tracked vehicles is based on the transmission control unit (TCU), performing complex gear change control functions, and diagnostic algorithms (a TCU's self-diagnostic and reporting capability of malfunction data through CAN communication). Since all functions of TCU are implemented by embedded-software, it is hardly possible to analyze specifications by reverse engineering. In this paper a real-time transmission simulator adaptable to TCU is presented, for analysis of diagnosis algorithm and standards. Signal simulation circuits are deliberately designed considering electrical characteristics of TCU inputs and various analysis tools, such as analog input auto scan function, and global output enable switch, are implemented in software. Test results from hardware-in-the-loop simulator verify tolerance time for each error, as well as cause of fault, error reset conditions.

• Asian Pacific Journal of Cancer Prevention
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• 제17권2호
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• pp.835-838
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• 2016

Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

• 한국CDE학회논문집
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• 제6권1호
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• pp.17-23
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• 2001

Lasting machines for shoe manufacturing are continuously developed with the aid of automation and CAM(Computer Aided Manufacturing). Although automation and CAM techniques have tremendously reduced the labor in shoe manufacturing, there still remain some parts manufactured by experts. In order to enhance the capability and efficiency of machines for labor-free shoe manufacturing, CAD data of a shoe last is essential. While CAD datarization takes the fundamental role in the shoe design and manufacturing, there has been little research for the CAD datarization of a shoe last. In this paper, a new procedure for CAD datarization of a shoe last using finite element patches and a tension sl)line method is proposed for application to shoe manufacturing machines. The outer line of a shoe-last sole is interpolated by a tension spline method and bonding lines are extracted from the shoe CAD data. Data set for a control algorithm of the tasting machine can be produced from the CAD data.