• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.881-884
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• 2017

스마트 디바이스가 대중화 되면서 각종 사건 사고에 대한 데이터가 SNS 상에 실시간으로 업데이트 된다. SNS의 이런 특성을 이용하여 이용자 개개인이 사고감지센서의 역할을 하면 빠른 사고감지가 가능하다. 하지만 기존 연구들은 단순히 키워드의 출현 빈도로 사고를 판단하는 방식과, 문법파괴 요소가 많은 트위터의 특성으로 인해 정확성에서 한계를 보인다. 본 연구에서는 사고감지의 정확도를 높이기 위해 형태소로 분석한 트윗을 벡터화하여 다층퍼셉트론신경망으로 학습시키는 모델을 구현하였다. 연구 결과 일반명사로 이루어진 40개의 단어를 사용했을 때 가장 높은 82.58%의 정확도를 얻었다.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.70-77
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• 2019

Breast ultrasound readings are very important to diagnose early breast cancer. In Ultrasonic inspection, it shows a significant difference in image quality depending on the ultrasonic equipment, and there is a large difference in diagnosis depending on the experience and skill of the inspector. Therefore, objective criteria are needed for accurate diagnosis and treatment. In this study, we analyzed texture characteristics by applying GLCM (Gray Level Co-occurrence Matrix) algorithm and extracted characteristic parameters and diagnosed breast cancer using neural network classifier. Breast ultrasound images were classified into normal, benign and malignant tumors and six texture parameters were extracted. Fourteen cases of normal, malignant and benign tumor diagnosed by mammography were studied by using the extracted six parameters and learning by multi - layer perceptron neural network back propagation learning method. As a result of classification using 51 normal images, 62 benign tumor images, and 74 malignant tumor images of the learned model, the classification rate was 95.2%.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.1-8
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• 2011

In multimedia fingerprinting field, it is many used a code based on BIBD, which has a strong resiliency of anti-collusion. When a collusion-attack code is generated with a logical XOR operation using the code based on BIBD, then some cases are occurred that a colluded code could be generated to the same fingerprint of non-colluder on the other hand, the colluder is decided to the non-colluder so that he would be excepted in the colluder tracing. For solving the serious problem of the wrong decision of the colluder tracing in this paper, XOR-ACC is implemented using multi-layer perceptron neural network among (AND, OR, XOR and Averaging)-ACC by the measured correlation coefficient. Through the experiment, it confirms that XOR-ACC efficiency of multimedia fingerprinting code{7,3,1} based on BIBD is improved to 88.24% from the conventional 41.18%, so that a ratio of the colluder tracing is also improved to 100% from the conventional 53%. As a result, it could be traced and decided completely a sectional colluder and non-colluder about the collusion attacks.

• Smart Media Journal
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• v.11 no.1
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• pp.58-66
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• 2022

As Internet technology develops, SNS users are increasing. As SNS becomes popular, SNS-type crimes using the influence and anonymity of social networks are increasing day by day. In this paper, we propose a fake account classification method that applies machine learning and deep learning to statistical and image data for fake accounts classification. SNS account data used for training was collected by itself, and the collected data is based on statistical data and image data. In the case of statistical data, machine learning and multi-layer perceptron were employed to train. Furthermore in the case of image data, a convolutional neural network (CNN) was utilized. Accordingly, it was confirmed that the overall performance of account classification was significantly meaningful.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.338-342
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• 2007

In this paper we propose a user adaptive post-processing method to improve the accuracy of speaker dependent, isolated word speech recognition, particularly for mobile devices. Our method considers the recognition result of the basic recognizer simply as a high-level speech feature and processes it further for correct recognition result. Our method learns correlation between the output of the basic recognizer and the correct final results and uses it to correct the erroneous output of the basic recognizer. A multi-layer perceptron model is built for each incorrectly recognized word with high frequency. As the result of experiments, we achieved a significant improvement of 41% in recognition accuracy (41% error correction rate).

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.217-218
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• 2021

본 논문에서는 다층 퍼셉트론을 활용하여 League of Legends 게임의 승패를 예측하는 Deep Neural Network 프로그램을 설계하는 방법을 제안한다. 연구 방법으로 한국 서버의 챌린저 리그에서 행해진 약 26000 경기 데이터 셋을 분석하여, 경기 도중 15분 데이터 중 드래곤 처치 수, 챔피언 레벨, 정령, 타워 처치 수가 게임 결과에 유의미한 영향을 끼치는 것을 확인하였다. 모델 설계는 softmax 함수보다 sigmoid 함수를 사용했을 때 더 높은 정확도를 얻을 수 있었다. 실제 LOL의 프로 게임 16경기를 예측한 결과 93.75%의 정확도를 도출했다. 게임 평균시간이 34분인 것을 고려하였을 때, 게임 중반 정도에 게임의 승패를 예측할 수 있음이 증명되었다. 본 논문에서 설계한 이 프로그램은 전 세계 E-sports 프로리그의 승패예측과 프로팀의 유용한 훈련지표로 활용 가능하다고 사료된다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.27-34
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• 2005

Ventricular fibrillation(VF) is generally caused by chaotic behavior of electrical propagation in heart and may result in sudden cardiac death. In this study, we proposed a ventricular fibrillation detection algorithm based on support vector machine classifier, which could offer benefits to reduce the teaming costs as well as good classification performance. Before the extraction of input features, raw ECG signal was applied to preprocessing procedures, as like wavelet transform based bandpass filtering, R peak detection and segment assignment for feature extraction. We selected input features which of some are related to the rhythm information and of others are related to wavelet coefficients that could describe the morphology of ventricular fibrillation well. Parameters for SVM classifier, C and ${\alpha}$, were chosen as 10 and 1 respectively by trial and error experiments. Each average performance for normal sinus rhythm ventricular tachycardia and VF, was 98.39%, 96.92% and 99.88%. And, when the VF detection performance of SVM classifier was compared to that of multi-layer perceptron and fuzzy inference methods, it showed similar or higher values. Consequently, we could find that the proposed input features and SVM classifier would one of the most useful algorithm for VF detection.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.85-85
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• 2017

효율적 물관리란 거대한 물순환 과정에서 인간이 편안한 삶을 사는데 필요한 물의 이용효율을 극대화하는 것이다. 과거의 물관리는 이원화된 수량과 수질관리, 수량중심에서는 용수공급과 홍수조절이 주요한 관심사였다. 현재는 과거의 물관리에 친수와 환경을 더한 복잡한 분야로 확대되고 있다. 통합물관리란 물을 최적으로 관리하기 위해 물관리 이해당사자간의 소통과 물 기술의 고도화를 기반으로 기존에 분산된 물관리 구성요소들(시설 정보, 수량 수질 등)을 권역적으로 관리하는 것을 말한다. 본 연구에서는 대청댐 방류에 따른 금강 하류부의 홍수추적을 위해 수행한 댐하류 소유역별 강우량 빈도분석 과정, 용담댐 방류를 고려한 대청댐 홍수도달시간 검토, Poincare Section과 신경망기법을 이용한 수문자료 예측, 추계학적 다변량 해석과 다변량 신경망해석에 의한 대청댐 유입량 산정과정, 보조여수로 건설에 따른 주여수로와 보조여수로간의 연계운영방안, 단계(관심, 주의, 경계, 심각)를 고려한 대청댐 확보수위 산정, 저수지 중장기 운영계획 수립과 댐 운영 기준수위를 결정하기 위해 누가차분방식으로 적용되는 갈수기 유입량 빈도분석에 대한 실무적용 사례를 소개하고자 한다. 강우량 빈도분석 과정은 L-모멘트방법(Hosking과 Wallis, 1993)을 적용하였고, 홍수도달시간 검토는 평균유속, 하류 수위상승 기점 영향검토, 수리학적 모형(FLDWAV, Progressive lag method 등)을 활용하였다. 카오스 이론을 도입하여 대청댐 수문자료의 상관성 검토 및 추계학적 모형을 이용한 모의발생을 유도하여 수문자료 예측을 시행하였다. 추계학적 모형과 신경망모형 연구의 대상은 대청댐으로, 시계열 자료는 댐의 월강우량, 월유입량, 최고기온, 평균기온, 최소기온, 습도, 증발량 등의 자료를 기반으로 하였다. 적용기간은 1981~2009년의 자료를 이용하여 2010년 1월부터 12월까지 12개월 동안의 월유입량을 예측하였다. 수문자료 해석의 기본이 되는 약 30년간의 자료를 이용하여 분석을 실시하였다. 대청댐의 유입량 예측을 위해 적용된 모형으로는 추계학적 모형인 ARMA모형, TF모형, TFN 모형 등이 적용되었고, 또한 신경망 모형의 종류인 다층 퍼셉트론, PCA모형 등을 활용하여 실측치와 가장 가깝게 근사화시키는 방법론을 찾고자 하였다. 또한, 기존여수로와 보조여수로 연계운영을 위해 3차원 수치해석을 통한 댐하류 안정성 검토 및 확보수위 산정을 통해 단계(관심, 주의, 경계, 심각)별로 대처가 가능한 수위를 산정하였다.