• 한국정보과학회논문지:소프트웨어및응용
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• 제29권3호
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• pp.180-191
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• 2002

본 논문은 MLP와 MultiCAMShift 알고리즘을 이용한 텍스춰 기반의 영상 내 문자 추출 방법을 제안한다. MLP를 이용한 텍스춰 분석기는 별도의 특징값 추출 단계 없이 다양한 환경의 입력 영상에 대해 효과적으로 문자 확률 영상을 생성하며, 문자 확률 영상 상에서 수행되는 MultiCAMShift 알고리즘은 국소 탐색만으로 효율적으로 문자 영역을 추출할 수 있다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2002년도 추계학술발표논문집 (상)
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• pp.377-380
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• 2002

신경회로망은 문자인식, 자동제어 등의 여러 분야에 널리 쓰이는 방식이다. 그러나 신경회로망을 구현하는데는 연산량이 많아서 실시간으로 구현하기에 어려움이 많이 따른다. 본 논문은 신경회로망을 구현하는데 필요한 연산을 살펴보고 그 연산을 구현하는 방법을 비교 분석하였다. 신경회로망을 구현하기 위해 DSP(Digital Signal Processor), PC의 FPU(Floating Point Unit), Intel사의 Pentium 계열 프로세서에서 지원하는 SIMD(Single Instruction Multiple Data) 기술을 사용하여 결과를 비교 분석 하였다. 신경회로망의 핵심인 MLP(Multi Layer Perceptron) 연산에 대해 실험한 결과 SIMD 기술을 이용하는 방법이 다른 방법에 비해 2배이상 좋은 결과를 나타내었다.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.131-135
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• 2021

Numbers of studies related to optimization of design of organic light emitting diodes(OLED) through machine learning are increasing. We propose the generative method of the image to assess the performance of the device combining with machine learning technique. Principle parameter regarding dark spot growth mechanism of the OLED can be the key factor to determine the long-time performance. Captured images from actual device and randomly generated images at specific time and initial pinhole state are fed into the deep neural network system. The simulation reinforced by the machine learning technique can predict the device parameters accurately and faster. Similarly, the inverse design using multiple layer perceptron(MLP) system can infer the initial degradation factors at manufacturing with given device parameter to feedback the design of manufacturing process.

• Computers and Concrete
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• 제32권3호
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1220-1225
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• 2010

This paper proposes a novel active noise control (ANC) approach that uses an IIR filter and neural network techniques to effectively reduce interior noise. We construct a multiple-channel IIR filter module which is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimization based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Lastly, we present the results of a numerical study to test our ANC methodology with realistic interior noise measurement obtained from Korean railway trains.

• 콘크리트학회논문집
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• 제27권4호
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• pp.331-341
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• 2015

본 연구는 섬유보강 콘크리트의 실무 적용을 위한 성능 평가에 대해 재료 시험으로 낭비되던 시간과 노력을 최소화하고 적용에 있어서의 이론적인 배경을 확보하기 위해, 기존의 가이드라인 및 시험 기준에 따른 실험 결과의 수집과 통계적 분석을 통한, 콘크리트의 압축강도에 기반한 주요 특성들을 특정하기 위해 수행되었다. 섬유보강콘크리트는 다양한 변수에 영향을 받게 되므로 이론적인 접근이 어려운 측면이 있어 본 연구에서는 현재 실무에서 다방면으로 사용되고 있는 100MPa 이하의 압축강도를 가지는 콘크리트를 중심으로 0.25%에서 2% 사이의 강섬유 혼입량에 대한 압축강도와 인장강도 시험을 수행하였다. 인장강도 시험은 표준기관에서 정하고 있는 시험방법인 쪼갬인장강도와 휨인장강도에 대해 수행하였다. 섬유보강콘크리트의 재료시험 결과 쪼갬인장강도와 휨인장강도 모두 압축강도의 증가에 따라 증가하는 추세를 보였으며 강도의 증진률은 압축강도 증가와 함께 감소하는 추세를 보였다. 또한섬유의 혼입량 증가는 인장강도의 증가를 유발하는 것을 확인할 수 있었으며, 압축강도 증가에 따른 인장강도 증진률 감소를 막아 콘크리트 압축강도 증가에 선형적으로 인장강도가 증가하도록 해주는 것을 확인할 수 있었다. 기존 연구들로부터 구축한 데이터베이스를 통한 섬유보강콘크리트의 기계적 성질에 대한 검토를 수행하였다. 다양한 변수에 따른 인장강도의 추정을 위해 인공신경망을 적용하였다. 인공신경망은 multi layer perceptron으로 구성하였으며 전달함수로는 sigmoid 함수를 사용하였고 역전파 알고리즘을 통해 학습을 수행하였다. 인공신경망을 사용한 콘크리트 인장강도의 추정 결과 시험 결과와 추정결과가 유사하게 나타나는 것을 확인할 수 있었다. 인공신경망에서 결합력이 큰 변수들은 물-시멘트비와 섬유의 혼입량으로 나타났으며 섬유보강콘크리트의 인장강도는 물-시멘트비에 영향을 받는 압축강도와 혼입량을 통해 추정할 수 있을 것으로 판단된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권7호
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• pp.1657-1670
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• 2013

The increase in the number of music files in smart phone and MP3 player makes it difficult to find the music files which people want. So, Query-by-Singing/Humming (QbSH) systems have been developed to retrieve music from a user's humming or singing without having to know detailed information about the title or singer of song. Most previous researches on QbSH have been conducted using musical instrument digital interface (MIDI) files as reference songs. However, the production of MIDI files is a time-consuming process. In addition, more and more music files are newly published with the development of music market. Consequently, the method of using the more common MPEG-1 audio layer 3 (MP3) files for reference songs is considered as an alternative. However, there is little previous research on QbSH with MP3 files because an MP3 file has a different waveform due to background music and multiple (polyphonic) melodies compared to the humming/singing query. To overcome these problems, we propose a new QbSH method using MP3 files on mobile device. This research is novel in four ways. First, this is the first research on QbSH using MP3 files as reference songs. Second, the start and end positions on the MP3 file to be matched are estimated by using multi-layered perceptron (MLP) prior to performing the matching with humming/singing query file. Third, for more accurate results, four MLPs are used, which produce the start and end positions for dynamic time warping (DTW) matching algorithm, and those for chroma-based DTW algorithm, respectively. Fourth, two matching scores by the DTW and chroma-based DTW algorithms are combined by using PRODUCT rule, through which a higher matching accuracy is obtained. Experimental results with AFA MP3 database show that the accuracy (Top 1 accuracy of 98%, with an MRR of 0.989) of the proposed method is much higher than that of other methods. We also showed the effectiveness of the proposed system on consumer mobile device.

• 정보처리학회논문지B
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• 제11B권6호
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• pp.735-742
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• 2004

본 논문은 일반적인 그래픽스 하드웨어를 이용하여 더욱 빠른 신경망을 구현하고, 구현된 시스템을 영상 처리 분야에 적용함으로써 효용성을 검증한다. GPU의 병렬성을 효율적으로 사용하기 위하여, 다수의 입력벡터와 연결가중치벡터를 모아서 많은 내적연산을 하나의 행렬곱 연산으로 대체하였고, 시그모이드와 바이어스 항 덧셈 연산도 GPV 상에서 픽셀세이더로 구현하였다. ATI RADEON 9800 XT 보드를 이용하여 구현된 신경망 시스템은 CPU를 사용한 기존의 시스템과 비교하여 정확도의 차이 없이 30배 정도의 속도 향상을 얻을 수 있었다.

• 한국산업융합학회 논문집
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• 제23권2_2호
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• pp.163-171
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• 2020

The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.