• 한국지능시스템학회논문지
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• 제18권3호
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• pp.373-380
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• 2008

지금까지 기계 기반의 필기 숫자 인식 방법에 대한 많은 연구가 진행되어 왔다. 그러나 여전히 인간이 만족할 만한 인식 성능을 이루지는 못하였다. 이러한 배경에는 단순히 인식률을 나타내는 수치가 낮은 것도 한 부분을 차지 하지만, 인간이 수긍할 수 없는 오류 성향도 중요한 부분을 차지한다. 그러므로 본 논문에서는 실제 인간의 숫자 인식이 어떻게 이루어지는지를 확인하는 실험을 먼저 수행하고, 이것에 근거하여 기계 인식을 위하여 필요한 요소들이 무엇인지를 고찰하도록 하였다. 실험결과 한쪽 또는 양쪽 방향으로 혼동하는 숫자 쌍, 전혀 혼동하지 않는 숫자 쌍, 오류 발생의 중복성 등의 결과를 비교 분석하여 인간이 인식과정에서 중요하게 고려하는 특징들을 찾아냈고, 그 결과에 근거하여 기계 인식에 있어서 더 높은 인식 성능을 발휘할 수 있고, 더 나아가 인간적인 측면에서 보다 더 신뢰할 수 있는 인식결과를 이끌어 낼 수 있는 접근 방향에 대하여 제시하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.201-203
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• 2005

The best method to study language is to talking with a native speaker. A voice recognition technology can be used to develope a language study machine. SD(Speaker dependant) and SI(speaker independant) voice recognition method is used for the language study machine. MP3 Player. FM Radio. Alarm clock functions are added to enhance the value of the product. The machine is designed with a DSP(Digital Signal Processing) chip for voice recognition. MP3 encoder/decoder chip. FM tumer and SD flash memory card. This paper deals with the application of SD ad SD voice recognition. flash memory file system. PC download function using USB ports, English conversation text function by the use of SD flash memory. LCD display control. MP3 encoding and decoding, etc. The study contents are saved in SD flash memory. This machine can be helpful from child to adult by changing the SD flash memory.

• 한국멀티미디어학회논문지
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• 제24권4호
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

• 센서학회지
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• 제30권2호
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• 한국기계가공학회지
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• 제8권2호
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• pp.3-9
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• 2009

When washing machine is rotated in the laundry, it tends to lean toward one side. This tendency causes a serious vibration. The balance of washing machine plays an important role in order to reduce the vibration by injecting the sand or the salt water into the balance of washing machine. The hot plate welder is used to prevent from outflow of contents. The hot plate welder brings about many problems which is concerned with accidents. The direction recognition and location information of the balance are required in this system. In this paper, the recognition direction of balance in washing machine using machine vision system is studied. The template matching algorithm compares sub-image with original image acquired in real-time to obtain a center point of balance image. The mid points and the edges of balance are estimated by the edge detection and gauging algorithms. The data acquired by these results is used for recognition direction of balance. The automation software for image processing is developed by using LabVIEW.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.69-73
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• 2017

본 논문에서는 기계학습 기반의 실시간 이미지 인식 알고리즘을 개발하고 개발한 알고리즘의 성능을 테스트 하였다. 실시간 이미지 인식 알고리즘은 기계 학습된 이미지 데이터를 바탕으로 실시간으로 입력되는 이미지를 인식한다. 개발한 실시간 이미지 인식 알고리즘의 성능을 테스트하기 위해 자율주행 자동차 분야에 적용해보았고 이를 통해 개발한 실시간 이미지 인식 알고리즘의 성능을 확인해보았다.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.361-370
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• 2020

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1032-1035
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• 2003

The field of handwriting recognition has been researched for many years. A hybrid classifier has been proven to be able to increase the recognition rate compared with a single classifier. In this paper, we combine support vector machine (SVM) and hidden Markov model (HMM) for offline handwritten numeral recognition. To improve the performance, we extract features adapted for each classifier and propose the modified SVM decision structure. The experimental results show that the proposed method can achieve improved recognition rate for handwritten numeral recognition.

• 한국정밀공학회지
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• 제16권12호
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• pp.119-125
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• 1999

This paper gives an overview of the various approaches to designing statistical pattern recognition scheme based on Bayes discrimination rule and the artificial neural networks for rotating machine condition classification. Concerning to Bayes discrimination rule, this paper contains the linear discrimination rule applied to classification into several multivariate normal distributions with common covariance matrices, the quadratic discrimination rule under different covariance matrices. Also we discribes k-nearest neighbor method to directly estimate a posterior probability of each class. Five features are extracted in time domain vibration signals. Employing these five features, statistical pattern classifier and neural networks have been established to detect defects on rotating machine. Four different cases of rotation machine were observed. The effects of k number and neural networks structures on monitoring performance have also been investigated. For the comparison of diagnosis performance of these two method, their recognition success rates are calculated form the test data. The result of experiment which classifies the rotating machine conditions using each method presents that the neural networks shows the highest recognition rate.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.