• 방송공학회논문지
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• 제26권1호
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• pp.14-25
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• 2021

서로 다른 시간에 촬영된 같은 위치의 원격 탐사 영상에서 변화된 사항을 찾는 변화 탐지는 다양한 영역에 적용되기 때문에 매우 중요하다. 그러나 정합 오차, 건물 변위 오차, 그림자 오차 등이 오탐지를 발생시킨다. 이러한 문제점을 해결하기 위해 본 논문은 CADNet(Change Attention Dense Siamese Network)을 제안한다. CADNet은 다양한 크기의 변화 영역을 탐지하기 위해 FPN(Feature Pyramid Network)을 사용하며, 변화 영역에 주목하는 변화 주목 모듈을 적용하고, 낮은 수준 (Low-level)의 특징과 높은 수준 (High-level)의 특징을 모두 포함하고 있는 피처 맵을 변화 탐지에 사용하기 위해 DenseNet을 피처 추출기로 사용한다. CADNet의 성능을 Precision, Recall, F1 측면에서 측정하였을 때 WHU 데이터 세트에 대하여 98.44%, 98.47%, 98.46%이었고, LEVIR-CD 데이터 세트에 대해 90.72%, 91.89%, 91.30%이었다. 이 실험의 결과는 CADNet이 기존 변화 탐지 방법들보다 향상된 성능을 제공한다는 것을 보여준다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.942-943
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• 2021

The present work proposes a classification system for the HEp-2 cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cell images. The network takes the original cell images as the inputs and learns to reconstruct them in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning based state-of-the-art methods.

• 산업경영시스템학회지
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• 제24권66호
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• pp.1-10
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• 2001

Simulation-Based Expert System(SIMBES) is a very effective tool to solve complex antral hard problems. The SIMBES model includes a simulator, a feature extractor, a machine learning system, a performance evaluator, and a Knowledge-Based Expert System(KBES). Since SIMBES depends on Problem domains, a schedule-based material requirements planning problem, which is NP-hard, was selected to exemplify the SIMBES model. To implement the SIMBES application in Smalltalk paradigm, a system class hierarchy was constructed. The hierarchy consists of five large classes such as Job Generator, Job Scheduler, Job Evaluator, Inference Engine, and Executive System. Several classes inside these classes were identified. Additionally, instance protocols about all classes have been described in terms of messages and pseudo methods. These protocols can be implemented easily by any other object-oriented languages. Furthermore, these results may be used as a skeletal system to develop a new SIMBES efficiently, especially when the application is related to other scheduling problems.

• 한국정밀공학회지
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• 제24권11호
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• pp.126-133
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• 2007

Tuberculosis (TB) is a chronic or acute infectious disease which damages more people than any other infectious diseases according to WHO estimates. In this paper, a new automatic detection system of tuberculosis bacilli by using their color information is proposed. Through the deep investigation of color and intensity compositions of tuberculosis images, new pre-processing and segmentation algorithms are suggested. Specific features of bacilli are extracted from the processed images and number counting is done by using domain-specific knowledge rules.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 춘계학술대회
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• pp.51-55
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• 1990

In this study, a peripheral auditory model used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean placenames and syllables. As a result of this study, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation. The recurrent network in this study reflects well time information of temporal speech signal.

• 대한의용생체공학회:의공학회지
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• 제11권1호
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• pp.157-162
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• 1990

In this study, a peripheral auditory model is used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean place names and syllables. In the case of using the general learning rule, it is found that the weights are diverged for a long sequence because of the characteristics of the node function in the hidden and output layers. So, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation and to use long data. The recognition results are considerably good, even if time worping and endpoint detection are omitted and learning patterns and test patterns are made of average length of data. The recurrent network used in this study reflects well time information of temporal speech signal.

• 대한의용생체공학회:의공학회지
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• 제5권1호
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• pp.69-76
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• 1984

An automatic white blood cell differential counting system was developed, which consists of feature extractor, main control computer, auto focus and search part and data acquisition part. This system is used as a clinical instrument whose purpose is to classify white blood cell images. It may also be used for other binary image processing.

• 전자공학회논문지B
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• 제31B권12호
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• pp.116-123
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• 1994

A modified Hybrid learning rule(MHLR) is proposed, which is derived from combining the Back Propagation algorithm that is known as an excellent classifier with modified Hebbian by changing the orginal Hebbian which is a good feature extractor. The network architecture of MHLR is multi-layered neural network. The weights of MHLR are calculated from sum of the weight of BP and the weight of modified Hebbian between input layer and higgen layer and from the weight of BP between gidden layer and output layer. To evaluate the performance, BP, MHLR and the proposed Hybrid learning rule (HLR) are simulated by Monte Carlo method. As the result, MHLR is the best in recognition rate and HLR is the second. In learning speed, HLR and MHLR are much the same, while BP is relatively slow.

• 대한산업공학회지
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• 제18권2호
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• pp.99-107
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• 1992

Both simulation and expert systems are popular ways to solve complex and hard problems. However, the results of the simulation, which include a large amount of valuable information as a good knowledge source, are not used efficiently. Furthermore, the development of the expert systems can fail because there is no expert or an expert is not available. A new Simulation-Based Expert System(SIMBES) paradigm has been constructed to overcome these problems. It consists of simulator, feature extractor, machine learning system, performance evaluator and Knowledge-Based Expert System(KBES). A SIMBES was implemented for an existing schedule-based MRP system in Smalltalk/V to show how this paradigm works and experimented for a large number of jobs. The KBES and the existing system produced better schedules for 72 percent and 28 percent of the jobs, respectively.

• Asia pacific journal of information systems
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• 제1권1호
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• pp.63-86
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• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.