• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.37-45
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• 2013

Context dependent fusion (CDF) is a fusion algorithm that combines multiple outputs from different classifiers to achieve better performance. CDF tries to divide the problem context into several homogeneous sub-contexts and to fuse data locally with respect to each sub-context. CDF showed better performance than existing methods, however, it is sensitive to noise due to the large number of parameters optimized and the innate linearity limits the application of CDF. In this paper, a variant of CDF using support vector machines (SVMs) for fusion and kernel principal component analysis (K-PCA) for context extraction is proposed to solve the problems in CDF, named CDF-SVM. Kernel PCA can shape irregular clusters including elliptical ones through the non-linear kernel transformation and SVM can draw a non-linear decision boundary. Regularization terms is also included in the objective function of CDF-SVM to mitigate the noise sensitivity in CDF. CDF-SVM showed better performance than CDF and its variants, which is demonstrated through the experiments with a landmine data set.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.4
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• pp.41-53
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• 2018

Recently, the development of machine learning technology has led to the application of deep learning technology to existing image based application systems. In this context, some researches have been made to apply CNN (Convolutional Neural Network) to the field of fire detection. To verify the effects of existing preprocessing and feature extraction methods on fire detection when combined with CNN, in this paper, the recognition performance and learning time are evaluated by changing the VGG19 CNN structure while gradually increasing the convolution layer. In general, the accuracy is better when the image is not preprocessed. Also it's shown that the preprocessing method and the feature extraction method have many benefits in terms of learning speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.97-100
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• 2007

In order to overcome many defects in the current JPEG standard of still image compression, the new JPEG2000 standard has been development. The JPEG2000 standard is based on the principles of DWT and EBCOT Entropy Coding. EBCOT(Embedded block coding with optimized truncation) is the most important technology in the latest image-coding standard, JPEG2000. However, EBCOT occupies the highest computation time to operate bit-level processing. Therefore, many researches have achieved methods to minimize computation speed of EBCOT. Thus, this paper proposes the method of context-extraction that improves computational architecture. This paper proposes efficient context coding method. The proposed algorithm would apply to hard-wired JPEG2000 Encoder that is used for compression of high resolution image.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.653-662
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• 2006

In ubiquitous computing that is to build computing environments to provide proper services according to user's context, human being's emotion recognition based on facial expression is used as essential means of HCI in order to make man-machine interaction more efficient and to do user's context-awareness. This paper addresses a problem of rigidly basic emotion recognition in context-sensitive facial expressions through a new Bayesian classifier. The task for emotion recognition of facial expressions consists of two steps, where the extraction step of facial feature is based on a color-histogram method and the classification step employs a new Bayesian teaming algorithm in performing efficient training and test. New context-sensitive Bayesian learning algorithm of EADF(Extended Assumed-Density Filtering) is proposed to recognize more exact emotions as it utilizes different classifier complexities for different contexts. Experimental results show an expression classification accuracy of over 91% on the test database and achieve the error rate of 10.6% by modeling facial expression as hidden context.

• Land and Housing Review
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• v.14 no.4
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• pp.95-102
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• 2023

Numerous investigations have explored the integration of machine leaning algorithms with high-resolution drone image for object detection in urban settings. However, a prevalent limitation in vehicle extraction studies involves the reliance on bounding boxes rather than instance segmentation. This limitation hinders the precise determination of vehicle direction and exact boundaries. Instance segmentation, while providing detailed object boundaries, necessitates labour intensive labelling for individual objects, prompting the need for research on automating unsupervised instance segmentation in vehicle extraction. In this study, a novel approach was proposed for vehicle extraction utilizing unsupervised SVM classification applied to vehicle bounding boxes in drone images. The method aims to address the challenges associated with bounding box-based approaches and provide a more accurate representation of vehicle boundaries. The study showed promising results, demonstrating an 89% accuracy in vehicle extraction. Notably, the proposed technique proved effective even when dealing with significant variations in spectral characteristics within the vehicles. This research contributes to advancing the field by offering a viable solution for automatic and unsupervised instance segmentation in the context of vehicle extraction from image.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.123-131
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• 2020

Along with the rapid development of artificial intelligence technology, natural language processing, which deals with human language, is also actively studied. In particular, BERT, a language model recently proposed by Google, has been performing well in many areas of natural language processing by providing pre-trained model using a large number of corpus. Although BERT supports multilingual model, we should use the pre-trained model using large amounts of Korean corpus because there are limitations when we apply the original pre-trained BERT model directly to Korean. Also, text contains not only vocabulary, grammar, but contextual meanings such as the relation between the front and the rear, and situation. In the existing natural language processing field, research has been conducted mainly on vocabulary or grammatical meaning. Accurate identification of contextual information embedded in text plays an important role in understanding context. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn context easily from computer. In this paper, we propose a system to extract Korean contextual information using pre-trained BERT model with Korean language corpus and knowledge graph. We build models that can extract person, relationship, emotion, space, and time information that is important in the text and validate the proposed system through experiments.

• Journal of Mushroom
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• v.18 no.3
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• pp.244-253
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• 2020

The aim of this study was to investigate the biological activity of extracts obtained from the mycelium, sclerotium, and fruiting body of Wolfiporia cocos using different extraction solvents (hot water, 70% ethanol, and 70% methanol). Among the three developmental stages, the mycelium extracts showed the highest DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity, ferric reducing antioxidant power (FRAP), nitrite scavenging activity, and total polyphenolic content. Among the extraction solvents in the context of the W. cocos mycelium, the DPPH radical scavenging activity, FRAP, and total polyphenol content in the hot-water extracts were significantly higher than those in the other extracts. In the case of the sclerotium, the reducing power, nitrite scavenging activity, and total polyphenol content were significantly higher in 70% ethanolic extracts. The fruiting body showed the highest DPPH radical scavenging activity, reducing power, nitrite scavenging activity, and total polyphenol content in the context of hot-water extraction. Moreover, the β-glucan content was significantly higher in the sclerotium versus the mycelium or fruiting body. The total amino acid and total essential amino acid contents were remarkably higher in the mycelium and fruiting body than in the sclerotium; of note, and arginine (Arg) and phenylalanine (Phe) were highly detected among the amino acid components.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.115-117
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• 2004

인간과 인간 사이에 컨텍스트의 역할이 중요한 것처럼 기계가 컨텍스트를 인식할 수 있는 능력을 갖추는 것은 중요하다. 특히 지능적인 서비스를 제공하기 위해서는 고수준 컨텍스트를 추출하는 것이 필요하고, 최근 베이지안 네트워크를 이용해 컨텍스트를 추출하려는 연구가 많이 있었다. 그러나 대부분은 단순한 컨텍스트를 추출하는 연구들이고, 상황이나 사용자에 따라 다른 특성을 보이는 경우에 대한 처리는 하지 못하고 있다. 본 논문은 퍼지 소속 함수를 통해 각 센서에서 오는 정보를 전 처리하고, 이를 베이지안 네트워크를 이용해 고수준 컨텍스트로 추출하는 방법을 제안한다. 특히 여러 개의 퍼지 노드가 있을 경우 퍼지 소속값의 곱을 사용하여 베이지안 추론에 적용하였다. 각 센서의 정보를 처리하는 퍼지 소속 함수는 사용자가 쉽게 설계할 수 있고, 컨텍스트 추출모듈과 별개로 설계가 가능하기 때문에 베이지안 네트워크의 유연하고 적응적인 특성을 유지하면서 개인화가 가능하다. 제안한 방법의 유용성을 보이기 위해 실제 세계의 문제를 모델링한 베이지안 네트워크의 예를 보이고 이를 분석한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.343-350
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• 2015

This paper presents a context recognition method using environmental sound signals, which is applied to a mobile-based client monitoring system. Seven acoustic contexts are defined and the corresponding environmental sound signals are obtained for the experiments. To evaluate the performance of the context recognition, MFCC and LPCC method are employed as feature extraction, and statistical pattern recognition method are used employing GMM and HMM as acoustic models, The experimental results show that LPCC and HMM are more effective at improving context recognition accuracy compared to MFCC and GMM respectively. The recognition system using LPCC and HMM obtains 96.03% in recognition accuracy. These results demonstrate that LPCC is effective to represent environmental sounds which contain more various frequency components compared to human speech. They also prove that HMM is more effective to model the time-varying environmental sounds compared to GMM.

• International Journal of Advanced Culture Technology
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• v.6 no.2
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• pp.117-122
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• 2018

As evidenced through rapidly growing digital repositories and web resources, automatic metadata generation is becoming ever more critical, especially considering the costly and complex operation of manual metadata creation. Also, automatic metadata generation is apt to consistent metadata application. In this sense, metadata quality and interoperability can be enhanced by utilizing a mechanism for automatic metadata generation. In this article, a mechanism of automatic metadata extraction called ExMETA is introduced in order to alleviate issues dealing with inconsistent metadata application and semantic interoperability across ever-growing digital collections. Conceptual graph, one of formal languages that represent the meanings of natural language sentences, is utilized for ExMETA as a mediation mechanism that enhances the metadata quality by disambiguating semantic ambiguities caused by isolation of a metadata element and its corresponding definition from the relevant context. Hence, automatic metadata generation by using ExMETA can be a good way of enhancing metadata quality and semantic interoperability.