• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.37-49
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• 2008

Services are reusable units in business level. Ubiquitous computing provides computing services anytime and anywhere. The combination of both is becoming an important paradigm of computing environment. Fundamentals of services require flexibility and interoperability, and key elements of ubiquitous modeling require interoperability and context-awareness. There are two kinds of methods to identify services. The top-down approach is based on business process, and the bottom-up approach is based on components. The first approach depends on experts' intuitions, while the second approach suffers the incapability of expressing non-functional expression through components. Although a feature-based approach is capable of expressing non-functional expression and identifying services in ubiquitous environment, the research on this issue is not adequately addressed by far. To promote this research, this paper proposes a feature-based method to identify services in ubiquitous computing. The method extracts initial-candidate-services from a feature model. Then, the ultimate services are identified through optimizing and analyzing the candidate-services. The proposed method is expected to enhance the service reusability by effectively analyzing ubiquitous domain based on feature, and varying reusable service units.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.342-345
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• 2014

Network intrusion detection system based on machine learning methods is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features from generally used features to detect network intrusion requires extensive computing resources. For instance, the number of possible feature combinations from given n features is $2^n-1$. In this paper, to tackle this problem we propose a optimal feature selection algorithm. Proposed algorithm is based on the local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In addition, the accuracy of clusters which obtained using selected feature components and k-means clustering algorithm is adopted to evaluate a feature assembly. In order to estimate the performance of our proposed algorithm, comparing with a method where all features are used on NSL-KDD data set and multi-layer perceptron.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.17-24
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• 2022

Mobile malicious apps are increasing rapidly, and Android, which accounts for most of the global mobile OS market, is becoming a major target of mobile cyber security threats. Therefore, in order to cope with rapidly evolving malicious apps, there is a need for detection techniques of malicious apps using machine learning, one of artificial intelligence implementation technologies. In this paper, we propose a selected feature method using feature selection and feature extraction that can improve the detection performance of malicious apps. In the feature selection process, the detection performance improved according to the number of features, and the API showed relatively better detection performance than the permission. Also combining the two characteristics showed high precision of over 93% on average, confirming that the appropriate combination of characteristics could improve the detection performance.

• Current Optics and Photonics
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• v.8 no.3
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• pp.215-224
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• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.

• Korean Institute of Interior Design Journal
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• v.23 no.5
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• pp.76-85
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• 2014

When conscious and unconscious exploring information of space-visitors which is contained in the information acquired in the process of seeing any space is analyzed, it can be found what those visitors pick up as factors in the space for its selection as visual information in order to put it into action. This study, with the object of the space reproduced in three dimensions from the cafe which was visited for conversation, has analyzed the process of acquiring space-information by sex to find out the features of scanning path, findings of which are the followings. First, the rate of scanning type of males was "Combination (50.5%)- Circulation (31.0%) and that of females "Horizontal (32.5%) - Combination (32.1%)", which shows that there was a big difference by sex in the scanning path which took place in the process of observing any space. Second, when the features of continuous observation frequency by sex is looked into, the trends of increased "horizontal" scanning and decreased "Combination" scanning of both showed the same as the frequency of continuous observations increased, while in case of "Circulation" scanning, that of females was found to decrease but that of males showed the aspect of confusion. Third, the 'Combination' scanning of males was found strong at the short observation time with three times of continuous observation frequency defined as "Attention Concentration" while the distinct feature was seen that the scanning type was dispersed to "combination-circulation" as the frequency of continuous observation increased. Females start the information acquirement with "combination-circulation" but in the process of visual appreciation they showed a strong "Horizontal" These scanning features can be defined as those by sex for acquiring space information and therefore are very significant because they are fundamental studies which will enable any customized space-design by sex.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.261-268
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• 2014

Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method's classification rate as well as detection rate under equal false positive rate are higher than conventional one.

• MALSORI
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• no.52
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• pp.145-160
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• 2004

In this paper, a new noise compensation method based on the eigenvoice framework in feature space is proposed to reduce the mismatch between training and testing environments. The difference between clean and noisy environments is represented by the linear combination of K eigenvectors that represent the variation among environments. In the proposed method, the performance improvement of speech recognition systems is largely affected by how to construct the noisy models and the bias vector set. In this paper, two methods, the one based on MAP adaptation method and the other using stereo DB, are proposed to construct the noisy models. In experiments using Aurora 2 DB, we obtained 44.86% relative improvement with eigen-environment method in comparison with baseline system. Especially, in clean condition training mode, our proposed method yielded 66.74% relative improvement, which is better performance than several methods previously proposed in Aurora project.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.370-384
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• 2016

Computer-aided materials design requires new modeling approaches to characterize and represent fine-grained geometric structures and material compositions at multiple scales. Recently, a dual-Rep approach was developed to model materials microstructures based on a new basis function, called surfacelet. As a combination of implicit surface and wavelets, surfacelets can efficiently identify and represent planar, cylindrical, and ellipsoidal geometries in material microstructures and describe the distribution of compositions and properties. In this paper, these primitive surfacelets are extended and composite surfacelets are proposed to model more complex geometries. Composite surfacelets are constructed by Boolean operations on the primitives. The surfacelet transform is applied to match geometric features in three-dimensional images. The composition of the material near the identified features can then be modeled. A cubic surfacelet and a v-joint surfacelet are developed to demonstrate the reverse engineering process of retrieving material compositions from material images.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.144-150
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• 2000

Surface roughness is one of the most important parameters to estimate quality of products. As this reason so many studies were car-ried out through various attempts that were contact or non-contact using computer vision. Even through these efforts there were few good results in this research., however texture analysis making a important role to solve these problems in various fields including universe aviation living thing and fibers. In this study feature value of co-occurrence matrix was calculated by statistic method and roughness value of worked surface was classified, of it. Experiment was carried out using input vector of neural network with characteristic value of texture calculated from worked surface image. It's found that recognition rate of 74% was obtained when adapting texture features. In order to enhance recogni-tion rate combination type in characteristics value of texture was changed into input vector. As a result high recognition rate of 92.6% was obtained through these processes.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2E
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• pp.98-104
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• 2002

The present study describes a combination method to recognize the human affective states such as anger, happiness, sadness, or surprise. For this, we extracted emotional features from voice signals and facial expressions, and then trained them to recognize emotional states using hidden Markov model (HMM) and neural network (NN). For voices, we used prosodic parameters such as pitch signals, energy, and their derivatives, which were then trained by HMM for recognition. For facial expressions, on the other hands, we used feature parameters extracted from thermal and visible images, and these feature parameters were then trained by NN for recognition. The recognition rates for the combined parameters obtained from voice and facial expressions showed better performance than any of two isolated sets of parameters. The simulation results were also compared with human questionnaire results.