• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4658-4679
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• 2017

Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.

• 방송공학회논문지
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• 제18권5호
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• pp.771-774
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• 2013

본 연구는 감정상태와 음색특성의 관계를 확인하고, 추가로 cepstral 피쳐와 조합하여 감정인식을 진행하였다. Open quotient, harmonic-to-noise ratio, spectral tilt, spectral sharpness를 포함하는 특징들을 음색검출을 위해 적용하였고, 일반적으로 사용되는 피치와 에너지를 기반한 운율피쳐를 적용하였다. ANOVA분석을 통해 각 특징벡터의 유효성을 살펴보고, sequential forward selection 방법을 적용하여 최종 감정인식 성능을 분석하였다. 결과적으로, 제안된 피쳐들으로부터 성능이 향상되는 것을 확인하였고, 특히 화남과 기쁨에 대하여 에러가 줄어드는 것을 확인하였다. 또한 음색관련 피쳐들이 cepstral 피쳐와 결합할 경우 역시 인식 성능이 향상되었다.

• 한국실내디자인학회논문집
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• 제15권6호
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• pp.150-158
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• 2006

The purpose of this study is to evaluate environment-friendly planning feature in Environment-friendly Certificated apartments and to use the basic planning data of housing. Seven Environment-friendly Certificated apartments have been analyzed. The findings of this study are as follows: Environment-friendly planning features are categorized into 4 items; floor planning feature, material planning feature, universal planning feature and environmental planning feature. Among floor planning features, natural sunlight, built-in closets, and differentiated floor plans are well considered, but the flexible floor plan for resident's lifestyle and green space are lack. Among material planning features, environment-friendly finishing materials, environment-friendly products and energy-efficient double-pane windows are well considered, but implementation for resource savings using recycled materials are lack. Among universal design planning features, removal of threshold and installation of safety device in bathroom are not well considered. Among environmental planning features, usage of alternative energy like solar energy are not applied. The environment-friendly planning features in interior space should be introduced in diverse ways.

• 정보관리학회지
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• 제27권4호
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• pp.89-107
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• 2010

오픈액세스 학술지는 무료로 원문에 접근할 수 있다는 특성을 부각시키면서 학술 연구자들이 학술정보를 찾는 주요 정보서비스로 자리를 잡아가고 있다. 그러나 최근 들어 정보환경이 변화하면서 오픈액세스 학술지 서비스의 실효성을 확보하기 위해서 서비스의 개선과 확장에 대한 필요성이 대두되고 있다. 이 연구에서는 이와 같은 환경적 변화를 반영하여 차세대 오픈액세스 학술지 서비스에 대한 방안을 기존 학술정보서비스의 서비스 요소를 분석하여 도출하고자 하였다. 분석결과 기존 학술정보서비스에서는 이용자의 참여를 유도하는 서비스가 부족한 것으로 나타났고 개방성 역시 미흡한 것으로 나타났다. 오픈액세스 차세대 모형을 위해 제시된 요소는 총 4개 영역으로 학술논문 구성 요소별 제공 및 멀티미디어 제공, 부가 정보 검색, 개방형 학술정보 공유 서비스, 모바일 서비스 등이다.

• 한국멀티미디어학회논문지
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• 제11권12호
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• pp.1635-1648
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• 2008

The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we applied three-dimensional (3D) texture feature extraction methods to cell nuclei images and evaluated the validity of them for computerized cell nuclei grading. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray-level co-occurrence matrices (3D GLCM) and 3D run length matrices (3D GLRLM). Finally, to demonstrate the suitability of 3D texture features for grading, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%. As a comparative study, we also performed a stepwise feature selection. Using the 4 optimized features, we could obtain more improved accuracy of 84.32%. Three dimensional texture features have potential for use as fundamental elements in developing a new nuclear grading system with accurate diagnosis and predicting prognosis.

• International Journal of Contents
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• 제17권4호
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• ETRI Journal
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• 제45권3호
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• pp.505-517
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• 2023

Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content-based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.25-33
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• 2023

The purpose of this study is to evaluate the impact of intermediate features on FER performance. To achieve this objective, intermediate features were extracted from the input images at specific layers (FM1~FM4) of the pre-trained network (Resnet-18). These extracted intermediate features and original images were used as inputs to the vision transformer (ViT), and the FER performance was compared. As a result, when using a single image as input, using intermediate features extracted from FM2 yielded the best performance (training accuracy: 94.35%, testing accuracy: 75.51%). When using the original image as input, the training accuracy was 91.32% and the testing accuracy was 74.68%. However, when combining the original image with intermediate features as input, the best FER performance was achieved by combining the original image with FM2, FM3, and FM4 (training accuracy: 97.88%, testing accuracy: 79.21%). These results imply that incorporating intermediate features alongside the original image can lead to superior performance. The findings can be referenced and utilized when designing the preprocessing stages of a deep learning model in FER. By considering the effectiveness of using intermediate features, practitioners can make informed decisions to enhance the performance of FER systems.

• 천문학회보
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• 제40권1호
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• pp.68.2-69
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• 2015

We present [Fe II] $1.644{\mu}m$ features around ultracompact H II regions (UCHIIs) found on a quest for the "footprint" outflow features of UCHIIs -the features produced by outflowing materials ejected during an earlier, active accretion phase of massive young stellar objects (MYSOs). We surveyed 237 UCHIIs in the first Galactic quadrant, employing the CORNISH UCHII catalog and UWIFE data, which is an imaging survey in [Fe II] $1.644{\mu}m$ performed with UKIRT-WFCAM under ~0.''8 seeing conditions. The [Fe II] features were found around five UCHIIs. We interpret the [Fe II] features to be shock-excited by outflows from YSOs and estimate the outflow mass-loss rates from the [Fe II] flux which are ${\sim}1{\times}10^{-6}-4{\times}10^{-5}M{\odot}yr^{-1}$. We propose that the [Fe II] features might be the "footprint" outflow features, but more studies are required to clarify whether or not this is the case. This is based on the morphological relation between the [Fe II] and 5 GHz radio features, the outflow mass-loss rate, the travel time of the [Fe II] features, and the existence of several YSO candidates near the UCHIIs.

• 한국실내디자인학회논문집
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• 제24권6호
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• pp.128-136
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• 2015

This research, which has been planned to appreciate the features of continuous observation of space, has applied the procedure of acquiring continuous visual information when the act of watching takes place along the time to analyze the space characteristics through the scenes and time so that the features of attention shown in the process of acquiring visual information at the time of observing continuous scenes might be estimated. For analysis of the features of continuous observation was set up the premise that the features of observation and perception vary depending on gender, when the women shops in department stores were selected as research objects. The observation features found at the time of continuous observation of selling spaces in department stores were focused on two analysis methods in order to compare the differences and characteristics of the two. The followings are the findings. First, the area with predominant observation was found to be 87.1% in both methods. It was found that the analysis of observation features by "Analysis I" was useful for inter-sectional comparison of continuous images. Second, in case of extracting predominant sections, the ceiling or the structures which are the backgrounds rarely attracted any eyes. Depending on analysis method, there was the gap of 14.3%~25.0% between observed sections. Third, in case that the hall is curved, the eyes were found to be expanded from side to side and up and down. The review of observation numbers of predominant sections makes it possible to decide whether it should be regarded as (1) unstability or (2) expanding search, and when the images are enlarged from distant view to close-range view, the weakening vanishing point results in the increase of expanded search of surroundings. Accordingly, it was found that the characteristics of images has effects on the observation features when any space was continuously observed. Furthermore, the difference of analysis methods also was found to be likely to cause big differences in the results of analyzing observation features.