• Journal of Agricultural Extension & Community Development
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• v.14 no.2
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• pp.437-469
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• 2007

The objectives of this study were 1) to explore the change features of rural development policy system, and 2) to get some policy counter measures for construction of desirable rural development policy system. First, the change features of rural development policy system are 1) to expand the rural development organization of local government, 2) to strengthen the finance basis for rural development policy, 3) to attempt building the cooperation network between the various local groups. And the policy counter measures derived from this study are 1) to convert the rural development policy system to that of local leading system, 2) to expand the actual rural inhabitant participation in policy making & performance process, 3) to prepare the effective governance system in local level.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.834-842
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• 2015

We propose a method to construct composite feature vector based on discriminant analysis for face recognition. For this, we first extract the holistic- and local-features from whole face images and local images, which consist of the discriminant pixels, by using a discriminant feature extraction method. In order to utilize both advantages of holistic- and local-features, we evaluate the amount of the discriminative information in each feature and then construct a composite feature vector with only the features that contain a large amount of discriminative information. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed composite feature vector has improvement of face recognition performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2407-2424
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• 2022

Recently, Transformer has made great progress in video retrieval tasks due to its high representation capability. For the structure of a Transformer, the cascaded self-attention modules are capable of capturing long-distance feature dependencies. However, the local feature details are likely to have deteriorated. In addition, increasing the depth of the structure is likely to produce learning bias in the learned features. In this paper, an improved Transformer structure named TransDCS (Transformer with Dynamic Convolution and Shortcut) is proposed. A Multi-head Conv-Self-Attention module is introduced to model the local dependencies and improve the efficiency of local features extraction. Meanwhile, the augmented shortcuts module based on a dual identity matrix is applied to enhance the conduction of input features, and mitigate the learning bias. The proposed model is tested on MSRVTT, LSMDC and Activity-Net benchmarks, and it surpasses all previous solutions for the video-text retrieval task. For example, on the LSMDC benchmark, a gain of about 2.3% MdR and 6.1% MnR is obtained over recently proposed multimodal-based methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.155-164
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• 2014

Local histogram equalization is one of the most popular ways of enhancing the local brightness features of an input image. However, local histogram equalization reveals some problems. First, undesired artifacts are produced by over-enhancing the local features. Second, the enhancement of local features does not always result in global contrast enhancement. To cope with these problems, we propose an illumination driven local histogram equalization method. First, to estimate the illumination information, the proposed method combines the input image and the blurred image produced through the process of the down-sampling and the up-sampling. Next, the proposed method adaptively adjusts the mapping function estimated by the local histogram equalization using the information of the illumination. As a result, the proposed illumination information driven local histogram equalization method simultaneously enhances the global and the local contrast levels while preventing any local artifacts. Experimental results show that the proposed algorithm outperforms the conventional methods on objective and subjective criteria.

• The Transactions of the Korea Information Processing Society
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• v.2 no.5
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• pp.645-654
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• 1995

A distributed DBMS integrates local schemas that are independently designed, maintained, and managed by different users at each site providing a global virtual schema. This global schema supports users at a specific site to transparently utilize local database at different sites. The security features of the local schema should also not be changed in the global schema integrating security features of each local schema. Researches on the integration of security features into local schema in distributed DBMS environment, however, are very rate. This pater using the multilevel secure object-oriented database model(as the model for the definition of a local schema in distributed environment) which is an extension of the object-oriented models. It also suggests eight integration methods that can maintain the security features of local schemas. The eight methods are classified by the object classes and by relationships among them.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2078-2093
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• 2019

Visual smoke recognition is a challenging task due to large variations in shape, texture and color of smoke. To improve performance, we propose a novel smoke recognition method by combining dual-encoded features that are extracted from both spatial and Curvelet domains. A Curvelet transform is used to filter an image to generate fifty sub-images of Curvelet coefficients. Then we extract Local Binary Pattern (LBP) maps from these coefficient maps and aggregate histograms of these LBP maps to produce a histogram map. Afterwards, we encode the histogram map again to generate Dual-encoded Local Binary Patterns (Dual-LBP). Histograms of Dual-LBPs from Curvelet domain and Completed Local Binary Patterns (CLBP) from spatial domain are concatenated to form the feature for smoke recognition. Finally, we adopt Gaussian Kernel Optimization (GKO) algorithm to search the optimal kernel parameters of Support Vector Machine (SVM) for further improvement of classification accuracy. Experimental results demonstrate that our method can extract effective and reasonable features of smoke images, and achieve good classification accuracy.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.5
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• pp.3-10
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• 2019

This study has a purpose to profile local sectors into meaningful groups by using facilities rates of Social Overhead Capital(SOC) for daily life. Comparing SOC for daily life among the meaningful groups, the profiling and comparison results bring the comprehensive understanding about the educational facilities in local sectors. For the research purpose, this study utilized Latent Profile Analysis(LPA) by using variables such as population, road information, SOC for daily life, usage of land, possession of land, and appraised value of land from the 2018 Geographic Information System(GIS) dataset of Gwangjin-gu, where is one of the administrative district of Seoul City. Results showed that there are four latent groups of sectors among 904 local sectors(100 squared-meters sector per each) in Gwangjin-gu. By comparing the four latent groups by using LPA, the results diagnose each sector's status and help to improve the policy about educational facilities. Specifically, by using dataset for SOC of daily life, there are four groups of local sectors and each group has different features. Based on the different features of local sector groups, there can be improved management of educational facilities matching with each group's features.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.570-575
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• 2012

In a wireless communication RF transmitter, the output of a quadrature modulator (QM) is distorted by not only the linear imperfection features such as in/quadrature-phase (I/Q) input gain imbalance, local phase imbalance, and local gain imbalance but also the nonlinear imperfection features such as direct current (DC) offset and mixer nonlinearity related to in-band spurious signal. In this paper, we propose the unified QM model to analyze the combined effects of the linear and nonlinear imperfection features on the performance of the QM. The unified QM model consists of two identical nonlinear systems and modified I/Q inputs based on the two-port nonlinear mixer model. The unified QM model shows that the output signals can be expressed by mixer circuit parameters such as intercept point and gain as well as the imperfection features. The proposed approach is validated by not only simulation but also measurement.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1851-1863
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• 2015

– In the paper, to handle the problem of traditional bag-of-features model ignoring the spatial relationship of local features in human action recognition, we proposed a Multiscale Spatial Position Coding under Locality Constraint method. Specifically, to describe this spatial relationship, we proposed a mixed feature combining motion feature and multi-spatial-scale configuration. To utilize temporal information between features, sub spatial-temporal-volumes are built. Next, the pooled features of sub-STVs are obtained via max-pooling method. In classification stage, the Locality-Constrained Group Sparse Representation is adopted to utilize the intrinsic group information of the sub-STV features. The experimental results on the KTH, Weizmann, and UCF sports datasets show that our action recognition system outperforms the classical local ST feature-based recognition systems published recently.