• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3689-3696
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• 2011

In decision support system, knowledge workers require aggregation operations of the large data and are more interested in the trend analysis rather than in the punctual analysis. Therefore, it is necessary to provide fast approximate answers rather than exact answers, and to research approximate query answering techniques. In this paper, we propose a new approximation query answering method which is based on Fuzzy C-means clustering (FCM) method and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method using FCM-ANFIS can compute aggregate queries without accessing massive multidimensional data cube by producing the KR model of multidimensional data cube. In our experiments, we show that our method using the KR model outperforms the NMF method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.23-29
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• 2011

There have been many studies on red tide due to increasing damage from red tide on fishing and aquaculture industry. However, internal study of automatic red tide image classification is not enough. Recognition of red tide algae is difficult because they do not have matching center features for recognizing algae image object. Previously studies used a few type of red tide algae for image classification. In this paper, we proposed the red tide image recognition method using semantic features of NMF and roundness of image objects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.392-393
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• 2012

This paper proposes a new document clustering method using clustering and Wikipedia. The proposed method can well represent the concept of cluster topics by means of NMF. It can solve the problem of "bags of words" to be not considered the meaningful relationships between documents and clusters, which expands the important terms of cluster by using of the synonyms of Wikipedia. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6028-6042
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• 2019

Since Sparse Nonnegative Matrix Factorization (SNMF) method can control the sparsity of the decomposed matrix, and then it can be adopted to control the sparsity of facial feature extraction and recognition. In order to improve the accuracy of SNMF method for facial feature recognition, new additive iterative rules based on the improved iterative step sizes are proposed to improve the SNMF method, and then the traditional multiplicative iterative rules of SNMF are transformed to additive iterative rules. Meanwhile, to further increase the sparsity of the basis matrix decomposed by the improved SNMF method, a threshold-sparse constraint is adopted to make the basis matrix to a zero-one matrix, which can further improve the accuracy of facial feature recognition. The improved SNMF method based on the additive iterative rules and threshold-sparse constraint is abbreviated as ASNMF, which is adopted to recognize the ORL and CK+ facial datasets, and achieved recognition rate of 96% and 100%, respectively. Meanwhile, from the results of the contrast experiments, it can be found that the recognition rate achieved by the ASNMF method is obviously higher than the basic NMF, traditional SNMF, convex nonnegative matrix factorization (CNMF) and Deep NMF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.1
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• pp.18-26
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• 2008

The invariance relation existing in the non-negative matrix factorization (NMF) is used for constructing robust image hashes in this work. The image is first re-scaled to a fixed size. Low-pass filtering is performed on the luminance component of the re-sized image to produce a normalized matrix. Entries in the normalized matrix are pseudo-randomly re-arranged under the control of a secret key to generate a secondary image. Non-negative matrix factorization is then performed on the secondary image. As the relation between most pairs of adjacent entries in the NMF's coefficient matrix is basically invariant to ordinary image processing, a coarse quantization scheme is devised to compress the extracted features contained in the coefficient matrix. The obtained binary elements are used to form the image hash after being scrambled based on another key. Similarity between hashes is measured by the Hamming distance. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as Gaussian filtering, moderate noise contamination, JPEG compression, re-scaling, and watermark embedding. Hashes of different images have very low collision probability. Tampering to local image areas can be detected by comparing the Hamming distance with a predetermined threshold, indicating the usefulness of the technique in digital forensics.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.48-52
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• 2008

In this paper, we propose a new selective subspace projection method in order to recognize the occlusive face image effectively. The conventional subspace projection method is project to basis image using a full image of face. The face recognition rate has reduced because the face characteristic is easy to be distorted by occlusion. To overcome this problem, the proposed method first decide to occlusion. If it hasn't an occlusion, we get the feature vectors with total basis projection using the conventional subspace projection method. If it has an occlusion, we get one with partial basis projection. We get better recognition rate than conventional PCA and NMF using AR face database with occlusive face images.

• The Journal of the Acoustical Society of Korea
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• v.31 no.5
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• pp.332-339
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• 2012

Beamspace transform algorithm transforms spatial-domain data - such as x, y, z dimension - into incidence-angle-domain data, which is called beamspace-domain data. The beamspace transform method is generally used in source localization and tracking, and adaptive beamforming problem. When the beamspace transform method is used in multichannel audio source separation, the inverse beamspace transform is also important because the source image have to be reconstructed. This paper studies the beamspace transform and inverse transform algorithms for multichannel audio source separation system, especially for the beamspace-domain multichannel NMF algorithm.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.20 no.4
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• pp.225-235
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• 2010

This study was aimed to assess the status of working environment, health care management status and cause of exposure in manufactories using dimethylformamide (DMF). For the purpose, airborne concentration of DMF in the workplaces and N-methylformamide (NMF) in worker's urine were measured with job type and process. In addition, management of local exhaust ventilation system (LEV) and personal protective equipment (PPE) was evaluated at 35 work places (107 workers) located in Busan and Gimhae area. Mean DMF concentrations in work places by job type and process were of high level measured in printing and record media reproduction (5.23 ppm) and flaking process (2.48 ppm). Workers in adhesive job were measured a large amount of urine NMF (21.59 mg/${\ell}$). 98.1% of DMF handling workers were provided respirators, but 67.3% of those workers used them. The main reasons for not using respirators were inconvenience and difficulty of breathing. Airborne concentrations of DMF were higher in the workplaces in which LEVs were working abnormally, but there was not statistically significant. In addition, the urine NMF levels were correlated with management of LEV within the workers who did not use the respirators (p<0.048). These results implied that LEV should be installed and maintained properly to protect the workers from the exposure to DMF. Management of PPE should be also necessary to protect the workers from chemical hazards.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.42 no.4
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• pp.343-349
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• 2016

Many minerals and nutrient salts are abundant in Jeju lava sea water. The objective of this study was to evaluate the skin hydration effects of Jeju lava sea water. The skin barrier serves as a protective barrier that prevents the loss of moisture. The water holding capacity and water transport of the epidermis have been proposed to be important determinants of skin hydration. Jeju lava sea water increased the mRNA expression of filaggrin and caspase-14 which is related to natural moisturizing factor (NMF) formation. Aquaporins 3 (AQP3) are proteins that facilitate the transport of water across cell membranes. Jeju lava sea water increased the mRNA expression and protein expression of AQP3. We employed a skin equivalent model to assess the efficacy of Jeju lava sea water. In a skin equivalent model, Jeju lava sea water increased the CD44 (hyaluronic acid receptor) which is related to skin hydration. From these results, we found out Jeju lava sea water maybe help to skin hydration.