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  • Title/Summary/Keyword: Matrix factorization

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A Study on the RFID Biometrics System Based on Hippocampal Learning Algorithm Using NMF and LDA Mixture Feature Extraction (NMF와 LDA 혼합 특징추출을 이용한 해마 학습기반 RFID 생체 인증 시스템에 관한 연구)

  • Oh Sun-Moon;Kang Dae-Seong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.4 s.310
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    • pp.46-54
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    • 2006
  • Recently, the important of a personal identification is increasing according to expansion using each on-line commercial transaction and personal ID-card. Although a personal ID-card embedded RFID(Radio Frequency Identification) tag is gradually increased, the way for a person's identification is deficiency. So we need automatic methods. Because RFID tag is vary small storage capacity of memory, it needs effective feature extraction method to store personal biometrics information. We need new recognition method to compare each feature. In this paper, we studied the face verification system using Hippocampal neuron modeling algorithm which can remodel the hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature vector of the face images very fast. and construct the optimized feature each image. The system is composed of two parts mainly. One is feature extraction using NMF(Non-negative Matrix Factorization) and LDA(Linear Discriminants Analysis) mixture algorithm and the other is hippocampal neuron modeling and recognition simulation experiments confirm the each recognition rate, that are face changes, pose changes and low-level quality image. The results of experiments, we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to the existing method.

The Suggestion of LINF Algorithm for a Real-time Face Recognition System (실시간 얼굴인식 시스템을 위한 새로운 LINF 알고리즘의 제안)

  • Jang Hye-Kyoung;Kang Dae-Seong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.4 s.304
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    • pp.79-86
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    • 2005
  • In this paper, we propose a new LINF(Linear Independent Non-negative Factorization) algorithm for real-time face recognition systea This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction Part we applied subtraction image, the detection of eye and mouth region , and normalization method, and then in the face recognition Part we used LINF in extracted face candidate region images. The existing recognition system using only PCA(Principal Component Analysis) showed low recognition rates, and it was hard in the recognition system using only LDA(Linear Discriminants Analysis) to apply LDA directly when the training set is small. To overcome these shortcomings, we reduced dimension as the matrix that had non-negative value to be different from former eigenfaces and then applied LDA to the matrix in the proposed system We have experimented using self-organized DAIJFace database and ORL database offered by AT(')T laboratory in Cambridge, U.K. to evaluate the performance of the proposed system. The experimental results showed that the proposed method outperformed PCA, LDA, ICA(Independent Component Analysis) and PLMA(PCA-based LDA mixture algorithm) method within the framework of recognition accuracy.

Nonnegative Matrix Factorization Based Direction-of-Arrival Estimation of Multiple Sound Sources Using Dual Microphone Array (이중 마이크로폰을 이용한 비음수 행렬분해 기반 다중음원 도래각 예측)

  • Jeon, Kwang Myung;Kim, Hong Kook;Yu, Seung Woo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.2
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    • pp.123-129
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    • 2017
  • This paper proposes a new nonnegative matrix factorization (NMF) based direction-of-arrival (DOA) estimation method for multiple sound sources using a dual microphone array. First of all, sound signals coming from the dual microphone array are segmented into consecutive analysis frames, and a steered-response power phase transform (SRP-PHAT) beamformer is applied to each frame so that stereo signals of each frame are represented in a time-direction domain. The time-direction outputs of SRP-PHAT are stored for a pre-defined number of frames, which is referred to as a time-direction block. Next, In order to estimate DOAs robust to noise, each time-direction block is normalized along the time by using a block subtraction technique. After that, an unsupervised NMF method is applied to the normalized time-direction block in order to cluster the directions of each sound source in a multiple sound source environments. In particular, the activation and basis matrices are used to estimate the number of sound sources and their DOAs, respectively. The DOA estimation performance of the proposed method is evaluated by measuring a mean absolute error (MAE) and the standard deviation of errors between the oracle and estimated DOAs under a three source condition, where the sources are located in [35, 5m], [12, 4m], and [38, 4.m] from the dual microphone array. It is shown from the experiment that the proposed method could relatively reduce MAE by 56.83%, compared to a conventional SRP-PHAT based DOA estimation method.

Health Risk Assessment with Source Apportionment of Ambient Volatile Organic Compounds in Seoul by Positive Matrix Factorization (수용체 모델(PMF)를 이용한 서울시 대기 중 VOCs의 배출원에 따른 위해성평가)

  • Kwon, Seung-Mi;Choi, Yu-Ri;Park, Myoung-Kyu;Lee, Ho-Joon;Kim, Gwang-Rae;Yoo, Seung-Sung;Cho, Seog-Ju;Shin, Jin-Ho;Shin, Yong-Seung;Lee, Cheolmin
    • Journal of Environmental Health Sciences
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    • v.47 no.5
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    • pp.384-397
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    • 2021
  • Background: With volatile organic compounds (VOCs) containing aromatic and halogenated hydrocarbons such as benzene, toluene, and xylene that can adversely affect the respiratory and cardiovascular systems when a certain concentration is reached, it is important to accurately evaluate the source and the corresponding health risk effects. Objectives: The purpose of this study is to provide scientific evidence for the city of Seoul's VOC reduction measures by confirming the risk of each VOC emission source. Methods: In 2020, 56 VOCs were measured and analyzed at one-hour intervals using an online flame ionization detector system (GC-FID) at two measuring stations in Seoul (Gangseo: GS, Bukhansan: BHS). The dominant emission source was identified using the Positive Matrix Factorization (PMF) model, and health risk assessment was performed on the main components of VOCs related to the emission source. Results: Gasoline vapor and vehicle combustion gas are the main sources of emissions in GS, a residential area in the city center, and the main sources are solvent usage and aged VOCs in BHS, a greenbelt area. The risk index ranged from 0.01 to 0.02, which is lower than the standard of 1 for both GS and BHS, and was an acceptable level of 5.71×10-7 to 2.58×10-6 for carcinogenic risk. Conclusions: In order to reduce the level of carcinogenic risk to an acceptable safe level, it is necessary to improve and reduce the emission sources of vehicle combustion and solvent usage, and eco-car policies are judged to contribute to the reduction of combustion gas as well as providing a response to climate change.

Antibiotics-Resistant Bacteria Infection Prediction Based on Deep Learning (딥러닝 기반 항생제 내성균 감염 예측)

  • Oh, Sung-Woo;Lee, Hankil;Shin, Ji-Yeon;Lee, Jung-Hoon
    • The Journal of Society for e-Business Studies
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    • v.24 no.1
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    • pp.105-120
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    • 2019
  • The World Health Organization (WHO) and other government agencies aroundthe world have warned against antibiotic-resistant bacteria due to abuse of antibiotics and are strengthening their care and monitoring to prevent infection. However, it is highly necessary to develop an expeditious and accurate prediction and estimating method for preemptive measures. Because it takes several days to cultivate the infecting bacteria to identify the infection, quarantine and contact are not effective to prevent spread of infection. In this study, the disease diagnosis and antibiotic prescriptions included in Electronic Health Records were embedded through neural embedding model and matrix factorization, and deep learning based classification predictive model was proposed. The f1-score of the deep learning model increased from 0.525 to 0.617when embedding information on disease and antibiotics, which are the main causes of antibiotic resistance, added to the patient's basic information and hospital use information. And deep learning model outperformed the traditional machine hospital use information. And deep learning model outperformed the traditional machine learning models.As a result of analyzing the characteristics of antibiotic resistant patients, resistant patients were more likely to use antibiotics in J01 than nonresistant patients who were diagnosed with the same diseases and were prescribed 6.3 times more than DDD.

Analysis and Compression of Spun-yarn Density Profiles using Adaptive Wavelets

  • Kim, Joo-Yong
    • Textile Coloration and Finishing
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    • v.18 no.5 s.90
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    • pp.88-93
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    • 2006
  • A data compression system has been developed by combining adaptive wavelets and optimization technique. The adaptive wavelets were made by optimizing the coefficients of the wavelet matrix. The optimization procedure has been performed by criteria of minimizing the reconstruction error. The resulting adaptive basis outperformed such conventional basis as Daubechies-5 by 5-10%. It was also shown that the yarn density profiles could be compressed by over 95% without a significant loss of information.

A Study of the effective method of LU factorization for Newton-Raphson Load Flow (Newton-Raphson법을 이용한 조류계산을 위한 효율적인 LU분해 계산 방법에 관한 연구)

  • Gim, Jae-Hyeon;Lee, So-Young
    • Proceedings of the KIEE Conference
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    • 2000.07a
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    • pp.274-275
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    • 2000
  • This paper introduces new ordering algorithms using the graph of data structure and forward/backward substitution of LU decomposition using recursive function. The performance of the algorithm is compared with Tinney's algorithm using 14 bus systems. Test results show that the new fill-in element of Jacobian matrix using the proposed ordering algorithm is same as that of Tinner scheme 3 and the forward/backward substitution can reduce the computation time

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A New Method for Robust and Secure Image Hash Improved FJLT

  • Xiu, Anna;Kim, Hyoung-Joong
    • 한국정보통신설비학회:학술대회논문집
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    • 2009.08a
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    • pp.143-146
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    • 2009
  • There are some image hash methods, in the paper four image hash methods have been compared: FJLT (Fast Johnson- Lindenstrauss Transform), SVD (Singular Value Decomposition), NMF (Non-Negative Matrix Factorization), FP (Feature Point). From the compared result, FJLT method can't be used in the online. the search time is very slow because of the KNN algorithm. So FJLT method has been improved in the paper.

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CONVERGENCE OF MULTI-RELAXED NONSTATIONARY MULTISPLITTING METHODS

  • Oh, Se-Young;Yun, Jae-Heon
    • Journal of applied mathematics & informatics
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    • v.29 no.3_4
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    • pp.753-762
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    • 2011
  • Recently, Cheng et al. [3] introduced new nonstationary multisplitting methods with multi-relaxed parameters. In this paper, we first provide correct proofs for convergence results of the multi-relaxed nonstationary multisplitting method which have not been proved completely by Cheng et al., and then we provide new convergence results for the multirelaxed nonstationary two-stage multisplitting method.

PM-10 Source Estimation Using Positive Matrix Factorization (PMF를 이용한 PM-10의 오염원 추정)

  • 황인조;김동술
    • Proceedings of the Korea Air Pollution Research Association Conference
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    • 2000.04a
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    • pp.291-293
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    • 2000
  • 대기 연구자들은 대기오염의 일반적인 현황과 대기오염 유발의 근본 원인 파악, 저감 대책 등에 대한 연구를 활발히 수행하고 있다 하지만 이러한 연구들 중에서 대기오염의 근본 원인을 파악하기 위한 오염원 (Source) 추정 연구는 국내외적으로 매우 미진하다. 대기질의 평가와 예측은 분산모델과 수용방법론을 이용하는데, 분산모델에 내재되어 있는 한계성과 제약점 때문에 수용체에서 오염물질의 특성을 분석한 후, 오염원의 기여도를 평가하는 수용방법론이 지속적으로 개발되고 있다. (중략)

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