• Title/Summary/Keyword: Matrix factorization

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On the Design of Orthogonal Pulse-Shape Modulation for UWB Systems Using Hermite Pulses

  • Giuseppe, Thadeu Freitas de Abreu;Mitchell, Craig-John;Kohno, Ryuji
    • Journal of Communications and Networks
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    • v.5 no.4
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    • pp.328-343
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    • 2003
  • Orthogonal pulse-shape modulation using Hermite pulses for ultra-wideband communications is reviewed. Closedform expressions of cross-correlations among Hermite pulses and their corresponding transmit and receive waveforms are provided. These show that the pulses lose orthogonality at the receiver in the presence of differentiating antennas. Using these expressions, an algebraic model is established based on the projections of distorted receive waveforms onto the orthonormal basis given by the set of normalized orthogonal Hermite pulses. Using this new matrix model, a number of pulse-shape modulation schemes are analyzed and a novel orthogonal design is proposed. In the proposed orthogonal design, transmit waveforms are constructed as combinations of elementary Hermites with weighting coefficients derived by employing the Gram-Schmidt (QR) factorization of the differentiating distortion model’s matrix. The design ensures orthogonality of the vectors at the output of the receiver bank of correlators, without requiring compensation for the distortion introduced by the antennas. In addition, a new set of elementary Hermite Pulses is proposed which further enhances the performance of the new design while enabling a simplified hardware implementation.

A Simplified Efficient Algorithm for Blind Detection of Orthogonal Space-Time Block Codes

  • Pham, Van Su;Mai, Linh;Lee, Jae-Young;Yoon, Gi-Wan
    • Journal of information and communication convergence engineering
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    • v.6 no.3
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    • pp.261-265
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    • 2008
  • This work presents a simplified efficient blind detection algorithm for orthogonal space-time codes(OSTBC). First, the proposed decoder exploits a proper decomposition approach of the upper triangular matrix R, which resulted from Cholesky-factorization of the composition channel matrix, to form an easy-to-solve blind detection equation. Secondly, in order to avoid suffering from the high computational load, the proposed decoder applies a sub-optimal QR-based decoder. Computer simulation results verify that the proposed decoder allows to significantly reduce computational complexity while still satisfying the bit-error-rate(BER) performance.

Modified Partial Matrix Refactorization (수정 제분행렬 재 인수화법)

  • 강기문;지용량
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.37 no.11
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    • pp.753-761
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    • 1988
  • Partial Matrix Refactorization (PR) has been available for refactorization repeatedly. But this paper aims to present Modified PR(MPR), which is faster in (re) factorization speed and simpler in program than PR, but storage is almost as big as that of PR. MPR substitutes refactorization process of PR1 for Modifide Trifactortzation (MT) and, instead of PR 2, adds to PR1 the algoritm that calculates modified element values of modified row / cols. MT, which is subalgorithm for MPR, simplifies the algorithm by applying information vectors to currently used Trifactorization, and trifactorizes and refactorizes in high speed. The test results of Fast Decoupled Load Flow (FDLF) and Contingency Analysis useing Indexing Scheme and Optimal Ordering also prove that MPR is faster than PR.

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Gender Classification using Non-Negative Matrix Analysis with Sparse Logistic Regression (Sparse Logistic Regression 기반 비음수 행렬 분석을 통한 성별 인식)

  • Hur, Dong-Cheol;Wallraven, Christian;Lee, Seong-Whan
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06c
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    • pp.373-376
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    • 2011
  • 얼굴 영상에서 구성요소(눈썹, 눈, 코, 입 등)의 존재에 따라 보는 사람의 얼굴 인식 정확도는 큰 영향을 받는다. 이는 인간의 뇌에서 얼굴 정보를 처리하는 과정은 얼굴 전체 영역 뿐만 아니라, 부분적인 얼굴 구성요소의 특징들도 고려함을 말한다. 비음수 행렬 분해(NMF: Non-negative Matrix Factorization)는 이러한 얼굴 영역에서 부분적인 특징들을 잘 표현하는 기저영상들을 찾아내는데 효과적임을 보여주었으나, 각 기저영상들의 중요도는 알 수 없었다. 본 논문에서는 NMF로 찾아진 기저영상들에 대응되는 인코딩 정보를 SLR(Sparse Logistic Regression)을 이용하여 성별 인식에 중요한 부분 영역들을 찾고자 한다. 실험에서는 주성분분석(PCA)과 비교를 통해 NMF를 이용한 기저영상 및 특징 벡터 추출이 좋은 성능을 보여주고, 대표적 이진 분류 알고리즘인 SVM(Support Vector Machine)과 비교를 통해 SLR을 이용한 특징 벡터 선택이 나은 성능을 보여줌을 확인하였다. 또한 SLR로 확인된 각 기저영상에 대한 가중치를 통하여 인식 과정에서 중요한 얼굴 영역들을 확인할 수 있다.

Email Classification using Dynamic Category Hierarchy and Non-negative Matrix Factorization (비음수 행렬 분해와 동적 분류체계를 사용한 이메일 분류)

  • Park, Sun;An, Dong Un
    • Annual Conference on Human and Language Technology
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    • 2009.10a
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    • pp.35-39
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    • 2009
  • 이메일의 사용증가로 수신 메일을 효율적이면서 정확하게 분류할 필요성이 점차 증가하고 있다. 현재의 이메일 분류는 베이지안, 규칙 기반 등을 이용하여 스팸 메일을 필터링하기 위한 이원 분류가 주를 이루고 있다. 클러스터링을 이용한 다원 분류 방법은 분류의 정확도가 떨어지는 단점이 있다. 본 논문에서는 비음수 행렬 분해(NMF, Non-negative Matrix Factrazation)를 기반으로 한 자동 분류 주제 생성 방법과 동적 분류 체계(DCH, Dynamic Category Hierachy) 방법을 결합한 새로운 이메일 분류 방법을 제안한다. 이 방법은 수신되는 이메일을 자동으로 분류하여 대량의 메일을 효율적으로 관리할 수 있으며, 분류 결과 사용자의 요구사항을 만족하지 못하면 메일을 동적으로 재분류 하여 분류 정확률을 높일 수 있다.

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Design of Horn Antenna for HAPS(High Altitude Platform Station) in 48/47 GHz Bands

  • Ku, Bon-Jun;Ahn, Do-Seob;Park, Jong-Min
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2001.10a
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    • pp.222-225
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    • 2001
  • This paper describes design and performance test of dual-mode horn antenna for HAPS (High Altitude Platform Station) in 47.2 - 47.5 GHz and 47.9 - 48.2 GHz bands. To obtain the optimal geometry parameters of it. the conical section is represented by a stepped transition composed of a set of cylindrical waveguide sections. For each step. the corresponding generalized scattering matrix is calculated. The elements of the matrices at the open end of the horn, are calculated by the rigorous formulas of the factorization method. To verify the theoretical results, a horn breadboard was manufactured for the medium frequency of 47.7 GHz and its radiation beam patterns were measured. The calculated and theoretical results are in good agreement.

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On Factorizing the Discrete Cosine Transform Matrix (DCT 행렬 분해에 관한 연구)

  • 최태영
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.16 no.12
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    • pp.1236-1248
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    • 1991
  • A new fast algorithm for computing the discrete cosine transform(DCT) Is developed decomposing N-point DCT into an N /2-point DCT and two N /4 point transforms(transpose of an N /4-point DCT. TN/t'and)It has an important characteristic that in this method, the roundoff noise power for a fixed point arithmetic can be reduced significantly with respect to the wellknown fast algorithms of Lee and Chen. since most coefficients for multiplication are distributed at the nodes close to the output and far from the input in the signal flow graph In addition, it also shows three other versions of factorization of DCT matrix with the same number of operations but with the different distributions of multiplication coefficients.

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KOREAN TOPIC MODELING USING MATRIX DECOMPOSITION

  • June-Ho Lee;Hyun-Min Kim
    • East Asian mathematical journal
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    • v.40 no.3
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    • pp.307-318
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    • 2024
  • This paper explores the application of matrix factorization, specifically CUR decomposition, in the clustering of Korean language documents by topic. It addresses the unique challenges of Natural Language Processing (NLP) in dealing with the Korean language's distinctive features, such as agglutinative words and morphological ambiguity. The study compares the effectiveness of Latent Semantic Analysis (LSA) using CUR decomposition with the classical Singular Value Decomposition (SVD) method in the context of Korean text. Experiments are conducted using Korean Wikipedia documents and newspaper data, providing insight into the accuracy and efficiency of these techniques. The findings demonstrate the potential of CUR decomposition to improve the accuracy of document clustering in Korean, offering a valuable approach to text mining and information retrieval in agglutinative languages.

A Comparative Study on the Efficient Reordering Methods of Sparse Matrix Problem for Large-scale Surveying Network Adjustment (대규모 측지망 조정을 위한 희소 행렬의 효율적인 재배열 방법에 대한 비교 연구)

  • Woo, Sun-Kyu;Yun, Kong-Hyun;Heo, Joon
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.26 no.1
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    • pp.85-91
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    • 2008
  • When a large sparse matrix is calculated for a horizontal geodetic network adjustment, it needs to go through the process of matrix reordering for the efficiency of time and space. In this study, several reordering methods for sparse matrix were tested, using Sparse Matrix Manipulation System(SMMS) program, total processing time and Fill-in number produced in factorization process were measured and compared. As a result, Minimum Degree(MD) and Mutiple Minimum Degree(MMD), which are based on Minimum Degree are better than Gibbs-Poole-Stockmeyer(GPS) and Reverse Cuthill-Mckee(RCM), which are based on Minimum Bandwidth. However, the method of the best efficiency can be changed dependent on distribution of non-zero elements in a matrix. This finding could be applied to heighten the efficiency of time and storage space for national datum readjustment and other large geodetic network adjustment.

Recommender system using BERT sentiment analysis (BERT 기반 감성분석을 이용한 추천시스템)

  • Park, Ho-yeon;Kim, Kyoung-jae
    • Journal of Intelligence and Information Systems
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    • v.27 no.2
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    • pp.1-15
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    • 2021
  • If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.