• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.6
• /
• pp.2497-2517
• /
• 2020

For compressed sensing (CS) applications, it is significant to construct deterministic measurement matrices with good practical features, including good sensing performance, low memory cost, low computational complexity and easy hardware implementation. In this paper, a deterministic construction method of bipolar measurement matrices is presented based on binary sequence family (BSF). This method is of interest to be applied for sparse signal restore and image block CS. Coherence is an important tool to describe and compare the performance of various sensing matrices. Lower coherence implies higher reconstruction accuracy. The coherence of proposed measurement matrices is analyzed and derived to be smaller than the corresponding Gaussian and Bernoulli random matrices. Simulation experiments show that the proposed matrices outperform the corresponding Gaussian, Bernoulli, binary and chaotic bipolar matrices in reconstruction accuracy. Meanwhile, the proposed matrices can reduce the reconstruction time compared with their Gaussian counterpart. Moreover, the proposed matrices are very efficient for sensing performance, memory, complexity and hardware realization, which is beneficial to practical CS.

• Proceedings of the KOSOMBE Conference
• /
• v.1993 no.05
• /
• pp.66-69
• /
• 1993

We have developed a software package for the analysis of electric field distribution in human body. It includes the graphical finite element mesh generator, linear system of equations solver using sparse matrix and vector technique, and post-processor for the display of the results. This software package can be used in various research areas of biomedical engineering where we inject current or apply voltage to human body. The software package was developed on Macintosh II computer and the size of the model is only limited by the main memory.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.7
• /
• pp.1078-1082
• /
• 2017

This paper presents an L1 optimization based filtering technique which effectively eliminates the optical misalignment effects encountered in the squint guidance mode with strapdown seekers. We formulated a series of L1 optimization problems in order to separate the bias and the gradient components from the measured data, and solved them via the alternating direction method of multipliers (ADMM) and sparse matrix decomposition techniques. The proposed technique was able to rapidly detect arbitrary discontinuities and gradient changes from the measured signals, and was shown to effectively cancel the undesirable effects coming from the seeker misalignment angles. The technique was implemented on embedded flight computers and the real-time operational performance was verified via the hardware-in-the-loop simulation (HILS) tests in parallel with the automatic target recognition algorithms and the intra-red synthetic target images.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.6A
• /
• pp.825-832
• /
• 2000

광섬유 통신 시스템이 고속화되고 장거리를 전송하게 될 수록 편광모드 분산의 중요성은 더욱 부각되어 있다. 따라서 본 논문에서는 복굴절 광섬유에서 비선형 광펄스의 전파특성을 편광 모드 분산의 영향을 고려하여 시뮬레이션하였으며 이러한 현상이 발생되는 것을 알 수 있었다. 그리고 광섬유 비션형성에 의해서 GVD(Group Velocity Dispersion)와 마찬가기로 PMD(Polarization Mode Dispersion)에서도 부분적인 보상 현상이 나타남을 수치 결과를 통해 알 수 있었다. 이러한 광 전송 시뮬레이션을 구현하기 위해서 기존의 단계분할 푸리에 방식 (SS-FM, Split-Step Fourier Method)보다 장거리 전송시 오차의 발생이 적은 단계 분할 유한 요소법)SS-FEM, Split-Step Finite Element Method)을 적용하였으며, 또한 그 단점인 수행 속도를 개선한 희귀 행렬 근사 단계 분할 유한 요소법을 제안하였다. 그 결과 제안된 방법이 기존의 푸리에 연산법이나 일반적인 유한 요소법과 비교하여 더 빠른 수행 속도를 나타내는 것을 알 수 있었다.

• IE interfaces
• /
• v.11 no.2
• /
• pp.149-157
• /
• 1998

In this paper, various techniques considering the characteristics of computer memory management are suggested, which can be used in the implementation of simplex method. First, reduction technique of indirect addressing, redundant references of memory, and scatter/gather technique are implemented, and the effectiveness of the techniques is shown. Loop-unrolling technique, which exploits the arithmetic operation mechanism of computer, is also implemented. Second, a subroutine frequently called is written in low-level language, and the effectiveness is proved by experimental results. Third, row-column linked list and Gustavson's data structure are compared as the data structure for the large sparse matrix in LU form. Last, buffering technique and memory-mapped file which can be used in reading large data file are implemented and the effectiveness is shown.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.05a
• /
• pp.45-47
• /
• 2021

APU(Accelerated Processing Unit)는 CPU와 GPU가 통합되어있는 프로세서이며 같은 메모리 공간을 사용한다. CPU와 GPU가 분리되어있는 기존 이종 컴퓨팅 환경에서는 GPU가 작업을 처리하기 위해 CPU에서 GPU로 메모리 복사가 이루어졌지만, APU는 같은 메모리 공간을 사용하므로 메모리 복사 없이 가상주소 할당으로 같은 물리 주소에 접근할 수 있으며 이를 Zero Copy라 한다. Zero Copy 성능을 테스트하기 위해 희소행렬 연산을 사용하였으며 기존 메모리 복사대비 크기가 큰 데이터는 약 4.67배, 크기가 작은 데이터는 약 6.27배 빨랐다.

• Korean Journal of Plant Resources
• /
• v.35 no.4
• /
• pp.515-540
• /
• 2022

The ultramafic serpentine area, the small size of 3 km², remains in Andong, South Korea. We researched the ecological flora and its structure through the 12 times field investigations from 2013 till 2018. A total of 527 taxa including the previously recorded species-list was analyzed. Among them, 331 taxa were filed up as a real flora of the serpentine area. On the vegetation land-cover map describing a characteristic aspect of species distribution, a matrix of the sparse forest by Pinus densiflora and the grassland patches were the main landscape. The study area was acknowledged as a home for the ethnobotanical species and grassland components, and clearly distinctive from the non-serpentine area. The original habitat was too deteriorated by introducing the non-site soils and exotic plants. Conclusionally a designation of a protected area and the long-term ecological monitoring were requested.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.129-142
• /
• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• The KIPS Transactions:PartA
• /
• v.10A no.2
• /
• pp.137-140
• /
• 2003

Recently, CG (Conjugate Gradient) scheme for the optimization of the Rayleigh quotient has been proven a very attractive and promising technique for interior eigenvalues for the following eigenvalue problem, Ax＝λx (1) The given matrix A is assummed to be large and sparse, and symmetric. Also, the method is very amenable to parallel computations. A proper choice of the preconditioner significantly improves the convergence of the CG scheme. We compare the parallel preconditioners for the computation of the interior eigenvalues of a symmetric matrix by CG-type method. The considered preconditioners are Point-SSOR, ILU (0) in the multi-coloring order, and Multi-Color Block SSOR (Symmetric Succesive OverRelaxation). We conducted our experiments on the CRAY­T3E with 128 nodes. The MPI (Message Passing Interface) library was adopted for the interprocessor communications. The test matrices are up to $512{\times}512$ in dimensions and were created from the discretizations of the elliptic PDE. All things considered the MC-BSSOR seems to be most robust preconditioner.

• Journal of Korea Multimedia Society
• /
• v.22 no.4
• /
• pp.443-454
• /
• 2019

In recent years, there has been active research on a recommender system that considers three or more inputs in addition to users and goods, making it a multi-dimensional array, also known as a tensor. The main issue with using tensor is that there are a lot of missing values, making it sparse. In order to solve this, the tensor can be shrunk using the tensor decomposition algorithm into a lower dimensional array called a factor matrix. Then, the tensor is reconstructed by calculating factor matrices to fill original empty cells with predicted values. This is called tensor reconstruction. In this paper, we propose a user-based Top-K recommender system by normalized PARAFAC tensor reconstruction. This method involves factorization of a tensor into factor matrices and reconstructs the tensor again. Before decomposition, the original tensor is normalized based on each dimension to reduce overfitting. Using the real world dataset, this paper shows the processing of a large amount of data and implements a recommender system based on Apache Spark. In addition, this study has confirmed that the recommender performance is improved through normalization of the tensor.