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Effective identification of dominant fully absorbing sets for Raptor-like LDPC codes

  • Woncheol Cho;Chanho Yoon;Kapseok Chang;Young-Jo Ko
    • ETRI Journal
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    • v.45 no.1
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    • pp.7-17
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    • 2023
  • The error-rate floor of low-density parity-check (LDPC) codes is attributed to the trapping sets of their Tanner graphs. Among them, fully absorbing sets dominantly affect the error-rate performance, especially for short blocklengths. Efficient methods to identify the dominant trapping sets of LDPC codes were thoroughly researched as exhaustively searching them is NP-hard. However, the existing methods are ineffective for Raptor-like LDPC codes, which have many types of trapping sets. An effective method to identify dominant fully absorbing sets of Raptor-like LDPC codes is proposed. The search space of the proposed algorithm is optimized into the Tanner subgraphs of the codes to afford time-efficiency and search-effectiveness. For 5G New Radio (NR) base graph (BG) 2 LDPC codes for short blocklengths, the proposed algorithm finds more dominant fully absorbing sets within one seventh of the computation time of the existing search algorithm, and its search-effectiveness is verified using importance sampling. The proposed method is also applied to 5G NR BG1 LDPC code and Advanced Television Systems Committee 3.0 type A LDPC code for large blocklengths.

Fault-Tolerant Adaptive Routing : Improved RIFP by using SCP in Mesh Multicomputers (적응적 오류 허용 라우팅 : SCP를 이용한 메쉬 구조에서의 RIFP 기법 개선)

  • 정성우;김성천
    • Journal of KIISE:Computer Systems and Theory
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    • v.30 no.11
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    • pp.603-609
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    • 2003
  • Adaptive routing methods are studied for effective routing in many topologies where occurrence of the faulty nodes are inevitable. Mesh topology provides simplicity in implementing these methods. Many routing methods for mesh are able to tolerate a large number of faults enclosed by a rectangular faulty block. But they consider even good nodes in the faulty block as faulty nodes. Hence, it results the degradation of node utilization. This problem is solved by a method which transmits messages to destinations within faulty blocks via multiple “intermediate nodes”. It also divides faulty block into multiple expanded meshes. With these expanded meshes, DAG(Directed Acyclic Graph) is formed and a message is able to be routed by the shortest path according to the DAG. Therefore, the additional number of hops can be resulted. We propose a method that reduces the number of hops by searching direct paths from the destination node to the border of the faulty block. This path is called SCP(Short-Cut Path). If the path and the traversing message is on the same side of outside border of the faulty block, the message will cut into the path found by our method. It also reduces the message traverse latency between the source and the destination node.

Harmony Search Algorithm-Based Approach For Discrete Size Optimization of Truss Structures

  • Lee Kang-Seok;Kim Jeong-Hee;Choi Chang-Sik;Lee Li-Hyung
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2005.04a
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    • pp.351-358
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    • 2005
  • Many methods have been developed and are in use for structural size optimization problems, In which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary In this paper, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through a standard truss example. The numerical results reveal that the proposed method is a powerful search and design optimization tool for structures with discrete-sized members, and may yield better solutions than those obtained using current method.

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A Comparative Study on OCR using Super-Resolution for Small Fonts

  • Cho, Wooyeong;Kwon, Juwon;Kwon, Soonchu;Yoo, Jisang
    • International journal of advanced smart convergence
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    • v.8 no.3
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    • pp.95-101
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    • 2019
  • Recently, there have been many issues related to text recognition using Tesseract. One of these issues is that the text recognition accuracy is significantly lower for smaller fonts. Tesseract extracts text by creating an outline with direction in the image. By searching the Tesseract database, template matching with characters with similar feature points is used to select the character with the lowest error. Because of the poor text extraction, the recognition accuracy is lowerd. In this paper, we compared text recognition accuracy after applying various super-resolution methods to smaller text images and experimented with how the recognition accuracy varies for various image size. In order to recognize small Korean text images, we have used super-resolution algorithms based on deep learning models such as SRCNN, ESRCNN, DSRCNN, and DCSCN. The dataset for training and testing consisted of Korean-based scanned images. The images was resized from 0.5 times to 0.8 times with 12pt font size. The experiment was performed on x0.5 resized images, and the experimental result showed that DCSCN super-resolution is the most efficient method to reduce precision error rate by 7.8%, and reduce the recall error rate by 8.4%. The experimental results have demonstrated that the accuracy of text recognition for smaller Korean fonts can be improved by adding super-resolution methods to the OCR preprocessing module.

Vector Approximation Bitmap Indexing Method for High Dimensional Multimedia Database (고차원 멀티미디어 데이터 검색을 위한 벡터 근사 비트맵 색인 방법)

  • Park Joo-Hyoun;Son Dea-On;Nang Jong-Ho;Joo Bok-Gyu
    • The KIPS Transactions:PartD
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    • v.13D no.4 s.107
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    • pp.455-462
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    • 2006
  • Recently, the filtering approach using vector approximation such as VA-file[1] or LPC-file[2] have been proposed to support similarity search in high dimensional data space. This approach filters out many irrelevant vectors by calculating the approximate distance from a query vector using the compact approximations of vectors in database. Accordingly, the total elapsed time for similarity search is reduced because the disk I/O time is eliminated by reading the compact approximations instead of original vectors. However, the search time of the VA-file or LPC-file is not much lessened compared to the brute-force search because it requires a lot of computations for calculating the approximate distance. This paper proposes a new bitmap index structure in order to minimize the calculating time. To improve the calculating speed, a specific value of an object is saved in a bit pattern that shows a spatial position of the feature vector on a data space, and the calculation for a distance between objects is performed by the XOR bit calculation that is much faster than the real vector calculation. According to the experiment, the method that this paper suggests has shortened the total searching time to the extent of about one fourth of the sequential searching time, and to the utmost two times of the existing methods by shortening the great deal of calculating time, although this method has a longer data reading time compared to the existing vector approximation based approach. Consequently, it can be confirmed that we can improve even more the searching performance by shortening the calculating time for filtering of the existing vector approximation methods when the database speed is fast enough.

Relational Database Structure for Preserving Multi-role Topics in Topic Map (토픽맵의 다중역할 토픽 보존을 위한 관계형 데이터베이스 구조)

  • Jung, Yoonsoo;Y., Choon;Kim, Namgyu
    • The Journal of Information Systems
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    • v.18 no.3
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    • pp.327-349
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    • 2009
  • Traditional keyword-based searching methods suffer from low accuracy and high complexity due to the rapid growth in the amount of information. Accordingly, many researchers attempt to implement a so-called semantic search which is based on the semantics of the user's query. Semantic information can be described using a semantic modeling language, such as Topic Map. In this paper, we propose a new method to map a topic map to a traditional Relational Database (RDB) without any information loss. Although there have been a few attempts to map topic maps to RDB, they have paid scant attention to handling multi-role topics. In this paper, we propose a new storage structure to map multi-role topics to traditional RDB. The proposed structure consists of a mapping table, role tables, and content tables. Additionally, we devise a query translator to convert a user's query to one appropriate to the proposed structure.

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Fractal Image Compression Using QR Algorithm (QR 알고리즘을 이용한 프렉탈 영상압축)

  • Han, Kun-Hee;Kim, Tae-Ho;Jun, Byoung-Min
    • Journal of the Korean Society of Industry Convergence
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    • v.3 no.4
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    • pp.369-378
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    • 2000
  • Conventional fractal image compression methods have many problems in searching time for matching domain block. Proposed method is an improved method of Fisher's Quadtree Decomposition in terms of time, compression ratio, and PSNR. This method determines range block in advance using QR algorithm. First, input image is partitioned to $4{\times}4$ range block and then recomposition is performed from bottom level to specified level. As a result, this proposed method achieves high encoding and decoding speed, high compression ratio, and high PSNR than Fisher's Quadtree Decomposition method.

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Clinical Evaluation of Empyema Thoracis (농흉의 임상적 고찰)

  • 김영진
    • Journal of Chest Surgery
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    • v.25 no.6
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    • pp.637-644
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    • 1992
  • The incidence of thoracic empyema has been reduced with the advent of antimicrobial agents. But, there are remained many significant problems in the management of thoracic empyema because of the empyema associated with bronchopleural fistula, other complications, This is a clinical analysis of 76 cases of thoracic empyema who had been treated from August 1975 to July 1991 in the Chest Surgery Department, Chung-Ang University Hospital. This report dealed with the incidence, etiology and symptoms, duration of hospital stay, therapeutic methods and review of literatures in the aspect of thoracic empyema, The results were as follows: 1. Predominance of male [3 : 1] and right side [1.5 : 1] were recorded. 2. The main symptom was the chest pain [55%], dyspnea[36%], fever[33%], cough [23%] and others. 3, The most common predisposing causatic diseases were pulmonary tuberculosis[33%] and pneumonia[31%], but also uncertain cases were 15%.4. Searching for the causatic organisms, there were not-identified[49%], streptoccocci [17%], staphylococci[12%], mixed infection[12%], AFB bacilli[7%]. 5. The range of hospital stay was from 6 to 146 days and the average duration was 29.4 days, 6. The results were good as the methods of closed thoracostomy[52%], decortication [23%], thoracentesis[15%], rib resection and drainage[4%], open drainage[4%], pleuropneumonectomy [4%]. 7. The serious complications or mortality didn`t developed.

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Developing Stock Pattern Searching System using Sequence Alignment Algorithm (서열 정렬 알고리즘을 이용한 주가 패턴 탐색 시스템 개발)

  • Kim, Hyong-Jun;Cho, Hwan-Gue
    • Journal of KIISE:Computer Systems and Theory
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    • v.37 no.6
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    • pp.354-367
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    • 2010
  • There are many methods for analyzing patterns in time series data. Although stock data represents a time series, there are few studies on stock pattern analysis and prediction. Since people believe that stock price changes randomly we cannot predict stock prices using a scientific method. In this paper, we measured the degree of the randomness of stock prices using Kolmogorov complexity, and we showed that there is a strong correlation between the degree and the accuracy of stock price prediction using our semi-global alignment method. We transformed the stock price data to quantized string sequences. Then we measured randomness of stock prices using Kolmogorov complexity of the string sequences. We use KOSPI 690 stock data during 28 years for our experiments and to evaluate our methodology. When a high Kolmogorov complexity, the stock price cannot be predicted, when a low complexity, the stock price can be predicted, but the prediction ratio of stock price changes of interest to investors, is 12% prediction ratio for short-term predictions and a 54% prediction ratio for long-term predictions.

Application Design for Food Allergy Management (식품 알레르기 관리에 관한 애플리케이션 설계)

  • Ji-Uk Han;Nam-Bin Kim;Ye-Won Lee;Byeong-Seung Yang;Won-Whoi Huh
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.2
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    • pp.197-203
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    • 2024
  • Food allergies are common and accidents occur annually. However, many people lack knowledge of the severity of allergies and food ingredients. Allergy-related applications currently on the market have problems such as providing information by relying only on certain certified products, food ingredients, and barcodes. This design plans a customized service application for food allergy patients. In this application, after extracting the text of the image using OCR technology, the food ingredients were read and displayed in large letters. In addition, if the user selects an ingredient that cannot be consumed through filtering technology, the restricted food is quickly and conveniently shown when searching for food ingredients. Finally, when scanning a barcode or searching for a product, food ingredient information is provided through barcode scanning and search engine technology that provides ingredient information of the product. Therefore, the purpose of this paper is to design an app in which users with food allergies can easily check food ingredients and avoid allergic reactions using databases and various information search methods.