• Title/Summary/Keyword: Rank algorithm

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Computer-Aided Decision Analysis for Improvement of System Reliability

  • Ohm, Tai-Won
    • Journal of the Korea Safety Management & Science
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    • v.2 no.4
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    • pp.91-102
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    • 2000
  • Nowadays, every kind of system is changed so complex and enormous, it is necessary to assure system reliability, product liability and safety. Fault tree analysis(FTA) is a reliability/safety design analysis technique which starts from consideration of system failure effect, referred to as “top event”, and proceeds by determining how these can be caused by single or combined lower level failures or events. So in fault tree analysis, it is important to find the combination of events which affect system failure. Minimal cut sets(MCS) and minimal path sets(MPS) are used in this process. FTA-I computer program is developed which calculates MCS and MPS in terms of Gw-Basic computer language considering Fussell's algorithm. FTA-II computer program which analyzes importance and function cost of VE consists. of five programs as follows : (l) Structural importance of basic event, (2) Structural probability importance of basic event, (3) Structural criticality importance of basic event, (4) Cost-Failure importance of basic event, (5) VE function cost analysis for importance of basic event. In this study, a method of initiation such as failure, function and cost in FTA is suggested, and especially the priority rank which is calculated by computer-aided decision analysis program developed in this study can be used in decision making determining the most important basic event under various conditions. Also the priority rank can be available for the case which selects system component in FMEA analysis.

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Development of Audio Melody Extraction and Matching Engine for MIREX 2011 tasks

  • Song, Chai-Jong;Jang, Dalwon;Lee, Seok-Pil;Park, Hochong
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2012.07a
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    • pp.164-166
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    • 2012
  • In this paper, we proposed a method for extracting predominant melody of polyphonic music based on harmonic structure. Harmonic structure is an important feature parameter of monophonic signal that has spectral peaks at the integer multiples of its fundamental frequency. We extract all fundamental frequency candidates contained in the polyphonic signal by verifying the required condition of harmonic structure. Then, we combine those harmonic peaks corresponding to each extracted fundamental frequency and assign a rank to each after calculating its harmonic average energy. We run pitch tracking based on the rank of extracted fundamental frequency and continuity of fundamental frequency, and determine the predominant melody. For the query by singing/humming (QbSH) task, we proposed Dynamic Time Warping (DTW) based matching engine. Our system reduces false alarm by combining the distances of multiple DTW processes. To improve the performance, we introduced the asymmetric sense, pitch level compensation, and distance intransitiveness to DTW algorithm.

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MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System

  • Zhao, Jianli;Fu, Zhengbin;Sun, Qiuxia;Fang, Sheng;Wu, Wenmin;Zhang, Yang;Wang, Wei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.5
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    • pp.2381-2399
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    • 2019
  • Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

Assignment-Change Optimization for the Problem of Bid Evaluation (입찰 평가 문제의 배정-변경 최적화)

  • Lee, Sang-Un
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.4
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    • pp.171-176
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    • 2021
  • This paper deals with bid evaluation problem that chooses the vendors and quantity with minimum purchasing cost for bid information of setup cost and unit price. For this problem, the branch-and-bound(BB) and branch-and-cut(BC) methods are well-known. But these methods can be fail to obtain the optimal solution. This paper gets the initial feasible solution with procuring quantity assignment principle in accordance with the unit price or setup cost rank-first. Then procuring quantity moving optimization(vendor change) is execute take account of unit price or setup cost rank. As a result of experimentation, the propose algorithm is significantly lower compared to BB and BC.

User Reputation Evaluation Using Co-occurrence Feature and Collective Intelligence (동시출현 자질과 집단 지성을 이용한 지식검색 문서 사용자 명성 평가)

  • Lee, Hyun-Woo;Han, Yo-Sub;Kim, Lae-Hyun;Cha, Jeong-Won
    • Korean Journal of Cognitive Science
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    • v.19 no.4
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    • pp.459-476
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    • 2008
  • The user needs to find the answer to your question is growing fast at the service using collective intelligent knowledge. In the previous researches, it was proven that the non-text information like view counting, referrer number, and number of answer is good in evaluating answers. There were also many works about evaluating answers using the various kinds of word dictionaries. In this work, we propose new method to evaluate answers to question effectively using user reputation that estimated by the social activity. We use a modified PageRank algorithm for estimating user reputation. We also use the similarity between question and answer. From the result of experiment in the Naver GisikiN corpus, we can see that the proposed method gives meaningful performance to complement the answer selection rate.

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Self Health Diagnosis System of Oriental Medicine Using Enhanced Fuzzy ART Algorithm (개선된 퍼지 ART 알고리즘을 이용한 한방 자가 진단 시스템)

  • Kim, Kwang-Baek;Woo, Young-Woon
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.2
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    • pp.27-34
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    • 2010
  • Recently, lots of internet service companies provide on-line health diagnosis systems. But general persons not having expert knowledge are difficult to use, because most of the health diagnosis systems present prescriptions or dietetic treatments for diseases based on western medicine. In this paper, a self health diagnosis system of oriental medicine coinciding with physical characteristics of Korean using fuzzy ART algorithm, is proposed. In the proposed system, three high rank of diseases having high similarity values are derived by comparing symptoms presented by a user with learned symptoms of specific diseases based on treatment records using neural networks. And also the proposed system shows overall symptoms and folk remedies for the three high rank of diseases. Database on diseases and symptoms is built by several oriental medicine books and then verified by a medical specialist of oriental medicine. The proposed self health diagnosis system of oriental medicine showed better performance than conventional health diagnosis systems by means of learning diseases and symptoms using treatment records.

Effective Noise Reduction using STFT-based Content Analysis (STFT 기반 영상분석을 이용한 효과적인 잡음제거 알고리즘)

  • Baek, Seungin;Jeong, Soowoong;Choi, Jong-Soo;Lee, Sangkeun
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.4
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    • pp.145-155
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    • 2015
  • Noise reduction has been actively studied in the digital image processing and recently, block-based denoising algorithms are widely used. In particular, a low rank approximation employing WNNM(Weighted Nuclear Norm Minimization) and block-based approaches demonstrated the potential for effective noise reduction. However, the algorithm based on low rank a approximation generates the artifacts in the image restoration step. In this paper, we analyzes the image content using the STFT(Short Time Fourier Transform) and proposes an effective method of minimizing the artifacts generated from the conventional algorithm. To evaluate the performance of the proposed scheme, we use the test images containing a wide range of noise levels and compare the results with the state-of-art algorithms.

A DDoS Attack Detection Technique through CNN Model in Software Define Network (소프트웨어-정의 네트워크에서 CNN 모델을 이용한 DDoS 공격 탐지 기술)

  • Ko, Kwang-Man
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.13 no.6
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    • pp.605-610
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    • 2020
  • Software Defined Networking (SDN) is setting the standard for the management of networks due to its scalability, flexibility and functionality to program the network. The Distributed Denial of Service (DDoS) attack is most widely used to attack the SDN controller to bring down the network. Different methodologies have been utilized to detect DDoS attack previously. In this paper, first the dataset is obtained by Kaggle with 84 features, and then according to the rank, the 20 highest rank features are selected using Permutation Importance Algorithm. Then, the datasets are trained and tested with Convolution Neural Network (CNN) classifier model by utilizing deep learning techniques. Our proposed solution has achieved the best results, which will allow the critical systems which need more security to adopt and take full advantage of the SDN paradigm without compromising their security.

Automatic Document Title Generation with RNN and Reinforcement Learning (RNN과 강화 학습을 이용한 자동 문서 제목 생성)

  • Cho, Sung-Min;Kim, Wooseng
    • Journal of Information Technology Applications and Management
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    • v.27 no.1
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    • pp.49-58
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    • 2020
  • Lately, a large amount of textual data have been poured out of the Internet and the technology to refine them is needed. Most of these data are long text and often have no title. Therefore, in this paper, we propose a technique to combine the sequence-to-sequence model of RNN and the REINFORCE algorithm to generate the title of the long text automatically. In addition, the TextRank algorithm was applied to extract a summarized text to minimize information loss in order to protect the shortcomings of the sequence-to-sequence model in which an information is lost when long texts are used. Through the experiment, the techniques proposed in this study are shown to be superior to the existing ones.

Recirculating Shuffle-Exchange Interconnection ATM Switching Network Based on a Priority Control Algorithm (우선순위 제어기법을 기반으로 한 재순환 Shuffle-Exchage 상호연결 ATM 스위치)

  • Park, Byeong-Su
    • The Transactions of the Korea Information Processing Society
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    • v.7 no.6
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    • pp.1949-1955
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    • 2000
  • This paper proposes a multistage interconnection ATM switching network without internal blocking. The first is recirculating shuffle-exchange network improved on hardware complexity. The next is connected to Rank network with tree structure. In this network, after the packets transferred to the same output ports are given each priority, only a packet with highest priority is sent to the next, an the others are recirculated to the first. Rearrangeability through decomposition and composition algorithm is applied for the transferred packets in hanyan network and all they arrive at a final destinations. To analyze throughput, waiting time and packet loss ratio according tothe size of buffer, the probabilities are modeled by a binomial distribution of packet arrival.

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