• Title/Summary/Keyword: Incremental Algorithm

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An Effective Increment리 Content Clustering Method for the Large Documents in U-learning Environment (U-learning 환경의 대용량 학습문서 판리를 위한 효율적인 점진적 문서)

  • Joo, Kil-Hong;Choi, Jin-Tak
    • Journal of the Korea Computer Industry Society
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    • v.5 no.9
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    • pp.859-872
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    • 2004
  • With the rapid advance of computer and communication techonology, the recent trend of education environment is edveloping in the ubiquitous learning (u-learning) direction that learners select and organize the contents, time and order of learning by themselves. Since the amount of education information through the internet is increasing rapidly and it is managed in document in an effective way is necessary. The document clustering is integrated documents to subject by classifying a set of documents through their similarity among them. Accordingly, the document clustering can be used in exploring and searching a document and it can increased accuracy of search. This paper proposes an efficient incremental clustering method for a set of documents increase gradually. The incremental document clustering algorithm assigns a set of new documents to the legacy clusters which have been identified in advance. In addition, to improve the correctness of the clustering, removing the stop words can be proposed.

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Incremental Techniques for Timing Analysis Considering Timing and Circuit Structure Changes (지연시간과 회로 구조 변화를 고려한 증가적 타이밍 분석)

  • O, Jang-Uk;Han, Chang-Ho
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.8
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    • pp.2204-2212
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    • 1999
  • In this paper, we present techniques which perform incremental timing analysis using Timed Boolean Algebra that solves the false path problem and extracts the timing information in combinational circuits. Our algorithm sets histories of internal inputs that are substituted for internal output and extracts maximal delays through checking sensitizability of primary outputs. Once finding the sum of primitive delay terms, then it applies modified delay with referencing histories of primary output and it can extract maximal delays of primary outputs fast and efficiently. When the structure of circuit is changed, there is no need to compute the whole circuit again. We can process partial timing analysis of computing on the gates that are need to compute again. These incremental timing analysis methods are considered both delay changes and structure of circuit, and can reduce the costs of a trial error in the circuit design.

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An Active Candidate Set Management Model on Association Rule Discovery using Database Trigger and Incremental Update Technique (트리거와 점진적 갱신기법을 이용한 연관규칙 탐사의 능동적 후보항목 관리 모델)

  • Hwang, Jeong-Hui;Sin, Ye-Ho;Ryu, Geun-Ho
    • Journal of KIISE:Databases
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    • v.29 no.1
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    • pp.1-14
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    • 2002
  • Association rule discovery is a method of mining for the associated item set on large databases based on support and confidence threshold. The discovered association rules can be applied to the marketing pattern analysis in E-commerce, large shopping mall and so on. The association rule discovery makes multiple scan over the database storing large transaction data, thus, the algorithm requiring very high overhead might not be useful in real-time association rule discovery in dynamic environment. Therefore this paper proposes an active candidate set management model based on trigger and incremental update mechanism to overcome non-realtime limitation of association rule discovery. In order to implement the proposed model, we not only describe an implementation model for incremental updating operation, but also evaluate the performance characteristics of this model through the experiment.

Numerical Method for Nonlinear Analysis of Composite Shells under Constant Lateral Pressure and Incremented In-plane Compression (일정 횡압력과 증분 압축하중을 동시에 받는 복합재 쉘의 비선형 해석을 위한 수치기법 연구)

  • 김진호;권진희
    • Composites Research
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    • v.13 no.1
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    • pp.69-77
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    • 2000
  • This paper presents a modified arc-length method for the nonlinear finite element analysis of a structure which is loaded in incremental and fixed forces, simultaneously. The main idea of the method is to separate the displacement term by the constant force from that by the incremental force. Presented method is applied to the nonlinear analysis of isotropic shell structures separately loaded by lateral pressure or compression, and shows the excellent agreement with previous results. As an illustrative example of the applicability of the present algorithm, a parametric study is performed on the nonlinear buckling analysis of composite cylindrical panels under the combined load of the incremented compression and the constant lateral pressure.

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Modular Line-connected Photovoltaic PCS (모듈형 계통연계 태양광 PCS)

  • Seo, Hyun-Woo;Kwon, Jung-Min;Kim, Eung-Ho;Kwon, Bong-Hwan
    • The Transactions of the Korean Institute of Power Electronics
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    • v.13 no.2
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    • pp.119-127
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    • 2008
  • In this paper, the modular line-connected photovoltaic PCS (photovoltaic power conditioning system) is proposed. A step-up DC-DC converter using a active-clamp circuit and a dual series-resonant rectifier is proposed to achieve a high efficiency and a high input-output voltage ratio efficiently. An IncCond (incremental conductance) MPPT (maximum power point tracking) algorithm that improves MPPT characteristic is used. The PV module current is estimated without using a DC current sensor. By control a inverter using a linearized output current controller, a unity power factor is achieved. All algorithms and controllers are implemented on a single-chip microcontroller and the superiority of the proposed DC-DC converter and controllers is proved by experiments.

Prefix Cuttings for Packet Classification with Fast Updates

  • Han, Weitao;Yi, Peng;Tian, Le
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.4
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    • pp.1442-1462
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    • 2014
  • Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.

A Study on Formation of Incremental Palette for Lower Quality Display (저 사양 디스플레이를 위한 동적 팔래트 생성 방법에 관한 연구)

  • Paik, Doo-Won;Lim, Hun-Gyu;Hwang, Joo-Yeon
    • Journal of the Korea Society of Computer and Information
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    • v.13 no.1
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    • pp.169-173
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    • 2008
  • A navigation system for virtual environments using low-quality HMD(head mounted display) must quantize images when the system presents true-color image with restricted number of colors. Such navigation system quantizes an image by using fixed palette. If the system represents an image by using a variable palette which is made considering a region around the viewpoint then user can perceive a virtual environments more vividly because human visual system is sensitive to the colors variation in the region around the viewpoint. In this paper we propose a color quantization algorithm that quantize a region around the viewpoint more finely than other regions at each variation of viewpoint for virtual environments navigation system.

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ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation

  • Kang, Jungyu;Han, Seung-Jun;Kim, Nahyeon;Min, Kyoung-Wook
    • ETRI Journal
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    • v.43 no.4
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    • pp.630-639
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    • 2021
  • Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.

The Incremental Learning Method of Variable Slope Backpropagation Algorithm Using Representative Pattern (대표 패턴을 사용한 가변 기울기 역전도 알고리즘의 점진적 학습방법)

  • 심범식;윤충화
    • Journal of the Korea Society of Computer and Information
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    • v.3 no.1
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    • pp.95-112
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    • 1998
  • The Error Backpropagation algorithm is widely used in various areas such as associative memory, speech recognition, pattern recognition, robotics and so on. However, if and when a new leaning pattern has to be added in order to drill, it will have to accomplish a new learning with all previous learning pattern and added pattern from the very beginning. Somehow, it brings about a result which is that the more it increases the number of pattern, the longer it geometrically progress the time required by leaning. Therefore, a so-called Incremental Learning Method has to be solved the point at issue all by means in case of situation which is periodically and additionally learned by numerous data. In this study, not only the existing neural network construction is still remained, but it also suggests a method which means executing through added leaning by a model pattern. Eventually, for a efficiency of suggested technique, both Monk's data and Iris data are applied to make use of benchmark on machine learning field.

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Incremental Image-Based Motion Rendering Technique for Implementation of Realistic Computer Animation (사실적인 컴퓨터 애니메이션 구현을 위한 증분형 영상 기반 운동 렌더링 기법)

  • Han, Young-Mo
    • The KIPS Transactions:PartB
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    • v.15B no.2
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    • pp.103-112
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    • 2008
  • Image-based motion capture technology is often used in making realistic computer animation. In this paper we try to implement image-based motion rendering by fixing a camera to a PC. Existing image-based rendering algorithms have disadvantages of high computational burden or low accuracy. The former disadvantage causes too long making-time of an animation. The latter disadvantage degrades reality in making realistic animation. To compensate for those disadvantages of the existing approaches, this paper presents an image-based motion rendering algorithm with low computational load and high estimation accuracy. In the proposed approach, an incremental motion rendering algorithm with low computational load is analyzed in the respect of optimal control theory and revised so that its estimation accuracy is enhanced. If we apply this proposed approach to optic motion capture systems, we can obtain additional advantages that motion capture can be performed without any markers, and with low cost in the respect of equipments and spaces.