• Title/Summary/Keyword: 성공의 확률

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A Study on New DCF Algorithm in IEEE 802.11 WLAN by Simulation (시뮬레이션에 의한 IEEE 802.11 WLAN에서의 새로운 DCF 알고리즘에 관한 연구)

  • Lim, Seog-Ku
    • Journal of Digital Contents Society
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    • v.9 no.1
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    • pp.61-67
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    • 2008
  • In this paper, MAC algorithm for the IEEE 802.11 DCF improving the performance is proposed and analyzed by simulation. The MAC of IEEE 802.11 WLAN to control data transmission uses two control methods called DCF(Distributed Coordination Function) and PCF(Point Coordination Function). The DCF controls the transmission based on CSMA/CA(Carrier Sense Multiple Access with Collision Avoidance), that decides a random backoff time with the range of CW(Contention Window) for each station. Normally, each station increase the CW to double after collision, and reduces the CW to the minimum after successful transmission. The DCF shows excellent performance relatively in situation that competition station is less but has a problem that performance is fallen from throughput and delay viewpoint in situation that competition station is increased. This paper proposes an enhanced DCF algorithm that increases the CW to maximal CW after collision and decreases the CW smoothly after successful transmission in order to reduce the collision probability by utilizing the current status information of WLAN. To prove efficiency of proposed algorithm, a lots of simulations are conducted and analyzed.

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Predicting Interesting Web Pages by SVM and Logit-regression (SVM과 로짓회귀분석을 이용한 흥미있는 웹페이지 예측)

  • Jeon, Dohong;Kim, Hyoungrae
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.3
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    • pp.47-56
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    • 2015
  • Automated detection of interesting web pages could be used in many different application domains. Determining a user's interesting web pages can be performed implicitly by observing the user's behavior. The task of distinguishing interesting web pages belongs to a classification problem, and we choose white box learning methods (fixed effect logit regression and support vector machine) to test empirically. The result indicated that (1) fixed effect logit regression, fixed effect SVMs with both polynomial and radial basis kernels showed higher performance than the linear kernel model, (2) a personalization is a critical issue for improving the performance of a model, (3) when asking a user explicit grading of web pages, the scale could be as simple as yes/no answer, (4) every second the duration in a web page increases, the ratio of the probability to be interesting increased 1.004 times, but the number of scrollbar clicks (p=0.56) and the number of mouse clicks (p=0.36) did not have statistically significant relations with the interest.

Flood Alert and Warning Scheme Based on Intensity-Duration-Quantity (IDQ) Curve considering Antecedant Moisture Condition (선행함수지수를 고려한 강우강도-지속시간-홍수량(IDQ) 곡선기반의 홍수예경보기법)

  • Kim, Jin-Gyeom;Kang, Boosik
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.35 no.6
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    • pp.1269-1276
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    • 2015
  • The methodology of utilizing Intensity-Duration-flood Quantity (IDQ) curve for flood alert and warning was introduced and its performance was evaluated. For this purpose the lumped parameter model was calibrated and validated for gauged basin data set and the index precipitation equivalent to alert and warning flood was estimated. The index precipitation and IDQ curves associated by three different Antecedant Moisture Conditions (AMCs) are made provision for various possible flood scenarios. The test basin is Wonju-cheon basin ($94.4km^2$) located in Gangwon province, Korea. The IDQ curves corresponding to alert (50% of design flood level) and warning (70% of design flood level) level was estimated using the Clark unit hydrograph based lumped parameter model. The performance evaluation showed 0.704 of POD (Probability of Detection), 0.136 of FAR (False Alarm Ratio), and 0.633 of CSI (Critical Success Index), which is improved from the result of IDQ with single fixed AMC.

A Study on Characteristics of the Desiccation Shrinkage in Reclaimed Hydraulic Fills (준설매립지반의 건조수축특성에 관한 연구)

  • 홍병만;김상규;김석열;김승욱;김홍택;강인규
    • Journal of the Korean Geotechnical Society
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    • v.15 no.6
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    • pp.219-238
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    • 1999
  • In the present study, laboratory tests including the seepage-induced consolidation test, suction test, and desiccation shrinkage test are performed to investigate characteristics of the desiccation shrinkage in reclaimed hydraulic fills. Soil samples for laboratory tests are obtained from three sites (districts of Haenam, Kogeum and Koheung in Chunnam area). Desiccation shrinkage settlement caused by three dimensional volume change is numerically evaluated using finite difference technique based on the governing equation proposed by Abu-Hejleh & Znidarcic. Also characteristics of the desiccation shrinkage analyzed from the test results are used as input data for numerical evaluations. Further predicted total settlements including the self-weight consolidation settlement are compared with values measured at the site of Haenam district. Finally, effects of parameters related to the desiccation shrinkage on settlements are examined.

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A heuristic path planning method for robot working in an indoor environment (실내에서 작업하는 로봇의 휴리스틱 작업경로계획)

  • Hyun, Woong-Keun
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.8
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    • pp.907-914
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    • 2014
  • A heuristic search algorithm is proposed to plan a collision free path for robots in an indoor environment. The proposed algorithm is to find a collision free path in the gridded configuration space by proposed heuristic graph search algorithm. The proposed algorithm largely consists of two parts : tunnel searching and path searching in the tunnel. The tunnel searching algorithm finds a thicker path from start grid to goal grid in grid configuration space. The tunnel is constructed with large grid defined as a connected several minimum size grids in grid-based configuration space. The path searching algorithm then searches a path in the tunnel with minimum grids. The computational time of the proposed algorithm is less than the other graph search algorithm and we analysis the time complexity. To show the validity of the proposed algorithm, some numerical examples are illustrated for robot.

A Cooperative ARQ Scheme for Single-hop and Multi-hop Underwater Acoustic Sensor Networks (단일-홉과 다중-홉 수중 음향 센서 네트워크에서의 효율적인 협력 재전송 기법)

  • Lee, Jae-Won;Cho, Ho-Shin
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.5B
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    • pp.539-548
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    • 2011
  • We propose an efficient cooperative ARQ (Automatic Repeat reQuest) scheme for single-hop and multi-hop underwater acoustic communications, in which cooperative nodes are used to provide more reliable alternative paths for a specific source-to-destination connection. This alternative path has higher channel quality than that of the direct source-destination path. In addition, during a packet-relay through multiple hops, the typical acknowledgement (ACK) signal is replaced with overhearing data packet returned back from the next hop. The usage of overhearing as an ACK improves the system performance. In this paper, we evaluate the proposed scheme by comparing it with a conventional S&W ARQ in terms of throughput efficiency. Computer simulation results show that the proposed cooperative retransmission scheme can significantly improve the throughput by increasing the probability of successful retransmission.

Comparative Simulation Studies on Generalized Binomial Models (일반화 이항모형의 적합도 평가)

  • Baik, E.J.;Kim, K.Y.
    • Communications for Statistical Applications and Methods
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    • v.18 no.4
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    • pp.507-516
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    • 2011
  • Comparative studies on generalized binomial models (Moon, 2003; Ng, 1989; Paul, 1985; Kupper and Haseman, 1978; Griffiths, 1973) are restrictive in that the models compared are rather limited and MSE of the estimates is the only measure considered for the model adequacy. This paper is aimed to report simulation results which provide possible guidelines for selecting a proper model. We examine Pearson type of goodness-of-fit statistic to its degrees of freedom and AIC for the overall model quality. MSE and Bias of the individual estimates are also considered as the component fit measures. Performance of some models varies widely for a certain range of the parameter space while most of the models are quite competent. Our evaluation shows that the Extended Beta-Binomial model (Prentice, 1986) turns out to be particularly favorable in the point that it provides consistently excellent fit almost all over the values of the intra-class correlation coefficient and the probability of success.

Elliptical Clustering with Incremental Growth and its Application to Skin Color Region Segmentation (점증적으로 증가하는 타원형 군집화 : 피부색 영역 검출에의 적용)

  • Lee Kyoung-Mi
    • Journal of KIISE:Software and Applications
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    • v.31 no.9
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    • pp.1161-1170
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    • 2004
  • This paper proposes to segment skin color areas using a clustering algorithm. Most of previously proposed clustering algorithms have some difficulties, since they generally detect hyperspherical clusters, run in a batch mode, and predefine a number of clusters. In this paper, we use a well-known elliptical clustering algorithm, an EM algorithm, and modify it to learn on-line and find automatically the number of clusters, called to an EAM algorithm. The effectiveness of the EAM algorithm is demonstrated on a task of skin color region segmentation. Experimental results present the EAM algorithm automatically finds a right number of clusters in a given image without any information on the number. Comparing with the EM algorithm, we achieved better segmentation results with the EAM algorithm. Successful results were achieved to detect and segment skin color regions using a conditional probability on a region. Also, we applied to classify images with persons and got good classification results.

Neural Predictive Coding for Text Compression Using GPGPU (GPGPU를 활용한 인공신경망 예측기반 텍스트 압축기법)

  • Kim, Jaeju;Han, Hwansoo
    • KIISE Transactions on Computing Practices
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    • v.22 no.3
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    • pp.127-132
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    • 2016
  • Several methods have been proposed to apply artificial neural networks to text compression in the past. However, the networks and targets are both limited to the small size due to hardware capability in the past. Modern GPUs have much better calculation capability than CPUs in an order of magnitude now, even though CPUs have become faster. It becomes possible now to train greater and complex neural networks in a shorter time. This paper proposed a method to transform the distribution of original data with a probabilistic neural predictor. Experiments were performed on a feedforward neural network and a recurrent neural network with gated-recurrent units. The recurrent neural network model outperformed feedforward network in compression rate and prediction accuracy.

Time Series Representation Combining PIPs Detection and Persist Discretization Techniques for Time Series Classification (시계열 분류를 위한 PIPs 탐지와 Persist 이산화 기법들을 결합한 시계열 표현)

  • Park, Sang-Ho;Lee, Ju-Hong
    • The Journal of the Korea Contents Association
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    • v.10 no.9
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    • pp.97-106
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    • 2010
  • Various time series representation methods have been suggested in order to process time series data efficiently and effectively. SAX is the representative time series representation method combining segmentation and discretization techniques, which has been successfully applied to the time series classification task. But SAX requires a large number of segments in order to represent the meaningful dynamic patterns of time series accurately, since it loss the dynamic property of time series in the course of smoothing the movement of time series. Therefore, this paper suggests a new time series representation method that combines PIPs detection and Persist discretization techniques. The suggested method represents the dynamic movement of high-diemensional time series in a lower dimensional space by detecting PIPs indicating the important inflection points of time series. And it determines the optimal discretizaton ranges by applying self-transition and marginal probabilities distributions to KL divergence measure. It minimizes the information loss in process of the dimensionality reduction. The suggested method enhances the performance of time series classification task by minimizing the information loss in the course of dimensionality reduction.