• Title/Summary/Keyword: Stochastic media

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Integrated Stochastic Admission Control Policy in Clustered Continuous Media Storage Server (클리스터 기반 연속 미디어 저장 서버에서의 통합형 통계적 승인 제어 기법)

  • Kim, Yeong-Ju;No, Yeong-Uk
    • The KIPS Transactions:PartA
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    • v.8A no.3
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    • pp.217-226
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    • 2001
  • In this paper, for continuous media access operations performed by Clustered Continuous Media Storage Server (CCMSS) system, we present the analytical model based on the open queueing network, which considers simultaneously two critical delay factors, the disk I/O and the internal network, in the CCMSS system. And we derive by using the analytical model the stochastic model for the total service delay time in the system. Next, we propose the integrated stochastic admission control model for the CCMSS system, which estimate the maximum number of admittable service requests at the allowable service failure rate by using the derived stochastic model and apply the derived number of requests in the admission control operation. For the performance evaluation of the proposed model, we evaluated the deadline miss rates by means of the previous stochastic model considering only the disk I/O and the propose stochastic model considering the disk I/O and the internal network, and compared the values with the results obtained from the simulation under the real cluster-based distributed media server environment. The evaluation showed that the proposed admission control policy reflects more precisely the delay factors in the CCMSS system.

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A PROPOSAL ON ALTERNATIVE SAMPLING-BASED MODELING METHOD OF SPHERICAL PARTICLES IN STOCHASTIC MEDIA FOR MONTE CARLO SIMULATION

  • KIM, SONG HYUN;LEE, JAE YONG;KIM, DO HYUN;KIM, JONG KYUNG;NOH, JAE MAN
    • Nuclear Engineering and Technology
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    • v.47 no.5
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    • pp.546-558
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    • 2015
  • Chord length sampling method in Monte Carlo simulations is a method used to model spherical particles with random sampling technique in a stochastic media. It has received attention due to the high calculation efficiency as well as user convenience; however, a technical issue regarding boundary effect has been noted. In this study, after analyzing the distribution characteristics of spherical particles using an explicit method, an alternative chord length sampling method is proposed. In addition, for modeling in finite media, a correction method of the boundary effect is proposed. Using the proposed method, sample probability distributions and relative errors were estimated and compared with those calculated by the explicit method. The results show that the reconstruction ability and modeling accuracy of the particle probability distribution with the proposed method were considerably high. Also, from the local packing fraction results, the proposed method can successfully solve the boundary effect problem. It is expected that the proposed method can contribute to the increasing of the modeling accuracy in stochastic media.

Roof failure of shallow tunnel based on simplified stochastic medium theory

  • Huang, Xiaolin;Zhou, Zhigang;Yang, X.L.
    • Geomechanics and Engineering
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    • v.14 no.6
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    • pp.571-580
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    • 2018
  • The failure mechanism of tunnel roof is investigated with upper bound theorem of limit analysis. The stochastic settlement and nonlinear failure criterion are considered in the present analysis. For the collapse of tunnel roof, the surface settlement is estimated by the simplified stochastic medium theory. The failure curve expressions of collapse blocks in homogeneous and in layered soils are derived, and the effects of material parameters on the potential range of failure mechanisms are discussed. The results show that the material parameters of initial cohesion, nonlinear coefficient and unit weight have significant influences on the potential range of collapse block in homogeneous media. The proportion of collapse block increases as the initial cohesion increases, while decreases as the nonlinear coefficient and the unit weight increase. The ground surface settlement increases with the tunnel radius increasing, while the possible collapse proportion decreases with increase of the tunnel radius. In layered stratum, the study is investigated to analyze the effects of material parameters of different layered media on the proportion of possible collapse block.

Stochastic analysis of elastic wave and second sound propagation in media with Gaussian uncertainty in mechanical properties using a stochastic hybrid mesh-free method

  • Hosseini, Seyed Mahmoud;Shahabian, Farzad
    • Structural Engineering and Mechanics
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    • v.49 no.1
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    • pp.41-64
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    • 2014
  • The main objective of this article is the exploitation of a stochastic hybrid mesh-free method based on stochastic generalized finite difference (SGFD), Newmark finite difference (NFD) methods and Monte Carlo simulation for thermoelastic wave propagation and coupled thermoelasticity analysis based on GN theory (without energy dissipation). A thick hollow cylinder with Gaussian uncertainty in mechanical properties is considered as an analyzed domain for the problem. The effects of uncertainty in mechanical properties with various coefficients of variations on thermo-elastic wave propagation are studied in details. Also, the time histories and distribution on thickness of cylinder of maximum, mean and variance values of temperature and radial displacement are studied for various coefficients of variations (COVs).

A Selection of Optimal EEG Channel for Emotion Analysis According to Music Listening using Stochastic Variables (확률변수를 이용한 음악에 따른 감정분석에의 최적 EEG 채널 선택)

  • Byun, Sung-Woo;Lee, So-Min;Lee, Seok-Pil
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.62 no.11
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    • pp.1598-1603
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    • 2013
  • Recently, researches on analyzing relationship between the state of emotion and musical stimuli are increasing. In many previous works, data sets from all extracted channels are used for pattern classification. But these methods have problems in computational complexity and inaccuracy. This paper proposes a selection of optimal EEG channel to reflect the state of emotion efficiently according to music listening by analyzing stochastic feature vectors. This makes EEG pattern classification relatively simple by reducing the number of dataset to process.

A Stochastic Model for the Nuclide Migration in Geologic Media Using a Continuous Time Markov Process (연속시간 마코프 프로세스를 이용한 지하매질에서의 통계적 핵종이동 모델)

  • Lee, Y.M.;Kang, C.H.;Hahn, P.S.;Park, H.H.;Lee, K.J.
    • Nuclear Engineering and Technology
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    • v.25 no.1
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    • pp.154-165
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    • 1993
  • A stochastic method using continuous time Markov process is presented to model the one-dimensional convective nuclide transport in geologic media, which have usually heterogeneous feature in physical/geochemical parameters such as velocity, dispersion coefficient, and retardation factor resulting poor description by conventional deterministic advection-dispersion model. The primary desired quantities from a stochastic model are the mean values and variance of the state variables as a function of time. The time-dependent probability distributions of nuclides are presented for each discretized compartment given the volumetric groundwater flux and the intensity of transition. Since this model is discrete in medium space, physical/geochemical parameters which affect nuclide transport can be easily incorporated for the heterogeneous media as well as remarkably layered media having spatially varied parameters. Even though the Markov process model developed in this study was shown to be sensitive to the number of discretized compartments showing numerical dispersion as the number of compartments are increased, this could be easily calibrated by comparing with the analytical deterministic model.

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Stochastic Mixture Modeling of Driving Behavior During Car Following

  • Angkititrakul, Pongtep;Miyajima, Chiyomi;Takeda, Kazuya
    • Journal of information and communication convergence engineering
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    • v.11 no.2
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    • pp.95-102
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    • 2013
  • This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.

Stochastic Low-Power and Buffer-Stable Routing for Gigabit Wireless Video Networks (기가빗 비디오 네트워크에서의 추계적 저전력 버퍼안정 라우팅)

  • Kim, Joongheon;Ryu, Eun-Seok
    • Journal of Broadcast Engineering
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    • v.18 no.3
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    • pp.491-494
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    • 2013
  • This paper proposes a stochastic/dynamic routing protocol which aims the minimization of the summation of time average expected power expenditure with buffer stability in mobile ad-hoc 60 GHz wireless networks. By using 60 GHz RF, the wireless devices can transmit/receive 1080p HD video signals without compression. In addition, our algorithm works without centralized controller, so that the distributed operation is available. The novelty of the proposed algorithm was also verified by simulations.

Generating various NPCs Behavior using Inference of Stochastic Finite Automata (확률 유한오토마타의 추론을 이용한 다양한 NPC의 행동양식 생성에 관한 기법 연구)

  • Cho, Kyung-Eun;Cho, Hyung-Je
    • Journal of Korea Game Society
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    • v.2 no.2
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    • pp.52-59
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    • 2002
  • This paper introduces FSM, statistical FSM and NFA that are used for assigning behaviors of NPCs in computer games. We propose a new method for remedy of the weakness of previous studies. We use the method of inferencing stochastic grammars to generate NPCs behaviors. Using this method we can generate a lot of MPCs or Computer Players behaviors automatically and the games will be more enjoyable.

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Stochastic Weight Averaging for Improving the Performance of Image Super-Resolution (Stochastic Weight Averaging 알고리즘을 이용한 이미지 초해상도 성능 개선)

  • Yoon, Jeong Hwan;Cho, Nam Ik
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2021.06a
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    • pp.345-347
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    • 2021
  • 단일 이미지 초해상도는 딥러닝의 발전과 함께 놀라운 성능 향상이 이루어 졌다. 이러한 딥러닝 모델은 매우 많은 파라미터를 갖고 있어 많은 연산량과 메모리를 필요로 한다. 하지만 사용할 수 있는 리소스는 한정되어 있기 때문에 네트워크를 경량화 시키려는 연구도 지속되어 왔다. 본 논문에서는 Stochastic Weight Averaging (SWA) 알고리즘을 이용하여 상대적으로 적은 양의 메모리와 연산을 추가해 이미지 초해상도 모델의 성능을 높이고 안정적인 학습을 달성하였다. SWA 알고리즘을 적용한 모델은 그렇지 않은 모델에 비해 테스트셋에서 최대 0.13dB 의 성능 향상을 보였다.

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