• Title/Summary/Keyword: branch predict

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The Prediction of Yield Load in Circular Tubular T-type Cross Sections on the Truss Structures (강관트러스의 T형 격점부의 항복하중 예측에 관한 연구)

  • Park, Il Min
    • Journal of Korean Society of Steel Construction
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    • v.13 no.1
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    • pp.9-18
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    • 2001
  • many steel tubular truss as roof structures are used of the large span structures Steel tubular sectioned truss has the structural merits in compared with other sections such as H, L-shape sections However it occurs local buckling at the joint of branch in truss and it makes the deterioration of loading capacity Loading capacity and deformation characteristics of truss joints are very complicate so it is very hard to predict exact solution of them Therefore this thesis dealt with T-type joints of steel circular hollow sectioned truss. A series of experimental scheme were planned and mainly experimental parameters were : ratio of diameter of branch-diameter of main chord(d/D). diameter-thickness(T/D) of main chord. In this paper predicted yield load capacity using by closed ring analysis method additionally compared with that of suggested by closed ring analysis method additionally compared with that of suggested by other countries.

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APPLICATION OF CFD SIMULATION IN SIC-CVD PROCESS (SiC-CVD 공정에서 CFD 시뮬레이션의 응용)

  • Kim, J.W.;Han, Y.S.;Choi, K.;Lee, J.H.
    • Journal of computational fluids engineering
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    • v.18 no.3
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    • pp.67-71
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    • 2013
  • Recently, the rapid development of the semiconductor industry induces the prompt technical progress in the area of device integration and the application of large diameter wafers for the price competitiveness. As a result of the usage of large wafers in the semiconductor industry, the silicon carbide components which have layers of silicon carbide on graphite or RBSC substrates is getting widely used due to the advantages of SiC such as high hardness and strength, chemical and ionic resistant to all the environments superior than other ceramic materials. For the uniform and homogeneous deposition of silicon carbide on these huge components, it needs to know about the gas flow in the CVD reactor, not only for the delicate adjustment of the process variables but more essentially for the cost reduction for the shape change of specimens and their holders on the stage of reactor. In this research, the CFD simulation is challenged for the prediction of the inner distribution of the gas velocity. Chemical reaction simulation is used to predict the distribution of concentration of the reacting gas with the rotating velocity of the stage. With the increase of the rotating speed, more uniform distribution of the reacting gas on the surface of the stage was obtained.

Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors

  • Chahnasir, E. Sadeghipour;Zandi, Y.;Shariati, M.;Dehghani, E.;Toghroli, A.;Mohamad, E. Tonnizam;Shariati, A.;Safa, M.;Wakil, K.;Khorami, M.
    • Smart Structures and Systems
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    • v.22 no.4
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    • pp.413-424
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    • 2018
  • The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.

Analytical Models and their Performance Analysis of Superscalar Processors (수퍼스칼라 프로세서의 해석적 모델 및 성능 분석)

  • Kim, Hak-Jun;Kim, Seon-Mo;Choe, Sang-Bang
    • Journal of KIISE:Computer Systems and Theory
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    • v.26 no.7
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    • pp.847-862
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    • 1999
  • 본 논문에서는 유한버퍼의(finite-buffered) 동기화된(synchronous) 큐잉모델(queueing model)을 이용하여 명령어들간의 병렬성, 분기명령의 빈도수, 분기예측(branch prediction)의 정확도, 캐쉬미스 등의 파라미터들을 고려하여 프로세서의 명령어 실행율을 예측하며 캐쉬의 성능과 파이프라인 성능간의 관계를 분석할 수 있는 새로운 해석적 모델을 제안하였다. 해석적 모델은 모델의 타당성을 검증하기 위해서 시뮬레이션을 수행하여 얻은 결과와 비교하였다. 해석적 모델과 시뮬레이션을 비교한 결과 대부분 10% 오차 내에서 일치하였다. 본 연구를 통하여 얻은 해석적 모델을 사용하면 시뮬레이션에서는 드러나지 않는 성능제약의 원인에 대한 명확한 규명이 가능하기 때문에 성능향상을 위한 설계자료를 얻을 수 있으며, 시스템 성능 밸런스를 위한 캐쉬와 비순차이슈 파이프라인 성능간의 관계에 대한 정확한 분석이 가능하다.Abstract This research presents a novel analytic model to predict the instruction execution rate of superscalar processors using the queuing model with finite-buffer size and synchronous operation mode. The proposed model is also able to analyze the performance relationship between cache and pipeline. The proposed model takes into account various kinds of architectural parameters such as instruction-level parallelism, branch probability, the accuracy of branch prediction, cache miss, and etc.. To prove the correctness of the model, we performed extensive simulations and compared the results with the analytic model. Simulation results showed that the proposed model can estimate the average execution rate accurately within 10% error compared to simulation results. The proposed model can explain the causes of performance bottleneck which cannot be uncovered by the simulation method only. The model is also able to show the effect of the cache miss on the performance of out-of-order issue superscalar processors, which can provide an valuable information in designing a balanced system.

Regression and ANN models for durability and mechanical characteristics of waste ceramic powder high performance sustainable concrete

  • Behforouz, Babak;Memarzadeh, Parham;Eftekhar, Mohammadreza;Fathi, Farshid
    • Computers and Concrete
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    • v.25 no.2
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    • pp.119-132
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    • 2020
  • There is a growing interest in the use of by-product materials such as ceramics as alternative materials in construction. The aim of this study is to investigate the mechanical properties and durability of sustainable concrete containing waste ceramic powder (WCP), and to predict the results using artificial neural network (ANN). In this order, different water to binder (W/B) ratios of 0.3, 0.4, and 0.5 were considered, and in each W/B ratio, a percentage of cement (between 5-50%) was replaced with WCP. Compressive and tensile strengths, water absorption, electrical resistivity and rapid chloride permeability (RCP) of the concrete specimens having WCP were evaluated by related experimental tests. The results showed that by replacing 20% of the cement by WCP, the concrete achieves compressive and tensile strengths, more than 95% of those of the control concrete, in the long term. This percentage increases with decreasing W/B ratio. In general, by increasing the percentage of WCP replacement, all durability parameters are significantly improved. In order to validate and suggest a suitable tool for predicting the characteristics of the concrete, ANN model along with various multivariate regression methods were applied. The comparison of the proposed ANN with the regression methods indicates good accuracy of the developed ANN in predicting the mechanical properties and durability of this type of concrete. According to the results, the accuracy of ANN model for estimating the durability parameters did not significantly follow the number of hidden nodes.

Development of a predictive functional control approach for steel building structure under earthquake excitations

  • Mohsen Azizpour;Reza Raoufi;Ehsan Kazeminezhad
    • Earthquakes and Structures
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    • v.25 no.3
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    • pp.187-198
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    • 2023
  • Model Predictive Control (MPC) is an advanced control approach that uses the current states of the system model to predict its future behavior. In this article, according to the seismic dynamics of structural systems, the Predictive Functional Control (PFC) method is used to solve the control problem. Although conventional PFC is an efficient control method, its performance may be impaired due to problems such as uncertainty in the structure of state sensors and process equations, as well as actuator saturation. Therefore, it requires the utilization of appropriate estimation algorithms in order to accurately evaluate responses and implement actuator saturation. Accordingly, an extended PFC is presented based on the H-ifinity (H∞) filter (HPFC) while considering simultaneously the saturation actuator. Accordingly, an extended PFC is presented based on the H-ifinity (H∞) filter (HPFC) while considering the saturation actuator. Thus, the structural responses are formulated by two estimation models using the H∞ filter. First, the H∞ filter estimates responses using a performance bound (𝜃). Second, the H∞ filter is converted into a Kalman filter in a special case by considering the 𝜃 equal to zero. Therefore, the scheme based on the Kalman filter (KPFC) is considered a comparative model. The proposed method is evaluated through numerical studies on a building equipped with an Active Tuned Mass Damper (ATMD) under near and far-field earthquakes. Finally, HPFC is compared with classical (CPFC) and comparative (KPFC) schemes. The results show that HPFC has an acceptable efficiency in boosting the accuracy of CPFC and KPFC approaches under earthquakes, as well as maintaining a descending trend in structural responses.

Experimental research on the behavior of circular SFRC columns reinforced longitudinally by GFRP rebars

  • Iman Saffarian;Gholam Reza Atefatdoost;Seyed Abbas Hosseini;Leila Shahryari
    • Computers and Concrete
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    • v.31 no.6
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    • pp.513-525
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    • 2023
  • This research presents the experimental and theoretical evaluations on circular steel-fiber-reinforced-concrete (SFRC) columns reinforced by glass-fiber-reinforced-polymer (GFRP) rebar under the axial compressive loading. Test programs were designed to investigate and compare the effect of different parameters on the structural behavior of columns by performing tests. Theses variables included conventional concrete (CC), fiber concrete (FC), steel/GFRP longitudinal rebars, and transversal rebars configurations. A total of 16 specimens were constructed and categorized into four groups in terms of different rebar-concrete configurations, including GFRP-rebar-reinforced-CC columns (GRCC), GFRP-rebar-reinforced-FC columns (GRFC), steel-rebar-reinforced-CC columns (SRCC) and steel-rebar- reinforced-FC columns (SRFC). Experimental observations displayed that failure modes and cracking patterns of four groups of columns were similar, especially in pre-peak branches of load-deflection curves. Although the average ultimate axial load of columns with longitudinal GFRP rebars was obtained by 17.9% less than the average ultimate axial load of columns with longitudinal steel rebars, the average axial ductility index (DI) of them was gained by 10.2% higher than their counterpart columns. Adding steel fibers (SFs) into concrete led to the increases of 7.7% and 6.7% of the axial peak load and the DI of columns than their counterpart columns with CC. The volumetric ratio had greater efficiency on peak loads and DIs of columns than the type of transversal reinforcement. A simple analytical equation was proposed to predict the axial compressive capacity of columns by considering the axial involvement of longitudinal GFRP rebars, volumetric ratio, and steel spiral/hoop rebar. There was a good correlation between test results and predictions of the proposed equation.

Efficient Indirect Branch Predictor Based on Data Dependence (효율적인 데이터 종속 기반의 간접 분기 예측기)

  • Paik Kyoung-Ho;Kim Eun-Sung
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.4 s.310
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    • pp.1-14
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    • 2006
  • The indirect branch instruction is a most substantial obstacle in utilizing ILP of modem high performance processors. The target address of an indirect branch has the polymorphic characteristic varied dynamically, so it is very difficult to predict the accurate target address. Therefore the performance of a processor with speculative methodology is reduced significantly due to the many execution cycle delays in occurring the misprediction. We proposed the very accurate and novel indirect branch prediction scheme so called data-dependence based prediction. The predictor results in the prediction accuracy of 98.92% using 1K entries, and. 99.95% using 8K But, all of the proposed indirect predictor including our predictor has a large hardware overhead for restoring expected target addresses as well as tags for alleviating an aliasing. Hence, we propose the scheme minimizing the hardware overhead without sacrificing the prediction accuracy. Our experiment results show that the hardware is reduced about 60% without the performance loss, and about 80% sacrificing only the performance loss of 0.1% in aspect of the tag overhead. Also, in aspect of the overhead of storing target addresses, it can save the hardware about 35% without the performance loss, and about 45% sacrificing only the performance loss of 1.11%.

Finite Element Analysis of Gaskets for Hydrogen Fuel Cells (수소 연료전지용 가스켓의 유한요소해석)

  • Cheon, Kang-Min;Jang, Jong-Ho;Hur, Jang-Wook
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.20 no.10
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    • pp.95-101
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    • 2021
  • An analysis was conducted to predict the behavior of gasket by applying an optimal-strain energy-density function selected through a uniaxial tensile test and an analysis of the gasket used in an actual hydrogen fuel cell. Among the models compared to predict the materials' properties, the Mooney-Rivlin secondary model showed the behavior most similar to the test results. The maximum stress of the gasket was not significantly different, depending on the location. The maximum surface pressure of the gasket was higher at positions "T" and "Y" than at other positions, owing to the branch-shape effect. In the future, a jig that can measure the surface pressure will be manufactured and a comparative verification study will be conducted between the test results and the analysis results.

Prediction of unmeasured mode shapes and structural damage detection using least squares support vector machine

  • Kourehli, Seyed Sina
    • Structural Monitoring and Maintenance
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    • v.5 no.3
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    • pp.379-390
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    • 2018
  • In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.