• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1009-1012
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• 1999

An analytical performance model that can predict the performance of a superscalar processor employing multiple branch prediction is introduced. The model is based on the conditional independence probability and the basic block size of instructions, with the degree of multiple branch prediction, the fetch rate, and the window size of a superscalar architecture. Trace driven simulation is performed for the subset of SPEC integer benchmarks, and the measured IPCs are compared with the results derived from the model. As the result, our analytic model could predict the performance of the superscalar processor using multiple branch prediction within 6.6 percent on the average.

• Journal of Distribution Science
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• v.11 no.6
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• pp.13-19
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• 2013

Purpose - This study aims to predict the audit reports of listed companies on the Tehran Stock Exchange by using meta-heuristic algorithms. Research design, data, methodology - This applied research aims to predict auditors reports' using meta-heuristic methods (i.e., neural networks, the ANFIS, and a genetic algorithm). The sample includes all firms listed on the Tehran Stock Exchange. The research covers the seven years between 2005 and 2011. Results - The results show that the ANFIS model using fuzzy clustering and a least-squares back propagation algorithm has the best performance among the tested models, with an error rate of 4% for incorrect predictions and 96% for correct predictions. Conclusion - A decision tree was used with ten independent variables and one dependent variable the less important variables were removed, leaving only those variables with the greatest effect on auditor opinion (i.e., net-profit-to-sales ratio, current ratio, quick ratio, inventory turnover, collection period, and debt coverage ratio).

• Earthquakes and Structures
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• v.22 no.4
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• pp.401-419
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• 2022

A novel hybrid extreme machine learning-multiverse optimizer (ELM-MVO) framework is proposed to predict the liquefaction phenomenon in seismically excited tunnel lining inside the sand lens. The MVO is applied to optimize the input weights and biases of the ELM algorithm to improve its efficiency. The tunnel located inside the liquefied sand lens is also evaluated under various near- and far-field earthquakes. The results demonstrate the superiority of the proposed method to predict the liquefaction event against the conventional extreme machine learning (ELM) and artificial neural network (ANN) algorithms. The outcomes also indicate that the possibility of liquefaction in sand lenses under far-field seismic excitations is much less than the near-field excitations, even with a small magnitude. Hence, tunnels designed in geographical areas where seismic excitations are more likely to be generated in the near area should be specially prepared. The sand lens around the tunnel also has larger settlements due to liquefaction.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.619-622
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• 2000

This research presents an analytical model to predict the instruction fetch rate on superscalar Processors. The proposed model is also able to analyze the performance relationship between cache miss and branch prediction miss. The proposed model takes into account various kind of architectural parameters such as branch instruction probability, cache miss rate, branch prediction miss rate, and etc.. To prove the correctness of the proposed model, we performed extensive simulations and compared the results with those of the analytical models. Simulation results showed that the pro-posed model can estimate the instruction fetch rate accurately within 10% error in most cases. The model is also able to show the effects of the cache miss and branch prediction miss on the performance of instruction fetch rate, which can provide an valuable information in designing a balanced system.

• Structural Engineering and Mechanics
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• v.54 no.6
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• pp.1061-1073
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• 2015

This paper presents a nonlocal sinusoidal shear deformation beam theory (SDBT) for the nonlinear vibration of single walled carbon nanotubes (CNTs). The present model is capable of capturing both small scale effect and transverse shear deformation effects of CNTs, and does not require shear correction factors. The surrounding elastic medium is simulated based on Pasternak foundation. Based on the nonlocal differential constitutive relations of Eringen, the equations of motion of the CNTs are derived using Hamilton's principle. Differential quadrature method (DQM) for the natural frequency is presented for different boundary conditions, and the obtained results are compared with those predicted by the nonlocal Timoshenko beam theory (TBT). The effects of nonlocal parameter, boundary condition, aspect ratio on the frequency of CNTs are considered. The comparison firmly establishes that the present beam theory can accurately predict the vibration responses of CNTs.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.1-10
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• 2009

Precise branch predictor has a profound impact on system performance in modern processor architectures. Recent works show that prediction latency as well as prediction accuracy has a critical impact on overall system performance as well. However, prediction latency tends to be overlooked. In this paper, we propose Branch Pre-Prediction policy to tolerate branch prediction latency. The proposed solution allows that branch predictor can proceed its prediction without any information from the fetch engine, separating the prediction engine from fetch stage. In addition, we propose newly modified BTE structure to support our solution. The simulation result shows that proposed solution can hide most prediction latency with still providing the same level of prediction accuracy. Furthermore, the proposed solution shows even better performance than the ideal case, that is the predictor which always takes a single cycle prediction latency. In our experiments, IPC improvement is up to 11.92% and 5.15% in average, compared to conventional predictor system.

• Structural Engineering and Mechanics
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• v.86 no.2
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• pp.181-195
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• 2023

To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, L-box ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.4
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• pp.507-513
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• 2006

Changes of in vitro true digestibility (IVTD) of Napiergrass (Pennisetum purpureum) were determined by a filter bag system, and their relationships to chemical composition, leaf to stem ratio, plant height, geographic location, climatic factors and harvest interval were studied and used to develop prediction models for the crude protein (CP), acid-detergent fiber (ADF), and neutral-detergent fiber (NDF) contents and IVTD. Partitioning the total variance of IVTD of Napiergrass showed that 80% was attributable to the effect of harvest interval. Days of growth, plant height, leaf/stem ratio, CP, ADF and NDF of Napiergrass had highly significant relationships (p<0.01) with IVTD. The highest coefficient of correlation between the ADF, NDF, and IVTD of Napiergrass and growth degree days was obtained when the base temperature was set at $0^{\circ}C$. Growth degree days could predict ADF, NDF, and IVTD of Napiergrass more accurately than plant height, and plant height is not suitable to predict IVTD.

• Advances in concrete construction
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• v.14 no.1
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• pp.45-55
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• 2022

In this article, the flexural and shear capacity of ultra-high-performance fiber-reinforced concrete beams (UHPFRC) using two kinds of rebars, including GFRP and steel rebars, are experimentally investigated. For this purpose, six UHPFRC beams (250 × 300 × 1650 mm) with three reinforcement ratios (ρ) of 0.64, 1.05, and 1.45 were constructed using 2% steel fibers by volume. Half of the specimens were made of UHPFRC reinforced with GFRP rebars, while the other half were reinforced with conventional steel rebars. All specimens were tested to failure in four-point bending. Both the load-deformation at mid-span and the failure pattern were studied. The results showed that utilizing GFRP bars increases the flexural strength of UHPFRC beams in comparison to those made of steel bars, but at the same time, it reduces the post-cracking strain hardening. Furthermore, by increasing the percentage of longitudinal bars, both the post-cracking strain hardening and load-bearing capacity increase. Comparing the experiment results with some of the available equations and provisions cited in the valid design codes reveals that some of the equations to predict the flexural strength of UHPFRC beams reinforced with conventional steel and GFRP bars are reasonably conservative, while Khalil and Tayfur model is un-conservative. This issue makes it essential to modify the presented equations in this research for predicting the flexural strength of UHPFRC beams using GFRP bars.

• Earthquakes and Structures
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• v.15 no.4
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• pp.431-441
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• 2018

This study provides fragility-based assessment of seismic performance of reinforced concrete bridges. Seismic fragility curves were created using nonlinear analysis (NA) and artificial neural networks (ANNs). Nonlinear response history analyses were performed, in order to calculate the seismic performances of the bridges. To this end, 306 bridge-earthquake cases were considered. A multi-layered perceptron (MLP) neural network was implemented to predict the seismic performances of the selected bridges. The MLP neural networks considered herein consist of an input layer with four input vectors; two hidden layers and an output vector. In order to train ANNs, 70% of the numerical results were selected, and the remained 30% were employed for testing the reliability and validation of ANNs. Several structures of MLP neural networks were examined in order to obtain suitable neural networks. After achieving the most proper structure of neural network, it was used for generating new data. A total number of 600 new bridge-earthquake cases were generated based on neural simulation. Finally, probabilistic seismic safety analyses were conducted. Herein, fragility curves were developed using numerical results, neural predictions and the combination of numerical and neural data. Results of this study revealed that ANNs are suitable tools for predicting seismic performances of RC bridges. It was also shown that yield stresses of the reinforcements is one of the important sources of uncertainty in fragility analysis of RC bridges.