• 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.

• Journal of Distribution Science
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• v.11 no.3
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• pp.5-11
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• 2013

Purpose - Going concern is one of fundamental concepts in accounting and auditing and sometimes the assessment of a company's going concern status that is a tough process. Various going concern prediction models' based on statistical and data mining methods help auditors and stakeholders suggested in the previous literature. Research design - This paper employs a data mining approach to prediction of going concern status of Iranian firms listed in Tehran Stock Exchange using Particle Swarm Optimization. To reach this goal, at the first step, we used the stepwise discriminant analysis it is selected the final variables from among of 42 variables and in the second stage; we applied a grid-search technique using 10-fold cross-validation to find out the optimal model. Results - The empirical tests show that the particle swarm optimization (PSO) model reached 99.92% and 99.28% accuracy rates for training and holdout data. Conclusions - The authors conclude that PSO model is applicable for prediction going concern of Iranian listed companies.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2104-2125
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• 2021

This paper presents the verification and validation (V&V) of a calculation module for isotope inventory prediction to control the back-end cycle of spent nuclear fuel (SNF). The calculation method presented herein was implemented in a two-step code system of a lattice code STREAM and a nodal diffusion code RAST-K. STREAM generates a cross section and provides the number density information using branch/history depletion branch calculations, whereas RAST-K supplies the power history and three history indices (boron concentration, moderator temperature, and fuel temperature). As its primary feature, this method can directly consider three-dimensional core simulation conditions using history indices of the operating conditions. Therefore, this method reduces the computation time by avoiding a recalculation of the fuel depletion. The module for isotope inventory calculates the number densities using the Lagrange interpolation method and power history correction factors, which are applied to correct the effects of the decay and fission products generated at different power levels. To assess the reliability of the developed code system for back-end cycle analysis, validation study was performed with 58 measured samples of pressurized water reactor (PWR) SNF, and code-to-code comparison was conducted with STREAM-SNF, HELIOS-1.6 and SCALE 5.1. The V&V results presented that the developed code system can provide reasonable results with comparable confidence intervals. As a result, this paper successfully demonstrates that the isotope inventory prediction code system can be used for spent nuclear fuel analysis.

• Journal of ILASS-Korea
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• v.1 no.3
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• pp.37-50
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• 1996

Computational fluid dynamics was used to optimize an A/C duct. Three dimensional flow analysis in an automotive A/C duct was performed computationally using various turbulence models and compared numerical predictions such as outlet flow split, surface pressure distribution along the duct to experimental data. Additionally, we studied the effect of location variation of 2nd branch on exit flow ratio and could find optimal location of 2nd branch. The design of an A/C duct was modeled and calculated to enhance the airflow distribution in each outlet using the STAR-CD computational fluid dynamics software. In results, modified $k-\varepsilon$ turbulence model allows a successful prediction of static pressure distribution particulary at around strong curvature but little improvement flow split. In the future, adoption of CFD to design an A/C duct with modified $k-\varepsilon$ model will bring benefits of producing more accurate prediction, and also give designers more detail information much more than now.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.2
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• pp.126-136
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• 1999

We studied on crack growth retardation in single overloading condition. Crack tip branching which as the second mechanism on crack growth retardation was examined. Crack tip branching was observed to kinked type and forked type. It was found that the branching angle range was from 25 to 53 degree. The variations of crack driving force with branching angle were calculated with finite element method The variation of {{{{ KAPPA _I}}}}, {{{{ KAPPA _II}}}} and total crack driving force(K) were examined respectively So {{{{ KAPPA _I}}}}, {{{{ KAPPA _II}}}} and K mean to mode I, II and total crack driving force. Present model(Willenborg's model) for crack growth retardation prediction was modified to take into consideration the effects of crack tip branching When we predicted retardation with modified model. it was confirmed that predicted and experimental results coincided with well each other.

• Computers and Concrete
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• v.33 no.1
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• pp.1-11
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• 2024

Aggressive environmental conditions, and especially the acidic effects of sulfate ion penetration, have reduced the lifetime of concrete structures in some areas, especially coastal and marine areas. In this research, at first, samples made of type II and V cement were kept in a solution of magnesium sulfate (MgSO₄) for a period of 90 and 180 days, the change of appearance. Field Emission Scanning Electron Microscopy (FE-SEM) and X-Ray Diffraction (XRD), were used to analyze the microstructure and the complex mineral composition of the concrete after exposure to corrosive environments. Then solving the differential equation governing the sulfate ion penetration, which is based on the second Fick law, it has been tried to determine the concentration of sulfate ions inside the concrete. In the following, an attempt has been made to improve the prediction of sulfate ion concentration in concrete by using Crank's penetration equation. At the same time, the coefficient in the Crank's solution have been optimized by using the Particle Swarm Optimization (PSO algorithm). The comparison between the results shows that the values obtained from Crank's relation are closer to the experimental results than the equation obtained from Fick's second law and shows a more accurate prediction.

• The KIPS Transactions:PartA
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• v.19A no.2
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• pp.77-84
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• 2012

Speculative execution for improving instruction-level parallelism is widely used in high-performance processors. In the speculative execution technique, the most important factor is the accuracy of branch predictor. Unfortunately, complex branch predictors for improving the accuracy can cause serious thermal problems in 3D multicore processors. Thermal problems have negative impact on the processor performance. This paper analyzes two methods to solve the thermal problems in the branch predictor of 3D multi-core processors. First method is dynamic thermal management which turns off the execution of the branch predictor when the temperature of the branch predictor exceeds the threshold. Second method is thermal-aware branch predictor placement policy by considering each layer's temperature in 3D multi-core processors. According to our evaluation, the branch predictor placement policy shows that average temperature is $87.69^{\circ}C$, and average maximum temperature gradient is $11.17^{\circ}C$. And, dynamic thermal management shows that average temperature is $89.64^{\circ}C$ and average maximum temperature gradient is $17.62^{\circ}C$. Proposed branch predictor placement policy has superior thermal efficiency than the dynamic thermal management. In the perspective of performance, the proposed branch predictor placement policy degrades the performance by 3.61%, while the dynamic thermal management degrades the performance by 27.66%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.289-292
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• 2008

This paper presents a branch prediction algorithm and a 4-way set-associative cache for performance improvement of OpenRISC processor and a clock gating algorithm using ODC (Observability Don't Care) operation for a low-power processor. The branch prediction algorithm has a structure using BTB(Branch Target Buffer) and 4-way set associative cache has lower miss rate than direct-mapped cache. The clock gating algorithm reduces dynamic power consumption. As a result of estimation of performance and dynamic power, the performance of the OpenRISC processor using the proposed algorithm is improved about 8.9% and dynamic power of the processor using samsung $0.18{\mu}m$ technology library is reduced by 13.9%.

• 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.

• 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.