• The Journal of Asian Finance, Economics and Business
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• v.2 no.2
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• pp.13-18
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• 2015

In this paper the DRC model is presented for solving multi objective problem. The proposed model is a combination of data envelopment analysis, Cuckoo algorithm and the response surface method. Due to reasons like costs, time and irreversible damages, it is not possible to analyze each and every one of the proposed models in practice, so the simulation is used. Since the number of experiments for simulation process is high then the optimization has gone to practice and directs the simulation process. The response surface method is used as one of the approaches of simulation optimization. Furthermore, data envelopment analysis is used to consider several response surfaces as efficiency response surface. Then this efficiency response surface is solved by Cuckoo algorithms. The main advantage of DRC model is to make one efficiency response surface function instate of multi surface function for every output and also using the advantages of Cuckoo algorithms. In order to demonstrate the effectiveness of the proposed approach, the branches of Refah bank in Mashhad is analyzed and the results are presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.145-146
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• 2010

This paper proposed a new concept of global healthcare system based on M2M technology with the combination of networks by using IPv6 techniques. The proposed system consists of 6LoWPAN based wearable sensors, gateway for the connection of different networks, and server program offering health information. Thus our approach presents an intelligent system which allows direct exchange of information between machines without human assistance with the epoch-making extension of measurement environment in healthcare areas appropriately.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.4
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• pp.77-84
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• 2011

Tire manufactures have dealt with noise problem by varying the pitch of the tread. The various formulas for the variations are generally determined differently, however. Often these variations are based on a combination of trial and error, intuition, and economics. Some manufactures have models and analogs to test tread patterns and their variations. These efforts, however practical, do not determine the best variation beforehand or guarantee the best results. For this reason it was felt that a general mathematical approach for determining the best variation was needed. Moreover, the method should be completely general, easy to use, and sufficiently accurate. This paper discusses a mathematical method called Mechanical Frequency Modulation(MFM) which meets the above requirements. Thus, MFM pertains to computing an irregular time sequence of events so that the resulting excitation spectrum is shaped to a preferred form. The first part of this paper treats the theoretical basis for computing an optimum variation ; the second part discusses experimental results and simulation program which corroborate the theory.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.212-212
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• 2015

So far, geomorphologic dispersion due to the heterogeneity characteristics of flow paths in a basin has been demonstrated as a major factor affecting to the hydrologic response function of a catchment. This effect has considered by many previous studies taking into account flow path length factors, especially in the application of width function. Based upon the analysis of topographic index, another important geomorphologic factor extracted from DEM data, this work presents a new factor named saturation to evaluate its effects to the formation of the well-known instantaneous unit hydrograph (IUH) in Nash model and drainage structure in a river basin. First, the geomorphologic parameters corresponding to different saturation conditions are computed from DEM data with the support of GIS software. Then, in the combination of hydrologic and geomorphologic data, effective rainfall in each saturation degree and the Nash parameters are calculated using excel. Finally, the verification process with direct runoff data is conducted using Fortran programming. This process is applied to five sub-watersheds in Bocheong catchment ($485.21km^2$) in Korea where the necessary data are available and believable. The results from this approach will improve researchers and students'understandings about the relationship between rainfall and runoff and its relation with drainage structure within a catchment.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• Structural Engineering and Mechanics
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• v.41 no.3
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• pp.313-337
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• 2012

A three-dimensional formulation is proposed to analyze the lateral loading distribution of external actions in high-rise buildings. The method is extended to encompass any combination of bracings, including bracings with open thin-walled cross-sections, which are analyzed in the framework of Timoshenko-Vlasov's theory of sectorial areas. More in detail, the proposed unified approach is a tool for the preliminary stages of structural design. It considers infinitely rigid floors in their own planes, and allows to better understand stress and strain distributions in the different bearing elements if compared to a finite element analysis. Numerical examples, describing the structural response of tall buildings characterized by bracings with different cross-section and height, show the effectiveness and flexibility of the proposed method. The accuracy of the results is investigated by a comparison with finite element solutions, in which the bracings are modelled as three-dimensional structures by means of shell elements.

• Structural Engineering and Mechanics
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• v.47 no.1
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• pp.119-129
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• 2013

Nonlinear Static Procedures (NSPs) have been developed as a practical tool to estimate the seismic demand of structures. Several researches have accomplished to minimize errors of NSPs, namely pushover procedures, in the Nonlinear Time History Analysis (NTHA), as the most exact method. The most important issue in a typical pushover procedure is the pattern and technique of loading which are extracted based on structural dynamic fundamentals. In this paper, the coefficients of modal force combination is focused involving a meta-heuristic optimization algorithm to find the optimum load pattern which results in a response with minimum amount of errors in comparison to the NTHA counterpart. Other parameters of the problem are based on the FEMA recommendations for pushover analysis of building structures. The proposed approach is implemented on a high-rise 20 storey concrete moment resisting frame under three earthquake records. In order to demonstrate the effectiveness and robustness of the studied procedure the results are presented beside other well-known pushover methods such as MPA and the FEMA procedures, and the results show the efficiency of the proposed load patterns.

• STI Policy Review
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• v.5 no.2
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• pp.20-42
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• 2014

The Brazil, Russia, India China and South Africa (BRICS) group has emerged as a collection of large economies which are outside the traditional groups of industrialised "first world" economies and which have altered the global distribution of economic power. The basis of their emergence is a combination of their size and growth rates, and the fact that they lie outside the established centres of global economic power. As such, they have "diversified" the power base of the global economic order. The question which is asked in this paper is whether the phenomenon of the BRICS goes beyond this to mark the start of a possible challenge to the neoliberal orthodoxy which emerged as the globally dominant policy paradigm since the collapse of the Soviet Union. This paper develops and uses a "modes of innovation" approach to explore the potential of the BRICS to constitute a structural rupture in the current globally dominant neoliberal mode of innovation. This question is important since, in the absence of this rupture, the remarkable development trajectory of the BRICS will serve to reinforce the legitimacy of the global orthodoxy. The paper first articulates the modes of innovation concept and then proceeds to locate the BRICS systems of innovation within the current globally dominant mode. On this basis it then provides an appraisal of the possible impact of the BRICS on the evolutionary path of the global system of innovation.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.95-105
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• 2021

A prerequisite of a sound financial system is effective channeling of financial resources to efficient users; hence maximizing economic and societal welfare. To that end, the prevalence of bad loans in banks in emerging economies is a major policy concern. In an attempt to add to the growing body of literature explaining the interrelationship between macroeconomic and firm-specific factors, and non-performing loans (NPL), this paper examines data from 24 scheduled commercial banks in Bangladesh from 2008 to 2019. Macroeconomic factors as well as firm-specific factors related to profitability, capital strength, and efficiency are considered. Panel data regression analysis is performed to estimate pooled OLS, fixed effects, and random effects models. Following the necessary testing, it was found that the fixed effects model with robust standard error is appropriate. Results show that return on assets and inflation have a negative influence on NPL, but GDP growth has a favorable impact. The paper concludes by asserting that the evidence supports similar findings from studies both in Bangladesh and elsewhere and it is noted that a combination of these macroeconomic and firm-specific factors explains only a small portion of the total variation in NPL.