Variable Scaling Factor Research Articles

A spherical evolution algorithm with two-stage search for global optimization and real-world problems

This paper proposes a spherical evolution algorithm with two-stage search. Spherical search and hypercube search are combined to achieve individuals' evolution. A self-adaptive Gaussian scale factor and a variable scale factor are designed to adaptively control individuals' spherical and hypercube search area according to their search situations. Two search stages frequently switch with four search cases to enhance the balance between exploration and exploitation processes. A directed adjacency matrix is devised to analyze the relationship among individuals from the perspective of graph theory. Experiments compare the proposed algorithm with five algorithms with distinctive search patterns on twenty nine CEC2017 benchmark functions. The diversity analysis and graph theory analysis show the good population diversity and effective information spreading of the proposed algorithm. Twenty two real-world problems evaluate the practicality and optimization ability of the proposed algorithm. Finally, the computational time complexity demonstrates that the proposed algorithm is more efficient than the original spherical evolution algorithm.

Radix-4 CORDIC algorithm based low-latency and hardware efficient VLSI architecture for Nth root and Nth power computations

In this article, a low-complexity VLSI architecture based on a radix-4 hyperbolic COordinate Rotion DIgital Computer (CORDIC) is proposed to compute the N{{rm th}} root and N{{rm th}} power of a fixed-point number. The most recent techniques use the radix-2 CORDIC algorithm to compute the root and power. The high computation latency of radix-2 CORDIC is the primary concern for the designers. N{{rm th}} root and N{{rm th}} power computations are divided into three phases, and each phase is performed by a different class of the proposed modified radix-4 CORDIC algorithms in the proposed architecture. Although radix-4 CORDIC can converge faster with fewer recurrences, it demands more hardware resources and computational steps due to its intricate angle selection logic and variable scale factor. We have employed the modified radix-4 hyperbolic vectoring (R4HV) CORDIC to compute logarithms, radix-4 linear vectoring (R4LV) to perform division, and the modified scaling-free radix-4 hyperbolic rotation (R4HR) CORDIC to compute exponential. The criteria to select the amount of rotation in R4HV CORDIC is complicated and depends on the coordinates X^j and Y^j of the rotating vector. In the proposed modified R4HV CORDIC, we have derived the simple selection criteria based on the fact that the inputs to R4HV CORDIC are related. The proposed criteria only depend on the coordinate Y^j that reduces the hardware complexity of the R4HV CORDIC. The R4HR CORDIC shows the complex scale factor, and compensation of such scale factor necessitates the complex hardware. The complexity of R4HR CORDIC is reduced by pre-computing the scale factor for initial iterations and by employing scaling-free rotations for later iterations. Quantitative hardware analysis suggests better hardware utilization than the recent approaches. The proposed architecture is implemented on a Virtex-6 FPGA, and FPGA implementation demonstrates 19% less hardware utilization with better error performance than the approach with the radix-2 CORDIC algorithm.

On the integral transform of fractal interpolation functions

This paper explores the integral transform of two distinct fractal interpolation functions, namely the linear fractal interpolation function and the hidden variable fractal interpolation function with variable scaling factors. Further, with a particular application of kernel functions, we investigate the integral transform of fractal functions, such as the Laplace transform and the Laplace Carson transform. Moreover, we show that the compositeness of two fractal interpolation functions, f1 in {tɛ,xɛ} and f2 in {xɛ,zɛ} remains a fractal interpolation function. It also generates iterated function system from given iterated function systems. In addition to this, the study is carried out on the composite linear fractal interpolation function of the integral transform, the Laplace transform, and the Laplace Carson transform.

Cosmic expansion of the universe characterized by anisotropic viscous fluid cosmological models

In this paper, the cosmic expansion of the universe filled with viscous fluid has been discussed in Bianchi type-I space-time. To solve Einstein’s field equations, a time-varying deceleration parameter, ( q( t)) yields a time variable scale factor [Formula: see text], with " α" and " n" are positive constants. We also consider a time-varying gravitational constant ( G) via the formulation 8π G = G0 a m , where " G0" and " m" are positive constants. Further, based on the third constraint, two different viscous cosmological models, one by considering the perfect gas equation of state p = ωρ and the other by considering time-varying bulk viscosity coefficient ( ξ( t)), have been constructed. It is mentioned here that this particular choice of scale factor generates a class of accelerating models for n < 1, while for n > 1, the transition of the universe from early deceleration to current acceleration takes place. An important feature of the models is decreasing dark energy candidate, i.e., cosmological constant (Λ) with time, fine-tuning with the results from recent supernovae Ia observations. The various physical parameters involved in the models have been discussed analytically and graphically and are found in good agreement with recent observations. For the physical acceptability of both the models, energy conditions are shown graphically. The models are found to be physically acceptable, particularly in identifying the universe’s transition. We have discussed the statefinder and observed that all the trajectories indicate dark energy candidates as Chaplygin gas and quintessence for different constraints ( α, n). We have also calculated the cosmographic parameters such as jerk, lerk, and maxout to analyze the physical behavior of the DE models.

ON THE CLASSICAL INTEGRAL OF FRACTAL FUNCTIONS

In this paper, the integral of classical fractal interpolation function (FIF) and A-fractal function is explored for both the cases of constant and variable scaling factors. The definite integral for the classical FIF in the closed interval of [Formula: see text] is estimated. The novel notion of affine-quadratic FIF is introduced and integrated for both constant and variable scaling factors. It is demonstrated that its integral is not an affine-quadratic FIF, however it is a FIF. Similarly, by choosing the vertical scaling factors as constants and variables, A-fractal function is integrated. Further, by assuming certain condition on the block matrix, it is shown that like the original A-fractal function its integral is also an attractor for the iterated function system.

Simulation Research of Electrostatic Precipitator Power Supply Voltage Control System Based on Finite Element Differential Equation

Abstract An electrostatic precipitator is an essential piece of equipment to eliminate atmospheric dust pollution. Its power supply is of great significance to the working efficiency and reliability of the dust collector. This paper uses field-programmable gate array (FPGA) technology to design an electrostatic precipitator power controller. In this paper, a fuzzy control scheme with variable scale factors is adopted to make the highest average voltage between the two poles of the precipitator better approach the critical breakdown voltage. In this paper, the transfer function is derived based on the finite element differential equation and the method of three-dimensional drawing. In this way, the influence of parameters such as the coupling inductance of the pulse power supply, the coupling coefficient, and the parasitic resistance of the dust removal chamber on the load oscillation can be obtained. It is found that the control system can control the electrostatic precipitator power well. It can significantly improve dust removal efficiency.

Emergent decoherence induced by quantum chaos in a many-body system: A Loschmidt echo observation through NMR

In the long quest to identify and compensate the sources of decoherence in many-body systems far from the ground state, the varied family of Loschmidt echoes (LEs) became an invaluable tool in several experimental techniques. A LE involves a time-reversal procedure to assess the effect of perturbations in a quantum excitation dynamics. However, when addressing macroscopic systems one is repeatedly confronted with limitations that seem insurmountable. This led to the formulation of the central hypothesis of irreversibility stating that the timescale of decoherence, ${T}_{3}$, is proportional to the timescale of the many-body interactions we reversed, ${T}_{2}$. We test this by implementing two experimental schemes based on Floquet Hamiltonians where the effective strength of the dipolar spin-spin coupling, i.e., $1/{T}_{2}$, is reduced by a variable scale factor $k$. This extends the perturbation timescale, ${T}_{\mathrm{\ensuremath{\Sigma}}}$, in relation to ${T}_{2}$. Strikingly, we observe the superposition of the normalized Loschmidt echoes for the bigger values of $k$. This manifests the dominance of the intrinsic dynamics over the perturbation factors, even when the Loschmidt echo is devised to reverse that intrinsic dynamics. Thus, in the limit where the reversible interactions dominate over perturbations, the LE decays within a timescale, ${T}_{3}\ensuremath{\approx}{T}_{2}/R$ with $R=(0.15\ifmmode\pm\else\textpm\fi{}0.01)$, confirming the emergence of a perturbation independent regime. These results support the central hypothesis of irreversibility.

0277 Deep learning revealed associations between altered temporal correlations in motor activity and Parkinson’s risk

Abstract Introduction Motor activity in healthy young adults displays fractal patterns with similar temporal correlations at different timescales. Altered fractal patterns were observed in patients with Parkinson’s disease. This study aimed to determine whether altered fractal patterns also predict the risk of Parkinsonism. Methods We studied 982 participants (age: 80.12±7.27 [SD]; 750 females) from the Rush Memory and Aging Project, who had at least one actigraphy assessment, had no symptoms of Parkinsonism at actigraphy baseline, and had follow-up clinical assessments. Detrended fluctuation analysis was performed on baseline actigraphy to determine the fractal patterns. Specifically, the activity fluctuation (around the trend) was computed at multiple timescales (n) ranging from 3-600 min. An exponential function with a variable scaling factor α was used to fit the local fluctuation function with respect to n. The α(n) that represented the temporal correlations was fed into a convolutional neural network (CNN) model whose output was further used as the input of a Cox proportional hazards model to predict the time to incident Parkinsonism. Covariates at baseline considered include age, sex, education, cognition, motor function, chronic health assessment, and actigraphy-derived measures including physical activity level, rest-activity/activity-rest transition probabilities, interdaily stability, and intradaily variability. Results There were 412 subjects who developed parkinsonism (in 4.75±3.13 [SD] years from baseline). Based on the gradient of hazard function (with respect to α) from the CNN model (estimated feature importance), the α in three timescale regions (i.e., 3-5 min, 12-20 min, and 270-600 min) contributed significantly to the prediction. Consistently, in separate Cox models with adjustment of age, sex, and education, the mean α at timescales 3-5 min was inversely associated with incident parkinsonism (for 1-SD increase, hazard ratio [HR]=0.82, 95% CI: 0.78-0.92, p<0.0001); The mean α at timescales 270-600 min was also inversely associated with incident Parkinsonism (HR=0.87, 95% CI: 0.78-0.96, p=0.008); And the mean α at timescales 10-25 min was marginally, positively associated with incident Parkinsonism (HR=1.10, 95% CI: 0.99-1.22, p=0.08). Conclusion Altered temporal correlations at specific timescales in motor activity predicted the risk of Parkinsonism. Support (If Any) NIH RF1AG064312, RF1AG059867, R01AG017917, R01AG56352, the BrightFocus Foundation A2020886S.

Enhanced Salp Swarm Algorithm for Multimodal Optimization and Fuzzy Based Grid Frequency Controller Design

In the present study, an Enhanced SSA (ESSA) has been proposed where the parameter of the SSA technique, which balances the exploration and exploitation phases, has been modified. Additionally, the variable scaling factor is engaged to regulate the salp’s position during the search procedure to minimize the random movement of salps. To demonstrate the effectiveness of the enhanced SSA (ESSA), a set of multimodal test functions are engaged. The statistical outcomes demonstrate that ESSA profits from local optima evasion and quick convergence speed, which aids the proposed ESSA algorithm to outclass the standard SSA and other recent algorithms. The fair analysis displays that ESSA delivers very promising results and outclass current methods. Next, frequency control of power systems is executed by designing a Combined Fuzzy PID (CFPID) controller with the projected ESSA method. Next, a Partially Distributed CFPID (PD-CFPID) controller is designed for a distributed power system (DPS). It is shown that the ESSA method outclasses the SSA method in engineering problems. It is also noted that the ESSA-based PD-CFPID scheme has become more operative in monitoring the frequency than similar structured centralized fuzzy PID (CFPID) as well as PID controller. Finally, the outcomes of the PD-CFPID controller are equated with CFPID and PID for various uncertain situations to validate the benefit of the proposed control approach.

Design and analysis of an auxetic metamaterial with tuneable stiffness

Auxetic materials have attracted a considerable attention due to their excellent properties, e.g., fracture resistance, shear resistance, energy dissipation, etc. However, the stiffness of auxetics tends to be much weaker than solid structure because of the existence of internal holes. Inspired by tuning the compacted point of auxetic structures to enhance their stiffness, a systematic methodology for defining a single parametric of variable stiffness scale factor (VSF) to generate auxetic unit cell with variable stiffness has been proposed and verified in this study. Two models with different VSF proportions were investigated experimentally. Different centres of rotation, heights of deformation area, and VSF percentages were analyzed to prove the effectiveness of the method numerically. The results indicate that the compacted strain can be tuned effectively using the designed VSF proportions, and the difference between the designed VSF and real VSF could be reduced by slightly changing the height of deformation area. These desirable characteristics provide a new idea for the optimal design of auxetics, with potential application in protective structures.

Two nondimensional parameters for characterizing the nuclear morphology

Nuclear morphology is an important indicator of cell function. It is regulated by a variety of factors such as the osmotic pressure difference between the nucleoplasm and cytoplasm, cytoskeletal forces, elasticity of the nuclear envelope and chromosomes. Nucleus shape and size are typically quantified using multiple geometrical quantities that are not necessarily independent of one another. This interdependence makes it difficult to decipher the implications of changes in nuclear morphology. We resolved this by analyzing nucleus shapes of populations for multiple cell lines using a mechanics-based model. We deduced two independent nondimensional parameters, namely, flatness index and isometric scale factor. We show that nuclei in a cell population have similar flatness but variable scale factor. Furthermore, nuclei of different cell lines segregate according to flatness. Cellular perturbations using biochemical and biomechanical techniques suggest that the flatness index correlates with actin tension and the scale factor anticorrelates with elastic modulus of nuclear envelope. We argue that nuclear morphology measures such as volume, projected area, height etc., are subsumed by flatness and scale factor, which can unambiguously characterize nuclear morphology.

Graph-directed random fractal interpolation function

"Barnsley introduced in [1] the notion of fractal interpolation function (FIF). He said that a fractal function is a (FIF) if it possess some interpolation properties. It has the advantage that it can be also combined with the classical methods or real data interpolation. Hutchinson and Ruschendorf [7] gave the stochastic version of fractal interpolation function. In order to obtain fractal interpolation functions with more exibility, Wang and Yu [9] used instead of a constant scaling parameter a variable vertical scaling factor. Also the notion of fractal interpolation can be generalized to the graph-directed case introduced by Deniz and  Ozdemir in [5]. In this paper we study the case of a stochastic fractal interpolation function with graph-directed fractal function."

Logarithmic Hyperbolic Cosine Adaptive Filter and Its Performance Analysis

The hyperbolic cosine function with high-order errors can be utilized to improve the accuracy of adaptive filters. However, when initial weight errors are large, the hyperbolic cosine-based adaptive filter (HCAF) may be unstable. In this paper, a novel normalization based on the logarithmic hyperbolic cosine function is proposed to achieve the stabilization for the case of large initial weight errors, which generates a logarithmic HCAF (LHCAF). Actually, the cost function of LHCAF is the logarithmic hyperbolic cosine function that is robust to large errors and smooth to small errors. The transient and steady-state analyses of LHCAF in terms of the mean-square deviation (MSD) are performed for a stationary white input with an even probability density function in a stationary zero-mean white noise. The convergence and stability of LHCAF can be therefore guaranteed as long as the filtering parameters satisfy certain conditions. The theoretical results based on the MSD are supported by the simulations. In addition, a variable scaling factor and step-size LHCAF (VSS-LHCAF) is proposed to improve the filtering accuracy of LHCAF further. The proposed LHCAF and VSS-LHCAF are superior to HCAF and other robust adaptive filters in terms of filtering accuracy and stability.

Single Image Super Resolution Enhancement Method Based on Fractal Interpolation of Iteration Function System

Based on the similarity and transitivity of local features of image data, self similarity and scale invariance of fractal features, fractal method of Iterated Function System is used to construct fractal interpolation function, and variable vertical scale factor is used to realize super-resolution enhancement of two-dimensional image data. The experimental results show that fractal interpolation method of iterated function system can keep the image details better, and the reconstruction error is smaller, which is better than bilinear interpolation and bicubic interpolation methods.

Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm

Improved characterization of ambient PM2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM2.5 concentrations (R2 value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 μg m−3, and the normalized mean bias (NMB) was lowered from −46.0 to 4.6%). The HSRL-CH method showed statistics (R2 = 0.75, RMSE = 8.6 μgm−3, and NMB = 24.0%), which were better than the CMAQ prediction of PM2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM2.5 concentration and chemical composition where HSRL data are available.

Mixed-radix, virtually scaling-free CORDIC algorithm based rotator for DSP applications

In this work, we proposed a novel Coordinate Rotation DIgital Computer (CORDIC) rotator algorithm that converges faster by performing radix-2,4 and 16 CORDIC iterations while maintaining the scale factor implicitly constant. A mixed-radix is used to achieve convergence faster to reduce the computational latency of the CORDIC algorithm. The main concern of the higher radix CORDIC algorithm is the compensation of a variable scale factor. To solve this problem, the Taylor series approximation of sine and cosine is proposed for a higher radix CORDIC algorithm to achieve the scaling-free rotation of the two-dimensional vector. The scaling-free rotation of the proposed CORDIC algorithm removes the read-only memory (ROM) needed to store scale factor of higher radix CORDIC algorithm. Further, the proposed CORDIC algorithm is designed in rotation mode and optimized by removing the Z datapath for the digital signal processing (DSP) applications for which the angle of rotation is known in advance. Finally, the multipath delay commutator (MDC) fast Fourier transform (FFT) algorithm is implemented with the proposed CORDIC algorithm based rotator on FPGA. The proposed design is compared with existing designs. In a comparison between the radix-16 CORDIC rotator based FFT implementation and our proposed implementation, it has been found out that implementation proposed in this article has used 17% fewer resources.

Scalable Output Linear Actuators, a Novel Design Concept Using Shape Memory Alloy Wires Driven by Fluid Temperature

In this paper, the concept of scalability for actuators is introduced and explored, which is the capability to freely change the output characteristics on demand: displacement and force for a linear actuator, angular position and torque for a rotational actuator. This change can either be used to obtain power improvement (with a constant scale factor), or to improve the usability of a robotic system according to variable conditions (with a variable scale factor). Some advantages of a scalable design include the ability to adapt to changing environments, variable resolution of step size, ability to produce designs that are adequate for restricted spaces or that require strict energy efficiency, and intrinsically safe systems. Current approaches for scalability in actuators have shortcomings: the method to achieve scalability is complex, so obtaining a variable scaling factor is challenging, or they cannot scale both output characteristics simultaneously. Shape Memory Alloy (SMA) wire-based actuators can overcome these limitations, because its two output characteristics, displacement and force, are physically independent from each other. In this paper we present a novel design concept for linear scalable actuators that overcome SMA design and scalability limitations by using a variable number of SMA wires mechanically in parallel, immersed in a liquid that transmits heat from a separate heat source (wet activation). In this concept, more wires increase the maximum attainable force, and longer wires increase the maximum displacement. Prototypes with different number of SMA wires were constructed and tested in isometric experiments to determine force vs. temperature behavior and time response. The heat-transmitting liquid was either static or flowing using pumps. Scalability was achieved with a simple method in all tested prototypes with a linear correlation of maximum force to number of SMA wires. Flowing heat transmission achieved higher actuation bandwidth.

A variable-scale S-type kernel fractional low-power adaptive filtering algorithm

The adaptive kernel algorithms usually achieve a good convergence performance and a tracking performance due to the universal approximator, offering an excellent solution to many problems with nonlinearities. However, as is well known, the convergence rate and steady-state error of adaptive filtering algorithm are a pair of inherent contradictions, and the kernel method is not exceptional. For this problem, a robust kernel adaptive filtering algorithm, called the variable-scaling factor kernel fractional lower power adaptive filtering algorithm based on the Sigmoid function, is developed by creating a new framework of cost function which combines the kernel fractional low power error criterion with the Sigmoid function for system identification of different noise environments. This new cost framework incorporates a scaling factor into the cost function of the Sigmoid kernel fractional lower power adaptive filtering algorithm (VS-SKFLP) in this paper. One of the main features in the new framework is its scaling factor. This scaling factor is used to control the steepness of the Sigmoid function, and the steepness can affect the convergence speed of filtering algorithm. The scaling factor provides a tradeoff between the convergence rate and the steady-state mean square error (MSE), which improves the convergence rate under the same steady-state mean square error. However, it is also an important problem to choose an appropriate scale factor. Therefore, a variable-scale factor SKFLP algorithm is also proposed to improve the convergence rate and steady-state MSE, simultaneously. The proposed variable-scale factor structure consists of a function of error, featuring the adaptive updates of their parameter estimated by making discerning use of the error. In this paper, the nonlinear saturation characteristic of the Sigmoid function and low order norm criterion are used to overcome the performance degradation of training data destroyed by non-Gaussian impulse noise and colored noise. Through the convergence analysis, the parameter estimation sequence of our proposed algorithm proves convergent. Simulation results show that the proposed algorithm (VS-SKFLP) outperforms other kernel adaptive filtering algorithms in system recognition with different noise environments.

DoNOF: An open-source implementation of natural-orbital-functional-based methods for quantum chemistry

The natural orbital functional theory (NOFT) has emerged as an alternative formalism to both density functional (DF) and wavefunction methods. In NOFT, the electronic structure is described in terms of the natural orbitals (NOs) and their occupation numbers (ONs). The approximate NOFs have proven to be more accurate than those of the density for systems with a significant multiconfigurational character, on one side, and scale better with the number of basis functions than correlated wavefunction methods, on the other side. A challenging task in NOFT is to efficiently perform orbital optimization. In this article we present DoNOF, our open source implementation based on diagonalizations that allows to obtain the resulting orbitals automatically orthogonal. The one-particle reduced-density matrix (1RDM) of the ensemble of pure-spin states provides the proper description of spin multiplets. The capabilities of the code are tested on the water molecule, namely, geometry optimization, natural and canonical representations of molecular orbitals, ionization potential, and electric moments. In DoNOF, the electron-pair-based NOFs developed in our group PNOF5, PNOF7 and PNOF7s are implemented. These JKL-only NOFs take into account most non-dynamic effects plus intrapair-dynamic electron correlation, but lack a significant part of interpair-dynamic correlation. Correlation corrections are estimated by the single-reference NOF-MP2 method that simultaneously calculates static and dynamic electron correlations taking as a reference the Slater determinant formed with the NOs of a previous PNOF calculation. The NOF-MP2 method is used to analyze the potential energy surface (PES) and the binding energy for the symmetric dissociation of the water molecule, and compare it with accurate wavefunction-based methods.

The undrained seismic response of the Daikai Station

The Daikai Station failed during the 1995 Kobe earthquake. Detailed analyses of the seismic response of the station had been conducted under the assumption of a drained response of the ground; that is, no excess pore pressure accumulate during the cycles of loading. Such assumption is supported by the location of the water table around the station, which was depressed, compared to locations far from the station, because the underground structure worked as a drain. The question that arises is whether such effect was beneficial or detrimental. In other words, what would have been the undrained response of the station during the earthquake? To that effect, a hyperbolic elastoplastic constitutive model has been developed to simulate the drained and undrained seismic response of geomaterials under small to moderate strains. The model captures the coupling between the shear and volumetric response of the ground, which produces compression volumetric strains during drained cyclic loading or positive excess pore pressures accumulation during undrained cyclic loading. The hysteretic behavior is controlled by variable scaling factors that simulate softening or hardening during cyclic loading. The dependence of the shear modulus for very small strains on the effective mean stress is considered. Accurate performance of the model is observed when comparing simulations with results from cyclic simple shear laboratory tests for drained and undrained loading, and from a centrifuge test on saturated sand with excess pore pressures generation. Model capabilities to evaluate soil-structure interaction are verified by comparing results of a centrifuge seismic test of a tunnel placed in sand. The model is used to evaluate the response of the Daikai station and shows the importance of using appropriate constitutive models and loading conditions (drained or undrained) to approximate the seismic behavior of underground structures. Important differences are seen in the tunnel distortions, depending on the drainage condition.