An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

Design Optimization of Field Emission From A Stacked Carbon Nanotube Array

Fluctuation of field emission in carbon nanotubes (CNTs) is riot desirable in many applications and the design of biomedical x-ray devices is one of them. In these applications, it is of great importance to have precise control of electron beams over multiple spatio-temporal scales. In this paper, a new design is proposed in order to optimize the field emission performance of CNT arrays. A diode configuration is used for analysis, where arrays of CNTs act as cathode. The results indicate that the linear height distribution of CNTs, as proposed in this study, shows more stable performance than the conventionally used unifrom distribution.

Seismic damage estimation through measurable dynamic characteristics

Ductility based design of reinforced concrete structures implicitly assumes certain damage under the action of a design basis earthquake. The damage undergone by a structure needs to be quantified, so as to assess the post-seismic reparability and functionality of the structure. The paper presents an analytical method of quantification and location of seismic damage, through system identification methods. It may be noted that soft ground storied buildings are the major casualties in any earthquake and hence the example structure is a soft or weak first storied one, whose seismic response and temporal variation of damage are computed using a non-linear dynamic analysis program (IDARC) and compared with a normal structure. Time period based damage identification model is used and suitably calibrated with classic damage models. Regenerated stiffness of the three degrees of freedom model (for the three storied frame) is used to locate the damage, both on-line as well as after the seismic event. Multi resolution analysis using wavelets is also used for localized damage identification for soft storey columns.

A dynamic renormalization group study of active nematics

We carry out a systematic construction of the coarse-grained dynamical equation of motion for the orientational order parameter for a two-dimensional active nematic, that is a nonequilibrium steady state with uniaxial, apolar orientational order. Using the dynamical renormalization group, we show that the leading nonlinearities in this equation are marginally irrelevant. We discover a special limit of parameters in which the equation of motion for the angle field bears a close relation to the 2d stochastic Burgers equation. We find nevertheless that, unlike for the Burgers problem, the nonlinearity is marginally irrelevant even in this special limit, as a result of a hidden fluctuation-dissipation relation. 2d active nematics therefore have quasi-long-range order, just like their equilibrium counterparts.

Dynamic simulation of activated sludge based wastewater treatment processes: Case studies with Titagarh Sewage Treatment Plant, India

STOAT has been extensively used for the dynamic simulation of an activated sludge based wastewater treatment plant in the Titagarh Sewage Treatment Plant, near Kolkata, India. Some alternative schemes were suggested. Different schemes were compared for the removal of Total Suspended Solids (TSS), b-COD, ammonia, nitrates etc. A combination of IAWQ#1 module with the Takacs module gave best results for the existing scenarios of the Titagarh Sewage Treatment Plant. The modified Bardenpho process was found most effective for reducing the mean b-COD level to as low as 31.4 mg/l, while the mean TSS level was as high as 100.98 mg/l as compared to the mean levels of TSS (92 62 mg/l) and b-COD (92.0 mg/l) in the existing plant. Scheme 2 gave a better scenario for the mean TSS level bringing it down to a mean value of 0.4 mg/l, but a higher mean value for the b-COD level at 54.89 mg/l. The Scheme Final could reduce the mean TSS level to 2.9 mg/l and the mean b-COD level to as low as 38.8 mg/l. The Final Scheme looks to be a technically viable scheme with respect to the overall effluent quality for the plant. (C) 2009 Elsevier B.V. All rights reserved.

Compiler-assisted instruction decoder energy optimization for clustered VLIW architectures

Traditionally, an instruction decoder is designed as a monolithic structure that inhibit the leakage energy optimization. In this paper, we consider a split instruction decoder that enable the leakage energy optimization. We also propose a compiler scheduling algorithm that exploits instruction slack to increase the simultaneous active and idle duration in instruction decoder. The proposed compiler-assisted scheme obtains a further 14.5% reduction of energy consumption of instruction decoder over a hardware-only scheme for a VLIW architecture. The benefits are 17.3% and 18.7% in the context of a 2-clustered and a 4-clustered VLIW architecture respectively.

Discrete parameter simulation optimization algorithms with applications to admission control with dependent service times

We propose certain discrete parameter variants of well known simulation optimization algorithms. Two of these algorithms are based on the smoothed functional (SF) technique while two others are based on the simultaneous perturbation stochastic approximation (SPSA) method. They differ from each other in the way perturbations are obtained and also the manner in which projections and parameter updates are performed. All our algorithms use two simulations and two-timescale stochastic approximation. As an application setting, we consider the important problem of admission control of packets in communication networks under dependent service times. We consider a discrete time slotted queueing model of the system and consider two different scenarios - one where the service times have a dependence on the system state and the other where they depend on the number of arrivals in a time slot. Under our settings, the simulated objective function appears ill-behaved with multiple local minima and a unique global minimum characterized by a sharp dip in the objective function in a small region of the parameter space. We compare the performance of our algorithms on these settings and observe that the two SF algorithms show the best results overall. In fact, in many cases studied, SF algorithms converge to the global minimum.

Improved procedures for static and dynamic analyses of wrinkled membranes

An analysis of large deformations of flexible membrane structures within the tension field theory is considered. A modification-of the finite element procedure by Roddeman et al. (Roddeman, D. G., Drukker J., Oomens, C. W J., Janssen, J. D., 1987, ASME J. Appl. Mech. 54, pp. 884-892) is proposed to study the wrinkling behavior of a membrane element. The state of stress in the element is determined through a modified deformation gradient corresponding to a fictive nonwrinkled surface. The new model uses a continuously modified deformation gradient to capture the location orientation of wrinkles more precisely. It is argued that the fictive nonwrinkled surface may be looked upon as an everywhere-taut surface in the limit as the minor (tensile) principal stresses over the wrinkled portions go to zero. Accordingly, the modified deformation gradient is thought of as the limit of a sequence of everywhere-differentiable tensors. Under dynamic excitations, the governing equations are weakly projected to arrive at a system of nonlinear ordinary differential equations that is solved using different integration schemes. It is concluded that, implicit integrators work much better than explicit ones in the present context.

Reactive Pulsed Laser Deposition of titanium nitride thin film: Optimization of process parameters using Secondary Ion Mass Spectrometry

Reactive Pulsed Laser Deposition is a single step process wherein the ablated elemental metal reacts with a low pressure ambient gas to form a compound. We report here a Secondary Ion Mass Spectrometry based analytical methodology to conduct minimum number of experiments to arrive at optimal process parameters to obtain high quality TiN thin film. Quality of these films was confirmed by electron microscopic analysis. This methodology can be extended for optimization of other process parameters and materials. (C) 2009 Elsevier B.V. All rights reserved.

Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

Simultaneous material selection and geometry design of statically determinate trusses using continuous optimization

In this work, we explore simultaneous geometry design and material selection for statically determinate trusses by posing it as a continuous optimization problem. The underlying principles of our approach are structural optimization and Ashby’s procedure for material selection from a database. For simplicity and ease of initial implementation, only static loads are considered in this work with the intent of maximum stiffness, minimum weight/cost, and safety against failure. Safety of tensile and compression members in the truss is treated differently to prevent yield and buckling failures, respectively. Geometry variables such as lengths and orientations of members are taken to be the design variables in an assumed layout. Areas of cross-section of the members are determined to satisfy the failure constraints in each member. Along the lines of Ashby’s material indices, a new design index is derived for trusses. The design index helps in choosing the most suitable material for any geometry of the truss. Using the design index, both the design space and the material database are searched simultaneously using gradient-based optimization algorithms. The important feature of our approach is that the formulated optimization problem is continuous, although the material selection from a database is an inherently discrete problem. A few illustrative examples are included. It is observed that the method is capable of determining the optimal topology in addition to optimal geometry when the assumed layout contains more links than are necessary for optimality.

Target reliability based design optimization of anchored cantilever sheet pile walls

In this study, the stability of anchored cantilever sheet pile wall in sandy soils is investigated using reliability analysis. Targeted stability is formulated as an optimization problem in the framework of an inverse first order reliability method. A sensitivity analysis is conducted to investigate the effect of parameters influencing the stability of sheet pile wall. Backfill soil properties, soil - steel pile interface friction angle, depth of the water table from the top of the sheet pile wall, total depth of embedment below the dredge line, yield strength of steel, section modulus of steel sheet pile, and anchor pull are all treated as random variables. The sheet pile wall system is modeled as a series of failure mode combination. Penetration depth, anchor pull, and section modulus are calculated for various target component and system reliability indices based on three limit states. These are: rotational failure about the position of the anchor rod, expressed in terms of moment ratio; sliding failure mode, expressed in terms of force ratio; and flexural failure of the steel sheet pile wall, expressed in terms of the section modulus ratio. An attempt is made to propose reliability based design charts considering the failure criteria as well as the variability in the parameters. The results of the study are compared with studies in the literature.

Process variability-aware statistical hybrid modeling of dynamic power dissipation in 65 nm CMOS designs

A generalized technique is proposed for modeling the effects of process variations on dynamic power by directly relating the variations in process parameters to variations in dynamic power of a digital circuit. The dynamic power of a 2-input NAND gate is characterized by mixed-mode simulations, to be used as a library element for 65mn gate length technology. The proposed methodology is demonstrated with a multiplier circuit built using the NAND gate library, by characterizing its dynamic power through Monte Carlo analysis. The statistical technique of Response. Surface Methodology (RSM) using Design of Experiments (DOE) and Least Squares Method (LSM), are employed to generate a "hybrid model" for gate power to account for simultaneous variations in multiple process parameters. We demonstrate that our hybrid model based statistical design approach results in considerable savings in the power budget of low power CMOS designs with an error of less than 1%, with significant reductions in uncertainty by atleast 6X on a normalized basis, against worst case design.

Optimization of the distance between source and substrate for device-grade SnS films grown by the thermal evaporation technique

Tin monosulfide (SnS) films with varying distance between the source and substrate (DSS) were prepared by the thermal evaporation technique at a temperature of 300 degrees C to investigate the effect of the DSS on the physical properties. The physical properties of the as-deposited films are strongly influenced by the variation of DSS. The thickness, Sn to S at.% ratio, grain size, and root mean square (rms) roughness of the films decreased with the increase of DSS. The films grown at DSS = 10 and 15 cm exhibited nearly single-crystalline nature with low electrical resistivity. From Hall-effect measurements, it is observed that the films grown at DSS <= 15 cm have p-type conduction and the films grown at higher distances have n-type conduction due to the variation of the Sn/S ratio. The films grown at DSS = 15 cm showed higher optical band gap of 1.36 eV as compared with the films grown at other distances. The effect of the DSS on the physical properties of SnS films is discussed and reported.