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The purpose of this paper is to expose computational methods as applied to engineering systems and evolutionary processes with randomness in external actions and inherent parameters.

In total, two approaches are distinguished that rely on solvers from deterministic algorithms. Probabilistic analysis is referred to as the approximation of the response by a Taylor series expansion about the mean input. Alternatively, stochastic simulation implies random sampling of the input and statistical evaluation of the output.

Beyond the characterization of random response, methods of reliability assessment are discussed. Concepts of design improvement are presented. Optimization for robustness diminishes the sensitivity of the system to fluctuating parameters.

Deterministic algorithms available for the primary problem are utilized for stochastic analysis by statistical Monte Carlo sampling. The computational effort for the repeated solution of the primary problem depends on the variability of the system and is usually high. Alternatively, the analytic Taylor series expansion requires extension of the primary solver to the computation of derivatives of the response with respect to the random input. The method is restricted to the computation of output mean values and variances/covariances, with the effort determined by the amount of the random input. The results of the two methods are comparable within the domain of applicability.

The present account addresses the main issues related to the presence of randomness in engineering systems and processes. They comprise the analysis of stochastic systems, reliability, design improvement, optimization and robustness against randomness of the data. The analytical Taylor approach is contrasted to the statistical Monte Carlo sampling throughout. In both cases, algorithms known from the primary, deterministic problem are the starting point of stochastic treatment. The reader benefits from the comprehensive presentation of the matter in a concise manner.

The purpose of this paper considers optimal input signal design for flutter model parameters identification, as input signal is the first step during the whole identification process. According to the constructed flutter stochastic model with observed noises, separable least squares identification and set membership identification are proposed to identify those unknown model parameters for statistical noise and unknown but bounded noise, respectively. The common trace operation with respect to the asymptotic variance matrix is minimized to solve the power spectral for the optimal input signal in the framework of statistical noise. Moreover, for the unknown bout bounded noise, the radius of information, corresponding to the established parameter uncertainty interval, is minimized to give the optimal input signal.

First, model identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter statistical model with statistical noise, separable least squares identification is proposed to identify the unknown model parameters, then the optimal input signal is designed to satisfy one given performance function. Third, for aircraft flutter model with unknown but bounded noise, set membership identification is proposed to solve the parameter set for each unknown model parameter. Then, the optimal input signal is designed by applying the idea of the radius of information with unknown but bounded noise.

This aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Then identification strategy and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise and unknown but bounded noise, respectively.

To the best knowledge of the authors, this problem of the model parameter identification for aircraft flutter was proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes two novel identification strategies and opens a new subject about optimal input signal design for statistical noise and unknown noise, respectively.

The purpose of this paper is to report a study in which the feasibility of conducting probabilistic finite element analysis (FEA) for crane hook design has been explored.

This paper presents the results of probabilistic analysis, where in the input random variables are varied and corresponding variations in the output parameters were observed. In this study, material properties and load have been considered as random input variables and the maximum stress, maximum deflection variations were considered as output random variables.

The probability of occurrence of output variation and the sensitivity of output variables on the input variables are the important results generated from this analysis. By performing this probabilistic analysis, the random selection of factor of safety could be avoided.

The implementation study has been carried out for a single product.

The usage of the approach will indicate the importance of probabilistic analysis in product design and development process. This process will enable the organization to compete in the global market.

A case study has been reported to indicate the feasibility of performing probabilistic FEA for crane hook design. Hence, the contributions are original.

The purpose of this paper is to present the methodology that was used to design a system identification experiment of a generic spinning gasodynamic projectile. For this object, because the high-speed spinning motion, it was not possible to excite the aircraft motion along body axes independently. Moreover, it was not possible to apply simultaneous multi-axes excitations because of the short time in which system identification experiments can be performed (multi-step inputs) or because it is not possible to excite the aircraft with a complex input (multi-sine signals) because of the impulse gasodynamic engines (lateral thrusters) usage.

A linear projectile model was used to obtain information about identifiability regions of stability and control derivatives. On this basis various sets of lateral thrusters’ launching sequences, imitating continuous multi-step inputs were used to excite the nonlinear projectile model. Subsequently, the nonlinear model for each excitation set was identified from frequency responses, and the results were assessed. For comparison, the same approach was used for the same projectile exited with aerodynamic controls.

It was found possible to design launching sequences of lateral thrusters that imitate continuous multi-step input and allow to obtain accurate system identification results in specified frequency range.

The designed experiment can be used during polygonal shooting to obtain a true projectile aerodynamic model.

The paper proposes a novel approach to gasodynamic projectiles system identification and can be easily applied for similar cases.

Involvement in the effective design and use of computer‐based information systems is essential for the manager of the 1990s. To be most effective, systems must be designed for the requirements of the manager‐user. Too often there is a communication gap between managers who are too busy, uninterested or unwilling to become directly involved, on the one hand, and on the other, the consultant who is more usually engrossed in the special nature of the system. The author aims to provide an up‐to‐date and integrated treatment of organisation and management, as well as to emphasise the utilisation of management information systems to improve the art of managing.

The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters.

Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. Linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM.

It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric.

This can be used to assess the GSM of embroidered fabrics before starting the actual embroidery process. So, this model can help the embroidery designers significantly to pre-estimate the GSM of the embroidered fabrics and select the design parameters accordingly. Also, this model can be a useful tool for estimation of thread consumption and thread cost in embroidery.

The input parameters used here are very basic parameters related to design and materials, which can be easily available. And also, a simple linear multiple regression is used to make the prediction equation simple and easy to use. So, this model can help the embroidery designers or garment designers to select/adjust the embroidery parameters and thread parameters accordingly in the planning and designing stage itself to ensure that the GSM of embroidered fabrics remains within desirable range. Also, this prediction model developed hereby may be a very useful tool for estimation of the consumption and cost of embroidery threads.

This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.

The 60 GHz unlicensed band is being utilized for high-speed wireless networks with data rates in the gigabit range. To successfully make use of these high-speed signals in a digital system, a high-speed analog-to-digital converter (ADC) is necessary. This paper aims to present the use of a common collector (CC) input tree and Cherry Hooper (C-H) differential amplifier to enable analog-to-digital conversion at high frequencies.

The CC input tree is designed to separate the input Miller capacitance of each comparator stage. The CC stages are biased to obtain bandwidth speeds higher than the comparator stages while using less current than the comparator stages. The C-H differential amplifier is modified to accommodate the low breakdown voltages of the technology node and implemented as a comparator. The comparator stages are biased to obtain a high output voltage swing and have a small signal bandwidth up to 29 GHz. Simulations were performed using foundry development kits to verify circuit operation. A two-bit ADC was prototyped in IBM’s 130 nm SiGe BiCMOS 8HP technology node. Measurements were carried out on test printed circuit boards and compared with simulation results.

The use of the added CC input tree showed a simulated bandwidth improvement of approximately 3.23 times when compared to a basic flash architecture, for a two-bit ADC. Measured results showed an effective number of bits (ENOB) of 1.18, from DC up to 2 GHz, whereas the simulated result was 1.5. The maximum measured integral non-linearity and differential non-linearity was 0.33 LSB. The prototype ADC had a figure of merit of 42 pJ/sample.

The prototype ADC results showed that the group delay for the C-H comparator plays a critical role in ADC performance for high frequency input signals. For minimal component variation, the group delay between channels deviate from each other, causing incorrect output codes. The prototype ADC had a low gain which reduced the comparator performance. The two-bit CC C-H ADC is capable of achieving an ENOB close to 1.18, for frequencies up to 2 GHz, with 180 mW total power consumption.

Modular construction is widely adopted and used in the construction industry to improve construction performance with respect to both efficiency and productivity. The evaluation of design options for modular construction can be iterative, and thus automation is required to develop design alternatives. This research aims to explore the potential of utilizing the generative design approach to automate modular construction for residential building structures in urban areas such as New York City.

The proposed research methodology is investigated for a systematic approach to parametrize design parameters for modular construction layout design as well as incorporate design rules/parameters into modularizing design layouts in a Building Information Modeling (BIM) environment. Based on current building codes and necessary inputs by the user, the proposed approach enables providing recommendations in a generative design method and optimizes construction processes by performing analytical calculations.

The generative design has been found to be efficient in generating layout designs for modular construction based on parametric design. The integration of BIM and generative design can allow industry practitioners to fast generate design layout with evaluations from constructability perspectives.

This paper has proposed a new approach of incorporating generative design with BIM technologies to solve module layout generations by considering design and constructability constraints. The method can be further extended for evaluating modular construction design from manufacturability and assembly perspectives.

The purpose of this paper is to design a model‐based robust controller for autonomous hovering of a small‐scale helicopter.

The model is developed using prediction error minimization (PEM) system identification method implemented to flight data. Based on the extracted linear model, an H_∞ controller is synthesized for robustness against parametric uncertainties and disturbances.

The proposed techniques for modelling provide a linear state‐space model which correlates well with the recorded flight data. The synthesized H_∞ controller demonstrates an effective performance which rejects both sinusoidal and step input disturbances. The controller enables the attitude angle follow the reference target while keeping the attitude rate constant about zero for hover flight condition.

The synthesized controller is effective for hovering and low‐speed flight condition.

This work provides an efficient hovering/low‐speed autonomous helicopter flight control required in many civilian UAV applications such as aerial surveillance and photography.

The paper addresses the challenges of controlling a small‐scale helicopter during hover with inherent modelling uncertainties and disturbances.

The purpose of this paper is to extend the authors’ previous contributions on aircraft flutter model parameters identification. Because closed-loop condition is more widely used in today’s practice, a closed-loop stochastic model of the aircraft flutter test is constructed to model the aircraft flutter process, whose input–output signals are all corrupted by the observed noises. Through using a rational transfer function, the equivalent property between the aircraft flutter model parameters and polynomial coefficients is established, and then the problem of aircraft flutter model parameters identification is turned to one closed-loop identification problem. An iterative identification algorithm is proposed to identify the unknown polynomial coefficients, being benefit for the latter flutter model parameter identification. Furthermore, as the closed-loop output corresponds to the flutter amplitude, so from the point of the minimization with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.

First, model parameter identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter closed-loop statistical model with statistical noise, an iterative identification algorithm is proposed to identify the unknown model parameters. Third, from the point of minimizing with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.

This aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Then, identification algorithm and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise, respectively. It means the optimal input signal and optimal feedback controller are useful for the aircraft flutter model parameter identification within the constructed new closed-loop stochastic model.

To the best of the authors’ knowledge, this problem of the model parameter identification for aircraft flutter is proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes a new closed-loop stochastic model to construct the aircraft flutter test, and some related topics are considered about this closed-loop identification for aircraft flutter model parameter identification in the framework of closed-loop condition.