Search results

In the dynamical analysis of engineering systems, running a detailed high‐resolution finite element model can be expensive even for obtaining the dynamic response at few frequency points. To address this problem, this paper aims to investigate the possibility of representing the output of an expensive computer code as a Gaussian stochastic process.

The Gaussian process emulator method is discussed and then applied to both simulated and experimentally measured data from the frequency response of a cantilever plate excited by a harmonic force. The dynamic response over a frequency range is approximated using only a small number of response values, obtained both by running a finite element model at carefully selected frequency points and from experimental measurements. The results are then validated applying some adequacy diagnostics.

It is shown that the Gaussian process emulator method can be an effective predictive tool for medium and high‐frequency vibration problems, whenever the data are expensive to obtain, either from a computer‐intensive code or a resource‐consuming experiment.

Although Gaussian process emulators have been used in other disciplines, there is no knowledge of it having been implemented for structural dynamic analyses and it has good potential for this area of engineering.

In this work, the authors aim to provide a set of convenient methods for generating training data, and then develop a deep learning method based on point clouds to estimate the pose of target for robot grasping.

This work presents a deep learning method PointSimGrasp on point clouds for robot grasping. In PointSimGrasp, a point cloud emulator is introduced to generate training data and a pose estimation algorithm, which, based on deep learning, is designed. After trained with the emulation data set, the pose estimation algorithm could estimate the pose of target.

In experiment part, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor and a base platform with adjustable inclination. A data set that contains three subsets is set up on the experimental platform. After trained with the emulation data set, the PointSimGrasp is tested on the experimental data set, and an average translation error of about 2–3 mm and an average rotation error of about 2–5 degrees are obtained.

The contributions are as follows: first, a deep learning method on point clouds is proposed to estimate 6D pose of target; second, a convenient training method for pose estimation algorithm is presented and a point cloud emulator is introduced to generate training data; finally, an experimental platform is built, and the PointSimGrasp is tested on the platform.

Numerical models are being increasingly relied upon to evaluate wind turbine performance by simulating phenomena that are infeasible to measure experimentally. These numerical models, however, require a large number of input parameters that often need to be calibrated against available experiments. Owing to the unavoidable scarcity of experiments and inherent uncertainties in measurements, this calibration process may yield non-unique solutions, i.e. multiple sets of parameters may reproduce the available experiments with similar fidelity. The purpose of this paper is to study the trade-off between fidelity to measurements and the robustness of this fidelity to uncertainty in calibrated input parameters.

Here, fidelity is defined as the ability of the model to reproduce measurements and robustness is defined as the allowable variation in the input parameters with which the model maintains a predefined level of threshold fidelity. These two vital attributes of model predictiveness are evaluated in the development of a simplified finite element beam model of the CX-100 wind turbine blade.

Findings of this study show that calibrating the input parameters of a numerical model with the sole objective of improving fidelity to available measurements degrades the robustness of model predictions at both tested and untested settings. A more optimal model may be obtained by calibration methods considering both fidelity and robustness. Multi-criteria Decision Making further confirms the conclusion that the optimal model performance is achieved by maintaining a balance between fidelity and robustness during calibration.

Current methods for model calibration focus solely on fidelity while the authors focus on the trade-off between fidelity and robustness.

Partitioned analysis is an increasingly popular approach for modeling complex systems with behaviors governed by multiple, interdependent physical phenomena. Yielding accurate representations of reality from partitioned models depends on the availability of all necessary constituent models representing relevant physical phenomena. However, there are many engineering problems where one or more of the constituents may be unavailable because of lack of knowledge regarding the underlying principles governing the behavior or the inability to experimentally observe the constituent behavior in an isolated manner through separate-effect experiments. This study aims to enable partitioned analysis in such situations with an incomplete representation of the full system by inferring the behavior of the missing constituent.

This paper presents a statistical method for inverse analysis infer missing constituent physics. The feasibility of the method is demonstrated using a physics-based visco-plastic self-consistent (VPSC) model that represents the mechanics of slip and twinning behavior in 5182 aluminum alloy. However, a constituent model to carry out thermal analysis representing the dependence of hardening parameters on temperature is unavailable. Using integral-effect experimental data, the proposed approach is used to infer an empirical constituent model, which is then coupled with VPSC to obtain an experimentally augmented partitioned model representing the thermo-mechanical properties of 5182 aluminum alloy.

Results demonstrate the capability of the method to enable model predictions dependent upon relevant operational conditions. The VPSC model is coupled with the empirical constituent, and the newly enabled thermal-dependent predictions are compared with experimental data.

The method developed in this paper enables the empirical inference of a functional representation of input parameter values in lieu of a missing constituent model. Through this approach, development of partitioned models in the presence of uncertainty regarding a constituent model is made possible.

In Part 1 of the paper, the problems a would‐be user faces in accessing the contents of machine stored bibliographic databases are assessed; the online search services solve part of the difficulty, but become part of the problem; current training techniques are summarized and evaluated, and ways of improving training are suggested. In Part 2 of the paper, a new training program is described, the Hands on online multisystem multidatabase trainer, which delivers the training to the home terminal of the trainee, and in which emulations of bibliographic retrieval systems are provided that accept commands, search files, provide messages and displays to mimic the operational services, and in which the user trainee can develop the same skills he would learn in the ‘real’ system. The program has optionally available practice and instructional modules that guide the user in the protocols of telecommunication services, computer log in, file selection, search term negotiation, logical statement structure, interpretation of system messages and displays, and formatting of output from the search. The program can be used as needed with either the emulators or the online system itself. Training and design goals are detailed; namely the use and availability of the trainer outside the university, experience, use, evaluation of the training, extension and future development.

This paper aims to devise a first-of-its-kind methodology to determine the design, operating conditions and actuation strategy of pneumatic artificial muscles (PAMs) for assistive robotic applications. This requires extensive characterization, data set generation and meaningful modelling between PAM characteristics and design variables. Such a characterization should cover a wide range of design and operation parameters. This is a stepping stone towards generating a design guide for this highly popular compliant actuator, just like any conventional element of a mechanism.

Characterization of a large pool of custom fabricated PAMs of varying designs is performed to determine their static and dynamic behaviours. Metaheuristic optimizer-based artificial neural network (ANN) structures are used to determine eight different models representing PAM behaviour. The assistance of knee flexion during level walking is targeted for evaluating the applicability of the developed actuator by attaching a PAM across the joint. Accordingly, the PAM design and the actuation strategy are optimized through a tabletop emulator.

The dependence of passive length, static contraction, dynamic step response for inflation and deflation of the PAMs on their design dimensions and operating parameters is successfully modelled by the ANNs. The efficacy of these models is investigated to successfully optimize the PAM design, operation parameters and actuation strategy for using a PAM in assisting knee flexion in human gait.

Characterization of static and the dynamic behaviour of a large pool of PAMs with varying designs over a wide range of operating conditions is the novel feature in this article. A lucid customizable fabrication technique is discussed to obtain a wide variety of PAM designs. Metaheuristic-based ANNs are used for tackling high non-linearity in data while modelling the PAM behaviour. An innovative tabletop emulator is used for investigating the utility of the models in the possible application of PAMs in assistive robotics.

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

The results indicate that the proposed service resource management strategy, considering user co-creation in the service delivery, process improved both the service provider’s business revenue and users' individual benefits.

The findings may facilitate the design and implementation of health information services that can achieve a high user service experience with low service operation costs.

This study is amongst the first to propose a service resource management model in I-HISS, considering the value co-creation of the user in the service-dominant logic. The novel artificial intelligence algorithm is developed using the deep reinforcement learning method to learn the adaptive service resource management strategy. The results emphasise user engagement in the health information service process.

The Japanization debate in the UK has moved considerably since first mooted in 1987. On the one hand academics ‐ advocates as well as sceptics ‐ have questioned its continued usefulness as an analytical framework. On the other, there has been greater sophistication, refinement and clarity on what is being studied, and particularly surrounding aspects of the transferability of the Japanese model. This paper reports on a study of production supervisors in Japanese transplants in the UK, and data from emulating or comparable non‐Japanese owned organizations. It also draws on comparative data from Japan and North America. The study focuses on two industries ‐ consumer electronics and autos ‐ and uses a variety of methodologies.

Academia is changing rapidly; the concept of attaining a college degree now encompasses not only the traditional ideas of attending classes on‐site at campuses but also through state‐of‐the‐art methods that deliver telecourses, now referred to as distance education. Distance education has opened the door of opportunity to a population of potential students who, not so long ago, felt a college degree was an impossible endeavor because of geographic location or personal restrictions. This boon to the students has posed a new set of challenges for libraries in providing academic support for the students engaged in distance learning. The Eastern Oregon Information Network (EOIN) was developed to provide dial‐in, remote access to CD‐ROM indexes, an interlibrary loan module, and an electronic mail system, which bridges the critical gap for the off‐campus student.

Analytic hierarchy process (AHP) is a simple decision‐making tool to deal with complex, unstructured and multi‐attribute problems. Selection of the most suitable production planning system (push or pull systems) requires the development of a tool to address quantitative and qualitative parameters which influence success of push‐and‐pull systems’ implementation. This paper presents a multi‐criteria approach within AHP to classify the most appropriate production planning system, based on push, pull or hybrid systems’ methodologies.