Data-driven Construction Research Articles

Modeling for assembly simulations: Data description and a data-driven construction framework

Assembly simulations such as assembly process simulation and assembly tolerance simulation have become an effective means to evaluate and analyze product assembly design and assembly process planning. Being core aspect of simulation implementation, building an assembly simulation model is rather time-consuming because of its high complexity. Furthermore, modeling has a significant influence on the popularization and application of simulation technology. In this paper, data needed by assembly process and tolerance simulation are addressed to propose a data-driven approach for assembly simulation modeling. The application process and the architecture of modeling framework for assembly simulation are presented as well. An assembly sequence simulation example is given to illustrate the application of the framework. The framework provides a new idea for the realization of automatic modeling for assembly simulation.

Integrated, High-Throughput, Multiomics Platform Enables Data-Driven Construction of Cellular Responses and Reveals Global Drug Mechanisms of Action.

An understanding of how cells respond to perturbation is essential for biological applications; however, most approaches for profiling cellular response are limited in scope to pre-established targets. Global analysis of molecular mechanism will advance our understanding of the complex networks constituting cellular perturbation and lead to advancements in areas, such as infectious disease pathogenesis, developmental biology, pathophysiology, pharmacology, and toxicology. We have developed a high-throughput multiomics platform for comprehensive, de novo characterization of cellular mechanisms of action. Platform validation using cisplatin as a test compound demonstrates quantification of over 10 000 unique, significant molecular changes in less than 30 days. These data provide excellent coverage of known cisplatin-induced molecular changes and previously unrecognized insights into cisplatin resistance. This proof-of-principle study demonstrates the value of this platform as a resource to understand complex cellular responses in a high-throughput manner.

Application of Chebyshev theorem to data preparation in landslide susceptibility mapping studies: an example from Yenice (Karabük, Turkey) region

Landslide database construction is one of the most crucial stages of the landslide susceptibility mapping studies. Although there are many techniques for preparing landslide database in the literature, representative data selection from huge data sets is a challenging, and, to some extent, a subjective task. Thus, in order to produce reliable landslide susceptibility maps, data-driven, objective and representative database construction is a very important stage for these maps. This study mainly focuses on a landslide database construction task. In this study, it was aimed at building a representative landslide database extraction approach by using Chebyshev theorem to evaluate landslide susceptibility in a landslide prone area in the Western Black Sea region of Turkey. The study area was divided into two different parts such as training (Basin 1) and testing areas (Basin 2). A total of nine parameters such as topographical elevation, slope, aspect, planar and profile curvatures, stream power index, distance to drainage, normalized difference vegetation index and topographical wetness index were used in the study. Next, frequency distributions of the considered parameters in both landslide and nonlandslide areas were extracted using different sampling strategies, and a total of nine different landslide databases were obtained. Of these, eight databases were gathered by the methodology proposed by this study based on different standard deviations and algebraic multiplication of raster parameter maps. To evaluate landslide susceptibility, Artificial Neural Network method was used in the study area considering the different landslide and nonlandslide data. Finally, to assess the performances of the so-produced landslide susceptibility maps based on nine data sets, Area Under Curve (AUC) approach was implemented both in Basin 1 and Basin 2. The best performances (the greatest AUC values) were gathered by the landslide susceptibility map produced by two standard deviation database extracted by the Chebyshev theorem, as 0.873 and 0.761, respectively. Results revealed that the methodology proposed by this study is a powerful and objective approach in landslide susceptibility mapping.

An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings

Brain–computer interfacing (BCI) is a promising technique for regaining communication and control in severely paralyzed people. Many BCI implementations are based on the recognition of task-specific event-related potentials (ERP) such as P300 responses. However, because of the high signal-to-noise ratio in noninvasive brain recordings, reliable detection of single trial ERPs is challenging. Furthermore, the relevant signal is often heterogeneously distributed over several channels. In this paper, we introduce a new approach for recognizing a sequence of attended events from multi-channel brain recordings. The framework utilizes spatial filtering to reduce both noise and signal space considerably. We introduce different models that can be used to construct the spatial filter and evaluate the approach using magnetoencephalography (MEG) data involving P300 responses, recorded during a BCI experiment. Compared to the accuracy achieved in the BCI experiment performed without spatial filtering, the recognition rate increased significantly to up to 95.3% on average (SD: 5.3%). In combination with the data-driven spatial filter construction we introduce here, our framework represents a powerful method to reliably recognize a sequence of brain potentials from high-density electrophysiological data, which could greatly improve the control of BCIs.

Data-driven construction of Convex Region Surrogate models

With the increasing trend of solving more complex and integrated optimization problems, there is a need for developing process models that are sufficiently accurate as well as computationally efficient. In this work, we develop an algorithm for the data-driven construction of a type of surrogate model that can be formulated as a set of mixed-integer linear constraints, yet still provide good approximations of nonlinearities and nonconvexities. In such a surrogate model, which we refer to as Convex Region Surrogate (CRS), the feasible region is given by the union of convex regions in the form of polytopes, and for each region, the corresponding cost function can be approximated by a linear function. The general problem is as follows: given a set of data points in the parameter space and a scalar cost value associated with each data point, find a CRS model that approximates the feasible region and cost function indicated by the given data points. We present a two-phase algorithm to solve this problem and demonstrate its effectiveness with an extensive computational study as well as a real-world case study.

Fuzzy inductive reasoning: a consolidated approach to data-driven construction of complex dynamical systems

Fuzzy inductive reasoning (FIR) is a modelling and simulation methodology derived from the General Systems Problem Solver. It compares favourably with other soft computing methodologies, such as neural networks, genetic or neuro-fuzzy systems, and with hard computing methodologies, such as AR, ARIMA, or NARMAX, when it is used to predict future behaviour of different kinds of systems. This paper contains an overview of the FIR methodology, its historical background, and its evolution.

Developing assessment scales for large-scale speaking tests: a multiple-method approach

The process of constructing assessment scales for performance testing is complex and multi-dimensional. As a result, a number of different approaches, both empirically and intuitively based, are open to developers. In this paper we outline the approach taken in the revision of a set of assessment scales used with speaking tests, and present the value of combining methodologies to inform and refine scale development. We set the process in the context of the growing influence of the Common European Framework of Reference (Council of Europe 2001) and outline a number of stages in terms of the procedures followed and outcomes produced. The findings describe a range of data that was collected and analysed through a number of phases and used to inform the revision of the scales, including consultation with experts, and data-driven qualitative and quantitative research studies. The overall aim of the paper is to illustrate the importance of combining intuitive and data-driven scale construction methodologies, and to suggest a usable scale construction model for application or adaptation in a variety of contexts.

Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers

In this paper, we consider the problem of generating concise but accurate naive Bayes classifiers using taxonomy of propositionalized attributes. For the problem, we introduce propositionalized attribute taxonomy guided naive Bayes Learner (PAT-NBL), a machine learning algorithm that effectively utilizes taxonomy to generate compact classifiers. We extend classical naive Bayes learner to the PAT-NBL algorithm that traverses over a propositionalized taxonomy to search for a locally optimal cut. PAT-NBL uses bottom-up search to find the locally optimal cut on a given taxonomy. For the evaluation of candidate cuts, we apply conditional log-likelihood, conditional minimum description length, and conditional Akaike information criterion. The detected cut enables PAT-NBL to construct an instance space which corresponds to the taxonomy and the data. That is, after PAT-NBL determines a cut according to its information-theoretic criteria, the algorithm generates a concise naive Bayes classifier based on the cut. Our experimental results on UCI Machine Learning benchmark data sets indicate that the proposed algorithm can generate naive Bayes classifiers that are more compact and often comparably accurate to those produced by standard naive Bayes learners.

Bayesian learning of biological pathways on genomic data assimilation

Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision. The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways. The FORTRAN source codes are available at the URL http://daweb.ism.ac.jpyoshidar/GDA/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Coupling graph perturbation theory with scalable parallel algorithms for large-scale enumeration of maximal cliques in biological graphs

Data-driven construction of predictive models for biological systems faces challenges from data intensity, uncertainty, and computational complexity. Data-driven model inference is often considered a combinatorial graph problem where an enumeration of all feasible models is sought. The data-intensive and the NP-hard nature of such problems, however, challenges existing methods to meet the required scale of data size and uncertainty, even on modern supercomputers. Maximal clique enumeration (MCE) in a graph derived from such biological data is often a rate-limiting step in detecting protein complexes in protein interaction data, finding clusters of co-expressed genes in microarray data, or identifying clusters of orthologous genes in protein sequence data. We report two key advances that address this challenge. We designed and implemented the first (to the best of our knowledge) parallel MCE algorithm that scales linearly on thousands of processors running MCE on real-world biological networks with thousands and hundreds of thousands of vertices. In addition, we proposed and developed the Graph Perturbation Theory (GPT) that establishes a foundation for efficiently solving the MCE problem in perturbed graphs, which model the uncertainty in the data. GPT formulates necessary and sufficient conditions for detecting the differences between the sets of maximal cliques in the original and perturbed graphs and reduces the enumeration time by more than 80% compared to complete recomputation.

Regularized data-driven construction of fuzzy controllers

Abstract - This paper is devoted to the mathematical analysis and the numerical solution of data-driven construction of fuzzy controllers. We show that for a special class of controllers (so-called Sugeno controllers), the design problem is equivalent to a nonlinear least squares problem, which turns out to be ill-posed. Therefore we investigate the use of regularization in order to obtain stable approximations of the solution. We analyze a smoothing method, which is common in spline approximation, as well as Tikhonov regularization with respect to stability and convergence. In addition, we develop an iterative method for the regularized problems, which uses the special structure of the problem and test it in some typical numerical examples. We also compare the behavior of the iterations for the original and the regularized least squares problems. It turns out that the regularized problem is not only more robust but also favors solutions that are interpretable easily, which is an important criterion for fuzzy systems.

Improvement of the Empiricism in the BACK Equation of State via Hybrid Neural Networks

Artificial neural networks (ANNs) have proven to be a valuable tool for the data-driven construction of empirical models. The predictive capabilities of the ANN approximations are often validated within a statistical framework. For many applications, one can rely on a first-principles background that allows for the construction of hybrid models. By constrasting the predictions of the hybrid models with the theory, valuable insight can be obtained about the predictive capabilities of the approximation and the first-principles formulation itself. In this contribution, we illustrate the use of ANNs for the construction of hybrid models for the determination of mixing rules for the BACK thermodynamic equation of state (EOS). The hybrid approximations are constructed in three levels, with the progressive addition of more information to the empirical formulation. Using an ANN within the framework of an EOS substantially increases the potential for applications allowing for the estimation of thermodynamic proper...

Establishing criteria to ensure successful feedforward artificial neural network modelling of mechanical systems

The emulation of mechanical systems is a popular application of artificial neural networks in engineering. This paper examines general principles of modelling mechanical systems by feedforward artificial neural networks (FFANNs). The slow convergence issue associated with the highly parallel and redundant structure of FFANN systems is addressed by formulating criteria for constraining network parameters so that FFANNs may be reliably applied to mechanics problems. The existence of the FFANN mechanical model and its stability during construction, with respect to the error in the data, are analyzed. Also, a class of differential equations is analyzed for use with Tikhonov regularization. It is shown that the use of Tikhonov regularization can aid in FFANN data-driven construction with a priori mathematical models of varying degrees of physical fidelity. Criteria to ensure successful FFANN application from an engineering perspective are established.

Rule-based modeling: precision and transparency

This article is a reaction to recent publications on rule-based modeling using fuzzy set theory and fuzzy logic. The interest in fuzzy systems has recently shifted from the seminal ideas about complexity reduction toward data-driven construction of fuzzy systems. Many algorithms have been introduced that aim at numerical approximation of functions by rules, but pay little attention to the interpretability of the resulting rule base. We show that fuzzy rule-based models acquired from measurements can be both accurate and transparent by using a low number of rules. The rules are generated by product-space clustering and describe the system in terms of the characteristic local behavior of the system in regions identified by the clustering algorithm. The fuzzy transition between rules makes it possible to achieve precision along with a good qualitative description in linguistic terms. The latter is useful for expert evaluation, rule-base maintenance, operator training, control systems design, user interfacing, etc. We demonstrate the approach on a modeling problem from a recently published article.