Single-scale System Research Articles

Knowledge acquisition of multi-granularity ordered information systems

Knowledge reduction is an essential issue in data mining. However, most of the existing studies consider the problem of knowledge reduction in a single-scale system. When the system expands to a multi-scale information system (MIS), the existing methods are not available. To overcome this challenge, we design a universal framework to reduce attributes and select optimal scales (OSs) based on the dominance relation in ordered information systems (OISs). Firstly, we analyze the effect of scale on rough approximations and correlation measures in multi-scale ordered information systems (MOISs). Secondly, the relation between attribute reduction and OS selection is explored in OISs. Moreover, the corresponding algorithms are designed around both tasks. Due to our universal framework considering the decisions at the finest, optimal and coarsest scale, our study is an extension of the three-way decision. Thirdly, a novel MOIS construction algorithm is explored to transform an OIS into an MIS. Thus, the proposed reduction algorithms can be operated in any OIS. Experiments substantiate that models trained by data at the OS can improve their stability and the attribute reduction task is also suitable for the proposed algorithm.

Fabrication and characterization of PVA/CS-PCL/gel multi-scale electrospun scaffold: simulating extracellular matrix for enhanced cellular infiltration and proliferation

A new bi-component poly(vinylalcohol)(PVA)/chitosan(CS)-poly(e-caprolactone)(PCL)/gelatin(Gel) multiscale electrospun scaffold was developed and analyzed in comparison with several other single scale systems. To mimic the native extracellular matrix in composition and structure and promote the migration of cells inside the scaffold, PVA/CS composite nanofibers (102 ± 52 nm) and PCL/Gelcomposite microfiber (2.5 ± 1.0 µm) were simultaneously electrospun from the two opposite syringes and mixed on a rotating mandrel to generate a bi-component multi-scale membrane. The bi-component membrane was crosslinked by glutaraldehyde vapor to maintain its fiber morphology in the wet stage. Morphology, shrinkage and spectroscopic of the electrospun membranes were characterized. To test the newly developed multiscale membrane, we seeded mesenchymal stem cells (MSCs) derived from rabbit onto five different fiber scaffolds (PVA, PVA/CS, PCL, PCL/Gel and PVA/CS-PCL/Gel) and compared cell adhesion and proliferation between different groups for 3 days using scanning electron microscopy, inverted microscope observations assay and MTT colorimetric. Cell culture results suggest that the incorporation of chitosan and gelatin could enhance cell adhesion and cell spreading in comparison to the performance of single component scaffolds of PVA and PCL. The multiscale PVA/CS-PCL/Gel membrane scaffolds provide a better environment to increase the growth, adhesion, and proliferation of cells. Scanning electron microscopy (SEM) observations showed that the cells were not only adhered well and proliferated on the surface of the scaffolds, but were also able to infiltrate inside the scaffold within 3 days of culture. MTT assay and inverted microscope observations also showed that the PVA/CS-PCL/Gel complex fibrous membrane exhibited better activity than other single component/scale systems scaffolds. Our results provide the underlying insights needed to guide the design of the native extracellular matrix.

Transcale LQG tracking control for a class of discrete stochastic systems

A wavelet-based real-time transcale linear-quadratic-Gaussian (LQG) tracking control algorithm is presented and discussed, which extends the classical single-scale system control tool to a multiscale scenario. The new strategy determines the control law at the finest scale such that it can reflect the control laws at different coarser scales and guarantee the corresponding performance indexes of coarser scales to be minimized. Simulation result shows that significant improvements in computational efficiency are achieved.

Higher Efficiency Modeling of Surface Location Errors by Using a Multi-scale Milling Simulation

The dynamic process behavior during milling often leads to vibrations or even chatter and, thereby, to surface location errors. In this paper, a multi-scale simulation system is presented, which can predict these errors efficiently by combining a dexel-based workpiece representation with a CSG-based process model and an oscillator-based system for the process dynamics. The different modeling techniques are described, and the run-time of the combined system is compared to a single-scale system in which the CSG-based model is used for every process step. Furthermore, the simulation is validated by examining the generated surface of a micromachined workpiece.

A multiscale empirical modeling framework for system identification

A multiscale system identification methodology is presented and discussed, that extends, in a systematic way, the classical board of single-scale system identification tools to a multiscale context. The proposed approach is built upon a wavelet-based multiscale decomposition in a receding horizon sliding window that always includes the last measured values, in order to make it adequate for on-line use. Several examples are presented that illustrate different features of the multiscale modeling framework, such as its improved ability to perform prediction in output variables having most of its energy concentrated at intermediate or coarser time scales when compared to input variables, and its intrinsic smoothing capability.

The application of multiscale modelling to the process of development and prevention of stenosis in a stented coronary artery

The inherent complexity of biomedical systems is well recognized; they are multiscale, multiscience systems, bridging a wide range of temporal and spatial scales. While the importance of multiscale modelling in this context is increasingly recognized, there is little underpinning literature on the methodology and generic description of the process. The COAST (complex autonoma simulation technique) project aims to address this by developing a multiscale, multiscience framework, coined complex autonoma (CxA), based on a hierarchical aggregation of coupled cellular automata (CA) and agent-based models (ABMs). The key tenet of COAST is that a multiscale system can be decomposed into N single-scale CA or ABMs that mutually interact across the scales. Decomposition is facilitated by building a scale separation map on which each single-scale system is represented according to its spatial and temporal characteristics. Processes having well-separated scales are thus easily identified as the components of the multiscale model. This paper focuses on methodology, introduces the concept of the CxA and demonstrates its use in the generation of a multiscale model of the physical and biological processes implicated in a challenging and clinically relevant problem, namely coronary artery in-stent restenosis.

MULTISCALE MODELLING: APPLICATION OF THE COMPLEX AUTOMATA SIMULATION TECHNIQUE (COAST)

Computer simulations and rule-based modelling can be effective methodologies to describe complex systems and understand or control their behaviour. As many complex biological systems encompass several spatial and temporal scales, as well as several elementary components they cannot be described by a single homogenous model. COAST is developing a multi-scale, multi-science framework coined ‘Complex Automata’ for modelling and simulation of complex systems. The key tenet of COAST is that a multi-scale system can be decomposed into a number of single-scale Cellular Automata or agent-based models that mutually interact across the scales. Decomposition is facilitated by building a Scale Separation Map (SSM) on which each single-scale system is represented according to its spatial and temporal characteristics. Processes having well-separated scales are thus easily identified as the fundamental components of the multi-scale model.

Performance Analysis of 1-D Scale-Space Algorithms for Pulse Detection in Noisy Image Scans

Scale-space representation is a topic of active research in computer vision. The focus of the research so far has been on coarse-to-fine focusing methods, image reconstruction, and computational aspects. However, not much work has been done on the signal detection problem, i.e., detecting the presence or absence of signal models from noisy image scans using the scale-space. In this paper we propose four 1-D signal detection algorithms for separating pulse signals in an image scan from the background in the scale-space domain. These algorithms do not need any thresholding to detect the zero-crossings (zc′s) at any of the scales. The different algorithms are applicable to image scans with different noise and clutter characteristics. A simple algorithm works best for scans having low noise and clutter. When noise and clutter increase sufficiently, a more sophisticated algorithm must be used. The 1-D algorithms for pulse and edge detection can be used to detect 2-D closed objects in cluttered and noisy backgrounds. This is done by scanning the image row-wise (and column-wise) and working on the individual scans. Using this method, the algorithms are demonstrated on several real life images. Another objective of this paper is to conduct comparative analysis of (i) a single-scale system vs a multiscale system and (ii) white noise vs clutter. This is done by conducting an experimental statistical analysis on single-scale and multiscale systems corrupted by white noise or clutter. Performance indices such as probability of detection, probability of false alarms, and delocalization errors are computed. The results indicate that (i) the multiscale approach is better than the single-scale approach and (ii) the degradation in performance is greater with clutter than with white noise.