Knowledge-based functional design of industrial robots

This paper presents an expert functional design model and software modeling environment for designing the architecture of industrial robots. The modeling environment comprises an integrated knowledge base, an inference engine, a working memory, and an explanation unit and is implemented in CLIPS. The functional design model is based on the authors’ behaviour-driven, function-environment-structure (B-FES) formalism, which has been tailored to meet the special requirements of industrial robot design. A universal robot template has been created and a library of typical components of a robot has been compiled. Nine customized templates were generated from the universal template. Web links to the websites of manufacturers/suppliers provide easy access to data on robot components. The architectural design solutions are assessed by a set of user-defined performance criteria, such as precision, flexibility and short cycle time. Application of the approach is demonstrated through a case study of the functional design of a printed circuit board assembly robot. The authors argue that this approach is new for configuring robots and can significantly reduce the time, effort and number of errors made.     

Potential and limits of functional modelling in the CAD process

This paper gives a survey of the beginning and newest computer-aided functional modelling by German researchers. The basic principles of functional modelling are established and a representative function model is illustrated by the example of an emergency power unit. Some important methods for computer-aided modelling according to the function model are explained in detail. Following this the limits of functional modelling are outlined by describing the working arrangements of working principles using a flowchart representation of the function structure.     

Using basis expansions for estimating functional PLS regression: Applications with chemometric data

There are many chemometric applications, such as spectroscopy, where the objective is to explain a scalar response from a functional variable (the spectrum) whose observations are functions of wavelengths rather than vectors. In this paper, PLS regression is considered for estimating the linear model when the predictor is a functional random variable. Due to the infinite dimension of the space to which the predictor observations belong, they are usually approximated by curves/functions within a finite dimensional space spanned by a basis of functions. We show that PLS regression with a functional predictor is equivalent to finite multivariate PLS regression using expansion basis coefficients as the predictor, in the sense that, at each step of the PLS iteration, the same prediction is obtained. In addition, from the linear model estimated using the basis coefficients, we derive the expression of the PLS estimate of the regression coefficient function from the model with a functional predictor. The results provided by this functional PLS approach are compared with those given by functional PCR and discrete PLS and PCR using different sets of simulated and spectrometric data.     

Optimal measurement of signal over noise ratio with constrained filter transfer functions

The problem of optimal measurement of a signal in presence of noise is treated in detail by Baldinger and Franzen (Adv. Electron. Electron Phys. 8 (1956) 225), Radeka and Karlovac (Nucl. Instr. and Meth. 52 (1967) 86) and Gatti and Manfredi (La Rivista Nuovo Cimento 9(1) (1986) 1), and the filter transfer function optimizing the signal over noise ratio is well known. These calculations deals with unconstrained optimization, that is the filter transfer function may assume any value. In this paper functional analysis techniques are applied to optimize the filter transfer function in presence of linear constraints.     

Nanostructuring strategies in functional fine-particle synthesis towards resource and energy saving applications

As a low cost and environmentally-benign powder technology, appropriate nanostructuring of fine particles (for example to produce porous, core–shell, hollow, or multicomponent composite structures) has attracted significant attention. Nanostructured fine particles have superior properties, such as high specific surface area, low density, hybrid functionalities, and resistance to weathering. Research into ways of nanostructuring particles for advanced functional materials has progressed rapidly, because these particles offer two great advantages over bulk materials or single component spherical particles: high performance and reduction in the use of expensive raw materials. In particular, the current interest in nanostructured particles focuses on tailoring their inner structure and morphology towards particular practical applications. This article reviews recent research progress on fabrication strategies for nanostructured fine particles. The examples chosen are phosphors, photocatalysts, electrocatalysts, adsorbents and magnetic particles. These fabrication techniques pave the way towards saving both resources and energy in materials production.     

Application of functional ANOVA to the study of thermal stability of micro-nano silica epoxy composites

The main purpose of this work is to use a new technique that combines functional data analysis and design of experiments, functional ANOVA for a one way treatment, to measure the influence of adding fumed silica on the thermal degradation of an epoxy resin. To achieve this, a design of experiments with a treatment factor (the amount of fumed silica) at three different levels (0, 10 and 20 wt.%) is performed. The data are obtained through the use of Thermogravimetric Analysis (TG), resulting in five degradation curves per level. The functional ANOVA uses all the information of each curve or functional data. The results obtained using this methodology with the TG rescaled data and their derivatives (DTG) indicate that the amount of fumed silica significantly affects the thermal stability of the compound. These facts may be indicative of the interaction between the organic phase and the inorganic particles. In addition, pairwise comparisons using the functional ANOVA method and a bootstrap distance based test are carried out to discern which factor levels provide different ways of degradation.     

Patterning surfaces with functional polymers

The ability to pattern functional polymers at different length scales is important for research fields including cell biology, tissue engineering and medicinal science and the development of optics and electronics. The interest and capabilities of polymer patterning have originated from the abundance of functionalities of polymers and a wide range of applications of the patterns. This paper reviews recent advances in top-down and bottom-up patterning of polymers using photolithography, printing techniques, self-assembly of block copolymers and instability-induced patterning. Finally, challenges and future directions are discussed from the point of view of both applicability and strategies for the surface patterning of polymers.     

Detection of outliers in gas emissions from urban areas using functional data analysis

In this work a solution for the problem of the detection of outliers in gas emissions in urban areas that uses functional data analysis is described. Different methodologies for outlier identification have been applied in air pollution studies, with gas emissions considered as vectors whose components are gas concentration values for each observation made. In our methodology we consider gas emissions over time as curves, with outliers obtained by a comparison of curves instead of vectors. The methodology, which is based on the concept of functional depth, was applied to the detection of outliers in gas omissions in the city of Oviedo and results were compared with those obtained using a conventional method based on a comparison of vectors. Finally, the advantages of the functional method are reported.     

An approach to functional synthesis of solutions in mechanical conceptual design. Part III: Spatial configuration

These three papers describe an approach to the synthesis of solutions to a class of mechanical design problems; these involve transmission and transformation of mechanical forces and motion, and can be described by a set of inputs and outputs. The approach involves(1) identifying a set of primary functional elements and rules of combining them, and(2) developing appropriate representations and reasoning procedures for synthesizing solution concepts using these elements and their combination rules; these synthesis procedures can produce an exhaustive set of solution concepts, in terms of their topological as well as spatial configurations, to a given design problem.This paper (Part III) describes a constraint propagation procedure which, using a knowledge base of spatial information about a set of primary functional elements, can produce possible spatial configurations of solution concepts generated in Part II.     

A Methodology to Test Instrument Software: An Automotive Diagnostic System Application

This paper aims to describe a new methodology specifically designed for testing measurement and diagnostic software. A black-box procedure allows the user to verify whether the functional requirements of a software module under test are fulfilled. The robust experimental design techniques and statistical theories implemented to generate the software input test sets are described in detail. The reliability of the testing methodology is estimated by applying it to diagnostic software in wide use throughout the automotive industry. The results of validation tests carried out on data from a car engine measurement system are reported and analyzed.      

Block copolymers as templates for functional materials

There are applications and devices which require controlled distribution of material functionality (electrical, optical, catalytic, magnetic) in two or three dimensions. At the nanometer length scale, attempts to meet this challenge have included template-mediated materials chemistry [1] in which track-etched membranes, porous alumina and zeolites serve as the nanoscale reaction vessels for the synthesis of the functional materials. The ability to control both the length scale and the spatial organization of block copolymer morphologies makes these materials particularly attractive candidates for use as templates in the synthesis of functional nanocomposites. Appropriate choices of the repeat units of the block sequences renders them capable of selectivity sequestering preformed inorganic nanoclusters or selectively solubilizing inorganic reagents for in-situ cluster synthesis. Methods exist to produce nanoscale voids which percolate through the structure, leading to processes which coat or backfill the channels with functional materials.     

多孔阳极氧化铝膜在纳米功能材料制备中的应用

纳米材料具有一系列不同于块体材料的新异特性，在许多领域都有着广阔应用前景。阳极氧化铝膜具有独特的多孔结构，可作为制备各种纳米功能材料的模板，因而在纳米功能材料制备中占有重要地位。本文综述了多孔阳极氧化铝模板的结构特征、制作方法及由模板合成法制备的多种纳米功能材料的研究与应用现状，并介绍了模板及纳米功能材料的常用表征手段。     

Microstructure and functional properties of rock materials

The properties of stone materials maybe divided into intrinsic properties such as chemical and mineralogical composition, grain size and porosity, and into functional properties related to a technical application. The latter describe how the stone responds to conditions in the environment such as mechanical load, freeze–thaw cycles and exposure to moisture, and are controlled by the intrinsic properties. This implies that intrinsic properties can be used in order to assess the functional properties of the stone if it is possible to identify and quantify the related critical parameters. While this is an accepted approach for metals and ceramics, it has not been applied to the same extent to rock materials. The aim of this paper is to demonstrate that this approach also has potential in petrographic analysis.     

Prevalence and correlates of functional dependence among maintenance dialysis patients

Functional dependence is an important determinant of longevity and quality of life. The purpose of the current study was to determine the prevalence and correlates of functional dependence among patients with end‐stage renal disease (ESRD) receiving maintenance dialysis. We enrolled 148 participants with ESRD from five clinics. Functional status, as measured by basic and instrumental activities of daily living (ADL, IADL), was ascertained by validated questionnaires. Functional dependence was defined as needing assistance in at least one of seven IADLs or at least one of four ADLs. Demographic characteristics, chronic health conditions, anthropometric measurements, and laboratories were assessed by a combination of self‐report and chart review. Cognitive function was assessed with a neurocognitive battery, and depressive symptoms were assessed by questionnaire. Mean age of the sample was 56.2 ± 14.6 years. Eighty‐seven participants (58.8%) demonstrated dependence in ADLs or IADLs, 70 (47.2%) exhibited IADL dependence alone, and 17 (11.5%) exhibited combined IADL and ADL dependence. In a multivariable‐adjusted model, stroke, cognitive impairment, and higher systolic blood pressure were independent correlates of functional dependence. We found no significant association between demographic characteristics, chronic health conditions, depressive symptoms or laboratory measurements, and functional dependence. Impairment in executive function was more strongly associated with functional dependence than memory impairment. Functional dependence is common among ESRD patients and independently associated with stroke, systolic blood pressure, and executive function impairment.     

Human–machine function allocation: a functional modelling approach

Function allocation between humans and systems is an important factor regarding safety, reliability and efficiency of industrial processes. One should allocate functions in order to maximise the operator's situation understanding and ability to handle unexpected events. Functional models can be used to study function allocation in a process control environment, because they explicitly describe functions and tasks of both the plant and the operator. The Halden Reactor Project is currently engaged in such a project called function allocation methods (FAME), aimed specifically at the work in a nuclear power plant control room. This paper describes the main features of the approach, and discusses how functional modelling can be used to address the issue of how much information is necessary for the operator, and thereby give a basis for how functions should be allocated.