An introduction of the condition class space with continuous value discretization and rough set theory

The granularity of an information system has an incumbent effect on the efficacy of the analysis from many machine learning algorithms. An information system contains a universe of objects characterized and categorized by condition and decision attributes. To manage the concomitant granularity, a level of continuous value discretization (CVD) is often undertaken. In the case of the rough set theory (RST) methodology for object classification, the granularity contributes to the grouping of objects into condition classes with the same condition attribute values. This article exposits the effect of a level of CVD on the subsequent condition classes constructed, with the introduction of the condition class space—the domain within which the condition classes exist. This domain elucidates the association of the condition classes to the related decision outcomes—reflecting the inexactness incumbent when a level of CVD is undertaken. A series of measures is defined that quantify this association. Throughout this study and without loss of generality, the findings are made through the RST methodology. This further offers a novel exposition of the relationship between all the condition attributes and the RST‐related reducts (subsets of condition attributes). © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 173–191, 2006.     

Mining predecessor–successor rules from DAG data

Data mining extracts implicit, previously unknown, and potentially useful information from databases. Many approaches have been proposed to extract information, and one of the most important ones is finding association rules. Although a large amount of research has been devoted to this subject, none of it finds association rules from directed acyclic graph (DAG) data. Without such a mining method, the hidden knowledge, if any, cannot be discovered from the databases storing DAG data such as family genealogy profiles, product structures, XML documents, task precedence relations, and course structures. In this article, we define a new kind of association rule in DAG databases called the predecessor–successor rule, where a node x is a predecessor of another node y if we can find a path in DAG where x appears before y. The predecessor–successor rules enable us to observe how the characteristics of the predecessors influence the successors. An approach containing four stages is proposed to discover the predecessor–successor rules. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 621–637, 2006.     

Fault detection for control of wastewater treatment plants.

Interest in real-time model-based control is increasing as more and more facilities are being asked to meet stricter effluent requirements while at the same time minimizing costs. It has been identified that biological process models and automated process control technologies are being used at wastewater treatments plants throughout the world and that great potential for optimising biotreatment may exist with the integration of these two technology areas. According to our experience, wastewater treatment plants are indeed looking for ways to successfully integrate their modelling knowledge into their process control structure; however, there are practical aspects of this integration that must be addressed if the benefits of this integration are to be realised. This paper discusses the practical aspects of monitoring, filtering and analysing real sensor data with an aim to produce a reliable real-time data stream that might be used within a model-based control structure. Several real case study examples are briefly discussed in this paper.     

Comparison of total and faecal coliforms as faecal indicator in eutrophicated surface water.

The aim of this study was to evaluate the use of total coliforms (TC) and faecal coliforms (FC) using a membrane filtration method for precise monitoring of faecal pollution in Korean surface water. The samples were collected in Korea from both main rivers and their tributaries. Presumptive TC * FC were enumerated. The ratios of presumptive FC to TC were not constant, but varied widely, and TC were difficult to enumerate because of overgrowth by background colonies. For FC this was not the case. Seven hundred and three purified strains of presumptive TC * FC and their background colonies were biotyped using API 20E. Among 272 presumptive TC, non-faecal related species, Aeromonas hydrophila dominated (34.6%) and E. coli accounted for only 5.1%. In contrast, E. coli made up 89% of the 209 presumptive FC. Furthermore, of 164 background colonies on Endo Agar LES, 54.9% was A. hydrophila, while background colonies on m-FC Agar were few (58 strains), and despite their atypical colony appearance, most of them were biotyped as enteric bacteria. These results reveal that the detection of FC rather than TC using m-FC Agar is more appropriate for faecal pollution monitoring in eutrophicated surface water located in a temperate region.     

Optical-transmission characteristics of optical-fiber cables and installed optical-fiber cable networks for WDM systems

Many cables containing 1.3-/spl mu/m zero-dispersion single-mode (SM) optical fibers are installed in trunk and access networks. Recently, there have been a number of studies on wavelength-division-multiplexing (WDM) systems designed to increase transmission capacity and flexibility. If we can construct WDM systems using SM optical-fiber cable networks designed to transmit using wavelengths in the 1.3-/spl mu/m window (O-band), this will prove very effective in reducing construction costs. It is therefore important to examine the wavelength dependence of the transmission characteristics of SM optical-fiber cables and networks that have already been installed and in which several optical fibers are joined. In this paper, we describe the measured optical characteristics of SM optical-fiber cables and installed optical-fiber cable networks at various wavelengths. The optical characteristics were stable in the 1.46 to 1.625-/spl mu/m wavelength range and we confirmed that the installed SM optical-fiber cable networks could be used for WDM system applications.     

Kommunikationsoberfläche Membran‐ Vorbild für moderne Nanoanalytik

A central event in the life of a cellular system is the interaction between the exterior and the interior compartments. Biochemical signals arrive at the cellular surface, bind to their membrane bound receptor followed by a conformational change triggering the release of an internal chemical or electrical signal.This basic principle is followed by all our perceptive abilities like sense of smell or taste, but also by different signal transduction pathways involved in nerve conductivity, vision, sense of touch or hearing. To follow and mimic this principle of parallel registration is one of the aims of modern nanobiotechnology. If we are able to specifically biofunctionalize small arrays of a solid surface, which could be an electrode or a semiconductor, this approach will enable us to build up devices called “biochips” or “biosensors” that allow the determination of bioactive molecules with high specificity at lowest concentrations. Potential pharmacological active substrates might be screened as well as new receptors may be determined. Applications in genomics as well as proteomics are realistic. The major prerequisite for such a broad spectrum of applications is the fabrication of receptive surfaces. Biomolecules have to be surface‐adsorbed in a highly reproducible, oriented and well organised fashion, a task which in biology is taken by the cellular membranes as external or internal receptive surfaces. The physical principles like hydrogen bonds, electrostatic or hydrophobic interactions that lead to such an organized surface are well known. To synthesize molecular building blocks and to position them onto an otherwise unspecific surface is one of the challenges of nanobiotechnology combining biological knowledge and chemical skills with biophysical techniques that allow to handle or analyze even single molecules.     

Microscale Patterning of Mesoscopic PZN-PT Single Crystal Films by Crystal ion Slicing and Laser-Induced Etching

Advances in the fabrication of solid-solution single crystal relaxor ferroelectrics have made it possible to produce highly efficient piezoelectric crystals, and have attracted renewed interest in the use of these crystals for a new generation of piezoelectric transducers, actuators and sensors. Of particular interest is their incorporation into micro-electromechanical systems (MEMS). In this paper we report on the laser-induced wet chemical etching of lead zinc niobate-lead titanate (PZN-PT) in hydrochloric acid (HCl). Argon-ion laser radiation at power levels up to 4 W is focused to a spot diameter of about 15μm and results in the chemical etching of grooves at patterning speeds up to 5μm/sec. Crystal ion slicing, an ion-implant-based film separation technique, is used in combination with laser etching to form 5 to 10μm-thick patterned and freestanding films for incorporation into micro-electromechanical devices.     

Second harmonic generation from a ferroelectric film

We derive an expression for transmittivity (T_SHG) of second harmonic generation (SHG) signals from a ferroelectric (FE) film. Intensities of up and down fields in the medium are investigated in relation to T_SHG. The derivations are made based on undepletion of input fields and nonlinear wave equation derived from the Maxwell equations. We present two cases: film without mirrors and with partial mirrors. Expressions for the newly derived nonlinear susceptibility coefficients of SHG for real crystal symmetry [J. Opt. Soc. Am. B 19 (2002) 2007] are used to get more realistic results. Variations in T_SHG with respect to film thickness are illustrated.     

Training and optimization of an artificial neural network controlling a hybrid power filter

A hybrid power compensator (HPC) consisting of a static VAr compensator and a dynamic compensator needs to be optimally controlled during the compensation of nonlinear loads. The HPC must be controlled to meet minimum requirements in terms of power factor and harmonic distortion, while at the same time minimizing its total cost. An artificial neural network (ANN) is used to control the HPC amidst a very dynamic power system environment. The performance of a reference ANN is evaluated while controlling an HPC connected to a typical nonlinear industrial load. The training and performance of the ANN is then optimized in terms of training set size, training set packing and ANN topology and the performance compared to the reference ANN. This paper highlights the importance of optimising the mentioned ANN parameters to achieve optimum ANN training and modeling accuracy. The results obtained reveals that the application of an ANN in controlling an HPC is feasible given that the ANN parameters are chosen appropriately.