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.     

Platinum Binuclear Complexes as Phosphorescent Dopants for Monochromatic and White Organic Light‐Emitting Diodes

Efficient blue‐, green‐, and red‐light‐emitting organic diodes are fabricated using binuclear platinum complexes as phosphorescent dopants. The series of complexes used here have pyrazolate bridging ligands and the general formula C^∧NPt(μ‐pz)₂PtC^∧N (where C^∧N = 2‐(4′,6′‐difluorophenyl)pyridinato‐N,C^2′, pz = pyrazole ( 1 ), 3‐methyl‐5‐tert‐butylpyrazole ( 2 ), and 3,5‐bis(tert‐butyl)pyrazole ( 3 )). The Pt–Pt distance in the complexes, which decreases in the order 1 > 2 > 3 , solely determines the electroluminescence color of the organic light‐emitting diodes (OLEDs). Blue OLEDs fabricated using 8 % 1 doped into a 3,5‐bis(N‐carbazolyl)benzene (mCP) host have a quantum efficiency of 4.3 % at 120 Cd m^–2, a brightness of 3900 Cd m^–2 at 12 V, and Commission Internationale de L'Eclairage (CIE) coordinates of (0.11, 0.24). Green and red OLEDs fabricated with 2 and 3 , respectively, also give high quantum efficiencies (～ 6.7 %), with CIE coordinates of (0.31, 0.63) and (0.59, 0.46), respectively. The current‐density–voltage characteristics of devices made using dopants 2 and 3 indicate that hole trapping is enhanced by short Pt–Pt distances (< 3.1 Å). Blue electrophosphorescence is achieved by taking advantage of the binuclear molecular geometry in order to suppress dopant intermolecular interactions. No evidence of low‐energy emission from aggregate states is observed in OLEDs made with 50 % 1 doped into mCP. OLEDs made using 100 % 1 as an emissive layer display red luminescence, which is believed to originate from distorted complexes with compressed Pt–Pt separations located in defect sites within the neat film. White OLEDs are fabricated using 1 and 3 in three different device architectures, either with one or two dopants in dual emissive layers or both dopants in a single emissive layer. All the white OLEDs have high quantum efficiency (～ 5 %) and brightness (～ 600 Cd m^–2 at 10 V).     

Intelligent process analytics for CRM

Many organisations use decision models in their processes such as tables or trees to provide decision support to their operational divisions. For example, in fault management, customer contact centre operators usually use a decision model in the form of prescribed interviews. Based on the answers given by customers, the operator navigates through the decision model to reach an assessment of the problem. In order to achieve customer satisfaction and operational excellence, it is very important to constantly monitor the performance of a decision model not only on an overall level, but also on the level of individual decisions. In this paper we present a configurable business process analytics tool, known as the intelligent Universal Service Management System, that constantly monitors decision data and is capable of optimising the decisions based on high-level business objectives. We explain the various features of the software and show how it can be used to optimise decision processes. We also show how we can easily provide a customised version to monitor the performance of provision processes.     

Learning in multiple model adaptive control switch

Including a learning mechanism in SMMAC (switched, multiple-model adaptive control) avoids the need for a priori knowledge of the model set of the plant to control and leads to a significant performance improvement with respect to the sole inclusion of an adaptive control channel in combination with switched fixed local controllers.     

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.     

Profile-Driven Component Placement for Cluster-Based Online Services

The growth of the Internet and of various intranets has spawned a wealth of online services, most of which are implemented on local-area clusters using remote invocation (for example, remote procedure call/remote method invocation) among manually placed application components. Component placement can be a significant challenge for large-scale services, particularly when application resource needs are workload dependent. Automatic component placement has the potential to maximize overall system throughput. The key idea is to construct (offline) a mapping between input workload and individual-component resource consumption. Such mappings, called component profiles, then support high-performance placement. Preliminary results on an online auction benchmark based on J2EE (Java 2 Platform, Enterprise Edition) suggest that profile-driven tools can identify placements that achieve near-optimal overall throughput.     

Measurement and Identification of the Parameters of SPICE-Models of a Bipolar Transistor

A method of measuring and identifying the static parameters of a bipolar transistor is considered. The characteristic of the transistor, from which the parameters of the model are determined, is chosen depending on what group the calculated parameters belong to. The characteristics are measured in such a way that the equations of the model describing them can be reduced to the simplest form.     

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.