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.     

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.     

Finite element analysis of electromagnetic scattering from a cavity

A finite element method (FEM) is implemented to compute the radar cross section of a two-dimensional (2D) cavity embedded in an infinite ground plane. The method is based on the variational formulation which uses the Fourier transform to couple the fields outside the cavity and those inside the cavity; hence, the scattering problem can be reduced to a bounded domain. The convergence of the discrete finite element problem is analyzed. Numerical results are presented and compared with those obtained by the standard finite element-Green function method and by the 2D integral equation method.     

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.     

Chernoff bounds on pairwise error probabilities of space-time codes

We derive Chernoff bounds on pairwise error probabilities of coherent and noncoherent space-time signaling schemes. First, general Chernoff bound expressions are derived for a correlated Ricean fading channel and correlated additive Gaussian noise. Then, we specialize the obtained results to the cases of space-time-separable noise, white noise, and uncorrelated fading. We derive approximate Chernoff bounds for high and low signal-to-noise ratios (SNRs) and propose optimal signaling schemes. We also compute the optimal number of transmitter antennas for noncoherent signaling with unitary mutually orthogonal space-time codes.     

Self-reflective segmentation of human bodily motions using recurrent neural networks

For realizing a naturalistic collaboration between the human and the robot, we have to establish the intention sharing from the series of motion data that are observed and exchanged between the human and the machine. In a word, this is a problem to detect "meanings" out of the digitized data stream. In this paper, we propose a novel approach based on semiosis, and present a method of interpreting bodily motions using recurrent neural networks called Elman networks. We made some experiments using the raw data acquired while a human performs a simple task of fetching objects by stretching and folding his/her arm, and demonstrate that the network can learn invariant features of the generalized motion concepts, classify the motion by referring to self-organized memory structure, and understand a task structure of the observed human bodily motion. These capabilities are essential for machine intelligence to establishing the human-robot shared autonomy, a new style of human-machine collaboration proposed in the area of robotics.     

Control of polishing of diamond films using microactuation and an atmospheric pressure plasma

In this work, the diamond deposited by hot filament chemical vapor deposition (CVD) is polished using an atmospheric pressure plasma. In order to position the film relative to the plasma, a microactuator system is designed using a stack of domed piezoelectric actuators. A dynamic model based on the physical system is developed and the model parameters are measured experimentally. A system based on laser triangulation is used to measure the position of the diamond film relative to the plasma. Control techniques are used to reduce the oscillations during actuation and to eliminate the steady state positioning error. With the application of feedback control, the overshoot is reduced to 2% and the settling time is reduced to 0.3 s. A preliminary set of experiments is performed to relate process parameters to the final surface roughness of the diamond film. The parameters studied include the film's time of exposure to the plasma, the height of the film relative to the plasma, and the distance from the film to the center of the plasma. It is found that the optimum exposure time is 15 min and the reduction of surface roughness is greatest when the distance between the film and the plasma is at a minimum. The best results are obtained when the top of the film is even in elevation with the tip of the top electrode. The diamond film is translated laterally along the plasma. When feedback control is not used, there is no change in the surface roughness. With feedback control implemented, the surface roughness of the diamond film is reduced by 33%.     

Minimax estimation of deterministic parameters in linear models with a random model matrix

We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a random model matrix, with known second-order statistics. We first seek the linear estimator that minimizes the worst-case mean-squared error (MSE) across all parameter vectors whose (possibly weighted) norm is bounded above. We show that the minimax MSE estimator can be found by solving a semidefinite programming problem and develop necessary and sufficient optimality conditions on the minimax MSE estimator. Using these conditions, we derive closed-form expressions for the minimax MSE estimator in some special cases. We then demonstrate, through examples, that the minimax MSE estimator can improve the performance over both a Baysian approach and a least-squares method. We then consider the case in which the norm of the parameter vector is also bounded below. Since the minimax MSE approach cannot account for a nonzero lower bound, we consider, in this case, a minimax regret method in which we seek the estimator that minimizes the worst-case difference between the MSE attainable using a linear estimator that does not know the parameter vector, and the optimal MSE attained using a linear estimator that knows the parameter vector. For analytical tractability, we restrict our attention to the scalar case and develop a closed-form expression for the minimax regret estimator.     

X-ray or UV adjustment of MOS threshold voltage: Analytical and numerical modeling

A model is formulated that describes how radiation-induced charge accumulates in the gate oxide of a MOS structure and how it decays through tunneling and thermal emission. The model is used in a numerical analysis of the x-ray or UV adjustment of threshold voltage in MOS-circuit manufacture. The limits of this process technique are evaluated.