Genetic algorithms to solve the power system restoration planning problem

This study reports the use of a Genetic Algorithm (GA) to solve the Power System Restoration Planning Problem (PSRP). The solution to the PSRP is described by a series of operations or a plan to be used by the Power System operator immediately on the occurrence of a blackout in the electrical power supply. Our GA uses new initialization and crossover operators based on the electrical power network, which are able to generate and maintain the plans feasible along GA runs. This releases the Power Flow program, which represents the most computer demanding component, from computing the fitness function of unfeasible individuals. The method was designed for large transmission systems and results for three different electrical power networks are shown: IEEE 14-Bus, IEEE 30-Bus, and a large realistic system.     

A flexible,object-centric approach for business process modelling

Mainstream business process modelling techniques often promote a design paradigm wherein the activities that may be performed within a case, together with their usual execution order, form the backbone on top of which other aspects are anchored. This Fordist paradigm, while effective in standardised and production-oriented domains, breaks when confronted with processes in which case-by-case variations and exceptions are the norm. We contend that the effective design of flexible processes calls for a substantially different modelling paradigm. Motivated by requirements from the human services domain, we explore the hypothesis that a framework consisting of a small set of coordination concepts, combined with established object-oriented modelling principles, provides a suitable foundation for designing highly flexible processes. Several human service delivery processes have been designed using this framework, and the resulting models have been used to realise a system to support these processes in a pilot environment.     

Effects of the number of hidden nodes used in a structured-based neural network on the reliability of image classification

A structured-based neural network (NN) with backpropagation through structure (BPTS) algorithm is conducted for image classification in organizing a large image database, which is a challenging problem under investigation. Many factors can affect the results of image classification. One of the most important factors is the architecture of a NN, which consists of input layer, hidden layer and output layer. In this study, only the numbers of nodes in hidden layer (hidden nodes) of a NN are considered. Other factors are kept unchanged. Two groups of experiments including 2,940 images in each group are used for the analysis. The assessment of the effects for the first group is carried out with features described by image intensities, and, the second group uses features described by wavelet coefficients. Experimental results demonstrate that the effects of the numbers of hidden nodes on the reliability of classification are significant and non-linear. When the number of hidden nodes is 17, the classification rate on training set is up to 95%, and arrives at 90% on the testing set. The results indicate that 17 is an appropriate choice for the number of hidden nodes for the image classification when a structured-based NN with BPTS algorithm is applied.     

A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures

We introduce a new volumetric registration technique that effectively combines active surfaces with the finite element method. The method simultaneously aligns multi-label automatic structural segmentation results, which can be obtained by the application of existing segmentation software, to produce an anatomically accurate 3D registration. This registration is obtained by the minimization of a single energy functional. Just like registering raw images, obtaining a 3D registration this way still requires solving a fundamentally ill-posed problem. We explain through academic examples as well as an MRI dataset with manual anatomical labels, which are hidden from the registration method, how the quality of a registration method can be measured and the advantages our approach offers.     

AutoTutor: Incorporating Back-Channel Feedback and Other Human-Like Conversational Behaviors into an Intelligent Tutoring System

This paper describes our recent attempts to incorporate human-like conversational behaviors into the dialog moves delivered by an animated pedagogical agent that simulates human tutors. We first present a brief overview of the modules comprising AutoTutor, an intelligent tutoring system. The second section describes a set of conversational behaviors that are being incorporated into AutoTutor. The behaviors of interest involve variations in intonation, head movements, arm and hand movements, facial expressions, eye blinking, gaze direction, and back-channel feedback. The final section presents a recent empirical study concerned with back-channel feedback events during human-to-human tutoring sessions. The back-channel feedback events emitted by tutors are mostly positive (63%), mostly verbal (77%), and immediately follow speech-act boundaries or noun-phrase boundaries (83%). Tutors also deliver back-channelevents at a very high rate when students are emitting dialog, about 13 events per minute. Conversely, 88% of students' back-channel feedback events are head nods, and they occur at unbounded locations (63%).     

Sensory-motor enhancement in a virtual therapeutic environment

The sensory-motor skills of persons with neuromuscular disabilities have been shown to be enhanced by intensive and repetitive therapeutic interventions. This paper describes a form of low immersion virtual reality and a prototype, open source system that allow a user with significant physical disability to actively interact with computer-generated objects whose behaviors promote a game-like interaction. Unlike fully immersive and haptic virtual reality, this approach frees the user from head-mounted displays and gloves. It extracts the user’s real-time silhouette from the output of a remote video camera and uses that two-dimensional outline to interact with graphical objects on screen. In contrast to video games that have been modified with specialized interfaces, this virtual interaction system promotes the repetitive use of goal directed movements of the arms and body, which are essential to promote cortical reorganization, as well as discourage unwanted changes in muscle tissue that result in contracture. A prototype system demonstrates the potential of low immersion technology to motivate users and encourage participation in therapy. It also offers the potential of accommodating the sensory-motor skills of individuals with very significant impairment. The behaviors of the computer-generated graphics can be altered to allow use by those with very limited range of motion and/or motor control. These behaviors can be adjusted to provide a continuing challenge as the user’s skills improve. This prototype is described in terms of functional capabilities that include a silhouette extraction from a video image, and generation of graphical objects that interact with the silhouette. The work is extended with a discussion of a more sophisticated region of interest detection algorithm that can select specific parts of the body.     

Influence of age and proximity warning devices on collision avoidance in simulated driving

OBJECTIVE: We conducted a set of experiments to examine the utility of several different uni- and multimodal collision avoidance systems (CASs) on driving performance of young and older adult drivers in a high-fidelity simulator. BACKGROUND: Although previous research has examined the efficacy of different CASs on collision avoidance, there has been a dearth of studies that have examined such devices in different driving situations with different populations of drivers. METHOD: Several different CAS warnings were examined in varying traffic and collision configurations both without (Experiment 1a) and with (Experiment 2) a distracting in-vehicle task. RESULTS: Overall, collision avoidance performance for both potential forward and side object collisions was best for an auditory/visual CAS, which alerted drivers using both modalities. Interestingly, older drivers (60-82 years of age) benefited as much as younger drivers from the CAS, and sometimes they benefited more. CONCLUSION: These data suggest that CASs can be beneficial across a number of different driving scenarios, types of collisions, and driver populations. APPLICATION: These results have important implications for the design and implementation of CASs for different driver populations and driving conditions.     

Frequency multiplexed superconducting quantum interference device readout of large bolometer arrays for cosmic microwave background measurements

A technological milestone for experiments employing transition edge sensor bolometers operating at sub-Kelvin temperature is the deployment of detector arrays with 100s-1000s of bolometers. One key technology for such arrays is readout multiplexing: the ability to read out many sensors simultaneously on the same set of wires. This paper describes a frequency-domain multiplexed readout system which has been developed for and deployed on the APEX-SZ and South Pole Telescope millimeter wavelength receivers. In this system, the detector array is divided into modules of seven detectors, and each bolometer within the module is biased with a unique ～MHz sinusoidal carrier such that the individual bolometer signals are well separated in frequency space. The currents from all bolometers in a module are summed together and pre-amplified with superconducting quantum interference devices operating at 4 K. Room temperature electronics demodulate the carriers to recover the bolometer signals, which are digitized separately and stored to disk. This readout system contributes little noise relative to the detectors themselves, is remarkably insensitive to unwanted microphonic excitations, and provides a technology pathway to multiplexing larger numbers of sensors.     

Examining cognitive function across the lifespan using a mobile application

Many studies conducted in a laboratory or university setting are limited by funding, personnel, space, and time constraints. In the present study, we introduce a method of data collection using a mobile application that circumvents these typical experiment administration issues. Using the application, we examined cross-sectional age differences in cognitive function. We obtained data from more than 15,000 participants and replicated specific patterns of age-related differences in cognition. Using a subset of these participants, we also examined the processing speed account of age-related cognitive differences, and the association of exercise and leisure activity with cognitive function across the lifespan. We discuss the relative advantages and disadvantages of data collection with a mobile application, and provide recommendations for the use of this method in research.     

Probabilistic Belief Contraction

Probabilistic belief contraction has been a much neglected topic in the field of probabilistic reasoning. This is due to the difficulty in establishing a reasonable reversal of the effect of Bayesian conditionalization on a probabilistic distribution. We show that indifferent contraction, a solution proposed by Ramer to this problem through a judicious use of the principle of maximum entropy, is a probabilistic version of a full meet contraction. We then propose variations of indifferent contraction, using both the Shannon entropy measure as well as the Hartley entropy measure, with an aim to avoid excessive loss of beliefs that full meet contraction entails.