Automation of finite element aided design of induction motors using multi-slice 2D models

Purpose – To develop a practical design tool employing a general purpose electromagnetic finite element (FE) software package to perform automated simulation and performance analysis of induction motors in a design and optimisation process. Design/methodology/approach – Recent publications identified a suitable approach in applying 2D finite-element analysis to 3D problems. This, together with other similar work carried out on brushless DC motors, set out a framework for program development. Performance of the program was validated against practical test data. Findings – Finite-element analysis-based design tools can be realistically employed within a design office environment and are capable of providing solutions within acceptable time scales. Such tools no longer require user expertise in the underlying FE modelling method. The multiple slice technique was employed to model skew in three-phase induction motors and it was established that a four-slice model provides a good balance between accuracy and speed of computation. Research limitations/implications – Program development was based on one commercial FE software package and comparison with practical test data was not exhaustive. However, the approach outlined confirms the practical application. Future work could consider alternative approaches to optimisation. Practical implications – Computing hardware and commercially available 2D FE software have developed sufficiently to enable multi-slice techniques and optimisation to be employed in the analysis and design of machines. Originality/value – This paper provides a practical illustration of how commercial electromagnetic software can be employed as a design tool, demonstrating to industry that such tools no longer need to be bespoke and can realistically be used within a design office

Technology in Practice (Section 2.31 of the Comprehensive Clinical Psychology: Vol. 2. Professional Issues)

The contemporary practice of psychology requires a prudent balance of traditional and emerging communication methods. Interpersonal interactions in the context of human relationship (e.g., speech, emotional expressions, and nonverbal gestures) have been a vital part of emotional healing throughout many centuries, and research findings in the 1990s underscore the importance of relational factors in effective psychological interventions (Whiston & Sexton, 1993). In addition to the time honored interpersonal communication methods of professional psychology, rapid technological advances have propelled psychologists into another sphere of communication. Today\u27s professional psychologist is increasingly expected to attain mastery in both of these communication methods-the very old and the very new

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov 18-21, 201

Feasibility Study of RFID Technology for Construction Load Tracking

INE/AUTC 10.0

NASA Automated Rendezvous and Capture Review. Executive summary

In support of the Cargo Transfer Vehicle (CTV) Definition Studies in FY-92, the Advanced Program Development division of the Office of Space Flight at NASA Headquarters conducted an evaluation and review of the United States capabilities and state-of-the-art in Automated Rendezvous and Capture (AR&C). This review was held in Williamsburg, Virginia on 19-21 Nov. 1991 and included over 120 attendees from U.S. government organizations, industries, and universities. One hundred abstracts were submitted to the organizing committee for consideration. Forty-two were selected for presentation. The review was structured to include five technical sessions. Forty-two papers addressed topics in the five categories below: (1) hardware systems and components; (2) software systems; (3) integrated systems; (4) operations; and (5) supporting infrastructure

Automated processing of series of micro-CT scans

For some applications of high-resolution X-ray Tomography (micro-CT) scanning, a large set of similar samples is to be analyzed in order to obtain statistically significant results. The complete process, including the micro-CT scan itself, the reconstruction and the analysis is almost identical for every sample. However, in a typical workflow every step is manually performed for every individual sample. This could be optimised by automation of this process, which results in less human intervention and thus a smaller cost and a lower risk to human error. We developed a reliable method to semi-automatically scan several stacked samples and automatically reconstruct the resulting series of data sets. The reconstruction step includes the manual reconstruction of one data set in order to optimize the reconstruction parameters, which can then be used for the rest of the batch. In future work, the automatic handling of the next step in the micro-CT workflow, 3D analysis, will also be improved