55 research outputs found
Interview with Paromita Vohra: Remaking the “Political” in Social Documentary
No full textIn her films, Paromita Vohra is the trickster, the bahurupiya who entertains, unsettles, and ultimately encourages the spectator to think, reflect, and reconsider. Always penetrating in her analysis, Vohra is interested in the ways power is exercised through history and discourse. Her films are aesthetically distinct in the context of the formal histories of social documentary in India, which emerged at a moment of political crisis and subsequently came to be associated with instrumental use in education, advocacy, and public address. For Vohra, the textual stability of documentary address, representational regimes, and cinematic and verbal language are important areas of political interrogation. Each film is a complex construction in which the “real” is only one element in an affective and reflexive architecture of performance, fiction, poetry, and the intuitive. In this interview, Vohra and the author discuss the filmmaker's discomfort with the historical conventions of social documentary and how she reworks documentary's aesthetic terms through the prisms of the personal.</jats:p
Writing special procedures and subroutines on TK Solver to solve for linear/nonlinear electric circuits
No full textThe purpose of the study was to write special subroutines and procedures on the TK Solver, an equationsolving software, to solve for electrical engineering network problems and to apply the TK Solver to some other areas of electrical engineering. A main Model, System. TK , has been created to solve for linear electrical engineering networks. The model is expert in the sense that the user does not have to demonstrate his knowledge of network analysis by actually typing in the network equations. The user interaction with the software takes place on the screen, where the user has to declare the network components, their numerical values, number of nodes, the right-hand column matrix and the frequency for which the response is desired. The admittance matrix y is automatically created by the subroutines coge and cogel. The capability of expressing the result in both tabular and plot form has also been displayed. The supporting features of this model are gain, power and transfer function calculation. Another supporting model performing mesh analysis on resistive circuits has also been created and is called Matrix. TK . The user, in this model, has to feed in the number of loops and number of resistors along with the resistor values and then has to establish the presence of a resistor in a particular loop by writing 1 or 0 in a pre-generated matrix. Merely pressing F9 gives the values of loop currents. All the associated voltages can also be found out by the same model. These two models can be used by students to do problems, can be used by instructors to correct assignments and can be used in the design projects for synthesis purposes. As the second part of the thesis objective, various models have been created in different fields of electrical engineering to show the applicability of the TK Solver to those fields. Every model shows different capabilities of the TK Solver. In many of these models, special subroutines have been written by the author to accomplish the model objectives
Engineering and instructional technology as a team to build a better learning environment
No full textEnergy Conservation Projects to Benefit the Railroad Industry
No full textThe Energy Conservation Projects to benefit the railroad industry using the Norfolk Southern Company as a model for the railroad industry has five unique tasks which are in areas of importance within the rail industry, and specifically in the area of energy conservation. The NIU Engineering and Technology research team looked at five significant areas in which research and development work can provide unique solutions to the railroad industry in energy the conservation. (1) Alternate Fuels - An examination of various blends of bio-based diesel fuels for the railroad industry, using Norfolk Southern as a model for the industry. The team determined that bio-diesel fuel is a suitable alternative to using straight diesel fuel, however, the cost and availability across the country varies to a great extent. (2) Utilization of fuel cells for locomotive power systems - While the application of the fuel cell has been successfully demonstrated in the passenger car, this is a very advanced topic for the railroad industry. There are many safety and power issues that the research team examined. (3) Thermal and emission reduction for current large scale diesel engines - The current locomotive system generates large amount of heat through engine cooling and heat dissipation when the traction motors are used to decelerate the train. The research team evaluated thermal management systems to efficiently deal with large thermal loads developed by the operating engines. (4) Use of Composite and Exotic Replacement Materials - Research team redesigned various components using new materials, coatings, and processes to provide the needed protection. Through design, analysis, and testing, new parts that can withstand the hostile environments were developed. (5) Tribology Applications - Identification of tribology issues in the Railroad industry which play a significant role in the improvement of energy usage. Research team analyzed and developed solutions which resulted in friction modification to improve energy efficiency
Transporation Energy
No full textThis Transportation Energy Project is comprised of four unique tasks which work within the railroad industry to provide solutions in various areas of energy conservation. These tasks addressed: energy reducing yard related decision issues; alternate fuels; energy education, and energy storage for railroad applications. The NIU Engineering and Technology research team examined these areas and provided current solutions which can be used to both provide important reduction in energy usage and system efficiency in the given industry. This project also sought a mode in which rural and long-distance education could be provided. The information developed in each of the project tasks can be applied to all of the rail companies to assist in developing efficiencies
Intuition, expertise and emotion in the decision making of investment bank traders
No full textThe role of intuition may be especially dominant in organizations embedded in turbulent environments (Khatri & Ng, 2000). Dane and Pratt (2007) argue that intuition will be more likely to function as an effective component of decision making in performance domains that require significant experience and complex domain-relevant schema, a description that fits the world of financial trading. Traders are also frequently engaged in decision making that is characterized by time pressure, high risk, complexity and imperfect information. In a previous study (Fenton-O’Creevy et al., 2011), the second author found that many high performing traders deploy a reflective and critical approach to the use and development of intuition, which they understand as well-founded in prior experience. In this chapter we draw on our prior research to discuss the role of intuition in the work of professional traders. We bring together the results of our research on emotion regulation of investment bank traders (Fenton-O’Creevy et al., 2005, 2011, 2012; Vohra & Fenton-O’Creevy, 2011) with research on expertise and affect-based intuition (Baylor, 2001; Dane & Pratt, 2007; Simon, 1987; Sinclair & Ashkanasy, 2005; Weiss & Cropanzano, 1996) to argue that since more effective emotion regulation is associated with greater experience and more effective use of emotions in decision making (Fenton-O’Creevy et al., 2012) and emotions underpin the use of intuition (Lieberman, 2000; Sinclair & Ashkanasy, 2005), then effective emotion regulation will be essential in the deployment of expert intuition
A Community College/University Educational Program In Technology – Maximizing Participation Through Varied Modes Of Delivery
No full textStrategic Alliance Between Higher Education, Secondary Schools, And Community Business/Industry To Improve Mathematics, Science, Technology, And English Education: A National Science Foundation Project
No full textOn the sensitivity of Von Neuman and Morgenstern abstract stable sets : the stable and the individual stable bargaining set
No full textMulti-Modal End-to-End Learning for Real-Time Monitoring of Sustainable Energy Systems
No full textThe growth of renewable energy technologies is leading to energy systems that are more reliant than ever on renewables such as Wind and Photovoltaic (PV) power. Despite their benefits in terms of sustainability, their ubiquity poses challenges in maintaining grid stability given their intermittency, emphasising the prediction of power fluctuations. Physical models and statistical approaches, especially for nowcasting (forecasting for 0-6 hours in the future), have been superseded by Machine Learning (ML) methods in terms of forecast accuracy (below 3% Root Mean Squared Error (RMSE)). Within ML, Artificial Neural Network (ANN) methods seem to perform particularly well for nowcasting. This project focuses on predicting solar and wind meteorology with that level of accuracy, and on how to best use the prediction to minimize the cost of maintaining a balanced energy system, i.e. one where power consumption matches production at any moment. Producing accurate power predictions based on Multi-Modal (MM) data and the extent to which prediction accuracy reduces system cost are challenges to be addressed in this thesis. MM and End-to-End (E2E) training (with the system cost as the task of an ANN based algorithm) are investigated to this end. MM learning involves handling information from multiple types of input (audio and visual, for example) for performing a ML task such as regression or classification. It is of interest for this project because it has been shown to outperform other NN approaches in predicting sudden changes in solar irradiance. E2E learning entails an algorithm design which predicts the end goal of a ML process directly from the inputs. This is pursued because it addresses the true task (cost minimization) of system operators as the focus of the ML algorithm. The proposed method consists of a NN architecture that learns to fuse features from MM data (sky imagery and meteorological sensor data) at intermediate layers of the network in order to predict PV or Wind generation. This prediction is then used as an input to an Optimal Power Flow (OPF) problem (which seeks to minimize generation costs in a power system, considering power balance and transmission network constraints to ensure the twin goals of economic and secure system operation). The proposed model is trained E2E, therefore it is informed by the minimized cost solved by the optimization, rather than the intermediate power prediction (as conventional approaches would involve). In an IEEE 6-bus system with PV generation, a sequential training baseline results in costs 10% higher than a perfect forecast, while our proposed MM4-E2E approach achieves costs only 7% higher, a significant improvement. The intermediate prediction of PV power by MM4-E2E is also improved, with 18% lower RMSE by the proposed model compared to the baseline, explained by the enhancement of one modality by the other through MM learning. In a power system with two renewable sources, costs are reduced through the proposed model compared to a conventional approach (4% excess cost compared to 7%, measured against a perfect forecast), but power prediction accuracy is worse, sue to convergence to local minima.Electrical Engineering | Sustainable Energy Technolog
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