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A two step predictor-corrector method for voltage collapse point estimation
Get PDFVoltage Collapse is the system failure to obtain acceptable voltage levels in significant part of the power system, and it is often due to system failure to satisfy reactive power demand. Voltage Collapse can lead to blackout like the one occurred in 2003 in North America. Methods for on-line voltage stability monitoring were established, and indices to quantify it were proposed. However, estimations of voltage collapse point based on these indices are often inaccurate or time consuming. A well-established method of voltage collapse point estimation is the Continuation Power Flow (CPF). CPF is considered accurate but, it is very computationally expensive for large systems. This work aims to speed up the predictor-corrector process by using a VSI called P-index. An initial prediction is made, corrected using a continuation technique, and then updated after correction. The results are relatively accurate and it makes a significant improvement to the CPF computational time
Applying generative adversarial networks to intelligent subsurface imaging and identification
Get PDFTo augment training data for machine learning models in Ground Penetrating Radar (GPR) data classification and identification, this thesis focuses on the generation of realistic GPR data using Generative Adversarial Networks. An innovative GAN ar- chitecture is proposed for generating GPR B-scans, which is, to the author’s knowledge, the first successful application of GAN to GPR B-scans. As one of the major contri- butions, a novel loss function is formulated by merging frequency domain with time domain features. To test the efficacy of generated B-scans, a real time object classifier is proposed to measure the performance gain derived from augmented B-Scan images. The numerical experiment illustrated that, based on the augmented training data, the proposed GAN architecture demonstrated a significant increase (from 82% to 98%) in the accuracy of the object classifier
Low-cost deep learning UAV and Raspberry Pi solution to real time pavement condition assessment
Get PDFIn this thesis, a real-time and low-cost solution to the autonomous condition assessment of pavement is proposed using deep learning, Unmanned Aerial Vehicle (UAV) and Raspberry Pi tiny computer technologies, which makes roads maintenance and renovation management more efficient and cost effective. A comparison study was conducted to compare the performance of seven different combinations of meta-architectures for pavement distress classification. It was observed that real-time object detection architecture SSD with MobileNet feature extractor is the best combination for real-time defect detection to be used by tiny computers. A low-cost Raspberry Pi smart defect detector camera was configured using the trained SSD MobileNet v1, which can be deployed with UAV for real-time and remote pavement condition assessment. The preliminary results show that the smart pavement detector camera achieves an accuracy of 60% at 1.2 frames per second in raspberry pi and 96% at 13.8 frames per second in CPU-based computer
Interreligious dialogue in the religious styles perspective: a qualitative analysis of instrumental cases
Get PDFInterreligious dialogue (IRD) is considered a sacred religious practice ([PCID], 2017; Merdjanova, 2016) and has become increasingly present in interventions to address conflict resulting from exposure to religious diversity (Cornille, 2013; Patel, 2018). However, few empirical efforts have examined the efficacy and outcomes of IRD. A grounded theory approach (Creswell & Poth, 2017) is well-suited to describe the nuanced role of religion in intergroup processes in major theoretical frameworks. Purposeful sampling (Patton, 2005) of 20 cases were selected from archival data of Faith Development Interviews (Streib & Keller, 2018) collected as part of the Developmental change in Spirituality project. Experiences of IRD were explored and analyzed through descriptions of instrumental cases and religious style scores. A thematic analysis (Braun & Clarke, 2006) is used to identify common themes in IRD and the Religious Styles Perspective (Streib, 2001a). Implications of a theoretical framework for future research and application are discussed
An exploration in atmopsheric gas burners
Get PDFThe objective of this paper is to explore the significant factors for primary aeration in atmospheric gas burners. Historically linear relationships have been used to estimate primary aeration with a design’s geometry and features. With a given heating output a burners geometry is designed to meet safety, efficiency, reliability and customers’ expectations. This paper explores the significant factors and how they interact with primary aeration. Experimentally exploring, port area, port loading, injector axial position, injection angle and the throat diameter interaction with port area. The structure creates an outline for atmospheric burner design
Predictive Analytics and You
No full textThe explosion of interest in big data and talent analytics has brought predictive models based in machine learning to the fore in I-O psychology. In this presentation, Dr. McCloy will discuss the general logic behind many of the machine learning techniques, relating them to techniques many I-O students recognize. He will then lead a discussion about how I-O psychologists can remain valuable in an arena that has become more heterogeneous over the years, with data scientists and computer engineers tackling problems that have traditionally fallen solidly within our purview
Income as a Predictor of Employe Job Satisfaction and Motivation
Get PDFEmployee job satisfaction and motivation are linked to their income level. Employee job satisfaction refers to an individual\u27s contentment with his or her job. Employee motivation has two components; extrinsic motivation refers to external benefits an individual gain (i.e. pay), whereas intrinsic motivation refers to an individual\u27s inherent satisfaction with one’s job (i.e. pride in the work they do). Higher or lower income levels impact employee satisfaction and motivation. It is hypothesized that individuals with medium-income (139,999) will have higher job satisfaction and motivation than individual with low-income (44,999). An independent samples t-test will be conducted between the two groups and the researchers will use two surveys to determine employee satisfaction and motivation. A 36-item Job Satisfaction Survey and 18-item Work Extrinsic and Intrinsic Motivation Scale will be emailed to managers in food and service industry within 15 miles of Chattanooga City Hall. They will be incentivized to forward the survey to their employees, who will also be incentivized to complete the survey. We expect results to verify our hypothesis. Future research should examine how to potentially increase employee motivation by introducing and balancing more extrinsic and intrinsic factors in retail and food service positions
The impact of perceived subordinate support
No full textExisting research indicates that perceived support (Eisenberger, Huntington, Hutchison, & Sowa, 1986) is positively related to job performance and can influence perceptions of support by those with whom they interact (Eisenberger, Armeli, Rexwinkel, Lynch, & Rhoades, 2001). While there is a large body of research examining employees’ perceived support from their supervisors (i.e., perceived supervisor support: PSS; Eisenberger, Stinglhamber, Vandenberghe, Sucharski, & Rhoades, 2002) and the organization (i.e., perceived organizational support: POS, Eisenberger et al., 2001), little research has examined supervisors’ perceptions of support from their subordinates. The proposed study evaluates the relationship between Perceived Subordinate Support (PSubS; O’Leary, 2012) and organizational commitment, job satisfaction, and turnover intentions, and the moderating impact of Leader-Member Exchange (LMX; Wilson, Sin, & Conlon, 2010) on these relationships. Participants will be supervisors at a local manufacturing organization (N = 150). We hypothesize that supervisors who feel supported by their subordinates will express higher levels of organizational commitment, job satisfaction, and lower turnover intentions. We also expect the supervisor’s level of LMX with their immediate supervisor to moderate these relationships
Personality Function Pairs and their Effect on 360-Feedback Reports
Get PDFPersonality Function Pairs and their effect on 360-Feedback Reports Cooper Drose, Keith Eigel, Ph.D, Sara Musgrove, Ph.D. Abstract For years, researchers in psychology have researched the impact of one’s personality type and what effects it has on their everyday lives; however, there has been a lack of research on each person’s personality function pair. The personality function pair is the middle two letters in someone’s four-letter personality code, (Golden, 1979) often labeled as their decision-making style (Sefcik, Prerost, Arbet, 2009). For this study, we compiled data from the past 8 years and have a total of 609 participants. We sought to discover a relationship between subjects’ function pairs using the Golden Personality Type Indicator and their score on The Leaders Lyceum 360-Feedback Report (Eigel & Musgrove, 2013). We hypothesized that the “Sensing Feeling” function pair would score highest on our 360-feedback report based on The Ohio State Leadership studies. These studies found that subjects listed the two most important qualities with regard to leaders and effective leadership as “consideration” and “initiating structure” (Hemphill, Coons, 1957). Because SF’s are both empathetic and detail oriented, we hypothesized they would be most likely to score higher than the other function pairs on our 360. In our results we found that SF’s scored significantly higher than ST’s and NT’s on 26 of the 40 questions which showed significant differences, but were only significantly higher than NF’s on 3 of the 40 questions. In future research it will be important that the 360 is constructed in a way that has different portions tailored to the strengths of each function pair as this will allow for the results to better illustrate where the strengths and weaknesses of the types of minds exist
Quality assessment of work recovery activities: Guidance for recovering from work-related demands
Get PDFThe proposed study is designed to test a revised work recovery process model and gather data to provide guidance for work recovery activities based on their recovery quality value. Using an integrated and modified model of the stress-recovery process, recovery quality will be measured in terms of potential for psychological detachment, mastery, and control, with relaxation serving as an outcome state associated with the proposed three core recovery mechanisms. Underlying theoretical frameworks such as the Conservation of Resources Theory, the Effort-Recovery Model, and the Job-Demands Resource model served as the foundation to describe the importance of recovering depleted resources. Past research suggests active forms of recovery in natural environments hold the greatest potential for work recovery, but research has been limited to broad activity category classifications. In this study we take a more holistic approach to identifying specific recovery activities and their associated recovery experience quality by asking participants to list, rank order, and provide quality-related details regarding their three most common recovery activities. A variety of analyses will be used to compare average ratings of recovery quality elements and identify common recovery themes