Matching Items (30)

Optimization of Incoming Inspection

Description

The first step in process improvement is to scope the problem, next is measure the current process, but if data is not readily available and cannot be manually collected, then

The first step in process improvement is to scope the problem, next is measure the current process, but if data is not readily available and cannot be manually collected, then a measurement system must be implemented. General Dynamics Mission Systems (GDMS) is a lean company that is always seeking to improve. One of their current bottlenecks is the incoming inspection department. This department is responsible for finding defects on parts purchased and is critical to the high reliability product produced by GDMS. To stay competitive and hold their market share, a decision was made to optimize incoming inspection. This proved difficult because no data is being collected. Early steps in many process improvement methodologies, such as Define, Measure, Analyze, Improve and Control (DMAIC), include data collection; however, no measurement system was in place, resulting in no available data for improvement. The solution to this problem was to design and implement a Management Information System (MIS) that will track a variety of data. This will provide the company with data that will be used for analysis and improvement. The first stage of the MIS was developed in Microsoft Excel with Visual Basic for Applications because of the low cost and overall effectiveness of the software. Excel allows update to be made quickly, and allows GDMS to collect data immediately. Stage two would be moving the MIS to a more practicable software, such as Access or MySQL. This thesis is only focuses on stage one of the MIS, and GDMS will proceed with stage two.

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  • 2017-05

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The Relationship Between the Success and Composition of Modern Popular Electronic Dance Music and the Seasons and Climate

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This research covers the relationship between popular electronic dance music (EDM) and the seasons and climate. The precedence and relevance of the research is outlined through prior research initiatives by

This research covers the relationship between popular electronic dance music (EDM) and the seasons and climate. The precedence and relevance of the research is outlined through prior research initiatives by Karen Aplin and Paul Williams on the effect of weather on classical music. The hypothesis is that the climate will affect how music by artists residing within the climate and the seasons will affect the popularity of certain genres. Warmer climates will produce songs that are more upbeat and energetic while colder climates will result in songs that are more complex and heavy. The analysis of this hypothesis will be performed in two parts. The first will be a data driven analysis from Beatport.com's Top 100 EDM charts to observe the season's impact on genre popularity. The second will be a case study analysis of a number of artists from around the world to observe climate impact on EDM. From the analysis, we are able to draw the connection that climate does in fact have an impact on the types of music produced. Likewise, we are able to conclude that there is a distinct variation in deep house, techno/tech house, and house as a result of the seasons shifting. Techno/tech house is more popular in the warmer spring and summer months and house and deep house have a higher standing in the colder fall and winter months.

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  • 2015-05

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A Reliability Driven Model for Airline Crew Vacation Grid Optimization

Description

The crew planning problem in the airline industry presents a very computationally complex problem of high importance to the business. Airlines must schedule crew members to ensure that all flights

The crew planning problem in the airline industry presents a very computationally complex problem of high importance to the business. Airlines must schedule crew members to ensure that all flights are staffed while remaining in compliance with the business needs and regulatory requirements set by entities such as unions and FAA. With the magnitude of operation of the prominent players in the airline industry today, the crew staffing problem proves very large and has become heavily reliant on operations research solution methodologies. An area of opportunity that has not yet been extensively researched lies in the planning of crew vacation. This paper develops a model driven by the idea of system risk that constructs an optimal vacation grid for the time period of one year. The model generates a daily allocation that maximizes vacation offering while ensuring a given level of system reliability. The model is then implemented using data from US Airways and model improvements are provided for practical application in the airline industry based on the output.

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  • 2015-05

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Data and Predictive Analytics for Energy Use

Description

The overall energy consumption around the United States has not been reduced even with the advancement of technology over the past decades. Deficiencies exist between design and actual energy performances.

The overall energy consumption around the United States has not been reduced even with the advancement of technology over the past decades. Deficiencies exist between design and actual energy performances. Energy Infrastructure Systems (EIS) are impacted when the amount of energy production cannot be accurately and efficiently forecasted. Inaccurate engineering assumptions can result when there is a lack of understanding on how energy systems can operate in real-world applications. Energy systems are complex, which results in unknown system behaviors, due to an unknown structural system model. Currently, there exists a lack of data mining techniques in reverse engineering, which are needed to develop efficient structural system models. In this project, a new type of reverse engineering algorithm has been applied to a year's worth of energy data collected from an ASU research building called MacroTechnology Works, to identify the structural system model. Developing and understanding structural system models is the first step in creating accurate predictive analytics for energy production. The associative network of the building's data will be highlighted to accurately depict the structural model. This structural model will enhance energy infrastructure systems' energy efficiency, reduce energy waste, and narrow the gaps between energy infrastructure design, planning, operation and management (DPOM).

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  • 2016-12

Effectiveness of Augmented Reality as a Learning Tool to Advance Personalized Learning

Description

In this study, the implementation of educational technology and its effect on learning and user experience is measured. A demographic survey, pretest/posttest, and educational experience survey was used to collect

In this study, the implementation of educational technology and its effect on learning and user experience is measured. A demographic survey, pretest/posttest, and educational experience survey was used to collect data on the control and experimental groups. The experimental group was subjected to different learning material than the control group with the use of the Elements 4D mobile application by Daqri to learn basic chemical elements and compounds. The control group learning material provided all the exact information as the application, but in the 2D form of a printed packet. It was expected the experimental group would outperform the control group and have a more enjoyable experience and higher performance. After data analysis, it was concluded that the control group outperformed the experimental group on performance and both groups has similar experiences in contradiction to the hypothesis. Once the factors that contribute to the limitations of different study duration, learning the application beforehand, and only-memorization questions are addressed, the study can be conducted again. Application improvements may also alter the future results of the study and hopefully lead to full implementation into a curriculum.

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  • 2017-05

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Modeling Fantasy Baseball Player Popularity Using Twitter Activity

Description

Social media is used by people every day to discuss the nuances of their lives. Major League Baseball (MLB) is a popular sport in the United States, and as such

Social media is used by people every day to discuss the nuances of their lives. Major League Baseball (MLB) is a popular sport in the United States, and as such has generated a great deal of activity on Twitter. As fantasy baseball continues to grow in popularity, so does the research into better algorithms for picking players. Most of the research done in this area focuses on improving the prediction of a player's individual performance. However, the crowd-sourcing power afforded by social media may enable more informed predictions about players' performances. Players are chosen by popularity and personal preferences by most amateur gamblers. While some of these trends (particularly the long-term ones) are captured by ranking systems, this research was focused on predicting the daily spikes in popularity (and therefore price or draft order) by comparing the number of mentions that the player received on Twitter compared to their previous mentions. In doing so, it was demonstrated that improved fantasy baseball predictions can be made through leveraging social media data.

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  • 2017-05

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On Memory and Physiological Signals of Experts and Novices-Case Study: Chess

Description

Abstract Chess has been a common research topic for expert-novice studies and thus for learning science as a whole because of its limited framework and longevity as a game. One

Abstract Chess has been a common research topic for expert-novice studies and thus for learning science as a whole because of its limited framework and longevity as a game. One factor is that chess studies are good at measuring how expert chess players use their memory and skills to approach a new chessboard con�guration. Studies have shown that chess skill is based on memory, speci�cally, "chunks" of chess piece positions that have been previously encountered by players. However, debate exists concerning how these chunks are constructed in players' memory. These chunks could be constructed by proximity of pieces on the chessboard as well as their precise location or constructed through attack-defense relations. The primary objective of this study is to support which one is more in line with chess players' actual chess abilities based off their memory, proximity or attack/defense. This study replicates and extends an experiment conducted by McGregor and Howe (2002), which explored the argument that pieces are primed more by attack and defense relations than by proximity. Like their study, the present study examined novice and expert chess players' response times for correct and error responses by showing slides of game configurations. In addition to these metrics, the present study also incorporated an eye-tracker to measure visual attention and EEG to measure affective and cognitive states. They were added to allow the comparison of subtle and unconscious behaviors of both novices and expert chess players. Overall, most McGregor and Howe's (2002) results were replicated supporting their theory on chess expertise. This included statistically significance for skill in the error rates with the mean error rates on the piece recognition tests were 70.1% for novices and 87.9% for experts, as well as significance for the two-way interaction for relatedness and proximity with error rates of 22.4% for unrelated/far, 18.8% for related/far, 15.8% for unrelated
ear, and 29.3% for related
ear. Unfortunately, there were no statistically significance for any of the response time effects, which McGregor and Howe found for the interaction between skill and proximity. Despite eye-tracking and EEG data not either support nor confirm McGregor and Howe's theory on how chess players memorize chessboard configurations, these metrics did help build a secondary theory on how novices typically rely on proximity to approach chess and new visual problems in general. This was exemplified by the statistically significant results for short-term excitement for the two-way interaction of skill and proximity, where the largest short-term excitement score was between novices on near proximity slides. This may indicate that novices, because they may lean toward using proximity to try to recall these pieces, experience a short burst of excitement when the pieces are close to each other because they are more likely to recall these configurations.

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  • 2017-05

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Statistical Analysis of Power Differences between Experimental Design Software Packages

Description

Based on findings of previous studies, there was speculation that two well-known experimental design software packages, JMP and Design Expert, produced varying power outputs given the same design and user

Based on findings of previous studies, there was speculation that two well-known experimental design software packages, JMP and Design Expert, produced varying power outputs given the same design and user inputs. For context and scope, another popular experimental design software package, Minitab® Statistical Software version 17, was added to the comparison. The study compared multiple test cases run on the three software packages with a focus on 2k and 3K factorial design and adjusting the standard deviation effect size, number of categorical factors, levels, number of factors, and replicates. All six cases were run on all three programs and were attempted to be run at one, two, and three replicates each. There was an issue at the one replicate stage, however—Minitab does not allow for only one replicate full factorial designs and Design Expert will not provide power outputs for only one replicate unless there are three or more factors. From the analysis of these results, it was concluded that the differences between JMP 13 and Design Expert 10 were well within the margin of error and likely caused by rounding. The differences between JMP 13, Design Expert 10, and Minitab 17 on the other hand indicated a fundamental difference in the way Minitab addressed power calculation compared to the latest versions of JMP and Design Expert. This was found to be likely a cause of Minitab’s dummy variable coding as its default instead of the orthogonal coding default of the other two. Although dummy variable and orthogonal coding for factorial designs do not show a difference in results, the methods affect the overall power calculations. All three programs can be adjusted to use either method of coding, but the exact instructions for how are difficult to find and thus a follow-up guide on changing the coding for factorial variables would improve this issue.

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  • 2017-05

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A Simulation Model of the Effect of Workplace Structure on Productivity

Description

Workplace productivity is a result of many factors, and among them is the setup of the office and its resultant noise level. The conversations and interruptions that come along with

Workplace productivity is a result of many factors, and among them is the setup of the office and its resultant noise level. The conversations and interruptions that come along with converting an office to an open plan can foster innovation and creativity, or they can be distracting and harm the performance of employees. Through simulation, the impact of different types of office noise was studied along with other changing conditions such as number of people in the office. When productivity per person, defined in terms of mood and focus, was measured, it was found that the effect of noise was positive in some scenarios and negative in others. In simulations where employees were performing very similar tasks, noise (and its correlates, such as number of employees), was beneficial. On the other hand, when employees were engaged in a variety of different types of tasks, noise had a negative overall effect. This indicates that workplaces that group their employees by common job functions may be more productive than workplaces where the problems and products that employees are working on are varied throughout the workspace.

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  • 2017-05

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Multi-Layer Optical Coatings Composed of Silicon Nanoparticles

Description

To compete with fossil fuel electricity generation, there is a need for higher efficiency solar cells to produce renewable energy. Currently, this is the best way to lower generation costs

To compete with fossil fuel electricity generation, there is a need for higher efficiency solar cells to produce renewable energy. Currently, this is the best way to lower generation costs and the price of energy [1]. The goal of this Barrett Honors Thesis is to design an optical coating model that has five or fewer layers (with varying thickness and refractive index, within the above range) and that has the maximum reflectance possible between 950 and 1200 nanometers for normally incident light. Manipulating silicon monolayers to become efficient inversion layers to use in solar cells aligns with the Ira. A Fulton Schools of Engineering research themes of energy and sustainability [2]. Silicon monolayers could be specifically designed for different doping substrates. These substrates could range from common-used materials such as boron and phosphorus, to rare-earth doped zinc oxides or even fullerene blends. Exploring how the doping material, and in what quantity, affects solar cell energy output could revolutionize the current production methods and commercial market. If solar cells can be manufactured more economically, yet still retain high efficiency rates, then more people will have access to alternate, "green" energy that does not deplete nonrenewable resources.

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  • 2016-12