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Description
Advances in computational processing have made big data analysis in fields like music information retrieval (MIR) possible. Through MIR techniques researchers have been able to study information on a song, its musical parameters, the metadata generated by the song's listeners, and contextual data regarding the artists and listeners (Schedl, 2014). MIR research techniques have been applied within the field of music and emotions research to help analyze the correlative properties between the music information and the emotional output. By pairing methods within music and emotions research with the analysis of the musical features extracted through MIR, researchers have developed predictive models for emotions within a musical piece. This research has increased our understanding of the correlative properties of certain musical features like pitch, timbre, rhythm, dynamics, mel frequency cepstral coefficients (MFCC's), and others, to the emotions evoked by music (Lartillot 2008; Schedl 2014) This understanding of the correlative properties has enabled researchers to generate predictive models of emotion within music based on listeners' emotional response to it. However, robust models that account for a user's individualized emotional experience and the semantic nuances of emotional categorization have eluded the research community (London, 2001). To address these two main issues, more advanced analytical methods have been employed. In this article we will look at two of these more advanced analytical methods, machine learning algorithms and deep learning techniques, and discuss the effect that they have had on music and emotions research (Murthy, 2018). Current trends within MIR research, the application of support vector machines and neural networks, will also be assessed to explain how these methods help to address the two main issues within music and emotion research. Finally, future research within the field of machine and deep learning will be postulated to show how individuate models may be developed from a user or a pool of user's listening libraries. Also how developments of semi-supervised classification models that assess categorization by cluster instead of by nominal data, may be helpful in addressing the nuances of emotional categorization.
ContributorsMcgeehon, Timothy Makoto (Author) / Middleton, James (Thesis director) / Knowles, Kristina (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Concentrated Solar Power and Thermal Energy Storage are two technologies that are currently being explored as environmentally friendly methods of energy generation. The two technologies are often combined in an overall system to increase efficiency and reliability of the energy generation system. A collaborative group of researchers from Australia and the United States formed a project to design solar concentrators that utilize Concentrated Solar Power and Thermal Energy Storage. The collaborators from Arizona State designed a Latent Heat Thermal Energy Storage system for the project. It was initially proposed that the system utilize Dowtherm A as the Heat Transfer Fluid and a tin alloy as the storage material. Two thermal reservoirs were designed as part of the system; one reservoir was designed to be maintained at 240˚ C, while the other reservoir was designed to be maintained at 210˚ C. The tin was designed to receive heat from the hot reservoir during a charging cycle and discharge heat to the cold reservoir during a discharge cycle. From simulation, it was estimated that the system would complete a charging cycle in 17.5 minutes and a discharging cycle in 6.667 minutes [1]. After the initial design was fabricated and assembled, the system proved ineffective and did not perform as expected. Leaks occurred within the system under high pressure and the reservoirs could not be heated to the desired temperatures. After adding a flange to one of the reservoirs, it was decided that the system would be run with one reservoir, with water as the Heat Transfer Fluid. The storage material was changed to paraffin wax, because it would achieve phase change at a temperature lower than the boiling point of water. Since only one reservoir was available, charging cycle tests were performed on the system to gain insight on system performance. It was found that the paraffin sample only absorbs 3.29% of the available heat present during a charging cycle. This report discusses the tests performed on the system, the analysis of the data from these tests, the issues with the system that were revealed from the analyses, and potential design changes that would increase the efficiency of the system.
ContributorsKocher, Jordan Daniel (Author) / Wang, Robert (Thesis director) / Phelan, Patrick (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
This essay examines national leaders’ shaping of K-pop into a foreign export, specifically looking into how K-pop is used as a soft power for South Korea. I also examine how effective K-pop is as a soft power. Because of its growing global popularity and use of K-pop artists for international relations, such as Red Velvet performing for Kim Jong Un, we might expect K-pop to act as the gateway into South Korean culture, often being the first exposure that other countries have into this country’s way of life. Through a qualitative analysis of resources ranging from news articles, videos, and social media posts, we see that K-pop idols, a term for K-pop celebrities, are heavily groomed and shaped by their labels to promote the South Korean national brand. Combined with a well-made business model to appeal to different countries, they also create sentiment for South Korean culture throughout the world with the support of the government and a strong fanbase. This plan is extremely effective in generating revenue for a multitude of South Korean brands beyond K-pop and even fosters South Korean affection in North Korea.
ContributorsMendez, Audrey F (Author) / Ingram-Waters, Mary (Thesis director) / Sandoval, Mathew (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05