Spartan Daily, February 23, 2016

Volume 146, Issue 10https://scholarworks.sjsu.edu/spartan_daily_2016/1008/thumbnail.jp

The Effect Of Magnetic Bearing On The Vibration And Friction Of A Wind Turbine

Demands for sustainable energy have resulted in increased interest in wind turbines. Thus, despite widespread economic difficulties, global installed wind power increased by over 20% in 2011 alone. Recently, magnetic bearing technology has been proposed to improve wind turbine performance by mitigating vibration and reducing frictional losses. While magnetic bearing has been shown to reduce friction in other applications, little data has been presented to establish its effect on vibration and friction in wind turbines. Accordingly, this study provides a functional method for experimentally evaluating the effect of a magnetic bearing on the vibration and efficiency characteristics of a wind turbine, along with associated results and conclusions. The magnetic bearing under examination is a passive, concentric ring design. Vibration levels, dominant frequency components, and efficiency results are reported for the bearing as tested in two systems: a precision test fixture, and a small commercially available wind turbine. Data is also presented for a geometrically equivalent ball bearing, providing a benchmark for the magnetic bearing’s performance. The magnetic bearing is conclusively shown to reduce frictional losses as predicted by the original hypothesis. However, while reducing vibration in the precision test fixture, the magnetic bearing demonstrates increased vibration in the small wind turbine. This is explained in terms of the stiffness and damping of the passive test bearing. Thus, magnetic bearing technology promises to improve wind turbine performance, provided that application specific stiffness and damping characteristics are considered in the bearing design

Forensic Acquisition of IMVU: A Case Study

There are many applications available for personal computers and mobile devices that facilitate users in meeting potential partners. There is, however, a risk associated with the level of anonymity on using instant message applications, because there exists the potential for predators to attract and lure vulnerable users. Today Instant Messaging within a Virtual Universe (IMVU) combines custom avatars, chat or instant message (IM), community, content creation, commerce, and anonymity. IMVU is also being exploited by criminals to commit a wide variety of offenses. However, there are very few researches on digital forensic acquisition of IMVU applications. In this paper, we discuss first of all on challenges of IMVU forensics. We present a forensic acquisition of an IMVU 3D application as a case study. We also describe and analyse our experiments with this application

Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education

This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform. This platform is engineered to reduce cognitive load on learners by providing easy access to information, facilitating knowledge assessment, and delivering personalized learning support tailored to individual needs and learning styles. The AIIA's capabilities include understanding and responding to student inquiries, generating quizzes and flashcards, and offering personalized learning pathways. The research findings have the potential to significantly impact the design, implementation, and evaluation of AI-enabled Virtual Teaching Assistants (VTAs) in higher education, informing the development of innovative educational tools that can enhance student learning outcomes, engagement, and satisfaction. The paper presents the methodology, system architecture, intelligent services, and integration with Learning Management Systems (LMSs) while discussing the challenges, limitations, and future directions for the development of AI-enabled intelligent assistants in education.Comment: 29 pages, 10 figures, 9659 word

Using State-of-the-Art Speech Models to Evaluate Oral Reading Fluency in Ghana

This paper reports on a set of three recent experiments utilizing large-scale speech models to evaluate the oral reading fluency (ORF) of students in Ghana. While ORF is a well-established measure of foundational literacy, assessing it typically requires one-on-one sessions between a student and a trained evaluator, a process that is time-consuming and costly. Automating the evaluation of ORF could support better literacy instruction, particularly in education contexts where formative assessment is uncommon due to large class sizes and limited resources. To our knowledge, this research is among the first to examine the use of the most recent versions of large-scale speech models (Whisper V2 wav2vec2.0) for ORF assessment in the Global South. We find that Whisper V2 produces transcriptions of Ghanaian students reading aloud with a Word Error Rate of 13.5. This is close to the model's average WER on adult speech (12.8) and would have been considered state-of-the-art for children's speech transcription only a few years ago. We also find that when these transcriptions are used to produce fully automated ORF scores, they closely align with scores generated by expert human graders, with a correlation coefficient of 0.96. Importantly, these results were achieved on a representative dataset (i.e., students with regional accents, recordings taken in actual classrooms), using a free and publicly available speech model out of the box (i.e., no fine-tuning). This suggests that using large-scale speech models to assess ORF may be feasible to implement and scale in lower-resource, linguistically diverse educational contexts