MODELING OF HOPPER DISCHARGE

Hoppers are widely used in many engineering processes. The discharging of granular mate- rials from a hopper is a critical topic of industrial importance, and the discharge flow rate from hoppers is the focus of the current work. Many parameters influence the discharge rate including: the hopper outlet width, the angle of the hopper wall, the particle size, and particle friction, and so on. Due to the expensive of examining a large variety of particle types and hopper conditions, computational simulation has been widely studied in an effort to establish an alternative method of determining critical factors impacting hopper flow. In this thesis, the process of hopper discharge has been simulated by the Discrete Element Method (DEM), which is one of the most popular methods for granular flow simulation. To validate against existing experiments, all conditions were matched as closely as possible to those in the experiment. The particles used in our simulation are spheroids with diameters of 0.77 cm. The angles of the hoppers examined range from 0◦ to 90◦, while the opening sizes vary from 2.9 cm to 4.3 cm. Computationally, the friction coefficient has been adjusted several times and finally is set to 0.5 in the simulation in order to fit the experimental resultsas closely as possible. As a quantitative test of the simulation fidelity we compare the hopper empty time t – which is related to the hopper discharge rate – for these different hopper angles and hopper opening size. As a secondary test of the fit, the survival time τ, the normal force profile, the velocity profile, and the probability of jamming Ps are also computed and compared to existing experimental data from collaborators at Duke University. Ultimately, the goal of the work is to establish the degree of model fidelity necessary in order to closely mimic the experimental results obtained

How does a Gamification Design Influence Students’ Interaction in an Online Course?

This study created and examined a gamification design that aimed at improving students’ interaction in a graduate level online course. By using a design-based research approach, the study investigated the application of principles from Self-Determination Theory in the gamification design and its influence on students’ interaction in discussion forums in terms of quantity, interaction dynamic, and interaction quality. The gamification design included a positive feedback system, contextualized in a narrative environment that was based on the original course project design. Participants were 49 students enrolled in the online course in three versions of the course, which were the non-gamification version of the course in the 2016 summer semester (NGC), the prototype gamification version of the course in the 2016 summer semester (PGC), and the revised gamification version of the course in the 2016 summer semester (RGC). Students’ interaction data in the academic discussion forums were compared with each other. Students’ gamification performance data were presented and compared between the PGC and the RGC. Moreover, eight students from the RGC participated in semi-structured interviews and shared their experiences and perspectives about the revised gamification design. The results showed that students in the gamified courses posted more messages per week. When students were the facilitators for the week, they were more actively involved in the online discussion. The student facilitators in the gamified courses were more active compared to the student facilitators in the non-gamified course. Second, students’ interaction was more evenly distributed among students in the gamified courses. On average, students in the gamified courses received comments from more peers than students in the non-gamified course. The class level density scores were higher with smaller centralization scores in the gamified courses. Finally, the RGC discussion transcripts presented more knowledge building features on a weekly basis in comparison with the PGC and the NGC, while overall the online discussion in the three versions of the course fell into the lower phases in the knowledge building conceptual model. Students’ gamification performance was about the same in the two gamified courses. Nonetheless, the design adjustments made between the two design cycles and during the second cycle improved students’ participation in several gamification activities. Furthermore, students’ interaction was more stable during the six weeks in the RGC due to the design adjustments. The semi-structured interviews further revealed the RGC interviewees’ experiences in the course. The positive feedback system satisfied students’ competence needs. Nonetheless, to what degree their competence needs were satisfied depended on their experiences and understanding of gamification. In pursuit of competence needs, some interviewees’ autonomy needs were undermined. The peer evaluation, dynamic academic discussion, and the authentic course project satisfied students’ relatedness needs. But additional emotional support from peers was barely sufficient. The study provided an example of gamification design in online courses to improve students’ interactions in discussion forums. The results suggested a positive feedback system could be added in the course design to improve students’ performance of the targeted learning activities. The selection of learning activities, the design and development of the gamification elements, and the gamification algorithm should take both the subject matter and students’ characteristics into consideration. A narrative environment can help align the feedback system with the course context and students’ actions should result in development of the narrative

Augmented Kinesthetic Teaching: Enhancing Task Execution Efficiency through Intuitive Human Instructions

In this paper, we present a complete and efficient implementation of a knowledge-sharing augmented kinesthetic teaching approach for efficient task execution in robotics. Our augmented kinesthetic teaching method integrates intuitive human feedback, including verbal, gesture, gaze, and physical guidance, to facilitate the extraction of multiple layers of task information including control type, attention direction, input and output type, action state change trigger, etc., enhancing the adaptability and autonomy of robots during task execution. We propose an efficient Programming by Demonstration (PbD) framework for users with limited technical experience to teach the robot in an intuitive manner. The proposed framework provides an interface for such users to teach customized tasks using high-level commands, with the goal of achieving a smoother teaching experience and task execution. This is demonstrated with the sample task of pouring water

Random graph matching at Otter's threshold via counting chandeliers

We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex. For two Erd\H{o}s-R\'enyi graphs $\mathcal{G}(n,q)$ whose edges are correlated through a latent vertex correspondence, we show that this algorithm correctly matches all but a vanishing fraction of the vertices with high probability, provided that $nq\to\infty$ and the edge correlation coefficient $\rho$ satisfies $\rho^2>\alpha \approx 0.338$, where $\alpha$ is Otter's tree-counting constant. Moreover, this almost exact matching can be made exact under an extra condition that is information-theoretically necessary. This is the first polynomial-time graph matching algorithm that succeeds at an explicit constant correlation and applies to both sparse and dense graphs. In comparison, previous methods either require $\rho=1-o(1)$ or are restricted to sparse graphs. The crux of the algorithm is a carefully curated family of rooted trees called chandeliers, which allows effective extraction of the graph correlation from the counts of the same tree while suppressing the undesirable correlation between those of different trees

Optimal Workload Allocation for Distributed Edge Clouds With Renewable Energy and Battery Storage

This paper studies an optimal workload allocation problem for a network of renewable energy-powered edge clouds that serve users located across various geographical areas. Specifically, each edge cloud is furnished with both an on-site renewable energy generation unit and a battery storage unit. Due to the discrepancy in electricity pricing and the diverse temporal-spatial characteristics of renewable energy generation, how to optimally allocate workload to different edge clouds to minimize the total operating cost while maximizing renewable energy utilization is a crucial and challenging problem. To this end, we introduce and formulate an optimization-based framework designed for Edge Service Providers (ESPs) with the overarching goal of simultaneously reducing energy costs and environmental impacts through the integration of renewable energy sources and battery storage systems, all while maintaining essential quality-of-service standards. Numerical results demonstrate the effectiveness of the proposed model and solution in maintaining service quality as well as reducing operational costs and emissions. Furthermore, the impacts of renewable energy generation and battery storage on optimal system operations are rigorously analyzed

CrowdCache: A Decentralized Game-Theoretic Framework for Mobile Edge Content Sharing

Mobile edge computing (MEC) is a promising solution for enhancing the user experience, minimizing content delivery expenses, and reducing backhaul traffic. In this paper, we propose a novel privacy-preserving decentralized game-theoretic framework for resource crowdsourcing in MEC. Our framework models the interactions between a content provider (CP) and multiple mobile edge device users (MEDs) as a non-cooperative game, in which MEDs offer idle storage resources for content caching in exchange for rewards. We introduce efficient decentralized gradient play algorithms for Nash equilibrium (NE) computation by exchanging local information among neighboring MEDs only, thus preventing attackers from learning users' private information. The key challenge in designing such algorithms is that communication among MEDs is not fixed and is facilitated by a sequence of undirected time-varying graphs. Our approach achieves linear convergence to the NE without imposing any assumptions on the values of parameters in the local objective functions, such as requiring strong monotonicity to be stronger than its dependence on other MEDs' actions, which is commonly required in existing literature when the graph is directed time-varying. Extensive simulations demonstrate the effectiveness of our approach in achieving efficient resource outsourcing decisions while preserving the privacy of the edge devices