Design for Assembly Line Performance: The Link Between DFA Metrics and Assembly Line Performance Metrics

Design for Assembly (DFA) is a tool that has been in use for almost 40 years. While it has been a useful design tool, it is not explicitly linked to actual manufacturing line performance. The motivation for this research came from the desire to link DFA directly to line balance and cycle time performance. The natural question that arose was whether these issues could be considered at the design stage by using the metrics that are derived from a DFA analysis. It is known that the time required to assemble a product can be estimated from both a DFA analysis and from a manufacturing analysis. This work links these two analysis methods so that the manufacturing parameters can be estimated and used to guide the design of a product. The methodology developed begins with a DFA analysis of the product. The times and operations from the DFA analysis are used to determine the minimum number of workstations to balance the line while maintaining the production rate (takt time) and precedence constraints. The precedence constraints are systematically relaxed in order to generate measures on a component-by- component basis as to the impact it could have on reducing cycle time and improving Line Balancing performance. These measures, coupled with an understanding of precedence types, are used to identify design improvements to a product. To illustrate how product designer can consider assembly line performance issues during the design stage of the product, the methodology has been applied to an ABS brake assembly

Detecting Strong Ties Using Network Motifs

Detecting strong ties among users in social and information networks is a fundamental operation that can improve performance on a multitude of personalization and ranking tasks. Strong-tie edges are often readily obtained from the social network as users often participate in multiple overlapping networks via features such as following and messaging. These networks may vary greatly in size, density and the information they carry. This setting leads to a natural strong tie detection task: given a small set of labeled strong tie edges, how well can one detect unlabeled strong ties in the remainder of the network? This task becomes particularly daunting for the Twitter network due to scant availability of pairwise relationship attribute data, and sparsity of strong tie networks such as phone contacts. Given these challenges, a natural approach is to instead use structural network features for the task, produced by {\em combining} the strong and "weak" edges. In this work, we demonstrate via experiments on Twitter data that using only such structural network features is sufficient for detecting strong ties with high precision. These structural network features are obtained from the presence and frequency of small network motifs on combined strong and weak ties. We observe that using motifs larger than triads alleviate sparsity problems that arise for smaller motifs, both due to increased combinatorial possibilities as well as benefiting strongly from searching beyond the ego network. Empirically, we observe that not all motifs are equally useful, and need to be carefully constructed from the combined edges in order to be effective for strong tie detection. Finally, we reinforce our experimental findings with providing theoretical justification that suggests why incorporating these larger sized motifs as features could lead to increased performance in planted graph models.Comment: To appear in Proceedings of WWW 2017 (Web-science track

Mining, Modeling, and Analyzing Real-Time Social Trails

Real-time social systems are the fastest growing phenomena on the web, enabling millions of users to generate, share, and consume content on a massive scale. These systems are manifestations of a larger trend toward the global sharing of the real-time interests, affiliations, and activities of everyday users and demand new computational approaches for monitoring, analyzing, and distilling information from the prospective web of real-time content. In this dissertation research, we focus on the real-time social trails that reflect the digital footprints of crowds of real-time web users in response to real-world events or online phenomena. These digital footprints correspond to the artifacts strewn across the real-time web like posting of messages to Twitter or Facebook; the creation, sharing, and viewing of videos on websites like YouTube; and so on. While access to social trails could benefit many domains there is a significant research gap toward discovering, modeling, and leveraging these social trails. Hence, this dissertation research makes three contributions: • The first contribution of this dissertation research is a suite of efficient techniques for discovering non-trivial social trails from large-scale real-time social systems. We first develop a communication-based method using temporal graphs for discovering social trails on a stream of conversations from social messaging systems like instant messages, emails, Twitter directed or @ messages, SMS, etc. and then develop a content-based method using locality sensitive hashing for discovering content based social trails on a stream of text messages like Tweet stream, stream of Facebook messages, YouTube comments, etc. • The second contribution of this dissertation research is a framework for modeling and predicting the spatio-temporal dynamics of social trails. In particular, we develop a probabilistic model that synthesizes two conflicting hypotheses about the nature of online information spread: (i) the spatial influence model, which asserts that social trails propagates to locations that are close by; and (ii) the community affinity influence model, which asserts that social trail prop- agates between locations that are culturally connected, even if they are distant. • The third contribution of this dissertation research is a set of methods for social trail analytics and leveraging social trails for prognostic applications like real-time content recommendation, personalized advertising, and so on. We first analyze geo-spatial social trails of hashtags from Twitter, investigate their spatio-temporal dynamics and then use this analysis to develop a framework for recommending hashtags. Finally, we address the challenge of classifying social trails efficiently on real-time social systems

Surrogate - Assisted Optimisation -Based Verification & Validation

This thesis deals with the application of optimisation based Validation and Verification (V&V) analysis on aerospace vehicles in order to determine their worst case performance metrics. To this end, three aerospace models relating to satellite and launcher vehicles provided by European Space Agency (ESA) on various projects are utilised. As a means to quicken the process of optimisation based V&V analysis, surrogate models are developed using polynomial chaos method. Surro- gate models provide a quick way to ascertain the worst case directions as computation time required for evaluating them is very small. A sin- gle evaluation of a surrogate model takes less than a second. Another contribution of this thesis is the evaluation of operational safety margin metric with the help of surrogate models. Operational safety margin is a metric defined in the uncertain parameter space and is related to the distance between the nominal parameter value and the first instance of performance criteria violation. This metric can help to gauge the robustness of the controller but requires the evaluation of the model in the constraint function and hence could be computationally intensive. As surrogate models are computationally very cheap, they are utilised to rapidly compute the operational safety margin metric. But this metric focuses only on finding a safe region around the nominal parameter value and the possibility of other disjoint safe regions are not explored. In order to find other safe or failure regions in the param- eter space, the method of Bernstein expansion method is utilised on surrogate polynomial models to help characterise the uncertain param- eter space into safe and failure regions. Furthermore, Binomial failure analysis is used to assign failure probabilities to failure regions which might help the designer to determine if a re-design of the controller is required or not. The methodologies of optimisation based V&V, surrogate modelling, operational safety margin, Bernstein expansion method and risk assessment have been combined together to form the WCAT-II MATLAB toolbox

Vision-Based Autonomous Control Schemes for Quadrotor Unmanned Aerial Vehicle

This chapter deals with the development of vision-based sliding mode control strategies for a quadrotor system that would enable it to perform autonomous tasks such as take-off, landing and visual inspection of structures. The aim of this work is to provide a basic understanding of the quadrotor dynamical model, key concepts in image processing and a detailed description of the sliding mode control, a widely used robust non-linear control scheme. Extensive MATLAB simulations are presented to enhance the understanding of the controller on the quadrotor system subjected to bounded disturbances and uncertainties. The vision algorithms developed in this chapter would provide the necessary reference trajectory to the controller enabling it to exercise control over the system. This work also describes, in brief, the implementation of the developed control and vision algorithms on the DJI Matrice 100 to present real-time experimental data to the readers of this chapter

An Experimental Study of Structural Diversity in Social Networks

Several recent studies of online social networking platforms have found that adoption rates and engagement levels are positively correlated with structural diversity, the degree of heterogeneity among an individual's contacts as measured by network ties. One common theory for this observation is that structural diversity increases utility, in part because there is value to interacting with people from different network components on the same platform. While compelling, evidence for this causal theory comes from observational studies, making it difficult to rule out non-causal explanations. We investigate the role of structural diversity on retention by conducting a large-scale randomized controlled study on the Twitter platform. We first show that structural diversity correlates with user retention on Twitter, corroborating results from past observational studies. We then exogenously vary structural diversity by altering the set of network recommendations new users see when joining the platform; we confirm that this design induces the desired changes to network topology. We find, however, that low, medium, and high structural diversity treatment groups in our experiment have comparable retention rates. Thus, at least in this case, the observed correlation between structural diversity and retention does not appear to result from a causal relationship, challenging theories based on past observational studies.Comment: To appear in the Proceedings of International AAAI Conference on Web and Social Media (ICWSM 2020

Detecting collective attention spam

We examine the problem of collective attention spam, in which spammers target social media where user attention quickly coalesces and then collectively focuses around a phe-nomenon. Compared to many existing spam types, collec-tive attention spam relies on the users themselves to seek out the content – like breaking news, viral videos, and popular memes – where the spam will be encountered, potentially in-creasing its effectiveness and reach. We study the presence of collective attention spam in one popular service, Twitter, and we develop spam classifiers to detect spam messages generated by collective attention spammers. Since many in-stances of collective attention are bursty and unexpected, it is difficult to build spam detectors to pre-screen them before they arise; hence, we examine the effectiveness of quickly learning a classifier based on the first moments of a bursting phenomenon. Through initial experiments over a small set of trending topics on Twitter, we find encouraging results, suggesting that collective attention spam may be identified early in its life cycle and shielded from the view of unsus-pecting social media users