Earthquake Arrival Association with Backprojection and Graph Theory

The association of seismic wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become more sensitive, and particularly when earthquake rates are high. For instance, seismic waves arriving across a monitoring network from several sources may overlap in time, false arrivals may be detected, and some arrivals may be of unknown phase (e.g., P- or S-waves). We propose an automated method to associate arrivals with earthquake sources and obtain source locations applicable to such situations. To do so we use a pattern detection metric based on the principle of backprojection to reveal candidate sources, followed by graph-theory-based clustering and an integer linear optimization routine to associate arrivals with the minimum number of sources necessary to explain the data. This method solves for all sources and phase assignments simultaneously, rather than in a sequential greedy procedure as is common in other association routines. We demonstrate our method on both synthetic and real data from the Integrated Plate Boundary Observatory Chile (IPOC) seismic network of northern Chile. For the synthetic tests we report results for cases with varying complexity, including rates of 500 earthquakes/day and 500 false arrivals/station/day, for which we measure true positive detection accuracy of > 95%. For the real data we develop a new catalog between January 1, 2010 - December 31, 2017 containing 817,548 earthquakes, with detection rates on average 279 earthquakes/day, and a magnitude-of-completion of ~M1.8. A subset of detections are identified as sources related to quarry and industrial site activity, and we also detect thousands of foreshocks and aftershocks of the April 1, 2014 Mw 8.2 Iquique earthquake. During the highest rates of aftershock activity, > 600 earthquakes/day are detected in the vicinity of the Iquique earthquake rupture zone

Open and closed industry clusters: The social structure of innovation

In this paper we discuss knowledge and innovation in clusters and the benefits of clustering from a knowledge-based perspective. Knowledge-based resources and innovations are important sources of competitive advantage for firms. Aware of the importance of continuously seeking new knowledge firms increasingly seek knowledge-rich locations such as specific industry clusters across the world. These are locations characterized by the concentration of firms operating in related and supporting activities, a specialized work force and a specialized institutional environment that nurtures the industry. However, it is not likely that these clusters are always locations from which the firms will be able to draw the intended knowledge benefits. The social structure of the relationships between individuals and firms determines the extent to which knowledge will be created, will flow between co-located firms and bounds the knowledge benefits the firms may capture. We finish with a discussion of the need of further examination of the network dynamics involved in an industry cluster to obtain a clearer identification of the actual positive externalities that may accrue to co-locating firms.Strategy; Industry clusters; Innovation

Acoustic Emission Monitoring of Prefabricated and Prestressed Reinforced Concrete Bridge Elements and Structures

Prefabricated and pre-stressed reinforced concrete beams and girders are integral components of many highway structures, including those build by rapid construction techniques. Concerns exist regarding the development of cracks during curing, form removal, detensioning, transport, installation, and operation. Non-destructive, Acoustic Emission (AE) sensing techniques have the potential for detecting and locating cracking in prefabricated, prestressed concrete girders used as Prefabricated Bridge Elements and Systems (PBES) in rapid construction practices as part of a Quality Assurance/Quality Control (QA/QC) program. AE sensing records transient elastic waves produced by the release of stored elastic energy resulting in plastic deformations (i.e., crack nucleation and growth) with an array of point sensors. The AE instrument system is relatively portable which can allow for it to be an option for both off-site fabrication QA/QC as well as on-site field QA/QC. This report presents a multi-stage research initiative on acoustic emission measurements of prefabricated and pre-stressed concrete beams used in highway bridge construction during detensioning, craned removal from formwork and transport to bridge sites, along with supporting laboratory tests and numerical analysis. The project objectives are: 1. Identify suitable instruments to monitor pre-stressed and/or post-tensioned concrete girders for cracking activity; 2. Design and develop a reusable instrumentation package; 3. Measure performance and condition of concrete girders during fabrication and transport; 4. Identify test protocols and possible accept/fix/reject criteria for structural elements based on information from monitoring system; and 5. Develop plans for reusing monitoring instruments on multiple bridge projects. Presented are results from laboratory, full-scale girder fabrication, and transport monitoring, along with recommendations for future testing procedures and quality assurance protocol development

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions

A Machine Learning Approach to Indoor Localization Data Mining

Indoor positioning systems are increasingly commonplace in various environments and produce large quantities of data. They are used in industrial applications, robotics, asset and employee tracking just to name a few use cases. The growing amount of data and the accelerating progress of machine learning opens up many new possibilities for analyzing this data in ways that were not conceivable or relevant before. This paper introduces connected concepts and implementations to answer question how this data can be utilized. Data gathered in this thesis originates from an indoor positioning system deployed in retail environment, but the discussed methods can be applied generally. The issue will be approached by first introducing the concept of machine learning and more generally, artificial intelligence, and how they work on a general level. A deeper dive is done to subfields and algorithms that are relevant to the data mining task at hand. Indoor positioning system basics are also shortly discussed to create a base understanding on the realistic capabilities and constraints that these kinds of systems encase. These methods and previous knowledge from literature are put to test with the freshly gathered data. An algorithm based on existing example from literature was tested and improved upon with the new data. A novel method to cluster and classify movement patterns was introduced, utilizing deep learning to create embedded representations of the trajectories in a more complex learning pipeline. This type of learning is often referred to as deep clustering. The results are promising and both of the methods produce useful high level representations of the complex dataset that can help a human operator to discern the relevant patterns from raw data and to be used as an input for subsequent supervised and unsupervised learning steps. Several factors related to optimizing the learning pipeline, such as regularization were also researched and the results presented as visualizations. The research found that pipeline consisting of CNN-autoencoder followed by a classic clustering algorithm such as DBSCAN produces useful results in the form of trajectory clusters. Regularization such as L1 regression improves this performance. The research done in this paper presents useful algorithms for processing raw, noisy localization data from indoor environments that can be used for further implementations in both industrial applications and academia

Acoustic Emission Sensing for Crack Monitoring in Prefabricated and Prestressed Reinforced Concrete Bridge Girders

Prefabricated and pre-stressed reinforced concrete beams and girders are integral components of many highway structures, including those built by rapid construction techniques. Concerns exist regarding the development of cracks during curing, form removal, detensioning, transport, installation, and operation. Non-destructive, Acoustic Emission (AE) sensing techniques have the potential for detecting and locating cracking in prefabricated, pre-stressed concrete girders used as Prefabricated Bridge Elements and Systems (PBES) used in rapid construction practices as part of a Quality Assurance/Quality Control (QA/QC) program. AE sensing records transient elastic waves produced by the release of stored elastic energy resulting in plastic deformations (i.e., crack nucleation and growth) with an array of point sensors. The AE instrument system is relatively portable which can allow for it to be an option for both off-site fabrication QA/QC as well as on-site field QA/QC. This thesis presents a multi-stage research initiative on acoustic emission monitoring of prefabricated and pre-stressed reinforced concrete beams used in highway bridge construction during detensioning, craned removal from formwork and transport to bridge sites, along with supporting laboratory tests and numerical analysis. The specific objectives of this research were to: 1. Identify suitable instruments to monitor pre-stressed and/or post-tensioned concrete girders for cracking activity; 2. Design and develop a reusable instrumentation package; 3. Measure performance and condition of concrete girders during fabrication and transport; and 4. Identify test protocols and possible accept/fix/reject criteria for structural elements based on information from monitoring system. Presented are results from laboratory, full-scale girder fabrication, and transport monitoring, along with overall conclusions and recommendations for future research

The Rise of Innovation Districts: A New Geography of Innovation in America

As the United States slowly emerges from the great recession, a remarkable shify is occurring in the spatial geogrpahy of innovation. For the past 50 years, the landscape of innovation has been dominated by places like Silicon Valley - suburban corridors of spatially isolated corporate campuses, accessible only by car, with little emphasis on the quality of life or on integrating work, housing, and recreation. A new complementary urban model is now emerging, giving rise to what we and others are calling "innovation districts." These districts, by our definition, are geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators, and accelerators. They are also physically compact, transit-accessible, and technicall

A Comparison of U. S. and European University-Industry Relations in the Life Sciences

We draw on diverse data sets to compare the institutional organization of upstream life science research across the United States and Europe. Understanding cross-national differences in the organization of innovative labor in the life sciences requires attention to the structure and evolution of biomedical networks involving public research organizations (universities, government laboratories, nonprofit research institutes, and research hospitals), science-based biotechnology firms, and multinational pharmaceutical corporations. We use network visualization methods and correspondence analyses to demonstrate that innovative research in biomedicine has its origins in regional clusters in the United States and in European nations. But the scientific and organizational composition of these regions varies in consequential ways. In the United States, public research organizations and small firms conduct R&D across multiple therapeutic areas and stages of the development process. Ties within and across these regions link small firms and diverse public institutions, contributing to the development of a robust national network. In contrast, the European story is one of regional specialization with a less diverse group of public research organizations working in a smaller number of therapeutic areas. European institutes develop local connections to small firms working on similar scientific problems, while cross-national linkages of European regional clusters typically involve large pharmaceutical corporations. We show that the roles of large and small firms differ in the United States and Europe, arguing that the greater heterogeneity of the U. S. system is based on much closer integration of basic science and clinical development

Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns

This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-dimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators