Geometric, Semantic, and System-Level Scene Understanding for Improved Construction and Operation of the Built Environment

Recent advances in robotics and enabling fields such as computer vision, deep learning, and low-latency data passing offer significant potential for developing efficient and low-cost solutions for improved construction and operation of the built environment. Examples of such potential solutions include the introduction of automation in environment monitoring, infrastructure inspections, asset management, and building performance analyses. In an effort to advance the fundamental computational building blocks for such applications, this dissertation explored three categories of scene understanding capabilities: 1) Localization and mapping for geometric scene understanding that enables a mobile agent (e.g., robot) to locate itself in an environment, map the geometry of the environment, and navigate through it; 2) Object recognition for semantic scene understanding that allows for automatic asset information extraction for asset tracking and resource management; 3) Distributed coupling analysis for system-level scene understanding that allows for discovery of interdependencies between different built-environment processes for system-level performance analyses and response-planning. First, this dissertation advanced Simultaneous Localization and Mapping (SLAM) techniques for convenient and low-cost locating capabilities compared with previous work. To provide a versatile Real-Time Location System (RTLS), an occupancy grid mapping enhanced visual SLAM (vSLAM) was developed to support path planning and continuous navigation that cannot be implemented directly on vSLAM’s original feature map. The system’s localization accuracy was experimentally evaluated with a set of visual landmarks. The achieved marker position measurement accuracy ranges from 0.039m to 0.186m, proving the method’s feasibility and applicability in providing real-time localization for a wide range of applications. In addition, a Self-Adaptive Feature Transform (SAFT) was proposed to improve such an RTLS’s robustness in challenging environments. As an example implementation, the SAFT descriptor was implemented with a learning-based descriptor and integrated into a vSLAM for experimentation. The evaluation results on two public datasets proved the feasibility and effectiveness of SAFT in improving the matching performance of learning-based descriptors for locating applications. Second, this dissertation explored vision-based 1D barcode marker extraction for automated object recognition and asset tracking that is more convenient and efficient than the traditional methods of using barcode or asset scanners. As an example application in inventory management, a 1D barcode extraction framework was designed to extract 1D barcodes from video scan of a built environment. The performance of the framework was evaluated with video scan data collected from an active logistics warehouse near Detroit Metropolitan Airport (DTW), demonstrating its applicability in automating inventory tracking and management applications. Finally, this dissertation explored distributed coupling analysis for understanding interdependencies between processes affecting the built environment and its occupants, allowing for accurate performance and response analyses compared with previous research. In this research, a Lightweight Communications and Marshalling (LCM)-based distributed coupling analysis framework and a message wrapper were designed. This proposed framework and message wrapper were tested with analysis models from wind engineering and structural engineering, where they demonstrated the abilities to link analysis models from different domains and reveal key interdependencies between the involved built-environment processes.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155042/1/lichaox_1.pd

Models and Algorithms for Private Data Sharing

In recent years, there has been a tremendous growth in the collection of digital information about individuals. Many organizations such as governmental agencies, hospitals, and financial companies collect and disseminate various person-specific data. Due to the rapid advance in the storing, processing, and networking capabilities of the computing devices, the collected data can now be easily analyzed to infer valuable information for research and business purposes. Data from different sources can be integrated and further analyzed to gain better insights. On one hand, the collected data offer tremendous opportunities for mining useful information. On the other hand, the mining process poses a threat to individual privacy since these data often contain sensitive information. In this thesis, we address the problem of developing anonymization algorithms to thwart potential privacy attacks in different real-life data sharing scenarios. In particular, we study two privacy models: LKC-privacy and differential privacy. For each of these models, we develop algorithms for anonymizing different types of data such as relational data, trajectory data, and heterogeneous data. We also develop algorithms for distributed data where multiple data publishers cooperate to integrate their private data without violating the given privacy requirements. Experimental results on the real-life data demonstrate that the proposed anonymization algorithms can effectively retain the essential information for data analysis and are scalable for large data sets

Prognostics and Health Management of Electronics by Utilizing Environmental and Usage Loads

Prognostics and health management (PHM) is a method that permits the reliability of a system to be evaluated in its actual application conditions. Thus by determining the advent of failure, procedures can be developed to mitigate, manage and maintain the system. Since, electronic systems control most systems today and their reliability is usually critical for system reliability, PHM techniques are needed for electronics. To enable prognostics, a methodology was developed to extract load-parameters required for damage assessment from irregular time-load data. As a part of the methodology an algorithm that extracts cyclic range and means, ramp-rates, dwell-times, dwell-loads and correlation between load parameters was developed. The algorithm enables significant reduction of the time-load data without compromising features that are essential for damage estimation. The load-parameters are stored in bins with a-priori calculated (optimal) bin-width. The binned data is then used with Gaussian kernel function for density estimation of the load-parameter for use in damage assessment and prognostics. The method was shown to accurately extract the desired load-parameters and enable condensed storage of load histories, thus improving resource efficiency of the sensor nodes. An approach was developed to assess the impact of uncertainties in measurement, model-input, and damage-models on prognostics. The approach utilizes sensitivity analysis to identify the dominant input variables that influence the model-output, and uses the distribution of measured load-parameters and input variables in a Monte-Carlo simulation to provide a distribution of accumulated damage. Using regression analysis of the accumulated damage distributions, the remaining life is then predicted with confidence intervals. The proposed method was demonstrated using an experimental setup for predicting interconnect failures on electronic board subjected to field conditions. A failure precursor based approach was developed for remaining life prognostics by analyzing resistance data in conjunction with usage temperature loads. Using the data from the PHM experiment, a model was developed to estimate the resistance based on measured temperature values. The difference between actual and estimated resistance value in time-domain were analyzed to predict the onset and progress of interconnect degradation. Remaining life was predicted by trending several features including mean-peaks, kurtosis, and 95% cumulative-values of the resistance-drift distributions

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Road traffic incident management and situational awareness

Rietveld, P. [Promotor]Scholten, H.J. [Promotor]Vlist, M. van der [Copromotor

Forging a Stable Relationship?: Bridging the Law and Forensic Science Divide in the Academy

The marriage of law and science has most often been represented as discordant. While the law/science divide meme is hardly novel, concerns over the potentially deleterious coupling within the criminal justice system may have reached fever pitch. There is a growing chorus of disapproval addressed to ‘forensic science’, accompanied by the denigration of legal professionals for being unable or unwilling to forge a symbiotic relationship with forensic scientists. The 2009 National Academy of Sciences Report on forensic science heralds the latest call for greater collaboration between ‘law’ and ‘science’, particularly in Higher Education Institutions (HEIs) yet little reaction has been apparent amid law and science faculties. To investigate the potential for interdisciplinary cooperation, the authors received funding for a project: ‘Lowering the Drawbridges: Forensic and Legal Education in the 21st Century’, hoping to stimulate both law and forensic science educators to seek mutually beneficial solutions to common educational problems and build vital connections in the academy. A workshop held in the UK, attended by academics and practitioners from scientific, policing, and legal backgrounds marked the commencement of the project. This paper outlines some of the workshop conclusions to elucidate areas of dissent and consensus, and where further dialogue is required, but aims to strike a note of optimism that the ‘cultural divide’ should not be taken to be so wide as to be beyond the legal and forensic science academy to bridge. The authors seek to demonstrate that legal and forensic science educators can work cooperatively to respond to critics and forge new paths in learning and teaching, creating an opportunity to take stock and enrich our discipline as well as answer critics. As Latham (2010:34) exhorts, we are not interested in turning lawyers into scientists and vice versa, but building a foundation upon which they can build during their professional lives: “Instead of melding the two cultures, we need to establish conditions of cooperation, mutual respect, and mutual reliance between them.” Law and forensic science educators should, and can assist with the building of a mutual understanding between forensic scientists and legal professionals, a significant step on the road to answering calls for the professions to minimise some of the risks associated with the use of forensic science in the criminal process. REFERENCES Latham, S.R. 2010, ‘Law between the cultures: C.P.Snow’s The Two Cultures and the problem of scientific illiteracy in law’ 32 Technology in Society, 31-34. KEYWORDS forensic science education legal education law/science divid

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters