Delineating demographic units of woodland caribou (Rangifer tarandus caribou) in Ontario: cautions and insights

Delineating demographic structure across an organism’s range can reveal the extent to which population dynamics in different geographic areas are driven by local or external factors and can be crucial for effective conservation and management. Obtaining optimal data for such analyses can be time and resource-intensive and impending development and resource extraction pressures may necessitate the examination of existing data, even when they are less than ideal. We analyzed a historic telemetry dataset containing satellite radio-collar locations of 73 forest-dwelling woodland caribou in northern Ontario to determine demographic structure. We applied several clustering methods (i.e., agglomerative, divisive and fuzzy k-means) to median seasonal locations. Results were used to distinguish demographic units and minimum convex polygons and fixed-kernel density estimates were used to delineate unit boundaries and core areas. For areas where sampling was considered representative of the distribution of caribou on the landscape, we assessed demographic distinctness by evaluating intra-individual variation in cluster membership, membership strength and distance between boundaries and core areas of adjacent units. The number and composition of clusters identified was similar among methods and caribou were grouped into 6 general clusters. The distinctions between the three clusters identified in the central portion of the province (i.e., Lac Seul, Wabakimi, Geraldton) and the two clusters identified in the eastern portion of the province (i.e., Cochrane and Cochrane-Quebec) were determined to represent demographic structuring. Additional distinctions in other areas (i.e., between The Red Lake and Lac Seul clusters in the west and between the central and eastern clusters) may just be artifacts of the original sampling effort. Amongst demographic units, there was no evidence of individual flexibility in cluster membership and average membership strength was very high. There was little to no overlap between boundaries and core areas of adjacent units, but distances between adjacent unit boundaries were relatively low. Additional sampling effort is needed to further delineate demographic structure in Ontario caribou

Predicting software faults in large space systems using machine learning techniques

Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of engineering problems including the prediction of failure, fault, and defect-proneness as the space system software becomes complex. One of the most active areas of recent research in ML has been the use of ensemble classifiers. How ML techniques (or classifiers) could be used to predict software faults in space systems, including many aerospace systems is shown, and further use ensemble individual classifiers by having them vote for the most popular class to improve system software fault-proneness prediction. Benchmarking results on four NASA public datasets show the Naive Bayes classifier as more robust software fault prediction while most ensembles with a decision tree classifier as one of its components achieve higher accuracy rates

Predicting software faults in large space systems using machine learning techniques

Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of engineering problems including the prediction of failure, fault, and defect-proneness as the space system software becomes complex. One of the most active areas of recent research in ML has been the use of ensemble classifiers. How ML techniques (or classifiers) could be used to predict software faults in space systems, including many aerospace systems is shown, and further use ensemble individual classifiers by having them vote for the most popular class to improve system software fault-proneness prediction. Benchmarking results on four NASA public datasets show the Naive Bayes classifier as more robust software fault prediction while most ensembles with a decision tree classifier as one of its components achieve higher accuracy rates

Design and Electronic Implementation of Machine Learning-based Advanced Driving Assistance Systems

200 p.Esta tesis tiene como objetivo contribuir al desarrollo y perfeccionamiento de sistemas avanzados a la conducción (ADAS). Para ello, basándose en bases de datos de conducción real, se exploran las posibilidades de personalización de los ADAS existentes mediante técnicas de machine learning, tales como las redes neuronales o los sistemas neuro-borrosos. Así, se obtienen parámetros característicos del estilo cada conductor que ayudan a llevar a cabo una personalización automatizada de los ADAS que equipe el vehículo, como puede ser el control de crucero adaptativo. Por otro lado, basándose en esos mismos parámetros de estilo de conducción, se proponen nuevos ADAS que asesoren a los conductores para modificar su estilo de conducción, con el objetivo de mejorar tanto el consumo de combustible y la emisión de gases de efecto invernadero, como el confort de marcha. Además, dado que esta personalización tiene como objetivo que los sistemas automatizados imiten en cierta manera, y siempre dentro de parámetros seguros, el estilo del conductor humano, se espera que contribuya a incrementar la aceptación de estos sistemas, animando a la utilización y, por tanto, contribuyendo positivamente a la mejora de la seguridad, de la eficiencia energética y del confort de marcha. Además, estos sistemas deben ejecutarse en una plataforma que sea apta para ser embarcada en el automóvil, y, por ello, se exploran las posibilidades de implementación HW/SW en dispositivos reconfigurables tipo FPGA. Así, se desarrollan soluciones HW/SW que implementan los ADAS propuestos en este trabajo con un alto grado de exactitud, rendimiento, y en tiempo real

An Investigation in Efficient Spatial Patterns Mining

The technical progress in computerized spatial data acquisition and storage results in the growth of vast spatial databases. Faced with large amounts of increasing spatial data, a terminal user has more difficulty in understanding them without the helpful knowledge from spatial databases. Thus, spatial data mining has been brought under the umbrella of data mining and is attracting more attention. Spatial data mining presents challenges. Differing from usual data, spatial data includes not only positional data and attribute data, but also spatial relationships among spatial events. Further, the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships, so the mining of spatial patterns demands new techniques. In this thesis, several contributions were made. Some new techniques were proposed, i.e., fuzzy co-location mining, CPI-tree (Co-location Pattern Instance Tree), maximal co-location patterns mining, AOI-ags (Attribute-Oriented Induction based on Attributes’ Generalization Sequences), and fuzzy association prediction. Three algorithms were put forward on co-location patterns mining: the fuzzy co-location mining algorithm, the CPI-tree based co-location mining algorithm (CPI-tree algorithm) and the orderclique- based maximal prevalence co-location mining algorithm (order-clique-based algorithm). An attribute-oriented induction algorithm based on attributes’ generalization sequences (AOI-ags algorithm) is further given, which unified the attribute thresholds and the tuple thresholds. On the two real-world databases with time-series data, a fuzzy association prediction algorithm is designed. Also a cell-based spatial object fusion algorithm is proposed. Two fuzzy clustering methods using domain knowledge were proposed: Natural Method and Graph-Based Method, both of which were controlled by a threshold. The threshold was confirmed by polynomial regression. Finally, a prototype system on spatial co-location patterns’ mining was developed, and shows the relative efficiencies of the co-location techniques proposed The techniques presented in the thesis focus on improving the feasibility, usefulness, effectiveness, and scalability of related algorithm. In the design of fuzzy co-location Abstract mining algorithm, a new data structure, the binary partition tree, used to improve the process of fuzzy equivalence partitioning, was proposed. A prefix-based approach to partition the prevalent event set search space into subsets, where each sub-problem can be solved in main-memory, was also presented. The scalability of CPI-tree algorithm is guaranteed since it does not require expensive spatial joins or instance joins for identifying co-location table instances. In the order-clique-based algorithm, the co-location table instances do not need be stored after computing the Pi value of corresponding colocation, which dramatically reduces the executive time and space of mining maximal colocations. Some technologies, for example, partitions, equivalence partition trees, prune optimization strategies and interestingness, were used to improve the efficiency of the AOI-ags algorithm. To implement the fuzzy association prediction algorithm, the “growing window” and the proximity computation pruning were introduced to reduce both I/O and CPU costs in computing the fuzzy semantic proximity between time-series. For new techniques and algorithms, theoretical analysis and experimental results on synthetic data sets and real-world datasets were presented and discussed in the thesis

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Optimization Modeling and Machine Learning Techniques Towards Smarter Systems and Processes

The continued penetration of technology in our daily lives has led to the emergence of the concept of Internet-of-Things (IoT) systems and networks. An increasing number of enterprises and businesses are adopting IoT-based initiatives expecting that it will result in higher return on investment (ROI) [1]. However, adopting such technologies poses many challenges. One challenge is improving the performance and efficiency of such systems by properly allocating the available and scarce resources [2, 3]. A second challenge is making use of the massive amount of data generated to help make smarter and more informed decisions [4]. A third challenge is protecting such devices and systems given the surge in security breaches and attacks in recent times [5]. To that end, this thesis proposes the use of various optimization modeling and machine learning techniques in three different systems; namely wireless communication systems, learning management systems (LMSs), and computer network systems. In par- ticular, the first part of the thesis posits optimization modeling techniques to improve the aggregate throughput and power efficiency of a wireless communication network. On the other hand, the second part of the thesis proposes the use of unsupervised machine learning clustering techniques to be integrated into LMSs to identify unengaged students based on their engagement with material in an e-learning environment. Lastly, the third part of the thesis suggests the use of exploratory data analytics, unsupervised machine learning clustering, and supervised machine learning classification techniques to identify malicious/suspicious domain names in a computer network setting. The main contributions of this thesis can be divided into three broad parts. The first is developing optimal and heuristic scheduling algorithms that improve the performance of wireless systems in terms of throughput and power by combining wireless resource virtualization with device-to-device and machine-to-machine communications. The second is using unsupervised machine learning clustering and association algorithms to determine an appropriate engagement level model for blended e-learning environments and study the relationship between engagement and academic performance in such environments. The third is developing a supervised ensemble learning classifier to detect malicious/suspicious domain names that achieves high accuracy and precision