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Anomaly detection is a relevant problem in the field of Mechanical Engineering, because the analysis of mechanical systems often relies on identifying deviations from what is considered "normal". The mechanical sciences are represented by a heterogeneous collection of data types: some systems may be highly dimensional, may contain exclusively spatial or temporal data, may be spatiotemporally linked, or may be non-deterministic and best described probabilistically. Given the broad range of data types in this field, it is not possible to propose a single processing method that will be appropriate, or even usable, for all data types. This has led to human observation remaining a common, albeit costly and inefficient, approach to detecting anomalous signals or patterns in mechanical data. The advantages of automated anomaly detection in mechanical systems include reduced monitoring costs, increased reliability of fault detection, and improved safety for users and operators. This dissertation proposes a hierarchical framework for anomaly detection through machine learning, and applies it to three distinct and heterogeneous data types: state-based data, parameter-driven data, and spatiotemporal sensor network data. In time-series data, anomaly detection results were robust in synthetic data generated using multiple simulation algorithms, as well as experimental data from rolling element bearings, with highly accurate detection rates (>99% detection, <1% false alarm). Significant developments were shown in parameter-driven data by reducing the sample sizes necessary for analysis, as well as reducing the time required for computation. The event-space model extends previous work into a geospatial sensor network and demonstrates applications of this type of event modeling at various timescales, and compares the model to results obtained using other approaches. Each data type is processed in a unique way relative to the others, but all are fitted to the same hierarchical structure for system modeling. This hierarchical model is the key development proposed by this dissertation, and makes both novel and significant contributions to the fields of mechanical analysis and data processing. This work demonstrates the effectiveness of the developed approaches, details how they differ from other relevant industry standard methods, and concludes with a proposal for additional research into other data types. / Ph. D.

Nous nous intéressons dans cette thèse à l'organisation des systèmes distribués dynamiquesde grande taille : ensembles de machines capables de communiquer entre elles et pouvant à toutinstant se connecter ou se déconnecter. Nous proposons de partitionner le système en groupesconnexes, appelés clusters. Afin d'organiser des réseaux de grande taille, nous construisons unestructure hiérarchique imbriquée dans laquelle les clusters d'un niveau sont regroupés au seinde clusters du niveau supérieur. Pour mener à bien ce processus, nous mettons en place desmécanismes permettant aux clusters d'être les noeuds d'un nouveau système distribué exécutantl'algorithme de notre choix. Cela nécessite en particulier des mécanismes assurant la cohérence decomportement pour le niveau supérieur au sein de chaque cluster. En permettant aux clusters deconstituer un nouveau système distribué exécutant notre algorithme de clustering, nous construisonsune hiérarchie de clusters par une approche ascendante. Nous démontrons cet algorithme endéfinissant formellement le système distribué des clusters, et en démontrant que chaque exécutionde notre algorithme induit sur ce système une exécution de l'algorithme de niveau supérieur. Celanous permet, en particulier, de démontrer par récurrence que nous calculons bien un clusteringhiérarchique imbriqué. Enfin, nous appliquons cette démarche à la résolution des collisions dansles réseaux de capteurs. Pour éviter ce phénomène, nous proposons de calculer un clusteringadapté du système, qui nous permet de calculer un planning organisant les communications ausein du réseau et garantissant que deux messages ne seront jamais émis simultanément dans laportée de communication de l'un des capteurs / To manage and handle large scale distributed dynamic distributed systems, constitutedby communicating devices that can connect or disconnect at any time, we propose to computeconnected subgraphs of the system, called clusters. We propose to compute a hierarchical structure,in which clusters of a level are grouped into clusters of the higher level. To achieve this goal,we introduce mechanisms that allow clusters to be the nodes of a distinct distributed system,that executes an algorithm. In particular, we need mechanisms to maintain the coherence of thebehavior among the nodes of a cluster regarding the higher level. By allowing clusters to be nodesof a distributed system that executes a clustering algorithm, we compute a nested hierarchicalclustering by a bottom-up approach. We formally define the distributed system of clusters, andprove that any execution of our algorithm induces an execution of the higher level algorithm onthe distributed system of clusters. Then, we prove by induction that our algorithm computes anested hierarchical clustering of the system. Last, we use this approach to solve a problem thatappears in sensor networks : collision. To avoid collisions, we propose to compute a clusteringof the system. This clustering is then used to compute a communication schedule in which twomessages cannot be sent at the same time in the range of a sensor

Indiana University-Purdue University Indianapolis (IUPUI) / Associated to any finite simple graph $\Gamma$ is the {\em chromatic polynomial} $\P_\Gamma(q)$ whose complex zeros are called the {\em chromatic zeros} of $\Gamma$. A hierarchical lattice is a sequence of finite simple graphs $\{\Gamma_n\}_{n=0}^\infty$ built recursively using a substitution rule expressed in terms of a generating graph. For each $n$, let $\mu_n$ denote the probability measure that assigns a Dirac measure to each chromatic zero of $\Gamma_n$. Under a mild hypothesis on the generating graph, we prove that the sequence $\mu_n$ converges to some measure $\mu$ as $n$ tends to infinity. We call $\mu$ the {\em limiting measure of chromatic zeros} associated to $\{\Gamma_n\}_{n=0}^\infty$. In the case of the Diamond Hierarchical Lattice we prove that the support of $\mu$ has Hausdorff dimension two. The main techniques used come from holomorphic dynamics and more specifically the theories of activity/bifurcation currents and arithmetic dynamics. We prove a new equidistribution theorem that can be used to relate the chromatic zeros of a hierarchical lattice to the activity current of a particular marked point. We expect that this equidistribution theorem will have several other applications, and describe one such example in statistical mechanics about the Lee-Yang-Fisher zeros for the Cayley Tree.

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Human valuing is a topic of study in many disciplines concerned with the behavior of humans in terms of its relationship to individual as well as group behavior. Many disciplines provide a theory of how values effect behavior, however a behavior analytic approach may demonstrate utility in terms of both understanding the formation of values as well as procedures that incorporate valuing into interventions for common psychological problems. Relational Frame Theory (RFT), a psychological account of human language and cognition, which has its foundation in behavior analysis, may provide an empirically-valid account of the formation of values and the mechanisms though which it effects behavior. Language processes including hierarchical, or categorical, relational responding, the transformation of consequential stimulus function, and rule-governed behavior may contribute to the act of human valuing. Acceptance and Commitment Therapy (ACT), a clinical derivative of RFT, incorporates values as a central component of treatment. This series of three studies sought to add to the empirical understanding of human valuing through two basic and one translational study. Study 1 examined the transformation of consequential stimulus functions in accordance with hierarchical networks, completed in a multiple baseline design. Results of this study suggest that, given sufficient strength of derived relations, the transformation was demonstrated by five of six participants. Study 2 evaluated the motivative effect of stimuli in a hierarchical relational network, completed in a multiple baseline design. The results suggest that in the presence of directly trained stimuli the motivative augmentals did not influence responding for four of four participants, however they did in a novel context for three of three participants. Study 3 sought to measure the effect of an arbitrary symbol related to a values-focused hierarchy as a motivative augmental for academic performance with a sample of undergraduate university students in a classroom setting. Together, these studies reflect a number of the languages processes necessary if an RFT-focused conceptualization of human valuing is accurate.

The Hutchinsonian niche is the n dimensional hyper volume that allows for the persistence of a species. Castor canadensis, a large semi-aquatic rodent, is an ecosystem engineer and often a keystone species for many ecosystems. I examined the effect of multiple spatial scales on hierarchical habitat selection byC. canadensis using presence-only modeling techniques. I also determined individual trophic niche specialization in C. canadensis utilizing stable isotope analysis. I concluded that C. canadensis displayed scale independent habitat selection when comparing landscape and fine spatial scales. Individual trophic niche specialization occurred in colonies of the same resource availability. Also, individual trophic niches varied substantially between wetlands. These results have implications for the management of “generalist” species because populations can be composed of specialized individuals. Studies of niche across spatial and organizational scales are required for successful conservation and management strategies.

The scope of the current study focuses on the relationship between direct and indirect methods of measuring social skills in children. Participants included 33 children between the ages of 6 and 11 years old. The sample drew from elementary schools in 2 Southern states in the U.S., as well as social skills groups from a university-based clinic. While some participants had been previously identified has having disabilities impacting social performance, it was not an inclusionary requirement and the majority of children were not identified as having a disability clinically or through a special education eligibility domain. Teachers and clinicians leading social skills groups completed indirect measures, the Behavior Assessment Scale for Children Third Edition (BASC-3) and the Social Skills Improvement System (SSIS) related to the participants’ social skills. Direct observations of participants were completed using the Social Observation System (SOS) by graduate level research assistants. Hierarchical multiple regression analyses were conducted to determine the predictive value of the teacher informed indirect measures on the direct method of observation. Additionally, simple linear regression analyses were conducted to examine the reverse relationship of the direct observation’s ability to predict the variance observed in each indirect measure. Results indicated that both the indirect and direct methods of social skills assessment can significantly predict the other. However, while significant, a low to moderate amount of variance in the direct measure is explained by the indirect measures of social skills. The results and implications of the finding are discussed, as well as limitations and future directions.

Depression in mothers is an important risk factor for behavioral and emotional problems in their children (Elgar, McGrath, Waschbusch, Stewart, & Curtis 2004), and disrupted parenting is thought to mediate the influences of maternal depression on children. This 4-year longitudinal study examined whether mothers’ depression predicted parenting of children with behavioral problems across the preschool years. This study attempted to tease apart the correlates of enduring, chronic maternal depressive symptoms from those of transient depressive symptoms on parenting during the preschool years. In particular, it sought to predict both changes in parenting across the preschool years as well as to predict parenting practices as parents and children emerge from the preschool years. Participants were 199 mothers of 3-year-old children, with behavior problems who completed measures of depression and parenting yearly until children were 6 years old. Mothers with higher average depressive symptoms across the preschool years reported more overreactivity and laxness, and showed less warmth when their children were 6 years old. These mothers were also more likely to increase their self-reported overreactivity over time. Increases in depression were associated with increases in overreactivity and laxness, but not in warmth. These results provide stronger evidence than previous cross-sectional studies for a causal relation between depression and parenting, and point to the importance of providing adequate treatment and support to depressed mothers of preschool children.