Evaluation of Stream Assessment Protocols for the Evaluation of Habitat in Intermittent Headwater Streams

EPA and state water resource agencies are now placing greater emphasis on monitoring and managing headwater streams. Two EPA stream protocols are available for headwater stream assessment but little effort has been made to compare these two methods or their resulting habitat quality index scores. The objectives of this effort were to 1) compare data types of the two protocols; 2) compare how the two protocols assess intermittent streams using habitat quality index (HQI) scores; and 3) compare stream characteristic emphases (geomorphology, riparia, substrate, in-stream cover for biota and hydrology) between the two protocols and their effect on overall HQI scores. This study was conducted within the Northern Glaciated Plains Ecoregion (NGP) of South Dakota. Forty reference sites were chosen using EPA’s Analytical Tools Interface for Landscape Assessments (ATtILA). Twenty more sites were chosen to validate the reference sites condition. Ten of the validation sites were selected at random and the other ten were targeted sites selected through consultation with state officials. All sites were field validated using the “North Carolina Division of Water Quality’s Identification Methods for the origins of Intermittent and Perennial Streams” and the “Riparian, Channel, and Environmental Inventory for small streams in the agricultural landscape.” Habitat assessments of 60 total streams occurred monthly (April-August) during the summer of 2008 following EPA’s “Western Pilot Study: Field Operations Manual for Wadeable Streams” and “Field Operations Manual for Assessing the Hydrologic Permanence and Ecological Condition of Headwater Streams.” Headwater streams in the NGP can be summarized as low gradient ( X = 0.02%) streams showing little incision ( X = 0.4 m). Channel dimensions were variable (CV = 1306.1 width/depth ratio) with flat banks ( X = 27.4ºC) and homogenous thalwegs ( X CV = 48.9 %). Substrates consisted of mostly soft/small sediments with herbaceous vegetation as the most frequently occurring instream cover for biota. With the exception of the Prairie Coteau Escarpment Ecoregion (46l), riparian trees were rare. Peck’s protocol had 51 measurements with a mixture of ratio (n = 14), interval (n = 2), ordinal (n = 23) and nominal (n = 12) data types. Fritz’s protocol had 15 measurements yielding mostly ratio (n = 10) data types, and a few interval (n = 2) and nominal data (n = 3). Substrate type was assessed differently by the two protocols. Organic substrates occurred with a frequency of 65% using Peck’s protocol, while the substrate class “sand/silt/clay” occurred most frequently (89%) using Fritz’s protocol. HQI scores for both protocols were compared using a sign test and a Wilcoxon Rank Sum test, revealing that they were different (p \u3c 0.01). Reference HQI scores generated from Fritz metrics ( X = 71%) were higher (p \u3c 0.01) than Peck’s HQI’s ( X = 63%). Riparian metrics composed 51% of Peck’s measurements and 7% of Fritz’s measurements but Peck’s riparian HQI’s scored lower (p \u3c 0.01) than Fritz’s riparian HQI’s. Hydrologic metrics composed 36% of Fritz’s protocol and 4% of Peck’s protocol and still the HQI’s compared favorably between the two protocols. Evaluation of stream assessments within either protocol revealed high variability in stream characteristics within the NGP ecoregion. Stream habitat scores exhibited greater similarity within level IV EPA ecoregions than between ecoregions. This supports that regionalization by level IV ecoregions may be necessary to account for regional differences in landscape features. The use of more measurements for Peck’s protocol increased the ability to detect the influence of human management practices. However, some metrics were similar within Peck’s protocol, leading to high redundancy. Fritz’s protocol contained fewer metrics with less focus on riparian metrics, reducing the sensitivity of this protocol to human management practices. Data types also differed between and within the two protocols, complicating integration and analysis. Peck’s protocol included a large number of ordinal and nominal measurements, which require training and consistency to remain unbiased. Thus, Peck’s assessments were more subjective, adding another source of disparity between protocol assessments. Substrate was the only parameter measured by both protocols, but assessments differed due to the use of different substrate classes and a different cross-sectional methodology. Results of HQI differences provide evidence that the two protocols do not respond similarly to physical habitat changes. This can be attributed to the divergence in stream characteristics emphasized by the two protocols. Differences in metric emphasis reflect a focus on hydrologic permanence by the Fritz protocol and riparian metrics by the Peck protocol. Riparian condition reflect the influence of human activities more successfully based on HQI scores than hydrologic condition. This helps to explain differences seen in HQI scores and provides incentive for the continued use of riparian metrics in stream habitat assessments. A new combined habitat metric set is proposed which places more balance between riparian and hydrologic stream characteristics

Interactive sonification exploring emergent behavior applying models for biological information and listening

Sonification is an open-ended design task to construct sound informing a listener of data. Understanding application context is critical for shaping design requirements for data translation into sound. Sonification requires methodology to maintain reproducibility when data sources exhibit non-linear properties of self-organization and emergent behavior. This research formalizes interactive sonification in an extensible model to support reproducibility when data exhibits emergent behavior. In the absence of sonification theory, extensibility demonstrates relevant methods across case studies. The interactive sonification framework foregrounds three factors: reproducible system implementation for generating sonification; interactive mechanisms enhancing a listener's multisensory observations; and reproducible data from models that characterize emergent behavior. Supramodal attention research suggests interactive exploration with auditory feedback can generate context for recognizing irregular patterns and transient dynamics. The sonification framework provides circular causality as a signal pathway for modeling a listener interacting with emergent behavior. The extensible sonification model adopts a data acquisition pathway to formalize functional symmetry across three subsystems: Experimental Data Source, Sound Generation, and Guided Exploration. To differentiate time criticality and dimensionality of emerging dynamics, are applied between subsystems to maintain scale and symmetry of concurrent processes and temporal dynamics. Tuning functions accommodate sonification design strategies that yield order parameter values to render emerging patterns discoverable as well as , to reproduce desired instances for clinical listeners. Case studies are implemented with two computational models, Chua's circuit and Swarm Chemistry social agent simulation, generating data in real-time that exhibits emergent behavior. is introduced as an informal model of a listener's clinical attention to data sonification through multisensory interaction in a context of structured inquiry. Three methods are introduced to assess the proposed sonification framework: Listening Scenario classification, data flow Attunement, and Sonification Design Patterns to classify sound control. Case study implementations are assessed against these methods comparing levels of abstraction between experimental data and sound generation. Outcomes demonstrate the framework performance as a reference model for representing experimental implementations, also for identifying common sonification structures having different experimental implementations, identifying common functions implemented in different subsystems, and comparing impact of affordances across multiple implementations of listening scenarios

On Resilient Control for Secure Connected Vehicles: A Hybrid Systems Approach

According to the Internet of Things Forecast conducted by Ericsson, connected devices will be around 29 billion by 2022. This technological revolution enables the concept of Cyber-Physical Systems (CPSs) that will transform many applications, including power-grid, transportation, smart buildings, and manufacturing. Manufacturers and institutions are relying on technologies related to CPSs to improve the efficiency and performances of their products and services. However, the higher the number of connected devices, the higher the exposure to cybersecurity threats. In the case of CPSs, successful cyber-attacks can potentially hamper the economy and endanger human lives. Therefore, it is of paramount importance to develop and adopt resilient technologies that can complement the existing security tools to make CPSs more resilient to cyber-attacks. By exploiting the intrinsically present physical characteristics of CPSs, this dissertation employs dynamical and control systems theory to improve the CPS resiliency to cyber-attacks. In particular, we consider CPSs as Networked Control Systems (NCSs), which are control systems where plant and controller share sensing and actuating information through networks. This dissertation proposes novel design procedures that maximize the resiliency of NCSs to network imperfections (i.e., sampling, packet dropping, and network delays) and denial of service (DoS) attacks. We model CPSs from a general point of view to generate design procedures that have a vast spectrum of applicability while creating computationally affordable algorithms capable of real-time performances. Indeed, the findings of this research aspire to be easily applied to several CPSs applications, e.g., power grid, transportation systems, and remote surgery. However, this dissertation focuses on applying its theoretical outcomes to connected and automated vehicle (CAV) systems where vehicles are capable of sharing information via a wireless communication network. In the first part of the dissertation, we propose a set of LMI-based constructive Lyapunov-based tools for the analysis of the resiliency of NCSs, and we propose a design approach that maximizes the resiliency. In the second part of the thesis, we deal with the design of DOS-resilient control systems for connected vehicle applications. In particular, we focus on the Cooperative Adaptive Cruise Control (CACC), which is one of the most popular and promising applications involving CAVs

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

Predictive control approaches to fault tolerant control of wind turbines

This thesis focuses on active fault tolerant control (AFTC) of wind turbine systems. Faults in wind turbine systems can be in the form of sensor faults, actuator faults, or component faults. These faults can occur in different locations, such as the wind speed sensor, the generator system, drive train system or pitch system. In this thesis, some AFTC schemes are proposed for wind turbine faults in the above locations. Model predictive control (MPC) is used in these schemes to design the wind turbine controller such that system constraints and dual control goals of the wind turbine are considered. In order to deal with the nonlinearity in the turbine model, MPC is combined with Takagi-Sugeno (T-S) fuzzy modelling. Different fault diagnosis methods are also proposed in different AFTC schemes to isolate or estimate wind turbine faults.The main contributions of the thesis are summarized as follows:A new effective wind speed (EWS) estimation method via least-squares support vector machines (LSSVM) is proposed. Measurements from the wind turbine rotor speed sensor and the generator speed sensor are utilized by LSSVM to estimate the EWS. Following the EWS estimation, a wind speed sensor fault isolation scheme via LSSVM is proposed.A robust predictive controller is designed to consider the EWS estimation error. This predictive controller serves as the baseline controller for the wind turbine system operating in the region below rated wind speed.T-S fuzzy MPC combining MPC and T-S fuzzy modelling is proposed to design the wind turbine controller. MPC can deal with wind turbine system constraints externally. On the other hand, T-S fuzzy modelling can approximate the nonlinear wind turbine system with a linear time varying (LTV) model such that controller design can be based on this LTV model. Therefore, the advantages of MPC and T-S fuzzy modelling are both preserved in the proposed T-S fuzzy MPC.A T-S fuzzy observer, based on online eigenvalue assignment, is proposed as the sensor fault isolation scheme for the wind turbine system. In this approach, the fuzzy observer is proposed to deal with the nonlinearity in the wind turbine system and estimate system states. Furthermore, the residual signal generated from this fuzzy observer is used to isolate the faulty sensor.A sensor fault diagnosis strategy utilizing both analytical and hardware redundancies is proposed for wind turbine systems. This approach is proposed due to the fact that in the real application scenario, both analytical and hardware redundancies of wind turbines are available for designing AFTC systems.An actuator fault estimation method based on moving horizon estimation (MHE) is proposed for wind turbine systems. The estimated fault by MHE is then compensated by a T-S fuzzy predictive controller. The fault estimation unit and the T-S fuzzy predictive controller are combined to form an AFTC scheme for wind turbine actuator faults

Geographic Information Systems for Real-Time Environmental Sensing at Multiple Scales

The purpose of this investigation was to design, implement, and apply a real-time geographic information system for data intensive water resource research and management. The research presented is part of an ongoing, interdisciplinary research program supporting the development of the Intelligent River® observation instrument. The objectives of this research were to 1) design and describe software architecture for a streaming environmental sensing information system, 2) implement and evaluate the proposed information system, and 3) apply the information system for monitoring, analysis, and visualization of an urban stormwater improvement project located in the City of Aiken, South Carolina, USA. This research contributes to the fields of software architecture and urban ecohydrology. The first contribution is a formal architectural description of a streaming environmental sensing information system. This research demonstrates the operation of the information system and provides a reference point for future software implementations. Contributions to urban ecohydrology are in three areas. First, a characterization of soil properties for the study region of the City of Aiken, SC is provided. The analysis includes an evaluation of spatial structure for soil hydrologic properties. Findings indicate no detectable structure at the scales explored during the study. The second contribution to ecohydrology comes from a long-term, continuous monitoring program for bioinfiltration basin structures located in the study area. Results include an analysis of soil moisture dynamics based on data collected at multiple depths with high spatial and temporal resolution. A novel metric is introduced to evaluate the long-term performance of bioinfiltration basin structures based on soil moisture observation data. Findings indicate a decrease in basin performance over time for the monitored sites. The third contribution to the field of ecohydrology is the development and application of a spatially and temporally explicit rainfall infiltration and excess model. The model enables the simulation and visualization of bioinfiltration basin hydrologic response at within-catchment scales. The model is validated against observed soil moisture data. Results include visualizations and stormwater volume calculations based on measured versus predicted bioinfiltration basin performance over time

A survey on online monitoring approaches of computer-based systems

This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use