Role of kilometer-scale weak circular heterogeneities on upper crustal deformation patterns: Evidence from scaled analogue modeling and the Sudbury Basin, Canada

The Sudbury Basin is a non-cylindrical fold basin occupying the central portion of the Sudbury Impact Structure. The impact structure lends itself excellently to explore the structural evolution of continental crust containing a circular region of long-term weakness. In a series of scaled analogue experiments various model crustal configurations were shortened horizontally at a constant rate. In mechanically weakened crust, model basins formed that mimic several first-order structural characteristics of the Sudbury Basin: (1) asymmetric, non-cylindrical folding of the Basin, (2) structures indicating concentric shortening around lateral basin termini and (3) the presence of a zone of strain concentration near the hinge zones of model basins. Geometrically and kinematically this zone corresponds to the South Range Shear Zone of the Sudbury Basin. According to our experiments, this shear zone is a direct mechanical consequence of basin formation, rather than the result of thrusting following folding. Overall, the models highlight the structurally anomalous character of the Sudbury Basin within the Paleoproterozoic Eastern Penokean Orogen. In particular, our models suggest that the Basin formed by pure shear thickening of crust, whereas transpressive deformation prevailed elsewhere in the orogen. The model basin is deformed by thickening and non-cylindrical synformal buckling, while conjugate transpressive shear zones propagated away from its lateral tips. This is consistent with pure shear deformation of a weak circular inclusion in a strong matrix. The models suggest that the Sudbury Basin formed as a consequence of long-term weakening of the upper crust by meteorite impact.

Modeling and detection of complex attacks

A complex attack is a sequence of temporally and spatially separated legal and illegal actions each of which can be detected by various IDS but as a whole they constitute a powerful attack. IDS fall short of detecting and modeling complex attacks therefore new methods are required. This paper presents a formal methodology for modeling and detection of complex attacks in three phases: (1) we extend basic attack tree (AT) approach to capture temporal dependencies between components and expiration of an attack, (2) using enhanced AT we build a tree automaton which accepts a sequence of actions from input message streams from various sources if there is a traversal of an AT from leaves to root, and (3) we show how to construct an enhanced parallel automaton that has each tree automaton as a subroutine. We use simulation to test our methods, and provide a case study of representing attacks in WLANs.

Modeling and multiway analysis of chatroom tensors

This work identifies the limitations of n-way data analysis techniques in multidimensional stream data, such as Internet chat room communications data, and establishes a link between data collection and performance of these techniques. Its contributions are twofold. First, it extends data analysis to multiple dimensions by constructing n-way data arrays known as high order tensors. Chat room tensors are generated by a simulator which collects and models actual communication data. The accuracy of the model is determined by the Kolmogorov-Smirnov goodness-of-fit test which compares the simulation data with the observed (real) data. Second, a detailed computational comparison is performed to test several data analysis techniques including svd [1], and multi-way techniques including Tucker1, Tucker3 [2], and Parafac [3].

Low temperature pretreatment of sugarcane bagasse at atmospheric pressure using mixtures of ethylene carbonate and ethylene glycol

A low temperature lignocellulose pretreatment process was developed using acid-catalysed mixtures of alkylene carbonate and alkylene glycol. Pretreatment of sugarcane bagasse with mixtures of ethylene carbonate (EC) and ethylene glycol (EG) was more effective than that with mixtures of propylene carbonate (PC) and propylene glycol (PG). These mixtures were more effective than the individual components in making bagasse cellulose more amenable to cellulase digestion. Glucan digestibilities of ≥87% could be achieved with a wide range of EC to EG ratios from 9:1 to 1:1 (w/w). Pretreatment of bagasse by the EC/EG mixture with a ratio of 4:1 in the presence of 1.2% H2SO4 at 90 °C for 30 min led to the highest glucan enzymatic digestibility of 93%. The high glucan digestibilities obtained under these acidic conditions were due to (a) the ability of alkylene carbonate to cause significant biomass size reduction, (b) the ability of alkylene glycol to cause biomass defibrillation, (c) the ability of alkylene carbonate and alkylene glycol to remove xylan and lignin, and (d) the magnified above attributes in the mixtures of alkylene carbonate and alkylene glycol.

Error modeling and calibration of exteroceptive sensors for accurate mapping applications

Reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. In field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. This is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. The whole system is as reliable as the weakest link in this chain. In this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as LIDAR or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. The coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that captures the dominant geometric and temporal sources of mapping error. This allows the mapping accuracy to be calculated at run time. A generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. This allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. The mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. The techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. Consequently, they should be employed whenever maps are formed from range-based exteroceptive sensor data. © 2009 Wiley Periodicals, Inc.

Non-formal techniques for requirements elicitation, modeling, and early assessment for services

Designing systems for multiple stakeholders requires frequent collaboration with multiple stakeholders from the start. In many cases at least some stakeholders lack a professional habit of formal modeling. We report observations from two case studies of stakeholder-involvement in early design where non-formal techniques supported strong collaboration resulting in deep understanding of requirements and of the feasibility of solutions.

Modeling and sensorless direct torque and flux control of a dual-airgap axial flux permanent-magnet machine with field-weakening operation

This paper presents the modeling and motion-sensorless direct torque and flux control of a novel dual-airgap axial-flux permanent-magnet machine optimized for use in flywheel energy storage system (FESS) applications. Independent closed-loop torque and stator flux regulation are performed in the stator flux ( x-y) reference frame via two PI controllers. This facilitates fast torque dynamics, which is critical as far as energy charging/discharging in the FESS is concerned. As FESS applications demand high-speed operation, a new field-weakening algorithm is proposed in this paper. Flux weakening is achieved autonomously once the y-axis voltage exceeds the available inverter voltage. An inherently speed sensorless stator flux observer immune to stator resistance variations and dc-offset effects is also proposed for accurate flux and speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a machine prototype.

Modeling and position-sensorless control of a dual-airgap axial flux permanent magnet machine for flywheel energy storage systems

This paper presents the modeling and position-sensorless vector control of a dual-airgap axial flux permanent magnet (AFPM) machine optimized for use in flywheel energy storage system (FESS) applications. The proposed AFPM machine has two sets of three-phase stator windings but requires only a single power converter to control both the electromagnetic torque and the axial levitation force. The proper controllability of the latter is crucial as it can be utilized to minimize the vertical bearing stress to improve the efficiency of the FESS. The method for controlling both the speed and axial displacement of the machine is discussed. An inherent speed sensorless observer is also proposed for speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a prototype machine.

Modeling and analysis of a novel variable-speed cage induction generator

This paper introduces a novel cage induction generator and presents a mathematical model, through which its behavior can be accurately predicted. The proposed generator system employs a three-phase cage induction machine and generates single-phase and constant-frequency electricity at varying rotor speeds without an intermediate inverter stage. The technique uses any one of the three stator phases of the machine as the excitation winding and the remaining two phases, which are connected in series, as the power winding. The two-series-connected-and-one-isolated (TSCAOI) phase winding configuration magnetically decouples the two sets of windings, enabling independent control. Electricity is generated through the power winding at both sub- and super-synchronous speeds with appropriate excitation to the isolated single winding at any frequency of generation. A dynamic mathematical model, which accurately predicts the behavior of the proposed generator, is also presented and implemented in MATLAB/Simulink. Experimental results of a 2-kW prototype generator under various operating conditions are presented, together with theoretical results, to demonstrate the viability of the TSCAOI power generation. The proposed generator is simple and capable of both storage and retrieval of energy through its excitation winding and is expected to be suitable for applications, such as small wind turbines and microhydro systems.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Modelling of a magnetocaloric system for cooling in the kilowatt range

A numerical time-dependent model of an active magnetic regenerator (AMR) was developed for cooling in the kilowatt range. Earlier numerical models have been mostly developed for cooling power in the 0.4 kW range. In contrast, this paper reports the applicability of magnetic refrigeration to the 50 kW range. A packed bed active magnetic regenerator was modelled and the influence of parameters such as geometry and operating parameters were studied for different geometries. The pressure drop for AMR bed length and particle diameter was also studied. High cooling power and coefficient of performance (COP) were achieved by optimization of the diameter of the magnetocaloric powder particles and operating frequency. The optimum operating conditions of the AMR for a cooling capacity of 50 kW was determined for a temperature span of 15 K. The predicted coefficient of performance (COP) was found to be ∼6, making it an attractive alternative to vapour compression systems.

An integrated modeling and risk analysis tool for mine water interactions

Poor mine water management can lead to corporate, environmental and social risks. These risks become more pronounced as mining operations move into areas of water scarcity and/or increase climatic variability while also managing increased demand, lower ore grades and increased strip ratios. Therefore, it is vital that mine sites better understand these risks in order to implement management practices to address them. Systems models provide an effective approach to understand complex networks, particularly across multiple scales. Previous work has represented mine water interactions using systems model on a mine site scale. Here, we expand on that work by present an integrated tool that uses a systems modeling approach to represent mine water interactions on a site and regional scale and then analyses the risks associated with events stemming from those interactions. A case study is presented to represent three indicative corporate, environmental and social risks associated with a mine site that exists in a water scarce region. The tool is generic and flexible, and can be used in many scenarios to provide significant potential utility to the mining industry.

The capture and retention evaluation of a stormwater gross pollutant trap design

Gross pollutant traps (GPT) are designed to capture and retain visible street waste, such as anthropogenic litter and organic matter. Blocked screens, low/high downstream tidal waters and flows operating above/below the intended design limits can hamper the operations of a stormwater GPT. Under these adverse operational conditions, a recently developed GPT was evaluated. Capture and retention experiments were conducted on a 50% scale model with partially and fully blocked screens, placed inside a hydraulic flume. Flows were established through the model via an upstream channel-inlet configuration. Floatable, partially buoyant, neutrally buoyant and sinkable spheres were released into the GPT and monitored at the outlet. These experiments were repeated with a pipe-inlet configured GPT. The key findings from the experiments were of practical significance to the design, operation and maintenance of GPTs. These involved an optimum range of screen blockages and a potentially improved inlet design for efficient gross pollutant capture/retention operations. For example, the outlet data showed that the capture and retention efficiency deteriorated rapidly when the screens were fully blocked. The low pressure drop across the retaining screens and the reduced inlet flow velocities were either insufficient to mobilise the gross pollutants, or the GPT became congested.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Modeling and control of a CLC resonant dual active bridge

This paper proposes a linear large signal state-space model for a phase controlled CLC (Capacitor Inductor Capacitor) Resonant Dual Active Bridge (RDAB). The proposed model is useful for fast simulation and for the estimation of state variables under large signal variation. The model is also useful for control design because the slow changing dynamics of the dq variables are relatively easy to control. Simulation results of the proposed model are presented and compared to the simulated circuit model to demonstrate the proposed model's accuracy. This proposed model was used for the design of a Proportional-Integral (PI) controller and it has been implemented in the circuit simulation to show the proposed models usefulness in control design.