Flexible structure control laboratory development and technology demonstration

An experimental structure is described which was constructed to demonstrate and validate recent emerging technologies in the active control and identification of large flexible space structures. The configuration consists of a large, 20 foot diameter antenna-like flexible structure in the horizontal plane with a gimballed central hub, a flexible feed-boom assembly hanging from the hub, and 12 flexible ribs radiating outward. Fourteen electrodynamic force actuators mounted to the hub and to the individual ribs provide the means to excite the structure and exert control forces. Thirty permanently mounted sensors, including optical encoders and analog induction devices provide measurements of structural response at widely distributed points. An experimental remote optical sensor provides sixteen additional sensing channels. A computer samples the sensors, computes the control updates and sends commands to the actuators in real time, while simultaneously displaying selected outputs on a graphics terminal and saving them in memory. Several control experiments were conducted thus far and are documented. These include implementation of distributed parameter system control, model reference adaptive control, and static shape control. These experiments have demonstrated the successful implementation of state-of-the-art control approaches using actual hardware

Critical data detection for dynamically adjustable product quality in IIoT-enabled manufacturing

The IIoT technologies, due to the widespread use of sensors, generate massive data that are key in providing innovative and efficient industrial management, operation, and product quality control processes. The significance of data has prompted relevant research communities and application developers how to harness the values of these data in secure manufacturing. Critical data analysis, identification of critical factors to improve the manufacturing process and critical data associated with product quality have been investigated in the current literature. However, the current works on product quality control are mainly based on static data analysis, where data may change, but there is no way to adjust them dynamically. Thus, they are not applicable for product quality control, at which point their adjustment is instantly required. However, many manufacturing systems exist, like beverages and food, where ingredients must be adjusted instantaneously to maintain product quality. To address this research gap, we introduce a method that identifies the critical data based on their ranking by exploiting three criticality assessment criteria that capture the instantaneous product quality change during manufacturing. These three criteria are - (1) correlation, (2) percentage quality change and (3) sensitivity for the assessment of data criticality. The product quality is estimated using polynomial regression (POLY), SVM, and DNN. The proposed method is validated using wine manufacturing data. Our proposed method accurately identifies critical data, where SVM produces the lowest average production quality prediction error (10.40%) compared with that of POLY (11%) and DNN (14.40%). © 2013 IEEE

Technical Debt Management in Small and Medium-sized Enterprises

The need to release our products under tough time constraints has required us to take shortcuts during the implementation of our products and to postpone the correct implementation, thereby accumulating Technical Debt. In this work, we report the experience of a Finnish SME (Small and Medium-sized Enterprise) in managing Technical Debt (TD), investigating the most common types of TD they faced in the past, their causes, and their effects. The case company is a spin-off which sells one product. Its development was outsourced in the beginning and later continued with external developers. We set up a focus group in the case-company, involving different roles. The results showed that the most significant TD in the company stems from disagreements with the supplier and lack of test automation. Specification and test TD are the most significant types of TD. Budget and time constraints were identified as the most potential root causes of TD. TD occurs when time or budget is limited or the amount and content of work are not understood properly. However, not all postponed activities generated ”debt”. Sometimes the accumulation of TD helped meet deadlines without a major impact, while in other cases the cost for repaying the TD was much higher than the benefits. From this study, we found out that learning from customers, careful estimations and continuous improvement could be potential strategies to mitigate TD. These strategies include iterative validation with customers, efficient communication with stakeholders, improvement of meta-cognition in estimations, and value orientation in budgeting and scheduling

Total Constraint Management for Improving Construction Work Flow in Liquefied Natural Gas Industry

Australia has benefited and will continue to benefit significantly from Liquefied Natural Gas (LNG) investments underway. Managing these LNG projects is challenging as they become increasingly complex and technologically demanding. The primary goal of this thesis is to develop a Total Constraint Management (TCM) method to improve construction work flow during LNG construction. Five controlled experiments were conducted and results show that successful implementation of TCM can significantly improve construction productivity and reduce schedule overruns

Congestion control, energy efficiency and virtual machine placement for data centers

Data centers, facilities with communications network equipment and servers for data processing and/or storage, are prevalent and essential to provide a myriad of services and applications for various private, non-profit, and government systems, and they also form the foundation of cloud computing, which is transforming the technological landscape of the Internet. With rapid deployment of modern high-speed low-latency large-scale data centers, many issues have emerged in data centers, such as data center architecture design, congestion control, energy efficiency, virtual machine placement, and load balancing. The objective of this thesis is multi-fold. First, an enhanced Quantized Congestion Notification (QCN) congestion notification algorithm, called fair QCN (FQCN), is proposed to improve rate allocation fairness of multiple flows sharing one bottleneck link in data center networks. Detailed analysis on FQCN and simulation results is provided to validate the fair share rate allocation while maintaining the queue length stability. Furthermore, the effects of congestion notification algorithms, including QCN, AF-QCN and FQCN, are investigated with respect to TCP throughput collapse. The results show that FQCN can significantly enhance TCP throughput performance, and achieve better TCP throughput than QCN and AF-QCN in a TCP Incast setting. Second, a unified congestion detection, notification and control system for data center networks is designed to efficiently resolve network congestion in a uniform solution and to ensure convergence to statistical fairness with “no state” switches simultaneously. The architecture of the proposed system is described in detail and the FQCN algorithm is implemented in the proposed framework. The simulation results of the FQCN algorithm implemented in the proposed framework validate the robustness and efficiency of the proposed congestion control system. Third, a two-level power optimization model, namely, Hierarchical EneRgy Optimization (HERO), is established to reduce the power consumption of data center networks by switching off network switches and links while still guaranteeing full connectivity and maximizing link utilization. The power-saving performance of the proposed HERO model is evaluated by simulations with different traffic patterns. The simulation results have shown that HERO can reduce the power consumption of data center networks effectively with reduced complexity. Last, several heterogeneity aware dominant resource assistant heuristic algorithms, namely, dominant residual resource aware first-fit decreasing (DRR-FFD), individual DRR-FFD (iDRR-FFD) and dominant residual resource based bin fill (DRR-BinFill), are proposed for virtual machine (VM) consolidation. The proposed heuristic algorithms exploit the heterogeneity of the VMs’ requirements for different resources by capturing the differences among VMs’ demands, and the heterogeneity of the physical machines’ resource capacities by capturing the differences among physical machines’ resources. The performance of the proposed heuristic algorithms is evaluated with different classes of synthetic workloads under different VM requirement heterogeneity conditions, and the simulation results demonstrate that the proposed heuristics achieve quite similar consolidation performance as dimension-aware heuristics with almost the same computational cost as those of the single dimensional heuristics

Dynamic digital shearography for on-board robotic non-destructive testing of wind turbine blades

Structural integrity plays a critical role in development of infrastructural construction and support facilities. During the lifespan of most large-scale equipment, condition monitoring and periodic inspection is indispensable for ensuring structural health and evaluation of service condition. Wind turbine blades are the most important component of wind turbines and demands regular inspection to detect defects, which often occur underneath a blade surface. Current methods used to inspect wind turbine blades include to send NDT operators to climb the tower for on-site inspection of the blades’ surface or to dismantle the blades for inspection on the ground. These approaches are time-consuming, costly and pose risks of injury to human inspectors. Thus, it is necessary to develop a technological method for wind turbine blade on-site inspection of wind turbine blades. Digital shearography based on laser interferometry has demonstrated its prominent capability for inspecting composite material which is the main material used in the construction of wind turbine blades. Shearography is a ramification of holography interferometry and is more efficient to be used as a non-destructive testing (NDT) technique owing to its improved robustness and sensitivity to surface displacement. Robotic climbers, on the other hand, have recently drawn significant interest in NDT applications to replace human inspectors in extreme conditions. Thus, this thesis presents investigations into the development of a robotic NDT method using digital shearography for on-site inspection of wind turbine blades. The development of the shearography unit with correlation fringe pattern acquisition and the integration of this unit with the robotic climber adhering to wind turbine blades using vacuum generators are described in this thesis. The successful conduction of the indoor and outdoor trails for the integrated system verifies that shearography holds the ability to be used as an NDT tool for on-site wind turbine blade inspection, and that the climbing robot is able to access most areas of a wind turbine blade and stabilise itself to remove the impact on the shearography of the high frequencies from the climber’s vacuum motor and the low frequencies from the blade swing. Temporal phase shift shearography, and the fast phase map acquisition methods with less steps are evaluated in the thesis. Experiments are performed in lab with phase maps obtained using different algorithms. Apart from the conventional 4 steps and 3 steps phase shift algorithms, the modified 4+1 and 3+1 temporal phase shifting algorithms are developed for more suitability of semi-dynamic inspection by firstly calculating the correlation fringes and followed by the phase map calculations. The results of these modified methods are compared with the conventional 4 steps and 3 steps methods and are shown with equal qualities. Moreover, the reduced steps of phase shifting, i.e., 2+1 phase shifting methods are conducted for semi-dynamic phase map acquisition. It is found that the temporal phase shifting methods are not suitable for dynamic wind turbine blade inspection, however, the fast semi-dynamic temporal phase shift algorithms are able to produce phase maps with lower clarity. Pixelated spatial phase shift shearography is developed to remedy the limitation of temporal phase shift techniques. It adopts a micro-polarization sensor in the complementary metal oxide semiconductor (CMOS) camera, two linear polarizers, and a quarter waveplate as a new arrangement of optical path to replace the piezoelectric transducer stepper as the phase stepper. Three algorithms are introduced based on this novel developed system. Additionally, the site of view is enlarged for upgrading of the system. The development of the pixelated spatial phase shift shearography has mitigated the static processing limitation on temporal phase shift shearography, which caters for the demands of on-site NDT operation. At the same time, it remedies the current real-time shearography system which is not able to produce phase distributions for further quantitative analysis. The new developed pixelated spatial phase shift shearography system is thus more suitable for WTB on board inspection than both conventional and less-steps temporal phase shift shearography system. The field of view enlargement optimisation in the new developed spatial phase shift system indirectly reduces the distance for the inspection process and meanwhile enlarges the site of view, which consequently reduces the weight and structural complexity of the robotic-shearography integration system. The research addresses and resolves the difficulty of on-board wind turbine blade inspection with a novel robotic NDT approach using digital shearography. The approach is significant for real world industrial applications. Moreover, through the temporal and spatial phase shift evaluation, the research proves the feasibility of dynamically obtaining phase maps by the shearography system for further quantitative analysis without using temporal phase shift devices

Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings

In-silico research has grown considerably. Today's scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.Fil: Longo, Mathias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. University of Southern California; Estados UnidosFil: Rodriguez, Ana Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Systems engineering approach to develop guidance, navigation and control algorithms for unmanned ground vehicle

Despite the growing popularity of unmanned systems being deployed in the military domain, limited research efforts have been dedicated to the progress of ground system developments. Dedicated efforts for unmanned ground vehicles (UGV) focused largely on operations in continental environments, places where vegetation is relatively sparse compared to a tropical jungle or plantation estate commonly found in Asia. This research explore methods for the development of an UGV that would be capable of operating autonomously in a densely cluttered environment such as that found in Asia. This thesis adopted a systems engineering approach to understand the pertinent parameters affecting the performance of the UGV in order to evaluate, design and develop the necessary guidance, navigation and control algorithms for the UGV. The thesis uses methodologies such as the pure pursuit method for path following and the vector field histogram method for obstacle avoidance as the main guidance and control algorithm governing the movement of the UGV. The thesis then considers the use of feature recognition method of image processing to form the basis of the target identification and tracking algorithm.http://archive.org/details/systemsengineeri1094550579Outstanding ThesisMajor, Republic of Singapore ArmyApproved for public release; distribution is unlimited