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Digital innovation of the last few decades has meant that the Internet of Things paradigm (IoT) is increasingly applied to new sectors of the industry, such as rapid prototyping and 3D printers. This has allowed the digitization of production processes and the customisation of products through the Internet by users and consumers. A first analysis has highlighted that the last generation 3D printers have a resolution higher than the final achieved accuracy evaluated by comparing the CAD model and the printed object. The goal is to study the whole process, detect the operations that degrade the accuracy and reduce their influence. This study has highlighted that the accuracy degradation is in the discretization of the CAD model that is required by the printing platforms. In this paper, an innovative method reducing the effect of the discretization is proposed and preliminary results are shown to assess its effectiveness.

Broadband Emission (BEMI) pyrometry is a low-cost technique for indirect characterization of soot temperature fields in laboratory flames using images captured with an RGB camera. However, retrieving temperature from color images through classical deconvolution techniques requires solving ill-posed inverse problems, producing results that are highly sensitive to signal noise and the choice of regularization parameters. This paper proposes using Convolutional Neural Networks (CNNs) to improve the accuracy of estimated 2D soot temperature fields from images of canonical axisymmetric laminar flames. Using a dataset of physically-grounded simulated images of temperature fields in the flame and their corresponding convoluted projections in the camera plane, we trained a CNN to learn the relationship between the reference temperature and the measured signals. Experiments over simulated and experimental images show that the trained CNN outperforms classical inversion methods when retrieving temperature from noisy images, especially in areas of interest such as the center of the flame. Resilience to noise makes CNNs attractive for implementing low-cost techniques for soot pyrometry using equipment of different quality.

This paper presents results from comparison of an industry-accepted model of the western United States with two different synthetic network models of the same geographic area. The synthetic models were independently produced by different construction algorithms but based on public information concerning the location and size of generation and demand. New realism measures examined include Fiedler Vector Partitioning (FVP), the Spectrum of the Normalized Laplacian (SNL), Topological and Impedance Element Ranking (TIER) and Singular Value decomposition of the power flow Jacobian (SVJ). The synthetic models differ greatly in size and granularity, but each has similarities to the industry-accepted model. However, the industry-accepted model exhibits behaviour not replicated in either of the synthetic models. The suitability of each analysis to assess the realism of a network model is discussed.

Synchrophasors generate large volumes of data that greatly benefit from compression for its efficient storage and transfer. In this paper, we investigate the effect of the quality of phasor measurement data on lossy compression, and propose effective data conditioning approaches to improve the compression performance. Specifically, three conditioning methods are employed: a Hamming window-based low-pass filtering scheme to clean measurements corrupted by noise, an algorithm to identify and eliminate measurements with significant amount of noise, and a simple scaling scheme to normalize the magnitudes of different types of measurements. Numerical results obtained on PMU data showcase the capability of the proposed approaches in improving compression performance.

The next generation of radio instruments will generate an unprecedented amount of data, with the Square Kilometre Array (SKA) predicted to generate approximately 160 TB of raw data per second. The computational resources required to process this data are considerable, and alongside this challenge there is an associated one: How is this data going to be transported efficiently? Compute servers composing the processing backends of these instruments must be capable of receiving a high data throughput, with enough leftover resources to perform the required computations. In this paper we present a high-performance and flexible library for packet acquisition. This library was developed as part of the effort to prototype a single Low Frequency Aperture Array (LFAA) station, the low frequency component of the SKA. This resulting LFAA demonstrator, the Aperture Array Verification System (AAVS), generates several types of data streams which must be received and processed. The data acquisition (DAQ) library was designed to be capable of receiving and processing multiple data stream types simultaneously whilst minimizing system load. We present the philosophy behind the design and implementation of this library, as well as provide some benchmarking results.

Textbook treatments of the power flow and related problems are almost universally based on the bus admittance matrix Y bus , representing a choice of nodal analysis for the circuit equations. Many texts further simplify their presentation by insisting that all network components be represented in equivalents that are interconnections of two-terminal circuit elements; e.g., the pi-equivalent for transmission lines, the star-equivalent for three-winding transformers. The combined restrictions of nodal analysis and two-terminal elements disallow more general ideal circuit elements that could serve as useful modeling building blocks. This work will argue that these are unnecessary limitations that have outlived their usefulness in modern power system pedagogy. While research literature has often overcome these limitations with Modified Nodal Analysis, here we instead advocate Sparse Tableau Analysis (STA) as a general tool for power system instruction, with particular value in facilitating node-breaker models. Illustrative examples show that these concepts are easily accessible, and allow more systematic formulations to facilitate graduate research. As an application benefiting from this approach, STA is applied to Optimal Power Flow, with numerical examples to demonstrate that STA provides computational speed comparable to nodal analysis Y bus based formulations.

Power systems across the world are deploying large numbers of high sampling rate synchronized phasor measurement units at the bulk transmission level. As PMUs generate large volumes of data, transmitting and storing this data for future studies becomes challenging. An attractive solution to this problem is to compress the data using efficient compression algorithms. This paper presents a novel lossy compression algorithm which utilizes the characteristics of power system measurements to improve the compression. Specifically, ideas from image compression are adapted to exploit the spatio-temporal correlations induced by the network in the measurement data. The effectiveness of the compression algorithm is judged based on the space savings and accuracy of data reconstruction. The results presented show that compression ratios as high as 95:1 can be achieved using the algorithm.

Chinese Tile Processing Module (CTPM) developed by KLAASA is an all-digital beam forming back-end processor for SKA1-LOW. CTPM is capable of dual polarized 16-channel multi-beam forming within the bandwidth of 50MHz-350 MHz. Integrated with SKALA-2 testing facility at Cambridge, CTPM verified its DBF functionality in stand-alone mode and by expanded and combined, the module can further compose large-scale broadband DBF system

The Square Kilometre Array (SKA) [1] telescope consists in its first phase of two arrays, a dish array to be constructed in South Africa and a low frequency aperture array to be constructed in Western Australia. The aperture array, SKAI-Low, will consist of 512 stations, each with 256 wide bandwidth log periodic antennas. The frequency range of SKAI-Low is 50 to 350 MHz. The Low Frequency Aperture Array (LFAA) consortium is tasked to design the station, the infrastructure around them and the station signal processing.

Optimal power flow (OPF) approaches employing such methods as semi-definite programming have garnered considerable interest in the literature of the last decade. The OPF formulations for these approaches have almost universally relied on Y bus admittance matrix representations, which derive from nodal analysis, and restrict allowable network elements to be voltage-controlled only. Limitations of nodal analysis long been recognized, and to overcome these, commercial power system software often employs modified nodal analysis (MNA). However, with OPF requiring many monitored links with constrained flows, the work here argues for a formulation even more versatile than MNA: Sparse Tableau Formulation (STF) of network constraints, with multi-port representation of individual components. The vast majority of transmission network components (e.g., transmission lines, transformers) have multi-port voltage-current behavior that is well-modelled as linear. Therefore, nonlinearities appear only in equations associated with generation and load at each bus. Utilizing STF, these sources of nonconvexity are confined to constraints local to each bus. This opens the door to simple, engineering-based convex relaxations. We introduce a constant current source at each bus, with these sources' values bounded in a manner that contains any feasible current produced by the actual generator or load model. The tightness of the relaxation is shown to improve when angle constraints are provided, and a DCOPF-based heuristic method is proposed to obtain such angle bounds.

SnowEx is a winter airborne and field campaign designed to measure snow-water equivalent in forested landscapes. A major focus of Year 1 (2016–17) of NASA's SnowEx campaign will be an extensive field program involving dozens of participants from U.S. government agencies and from many universities and institutions, both domestic and foreign. Along with other instruments, two infrared (IR) sensors will be flown on a Naval Research Laboratory P-3 aircraft. Surface temperature is a critical input to hydrologic models and will be measured during the SnowEx mission. A Quantum Well Infrared Photodetector (QWIP) IR imaging camera system will be flown along with a KT-15 remote thermometer to aid in the calibration of the IR image data. Together, these instruments will measure surface temperature of snow and ice targets to an expected accuracy of

This work present results of an identifying and characterizing sand bank, located in the northern region of Santa Catarina Island to be used as borrow area for beach restoration projects. In this way, data from 36.2km of continuous seismic profiling were acquired using a multi-source sub bottom profilers (SBP). The applied SBP was a Boomer seismic source (frequency between 0.5 kHz and 1.5 kHz) and a high resolution Chirp (frequency between 0.5kHz and 12kHz); and 56.1 km of Side Scan Sonar data, using an interferometric sonar (multiphase bathymetry and sonography with a frequency of 540 kHz). The methodology included the processing of the data and analysis in the post-processed data. The steps of importing survey lines, data previews, bottom track and User Gain Control and filters (bandpass, swell) were performed. Finally, the identification, delimitation, analysis and quantification using the acoustic reflectors were carried out. As results, four different sites with different acoustic responses could be observed in the sonography data, which indicates the arrangement of sediments with different grain size at the studied site. In some seismic profiles was observed the occurrence of an acoustic reflector indicating the rocky basement. This profiles showed a layer of sediment about 13m thick. The volume of sediment estimated through the order of magnitude was 10.000.000 m3 for the area.

Missing data detection and estimation has been studied in many applications, such as image processing, computer network, and traffic information. In power and energy industry, it draws a lot of attentions as the number of Phasor Measurement Units (PMU) increases and many measurement-based applications are proposed. As accurate data is required to have better results in the applications, many bad data detection algorithms have been introduced to have reliable data. Motivated by low dimensional characteristics of PMU data, new bad PMU data detection and correction method is proposed and tested with synthetic PMU data. The proposed bad data detection and estimation method is based on subspace projection and lifting, and is strongly related to the least squares solution of overdetermined equations.

The Low Frequency Aperture Array (LFAA) component of the Square Kilometer Array (SKA) involves the processing of 218 signal chains, which will be performed on custom FPGA boards, the Tile Processing Module (TPM). These TPMs, as well as firmware running on them, need to be managed, monitored and controlled by the rest of the system. This requires access to on-board devices and registers on running firmware. This paper presents the software framework which has been developed to automatically generate VHDL code that exposes information on the memory map of running firmware, as well as a flexible software infrastructure for interacting with the board.

A monitoring and control system is being developed for the Aperture Array Verificaton System (AAVS) prototype of the Low Frequency Aperture Array (LFAA) component of the Squatre Kilometer Array (SKA). This software system will have to continuously monitor hundreds of thousands of antennas; schedule, run and monitor observations; control the data flow within the element and outwards; and provide the operational and diagnostic utilities for the system. This paper will describe the various elements of the software stack and the integration with the TANGO control system framework.

In this work we have developed a multiple object tracker that is capable of tracking objects that produce fragmented returns when ensonified. The fact that the types of objects that we are tracking (e.g., divers) produce multiple returns required us to develop an augmentation to the classical multi-hypothesis tracking approach. To account for the fragmented object returns, a novel adaptive Kalman filter R measurement matrix algorithm was developed. By distorting the R matrix and, thus, the measurement validation region, the object tracker was able to “catch” the fragmented blobs that belonged to it. Also, we demonstrated that the same object tracking algorithm can be used to track objects that do not produce fragmented returns, such as fish. We demonstrated the effectiveness of the multiple object tracker at providing long continuous tracks on data we collected at the Georgia Tech acoustic dive well and at NASA NEEMO Mission 20.

Methods to modify power system dynamics through the addition of synthetic inertia have gained increasing research attention over the past several years. Many researchers justify this focus by implicitly assuming that adding inertia increases a system's stability, and only focusing on the frequency nadir as a measure of performance. In this paper, we draw attention to the fact that adding synthetic inertia to a power system may not always increase the system stability when considering overall small signal behavior. We apply two different sets of dynamic parameters to the generators in the IEEE 14-bus test system and show an example in which increasing synthetic inertia exercised through loads may lead to problematic instabilities.

This paper focuses on the preventive security constrained optimal power flow problems (PSCOPF), comparing full nonlinear AC models to linearized model. Generator participation factors (droop coefficients) are introduced to account for active power imbalance created by component outages under contingencies. Moreover, we propose a novel representation of network elements' electrical behavior, employing transmission (“ABCD”) matrices for lines and transformers. We demonstrate that this allows straightforward representation of breaker action and controllable transformers compared to standard approaches based on the admittance matrix Ybus. The proposed technique is tested on the RTS-96 test system and our results demonstrate that differences between the linear and nonlinear representation become particularly critical in the context of security analysis.

Transmission has seen renewed investment in the U.S. over the past decade, as the nation seeks to modernize its aging power infrastructure. While the technical side of transmission planning seeks improvements to the grid to enhance reliability and/or economy of operation, transmission line siting (TLS) must include the very challenging process of routing transmission corridors to respect geographic and land-use constraints. Among other factors, acquisition of rights-of-way, compliance with state and federal agencies, and environmental impact of transmission lines are all investigated in transmission line siting. While strides have been made to improve the process in the past decade, transmission line siting remains a controversial and difficult process. Transmission line siting in a post-restructuring electric industry will be covered in this paper. We review the important role played by non-electric constraints such as geographic barriers, regulatory impact, public/stakeholder input, etc. in transmission expansion planning. We introduce an approach that provides a first step towards an automated, optimization-based approach incorporating geographic decision factors, and most critically, line length. This modification to the authors' previous transmission expansion work allows weighing of system-wide electrical performance contributions of a transmission line in conjunction with geographic impact.

We have constructed an algorithm that is able to classify generic sonar objects as divers or not divers. This process was made difficult by the fact that human divers produce amorphous blobs when ensonfied with 2D imaging sonar, which makes it difficult to use state-of-the-art Computer Vision classification algorithms to classify sonar objects.

This paper formulates a power system related optimal control problem, motivated by potential cyber-attacks on grid control systems, and ensuing defensive response to such attacks. The problem is formulated as a standard nonlinear program in the GAMS optimization environment, with system dynamics discretized over a short time horizon providing constraint equations, which are then treated via waveform relaxation. Selection of objective function and additional decision variables is explored first for identifying grid vulnerability to cyber-attacks that act by modifying feedback control system parameters. The resulting decisions for the attacker are then fixed, and the optimization problem is modified with a new objective function and decision variables, to explore a defender's possible response to such attacks.

Scanning L-band Active/Passive (SLAP) is an airborne remote sensing instrument developed at NASA Goddard Space Flight Center specifically as an airborne simulator of the Soil Moisture Active/Passive (SMAP) satellite instrument suite, for remote sensing of soil moisture, freeze-thaw state, ocean salinity, sea ice, and other physical phenomena that display characteristics at microwave L-band. This paper will present example observations from a recent soil freeze-thaw campaign in Winnipeg, Manitoba, Canada, as well as detail enhancements made to the SLAP RFI processor beyond the capabilities of the SMAP RFI processor.

This paper summarizes a set of six benchmark systems for the analysis and control of electromechanical oscillations in power systems, recommended by the IEEE Task Force on Benchmark Systems for Stability Controls of the Power System Dynamic Performance Committee. The benchmark systems were chosen for their tutorial value and particular characteristics leading to control the system design problems relevant to the research community. For each benchmark, the modeling guidelines are provided, along with eigenvalues and time-domain results produced with at least two simulation softwares, and one possible control approach is provided for each system as well. Researchers and practicing engineers are encouraged to use these benchmark systems when assessing new oscillation damping control strategies.