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1. A Hardware-in-the-Loop Platform for DC Protection

   https://doi.org/10.1109/JESTPE.2020.3017769  

   Vygoder, Mark ; Milton, Matthew ; Gudex, Jacob ; Cuzner, Robert ; Benigni, Andrea ( August 2020 , IEEE Journal of Emerging and Selected Topics in Power Electronics)

   null (Ed.)

   Real-time (RT) simulation of power and energy conversion systems allows engineers to interface both simulation- and hardware-based controls using controller hardware-in-the-loop (CHiL) simulation of networks of power electronic converters (PECs) in order to de-risk highly developmental systems such as next generation electrified transportation systems and dc microgrids. CHiL exploration and performance verification moves a design from Technology Readiness Level (TRL) 3 to TRL 4 without incurring significant cost investments in developmental hardware platforms, which otherwise discourages such endeavors. A real-time CHiL simulation platform suitable for explorations of protective equipment, protection schemes and networked PEC dc and mixed dc-ac power distribution architectures must be capable of simulating common-mode behavior, various grounding schemes, and fault transients at sufficiently high resolution. This paper demonstrates this capability using a Latency-Based Linear Multistep Compound (LB-LMC) simulation method implemented in a commercially sustainable, adaptable and expandable FPGA-based test and instrumentation platform. The proposed CHiL platform achieves real-time power system simulations, including detailed switching commutations of networked PECs, with 50 ns resolution, and faithfully produces resonant and transient behaviors associated with line-to-ground (LG) and line-to-line (LL) faults and fault recovery in ungrounded PEC-based dc systems. This resolution in RT cannot be achieved with today’s commercial off-the-shelf CHiL platforms. This paper demonstrates the need for high resolution RT simulation of LG and LL faults within dc systems, and demonstrates a CHiL approach that enables dc protection design explorations and protective control hardware testing while taking into account the realistic aspects that affect fault characteristics in PEC-based dc systems, such as cable current rating and length, cable and PEC parasitic LG capacitance and PEC i... 

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2. Fast EVM Tuning of MIMO Wireless Systems Using Collaborative Parallel Testing and Implicit Reward Driven Learning

   https://doi.org/10.1109/ITC44778.2020.9325270  

   Komarraju, Suhasini ; Chatterjee, Abhijit ( November 2020 , International Test Conference)

   null (Ed.)

   Modern 5G and projected 6G wireless systems deploy massive MIMO systems with antenna arrays and novel RF transceiver architectures that admit RF beamforming. Testing and tuning of the underlying transceiver arrays on a per-transceiver basis is expensive and can be expedited through the use of parallel testing and tuning techniques that stimulate the entire array transceiver system concurrently. State of the art parallel testing techniques require frequency separation between the tones applied to individual RF chains due to combining of RF signals before down-conversion in analog beamforming MIMO systems. Test schemes that allow some frequency overlap are limited to testing only third order distortion. In this paper, we first present a parallel testing scheme for testing large MIMO transceiver arrays that is amenable to higher order distortion (upto fifth order) in the RF chains considered. Second, we propose a tuning scheme for the entire MIMO array which implicitly tunes for EVM system specifications without explicit knowledge of the relationship between the system test response, the system tuning knobs and the corresponding EVM and SINR specification values. A cost metric is formulated that allows such a solution using reinforcement (multi-arm bandit) learning driven system tuning. Significant yield improvement using this approach is demonstrated by simulation experiments. 

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3. Unpaired data empowers association tests

   https://doi.org/10.1093/bioinformatics/btaa886  

   Gong, Mingming ; Liu, Peng ; Sciurba, Frank C ; Stojanov, Petar ; Tao, Dacheng ; Tseng, George C ; Zhang, Kun ; Batmanghelich, Kayhan ( October 2020 , Bioinformatics)

   Alfonso, Valencia (Ed.)

   Abstract Motivation There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test. Results We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system. Availability and implementation Code is available on https://github.com/batmanlab/Semi-paired-Association-Test. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. Testing Methodology for Spacecraft Precision Formation Flying Missions

   Kimmel, E. ( February 2023 , 45th Annual AAS Guidance and Control Conference)

   Distributed space systems, and specifically spacecraft formations, have been identified as a new paradigm for addressing important science questions. However, when it comes to verifying and validating these systems before launch, there is the added challenge of figuring out how to test the formation’s holistic operations on the ground since a full end-to-end mission simulation is likely infeasible due to the need for costly testing infrastructure/facilities. Building on established methods for single-spacecraft testing, this paper presents a two-phase testing methodology that can be applied to precision formation flying missions with budget, timeframe, and resource constraints. First, a testing plan with unique considerations to address the coordinated and coupled nature of precision formation flight is devised to obtain high system confidence on the ground, and second, the formation’s holistic behavior is refined on orbit during the mission’s in-space commissioning. This approach structures the pre-launch testing to make efficient use of the limited test infrastructure on hand and leverages a sequential configuration process combined with built-in operational flexibility on orbit to safely finish characterizing the formation’s performance so that it can meet mission requirements 

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5. Multi-scale Fisher’s independence test for multivariate dependence

   https://doi.org/10.1093/biomet/asac013  

   Gorsky, S ; Ma, L ( February 2022 , Biometrika)

   Summary Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them in the presence of massive sample sizes. Moreover, resampling is usually necessary to evaluate the statistical significance of the resulting test statistics at finite sample sizes, further worsening the computational burden. We introduce a scalable, resampling-free approach to testing the independence between two random vectors by breaking down the task into simple univariate tests of independence on a collection of $2\times 2$ contingency tables constructed through sequential coarse-to-fine discretization of the sample , transforming the inference task into a multiple testing problem that can be completed with almost linear complexity with respect to the sample size. To address increasing dimensionality, we introduce a coarse-to-fine sequential adaptive procedure that exploits the spatial features of dependency structures. We derive a finite-sample theory that guarantees the inferential validity of our adaptive procedure at any given sample size. We show that our approach can achieve strong control of the level of the testing procedure at any sample size without resampling or asymptotic approximation and establish its large-sample consistency. We demonstrate through an extensive simulation study its substantial computational advantage in comparison to existing approaches while achieving robust statistical power under various dependency scenarios, and illustrate how its divide-and-conquer nature can be exploited to not just test independence, but to learn the nature of the underlying dependency. Finally, we demonstrate the use of our method through analysing a dataset from a flow cytometry experiment. 

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