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The traditional method for transformer moisture diagnosis is to establish empirical equations between feature parameters extracted from frequency domain spectroscopy (FDS) and the transformer’s moisture content. However, the established empirical equation may not be applicable to a novel testing environment, resulting in an unreliable evaluation result. In this regard, it is acknowledged that FDS combined with machine learning is more suitable for estimating moisture content in a variety of test environments. Nonetheless, the accuracy of the estimation results obtained using the existing method is limited by the algorithm’s inability to generalize. To address this issue, we propose an AdaBoost algorithm-enhanced back-propagation neural network (BP_AdaBoost). This study creates a database by extracting feature parameters from the FDS that characterize the insulation states of the prepared samples. Then, using the BP_AdaBoost algorithm and the newly constructed database, the moisture estimation models are trained. Finally, the results of the estimation are discussed in terms of laboratory and field transformers. By comparing the proposed BP_AdaBoost algorithm to other intelligence algorithms, it is demonstrated that it not only performs better in generalization, but also maintains a high level of accuracy. [ABSTRACT FROM AUTHOR]

The Unlimited Sensing Framework (USF) was recently introduced to overcome the sensor saturation bottleneck in conventional digital acquisition systems. At its core, the USF converts a continuous-time high-dynamic-range (HDR) signal into folded, low-dynamic-range, modulo samples and allows the recovery of the HDR signal via algorithmic unfolding. In hardware, however, implementing ideal modulo folding requires careful calibration, analog design and high precision. At the interface of theory and practice, this paper explores a computational sampling strategy that relaxes strict hardware requirements via a novel, mathematically guaranteed reconstruction. We start with a generalized model for USF with two new parameters modeling hysteresis and folding transients in addition to the modulo threshold. Hysteresis accounts for mismatches between the reset threshold and the amplitude displacement at the folding time and the folding transient is a continuous transition period in the implementation of a reset. Both these effects are motivated by our hardware experiments and also occur in previous, domain-specific applications. We show that hysteresis is beneficial for USF and leverage it to derive the first recovery guarantees in the context of our generalized USF model for a certain sampling rate regime. Additionally, we show how the sampling rate requirement can be greatly reduced via a direct generalization of the proposed recovery. Our theoretical work is corroborated by hardware experiments with a hysteresis enabled, modulo ADC testbed comprising off-the-shelf electronic components. Thus, by capitalizing on a collaboration between hardware and algorithms, our paper enables an end-to-end pipeline for HDR sampling allowing more flexible hardware implementations. [ABSTRACT FROM AUTHOR]

This paper proposes a reconstruction algorithm for the geometric shapes of static overhead transmission lines that is based on the spatial coordinate data (obtained by field mapping) and the corresponding inclination data (obtained by attitude sensors). The algorithm takes the piecewise cubic Hermite interpolation function as the basic framework and corrects the aforementioned two types of data based on the “minimum energy principle” of the transmission line system. The introduction of a new energy function in modified energy method (MEM) overcame the shortcomings of conventional energy method (CEM) in fairing strategy, and the new Fairing Criterion made result curves more consistent with the mechanical properties of transmission lines. In the numerical simulation tests, the two energy methods were compared in terms of load type, horizontal span, elevation difference, and anti-disturbance ability, which showed that MEM has a higher reconstruction accuracy (average Euclidean distance is less than 1 cm) and better robustness than CEM. Meanwhile, the key parameters involved in the algorithm were discussed to guide the application in practical engineering. In addition, measurement tests under non-uniform icing were carried out in the field test station, and the results showed that the maximum relative error is less than 0.1%. [ABSTRACT FROM AUTHOR]

Vehicle and airborne navigation users are facing more and more complex signal interference and even spoofing. If vehicles and aircraft do not strengthen their anti-spoofing ability, their navigation performance is bound to be adversely affected. global navigation satellite system (GNSS) spoofing technology has gradually become a preferred interference method for spoofer because of its high concealment and great harm. For spoofer, user terminal increasingly adopts GNSS with inertial measurement unit (IMU): tightly-coupled GNSS/IMU, on this basis, user also configures a variety of anti-spoofing techniques to effectively deal with spoofing. Even if spoofer slowly changes user's positioning, if spoofing strategy is unreasonable, it will lead to the violation of parameter rationality of coupled filter output parameters and spoofing observation, which greatly increases the difficulty of spoofing. In view of the above problems, from the perspective of spoofer, in order to effectively counter the non cooperative target of assembling tightly-coupled GNSS/IMU by using GNSS spoofing, this paper establishes GNSS spoofing mathematical model, and proposes a slowly varying spoofing algorithm to avoid tightly-coupled GNSS/IMU with multiple anti-spoofing techniques based on the analysis of the influence mechanism of spoofing on the positioning of tightly-coupled GNSS/IMU, the algorithm proposes a measurement deviation determination method to avoid a variety of anti-spoofing techniques, which can gradually pull the positioning results of coupled system, and successfully avoid anti-spoofing techniques detection of least squares residual receiver autonomous integrity monitoring (RAIM) and parameter rationality check. The experimental results show that the algorithm can gradually change positioning of tightly-coupled GNSS/IMU within 30 s, and the north, east and down displacements basically achieve the spoofing effect, the errors with the expected offset are −0.5 m, 1.9 m and 12.7 m respectively. At the same time, the detection of the above anti-spoofing techniques is avoided. The mean value of test statistics for tightly-coupled system is reduced by 75.4% and does not exceed the alarm threshold, so as to achieve the purpose of spoofing, the effectiveness and high concealment of the spoofing algorithm are proved. [ABSTRACT FROM AUTHOR]

Power flow analysis is a key tool for planning, operation and control of power systems. With future hybrid Supergrids in mind, a wide variety of power flow algorithms for multi-terminal VSC AC/DC systems have been proposed. However, the nonlinear nature of these formulations represents a challenge for its use in real power systems. Whereas in the study of AC networks linear approximations are frequently utilized to speed up multi-scenario steady-state analysis, an equivalent tool has not been proposed for hybrid AC/DC grids. This paper fills that gap by formulating a linear MW-only power flow algorithm with losses for multi-terminal VSC AC/DC systems. The proposed algorithm provides a consistent estimation of converters' losses and allows modelling of all branches within the converter stations. Moreover, two novel schemes for the inclusion of DC network losses are proposed and a circuit-equivalent model of the linear power flow problem in multi-terminal VSC AC/DC systems is suggested. [ABSTRACT FROM AUTHOR]

In this paper, we propose to extend the standard Convolutional Dictionary Learning problem to a tensor representation where the activations are constrained to be “low-rank” through a Canonical Polyadic decomposition. We show that this additional constraint increases the robustness of the CDL with respect to noise and improve the interpretability of the final results. In addition, we discuss in detail the advantages of this representation and introduce two algorithms, based on ADMM or FISTA, that efficiently solve this problem. We show that by exploiting the low rank property of activations, they achieve lower complexity than the main CDL algorithms. Finally, we evaluate our approach on a wide range of experiments, highlighting the modularity and the advantages of this tensorial low-rank formulation. [ABSTRACT FROM AUTHOR]

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

For the quantitative applications of the Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS), the geolocation accuracy of its sensor data records must be quantified during its on-orbit operation. In this paper, a refined coastline inflection point method is used to evaluate the on-orbit geolocation accuracy of SNPP ATMS. It is disclosed that for SNPP ATMS, the static error term with scan-angle-dependent feature is a dominant part among all the geolocation error sources. A mathematical model is then developed to convert the in-track and cross-track geolocation errors to the beam pointing Euler angles defined in the spacecraft coordinate system, which can be further used to construct the correction matrix for on-orbit geolocation process. By using the correction matrix built in this paper, the geolocation error is obviously reduced both at nadir and at the edge of the scan. The total geolocation error at nadir before/after correction is 3.8/0.8 km at K-band, 5.6/0.8 km at Ka-band, 3.3/0.4 km at V-band, and 1.5/0.1 km at W-band. The geolocation bias at the edge of the scan line before/after correction is 4.6/1.3 km at K-band, 9.4/1.8 km at Ka-band, 4.4/2.4 km at V-band, and 3.2/0.8 km at W-band. After correction, the scan-angle-dependent feature in geolocation error is also largely reduced. [ABSTRACT FROM AUTHOR]

Critical process loads need to be safeguarded against power system disturbances and erroneous operations caused by human factors. During such events, a fast synchronism-supervised transfer scheme is required to ensure continuous operation. This paper discusses the implementation of a fast motor bus transfer scheme using existing protective relays. The scheme is designed to respond to local and remote (upstream) events in the substation. This makes possible an attempt to initiate a fast transfer for events that are remote to the motor bus. On a failure to achieve a fast transfer, the scheme resorts to an in-phase transfer and, if that fails, finally a residual transfer. Real-time dynamic modeling was performed to simulate motor bus decay and reacceleration. This paper provides the results of the simulations and the response of the transfer scheme. [ABSTRACT FROM AUTHOR]

The distributed power control algorithms available in this paper assume perfect estimation of interference power at the receivers and often, the availability of perfect local channel state information (CSI). However, perfect estimation of interference power or acquisition of channel information is impractical. In this paper, we consider a generic class of distributed power control algorithms and analyze the resulting performance loss when faced with errors in CSI and/or interference level estimation. We approximate such power control algorithms with linearized state space dynamics wherein the errors in the estimation of received interference or the CSI, can be modeled as additive observation noise. Based on the proposed model, we derive lower bounds on the norm of the errors in the transmit power vector and receive signal-to-interference-plus-noise ratio (SINR) vector. Our analysis suggests that when the target SINR vector approaches the Pareto-optimal bound, the errors in the transmit vectors could grow without bound. As a consequence, to maintain a performance loss within an acceptable range, either much longer estimation times or lower target SINRs are required. Our numerical results confirm the validity of our analysis and the validity of the proposed bounds. [ABSTRACT FROM PUBLISHER]

Terahertz (THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultrabroadband wireless systems, thanks to its ultrabroad bandwidth. Tailored for the unique properties of pulse-based communications in the THz band, two timing acquisition algorithms are proposed and analyzed thoroughly in this paper. First, a low-sampling-rate (LSR) synchronization algorithm is proposed, by extending the theory of sampling signals with finite rate of innovation in the communication context and exploiting the properties of the annihilating filter. The simulation results show that the timing accuracy at an order of ten picoseconds is achievable. In particular, the LSR algorithm has high performance with uniform sampling at $1/20$ of the Nyquist rate when the signal-to-noise ratio (SNR) is high (i.e., greater than 18 dB). Complementary to this, a maximum likelihood (ML) approach for timing acquisition is developed, which searches for the timing offsets by adopting a two-step acquisition procedure based on the ML criterion. The simulation results show that the ML-based algorithm is well suitable in the low SNR case with a half-reduced search space. For further evaluation, the error performance and the resulting bit-error-rate sensitivity to the timing errors in the LSR and the ML algorithms are both analytically and numerically studied. This paper provides very different and promising angles to efficiently and reliably solve the timing acquisition problem for pulse-based THz band wireless systems. [ABSTRACT FROM PUBLISHER]

A method to find the minimized state machine cover of live and safe Petri nets (PNs) is proposed in this paper. It is required that a cover be obtained in some implementation methods of PN-based control algorithms and other applications such as data mining. Most of the known methods of its computation have at least exponential time and space complexity; this is true not only for the methods which guarantee a minimal cover is obtained but also for some methods of finding approximate solutions. The proposed algorithm is an approximation algorithm and has polynomial computational complexity. The experimental verification shows a very high efficiency of the presented technique. The proposed method is especially valuable in the case of large systems where a solution is obtained hundreds of times quicker than by other methods. [ABSTRACT FROM AUTHOR]

We present an algorithm for passivity verification and enforcement of multivariate macromodels whose state-space matrices depend in closed form on a set of external or design parameters. Uniform passivity throughout the parameter space is a fundamental requirement of parameterized macromodels of physically passive structures, that must be guaranteed during model generation. Otherwise, numerical instabilities may occur, due to the ability of nonpassive models to generate energy. In this paper, we propose the first available algorithm that, starting from a generic parameter-dependent state-space model, identifies the regions in the frequency-parameter space where the model behaves locally as a nonpassive system. The approach we pursue is based on an adaptive sampling scheme in the parameter space, which iteratively constructs and perturbs the eigenvalue spectrum of suitable skew-Hamiltonian/Hamiltonian pencils, with the objective of identifying the regions where some of these eigenvalues become purely imaginary, thus pinpointing local passivity violations. The proposed scheme is able to detect all relevant violations. An outer iterative perturbation method is then applied to the model coefficients in order to remove such violations and achieve uniform passivity. Although a formal proof of global convergence is not available, the effectiveness of the proposed implementation of the passivity verification and enforcement schemes is demonstrated on several examples. [ABSTRACT FROM AUTHOR]

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences. [ABSTRACT FROM AUTHOR]

An automated transfer line balancing problem is investigated in this paper. The line produces a complex part at a high volume, such as cylinder heads, and incorporates identical machines that can be operated under different cutting conditions. The selection of cutting conditions significantly influences productivity and the cost of the transfer line. The production of cylinder heads requires machining different operations within a given takt time and satisfying precedence, inclusion, and exclusion relationships. The operations are located on different faces of the cylinder head and are processed by different cutting tools. The line balancing problem is studied to identify the optimal cutting conditions, number of machines and tools, and machining sequence of operations. The objective is to design a balanced transfer line at a minimum machining and tooling cost, and also with minimum nonproductive time. The problem is represented by a goal-programming model and solved for small transfer line configurations through linearization. An improved cuckoo search algorithm via Levy flight is developed to solve large-scale instances. Results of the cuckoo algorithm are promising since it reached optimal and close-to-optimal solutions to small problems and surpassed the results of a random local search approach for instances of medium and large problems. [ABSTRACT FROM AUTHOR]

This paper deals with signal reconstruction for multichannel high-resolution wide-swath synthetic aperture radar (HRWS-SAR) systems. One of the commonly used algorithms for processing HRWS-SAR data is Krieger's digital beamforming (DBF) algorithm. This algorithm works quite well for cases with uniform space–time sampling as well as moderately nonuniform sampling. However, for cases with highly nonuniform sampling, Krieger's DBF algorithm in its original form suffers from a severe degradation in the signal-to-noise ratio, and for cases with coinciding sampling, this algorithm even fails to function. In this paper, we will solve these problems such that an improved DBF (IDBF) algorithm is developed. The key of the IDBF algorithm consists of two procedures: The first one implements an operation called ambiguity-index screening, which is based on two novel quantities, termed the equivalent sampling spacing and the effective reconstructed Doppler bandwidth; and the other key procedure implements a Doppler-spectrum weighting based on a newly defined parameter that is termed the maximum-to-minimum ratio of the noise spectrum. Simulation results show that the newly proposed IDBF algorithm shares a pronounced improvement in the imaging performance over Krieger's DBF algorithm. Moreover, simulation results also demonstrate that the IDBF algorithm exhibits a more robust and consistent overall performance than several other existing reconstruction algorithms. [ABSTRACT FROM PUBLISHER]

This paper presents a comprehensive study of the computation of square roots over finite extension fields. We propose two novel algorithms for computing square roots over even field extensions of the form \BBF{q^2}, with q = p^n, p an odd prime and n \geq 1. Both algorithms have an associate computational cost roughly equivalent to one exponentiation in \BBF{q^2}. The first algorithm is devoted to the case when q \equiv 1\, mod\, 4, whereas the second one handles the case when q \equiv 3\, mod\,4. Numerical comparisons show that the two algorithms presented in this paper are competitive and in some cases more efficient than the square root methods previously known. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid phased locked loop (HPLL) algorithm for high power master-slave configured (MSC) centralized inverters for large photovoltaic power plants. Proposed PLL technique is a combination of double second order general integral (DSOGI) PLL and enhanced PLL (EPLL) with a zero crossing detector, which enables faster synchronization of slave inverters and enables early export of power into the grid. ${{3}}{\hbox{-}}\phi $ PLL structures such as synchronous reference frame (SRF), double decoupled SRF (DDSRF), EPLL, DSOGI, and DSOGI with frequency locked loop (FLL) are discussed, and a comparative analysis with the proposed PLL technique is presented. The proposed PLL technique is superior as compared with existing PLL techniques in master-slave configurations where it can provide faster synchronization, dynamic interleaving of the hardware timers generating carrier waves for master-slave converters, and low voltage ride through (LVRT) capability. The proposed PLL technique along with the existing techniques is simulated and compared in MATLAB/SIMULINK and digitally implemented in TMS320F2812 based discrete signal controller. The performance of the proposed HPLL technique is experimentally validated with unbalanced and distorted grid signals. Experimental results of proposed HPLL show faster grid synchronization in MSC-based grid-connected inverter, LVRT capability, and dynamic interleaving of MSC inverters. [ABSTRACT FROM AUTHOR]

This paper develops data-driven Type-2 Takagi–Sugeno (T–S) fuzzy modeling and control for bilateral teleoperation with dynamic uncertainties and time-varying delays. The Type-2 T–S fuzzy model identified based on input–output data samples describes the nonlinear teleoperation system by a weighted sum of a group of linear local models, which offers a platform to design robust control algorithms by means of mature linear theories. The fuzzy-model-based four-channel control laws are proposed to guarantee the motion synchronization and enhance the operator's force perception for the environment when the time-varying delays and large dynamic uncertainties, especially the gravity of a heavy end effector of the slave, exist. Markov processes are applied to model the time delays. The stability of the closed-loop system is proved by using the Lyapunov–Krasovskii functions. All the conditions are expressed as linear-matrix inequalities (LMI). By using the MATLAB LMI toolbox, the optimized control gains for each of the fuzzy rules are derived to achieve the optimal performance. Finally, experiments based on an experimental platform consisting of two haptic devices prove the superiority of the proposed strategy through comparison with previous work. [ABSTRACT FROM PUBLISHER]

This paper deals with the stability problem of selected induction motor speed estimators in the case of the regenerating mode operation. Two effective solutions are taken into consideration: adaptive full-order observer and model reference adaptive system-type estimator based on the current flux model and stator current estimation. The two estimation algorithms are compared in detail, namely their mathematical models, theoretical stability analysis, unstable operation areas, and stability improvement methods. The speed adaptation mechanism is extended with an additional shift angle in both cases. The theoretical analysis and simulation test results are validated by wide experimental tests. [ABSTRACT FROM PUBLISHER]

Pooling plays an important role in generating a discriminative video representation. In this paper, we propose a new semantic pooling approach for challenging event analysis tasks (e.g., event detection, recognition, and recounting) in long untrimmed Internet videos, especially when only a few shots/segments are relevant to the event of interest while many other shots are irrelevant or even misleading. The commonly adopted pooling strategies aggregate the shots indifferently in one way or another, resulting in a great loss of information. Instead, in this work we first define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event analysis. Next, we propose a new isotonic regularizer that is able to exploit the constructed semantic ordering information. The resulting nearly-isotonic support vector machine classifier exhibits higher discriminative power in event analysis tasks. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new and closed-form proximal steps. We conduct extensive experiments on three real-world video datasets and achieve promising improvements. [ABSTRACT FROM PUBLISHER]

Monolithic 3-D ICs (M3-D ICs) offer extremely high vertical interconnection density, significantly improving the power-performance envelope when compared to conventional 2-D ICs. However, process limitations lead to one tier having either degraded transistors or interconnects. This paper models the amount of degradation that can be expected at current and future nodes (45, 22, and 10 nm), develops a process development kit using these models to enable evaluation, and presents a block-level M3-D IC RTL-to-GDSII flow that is capable of mitigating some of this degradation. Experimental results indicate that at lower technology nodes, M3-D ICs offer more benefits. Results also indicate that the impact of transistor degradation is diminished at lower technology nodes while the impact of interconnect degradation becomes worse. Overall, perfect M3-D ICs close more than half the gap in the power-performance envelope between 2-D ICs and the “ideal” block-level design. While degraded tiers reduce the benefit of M3-D ICs, our degradation-aware floorplanner gives back up to 17% of the loss, and helps to obtain significant overall benefits compared to 2-D ICs. [ABSTRACT FROM AUTHOR]

This paper proposes the design of robust repetitive control with time-varying sampling periods. First, it develops a new frequency domain method to design a low-order, stable, robust, and causal IIR repetitive compensator using an optimization method to achieve fast convergence and high tracking accuracy. As such, a new stable and causal repetitive controller can be implemented independently to reduce the design complexity. The comprehensive analysis and comparison study are presented. Then, this paper extends the method to design a robust repetitive controller, which compensates time-varying periodic signals in a known range. A complete series of experiments is successfully carried out to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the use of first-order reliability methods to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in engineering applications. The prediction of RUL is affected by several sources of uncertainty, and it is important to systematically quantify their combined effect on the RUL prediction in order to aid risk assessment, risk mitigation, and decision-making. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical approaches are computationally cheaper, and sometimes they are better suited for online decision-making. Exact analytical algorithms may not be available for practical engineering applications, but effective approximations can be made using first-order reliability methods. This paper describes three first-order reliability-based methods for RUL uncertainty quantification: first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (inverse-FORM). The inverse-FORM methodology is particularly useful in the context of online health monitoring, and this method is illustrated using the power system of an unmanned aerial vehicle, where the goal is to predict the end of discharge of a lithium-ion battery. [ABSTRACT FROM AUTHOR]

This paper presents a method for determination of heat transfer coefficients for finite-element method housing model of medium-voltage switchgear cell. Suggested method is based on the particle swarm optimization algorithm. For that purpose, a real test model of partition wall has been created as well as an equivalent numerical FE model. Since the preciseness of thermal model is dependent on the preciseness of thermal coefficients, the goal of the optimization procedure is to assure even better match between the measured and the FE model calculated temperature values. Because of the fact that the temperature field is a consequence of eddy currents that appear in metal parts of switchgear devices, this paper deals with a coupled problem. Nonlinear calculation of magnetic field is also considered. [ABSTRACT FROM AUTHOR]

In this paper, a phase-based direct average strain estimation method is developed. A mathematical model is presented to calculate axial strain directly from the phase of the zero-lag cross-correlation function between the windowed precompression and stretched post-compression analytic signals. Unlike phase-based conventional strain estimators, for which strain is computed from the displacement field, strain in this paper is computed in one step using the secant algorithm by exploiting the direct phase?strain relationship. To maintain strain continuity, instead of using the instantaneous phase of the interrogative window alone, an average phase function is defined using the phases of the neighboring windows with the assumption that the strain is essentially similar in a close physical proximity to the interrogative window. This method accounts for the effect of lateral shift but without requiring a prior estimate of the applied strain. Moreover, the strain can be computed both in the compression and relaxation phases of the applied pressure. The performance of the proposed strain estimator is analyzed in terms of the quality metrics elastographic signal-to-noise ratio (SNRe), elastographic contrast-tonoise ratio (CNRe), and mean structural similarity (MSSIM), using a finite element modeling simulation phantom. The results reveal that the proposed method performs satisfactorily in terms of all the three indices for up to 2.5% applied strain. Comparative results using simulation and experimental phantom data, and in vivo breast data of benign and malignant masses also demonstrate that the strain image quality of our method is better than the other reported techniques. [ABSTRACT FROM AUTHOR]

Rule learning algorithms, for example, Ripper, induces univariate rules, that is, a propositional condition in a rule uses only one feature. In this paper, we propose an omnivariate induction of rules where under each condition, both a univariate and a multivariate condition are trained, and the best is chosen according to a novel statistical test. This paper has three main contributions: First, we propose a novel statistical test, the combined $(5\times 2)$ cv $(t)$ test, to compare two classifiers, which is a variant of the $(5\times 2)$ cv $(t)$ test and give the connections to other tests as $(5\times 2)$ cv $(F)$ test and $(k)$-fold paired $(t)$ test. Second, we propose a multivariate version of Ripper, where support vector machine with linear kernel is used to find multivariate linear conditions. Third, we propose an omnivariate version of Ripper, where the model selection is done via the combined $(5\times 2)$ cv $(t)$ test. Our results indicate that 1) the combined $(5\times 2)$ cv $(t)$ test has higher power (lower type II error), lower type I error, and higher replicability compared to the $(5\times 2)$ cv $(t)$ test, 2) omnivariate rules are better in that they choose whichever condition is more accurate, selecting the right model automatically and separately for each condition in a rule. [ABSTRACT FROM PUBLISHER]

Courses at the Master's level in automatic control and signal processing cover mathematical theories and algorithms for control, estimation, and filtering. However, giving students practical experience in how to use these algorithms is also an important part of these courses. A goal is that the students should not only be able to understand and derive these algorithms, but also be able to apply them to real-life technical problems. The latter is achieved by assigning more time to the laboratory tutorials and designing them in such a way that the exercises are open for interpretation; an example of this would be giving the students more freedom to decide how to acquire the data needed to solve the given exercises. The students are asked to hand in a laboratory report in which they describe how they solved the exercises. This paper presents a double-blind peer-review process for laboratory reports, introduced at the Department of Electrical Engineering, Linköping University, Sweden. A survey was administered to students, and the results are summarized in this paper. Also discussed are the teachers' experiences of peer review and of how students perform later in their education in writing their Master's theses. [ABSTRACT FROM AUTHOR]

This paper introduces the minimum-time arbitrarily constrained distributed scheduling problem. The optimal distributed nonsequential backtracking algorithm is used by agents to schedule tasks where the tasks are related by constraints and where the assignment of tasks to agents may change during scheduling. The problem is distributed in that no agent has full knowledge either of the communication network or of the scheduling constraints. Each agent knows the assignments of the agents with which it can communicate and the scheduled times of the tasks assigned to those agents. The minimum-time arbitrarily constrained distributed scheduling problem is for the agents to use this local knowledge to find a schedule that satisfies all constraints and minimizes the time needed to complete all tasks. The optimal distributed nonsequential backtracking algorithm is able to find minimum-time schedules that satisfy all constraints. This algorithm is designed to work concurrently with a distributed assignment algorithm that satisfies certain communication requirements detailed in this paper. This paper presents proof of the algorithm’s correctness, completeness, and optimality. [ABSTRACT FROM PUBLISHER]

Model checking is a formal method for verifying whether the system satisfies a user-defined specification. Compared to simulation, model checking is restricted in capacity. On the other hand, simulation is weak in detecting bugs that require long and complex sequences of events to be exposed. This paper combines model checking and simulation in an abstraction-refinement scheme to mitigate the problems of both methods. Abstraction refinement iteratively constructs a simplified model to verify the original model. While a simplified model mitigates the weakness of model checking, the set of simplified error traces model helps guide simulation toward deep bugs. In abstraction refinement, concretization—a process of deriving an error trace in the original model from the abstract ones—is used to invalidate spurious abstract error traces or to refute a property. In this paper, we describe a novel concretization algorithm that combines simulation with satisfiability to efficiently refute properties with very long error traces. [ABSTRACT FROM AUTHOR]

In this paper, we present a tubular structure segmentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient computed tomography angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert. [ABSTRACT FROM PUBLISHER]

This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG methods for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by designing an alternative rPPG method, where a projection plane orthogonal to the skin tone is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, a novel algorithm “firework optimization” (FO) is introduced, as well as its hybridization with generating set search (GSS). FO is an exploration algorithm, which intends to explore a vast region within the search space in order to determine regions that may contain a regional optimum. As the regions with potential are defined, GSS, the exploitation algorithm is applied to determine whether regions determined by FO contain a local or a global optimum. The validity of FO and GSS hybridization algorithm is verified using Branin and Six-Hump Camel test functions, and the hybridization algorithm is applied to an interior permanent magnet synchronous motor, in order to minimize torque ripple. [ABSTRACT FROM AUTHOR]

This paper investigates the use of a model-predictive control strategy to control a direct matrix converter. The proposed control method combines the features of the classical model-predictive control and the space vector modulation technique into a modulated model-predictive control. This new solution maintains all the characteristics of model-predictive control (such as fast transient response, multiobjective control using only one feedback loop, easy inclusion of nonlinearities and constraints of the system, and the flexibility to include other system requirements in the controller), adding the advantages of working at fixed switching frequency and improving the quality of the controlled waveforms. Simulation and experimental results employing the control method to a direct matrix converter are presented. [ABSTRACT FROM PUBLISHER]

This paper investigates the data-based optimal control for a class of networked industrial processes with a double-layer architecture. Without knowing the dynamics of subsystems at the device layer, the index prediction function is constructed via the input/output signals, and radial basis function neural networks. The tuning laws for the index prediction function are obtained through the optimal control strategy. Then, by treating the network-induced phenomenon as random round-trip time delay and introducing the predictive algorithm, the compensation scheme is designed at the operation layer to dynamically decompose the setpoints. Finally, two simulation examples are given to further illustrate the effectiveness of the proposed compensation strategy. [ABSTRACT FROM AUTHOR]

In this paper, we analyze a collaborative filter that answers the simple question: What is popular amongst your “friends”? While this basic principle seems to be prevalent in many practical implementations, there does not appear to be much theoretical analysis of its performance. In this paper, we partly fill this gap. While recent works on this topic, such as the low-rank matrix completion literature, consider the probability of error in recovering the entire rating matrix, we consider probability of an error in an individual recommendation [bit error rate (BER)]. For a mathematical model introduced by Aditya in 2009 and 2011, we identify three regimes of operation for our algorithm (named Popularity Amongst Friends) in the limit as the matrix size grows to infinity. In a regime characterized by large number of samples and small degrees of freedom (defined precisely for the model in the paper), the asymptotic BER is zero; in a regime characterized by large number of samples and large degrees of freedom, the asymptotic BER is bounded away from 0 and 1/2 (and is identified exactly except for a special case); and in a regime characterized by a small number of samples, the algorithm fails. We then compare these results with the performance of the optimal recommender. We also present numerical results for the MovieLens and Netflix datasets. We discuss the empirical performance in light of our theoretical results and compare with an approach based on low-rank matrix completion. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm is proposed to extract two illuminant invariant chromaticity features from three image sensor responses. The algorithm extracts these chromaticity features at pixel level and therefore can perform well in scenes illuminated with nonuniform illuminant. An approach is proposed to use the algorithm with cameras of unknown sensitivity. The algorithm was tested for separability of perceptually similar colors under the International Commission on Illumination standard illuminants and obtained a good performance. It was also tested for color-based object recognition by illuminating objects with typical indoor illuminants and obtained a better performance compared to other existing algorithms investigated in this paper. Finally, the algorithm was tested for skin detection invariant to illuminant, ethnic background and imaging device. In this investigation, daylight scenes under different weather conditions and scenes illuminated by typical indoor illuminants were used. The proposed algorithm gives a better skin detection performance compared to widely used standard color spaces. Based on the results presented, the proposed illuminant invariant chromaticity space can be used for machine vision applications including illuminant invariant color-based object recognition and skin detection. [ABSTRACT FROM AUTHOR]

This paper presents a closed-form noniterative transformation of the Maxwell-Garnett mixing rule for biphased mixtures to the triple-pole Debye relaxation formula. For the first time, it is formally proven that such a transformation is complete for conductive constituent materials. In other words, the Maxwell-Garnett representation of any biphased mixture of any conductive materials always has its formal equivalent in the Debye form with three poles at most. For specific aspect ratios of ellipsoidal inclusions, the number of poles reduces to one or two, which is formally proven herein, while in previous studies, a single-pole Debye model was arbitrarily assumed. The proposed transformation provides Debye parameters as an explicit function of a mixture composition, which is competitive to alternative techniques based on laborious curve-fitting algorithms. The newly proposed approach is of particular importance to time-domain modeling of dilute mixtures, where the Maxwell-Garnett mixing rule is usually approximated with available dispersive models. Computational examples given in this paper show advantages of the presented method over previous Maxwell-Garnett to Debye conversion algorithms, in terms of accuracy, robustness, and computational cost. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively prune those unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose PFS $^2$, a novel differentially private FSM algorithm. It is the first algorithm that supports the general gap-constrained FSM in the context of differential privacy. The gap constraints in FSM can be used to limit the mining results to a controlled set of frequent sequences. In our PFS$^2$ algorithm, the core is to utilize sample databases to prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which candidate sequences are potentially frequent. To improve the accuracy of such private estimations, a gap-aware sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to calibrate the amount of noise required by differential privacy, a gap-aware sensitivity computation method is proposed to obtain the sensitivity of the local support computations with different gap constraints. Furthermore, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS $^2$ algorithm is $\epsilon$ -differentially private. Extensive experiments on real datasets illustrate that our PFS$^2$ algorithm can privately find frequent sequences with high accuracy. [ABSTRACT FROM PUBLISHER]

We study optimal multirobot path planning on graphs (\textMPP ) over four minimization objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance, the total arrival time, and the total distance. Having established previously that these objectives are distinct and NP-hard to optimize, in this paper, we focus on efficient algorithmic solutions for solving these optimal \textMPP problems. Toward this goal, we first establish a one-to-one solution mapping between \textMPP and a special type of multiflow network. Based on this equivalence and integer linear programming (ILP), we design novel and complete algorithms for optimizing over each of the four objectives. In particular, our exact algorithm for computing optimal makespan solutions is a first that is capable of solving extremely challenging problems with robot-vertex ratios as high as $100\%$ . Then, we further improve the computational performance of these exact algorithms through the introduction of principled heuristics, at the expense of slight optimality loss. The combination of ILP model based algorithms and the heuristics proves to be highly effective, allowing the computation of $1.x$-optimal solutions for problems containing hundreds of robots, densely populated in the environment, often in just seconds. [ABSTRACT FROM AUTHOR]

Diagnosis by comparison is a realistic approach to detect faults of multiprocessor systems. This paper considers a pessimistic diagnostic strategy for hypercube-like multiprocessor systems under the comparison model. The pessimistic strategy is a diagnostic process whereby all faulty nodes can be correctly identified and at most one fault-free node may be misjudged as a faulty node. We propose a pessimistic diagnosis algorithm based on the largest component in the faulty system. For a system with N = 2n nodes and n ≥ 5 , when the number of faulty nodes is bounded by 2n − 2 , the algorithm can correctly identify all nodes except at most one node left undiagnosed. The time complexity of the algorithm is O(Nlog2N). [ABSTRACT FROM PUBLISHER]

In this paper we propose a modified Elliptic Net algorithm to compute pairings. By reducing the number of the intermediate variables which should be updated in the iteration loop of the Elliptic Net algorithm, we speed up the computation of pairings. Experimental results show that the proposed method is more efficient than the original Elliptic Net algorithm on Barreto-Naehrig (BN) curves which are the best choice for implementing pairings at 128-bit security level. [ABSTRACT FROM PUBLISHER]

Recent studies have shown high interest in statistical methods dedicated to the prediction of the maximum confidence levels in simulations and measurements for risk assessment in electromagnetic compatibility. In particular, it has been shown that one of the main issues remains the access to a number of samples allowing the assessment of the risks in regards to test setup random variables. In this paper, it is argued that a real-time bootstrapping module enables to optimize the number of experiments while estimating the maximum confidence levels of the accessible samples. An efficient technique is presented to substitute the full experimental requirements to a simple and low time-consuming mathematical algorithm. The benefits expected of this method are illustrated throughout experimental shielding effectiveness power measurements. Based upon an optimized statistical approach, the first statistical moments (e.g., mean and standard deviation) of power received inside a cabinet are provided jointly with an accurate assessment of power's quantiles, leading to a better evaluation of extreme values. [ABSTRACT FROM AUTHOR]

Gyroscopes are integral components of inertial measurement units, which are used for guidance and stabilization of many platforms. This paper presents an algorithm for estimating the statistical parameters that govern the performance of rate gyros, i.e., the spectral densities R and Q of the angle random walk and rate random walk components, respectively. Previous work on gyro modeling is based on computing the Allan variance of a gyro signal and using a well-known formula for its mean. The algorithm in this paper uses these as well as the following quantities, which are derived in this paper: the theoretical variance of the Allan variance and the covariance between different Allan variance points. The algorithm is developed using the formulation of the best linear unbiased estimator from statistical estimation theory. The performance of the algorithm is demonstrated using simulated and experimental data. A bound on the error in the integral of the gyro output, as a function of Q and R, is also derived. [ABSTRACT FROM PUBLISHER]

The end-of-life (EOL) disassembly sequencing problem (DSP) has become increasingly important in the process of handling EOL products. This paper proposes a solution procedure for the “EOL DSP”; the procedure is based on a novel soft-computing algorithm that utilizes modified “simplified swarm optimization,” and the procedure combines the precedence preservative operator, feasible solution generator, self-adaptive parameter control, and repetitive pairwise exchange procedures. By taking into consideration the non-deterministic polynomial time (NP)-complete nature of the problem, the proposed algorithm efficiently seeks the optimal disassembly sequence with a novel approach; this approach involves reducing the initial solution space and using a combination of soft-computing algorithms for achieving higher computational efficiency and solution quality. The results presented in this paper show that the proposed algorithm outperforms the existing algorithms in terms of solution quality achieved in a limited computation time. [ABSTRACT FROM PUBLISHER]

Effect-based weapon–target pairing assigns weapons to targets for the given desired effects on such targets. The most obvious and natural effects on targets are represented by the percentages of damage of these targets. In this paper, we focus on the generation of input for effect-based weapon–target pairing optimization. One way to generate such input is based on the Joint Munition Effectiveness Manual (JMEM). JMEM allows the evaluation of the weapons. It is a database that contains many tables, and each table contains many different data fields. Because of the sheer size of JMEM, the optimization of weapon–target pairing based on JMEM is currently focused mainly on one target at a time. In other words, the optimization of weapon–target pairing for many targets and weapons is not directly supported by JMEM, although all the necessary data is there. In this paper, we derive an input based on the given JMEM and desired effect(s), which should be useful in the follow-on effect-based weapon–target pairing optimization that is not limited to a single weapon or target. In particular, effect-based weapon–target pairing will rely on the scanning of the attack guidance table that we derive from JMEM to determine a preferred set of weapon combinations for engaging a given set of targets. [ABSTRACT FROM PUBLISHER]

In this paper, the application of optimal state estimation and optimal state feedback algorithms for real-time active magnetic bearing control is treated. A linear quadratic Gaussian controller, consisting of an extended Kalman filter and an optimal state feedback regulator, is implemented. It is shown that this controller yields improved rotor positioning accuracy, better system dynamics, higher bearing stiffness, and reduced control energy effort compared to the conventionally used proportional–integral–differential control approaches. In addition, a method for compensating unbalance-caused forces and vibrations of the magnetically levitated rotor is presented which is based on the estimation of unknown disturbance forces. All results achieved in this paper are verified by means of measurements. [ABSTRACT FROM PUBLISHER]

This paper presents a wireless risk assessment method to help an administrator manage wireless network security. The assessment method consists of a risk model and an assessment measure. The risk model is in charge of modeling the wireless network risk. Security requirements, wireless attacks, and system configurations are considered in the model. The assessment measure is an algorithm which determines the risk value of the wireless network according to the risk model. Our risk model is developed upon an extended analytic hierarchy process, which contains the 4 layers: the risk layer, the requirement layer, the attack layer, and the configuration layer. The separate layers of the risk model are helpful in dealing with the dynamics of a wireless network because only the related layers are introduced to the assessment measure when changes of the network are detected. Based on the risk model per device, our assessment measure evaluates the wireless network risk in consideration of the relations between devices, attacks, and configurations. Hence, our risk assessment method, composed of the risk model and the assessment measure, can determine the wireless network risk efficiently while considering the dependencies in the wireless network. Two examples are introduced in this paper to examine the feasibility of our method. In the first example, we demonstrate that the risk values derived by our method meet the ground truth by performing practical experiments. The second example shows that our method can evaluate the risk of a changing wireless network with efficiency, and can distinguish disparities in different wireless networks. [ABSTRACT FROM PUBLISHER]

The video surveillance market is increasingly moving toward cheaper, efficient, portable, and high resolution systems. A typical video surveillance system consists of several cameras, which issue warnings or initiate smart reactions according to the analysis results of the captured video data. Many algorithms in video surveillance systems assume fixed lighting conditions for the monitored area. The performance of these algorithms is severely affected by the illumination changes of the monitored area. This paper presents an efficient intensity correction algorithm for high definition video surveillance applications. The new algorithm corrects both the global and the local intensity changes. It uses an apparent gain factor to correct the global intensity changes. In addition it corrects the local intensity changes using the local intensity mean and standard deviation. The proposed algorithm shows a promising performance when compared with other intensity correction algorithms. It has a low computational cost that makes it a suitable choice for real-time hardware implementation. This paper also presents a hardware implementation of the proposed algorithm using Xilinx Spartan3A digital signal processor (DSP) XC3SD3400A device. The targeted resolution is 1920\,\times\,1080 at 30 f/s. The hardware prototype utilizes 17% of the slices, 21% of the block random access memories, and 24% of the DSP48As available in Spartan3A DSP XC3SD3400A device. [ABSTRACT FROM AUTHOR]

The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finite-dimensional (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that “better” explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Computer simulations and tests on real data corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced. [ABSTRACT FROM PUBLISHER]