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Tight assembly of stator windings with no insulation paper damage is a manufacturing challenge. We evaluate different sets of parts according to the following parameters: magnet wire thickness, stator slot smoothness, length of the straight magnet wire after the slot end, and type and amount of insulation cap at the end of the slot. These parameters have discrete values with small differences between them. The damage criterion is the decrease of the insulation paper breakdown voltage after assembly/disassembly of parts, assembled in a small set of designed experiments. Parameter values, i.e., levels at individual experiments are set by an orthogonal experiment matrix. Repetition of each experiment provides statistical significance. Data analysis shows that the additive model alone is not sufficient due to the high correlation of the parameters' influences. We extend the model to include interparameter influences, which we model by adding a virtual parameter. The extended additive model generates parameter values that do not degrade the insulation paper breakdown voltage within the manufacturing process. These values are verified by repetitions of the control experiment.

The frequency, nature, and scope of Soft Error Events (SEEs) is becoming an increasingly significant reliability concern as transistors continuously scale down into ever smaller nanometer memory chip geometries. As part of the on-going research efforts required to address this steadily developing situation, two significant research questions were addressed by the research work presented within this paper. The first research question addressed the possibility of crafting a novel methodology whose application would consistently lead to the creation of viable Monte Carlo SEE Simulators. Each of these Monte Carlo SEE Simulators would need to be capable of adequately profiling SEEs as well as simulating and modeling their impacts on the reliability of a variety of simulated 45-nm SRAM computer memories. The Standard 2-Parameter Weibull Soft Error Event (S2P-WSE) Simulator proposed within this paper as a direct application of this novel methodology successfully produced simulation results that were comparatively equivalent both directly and statistically to the results calculated by the empirically validated Weibull Row-Clustered Soft Error Reliability (WRCSER) model. This WRCSER Model was independently developed and empirically validated against proprietary reliability datasets obtained from testing a CISCO 45-nm SRAM computer memory device in a laboratory setting. The S2P-WSE Simulator itself was validated by means of a Kruskal–Wallis ANOVA test and by direct comparison of both the model and the simulator as a function of their respective failure probability and reliability results on 45-nm SRAM simulated computer memory devices subjected to the same SEE impacts over the course of an entire simulation run. The second research question sought to determine the significance of SEE topography versus a Row-Depth-Only bit error severity assumption on the reliability of simulated 45-nm SRAM computer memories shielded from the accumulating impacts of SEEs by variable error-correction codes, row-depth interleaving distances, and periodic scrubbing time intervals. The simulation runs for both the S2P-WSE Simulator and the T2P-WSE Simulator were carefully setup to ensure that only the potentially divergent factor between the results generated by the two simulators was the T2P-WSE Simulator's stochastically estimated topographical shape for each SEE impacting the simulated computer memory device. The work within this paper successfully demonstrated through direct plots of the two simulators’ failure probability and reliability results and the results of the Kruskal–Wallis ANOVA tests that the topography of SEEs does represent a significant impact on the overall failure probability and reliability of 45-nm SRAM computer memory devices. [ABSTRACT FROM PUBLISHER]

Prior research has shown that autocorrelation and variance in voltage measurements tend to increase as power systems approach instability. This paper seeks to identify the conditions under which these statistical indicators provide reliable early warning of instability in power systems. First, the paper derives and validates a semi-analytical method for quickly calculating the expected variance and autocorrelation of all voltages and currents in an arbitrary power system model. Building on this approach, the paper describes the conditions under which filtering can be used to detect these signs in the presence of measurement noise. Finally, several experiments show which types of measurements are good indicators of proximity to instability for particular types of state changes. For example, increased variance in voltages can reliably indicate both proximity to a bifurcation and the location of increased stress. On the other hand, growth of autocorrelation in certain line currents is related less to a specific location of stress but, rather, is a reliable indicator of stress occurring somewhere in the system; in particular, it would be a clear indicator of approaching instability when many nodes in an area are under stress. [ABSTRACT FROM AUTHOR]

Variability is a challenge for future scaling as process dimensions reduce. The emerging 3-D sequential stacking technology is more than Moore’s scaling alternative. The 3-D design flow requires the partitioning of the netlist between the tiers. This paper presents the variability analysis of circuits partitioned into different levels. A comparison among local and global variations effects on ring oscillators (ROs) and SRAM is demonstrated. The across-chip variations and correlation range are shown as a critical point for the 3-D very large-scale integrated circuits, where the local variability is dominant. The correlations between devices due to the distances or the allocation into different tiers are directly taken into account in the SPICE model due to a statically unified model applied to 3-D circuits based on Monte Carlo simulations. Design wise, the 3-D integration can further decrease the circuit variability as shown in RO output frequency and SRAM static noise margin. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework. [ABSTRACT FROM AUTHOR]

The generalized polynomial chaos (gPC) method recently advocated in the literature, exhibits impressive efficiency and accuracy in probabilistic power flow (PPF) calculations of small-scale power systems. However, it suffers from the “curse of dimensionality” and can only be applied to systems with input random variables that follow a set of standard probability distributions. This paper overcome these weaknesses by developing a hierarchical polynomial chaos analysis of variance (ANOVA) method that shows excellent performances in terms of accuracy, efficiency, rationality, and adaptability, for small- to large-scale power systems. By proving the equivalence between the polynomial chaos expansion and the ANOVA decomposition, which is executed at no extra computational cost, the dimensionality of the polynomial chaos expansion can be adaptively reduced to improve the computational efficiency of the generalized polynomial chaos method in high-dimensional problems. Furthermore, by resorting to the Stieltjes procedure, it is extended to any assumed probability distributions of the input random variables. Simulation results carried out on the IEEE 118-bus system and the 1354-bus European high voltage system with correlated renewable energy generations reveal that the developed method outperforms the generalized polynomial chaos method and the Monte Carlo (MC) method while being compatible with real-time applications in power systems. [ABSTRACT FROM AUTHOR]

Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost. [ABSTRACT FROM AUTHOR]

This paper describes a new method of power-grid signal variation measurement, consisting of a demodulation algorithm and a set of measures (indices). The measures that will be presented are the abstract functionals, operating on the complex envelope of a signal. This paper presents a demodulation algorithm, definitions of proposed functionals, results of simulations, as well as laboratory experiments. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of adaptive multidimensional/multichannel signal detection in homogeneous Gaussian disturbance with unknown covariance matrix and structured deterministic interference. The aforementioned problem corresponds to a generalization of the well-known Generalized Multivariate Analysis of Variance (GMANOVA). In this Part I of the paper, we formulate the considered problem in canonical form and, after identifying a desirable group of transformations for the considered hypothesis testing, we derive a Maximal Invariant Statistic (MIS) for the problem at hand. Furthermore, we provide the MIS distribution in the form of a stochastic representation. Finally, strong connections to the MIS obtained in the open literature in simpler scenarios are underlined. [ABSTRACT FROM AUTHOR]

This paper analyzes the variance of the estimated parametric best linear approximation (BLA) \mathhatGBLA(q, \theta) of a nonlinear system that is driven by random excitations. The estimated model \mathhatGBLA(q, \theta) varies not only due to the disturbing measurement and process noise but also over different realizations of the random excitation because the nonlinear distortions depend on the input realization. For the nonparametric frequency response function (FRF) estimate, it has been shown that the variance expression is still valid in the presence of nonlinear distortions, and the same formulas can be used in the linear as in the nonlinear case. This result does not hold for the variance \sigma\mathhat{GBLA(q, \theta)}^{2} on the parametric estimate \mathhatG \rm BLA(q, \theta). It is shown in this paper that it is still possible to upperbound the variance \sigma\mathhat{GBLA(q, \theta)}^{2} using linear expression by introducing an additional scaling factor that depends upon the maximal degree of nonlinearity. [ABSTRACT FROM PUBLISHER]

Image processing circuits are expected to play an important role in Internet of Things electronic systems. For this type of circuits, errors (e.g., those due to wear out) might not be perceptible to us due to our insensitivity to small variances in images or colors. In the case that errors are acceptable (imperceptible), the systems are very likely to still be functional but with only minor performance degradation. The lifetime of the systems thus can be extended. Although there have been several attributes proposed in the literature to test acceptability of errors, these attributes do not consider human beings’ sensitivity to structural variances of erroneous images. This may result in acceptability misclassification of errors. In this paper, we take this into consideration and propose a new test method to more accurately evaluate acceptability of errors. Compared with the available attributes that can also extract the structural information of images, our method has much lower computation complexity, which is advantageous for shortening test time. This also makes our method hardware efficient. It is shown that the incurred area overhead of our method is low. The proposed method is to be applied periodically in-field to examine the reliability of the target circuit. We thus consider errors that may occur during in-field use. Single/multiple stuck-at faults and noises are considered, and a total of 161 460 erroneous images are generated. The experimental results on these diverse images show that on average the test accuracy of our method is more than 99%. [ABSTRACT FROM AUTHOR]

The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters are popular solutions to the multitarget tracking problem due to their low complexity and ability to estimate the number and states of targets in cluttered environments. The PHD filter propagates the first-order moment (i.e. mean) of the number of targets while the CPHD propagates the cardinality distribution in the number of targets, albeit for a greater computational cost. Introducing the Panjer point process, this paper proposes a Second-Order PHD (SO-PHD) filter, propagating the second-order moment (i.e., variance) of the number of targets alongside its mean. The resulting algorithm is more versatile in the modeling choices than the PHD filter, and its computational cost is significantly lower compared to the CPHD filter. This paper compares the three filters in statistical simulations which demonstrate that the proposed filter reacts more quickly to changes in the number of targets, i.e., target births and target deaths, than the CPHD filter. In addition, a new statistic for multiobject filters is introduced in order to study the correlation between the estimated number of targets in different regions of the state space, and propose a quantitative analysis of the spooky effect for the three filters. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel motion reproduction method based on environmental disturbance (EnvDs) compensation. The preservation of skillful techniques is an important issue. Although the motion-copying system (MCS) was proposed to deal with this problem, a clear strategy still does not exist for adapting to the environmental variances between the motion saving and reproduction phases. Conventional methods cannot adapt to the difference properly because their control systems are just one-degree-of-freedom (DOF) control systems. In this paper, variances were defined as EnvDs, and the effect on the MCS was demonstrated. A 2-DOF control system structure was developed to compensate for EnvDs. The effectiveness of the proposed system was demonstrated through analysis and experimental results. [ABSTRACT FROM PUBLISHER]

For high-speed bearingless disk drives, certain topologies seem advantageous. The authors have published works on a bearingless disk drive for high speeds that is characterized by a slotless stator and a toroid winding set. Several different variations of this setup are imaginable. The ones which this paper focuses on are the variation of the number of phases, of the number of coils, and of the applied coil connection. A comparison of the constructed and tested setup with the possible variations is presented in the course of this paper. [ABSTRACT FROM AUTHOR]

Semiconductors are essential components in many electronic devices. Because wafers are produced quickly and in large quantities, defects occur that adversely affect semiconductor properties. This makes it necessary to install powerful and robust inspection systems which use artificial intelligence techniques in the early stages of the manufacturing chain in order to detect and classify those defects. This paper proposes a method for defect detection and classification on images of semiconductor wafer materials obtained by means of a scanning electron microscope based in the following stages: (i) use of computer vision techniques to isolate the defect from the background; (ii) use of several descriptors based on shape, size, texture, histogram, and key-points to create a feature vector for the characterization of the defect; (iii) application of an exhaustive search as a feature selection method to determine the optimal subset of feature descriptors; and (iv) evaluation of the feature descriptors by using a support vector machine classifier providing the optimal set with highest F1-score metrics. Finally, the effectiveness of the proposed approach is compared with five popular feature selection methods, reporting better classification results than the latter. [ABSTRACT FROM AUTHOR]

The kernelized correlation filter (KCF) is one of the state-of-the-art object trackers. However, it does not reasonably model the distribution of correlation response during tracking process, which might cause the drifting problem, especially when targets undergo significant appearance changes due to occlusion, camera shaking, and/or deformation. In this paper, we propose an output constraint transfer (OCT) method that by modeling the distribution of correlation response in a Bayesian optimization framework is able to mitigate the drifting problem. OCT builds upon the reasonable assumption that the correlation response to the target image follows a Gaussian distribution, which we exploit to select training samples and reduce model uncertainty. OCT is rooted in a new theory which transfers data distribution to a constraint of the optimized variable, leading to an efficient framework to calculate correlation filters. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves KCF, and achieves better performance than other state-of-the-art trackers. To encourage further developments, the source code is made available. [ABSTRACT FROM AUTHOR]

An iterative technique for the computation of approximate performance indices of a class of stochastic Petri nets models is presented. The proposed technique is derived from the mean value analysis algorithm for product form solution stochastic Petri nets. In this paper, we apply the approximation technique to stochastic marked graphs. In principle, the proposed technique can be used for other stochastic Petri net subclasses in the paper, some of these possible applications are presented. Several examples are presented in order to validate the approximate results. [ABSTRACT FROM AUTHOR]

The degrees of freedom (DoFs) of the $K$ -user multiple-input multiple-output (MIMO) interference channel are studied when perfect, but delayed channel state information is available at the transmitter side (delayed CSIT). Recent works have proposed schemes improving the DoF knowledge of the interference channel, but at the cost of developing transmission involving many channel uses (long delay), thus increasing the complexity at both transmitter and receiver side. This paper proposes three linear precoding strategies, limited to at most three phases, based on the concept of interference alignment, and built upon three main ingredients: delayed CSIT precoding, user scheduling, and redundancy transmission. In this respect, the interference alignment is realized by exploiting delayed CSIT to align the interference at the non-intended receivers along the space-time domain. Moreover, a new framework is proposed where the number of transmitted symbols and duration of the phases is obtained as the solution of a maximization problem, and enabling the introduction of complexity constraints, which allows deriving the achievable DoF as a function of the transmission delay, i.e., the achievable DoF-delay trade-off. Finally, the latter part of this paper settles that the assumption of time-varying channels common along all the literature on delayed CSIT is indeed unnecessary. [ABSTRACT FROM PUBLISHER]

Qorvo is a global leader in scalable and dynamic RF solutions for mobile, infrastructure, and defense. To ensure quality and compliance to customer specifications, a large number of product devices and die on wafers are tested every day. Typical data sets will contain unusually large or small values when compared with others in that data set, which may or may not pass specification limits. Such outliers will have negative effects on data analyses such as cpk-analysis, ANOVA, and regressions. More important, outliers, particularly those passing specification limits, may indicate defective parts or pose a reliability concern. For engineering teams in development and production, it is standard practice to identify or label outliers in their data sets, so that they can be removed to improve data analyses and to protect customers against receiving defective parts. The aim for this paper is to provide product engineering teams at Qorvo with reliable outlier labeling methods particularly for skewed distributions. While the ultimate goal is to label and to remove outliers, we also need to balance this with unnecessary yield loss for the financial bottom line. [ABSTRACT FROM AUTHOR]

Atomic clocks are essential elements in a variety of applications, such as global navigation satellite systems. Consequently, monitoring their performances is fundamental. The Allan variance is the key statistical tool for the performance characterization of atomic clocks. This paper proves that the Allan variance computed from frequency measurements with missing data is affected by a bias, which can make it dramatically different from the expected behavior in the full data case. Furthermore, it shows how to eliminate (or largely reduce) this bias by correcting the Allan variance. The corrected Allan variance is obtained for some of the most common atomic clock noise components, and it is validated through numerical simulations. [ABSTRACT FROM AUTHOR]

Activity recognition in videos in a deep-learning setting—or otherwise—uses both static and pre-computed motion components. The method of combining the two components, while keeping the burden on the deep network less, still remains uninvestigated. Moreover, it is not clear what the level of contribution of individual components is, and how to control the contribution. In this paper, we use a combination of convolutional-neural-network-generated static features and motion features in the form of motion tubes. We propose three schemas for combining static and motion components: based on a variance ratio, principal components, and Cholesky decomposition. The Cholesky-decomposition-based method allows the control of contributions. The ratio given by variance analysis of static and motion features matches well with the experimental optimal ratio used in the Cholesky decomposition-based method. The resulting activity recognition system is better or on par with the existing state-of-the-art when tested with three popular data sets. The findings also enable us to characterize a data set with respect to its richness in motion information. [ABSTRACT FROM AUTHOR]

In this paper, we present a methodology for the size optimization of an external magnetic system made of arc-shaped permanent magnets (ASMs). This magnetic system is able to remotely actuate a drug-release module embedded in a prototype of a capsule robot. The optimization of the magnetic system is carried out by using an accurate analytical model that is valid for any arbitrary dimensions of the ASMs. By using this analytical model, we perform parametric studies and conduct a statistical analysis [analysis of variance (ANOVA)] to investigate efficient ways to distribute the volume of the ASMs so that the dimensions and volume of the magnetic system are minimized while optimal flux densities and magnetic torques are obtained to actuate the drug delivery system (DDS). The ANOVA results, at 5% significance level, indicate that changes in the angular width followed by changes in the length of the ASMs have the highest impact on the magnetic linkage. Furthermore, our experimental results, which are in agreement with the analytical results, show that the size optimization of the magnetic system is effective for the actuation of the DDS in capsule robots. [ABSTRACT FROM AUTHOR]

This paper derives an effective rotor shape by the optimal design of hybrid magnetic torque converter using the multiple linear regression analysis (MLRA). In particular, using a non-linear finite-element method based on the 2-D numerical analysis, we conduct a correlation analysis between independent variables (selected by the Box-Behnken design) and reaction variable. In addition, we derive a regression equation for reaction variables according to the independent variables using the MLRA and analysis of variance. We assess the validity of the optimized design by comparing the characteristics of the optimized model derived from the response surface methodology and the initial model. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis (GSA) of voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows resorting to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with different numbers of uncertain sources with various uncertain levels and different PV distributions demonstrate that the proposed method can achieve accurate GSA. [ABSTRACT FROM AUTHOR]

A magnetic gear that is capable of noncontact torque transmission has replaced mechanical gear, and has advantages of high efficiency and improved reliability. However, it also has the problem of a low torque density, which requires improvement. In this paper, a novel pole piece shape is proposed in order to improve the problem of low torque density of the magnetic gear. In order to identify the functional relationship between design variables of the proposed pole piece shape and response variables such as torque and torque ripple, response surface methodology is used, and the Box-Behnken design is used to establish the design of experiment. In addition, using variance analysis and regression analysis of design variables and response variables, response surface of the response variable to the design variable, and its response surface equation are estimated to derive optimal design variables for the proposed pole piece shape. The experiment data required for predicting the relationships among them are obtained using finite-element method based on two-dimensional (2-D) numerical analysis. Therefore, this paper derived an optimal model for the magnetic gear with the novel pole piece using the Box-Behnken design, and the validity of the optimal design of the proposed pole piece shape through variance analysis and regression analysis demonstrated. [ABSTRACT FROM AUTHOR]

A glitter pattern is a set of bright spots. To have a bright spot on the water, the slope of a point on the sea surface must provide a specular reflection direction of the sunlight to the detector where the image will be created. This interaction is visible for all of us on a clear day with appropriate wind velocities on the sea surface. In this paper, a mathematical methodology was deduced, which is suggested to apply in real images of the sea. First, the suitable 2-D geometry of the reflection situation was determined. Then, the range of the slopes that satisfies the specular reflection condition for each direction was obtained; in addition, the 2-D statistical relationships between the glitter patterns of the sea surface and the sea surface slopes for different wind velocities on the sea surface were deduced using a 2-D Rect and a Gaussian glitter function, considering Gaussian and non-Gaussian probability density functions. Finally, doing parallel computing, the results consistent with the extension of the 1-D case were obtained. This method could be applied on real images, instead of using pressure sensors and others expensive electronic dispositives to model ocean behavior and other various oceanographic studies. [ABSTRACT FROM AUTHOR]

As human visual attention is naturally biased toward foreground objects in a scene, it can be used to extract salient objects in video clips. In this paper, we proposed a weakly supervised learning-based video saliency detection algorithm utilizing eye fixations information from multiple subjects. Our main idea is to extend eye fixations to saliency regions step by step. First, visual seeds are collected using multiple color space geodesic distance-based seed region mapping with filtered and extended eye fixations. This operation helps raw fixation points spread to the most likely salient regions, namely, visual seed regions. Second, in order to seize the essential scene structure from video sequences, we introduce the total variance-based pairwise interaction model to learn the potential pairwise relationship between foreground and background within a frame or across video frames. In this vein, visual seed regions eventually grow into salient regions. Compared with previous approaches the generated saliency maps have two most outstanding properties: $integrity$ and $purity$ , which are conductive to segment the foreground and significant to the follow-up tasks. Extensive quantitative and qualitative experiments on various video sequences demonstrate that the proposed method outperforms the state-of-the-art image and video saliency detection algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the degrees of freedom (DoF) of two-way multiple-input multiple-output (MIMO) butterfly networks, a class of two-way four-unicast networks, that consists of four source/destination nodes and three relay nodes. We first give upper and lower bounds on the sum DoF of such network with any number of antennas in each node. We also study the DoF of the two-way MIMO butterfly network with caching at the relays. For the special case that each source/destination node has $M$ antennas and each relay node has $N$ antennas, we obtain the exact DoF for some special cases. Specifically, if $N \leq M$ , then without caching, ${\rm DoF}=2N$ , which is equal to the DoF of a one-way MIMO butterfly network, and with relay caching, ${\rm DoF}=4N$. Moreover, if $N > 2M$ , then with or without relay caching, ${\rm DoF}=4M$ which doubles the DoF of a one-way MIMO butterfly network. Hence, for this MIMO butterfly network, when the number of relay antennas is large compared with the number of source/destination antennas, bidirectional transmission improves the DoF; and when the number of relay antennas is small, relay caching improves the DoF. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of the variance-based global sensitivity analysis for a topological derivative method in order to solve a stochastic nonlinear time-dependent magnetoquasi-static interface problem. To illustrate the approach a permanent magnet (PM) synchronous machine has been considered. Our key objective is to provide a robust design of the rotor poles and of the tooth base in a stator for the reduction of the torque ripple and electromagnetic losses, while taking material uncertainties into account. Input variations of material parameters are modeled using the polynomial chaos expansion technique, which is incorporated into the stochastic collocation method in order to provide a response surface model. Additionally, we can benefit from the variance-based sensitivity analysis. This allows us to reduce the dimensionality of the stochastic optimization problems, described by the random-dependent cost functional. Finally, to validate our approach, we provide the 2-D simulations and analysis, which confirm the usefulness of the proposed method and yield a novel topology of a PM synchronous machine. [ABSTRACT FROM AUTHOR]

Given a collection of entities (or nodes) in a network and our intermittent observations of activities from each entity, an important problem is to learn the hidden edges depicting directional relationships among these entities. Here, we study causal relationships (excitations) that are realized by a multivariate Hawkes process (MHP). The MHP and its variations (spatio-temporal point processes) have been used to study contagion in earthquakes, crimes, neural spiking activities, the stock and foreign exchange markets, and so on. In this paper, we consider the MHP with gaps (MHPG) in observations. We propose a variational problem for detecting sparsely hidden relationships with an MHP that takes into account the gaps from each entity. We bypass the problem of dealing with a large amount of missing events by introducing a small number of unknown boundary conditions. In the case where our observations are sparse (e.g., from 10% to 30%), we show through numerical simulations that robust recovery with MHPG is still possible even if the lengths of the observed intervals are small but they are chosen accordingly. The numerical results also show that the knowledge of gaps and imposing the right boundary conditions are very crucial in discovering the underlying patterns and hidden relationships. [ABSTRACT FROM PUBLISHER]

We propose a structure of a semiparametric two-component mixture model when one component is parametric and the other is defined through linear constraints on either its distribution function or its quantile measure. Estimation of a two-component mixture model with an unknown component is very difficult when no particular assumption is made on the structure of the unknown component. A symmetry assumption was used in the literature to simplify the estimation. Such method has the advantage of producing consistent and asymptotically normal estimators, and identifiability of the semiparametric mixture model becomes tractable. Still, existing methods, which estimate a semiparametric mixture model have their limits when the parametric component has unknown parameters or the proportion of the parametric part is either very high or very low. We propose in this paper a method to incorporate a prior linear information about the unknown component in order to better estimate the model when existing estimation methods fail. This linear information is either translated by linear constraints, such as moment-type constraints or L-moments constraints (linear constraints over the quantile). The new method is based on $\varphi -$ divergences and has a non classical form since the minimization is carried over both arguments of the divergence. The resulting estimators are proved to be consistent and asymptotically normal under standard assumptions. We show that using the Pearson’s \chi ^{2} divergence our algorithm has a linear complexity when the constraints are moment-type. Simulations on univariate and multivariate mixtures demonstrate the viability and the interest of our novel approach. [ABSTRACT FROM AUTHOR]

For circular trace scanning synthetic aperture radar (CTSSAR) with a circular track, the conventional hyperbolic equation becomes inadequate to express the range history of a point target accurately, and when it comes to the wide swath observation and imaging, the range variance makes it even harder to focus the target on the edge of the scene. Thus, an expression with high-order terms is needed to approximate the range history and the range variance should also be considered in the imaging algorithm. In this paper, based on the method of series reversion, a fourth-order approximated range model is established for the CTSSAR processing and the 2-D spectrum is derived for the echo signal in CTSSAR with circular trajectory. At the same time, in order to deal with the range-variant range cell migration problem in large-area CTSSAR imaging, a modified chirp scaling algorithm is proposed to realize precise wide swath CTSSAR focusing. Experiments and analyses are performed to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. [ABSTRACT FROM PUBLISHER]

This paper proposes a blanker with a threshold value that is adaptively updated based on average signal power in an impulsive noise environment. For conventional receivers, using a blanker as a preprocessor improves the bit error performance in an impulsive noise environment by removing received signal samples with large amplitudes in order to reduce the effect of impulsive noise. However, the conventional blanker shows a poor bit error performance in case of high signal-to-noise ratio (or large signal power) due to a fixed threshold. Therefore, the proposed blanker, called adaptive threshold blanker, overcomes this problem by adaptively updating the threshold and achieves an excellent bit error performance. In addition, this paper also introduces a simple method for calculating the quasi-optimal threshold value for the proposed blanker. This simple method allows an easy calculation of the threshold in practical systems. In order to evaluate the bit error performance of the proposed blanker, an approximated probability density function (PDF) using a sum of weighted Gaussian PDFs with different variances is also discussed. It is observed that simulation results agree well with the performance analysis. [ABSTRACT FROM AUTHOR]

This paper presents the experimental determination of the ZIP coefficients model to represent (static) modern loads under varying voltage conditions. ZIP are the coefficients of a load model comprised of constant impedance Z, constant current I, and constant power P loads. A ZIP coefficient load model is used to represent power consumed by a load as a function of voltage. A series of surveys was performed on typical residential, commercial, and industrial customers in New York City. Household appliances and industrial equipment found in the different locations were tested in the laboratory by varying the voltage from 1.1-p.u. voltage to 0 and back to 1.1 pu in steps of 3 V to obtain the individual P–V, Q–V, and I –V characteristics. Customer load tables were built using seasonal factors and duty cycles to form weighted contributions for each device in every customer class. The loads found in several residential classes were assembled and tested in the lab. It was found that modern appliances behave quite differently than older appliances even from only 10 years back. Models of the different customer classes were validated against actual recordings of load variations under voltage reduction. [ABSTRACT FROM AUTHOR]

The influence of complementarity balancing connected grid and control of offshore wind power generation on the economic and reliable operation of hybrid multiterminal HVDC (MTDC) and AC power system is investigated in the paper. The variances of distribution curve of both transmission loss of AC/DC system and voltage of critical nodes assessed by cumulant-based method are used to demonstrate how the different complementarity conditions, generation balancing strategies, MTDC grid topologies, and control schemes affect power system operation. In this paper, a study case of 16-generator 68-bus power system connected with three offshore wind farms is presented. The variance indices of transmission loss and voltage magnitude are calculated and then compared under different complementarity levels, active power balancing strategies, HVDC transmission systems, and controls, respectively. The analysis reveals that higher complementarity and multi-machine balancing strategy can effectively reduce system operational cost and enhance operational reliability. Compared to the point-to-point topology, the control pattern and parameter setting of multi-terminal topology are two key factors to determine its impact on system operation. [ABSTRACT FROM PUBLISHER]

The Rayleigh product channel model is useful in capturing the performance degradation due to rank deficiency of MIMO channels. In this paper, such a performance degradation is investigated via the distribution of mutual information assuming the block fading channels and the uniform power transmission scheme. Using techniques of free probability theory, the asymptotic variance of mutual information is derived when the dimensions of the channel matrices approach infinity. In this asymptotic regime, the mutual information is rigorously proven to be Gaussian distributed. Using the obtained results, a fundamental tradeoff between multiplexing gain and diversity gain of Rayleigh product channels under the uniform power transmission can be characterized by the closed-form expression at any finite signal-to-noise ratio. Numerical results are provided to compare the outage performance between the Rayleigh product channels and the conventional Rayleigh MIMO channels. [ABSTRACT FROM PUBLISHER]

This paper presents an algorithm for estimating the variance in simulated electromagnetic fields arising from uncertainties in electrical parameters in a model. The proposed algorithm is shown to be faster and more accurate than the stochastic finite difference time domain method. The main advantage of the algorithm lies in how does not slow down the main simulation of electromagnetic fields, since it is only performed on demand after the main simulation is completed. [ABSTRACT FROM PUBLISHER]

The impact of cooperation on interference management is investigated by studying an elemental wireless network, the so-called symmetric interference relay channel (IRC), from a generalized degrees of freedom (GDoF) perspective. This is motivated by the fact that the deployment of relays is considered as a remedy to overcome the bottleneck of current systems in terms of achievable rates. The focus of this paper is on the regime in which the interference link is weaker than the source-relay link in the IRC. Our approach toward studying the GDoF goes through the capacity analysis of the linear deterministic IRC (LD-IRC). New upper bounds on the sum capacity of the LD-IRC based on genie-aided approaches are established. These upper bounds together with some existing upper bounds are achieved by using four novel transmission schemes. Extending the upper bounds and the transmission schemes to the Gaussian case, the GDoF of the Gaussian IRC is characterized for the aforementioned regime. This completes the GDoF results available in the literature for the symmetric GDoF. It turns out that even if the incoming and outgoing links of the relay are both weaker than the desired channel, involving a relay can increase the GDoF. Interestingly, utilizing the relay in this case can increase the slope of the GDoF from −2 [in the interference channel (IC)] to −1 or 0. This shrinks the regime where ignoring the interference by treating it as noise is optimal. Furthermore, the analysis shows that if the relay ingoing and outgoing links are sufficiently strong, the relay is able to neutralize the interference completely. In this case, the bottleneck of the transmission will be the interference links, and hence, the GDoF increases if the interference link gets stronger. It is shown that in the strong interference regime, in contrast to the IC, the GDoF can be a monotonically decreasing function of the interference level. [ABSTRACT FROM PUBLISHER]

In this paper, we set out the drivers and patterns of adoption of new product development (NPD) tools in small high-technology firms. Despite previous findings that using NPD tools can improve NPD performance outcomes, surveys of large firms show reluctance to make full use of such tools. Our study is the first to look at NPD tool adoption in small high-technology firms. We draw on survey data from 99 firms in New Zealand, covering 76 NPD tools, including both functional and support tools, and tools from nontechnical aspects of NPD, such as the market perspective. The firms in our sample adopted fewer tools overall than studies report for larger firms, and fewer complex tools, but adopted just as many simple tools such as brainstorming, competitor analysis, project management, and alpha prototype. The firms typically did not have formalized NPD processes, but those that did also adopted more NPD tools. We found no significant differences in tool adoption by the level of project novelty to the firm or between industrial and consumer products. We conclude by discussing the case for small firms to formalize their NPD processes and to adopt a greater number and range of NPD tools. [ABSTRACT FROM PUBLISHER]

This paper proposes a new adaptive interference alignment (AIA) scheme in downlink cellular networks. The AIA scheme first selects the degree of freedom (DoF) mode from a set of feasible interference alignment (IA) configurations in accordance with the asymmetric data rate demands fed back from mobile users, which enables base stations to schedule the DoFs among different users. We then derive a unified zero-forcing algorithm to implement perfect IA in closed form, which requires only the feedback of combined channel state information (CSI) rather than that of both the desired and cross links. The proposed method shows good robustness in the case of limited feedback, where, regardless of feedback precision, intercell interference (ICI) can be completely canceled. When data rate demand information is not available, we propose a suboptimal yet effective geometry-based power allocation algorithm (GPA) for the AIA to maximize the sum rate. A two-user case study of power scheduling shows that the proposed Gerschgorin-disc-theory-based GPA significantly improves power efficiency and achieves a comparable sum rate to that of the waterfilling approach, even at a low signal-to-noise ratio. [ABSTRACT FROM PUBLISHER]

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]