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A novel but simple machine map-based modeling, identification, and optimal feedforward torque control (OFTC) approach for induction machines (IMs) is presented. It is based on, first, a generic, nonlinear transformer-like machine model considering nonlinear flux linkages (with magnetic saturation and cross coupling) and iron losses in the stator laminations in a novel, arbitrarily rotating but unique, robust, and reproducible ($d,q$)-reference frame; second, a holistic machine identification procedure, which evaluates steady-state measurements over a grid of ($d,q$) stator currents and produces temperature and frequency dependent machine maps, for example, flux linkages, torque, iron resistance, and efficiency; and third, a numerical offline optimization and extraction of different OFTC look-up tables (LUTs) for optimal current reference generation depending on reference torque and electrical frequency (and temperature). During the identification, stator winding temperature and electrical stator frequency of the IM are kept constant by an intelligent temperature and the speed control system. The presented measurement results for a squirrel-cage IM confirm that compared to constant flux operation or scalar V/Hz control, efficiency can be increased particularly in part-load operation by up to $\text{7} \,\%$ by Maximum Torque Per Losses minimizing copper and iron losses. [ABSTRACT FROM AUTHOR]

The accurate calculation of the natural frequencies of the stator is an important prerequisite for predicting and improving electromagnetic vibration in electrical machines. In this article, the stator core and the enclosure are regarded as cylindrical shells with axial ribs attached inside and outside the shells, respectively. Considering the moment of inertia and the shear deformation of the cylindrical shell, the displacement functions of the mid-surface in cylindrical shell are expressed as a superposition of orthogonal polynomials, the natural frequencies including axial, and circumferential modal shapes under different boundary conditions are obtained by combining the energy method and the Gram–Schmidt orthogonalization method, and the accuracy of the calculation results is verified by the finite element method and modal test. Considering the orthogonality of the stator material and specific slot structure, the natural frequencies of the stator including the stator core, windings, and enclosure are accurately obtained, the actual constraints of the end covers on the enclosure are accurately simulated by the simply supported boundary conditions. [ABSTRACT FROM AUTHOR]

Scalability of optimization algorithms is a major challenge in coping with the ever-growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly, population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird’s-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part I of the series covers two major algorithmic approaches to large-scale global optimization: 1) problem decomposition and 2) memetic algorithms. Part II of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally, touches upon the pitfalls and challenges of current research and identifies several potential areas for future research. [ABSTRACT FROM AUTHOR]

We design a decentralized stochastic optimization algorithm for machine-to-machine (M2M) networks, in which machines in the networks can attend the optimization process in a randomly-unexpected manner. This property of random attendance aims at capturing unexpected events that prevent the machines to successfully communicate with each other. The property, beside simulating the unforeseen nature of the networks, additionally helps reduce the communication cost of machines while still maintaining a reasonable optimization outcome. Our contribution gives practical insights for deploying optimization algorithms over machines in complex environments, including the Internet of Things. [ABSTRACT FROM AUTHOR]

Fast soil moisture content (SMC) mapping is necessary to support water resource management and to understand crop growth, quality, and yield. Therefore, earth observation (EO) plays a key role due to its ability of almost real-time monitoring of large areas at a low cost. This study aimed to explore the possibility of taking advantage of freely available Sentinel-1 (S1) and Sentinel-2 (S2) EO data for the simultaneous prediction of SMC with cycle-consistent adversarial network (CycleGAN) for time-series gap filling. The proposed methodology, first, learns latent low-dimensional representation of the satellite images, then learns a simple machine learning (ML) model on top of these representations. To evaluate the methodology, a series of vineyards, located in South Australia ’s Eden valley are chosen. Specifically, we presented an efficient framework for extracting latent features from S1 and S2 imagery. We showed how one could use S1 to S2 feature translation based on CycleGAN using S1 and S2 time series when there are missing images acquired over an area of interest. The resulting data in our study is then used to fill gaps in time-series data. We used the resulting latent representations to predict SMC with various ML tools. In the experiments, CycleGAN and the autoencoders were trained with data randomly chosen around the site of interest, so we could augment the existing dataset. The best performance was demonstrated with random forest (RF) algorithm, whereas linear regression model demonstrated significant overfitting. The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there are missing optical and synthetic-aperture radar (SAR) images. [ABSTRACT FROM AUTHOR]

Origami structures can transform their form and function by changing the direction of their folds. This reconfiguration can enable multifunctional robots, but doing so requires a fast, robust, and repeatable actuation method. In this article, we present an origami gripper that uses dynamic transformation to change its kinematic behavior in less than a second. We characterize individual vertices to show that the transformation is predictable and repeatable for different designs and orientations. We then apply it to a multivertex template that is capable of a wide range of shapes and motion patterns, indicating that transformation can be generalized to complex and functional machines. To demonstrate this, we built a transforming origami gripper on a robotic arm to pick up multiple objects. Demonstrations show that the gripper can quickly reconfigure between three different grasping modes and has sufficient stiffness to engage with and lift multiple objects with distinct geometries. [ABSTRACT FROM AUTHOR]

The need for combining two mechanical powers with electrical power is a vital part of many applications, such as hybrid electric vehicles. The magnetic geared surface permanent magnet (MGSPM) motor is a compact and efficient way of achieving this goal. However, their complex structure demands time-consuming optimizations, which requires a large number of simulations. In this context, a fast and accurate simulation method is required to reduce the design and optimization time. In addition to speed and accuracy in considering the complex structures of the machine, as the saturation effect has a major impact on the accuracy of the simulations in these machines, it must be included in the method. To tackle this problem, in this article, a hybrid subdomain (SD) and magnetic equivalent circuit (MEC) model is proposed, considering the saturation effect of the iron parts using current distributions in the SD model on the surfaces of modulators and rotor core, as well as in the stator slots. These currents are calculated using the MEC model of the respective parts and Ampere’s law. Furthermore, an example motor is simulated both using the proposed method and the FE method, and the accuracy of the method both for prediction of magnetic flux density inside iron parts and prediction of flux linkages of the machine for a variety of designs and excitations is verified. [ABSTRACT FROM AUTHOR]

The thermal modeling and design process of highly utilized electric machines is a complex task. The main heat paths within an electric drive are influenced by a variety of different thermal interfaces, that can mainly be described by thermal conduction and convection. The proper selection and estimation of these design parameters is challenging especially in the early design stage. This paper provides a systematic investigation evaluating inaccuracies and design influences of single thermal resistances, such as the convective heat transfer in the rotor-stator annulus, in the cooling jacket or interfaces between different machine components. Within the study, electromagnetic finite element models and thermal lumped parameter models of two traction drives are developed and validated using test bench measurements. The thermal design parameters are varied in a range resulting from intensive literature research and own laboratory experience. The influence of the design parameters in different operational points of the drives are studied in a sensitivity analysis. [ABSTRACT FROM AUTHOR]

Manual workstations play a critical role in flexible assembly lines by enabling human responses to reconfiguration that is faster than machine responses. As a result, human is more adaptive and sometimes unreplaceable by machines in complex assembly. However, with the increasing complexity of tasks, product quality has become highly susceptible to human error due to increments in operators’ cognitive load. One of the errors that affect assembly quality is the operator’s use of a handheld tool with an unfavorable working angle when handling the workpiece. In this case, an assistive mechanism to remind workers about the wrong working angle is necessary to support the process. To this end, this study proposes an angle monitoring system to inspect the working angle of handheld tools and provide feedback in real time with minimal interruption to the assembly process. The proposed system consists of an angle measurement model and an action recognition model, which are both built using deep-learning-based object detection algorithm. Besides, the system was designed with flexibility that it is applicable to different tools and assembly tasks. A case study on fastening a high-end graphical processing unit card is investigated to evaluate their performance. Results show 95.83% and 99.83% accuracies of the models. In practice, the proposed study is expected to facilitate assembly quality by preventing the failure of angle-related operations in a timely and reliable manner. [ABSTRACT FROM AUTHOR]

Fault prognosis under time-varying operating conditions is critical to predictive maintenance of complex industrial machines. However, prognostic methods proposed in the literature toward varying operating conditions typically focus on limited discrete states with clear boundaries, which are not always encountered in practical scenarios. This article aims to propose a more general method that can handle complex operating conditions with more nonlinearity and dynamics. In this work, a novel deep model named dual attention-based temporal convolutional network (DATCN), is developed to integrate multisensor data with time-varying operating settings for health assessment of industrial machines. An efficient deep temporal convolutional network is introduced to extract degradation features for sequence modeling. A novel dual attention model is developed to adaptively capture the temporal dynamics of sensor data and operating data, further enhancing the feature representations and highlighting the degradation information under time-varying operating settings. Besides, domain adaptation layer with multikernel maximum mean discrepancies (MK-MMD) is embedded to mitigate the domain-shift issue caused by varying operating conditions. The experimental results on the popular turbofan engine data set and a real-world Ion mill etching process validate the effectiveness of the proposed method and show its superiority in yielding significant improvements of prognostic accuracy relative to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Nonverbal signals play an important role in social interaction. Body orientation, posture, hand, and leg movements all contribute to successful communication, though research has typically focused on cues transmitted from the torso alone. Here, we explore lower body movements and address two issues. First, the empirical question of what social signals they provide. Second, the technical question of how these movements could be sensed unintrusively and in situations where traditional methods prove challenging. To approach these issues, we propose a soft, wearable sensing system for clothing. Bespoke "smart" trousers with embedded textile pressure sensors are designed and deployed in seated, multiparty conversations. Using simple machine learning techniques and evaluating individual and community models, our results show that it is possible to distinguish basic conversational states. With the trousers picking up speaking, listening, and laughing, they present an appropriate modality to ubiquitously sense human behavior. [ABSTRACT FROM AUTHOR]

Compiling queries to machine code is a very efficient way for executing queries. One often overlooked problem with compilation is the time it takes to generate machine code. Even with fast compilation frameworks like LLVM, generating machine code for complex queries often takes hundreds of milliseconds. Such durations can be a major disadvantage for workloads that execute many complex, but quick queries. To solve this problem, we propose an adaptive execution framework, which dynamically switches from interpretation to compilation. We also propose a fast bytecode interpreter for LLVM, which can execute queries without costly translation to machine code and dramatically reduces the query latency. Adaptive execution is fine-grained, and can execute code paths of the same query using different execution modes. Our evaluation shows that this approach achieves optimal performance in a wide variety of settings—low latency for small data sets and maximum throughput for large data sizes. Besides compilation time, we also focus on debugging, which is another important challenge of compilation-based query engines. To address this problem, we present a novel, database-specific debugger for compiling query engines. [ABSTRACT FROM AUTHOR]

A massively parallelized unstructured-grid plasma simulation code based on a multifluid plasma model has been developed and validated. The code uses a collocated cell-centered finite-volume method and is designed to simulate intermediate low-pressure and atmospheric-pressure (AP) plasma sources with complex machine geometries. One of the novel features of this code is that it is implemented in a highly flexible computational platform named ultrafast massively parallel processing (ultraMPP), which allows straightforward addition and integration of different partial differential equation (PDE) solvers in a self-consistent manner. As to the numerical methods, the Scharfetter–Gummel scheme is adopted to handle the drift-diffusion flux of electrons, whereas the Harten–Lax-van Leer (HLL)-type approximate Riemann solver is used to handle the convection terms of ion momentum equations. For discretization of the diffusion terms in an unstructured grid, the Taylor expansion is used to deal with the effects of nonorthogonality of the cells, and the cell-center gradient is calculated using a least-squares method. The simulation code with a local-field approximation (LFA) was validated for two cases of AP plasmas as well as a case of an argon capacitively coupled plasma generated in a Gaseous Electronic Conference (GEC) reference cell with local mean energy approximation (LMEA) at intermediate low pressure. The simulation results were found to be in good agreement with the previously published experimental and simulation data. [ABSTRACT FROM AUTHOR]

As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges. [ABSTRACT FROM AUTHOR]

This article proposes a general design method for dual-stator field modulation machines (DSFMMs) that can be separated into two field modulation machine (FMM) portions sharing the key components with each other. The existing permanent-magnet synchronous machines and FMMs can be categorized into R/S types, where R denotes the part that can generate the rotating magnetic field, and S represents the stationary modulator. R and S can be used to derive various DSFMM topologies that take advantage of the high toque density. The combined machines employ the multilayer topology that provides space for multi-PM-excitation and multimodulation. The general design principle, simplest R/S-type machine, and the coupled armature winding configuration are discussed, and the modulated air-gap working harmonic components are investigated. Four representative dual-stator machines are developed based on the proposed method. The proposed hybrid-magnet DSFMM (HM-DSFMM) has the merits of high power factor, low cogging torque, and low torque ripple. The prototype of the HM-DSFMM is manufactured and tested. The result comparisons of the finite-element-analysis prediction and measurement verify the electromagnetic design. [ABSTRACT FROM AUTHOR]

Megavolt ITER Injector and Concept Advancement (MITICA) is the full-size prototype of the neutral beam injector (NBI) for the ITER currently under construction at the neutral beam test facility (NBTF) (Consorzio RFX, Padova, Italy). One of the main issues of such a complex machine is to secure a stable voltage holding of the ion source—accelerator complex—which is biased at −1 MV and constitutes a very large (cathodic) area, with respect to the ground (the vessel). Coordinated with the experimental program of the HV test facilities in Japan (QST, Naka) and in Italy (high voltage Padova test facility, Consorzio RFX), a test campaign is foreseen for 2020–2021, using the real MITICA vacuum vessel and a mockup of the ion source. This mockup can constitute a simple geometry, where the MITICA ion source is represented by a sphere (cathode), connected to the high-voltage bushing. A planar electrode (anode) will cover the lower part of the beam source vessel (BSV) and will be positioned at an adjustable distance from the sphere, to assure fine control of the electrostatic configuration. The experiments will be devoted to characterize the breakdown voltage as a function of the gap length and vacuum pressure. In this article, the design activity of the electrodes to be used during the campaign is presented. [ABSTRACT FROM AUTHOR]

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) with typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets. For a finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoder NNs and one-class SVMs which, however, are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical examples support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for evaluating the performance of a closed-loop spare machines supply system when the time between failures as well as the repair times is generally distributed. The closed-loop system with finite number of machines and the generic time distribution assumption well represents real-life spare machines supply systems but at the cost of higher complexity. The increased complexity prohibits the use of existing models, which evaluate the performance of closed-loop systems assuming exponentially distributed failure and repair times. The Extended Bottleneck (EBOTT) algorithm in the literature estimates the performance measures of a closed-loop system with generally distributed repair service times. However, as our analysis indicates, the EBOTT algorithm may fall into an infinite loop even in assessing the performance of a simple spare machines supply system. The Modified EBOTT algorithm presented in this paper overcomes the shortcomings of the EBOTT algorithm and exhibits superior performance. Finally, through tradeoff analysis between spare machines inventory level and repair shop capacities, insights into the performance of such systems are presented. [ABSTRACT FROM AUTHOR]

Due to its special partitioned stator structure and multi-excitation with two different types of permanent magnet, a new double-stator multi-excitation flux-switching machine (DSME-FSM) has advantages of high torque density, wide speed range, and low cost. For this kind of machine with complex structure, in order to realize the optimization accurately and efficiently, a multi-objective comprehensive optimal design method considering the different weights of the design objectives is presented based on parameters sensitivity analysis and genetic algorithm optimization in this paper. The parametric model of the machine is built up, and a trade-off objective function considering the different weights of the design objectives is defined. The key size parameters with strong sensitive are selected through parameters sensitivity analysis and then optimized by using the genetic algorithm method. The performance comparison between the initial and the optimized machines verifies the proposed optimization method. The anti-demagnetization property and the flux-weakening capability for a wide-speed range operation are also analyzed, which further confirms the DSME-FSM an interesting candidate for EV propulsion. [ABSTRACT FROM AUTHOR]

An electrical variable transmission (EVT) is an electromagnetic power-split device with two mechanical and two electrical ports. It can be used in hybrid electric vehicles to split the power to the wheels in a part coming from the combustion engine and a part exchanged with the battery. Although crucial for the EVT design and evaluation, no papers are found to give a detailed overview of the different loss components in such a machine and how they can be calculated and measured. In contrast to conventional electrical machines, this machine has more degrees of freedom, which can be exploited to measure the different loss contributions separately. This paper proposes a methodology to identify and measure the different loss components in this kind of machines. The proposed method is able to identify the iron losses in stator and inner rotor, the copper losses, bearing losses, and slip ring friction losses separately. To this end, measurements of both torque and speed sensors in different operating points are combined. The methods are applied to identify the different loss contributions in a prototype permanent magnet assisted EVT, both in no-load operation as under load where its functionality as power-split device is evaluated. [ABSTRACT FROM PUBLISHER]

Proposed in this paper is a symbolic linearly elastodynamic model for the analysis and synthesis of microelectromechanical system. In particular, the strain energy in the compliant links is computed from the results of a previous work, by representing beam deflections with small-displacement screws. This allows for a systematic coherent approach based on screw theory. Two case studies are proposed to illustrate the application of the model. In the first example, the elastodynamic model of a simple accelerometer is derived and compared to one available in the literature. In the second example, a complex accelerometer is modeled, fabricated, and tested. Comparison between the simulated results and data from the literature or obtained experimentally shows the accuracy of the proposed model. [ABSTRACT FROM AUTHOR]

This paper studies the strength and reversibility of direction-of-motion stereotypes and response times for a digital counter with 2-D (four-way) lever controls. The results showed that the forward-for-increase (Fl), backward-for-decrease (BD), up-for-increase (UI), and down-for-decrease (DD) stereotypes were generally weak in the four-way fever-digital counter configuration, with the FI-BD and UI-DD stereotypes being dominant at the horizontal and vertical planes, respectively. The FI-BD stereotypes at the horizontal planes were generally stronger than the UI-DD stereotypes at the vertical planes, indicating that the horizontal planes are the desirable planes for positioning a four-way lever in working with the digital counter. In consideration of the mean stereotype strengths and indexes of reversibility, the compatibility of the lever control-digital counter was much lower than that of the four-way lever-circular display configuration found in a previous study. The analysis of the contribution of component principles also confirmed that the UI, DD, Fl, and BD stereotypes were much weaker than the stereotypes found in the four-way lever-circular display combination. The findings suggested that a circular display is a better partner than a digital counter with a four-way lever. [ABSTRACT FROM AUTHOR]

A typical antilock brake system (ABS) senses when the wheel lockup is to occur, releases the brakes momentarily, and then reapplies the brakes when the wheel spins up again. In this paper, a genetic neural fuzzy ABS controller is proposed that consists of a nonderivative neural optimizer and fuzzy-logic components (FLCs). The nonderivative optimizer finds the optimal wheel slips that maximize the road adhesion coefficient. The optimal wheel slips are for the front and rear wheels. The inputs to the FLC are the optimal wheel slips obtained by the nonderivative optimizer. The fuzzy components then compute brake torques that force the actual wheel slips to track the optimal wheel slips. The brake torques that force the actual wheel slips to track the optimal wheel slips minimize the vehicle stopping distance. The FLCs are tuned using a genetic algorithm. The performance of the proposed controller is compared with the case when maximal brake torques are applied causing a wheel lockup and with the case when wheel slips are kept constant while the road surface changes. [ABSTRACT FROM AUTHOR]