Search

Neural architecture search (NAS) has been widely studied to design high-performance network architectures automatically. However, existing approaches require more search time and substantial resource consumption due to their intensive architecture evaluations. Moreover, recently developed NAS algorithms are noncompetitive when combining multiple competing and conflicting objectives, e.g., the test accuracy and the number of parameters. In this paper, a low-cost NAS (LoNAS) method is proposed to address these problems. First, a variable-architecture encoding strategy based on a novel Reg Block is designed to construct high accuracy network architectures with few parameters. Second, a training-free proxy based on the neural tangent kernel (NTK) is proposed to accelerate the search process efficiently. Finally, a three-stage evolutionary algorithm (EA) based on multiple-criteria environmental selection and a set of block-based mutation operators are designed to balance exploration and exploitation better. The experimental results show that LoNAS finds network architectures with competitive performance compared to the state-of-the-art architectures in test accuracy and the number of parameters. Moreover, LoNAS uses less search time and fewer computational resources, consuming only 0.02 GPU Days with one GPU on CIFAR-10 and CIFAR-100. Furthermore, the architectures found by LoNAS on CIFAR-10 and CIFAR-100 exhibit good transferability to ImageNet-16-120, with the test accuracy surpassing that of the state-of-the-art network architectures. [ABSTRACT FROM AUTHOR]

H∞ control can guarantee the performance of a nominal ICPT system. However, the dynamic performance may suffer degradation when the parameter deviates from its nominal value. Thus, to enhance the overall dynamic performance of the ICPT system under parameter perturbations, an H∞ control method with a selected weighting function considering parameter perturbations is proposed in this article. First, a generalized state-space model is established to analyze the influence of parameter perturbations on the open-loop system in detail. After that, an uncertain model is established via frequency-domain analysis, generating a numerical solution of a weighting function used to describe the parameter perturbations. Then, the generalized plant with uncertainty is obtained and transferred into a standard H∞ optimal problem via small gain theory. Based on the proposed controller, the dynamic performance and robustness of the closed-loop system are analyzed and verified theoretically. Finally, simulation and experimental results further verify that the proposed H∞ control method can make the closed-loop system obtain good dynamic performance and strong robustness against parameter perturbations. The settling time is less than 6.8 ms without any overshoot when the system suffers parameter perturbations. Besides, the controller can maintain good dynamic performance in the startup and reference tracking experiments. [ABSTRACT FROM AUTHOR]

The soft switching of power components in inductive power transfer systems can reduce switching loss and switching stress and decrease electromagnetic interference. However, the realization of soft switching in an inductive power transfer system may change the system's constant voltage and constant current output characteristics. For this reason, in this article, a zero-voltage switching technology based on a full-bridge inverter that is decoupled from the output characteristics of the system is proposed and validated. First, the basic conditions for the full-bridge inverter to achieve zero voltage switching are derived by analyzing the working mode of the full-bridge inverter circuit. Then, the constant current, constant voltage output, and zero phase angle input conditions of a first-order/second-order/third-order compensation structure are studied using the high-order two-port network cascading rule. On this basis, a parameter adjustment strategy for an arbitrary compensation structure is proposed to achieve zero voltage switching without affecting the output. Then analytical verification of the constant current output characteristics of a double-sided inductor-capacitor-capacitor compensation structure is performed as an example. Finally, a MATLAB/Simulink model and a set of 3.3 kW prototypes are established to verify the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Despite pedestrian detectors having made significant progress by introducing convolutional neural networks, their performance still suffers degradation, especially in occlusion scenes with more false positives (FPs) and false negatives (FNs). To alleviate the problem, we propose a novel Sequential Attention-based Distinct Part Modeling (SA-DPM) for balanced pedestrian detection. It takes one step further in constructing more robust representation that supports detection with fewer FNs and FPs. Specifically, the Sequential Attention serves as one internal perception process that captures several distinct part areas step by step from each pedestrian proposal (full-body). Different from the previous either-or feature selection, the following Joint Learning attempts to seek a reasonable trade-off between part and full-body features, and combines both features for more accurate classification and regression. Evaluation on the widely used pedestrian datasets including Caltech and Citypersons shows that the proposed SA-DPM achieves promising performance for both non-occluded and occluded pedestrian detection tasks, especially on Caltech Heavy Occlusion set, which yields a new state-of-the-art miss rate by 30.18% and outperforms the second best detector by 6.32%. [ABSTRACT FROM AUTHOR]

In general, flexible ligand docking is used for docking simulations under the premise that the position of the binding site is already known, and meanwhile it can also be used without prior knowledge of the binding site. However, most of the optimization search algorithms used in popular docking software are far from being ideal in the first case, and they can hardly be directly utilized for the latter case due to the relatively large search area. In order to design an algorithm that can flexibly adapt to different sizes of the search area, we propose an effective swarm intelligence optimization algorithm in this paper, called diversity-controlled Lamarckian quantum particle swarm optimization (DCL-QPSO). The highlights of the algorithm are a diversity-controlled strategy and a modified local search method. Integrated with the docking environment of Autodock, the DCL-QPSO is compared with Autodock Vina, Glide and other two Autodock-based search algorithms for flexible ligand docking. Experimental results revealed that the proposed algorithm has a performance comparable to those of Autodock Vina and Glide for dockings within a certain area around the binding sites, and is a more effective solver than all the compared methods for dockings without prior knowledge of the binding sites. [ABSTRACT FROM AUTHOR]

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed. [ABSTRACT FROM AUTHOR]

In this letter, an infrared photoinverter with a GeSe 2-D/ $\alpha $ -PbSe heterostructure and a high detectivity ($5.8\times 10 ^{14}$ Jones) and responsivity ($1.26\times 10$ 6 V/W) has been proposed for applications in spectroscopy detectors and quick chemical gas identification. The design concept is based on three criteria: 1) an enhanced heterostructure constructed from a GeSe 2-D nanomaterial with PbSe and an abrupt PN junction; 2) a photoinverting device that can conveniently perform I-V conversion; and 3) a compatible sensing unit that includes a spectral ppb-level trace gas sensing module. Our work, which used a tunable DFB (Distributed Feedback) laser and retained high spectral sensitivity (1550-1750 nm), presents a novel technical method that meets the various requirements for cost-effective trace gas detection in the infrared region. [ABSTRACT FROM AUTHOR]

Accurate estimation of mutual inductance in wireless power transfer (WPT) system is a prerequisite for accurate identification of load parameters, which is critical in system constant output and efficiency tracking control. In this article, a general load decoupling method for WPT system considering soft-start process and battery initial voltage is proposed. Moreover, a double-sided LCC topology is used to explain how to calculate the boundary conditions of load decoupling. Based on the proposed load decoupling method, a mutual inductance identification method is proposed for WPT system with double-sided LCC topology. Within the frequency range recommended, the unique solution of mutual inductance can be obtained by solving high-ordered equivalent impedance equation. In this case, there is no need to discuss the situation for multiple solutions. Based on the WPT prototype, the effectiveness of the proposed load decoupling method is verified, and parameter sensitivity of the identification method is analyzed in detail. Finally, the proposed method is experimentally verified. Results show that high identification accuracy can be achieved even when the coils are misaligned. Compared with the existing identification methods, the proposed method does not need any additional switches and auxiliary circuits, which reduces the complexity and system cost effectively. [ABSTRACT FROM AUTHOR]

A novel diode-triggered silicon-controlled rectifier with embedded MOS and island diodes (DTSCR-EMOS-ID) is proposed and investigated. The DTSCR-EMOS-ID was fabricated in 0.18- $\mu \text{m}$ and 22-nm CMOS processes for verifying the process transplantation. The fabricated DTSCR-EMOS-ID presents a low trigger voltage of 1.3 V, a small turn-on resistance of 8 $\text{m}\Omega /\mu \text{m}$ and a small parasitic capacitance of 43 fF. Meanwhile, the effective failure current and response time of DTSCR-EMOS-ID are optimized to 4.3 A and 3.6 ns, respectively. The DTSCR-EMOS-ID shows much better ESD performance than the conventional LVTSCR and DTSCR fabricated in the same process, which can be attributed to the diode triggering, gate coupling and large injection effects. Moreover, the TCAD simulation results of DTSCR-EMOS-ID agree well with the transmission line pulse testing results, further confirming that the proposed DTSCR-EMOS-ID can be widely used as an effective ESD protection device for high-speed ICs, without the limitation of the fabrication process. [ABSTRACT FROM AUTHOR]

As part of video transmission, video quality assessment plays an important role in improving the user’s visual quality and reducing transmission bitrate. However, the existing assessment schemes for the tile-based transmission mechanism cannot accurately predict the video quality. In this paper, we propose a tile-based panoramic video quality assessment method to optimize the transmission strategy for tile-based streaming. Considering the distortion of panoramic video projection, the inverse of gnomonic projection and bilinear interpolation are utilized to convert the planar tiles to sphere. Video Multimethod Assessment Fusion (VMAF) is applied to obtain the tile quality score. Besides, the user’s head movement and eye movement are combined to generate a tile weight map, and generate the final score of the panoramic video through weighted averaging. In addition, considering the impact of the quality fluctuation of the tiles within field of view, the tile quality loss model is established from multiple dimensions including the encoding parameter, viewport position and video content. Experimental results show that there is a high correlation between the predicted results and actual subjective scores, under two video quality assessment datasets. Furthermore, we integrate the method into the panoramic video transmission system. Compared with the tile-based adaptive rate algorithm, the proposed method degrades the video quality by only 3%, while reducing the bandwidth consumption by 16.99%. [ABSTRACT FROM AUTHOR]

Objective quality estimation of media content plays a vital role in a wide range of applications. Though numerous metrics exist for 2D images and videos, similar metrics are missing for 3D point clouds with unstructured and non-uniformly distributed points. In this paper, we propose ${\sf GraphSIM}$ GraphSIM —a metric to accurately and quantitatively predict the human perception of point cloud with superimposed geometry and color impairments. Human vision system is more sensitive to the high spatial-frequency components (e.g., contours and edges), and weighs local structural variations more than individual point intensities. Motivated by this fact, we use graph signal gradient as a quality index to evaluate point cloud distortions. Specifically, we first extract geometric keypoints by resampling the reference point cloud geometry information to form an object skeleton. Then, we construct local graphs centered at these keypoints for both reference and distorted point clouds. Next, we compute three moments of color gradients between centered keypoint and all other points in the same local graph for local significance similarity feature. Finally, we obtain similarity index by pooling the local graph significance across all color channels and averaging across all graphs. We evaluate ${\sf GraphSIM}$ GraphSIM on two large and independent point cloud assessment datasets that involve a wide range of impairments (e.g., re-sampling, compression, and additive noise). ${\sf GraphSIM}$ GraphSIM provides state-of-the-art performance for all distortions with noticeable gains in predicting the subjective mean opinion score (MOS) in comparison with point-wise distance-based metrics adopted in standardized reference software. Ablation studies further show that ${\sf GraphSIM}$ GraphSIM can be generalized to various scenarios with consistent performance by adjusting its key modules and parameters. Models and associated materials will be made available at https://njuvision.github.io/GraphSIM or http://smt.sjtu.edu.cn/papers/GraphSIM. [ABSTRACT FROM AUTHOR]

Most existing random access schemes for machine-type communications (MTC) simply adopt a uniform preamble selection distribution, irrespective of the underlying device activity distributions. Hence, they may yield unsatisfactory access efficiency. In this paper, we model device activities for MTC as multiple Bernoulli random variables following an arbitrary multivariate Bernoulli distribution which can reflect both dependent and independent device activities. Then, we optimize preamble selection and access barring for random access in MTC according to the underlying joint device activity distribution. Specifically, we investigate three cases of the joint device activity distribution, i.e., the cases of perfect, imperfect, and unknown joint device activity distributions, and formulate the average, worst-case average, and sample average throughput maximization problems, respectively. The problems in the three cases are challenging nonconvex problems. In the case of perfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain stationary points of the original problem and an approximate problem, respectively. In the case of imperfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of an equivalent problem and a stationary point of an approximate problem, respectively. Finally, in the case of unknown joint device activity distribution, we develop an iterative algorithm to obtain a stationary point. The proposed solutions are widely applicable and outperform existing solutions for dependent and independent device activities. [ABSTRACT FROM AUTHOR]

This article addresses the aperiodic sampled-data control problem for flexible spacecraft with stochastic actuator failures. Flexible spacecraft dynamics are approximated by a group of T–S fuzzy models due to strong nonlinearity, and the multi-stochastic failures of spacecraft are depicted by a time-continuous and state-discrete Markov chain. To reduce the design conservativeness, a membership-sampling-dependent Lyapunov–Krasovskii functional (MSDLKF) is introduced to utilize the information of fuzzy membership functions and aperiodic sampling modes. Furthermore, a number of reliable fuzzy controllers are designed to obtain the exponential attitude stabilization under the circumstances of stochastic failures. At the same time, disturbance attenuation is ensured. The solution of the fuzzy controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). In the end, an example of the practical flexible spacecraft system is given to illustrate the feasibility and validity of the proposed fuzzy control methods. [ABSTRACT FROM AUTHOR]

This paper focuses on communication-efficient federated learning problem, and develops a novel distributed quantized gradient approach, which is characterized by adaptive communications of the quantized gradients. Specifically, the federated learning builds upon the server-worker infrastructure, where the workers calculate local gradients and upload them to the server; then the server obtain the global gradient by aggregating all the local gradients and utilizes it to update the model parameter. The key idea to save communications from the worker to the server is to quantize gradients as well as skip less informative quantized gradient communications by reusing previous gradients. Quantizing and skipping result in ‘lazy’ worker-server communications, which justifies the term Lazily Aggregated Quantized (LAQ) gradient. Theoretically, the LAQ algorithm achieves the same linear convergence as the gradient descent in the strongly convex case, while effecting major savings in the communication in terms of transmitted bits and communication rounds. Empirically, extensive experiments using realistic data corroborate a significant communication reduction compared with state-of-the-art gradient- and stochastic gradient-based algorithms. [ABSTRACT FROM AUTHOR]

Recently, many researches on convolution neural network (CNN) based in-loop filters have been proposed to improve coding efficiency. However, most existing CNN based filters tend to train and deploy multiple networks for various quantization parameters (QP) and frame types (FT), which drastically increases resources in training these models and the memory burdens for video codec. In this paper, we propose a novel variable CNN (VCNN) based in-loop filter for VVC, which can effectively handle the compressed videos with different QPs and FTs via a single model. Specifically, an efficient and flexible attention module is developed to recalibrate features according to QPs or FTs. Then we embed the module into the residual block so that these informative features can be continuously utilized in the residual learning process. To minimize the information loss in the learning process of the entire network, we utilize a residual feature aggregation module (RFA) for more efficient feature extraction. Based on it, an efficient network architecture VCNN is designed that can not only effectively reduce compression artifacts, but also can be adaptive to various QPs and FTs. To address training data imbalance on various QPs and FTs and improve the robustness of the model, a focal mean square error loss function is employed to train the proposed network. Then we integrate the VCNN into VVC as an additional tool of in-loop filters after the deblocking filter. Extensive experimental results show that our VCNN approach obtains on average 3.63%, 4.36%, 4.23%, 3.56% under all intra, low-delay P, low-delay, and random access configurations, respectively, which is even better than QP-Separate models. [ABSTRACT FROM AUTHOR]

Neural architecture search (NAS) has made incredible progress in medical image segmentation tasks, due to its automatic design of the model. However, the search spaces studied in many existing studies are based on U-Net and its variants, which limits the potential of neural architecture search in modeling better architectures. In this study, we propose a new NAS architecture named GNAS-U2Net for the joint segmentation of optic cup and optic disc. This architecture is the first application of NAS in a two-level nested U-shaped structure. The best performance achieved by the joint segmentation model designed by NAS on the REFUGE dataset has an average DICE of 92.88%. Compared to U2-Net and other related work, the model has better performance and uses only 34.79M parameters. We then verify the generalization of the model on two datasets, namely the Drishti-GS dataset and the GAMMA dataset, for which we obtain an average DICE of 92.32% and 92.11% respectively. [ABSTRACT FROM AUTHOR]

Microservice systems are complicated due to their runtime environments and service communications. Debugging a failure involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimalize failure-inducing deltas of circumstances (e.g., deployment, environmental configurations). Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas during delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimalize, our approach defines a set of circumstance dimensions that can affect the execution of microservice systems. To automate the testing of deltas, our approach includes the design of an infrastructure layer for automating deployment and manipulation of microservice systems. To optimize the delta debugging process, our approach includes the design of parallel execution for delta testing tasks. Our evaluation shows that our approach is scalable and efficient with the provided infrastructure resources and the designed parallel execution for optimization. Our experimental study on a medium-size microservice benchmark system shows that our approach can effectively identify failure-inducing deltas that help diagnose the root causes. [ABSTRACT FROM AUTHOR]

Reflection caused by glass often degrades the quality of an image and further makes it difficult to estimate depth. In this article, we propose joint reflection removal and depth estimation from a single image. We perform reflection removal (transmission recovery) and depth estimation jointly using a collaborative neural network that consists of four blocks: 1) encoder for feature extraction; 2) reflection removal subnetwork (RRN); 3) depth estimation subnetwork (DEN); and 4) depth refinement guided by the transmission layer. We achieve collaboration between reflection removal and depth estimation by concatenating intermediate features of DEN with RRN. Since the recovered transmission layer contains accurate edges of objects behind glass, we refine the estimated depth with its guidance by guided image filtering. The experimental results demonstrate that the proposed method achieves both reflection removal and depth estimation even for images with dominant reflections. Besides, this article offers a new way of treating reflections in images to introduce depth estimation into reflection removal and achieve reflection removal and depth estimation simultaneously. [ABSTRACT FROM AUTHOR]

pH of soilless culture substrate is an important and frequently measured property in facility agriculture. Compared with laboratory analysis, pH direct measurement (pH-DM) involving the direct insertion of a pH sensor into soil or substrates, is rapid, efficient, and inexpensive. This method requires a reliable pH sensor and exhibits a significant measuring error of 0.8 to 1.1. In this study, all-solid-stated pH sensors were fabricated on alumina substrate through magnetron sputtering. Substrates containing mixed peat and vermiculite were prepared to examine the effects of mass water content (MWC) and mixture ratio on the pH-DM method. Statistical analysis of the measured pH values demonstrated that MWC and mass ratio of peat ($\boldsymbol {\eta }_{{peat}}$) evidently influenced pH-DM. Specifically, in the unsaturated substrate, the errors of pH-DM were highly and negatively correlated with MWC but positively correlated with $\boldsymbol {\eta }_{{peat}}$. The measured pH values also exhibited wide dispersion in the substrates with high $\boldsymbol {\eta }_{{peat}}$ or extremely low MWC. Furthermore, a two-factor compensation model was established and evaluated. The compensation results indicated that the maximum error with pH-DM method decreased from −1.63 to −0.66 pH unit, and the fluctuation coefficient of pH-DM decreased from 0.796 to 0.264. The influence rules of MWC and $\boldsymbol {\eta }_{{peat}}$ on errors in the pH-DM method, as well as the proposed compensation model, are useful in improving the accuracy and robustness of pH-DM and can contribute to rapid and efficient pH measurements in soilless culture substrate. [ABSTRACT FROM AUTHOR]

For a drag-free satellite, one core technology required to achieve the mission requirements is drag-free control. The purpose of control is to offset the interference such as sunlight pressure, gravitational gradients of Earth and Moon, nonspherical perturbation, etc. Currently, the drag-free controller design is mainly aimed at the full frequency band. This will inevitably mix the control signal into the scientific measurement signal, thereby interfering with the processing of scientific data. Besides, the control at the full frequency band will increase fuel consumption. To solve the above problems, this article presented the controller design method for a drag-free satellite with scientific measurement constraints, which covers motion model establishing, coordinate axis decomposition, frequency separation control strategy formulation, H∞ mixed sensitivity controller design, controller stability analysis, and performance results. [ABSTRACT FROM AUTHOR]

In this letter, a dual-cavity TM01 and TM02 mixed modes to TM01 mode converter is proposed for a dual-mode relativistic backward-wave oscillator (RBWO) application. This converter is more compact than the conventional converters and the TM02 mode is cutoff in the output waveguide, which forces the purity of the output TM01 mode to 100%. Especially, the converter is adjustable, and by simply replacing the waveguides in it, the converter can be well-matched to different proportional mixed modes. Electromagnetic simulation results show that the conversion efficiency of the converter is higher than 99%. The converter is validated in the high-power microwave (HPM) generation experiment of the dual-mode RBWO and a conversion efficiency of 98% is realized. A pure TM01 microwave with power of 4.6 GW and pulse duration of 42 ns at 10 GHz was obtained. [ABSTRACT FROM AUTHOR]

This paper presents a dual camera system for high spatiotemporal resolution (HSTR) video acquisition, where one camera shoots a video with high spatial resolution and low frame rate (HSR-LFR) and another one captures a low spatial resolution and high frame rate (LSR-HFR) video. Our main goal is to combine videos from LSR-HFR and HSR-LFR cameras to create an HSTR video. We propose an end-to-end learning framework, AWnet, mainly consisting of a FlowNet and a FusionNet that learn an adaptive weighting function in pixel domain to combine inputs in a frame recurrent fashion. To improve the reconstruction quality for cameras used in reality, we also introduce noise regularization under the same framework. Our method has demonstrated noticeable performance gains in terms of both objective PSNR measurement in simulation with different publicly available video and light-field datasets and subjective evaluation with real data captured by dual iPhone 7 and Grasshopper3 cameras. Ablation studies are further conducted to investigate and explore various aspects, such as reference structure, camera parallax, exposure time, etc) of our system to fully understand its capability for potential applications. [ABSTRACT FROM AUTHOR]

Remote sensing images obtained from onboard linear array cameras suffer from geometric disturbances in the presence of attitude jitter from a satellite platform. Thus, platform jitter estimation is essential for improving satellite stability and enhancing remote sensing image quality. In this article, a novel integration framework is designed to estimate attitude fluctuation, which combines multispectral advanced spaceborne thermal emission and reflection/short-wave-infrared (ASTER/SWIR) data and blurred star images from onboard optical sensors. First, a multilevel XGBoost algorithm is applied to learn the nonlinear relationship between the star blurring and instantaneous jitter displacement. An image registration method and a deconvolutional process are then introduced to remove the satellite jitter from ASTER/SWIR data. Next, an augmented $H$$_\infty$ filter is proposed to fuse and estimate the satellite jitter from two algorithms. Simulating experiment results indicate that the jitter estimation error of the proposed integration framework is reduced by 40%. Compared with the existing jitter estimation methods only based on remote sensing image processing, our strategy has better robustness and accuracy, especially on extreme ground scenes. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) tumor is the most common primary brain malignant tumor. The precise identification of GBM tumors is very important for diagnosis and treatment. Hyperspectral imaging is a fast, noncontact, accurate, and safe modern medical detection technology, which is expected to be a new tool of intraoperative diagnosis. In order to make full use of the spectral and spatial information of hyperspectral images (HSIs) to achieve accurate GBM tumor identification, a method based on the fusion of multiple deep models is proposed for in vivo human brain HSI classification. The proposed method includes the following major steps: 1) spectral phasor analysis and data oversampling; 2) 1-D deep neural network (1D-DNN)-based spectral HSI feature extraction and classification; 3) 2-D convolution neural network (2D-CNN)-based spectral–spatial HSI feature extraction and classification; 4) edge-preserving filtering-based classification result fusion and optimization; and 5) fully convolutional network (FCN)-based background segmentation. To verify the capabilities of the proposed method, experiments are performed on two real human brain hyperspectral datasets, including 36 in vivo HSIs captured from 16 different patients. The proposed method can achieve an overall accuracy of 96.69% for four-class classification and overall accuracy of 96.34% for GBM tumor identification. Experimental results demonstrate that the proposed method exhibits competitive classification performance and can generate satisfactory thematic maps of the location of the GBM tumor, which can provide the surgeon with guidance on successful and precise tumor resection. [ABSTRACT FROM AUTHOR]

Channel estimation is essential for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with hybrid precoding. However, accurate channel estimation is a challenging task as the number of antennas is huge, while the number of RF chains is limited. Traditional methods of compressed sensing for channel estimation lead to serious loss of accuracy due to channel angle quantization. In this brief, we propose a new iterative reweight-based log-sum constraint channel estimation scheme. Specifically, we exploit the structure sparsity of the mmWave channels by formulating the channel estimation problem as an objective optimization problem. We utilize the log-sum as a constraint, via optimizing an objective function through the gradient descent method, the proposed algorithm can iteratively move the channel estimated angle-of-arrivals (AOAs) and angle-of-departures (AODs) towards the optimal solutions, and finally improve the angle estimation performance significantly. In addition, to guarantee the accuracy of channel estimation, we introduce a dynamic regularization factor to leverage between the channel sparsity and the data fitting error. Numerical experiments demonstrate that the proposed algorithm achieves better convergence behavior than conventional sparse signal recovery approaches. [ABSTRACT FROM AUTHOR]

Proxy re-encryption (PRE) allows a proxy to transform one ciphertext to another under different encryption keys while keeping the underlying plaintext secret. Because of the ciphertext transformability of PRE, there are many potential private communicating applications of this feature. However, existing PRE schemes are not as full-fledged as expected. The lack of necessary features makes them hard to apply in real-world scenarios. So far, there does not exist a unidirectional multihop PRE scheme with constant decryption efficiency and constant ciphertext size without extensions. Impractical performance and weak scalability also hinder PRE from most real-world applications. In this work, we present a new PRE scheme with secure hardware enclave named hPRESS (hardware-enhanced PRE scheme using secure enclave). To the best of our knowledge, hPRESS is the first unidirectional multihop PRE scheme which achieves both constant decryption efficiency and constant ciphertext size without extensions. A detailed security analysis demonstrates that our proposal is CCA secure based on the security of the underlying encryption schemes and the secure enclave. We also implement a prototype based on Intel SGX, one of the most popular secure enclave techniques in recent years, and evaluate its performance. The experimental results show that, compared with previous PRE schemes, our hPRESS is almost one order of magnitude faster in terms of the decryption and transformation. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of imperfect premise matching asynchronous H∞ output tracking control for Takagi–Sugeno fuzzy Markov jump systems. A hidden Markov model is established due to the fact that the modes information of the system may not be accurately transmitted to the controller, which is used to depict the asynchronous phenomenon between the system modes and controller modes. The packet loss in the communication process is described by a stochastic variable subject to Bernoulli distribution. Then, based on a novel Lyapunov function, the mode-dependent and fuzzy-basis-dependent stability criteria are derived and the asynchronous control scheme is developed subject to an H∞ tracking performance. Finally, two examples are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Traditional open-switch fault diagnosis methods suffer from poor rapidity or robustness. To solve this issue, a new differential current observer-based fault diagnosis method is proposed in this article. With the designed differential observer, fault symptoms (residuals) can be generated and adopted for fault diagnosis easily. Considering that the residuals are sensitive to motor operating condition in the conventional model-based method due to model error, an adaptive fault detection threshold is designed. As a result, the false detection and missed detection caused by the change of working condition can be avoided, and stronger robustness against speed, load, and parameter variations can be achieved with superior rapidity compared with existing methods. Finally, the rapidity and robustness of the proposed fault diagnosis method are verified sufficiently through experimental results. [ABSTRACT FROM AUTHOR]

This article proposes a new position estimation method based on an unsaturated flux linkage model for planar switched reluctance motors (PSRMs). The bipolar-voltage-injection method is used to measure the flux linkage, and it is found that ripples caused by iron loss and aluminum eddy current with different amplitudes appear at the troughs of the flux curve. The effect of the losses on the flux linkage measurement is revealed in this article, and the loss compensation models are built based on the analysis. The improved flux linkage model is proposed with the loss compensation models. Then, the position estimation method is given with the improved flux linkage model. Flux linkage measurement result shows that the model with loss compensation has higher accuracy than the conventional model. Initial and planar-motion position estimation experiments are carried out for the PSRM. The initial position estimation error is from −0.4 to 0.4 mm. The planar-motion position estimation errors are from −1.1 to 1.1 mm in the ${X}$ -axis and from −1.2 to 1.0 mm in the ${Y}$ -axis, and the effectiveness of the proposed flux linkage model is verified. [ABSTRACT FROM AUTHOR]

Interferences due to non-orthogonality of signals usually exist in wireless networks when the number of users is larger than the sequence length, such as non-orthogonality of the pilots in multi-cell systems and non-orthogonality of the signature sequences in overloaded code-division-multiple-access (CDMA) systems. We address this effect from the perspective of non-orthogonal sequence design in a two-cell multiple-antenna network. Specifically, we aim at designing pilot sequences to minimize the sum mean-squared-error (MSE) of channel estimation with a given sequence length $\tau $ where $\tau \in [K,2K]$ and $K$ is the number of users per cell. Considering the strength disparity between channels originating from the home cell and the neighbor cell, this problem boils down to minimizing the sum of squares of weighted correlations among sequences, whose lower bound is obtained in closed form and can be regarded as a generalization of the well-known Welch bound (Welch, 1974). We prove this extended Welch bound is achievable, and design an algorithm based on the Davies-Higham method to generate the interference-minimizing sequences. Three fundamental properties of the proposed sequences are presented. Finally, we derive closed-form expressions of the average signal-to-interference-plus-noise-ratio (SINR) and rate for data transmission, based on which the optimal training duration can be found. [ABSTRACT FROM AUTHOR]

Developmental dysplasia of the hip (DDH) is one of the most common orthopedic disorders in infants and young children. Accurately detecting and identifying the misshapen anatomical landmarks plays a crucial role in the diagnosis of DDH. However, the diversity during the calcification and the deformity due to the dislocation lead it a difficult task to detect the misshapen pelvis landmarks for both human expert and computer. Generally, the anatomical landmarks exhibit stable morphological features in part regions and rigid structural features in long ranges, which can be strong identification for the landmarks. In this paper, we investigate the local morphological features and global structural features for the misshapen landmark detection with a novel Pyramid Non-local UNet (PN-UNet). Firstly, we mine the local morphological features with a series of convolutional neural network (CNN) stacks, and convert the detection of a landmark to the segmentation of the landmark’s local neighborhood by UNet. Secondly, a non-local module is employed to capture the global structural features with high-level structural knowledge. With the end-to-end and accurate detection of pelvis landmarks, we realize a fully automatic and highly reliable diagnosis of DDH. In addition, a dataset with 10,000 pelvis X-ray images is constructed in our work. It is the first public dataset for diagnosing DDH and has been already released for open research. To the best of our knowledge, this is the first attempt to apply deep learning method in the diagnosis of DDH. Experimental results show that our approach achieves an excellent precision in landmark detection (average point to point error of 0.9286mm) and illness diagnosis over human experts. Project is available at http://imcc.ustc.edu.cn/project/ddh/. [ABSTRACT FROM AUTHOR]

Relativistic backward wave oscillator (RBWO) is a promising high-power microwave (HPM) generator. In past decades, many efforts have been devoted to improving its practicality, in which the repetitively operating RBWO has drawn much attention. However, two bottlenecks once limited its development. One is the generation of massive anode plasmas or even damage of the collector, and the other is the RF breakdown of electrodynamic structures. This article describes an RBWO which can operate repetitively with 3-GW microwave power by mitigating these two problems. First, a titanium collector, which shows better performance in resisting electron beam bombardment than the conventional stainless steel collector, is adopted. Meanwhile, water-cooling technology is applied to expedite heat dissipation of the collector. Second, since our investigations demonstrate that an anodic mechanism, not the conventional cathodic mechanism, is the main cause for RF breakdown of electrodynamic structures under strong guiding magnetic field, the titanium material is also used to manufacture the electrodynamic structures of the RBWO in order to increase its power capacity. The advantage of the titanium RBWO is verified in experiments. The application of these techniques, as well as some important surface treatment techniques such as polishing, ultrasonic washing, and vacuum baking, leads to the stable repetitive operation of the RBWO with 3-GW output power and 25-ns microwave duration under a repetition rate of 50 Hz. The successive operation time of the device is up to 20 s. [ABSTRACT FROM AUTHOR]

Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introduce PopPhy-CNN, a novel convolutional neural network (CNN) learning framework that effectively exploits phylogenetic structure in microbial taxa for host phenotype prediction. Our approach takes an input format of a 2D matrix representing the phylogenetic tree populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data. We show the competitiveness of our model compared to other available methods using nine metagenomic datasets of moderate size for binary classification. With synthetic and biological datasets, we show the superior and robust performance of our model for multi-class classification. Furthermore, we design a novel scheme for feature extraction from the learned CNN models and demonstrate improved performance when the extracted features. PopPhy-CNN is a practical deep learning framework for the prediction of host phenotype with the ability of facilitating the retrieval of predictive microbial taxa. [ABSTRACT FROM AUTHOR]

In this article, the iterative learning control (ILC) problem is investigated for a class of stochastic time-varying systems with variable pass lengths. The randomness of the pass lengths is described by the recursive interval Gaussian distribution, and a modified iteration-average operator is developed to construct the novel ILC scheme for overcoming the limitation of conventional ILC algorithms that every pass must end in a fixed time of duration throughout the repetition. The proposed ILC approach works effectively to guarantee the boundedness of the tracking errors, which is demonstrated by a practical case study on a type of robot manipulator with two joints. [ABSTRACT FROM AUTHOR]

Hyperspectral image super-resolution addresses the problem of fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to produce a high-resolution hyperspectral image (HR-HSI). In this paper, we propose a novel fusion approach for hyperspectral image super-resolution by exploiting the specific properties of matrix decomposition, which consists of four main steps. First, an endmember extraction algorithm is used to extract an initial spectral matrix from LR-HSI. Then, with the initial spectral matrix, we estimate the spatial matrix, i.e., the spatial-contextual information, from the degraded observations of HR-HSI. Third, the spatial matrix is further utilized to estimate the spectral matrix from LR-HSI by solving a least squares (LS)-based problem. Finally, the target HR-HSI is constructed by combing the estimated spectral and spatial matrixes. In particular, two models are proposed to estimate the spatial matrix. One is a simple case that involves a LS-based problem, and the other is an elaborate case that consists of two fidelity terms and a spatial regularizer, where the spatial regularizer aiming to restrain the range of solutions is achieved by exploiting the superpixel-level low-rank characteristics of HR-HSI. Experiment results conducted on both synthetic and real data sets demonstrate the effectiveness of the proposed approach as compared to other hyperspectral image super-resolution methods. [ABSTRACT FROM AUTHOR]

In this article, a position estimation method is proposed to realize sensorless control of planar switched reluctance motor (PSRM) with core loss. First, a square-wave voltage generated through bipolar pulsewidth modulation (PWM) is injected into the idle phase of a PSRM, thereby generating core loss. The corresponding core-loss average power (CLAP), which contains position information, is calculated in real time. Then, depending on the six-phase CLAP, the initial position can be estimated for the PSRM startup. To stabilize the position estimation in different regions of pole pitches, position data measured in a long stroke are utilized to determine a function mapping from the CLAP with respect to position. Furthermore, a sensorless control system and commutation strategy are designed. Finally, the capability of different methods for position estimation and sensorless control of a PSRM system are experimentally investigated. The feasibility and effectiveness of the proposed method are verified through the experimental results. [ABSTRACT FROM AUTHOR]

An accurate mover position is important for a planar switched reluctance motor (PSRM) to achieve precision positioning; however, position sensors are susceptible to the influence from harsh conditions and are relatively expensive. In this article, an assistant-mover-based position estimation method is proposed to increase the reliability and reduce the cost of a PSRM system. The method is based on a mapping relationship between the flux density and position of the assistant mover. First, the configuration of the assistant movers is presented. Then, an analytical model of the flux density of the assistant mover is developed by using an equivalent magnetic circuit method. Based on the analytical model, the parameters of the assistant mover are determined. According to the flux density characteristics, a position estimation algorithm with the advantages of not requiring previous experimental data, simple computation, and small memory consumption is developed. Moreover, considering the deviation in the characteristics of each assistant mover, a compensation strategy is proposed to obtain better position estimation accuracy. Finally, the proposed method is implemented with the PSRM system. The effectiveness of the proposed position estimation method is verified experimentally. [ABSTRACT FROM AUTHOR]

This paper investigates how to minimize the operational cost of cloud service provider (CSP) that operates urban neighboring data centers (DCs) in the same electricity market and can conduct workload transfer among DCs. Due to the substantial electricity demand of DCs, their market power which can have impact on the locational marginal prices (LMPs) of the electricity market should be taken into consideration. We formulate a bilinear bilevel problem which regards the CSP as a price maker and explores cost-minimizing workload transfer strategies. The upper level is the operational cost minimization problem of CSP and the lower level corresponds to the economic dispatch problem of independent system operator (ISO) of electricity market which determines the electricity prices. It is challenging to directly solve the bilevel problem with bilinear term in the objective function. Hence, we first reformulate the original problem into a single level problem and then based on the property of the problem we develop a polytope cutting algorithm that attains the global optimal solution. The proposed algorithm solves linear optimizations iteratively by cutting the non-convex polytope feasible set into convex sets. In addition, considering the varying communication environment in practice, we analyze the impact of transfer price uncertainty on total cost of CSP, and show that the expected cost surprisingly decreases with the increasing uncertainty. Simulations based on the standard IEEE test cases show that the cost of CSP is significantly reduced and a win-win result for both the CSP and independent system operator (ISO) is possible. [ABSTRACT FROM AUTHOR]

Screen content image (SCI) prevails because of the explosive growth of screen oriented applications. This leads to extensive studies on SCI quality assessment and modeling for application optimization. In this paper, we propose a full reference multiscale edge attention (MSEA) similarity index to efficiently measure the perceptual quality of a screen image. This model considers the perceptual impacts of fixation attention, edge structure and edge contrast jointly, to accurately capture the masking phenomena (e.g., frequency selectivity, luminance, contrast, etc.) of our human visual system (HVS) when viewing a screen image. Specifically, we decompose the images using Gaussian and Laplacian pyramids which are then used to derive the edge structure, and edge contrast feature maps. Together with the fixation attention map generated by weighted luminance difference between the reference and distorted SCIs, we could eventually offer a MSEA similarity map for final index score. We have evaluated this model using a publicly accessible screen image database. Simulation results have shown that the MSEA similarity index correlates with the collected subjective mean opinion score (MOS) very well. In fact, it is ranked at the first place for both Pearson linear correlation coefficient (PLCC) and Root mean squared error (RMSE), and ranked at the second place for Spearman rank-order correlation coefficient (SROCC) measurements, among existing quality metrics. [ABSTRACT FROM AUTHOR]

The effect of the local electric field on the radial oscillation of the electron beam in the low-magnetic-field foilless diode is studied theoretically and experimentally. The results obtained with different methods show good coherence. The radial oscillation amplitude of the electron beam affected by the local electric field depends on the electric field intensity and the phases of electrons entering and leaving the local electric field region. It is found that the enhanced radial electric field near the drift tube head results in the change of the radial oscillation amplitude with the anode–cathode gap. Meanwhile, a method to effectively suppress the radial oscillation is proposed. Optimizing the anode–cathode gap and the tilt angle of the drift tube head can effectively adjust the matching state of the electron phase and the local electric field, and thus the transverse momentum of the electron beam can be greatly reduced due to the negative effect of the radial electric field. [ABSTRACT FROM AUTHOR]

This paper is devoted to the stability and stabilization for Takagi–Sugeno fuzzy systems with time-varying delays. First, an improved matrix inequality is presented to bound both strictly and nonstrictly proper rational functions, which is more general than the existing versions of reciprocally convex lemmas. Second, by suitable operations on parameter-dependent polynomial multiplied by state rate, a couple of novel intermediary-polynomial-based functions (IPFs) are developed in delay-product types. Benefitting from slack matrices of IPFs, a certain degree of flexibility is furnished. More importantly than that, by feat of adjustment of the variable parameter, the resulting conditions will be further endowed with additive freedom, which relaxes the feasible space in a distinctive manner. Third, by utilizing IPFs along with triple integrals, the stability criteria and the controller design approach are derived by some advanced integral inequalities. Resorting to elaborate construction of IPFs, the strengths of bounding techniques are sufficiently exploited, and the information on delay derivative is adequately reflected. Consequently, more desirable performances are achieved, while without excessive computational complexity. Finally, the effectiveness of the proposed methods is verified by numerical examples. [ABSTRACT FROM AUTHOR]

Open-phase fault is a common failure in permanent magnet synchronous motor (PMSM), which would degrade motor performance and increase its loss due to the unbalanced phase currents. However, conventional fault-tolerant strategies suffer from the tracking problem of sinusoidal current references. In this paper, different from previous fault-tolerant strategies, a new fault-tolerant method for open-phase PMSM is proposed by designing a novel transformation matrix for current/voltage references. With the new transformation matrix, the voltage/current references in the d–q frame can be transformed to the phase voltage/current references of the two remaining phases. With the proposed method, open-phase fault-tolerant implementation can be much easier (proportional-integral controller is enough) and its performance can be much higher. Besides, as the tolerant method is designed based on the four-leg inverter, stronger fault-tolerance capability can be obtained compared with the three-phase inverter-based method. Finally, the experimental results confirm the validity of the proposed fault-tolerant method. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, a simple and effective classifier, i.e., the ridge regression for multivariate labels, is extended to its tensorial version by taking advantages of tensorial representation. Then, the discrimination information of different modes is exploited to further strengthen the capacity of the model. Moreover, the model can be simplified and solved easily. Different from traditional tensorial methods, the proposed model can be utilized to capture not only the intrinsic structure of data in a physical sense but also the generalized relationship of data in a logical sense. Our proposed approach is shown to be effective for different classification purposes on a series of instantiations. Specifically, our experiment results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer, the reflective optics spectrographic imaging system and the ITRES CASI-1500 demonstrate the effectiveness of the proposed approach as compared to other tensor-based classifiers and multiple kernel learning methods. [ABSTRACT FROM AUTHOR]

For the 360-degree virtual reality (VR) videos, to achieve the same user-perceived playout quality with traditional video, the overall video bitrate has to be much higher, which significantly increases the bandwidth requirement. To overcome this issue, most of rate adaptation schemes are proposed based on the users’ Field of View (FoV), since users only view part of the entire videos when watching 360-degree videos. However, the FoV prediction is only valid in short terms which incurs low robustness against network dynamics and results in frozen screen events. Since the playback buffer can effectively avoid re-buffering by storing videos in advance, we propose a two-layered hierarchical buffer-based rate adaptation scheme for 360-degree VR videos in this paper. The tiles will be updated to enhance the video quality in the short buffer layer based on FoV prediction, meanwhile the long buffer layer takes charge of downloading new chunks to guarantee the buffer occupancy. To better improve the quality of experience (QoE), we jointly consider the users’ FoV, channel conditions and buffer status. Our method achieves a good trade-off between video quality and buffer occupancy. Besides, the maximum buffer level selection is optimized to further improve the performance. The simulation results show that our proposed scheme and algorithms outperform other methods by about 5%-30% of QoE improvement. [ABSTRACT FROM AUTHOR]

Recently, a new structure of low density parity check (LDPC) code named raptor-like LDPC code has attracted much attention. It has better performance at low code rate. In this paper, a novel decoding scheme for raptor-like LDPC code is proposed. First, the Gaussian approximation density evolution (GADE) algorithm is used to track and analyze the message transmission during the decoding process. It is found that certain log likelihood ratio (LLR) messages can be approximated by “zero” setting in the early iteration of raptor-like LDPC decoding. In other words, some column and row operations could be eliminated without compromise the performance. Next, we propose a new decoding scheme, which can skip unnecessary column and row operations. In comparison with the traditional belief propagation (BP)-based LDPC decoding scheme, the proposed scheme can reduce the decoding complexity. Additionally, a new algorithm is developed to facilitate the selection of the early iteration number. With this novel design, the proposed decoding scheme performs almost the same as the traditional BP-based scheme. To proof the concept, the raptor-like LDPC codes in the ATSC3.0 digital TV system are used to evaluate the proposed scheme, in comparison with the traditional BP-based schemes, i.e., sum-product algorithm (SPA), offset min-sum algorithm (OMSA), and normalized min-sum algorithm (NMSA). The simulation results confirm that the proposed scheme can reduce the decoding complexity without sacrifice in performance for all SPA, OMSA, and NMSA methods. About 10% complexity reduction can be achieved. This will reduce the buttery consumption for Internet of Things (IoT) and handheld devices. [ABSTRACT FROM AUTHOR]

In order to effectively identify and classify weld defects of thin-walled metal canisters, a weld defect detection and classification algorithm based on machine vision is proposed in this paper. With the weld defects categorized, a modified background subtraction method based on Gaussian mixture models, is proposed to extract the feature areas of the weld defects. Then, we design an algorithm for weld detection and classification according to the extracted features. Next, by using the weld images sampled by the constructed weld defect detection system on a real-world production line, the parameters of the weld defect classifiers are determined empirically. Experimental results show that the proposed methods can identify and classify the weld defects with more than 95% accuracy rate. Moreover, the weld detection results obtained in the actual production line show that the detection and classification accuracy can reach more than 99%, which means that the system enhanced with the proposed method can meet the requirements for the best real-time and continuous weld defect detection systems available nowadays. [ABSTRACT FROM AUTHOR]

An oversized mode-selective relativistic backward wave oscillator is proposed. This device operates using the pure TM02 mode in the beam-wave interaction area and transmits a pure TM01 mode in the output waveguide. In the beam-wave interaction area, mode selection is well realized through oversized slow-wave structures with a coaxial inner conductor that provides perfect boundary conditions for the TM02 mode and simultaneously eliminates other modes. A large proportion of the TM02 mode field is eventually exported into the output waveguide in the form of a pure TM01 mode. With the large diameter and high mode purity, the device can be expected to have high power capacity. With a 770 kV, 11.0 kA beam guided by a 3.5 T magnetic field, a 3.2 GW microwave at 9.42 GHz has been obtained in our simulation. The device efficiency is 38%. [ABSTRACT FROM AUTHOR]

In this paper, a novel multiobjective evolutionary algorithm (MOEA/CT) is proposed to better manage convergence and distribution of solutions when MOEAs are used for solving multiobjective optimization problems. The coordinate transformation strategy, an external archive update strategy, and a diversity maintenance strategy are proposed in MOEA/CT. The coordinate transformation strategy in the objective space is designed to find more efficient solutions that can accelerate the convergence process. Based on the coordinate transformation strategy, a novel update strategy and diversity maintenance approach for selecting nondominated solutions from the external archive set are integrated in MOEA/CT for getting better distribution of the solutions. The proposed MOEA/CT is compared with eight state-of-art algorithms on six biobjective and seven tri-objective test problems. In terms of four performance metrics, the comparative experimental results demonstrate that MOEA/CT outperforms the other eight competitors and it can achieve solutions with better distribution and better convergence to the Pareto front. In addition, parameter sensitivity analysis is provided to investigate the effect of a key parameter in MOEA/CT; the proposed three strategies are also studied individually to investigate their contribution to MOEA/CT; the performance analysis along with the capacity of external archive is given to clearly make the influence in MOEA/CT; finally, the scalability performance of MOEA/CT is investigated and compared with five notable many-objective evolutionary algorithms on the DTLZ and WFG test suites with 5, 8, 10, and 15 objectives. [ABSTRACT FROM AUTHOR]

Due to the ironless structure of the toroidal winding in the tubular linear voice coil motor (TLVCM), the winding thickness is one of the critical parameters that have an impact on air-gap flux density, and hence affects the copper loss at rated thrust. This paper investigates and determines the thickness of the ironless toroidal winding of the TLVCM for the purpose of a minimum copper loss. Magnetic equivalent circuit (MEC) method is used to analyze the air-gap flux density with respect to winding thickness, and the corresponding copper loss at the rated thrust is assessed by considering the slot fill factor. The optimal solution is obtained according to the U-shaped curve of the copper loss. Moreover, the finite-element analysis is combined to validate the results of MEC method, and a good agreement is reached. Two prototypes of TLVCMs having similar slot fill factor are made, one of which is with the optimal winding thickness. The expected current at the rated thrust is obtained by a test platform of static thrust, so as to obtain the corresponding copper loss for each prototype. The experimental results show that the TLVCM with optimal winding thickness features a lower copper loss. Hence, the attempt to minimize the copper loss by designing the thickness of the ironless winding is highly effective. [ABSTRACT FROM AUTHOR]