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This work explores the application of hypothesis testing to enhance anomaly detection in time series data from the cigarette cutting production process, aiming to improve detection accuracy and robustness. Historical production data from the cigarette cutting workshop of the S Cigarette Factory are first collected. A deep learning-based anomaly detection model is developed using the Temporal Convolutional Network (TCN) algorithm, integrating TCN-Long Short-Term Memory (TCN-LSTM) and hypothesis testing. This model utilizes TCN-LSTM to extract features from the production data and applies the Wilcoxon signed-rank test for hypothesis testing. Results indicate that the model’s Mean Absolute Error, Mean Relative Square Error, and R2 values at a time step of 15 seconds are 11.54, 16.17, and 95.80%, respectively, improving by over 3% than other models. Additionally, the hypothesis testing further validates the model’s effectiveness and robustness at this optimal time step. Therefore, this model not only significantly enhances the accuracy of anomaly detection but also provides a new method and reference for intelligent monitoring and optimization of the cigarette cutting process.

This paper aims to improve the quality monitoring level in the cigarette silk production, thereby proposing an innovative model based on a combination of Wasserstein distance and deep learning algorithms. This model combines Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and attention mechanism to process the collected multi-dimensional process parameters and quality inspection data. The Wasserstein distance is used to calculate the differences between the distribution of process parameters in different time periods or batches, and it is input as a feature vector into the deep learning model. Finally, the performance of the model is experimentally evaluated. The results show that the proposed model significantly outperforms other models in key performance indicators such as accuracy, recall, and F1 value, with an accuracy of 96.08%. The recall and F1 values are 89.25% and 90.95%, respectively, and the prediction error is excellent, significantly reducing Mean Absolute Error (MAE) and Mean Relative Square Error (MRSE). The MAE and MRSE values of the proposed model reach 7.30 and 12.44, respectively. Therefore, the proposed model can improve the quality control level in the production process of cigarette silk production quality monitoring, providing solid data support and decision-making basis for production optimization.

Reducing the feature distribution shift caused by the factor of visual-environment changes, named visual-environment changes (VE-changes), is a hot issue in domain adaptation learning. However, in the semantic segmentation task of remote sensing imageries, besides VE-changes, the change of semantic-scene changes (SS-changes) is another factor raising the domain gap, which brings the label distribution shift. For example, although urban and rural share the same landcover label, there is still a gap in label distribution. If there is little relation that can be found in neither feature nor label space, forcibly adapting to a new domain could have a high risk of negative transfer. Hence, we propose a new Transitive Domain Adaptation method for Remote Sensing (TDARS) images. First, we introduce an intermediate domain to enlarge the relation between the given source and target domains. Second, we learn from primary and nonprimary confident classes to increase the likelihood of transferring valuable information. As a result, TDARS enables the given source and target domains to be connected through the selected intermediate domain and performs effective knowledge transfer among all domains. The proposed method is evaluated on three domain adaptation datasets of remote sensing images. Extensive experiments show that the approach can effectively handle the domain shift problem from remote sensing images compared to other state-of-the-art domain adaptation methods. [ABSTRACT FROM AUTHOR]

In this article, a new dual-permanent magnet spoke-type flux-reversal machine (dual-PM SFRM) for direct-drive applications is proposed. Tangentially magnetized permanent magnets (PMs) sandwiched with iron poles are set in stator slots. To increase the torque density within the geometric restrictions, PMs are introduced in the rotor as well and magnetized radially to form a consequent-pole rotor. Global optimizations are conducted and comparisons are made on torque, torque ripple, and torque/PM volume with the existing single-PM SFRM. It is found that the proposed dual-PM SFRM can produce 22.6% larger torque with an optimal PM volume and a 17.5% larger torque with the same PM volume compared with the single-PM counterpart. Moreover, major magnetic field space harmonics are derived analytically and verified by finite-element analysis (FEA) and the torque components produced by the working harmonics are calculated and compared in terms of different stator/rotor pole number combinations. Comparisons are made on back electromotive force, cogging torque, torque performance, and efficiency between dual-PM SFRM and conventional flux-reversal machine. The influence of different stator configurations, i.e., the slot opening, the iron bridge, and the unequal tooth, on torque performance in the proposed dual-PM SFRM has been studied and highlighted. A prototype has been built and tested to validate the FEA calculations. [ABSTRACT FROM AUTHOR]

This article proposes an improved multiple synchronous reference frame (MSRF) current harmonic regulation strategy for dual three phase permanent magnet synchronous machine drive system. A new vector space decomposition transformation combined with auxiliary currents is designed to decouple the current fundamental with current harmonics, and the auxiliary currents can be established by the π/9 phase shifting of the physical currents in ABC set and the π/18 phase shifting of the physical currents in XYZ set. By employing the proposed current decomposition, it is easy to detect them using low pass filters in MSRFs. A generic complex vector proportional integral regulator is then employed to control the detected current harmonics to track the current harmonic references. Compared with the existing MSRF-based methods, the proposed method provides a new current harmonic detection with reduced delay effect, which contributes to enhancing the dynamic performance of current harmonic regulation, and also stabilizing the control system. Comprehensive experimental results are given to validate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of stator/rotor pole number combinations and permanent magnet (PM) numbers on electromagnetic performance of consequent-pole hybrid excited flux reversal machines (CP-HEFRMs), which have different numbers of PM poles and iron poles and concentrated AC and DC windings on the stator and a salient pole rotor. PMs in the slots of one stator pole are magnetized radially in the same direction, while opposite on adjacent stator poles. Magnetic field paths of DC, PM, and hybrid excitations are investigated. The phenomenon of flux cancellation, i.e., PM and DC MMFs in CP-HEFRMs are of opposite polarities in the flux-enhancing operating mode, is revealed and verified, for the first time, as the key feature that makes CP-HEFRMs different from other hybrid excited machines. Consequently, the saturation in stator pole and yoke can be mitigated and hence the overload capability of DC excitation is enhanced, which is verified by finite element analysis (FEA). Global optimizations are utilized to obtain the optimal rotor and stator pole number combinations and PM numbers for each stator pole. Torque, torque ripple, inductance, unbalanced magnetic force, and flux regulation capability are compared for different PM numbers and rotor pole numbers. Comparisons are made on consequent-pole and non-consequent-pole topologies in terms of torque capacity, flux regulation capability, and PM consumption to further illustrate the advantages of consequent-pole topology. A prototype machine is built and tested to validate the analyses. [ABSTRACT FROM AUTHOR]

Traffic signal control is an important part of intelligent transportation. Efficient traffic signal control strategies not only alleviate traffic congestion, improve vehicle traffic efficiency, but also reduce exhaust pollution during the waiting period. The traditional multiple intersection signal light control method are generally faced with passive control and can not adapt to complex changes. In view of this, the study first combines reinforcement learning and Markov decision to construct a signal control problem model for multiple intersections. Secondly, deep Q-learning networks are introduced for decision solving, and multi-head attention mechanisms and graph convolutional networks are further introduced for optimization and improvement. Finally, a spatial lightweight model for multiple intersection adaptive graph convolution is proposed. The experimental results show that the maximum throughput of the multi-intersection adaptive control model can reach 195 vehicles/min, the shortest queue length is 4.31 vehicles, the shortest average delay is 1.23 minutes, the shortest average travel time is 2.07 minutes, and the attention value of adjacent intersections is close to 80%. The average green wave time under this model is 4 minutes, and the longest green wave section can reach 5 minutes. It can be seen that the new multi-intersection signal adaptive control model proposed in this study has advantages and feasibility compared with similar methods in various indexes, and can provide new ideas and methods for the further development of traffic signal automatic control technology.

This paper considers an agent identity privacy problem in Markov decision process. There are two types of agents with different instantaneous control reward functions, e.g., two types of energy consumption activities in smart grid. An eavesdropper is assumed to intercept the observations of agent and make a statistic inference on the agent identity, which is privacy-sensitive and can be utilized by the eavesdropper to further make corresponding malicious attacks. With regard to the agent identity privacy problem, a privacy-preserving Markov decision process is formulated and a novel adversarial reinforcement learning algorithm is further proposed by exploiting the ideas of deep reinforcement learning and variational method to design the agent policies with the aim to optimally tradeoff improving cumulative control reward and preventing agent identity privacy leakage. Experiments in a modified OpenAI Gym environment show different training process patterns and justify the effectiveness of the proposed algorithm.

Shallow underground explosive source localization technology is a key technology in the field of underground space localization. The existing approaches mainly aim to improve the localization accuracy, but need to deploy enormous sensors in the monitoring area, and rely on a large number of backend workstations to solve. These methods have the defects of considerable calculation and high time cost, and are hard to satisfy the precise and real-time requirements of onsite testing, ultimately resulting in slow localization speed and accurate localization failure. Fortunately, emerging deep reinforcement learning can effectively solve the problem of slow search policy by modeling the source localization as a Markov decision process (MDP). Therefore, a curiosity-driven deep dueling double Q-learning network (C-D3QN) is subsequently proposed to solve the above MDP. The overestimation problem is solved by decoupling selection and evaluation of the bootstrap action, and the action difference is effectively increased by introducing the dueling network that separately represents state values and action advantages. Meanwhile, the exploration is jointly reinforced by an intrinsic reward outputted from the curiosity module and an extrinsic reward supplied by the environment, guaranteeing the convergence to global optimal. Finally, extensive simulation results based on the outfield experiment data show that compared with other algorithms, the proposed scheme can significantly improve exploration ability and learning speed as well as generalization and robustness. In addition, compared to the baseline algorithm deep Q-learning network, the C-D3QN algorithm can offer an improved localization accuracy as high as 99.62% and an increased localization speed of 66.23%.

The effective disposal of healthcare waste is a highly concerned issue. Healthcare waste collection poses transport risks and temporary storing risks, and the deliberation undertaken by decision-makers concerning waste collection entails potential scenario wherein some hospitals have not yet submitted their service requests and may submit their requests during the healthcare waste collection procedure. A routing optimisation problem for healthcare waste collection with temporary storing risks and sequential uncertain service requests is introduced. A two-stage decision-making is proposed and mathematical models corresponding to each stage are developed. Different solution algorithms are developed for different stages or different scales of instances, including the improved $\varepsilon $ -constraint method and Non-dominated Sorting Genetic Algorithm-II for the solution procedure in Stage 1, and the Compare-choose-move algorithm for the solution procedure in Stage 2. Finally, the models and algorithms are tested by numerical instances and several suggestions for healthcare waste collection have been proposed based on sensitivity analysis.

Dual three-phase (DTP) permanent magnet synchronous machine (PMSM) systems exhibit high current harmonics due to low impedance to voltage disturbance such as inverter voltage errors and back-EMF harmonics. This article proposes a concept of virtual multi three-phase systems to achieve independent regulation of the current harmonics in a DTP PMSM system. The virtual multi three-phase currents are first reconstructed by appropriately shifting the phase of original physical DTP currents, and then decomposed with the help of vector space decomposition techniques. As a result, the fundamental and the major current harmonics, i.e., the 5th, 7th, 11th, and 13th, can be completely separated in several isolated subspaces, where the independent current control loops can be easily designed and implemented. Compared with existing methods, the steady-state and dynamic-state performance of current harmonic regulation are enhanced by the proposed virtual multi three-phase systems and the effectiveness is validated by experimental. [ABSTRACT FROM AUTHOR]

Consequent-pole (CP) permanent magnet (PM) machines have attracted considerable interest as a means of reducing machine cost through a marked reduction in the volume of PM required to meet a particular torque specification. However, the presence of a large torque ripple that can result from the CPPM structure can hinder their adoption in some applications, especially for the dominant third-order torque ripple. Although several design-specific modifications have been proposed to ameliorate torque ripple, the generalized principles underpinning this behavior have not been fully established. In this article, it will be illustrated that an aggregation of the fluctuations in inductance, back electromotive force, and cogging torque contributes to increased torque ripple. Meanwhile, an asymmetric pole shaping method is proposed to reduce the torque ripple, taking the dominant third-order torque ripple as an example. Both simulated and experimental results of the 12-slot/8-pole prototypes show that compared to the symmetrical pole shaping model and the plain pole model, the proposed asymmetric shaping method is effective in reducing torque ripple. [ABSTRACT FROM AUTHOR]

Consequent pole (CP) permanent magnet (PM) machines offer scope for reducing the quantity of PM material and hence cost compared to more conventional PM machine topologies of the same rating. In this type of machine, the rotor pole arc is usually optimized to realize the largest output torque with pole shaping methods used to reduce torque ripple. However, such approaches tend to be limited by constraints imposed on the pole shapes. It is a common practice to adopt similar PM and iron pole shapes, an approach that does not fully account for the different characteristics of PM and iron poles in CPPM machines, often leading to large even order harmonics in the airgap flux density. This article proposes a shaping method with a variable rotor profile and pole arc span being established by means of optimization by Genetic Algorithm. It is demonstrated that for a fixed quantity of PM material, different PM and iron pole shapes in combination with optimal PM and iron pole arc spans are essential for ensuring both maximum output torque and lower torque ripple when the due account is taken of flux leakage. It also demonstrates that since the flux density in the region under a PM pole is governed by the magnetic potential produced by magnets while it is governed by magnetic reluctance under iron pole, different PM pole and iron pole shapes are necessary to reduce the even-order harmonics in a CPPM machine and consequently to reduce torque ripple. The performances of optimized and more conventional CP machines are compared by finite-element method on 12-slot/8-pole prototype motors. [ABSTRACT FROM AUTHOR]

This article proposes and investigates two asymmetric consequent-pole hybrid excited flux reversal machine (AS-CP-HEFRM) topologies, which have same numbers of iron poles and permanent magnets (PMs) with concentrated ac and dc windings on each stator pole and a salient pole rotor. If PM number on one stator pole is larger than 1, PMs are magnetized radially in the same direction on one stator pole, while opposite on adjacent stator poles. The phenomenon of the phase shift between the flux linkages of dc and PM excitations in the AS-CP-HEFRMs is revealed for the first time, which will weaken the regulation of dc excitation field to PM produced field. The elimination condition of the phase shift is derived and verified by finite-element analysis in this article, which can be considered as design guidelines in AS-CP-HEFRMs. Airgap magnetic field space harmonics of AS-CP-HEFRM are also investigated and verified by finite-element analyses. Meanwhile, the relationship between the rotor pole number and the stator pole arc (PM ratio) is derived and verified by global optimization based on genetic algorithm for maximum average torque. The influence of stator/rotor pole number combinations is also studied and compared in terms of torque, torque ripple, and flux regulation capability. The torque components produced by airgap magnetic field space harmonics are calculated and compared. A prototype has been built and tested to validate the finite element calculations. [ABSTRACT FROM AUTHOR]

Optical satellites are affected by factors such as seasonal and atmospheric variation, illumination, and sensor distortion. Thus, satellite images covering large-scale area often show conspicuous color differences, resulting in poor color continuity of the mosaicked satellite image. This study proposes a novel combined model color correction (CMCC) method for high-resolution optical satellite images, which constructively combines a defogging model with a radiation correction model. First, this study analyzed the feasibility of using easily available low-resolution satellite images as external references to correct the color of high-resolution images and describes the selection criteria for external references. Second, considering the negative effects of atmosphere on the color and clarity of remote sensing images, we proposed an optical satellite image enhancement method, which is based on the content characteristics of remote sensing images and the dark channel prior defogging method. Finally, we designed a two-stage color correction process: 1) correcting the color of downsampled images via low-frequency modeling and replacement and 2) mapping the color of downsampled images to original images through local modeling and super-resolution color correction. Furthermore, this study proposes an indicator of quality considered mean absolute error (QCMAE) for quantitative evaluation of the color correction result. We selected 328 Gaofen-1 (GF-1) high-resolution images for the experiments. Visual effects and statistical results of images after being processed by the proposed CMCC are both superior to the three state-of-the-art methods, which verifies the effectiveness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

Remote sensing image (RSI) scene classification (RSISC) is a fundamental problem for understanding high-resolution RSIs. More recently, deep learning methods, especially convolutional neural networks (CNNs), and large data sets have greatly promoted the RSISC. However, deep learning methods rely heavily on the visual features extracted from the patches cropped from original RSIs, so the intraclass diversity and interclass similarity are two big challenges. To address these problems, in this letter, we propose a spatial information considered model to learn more discriminative features. By combining CNN and recurrent neural network, the proposed method can exploit both local and long-range spatial relation information to enhance the representational ability of the learned features. As the initial visual features of a single patch are transformed into higher-level features with spatial information, the proposed method achieves more accurate scene classification. Besides, we present an RSISC data set named as CSU-RSISC10 data set to preserve the spatial information between scenes in a new way of organization. Experiments demonstrate that the proposed method outperforms other three state-of-the-art methods in scene classification using CSU-RSISC10 data set. [ABSTRACT FROM AUTHOR]

Brushless modular spoke type permanent magnet (MSTPM) machine has exhibited prior electromagnetic (EM) performance over conventional surface-mounted PM machines for in-wheel traction system in electric vehicles (EVs). However, analysis of thermal behavior of MSTPM machines is rather insufficient, even accurate thermal analysis is of significant importance due to poor heat dissipation condition inside the wheel hub. Conventionally, the losses produced in electrical machines by EM prediction is equivalent to the heat source in normal thermal analysis, and only the resultant thermal behavior is investigated. However, the reaction of temperature rising on the EM performance is neglected. In this article, a bi-directional coupled electromagnetic-thermal analysis method is proposed and carried out by a combination of lumped parameter thermal network (LPTN) model and finite element method (FEM), where both steady-state temperature distribution and transient-state temperature rise are investigated for MSTPM machines. By finite number of iterations between magnetic and thermal fields, the electromagnetic performance and thermal behavior can be predicted more accurately due to the coupling effect considered. The coupled model-predicted results are verified by 3D-FEM and experimental measurement, which shows that the proposed method has advantages in both computational efficiency and accuracy as well as can be applied to other electrical machines. [ABSTRACT FROM AUTHOR]

This article presents a nonlinear analytical (NA) solution based on subdomains for predicting magnetic field and performances of a spoke array vernier permanent magnet (SAVPM) machine which has spoke array type permanent magnet (PM) excitation. On one hand, equivalent current sheets replace tangentially magnetized PMs to make the magnetic vector potential in the region with PMs satisfy a Laplace's equation, which simplifies the problem and reduces the workload of numerical calculation. On the other hand, in contrast to the existing literature, regions of soft-magnetic material such as a tooth and a rotor pole are subdivided into small pieces, and are no longer a whole with a single permeability for considering local magnetic saturation. Poisson's and Laplace's equations with the given permeability of soft-magnetic material are analytically solved by separation technique of variables in seven subdomains, and nonlinear iteration is used to determine the solution of NA and update soft-magnetic material permeability. NA solves the magnetic field and performances of a SAVPM machine. Frozen permeability method (FPM) is used to determine PM flux linkage and d-axis inductance under load. Results from the NA model have been compared with those from FEA and experiments. [ABSTRACT FROM AUTHOR]

Orbital angular momentum (OAM) mode division multiplexing (MDM) amplification in a seven-ring-core erbium-doped fiber (7-RC-EDF) has been experimentally demonstrated, in which the 1st–4th order OAM modes within each core of the 7-RC-EDF can be supported. By combining the OAM modes with the pump laser into the 7-RC-EDF, 112 OAM modes are predicted to be amplified in the C-band with a net gain of more than 22.36 dB with the differential modal gain (DMG) of less than 1.7 dB. The optical signal-to-noise ratio (OSNR) can be higher than 30 dB. As a proof of concept, signals with three wavelengths were individually converted into the 2nd–4th higher order OAM modes, respectively, and then injected together into one ring core of the 7-RC-EDF. The amplification with a net gain of more than 27.7 dB and OSNR of more than 22 dB was achieved. The proposed wavelength division multiplexing (WDM) combined with the OAM-MDM technique verified its potential applications in large-capacity OAM-MDM optical fiber communication systems.

While analyzing multiple data sequences, the following questions typically arise: how does a single sequence change over time, how do multiple sequences compare within a period, and how does such comparison change over time. This paper presents a visual technique named STBins to answer these questions. STBins is designed for visual tracking of individual data sequences and also for comparison of sequences. The latter is done by showing the similarity of sequences within temporal windows. A perception study is conducted to examine the readability of alternative visual designs based on sequence tracking and comparison tasks. Also, two case studies based on real-world datasets are presented in detail to demonstrate usage of our technique. [ABSTRACT FROM AUTHOR]

To improve the fault-tolerant performance of a five-phase flux-switching permanent magnet (FSPM) motor drive under open-circuit fault (OCF) condition, a model predictive current control (MPCC) with pre-selective method and duty ratio control (DRC) technology is proposed and investigated in this paper. First, on the principle of minimizing harmonic voltages in x–y subspace, two zero switching states and the switching state, which generates a larger voltage vector in α–β subspace are pre-selected. Second, voltage vector references in α–β subspace and x–y subspace are predicted to further select active voltage vector candidates. Consequently, the number of current predictions has been significantly reduced, resulting in the alleviation of the computational complexity and the increase of sampling frequency. Third, the DRC approach is applied in conjunction with the pre-selection-based MPCC to improve the steady-state performance. Finally, the effectiveness of the proposed MPCC method for the OCF tolerant five-phase FSPM motor drive is validated by comparative experiments. [ABSTRACT FROM AUTHOR]

As the spatial resolution of remote sensing images is improving gradually, it is feasible to realize “scene-object” collaborative image interpretation. Unfortunately, this idea is not fully utilized in vehicle detection from high-resolution aerial images, and most of the existing methods may be promoted by considering the variability of vehicle spatial distribution in different image scenes and treating vehicle detection tasks scene-specific. With this motivation, a scene context-driven vehicle detection method is proposed in this paper. At first, we perform scene classification using the deep learning method and, then, detect vehicles in roads and parking lots separately through different vehicle detectors. Afterward, we further optimize the detection results using different postprocessing rules according to different scene types. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms in terms of higher detection accuracy rate and lower false alarm rate. [ABSTRACT FROM AUTHOR]

Modular-spoke-type permanent-magnet (MSTPM) machines are novel in-wheel traction machines for electric vehicles (EVs). Considering the strict constraints including the mass and volume of electric machines in EVs, which are heavily dependent on the dissipation condition, an accurate thermal model and resultant temperature distribution analysis are key challenges. Therefore, in this paper, a lumped parameter thermal network (LPTN) model for thermal analysis of MSTPM machines is proposed, where the heat transfer coefficients are determined theoretically instead of empirically, which means that the proposed LPTN model can be applicable for the MSTPM machines with different specifications and parameters. Both the steady-state and transient-state temperature rises are analyzed by the LPTN model, and the results are verified by both 3-D finite element analysis (FEA) predictions and experiments, indicating that the proposed method has advantages in both computational efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Classical hardware development approaches for critical embedded systems are inefficient. Extensive resources must be committed before traditional hardware development can commence. Thus, an efficient method for circuit development is urgently required. In this paper, a computer-aided formal development method for a multifunction vehicle bus (MVB) is addressed. First, a formal modeling language is defined and its semantics are analyzed. Then, in terms of the modeling language, a formal computer-aided-design method is proposed for the design of complex MVB circuits. Core modules are modeled by extended timed automata. After the functionality of the model has been verified, the formal model is automatically converted into the hardware description language (HDL) code. Furthermore, code optimization algorithms are proposed to enhance the quality of the generated HDL code. Finally, the optimal formal MVB is designed and implemented as a circuit. [ABSTRACT FROM AUTHOR]

To reduce the thrust force ripple of a complementary and modular linear flux-switching permanent magnet machine, a model predictive thrust force control (MPTFC) is developed and implemented in this paper based on the active voltage vectors selection (AVVS) and two-voltage-vector synthesis (TVVS). First, the active voltage vectors are screened and assigned to the specific sector, instead of traversing all the possible vectors, and consequently, significantly decrease prediction workload. Second, for the elimination of the weighting factor in the conventional MPTFC, the control object is transformed from the thrust force and stator flux to the stator flux only by using the load angle. Third, to achieve the steady-state performance improvement, the TVVS approach is applied in each control period, where the optimal TVVS and its dwell time are simultaneously obtained by only one cost function. Both simulated and experimental results verify that the steady-state performance improvement can be realized by the developed MPTFC through comparing with normal hysteresis current control and conventional MPTFC strategies. [ABSTRACT FROM AUTHOR]

In this paper, comparisons between two permanent-magnet-free machines, namely a wound-field flux-switching machine (WFFSM) and a switched reluctance machine (SRM), are carried out. The topologies and operation principles of the WFFSM are illustrated first. Then, the effects of rotor skewing angle on static characteristics, e.g., open-circuit phase flux-linkage and back electromotive force due to field currents, electromagnetic torque, and torque ripple, have been investigated in depth by finite-element analysis (FEA). Consequently, the optimal rotor skewing angle is determined. After that, comparisons at the rated speed 1500 r/min and at the rated RMS current density of 5 A/mm2 among the 6-armature-slots/5-rotor-poles (6/5) straight- and skewed-WFFSMs, and two representative SRMs, namely conventional 12/8 SRM and the short-flux 12/10 SRM with the same stator outer diameter and stack length are conducted in terms of phase flux-linkage, inductance, and torque performance. The results indicate that for these two PM-free machines, at the rated speed and the rated RMS current density, the skewed-rotor WFFSM exhibits considerably larger torque capacity, much smaller torque ripple, stronger saturation withstand ability. In addition, the experiments of the skewed-WFFSM and the 12/8 SRM prototypes working at the mechanical character curve are conducted. The experimental results present that the power factor of the WFFSM is much higher and the efficiency is slightly lower than that of the 12/8 SRM. [ABSTRACT FROM AUTHOR]

A heterogeneous medium access control (MAC) scheme (with both scheduled and random communications) is proposed for airborne networks. These networks have a hierarchical topology with a small number of powerful aircraft nodes with multibeam antennas and long-distance links at the higher level, and high-density unmanned aerial vehicle (UAV) nodes with single-beam directional antennas at the lower level. Our proposed MAC scheme has the following three parts: First, multibeam, long-distance MAC for aircraft to aircraft links: a dynamic, scheduled MAC is proposed to take advantage of the multibeam features to achieve high-throughput transmissions, and the beam locking/synchronization issues are solved; second, uplink MAC for UAV to aircraft communication: conventional IEEE 802.11n MAC is enhanced with fame aggregation and compressive-sensing-based request polling; and third, MAC for UAV to UAV links: IEEE 802.11e DCF MAC is enhanced through parameter adjustment for channel access. Our simulation results show the significant performance improvement over conventional MAC protocols. [ABSTRACT FROM AUTHOR]

In the past decade, object localization and object classification using correlation filters, especially large margin correlation filters which combine with support vector machine (SVM), have become a hotspot. However, the large margin correlation filters do not consider the class distribution and the structural features within the class during training, which is easily affected by noise. This paper presents two methods to overcome this drawback: minimum class variance large margin correlation filter (MCVLMCF) and minimum class locality preserving variance correlation filter (MCLPVCF). First, the overall structure information of the target is obtained by the within-class scatter with MCVLMCF, and the spatial features of the sample are extracted by the intrinsic manifold structure of data with MCLPVCF. Then, we embed these two types of information into the optimal function of the large margin correlation filter, fuse the sample spatial features with the large margin principle and correlation filtering, and convert it to solve the filter in the frequency domain. Finally, object localization experiments in actual environments and classification experiments on different datasets demonstrate that our proposed methods can adapt to complex object changes and achieve better performances than some state-of-the-art methods.