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Traditional mode conversion ultrasonic motors are typically composed of the superposition of multiple vibration modes, making them more complex than single-mode ultrasonic motors. This complexity makes it difficult to miniaturize. A micro radial-torsional vibration mode ultrasonic motor is proposed and tested in this work. It is excited by a single-phase excitation signal. The proposed motor utilizes the radial in-plane vibration mode of the stator to convert the torsional vibration mode of the inner ring through the beams. The torsional vibration of the inner ring causes the rotor to rotate due to the friction force between the inner ring and the rotor. Based on the designed structure and verified using the finite element method, a prototype motor with a mass of 2.6 g and overall dimensions of Ô20 mm in diameter and 15 mm in height is fabricated and characterized. During the mechanical output performance test, the prototype achieved a maximum no-load speed of 3300 r/min and a stall torque of 0.2 mN $\cdot $ m at the driving voltage of 100 V $_{\mathrm {p-p}}$ .

Diabetic retinopathy (DR) is an essential factor that has caused vision loss and even blindness in middle-aged and older adults. A system that can automatically perform DR diagnosis can help ophthalmologists save a lot of tedious work, such as DR grading or lesion detection. At the same time, patients can find their diseases earlier and perform the correct treatment. However, most of the existing methods require many DR annotations to train the model, and the DR data will vary to different degrees due to various shooting tools. The above problems lead to the inefficient use of existing data in the experiment, limiting actual deployment. To alleviate this problem, we propose a novel Graph Adversarial Transfer Learning (GATL) for DR diagnosis in a deep model through transfer learning, including intra-domain alignment and inter-domain alignment. The proposed GATL enjoys several merits. First, our GATL adopts the self-supervised training to save the annotating cost in the target domain thus this domain adaptation method can significantly reduce annotation cost compared to the supervised approaches. Second, we introduce the graph neural network to extract potential features between unknown samples. Third, to enhance the robustness of the model, we use adversarial training to perform both inter-domain and intra-domain alignment to further improve the model’s classification accuracy. GATL achieved 94.3%, 97.5%, and 91.1% in accuracy, sensitivity, and specificity in the APTOS dataset and 92.7%, 95.7%, and 89.7% in the EyePACS dataset, respectively. Extensive experimental results on two challenging benchmarks, including APTOS 2019 and EyePACS, demonstrate that the proposed GATL performs favorably against baseline DR classification methods.

For high-order linear networked systems with switching topologies and the random communication silence, energy-constraint output formation problems are investigated. Firstly, the dynamics associated with the coordinated output is decomposed from the whole dynamics of each intelligent agent and a new control protocol is constructed on the basis of the coordinated outputs and the protocol states of neighboring intelligent agents, where switching topologies, the energy constraint and the random communication silence are contained. Then, by introducing the silent rate and the dwell time, the impacts of the random communication silence and switching topologies are dealt with and sufficient conditions for energy-constraint output formation design and analysis are proposed, where the output formation feasible conditions are required to guarantee the output formation achievement property. Especially, an explicit expression of the output formation center function is determined, which depicts the whole motion of a networked system when energy-constraint output formation is achieved. Finally, a numerical example is given to demonstrate theoretical conclusions.

This paper studies the classification of unbalanced data sets. First, this kind of data sets is briefly introduced, and then the classification methods of unbalanced data sets are analyzed in detail from different perspectives such as data sampling method, algorithm level, feature level, cost-sensitive function, and deep learning. In addition, the data sampling methods are divided into different technologies for introduction: unbalanced data set classification method based on synthetic minority over-sampling technology (SMOTE), support vector machine (SVM) technology, and k-nearest neighbor (KNN) technology, etc. Then, the advantages and disadvantages of these methods are compared. Finally, the evaluation criteria of the unbalanced data set classifier are summarized, and the future work directions are prospected and summarized.

In this paper, an aperture expanding direction finding algorithm using L-shaped non-uniform sparse array is proposed. Firstly, the virtual array is expanded by vectorizing, de-redundant and sorting of received data covariance matrix. Secondly, the full rank matrix is got by smoothing the virtual array data, and the signal subspace is obtained by matrix block processing. Finally, the elevation angle and azimuth angle are acquired by using the rotation invariant relation. The algorithm does not need to decompose the covariance matrix of the data, nor does it need to search the two-dimensional spectral peaks. It can greatly reduce the computational complexity when there are many array elements and large number of snapshots. The simulation results show that the sparse array arrangement can enlarge the array aperture, improve the parameter resolution and the accuracy of DOA estimation, and verify the effectiveness of the proposed algorithm.

In this paper, we propose a multiple-input single-output ghost imaging scheme, named MISO-GI. In the scheme, K spatiotemporal speckle pattern sources (multiple input) simultaneously generate K different spatiotemporal modulated speckle patterns which then are illuminated on an unknown object at the same time. Then, a non-spatial-resolution bucket detector (single output) is used to detect the light field interacting with the object. According to the orthogonality of speckle patterns from different spatiotemporal speckle pattern sources, the detection signal is separated into K components corresponding to K spatiotemporal modulated speckle patterns. With the effective recovery algorithm, an image is reconstructed by using all spatiotemporal modulated speckle patterns and their corresponding components of detection signals. Both experimental and simulation results verify the feasibility of the proposed MISO-GI scheme. Moreover, the results also show that the proposed MISO-GI scheme could reduce the total sampling time by K times and improve the imaging quality comparing to traditional GI, without the decrease of the number of speckle patterns and the picture time as well as the consumption of other light field dimensions. It provides a promising avenue to realize GI with limited sampling time and has potential for more applications.

Neural networks have been widely applied but they are still vulnerable to adversarial examples. More and more defense models have been proposed and they can resist the attacks to the neural networks. In order to generate adversarial examples with good transferability, we propose the restricted region based iterative gradient method (RRI-GM) for non-targeted attack, which aims at generating adversarial examples to make black-box defense models output wrong decision. We first use object detection algorithm to restrict some key regions in the images, since we regard perturbation in the key region affects more than the whole image. To improve the efficiency of attacks, we use gradient-based attack methods and they show good performance. In addition, the process is iterated for multiple rounds to generate adversarial examples with good transferability. Furthermore, we conduct extensive experiments to validate the effectiveness of the proposed method, and the results show that our method can achieve good attack performance against black-box defense models.

In the current paper, time-varying formation tracking (TVFT) problems for second-order multi-agent systems with external disturbances are studied. Based on the active disturbance rejection controller, a robust TVFT protocol is proposed, which contains the disturbance compensation. To restrain the influence of external disturbances, an extended state observer is designed to estimate the disturbance compensation using the control input and output information of the system. For the case that the desired formation trajectories are known, an approach to give the time-varying formation with a stationary transient process is proposed by constructing a tracking-differentiator, which actually plan a motion taking the shortest time along the desired trajectories. An algorithm is designed to determine the control parameter matrix of the protocol, in which the formation tracking feasibility condition is given. Finally, to verify the theoretical results, two numerical simulation examples are presented.

A vehicular ad-hoc network (VANET) enables vehicles to communicate with each other directly or via roadside infrastructure in order to improve road safety and efficiency. Within a VANET, communications can potentially alter vehicular mobility and conversely, that the mobility could potentially influence vehicular communications. Therefore, a VANET simulator environment is needed that can accurately model interactions between vehicular mobility and network protocols. In this paper, we present a novel vehicular network simulation environment designed using the MATLAB discrete event system (DES) in the SimEvents toolbox. The proposed simulation environment is a bit-accurate, discrete event simulator that integrates vehicular mobility operations with wireless network communication. This paper provides details on the design of the proposed simulator. Its computational costs are evaluated in terms of events quantities and execution time. The physical (PHY) layer of the proposed simulator shows a more realistic packet success rate (PSR) using bit-level processing techniques when compared with the packet-based NS-3 simulator. The performance of the priority-based media access control (MAC) layer proves the data with different priorities that can coexist in the same channel.

In this paper, a wideband circularly polarized (CP) crossed-dipole antenna is presented by using parasitic modified patches. Four polygonal metal patches are sequentially rotated around the crossed dipoles. By tuning the electromagnetic coupling characteristic between the patches and the crossed dipoles, the axial-ratio bandwidth (ARBW) of the lower frequency band can be enhanced significantly. Furthermore, four slots are etched on the parasitic patches in a sequential rotation way, modifying the current distribution. Therefore, an extra CP mode produces at the higher band. A wide 3-dB ARBW is realized by combing three CP modes, including the basic CP mode of the cross dipole at the middle frequency band. A prototype of the proposed broadband CP antenna was manufactured and measured to verify the design principle. The measurement results illustrate that the antenna has a wide 10-dB return loss bandwidth of 99.2% (1.24-3.68 GHz) and a broad 3-dB ARBW of 72.7% (1.41-3.02 GHz) for right-hand circular polarization (RHCP). Besides, stable gain and unidirectional radiation patterns are also obtained over the entire band of operation.

Intelligent motion control is one of the key technologies of ships. This paper studies the application of Adaptive Mutation Beetle Particle Swarm (AMBPS)-PID algorithm in ship motion control. Firstly, the ship MMG model is established. Then the BAS algorithm is introduced, and AMBPS algorithm is improved and designed on this basis. Secondly, ship heading and path following controllers are designed according to the algorithm, and rudder turning rate constraint is introduced to limit the rudder angle. Thirdly, through the test function effect analysis of AMBPS and other similar algorithms, the improved effect of this algorithm is verified. Finally, from manual tuning PID parameters to off-line and on-line optimizing parameters based on AMBPS algorithm, the optimal control parameters are obtained step by step, and the optimal heading and path following simulation results are achieved. Compared with the results of traditional PID, AMBPS-PID algorithm has a better adaptive control effect on ship motion control, reduces the error of manual tuning parameters and improves efficiency.

The operational skills are essential for the cybersecurity practitioners and the hands-on experiments can be utilized to train their practical skills. As malicious attacks are continually changing and new vulnerability appears quickly with the increasing number of new software, it has been a challenging problem to develop hands-on experiments rapidly and continuously which are consistent with realistic threat scenarios. Normally, skilled engineers are familiar with threat scenarios and the attack/defense skills, but they have no motivation to develop hands-on experiments for education. In contrast, the faculties are good at teaching and training but they have no resource or experience to deploy lots of hands-on experiments by themselves. In this paper, we propose a framework called RC2F by adopting the crowdsourcing approach, which acts as a platform for the engineers and the faculties as well as channels between them for resource exchanging. In the framework, we also introduce two incentive models to motivate the participants, where the constructor-oriented Forward Incentive Model (FIM for short) encourages the engineers by the incentive mechanism and the demand-oriented Backward Incentive Model (BIM for short) balances the educational resources for the faculties. We also provide a comprehensive case based on the practical data to analyze the proposed framework. And the results validate that the proposed framework as well as the FIM and BIM models can address the challenge in developing hands-on experiments.

Abstract Estimation for weather-related failure probability of overhead transmission lines is essential in the reliability assessment of a power system. This paper analyzes the outage and weather data of 110 kV overhead transmission lines in the Guangxi Zhuang Autonomous Region of China during 2011–2014. The result reveals obvious uneven distributions of outage events for time and space due to the spatial and temporal variation of severe weather. Based on the results, an estimation method is proposed in this paper. Split and aggregation is used to smooth the outage and weather data. The poisson model is adopted in our method to investigate the statistic characteristics of transmission line outage events. Regression analysis is applied to obtain the correlation between the weather intensity and history failure rate. Furthermore the method proposed is validated against the empirical outage data.

Most resource allocation algorithms used in wireless communication networks arrange each user's transmission rate based on the type of user data but neglect the quality of the users' communication channels. In this paper, the relationship between rate and bit error rate (BER) was established by considering both the communication channel decay and the error control mechanism. A resource allocation based on joint rate and BER scheduling (JRBS) was proposed to satisfy the transmission power requirement generated by the quality change in communication channels. The JRBS analyzes the maximum transmission capacity requirement in each time slot, determines the variable capacity and the available channels in each time slot, and decides the transmission priority of data packets based on various quality of service (QoS). Ultimately, the JRBS algorithm improves system capacity to satisfy the BER and QoS of various services by the simulations.

Guaranteed-performance time-varying formation analysis and design problems for general high-order swarm systems with communication constraints are investigated. First, a guaranteed-performance time-varying formation control protocol is proposed by using the intermittent relative local information, and the performance function is introduced to guarantee the formation regulation performance among neighboring agents. An explicit presentation of the formation center function is presented to describe the movement trajectory of the formation as a whole. Then, sufficient conditions for guaranteed-performance time-varying formation analysis and design are respectively given by linear matrix inequality techniques, and a guaranteed-performance cost is determined meanwhile. When the desired guaranteed-performance time-varying formation satisfies the time-varying formation feasibility condition, swarm systems with communication constraints can achieve the guaranteed-performance time-varying formation by designing the gain matrix of the guaranteed-performance time-varying formation control protocol if the nominal coverage rate is larger than a positive threshold. Finally, a numerical simulation is shown to illustrate theoretical results.

A high-stability wavelength-tunable single-frequency (SF) ytterbium-doped all-fiber compound ring laser has been demonstrated experimentally. The compound ring cavity is composed of a fiber optical tunable filter, a high-finesse ring filter, and a loop mirror filter. Three segments of ytterbium-doped fibers are employed as the gain medium or saturable absorber, respectively. The SF operation is observed to be stable during 1 h without mode hopping. The SF fiber laser achieves a wide-band tunability from 1030 to 1090 nm with maximum output slope efficiency of 7.95%. The power instability over 2 h gives standard deviation value of 1.34%. The average linewidth is 8.8 kHz and the relaxation oscillation frequency is at 62 kHz.