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This study presents a detailed numerical investigation into the web buckling behaviour exhibited by high-strength aluminium alloy channels, namely 7075-T6 and AA-6086, when subjected to concentrated loading. A nonlinear finite element (FE) model was established and verified using the experimental data reported by other researchers, and the material properties of 7075-T6 and AA-6086 high-strength aluminium alloy were obtained through the literature. A parametric study comprising 1024 models was performed using the validated FE models. Variables examined in this work included web slenderness ratio, internal corner radii, bearing lengths, and aluminium alloy grades. The numerical results generated by the parametric investigation were used to evaluate the applicability and reliability of the most recent design specifications given in the Australian and New Zealand Standards (AS/NZ S4600) (2018) and Australian Standards (AS/NZS 1664.1) (1997). The comparison indicated that the calculated design strength using AS/NZ S4600 was over-conservative by 41% and 43% for 7075-T6 and AA-6086 aluminium alloy, correspondingly, while the design strength computed using AS/NZS 1664.1 was marginally unconservative, compared to numerical results. Finally, using bivariate linear regression analysis, new design formulas with new coefficients for determining the web buckling behaviour of 7075-T6 and AA-6086 high-strength aluminium alloy channels were proposed. A reliability analysis was then undertaken, indicating that the proposed design equations possess the capability of accurately predicting the web buckling behaviour of these members. [ABSTRACT FROM AUTHOR]

Recently, new types of C-shaped members made from AA-6086 and 7075-T6 high-strength aluminium alloy have become more popular due to their high yield strength and lower cost. These members are often manufactured with pre-punched web perforations to simplify the installation of services, but this can reduce their strength. Also, such aluminium C-shaped members that contain perforated webs are vulnerable to web buckling failure, as aluminium alloy has a lower elastic modulus compared to steel. However, this influence has not been investigated for high-strength aluminium alloy sections to date. An extensive numerical investigation was undertaken to examine the effect of web perforations on the web buckling resistance of high-strength aluminium alloy C-shaped members under an end-two-flange (ETF) loading case, and this study focused on two types of aluminium alloys, namely 7075-T6 and AA-6086. To achieve this, a nonlinear finite element (FE) model was developed and validated using the test data in the literature. The material properties used in the FE models were obtained from the relevant literature. A parametric investigation was carried out, consisting of a total of 1458 models. In this investigation, a number of variables were examined, including the web hole size, web hole location, bearing length, fillet radius and aluminium alloy grades. The results showed that increasing the a/h ratio from 0.1 to 0.5 resulted in a decrease of 9.7% and 9.3% in the web buckling resistance for the 7075-T6 aluminium and AA-6086 aluminium, respectively. When the length of the bearing plates (N) varied from 100 mm to 200 mm, the web buckling resistance experienced an average increase of 61.7% for the 7075-T6 aluminium and 54.1% for the AA-6086 aluminium. Also, the web buckling resistance increased by 6.2% for the 7075-T6 aluminium alloy, while the strength increased by 4.0% for the AA-6086 aluminium alloy when the x/h ratio increased from 0.1 to 0.5. The numerical data generated from the parametric study were used to assess the accuracy and suitability of the latest design recommendations, and it was found that the design rules presented in the previous literature cannot provide reliable and safe predictions for estimating the web buckling resistance of aluminium C-shaped members that contain perforated webs under an ETF loading case. Finally, new design formulas were proposed in the form of strength reduction factors. A reliability assessment was then undertaken, and the results of this analysis indicated that the proposed design formulas can accurately predict the web buckling resistance of such members with perforated webs. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) is widely used in the automotive industry as it can provide point cloud information containing precise distances. Three-dimensional (3D) object detection based on LiDAR point clouds is significant for environment perception tasks. However, feature learning for point clouds remains challenging. This paper proposes a two-stage voxel-based LiDAR 3D object detector, referred to as density-aware and neighborhood attention (DenNet), that focuses on the neighborhood information of objects. DenNet mainly integrates two modules: voxel density-aware (VDA) and neighborhood attention (NA). VDA introduces density information of the point cloud. Here, point cloud density information was added as voxel features in the voxel-based framework to alleviate the information loss during voxelization. Additionally, to extract neighbor information, the characteristics of 3D objects were analyzed for traffic scenes. The NA mechanism was adopted, which localizes the receptive field for each query to its nearest neighboring points. DenNet yielded competitive results, as compared with state-of-the-art methods, for the KITTI and One Million Scenes (ONCE) datasets; notably, it afforded an improvement of 3.96% relative to the baseline mean average precision on the more challenging ONCE dataset. [ABSTRACT FROM AUTHOR]

Lodging is one of the primary factors that reduce wheat yield; therefore, rapid and accurate monitoring of wheat lodging helps to provide data support for crop loss and damage response and the subsequent settlement of agricultural insurance claims. In this study, we aimed to address two problems: (1) calculating the wheat lodging area. Through comparative experiments, the SegFormer-B1 model can achieve a better segmentation effect of wheat lodging plots with a higher prediction rate and a stronger generalization ability. This model has an accuracy of 96.56%, which realizes the accurate extraction of wheat lodging plots and the relatively precise calculation of the wheat lodging area. (2) Analyzing wheat lodging areas from various growth stages. The model established, based on the mixed-stage dataset, generally outperforms those set up based on the single-stage datasets in terms of the segmentation effect. The SegFormer-B1 model established based on the mixed-stage dataset, with its mIoU reaching 89.64%, was applicable to wheat lodging monitoring throughout the whole growth cycle of wheat. [ABSTRACT FROM AUTHOR]

Among the existing harmonic current suppression methods, it is difficult and complicated to suppress any nth harmonic current accurately for multi-phase permanent magnet synchronous motors (PMSMs). To solve this problem, this paper takes a five-phase dual-rotor PMSM fed with a current source inverter (CSI) as an example, and proposes an nth harmonic current suppression method based on the impulse current PWM algorithm. Firstly, the analysis is conducted and presented for the nth harmonic current in the mth harmonic space. Then, based on the Sliding Discrete Fourier Transformation (SDFT), a low-pass filter (LPF) named SDFT-LPF is designed. Additionally, the impulse current PWM technique for the five-phase CSI is realized. In this paper, the experiments have confirmed that the SDFT-LPF has good filter performance. Compared with the SVPWM, the impulse current PWM technique has the same DC-link current utilization rate, but it is easier to implement. Moreover, the proposed harmonic current control method can accurately control any nth harmonic current without changing the PWM technique, which has significantly reduced the complexity of the harmonic current control. Additionally, the proposed scheme is easy to implement and can be directly extended to the multiple harmonic current's control. [ABSTRACT FROM AUTHOR]

A fast and low-complexity blind chromatic dispersion (CD) estimation method is proposed by a stepwise optimal order search of fractional Fourier transform (FrFT) in coherent optical communications. The method can track the cumulative dispersion over time in future dynamic and flexible scenarios. The validity of the FrFT method is verified by analogy to the field of CD estimation from the energy concentration property of the chirp signal, and the estimation equation of the CD value is obtained by parameter transformation. Numerical simulations have been carried out in the CD range of 1600 ps/nm to 32,000 ps/nm for 28 GBaud PM-QPSK and 14 GBaud PM-16QAM signals. The results show that the proposed algorithm achieves a high CD estimation accuracy with an average estimation error of less than 50 ps/nm within the 4096 sampling points and an order search interval of 0.0001. The effects of the polarization mode dispersion, optical signal-to-noise ratio and nonlinearity on CD estimation performance are discussed. In addition, the stepwise optimal order search method can achieve the purpose of greatly reducing the computational complexity under the premise of ensuring the accuracy of CD estimation. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology and the increasing application of UAV in various fields, the security problems of unmanned aerial vehicle (UAV) communication network have become increasingly prominent. It has become an important scientific challenge to design a routing protocol that can provide efficient and reliable node to node packet transmission. In this paper, an efficient Digital Signature algorithm based on the elliptic curve cryptosystem is applied to routing protocol, and an improved security method suitable for on-demand routing protocol is proposed. The UAV communication network was simulated through the NS2 simulation platform, and the execution efficiency and safety of the improved routing protocol were analyzed. In the simulation experiment, the routing protocols of ad-hoc on demand distance vector (AODV), security ad-hoc on demand distance vector (SAODV), and improved security ad-hoc on demand distance vector (ISAODV) are compared in terms of the performance indicators of packet delivery rate, throughput, and end-to-end delay under normal conditions and when attacked by malicious nodes. The simulation results show that the improved routing protocol can effectively improve the security of the UAV communication network. [ABSTRACT FROM AUTHOR]

In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree–support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks. [ABSTRACT FROM AUTHOR]

A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks. [ABSTRACT FROM AUTHOR]

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection. [ABSTRACT FROM AUTHOR]