A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process parameters of relay production lines are studied based on the long-and-short-term memory network. Then, the Keras deep learning framework is utilized to build up a short-term relay quality prediction algorithm for the semi-finished product. A simulation model is used to study prediction algorithm. The simulation results show that the average prediction absolute error of the fraction is less than 5%. This work displays great application potential in the relay production lines.

Study on Plasticity Modification for Measuring High Residual Stress by Hole-Drilling Method

Hole-drilling method is a commonly used method for measuring residual stress. The calibration coefficients in ASTM E837-13 a would cause large errors due to the plasticity deformation of materials. In the study, calibration coefficients were modified in the plasticity deformation stage based on the distortion energy theory. The calibration experiment of calibration coefficients was simulated by the finite element model, and the plasticity modification formulas of 7075 aluminum alloy were obtained. From the results of uniaxial tensile loading test, the measuring errors of high residual stress are significantly reduced from-4.071%～53.440% to-5.140% ～ 0.609% after the plasticity modification. This work provides an effective way to expand the application of hole-drilling method.

Optimal Placement of Phasor Measurement Unit in Smart Grids Considering Multiple Constraints

The distribution of measurement noise is usually assumed to be Gaussian in the optimal phasor measurement unit(PMU)placement(OPP)problem.However,this is not always accurate in practice.This paper proposes a new OPP method for smart grids in which the effects of conventional measurements,limited channels of PMUs,zero-injection buses(ZIBs),single PMU loss contingency,state estimation error(SEE),and the maximum SEE variance(MSEEV)are considered.The SEE and MSEEV are both obtained using a robust t-distribution maximum likelihood estimator(MLE)because t-distribution is more flexible for modeling both Gaussian and non-Gaussian noises.The A-and G-optimal experimental criteria are utilized to form the SEE and MSEEV constraints.This allows the optimization problem to be converted into a linear objective function subject to linear matrix inequality observability constraints.The performance of the proposed OPP method is verified by the simulations of the IEEE 14-bus,30-bus,and 118-bus systems as well as the 211-bus practical distribution system in China.

A Local-Global Attention Fusion Framework With Tensor Decomposition for Medical Diagnosis

Dear Editor,In this letter,a novel hierarchical fusion framework is proposed to address the imperfect data property in complex medical image analysis(MIA)scenes.In particular,by combining the strengths of convolutional neural networks(CNNs)and transformers,the enhanced feature extraction,spatial modeling,and sequential context learning are realized to provide comprehensive insights on the complex data patterns.Integration of information in different level is enabled via a multi-attention fusion mechanism,and the tensor decomposition methods are adopted so that compact and distinctive representation of the underlying and high-dimensional medical image features can be accomplished[1].It is shown from the evaluation results that the proposed framework is competitive and superior as compared with some other advanced algorithms,which effectively handles the imperfect property of inter-class similarity and intra-class differences in diseases,and meanwhile,the model complexity is reduced within an acceptable level,which benefits the deployment in clinic practice.MIA has assumed a pivotal role in numerous critical clinical scenarios,where sophisticated image analysis techniques have proven instrumental in augmenting medical decision-making,facilitating individualized therapeutic interventions,and enhancing patient prognostication[2]−[4].

Surface Functionalization of PEO Nanofibers Using a TiO_(2) Suspension as Sheath Fluid in a Modified Coaxial Electrospinning Process

Convenient and integration fabrication process is a key issue for the application of functional nanofibers.A surface functionalization method was developed based on coaxial electrospinning to produce ultraviolet(UV)protection nanofibers.The titanium dioxide(TiO_(2))nanoparticles suspension was delivered through the shell channel of the coaxial spinneret,by which the aggregation of TiO_(2) nanoparticles was overcome and the distribution uniformity on the surface of polyethylene oxide(PEO)nanofiber was obtained.With the content of TiO_(2) increasing from 0 to 3%(mass fraction),the average diameter of nanofibers increased from(380±30)nm to(480±100)nm.The surface functionalization can be realized during the electrospinning process to gain PEO/TiO_(2) composite nanofibers directly.The uniform distribution of TiO_(2) nanoparticles on the surface of nanofibers enhanced the UV absorption and resistance performance.The maximum UV protection factor(UPF)value of composite nanofibers reaches 2751.This work presented a novel surface-functionalized way for the preparation of composite nanofiber,which has great application potential in the field of micro/nano system integration fabrication.

Surfaced-modified TiO_(2) Nanofibers with Enhanced Photodegradation Under Visible Light

Asynchronized surface modification method based on coaxial electrospinning was developed to fabricate high-efficiency photodegradative nanofiber for water purification. TiO_(2) nanoparticles assembled uniformly on the surface of polycaprolactone(PCL) nanofibers to form composite nanofibers through one step process. The maximal content of Ti element was 25.6%(mass fraction) in the PCL/TiO_(2) composite nanofibrous membrane, which exhibited hydrophilicity and excellent photodegradation under visible light in water. The Rhodamine B dye degraded 96.17% in 120 min under visible light by the PCL/TiO_(2) composite membrane. The adsorption behavior fitted Langmuir model well and indicated chemical related adsorption. This PCL/TiO_(2) composite nanofibrous membrane has super degradation properties and displays great application potential to environmental protection.