A Deep Latent Space Model for Graph Representation Learning

Graph representation learning is a fundamental problem for modeling relational data and benefits a number of downstream applications. Traditional Bayesian-based graph models and recent deep learning based GNN either suffer from impracticability or lack interpretability, thus combined models for undirected graphs have been proposed to overcome the weaknesses. As a large portion of real-world graphs are directed graphs (of which undirected graphs are special cases), in this paper, we propose a Deep Latent Space Model (DLSM) for directed graphs to incorporate the traditional latent variable based generative model into deep learning frameworks. Our proposed model consists of a graph convolutional network (GCN) encoder and a stochastic decoder, which are layer-wise connected by a hierarchical variational auto-encoder architecture. By specifically modeling the degree heterogeneity using node random factors, our model possesses better interpretability in both community structure and degree heterogeneity. For fast inference, the stochastic gradient variational Bayes (SGVB) is adopted using a non-iterative recognition model, which is much more scalable than traditional MCMC-based methods. The experiments on real-world datasets show that the proposed model achieves the state-of-the-art performances on both link prediction and community detection tasks while learning interpretable node embeddings. The source code is available at https://github.com/upperr/DLSM

Analysis of vibration traits of underwater vehicle propulsion shafting and optimization design of support parameters

In this paper, the calculation model of the propulsion shafting structure was established to solve the problem of flexural vibration of the shafting system for the underwater vehicle with relatively small scale. By using the transfer matrix method and the finite element method, the vibration characteristics of the shafting system subjected to the transverse unsteady excitation force were calculated by MATLAB software and ABAQUS software. Two aspects of the displacement response and the vibration power flow were analyzed and compared. Analysis showed that the results of the two methods were very close to each other and all met the requirements of vibration engineering calculation. The influence of the mass of propeller and the bearing stiffness in different positions on the vibration characteristics were analyzed by using the transfer matrix method. Finally, based on the transfer matrix method, the parameters of the bearing stiffness at different supports were optimized with design optimization, and then use ABAQUS software to verify the effectiveness of the optimization. The analysis results showed that, after optimization calculation, the vibration power flow input to the bases of different bearings were significantly decreased

Characteristic analysis of vibration isolation system based on high-static-low-dynamic stiffness

The purpose of this study is to investigate the characteristics of vibration isolation system with a single degree-of-freedom (SDOF) and a two-degree-of-freedom (2DOF) respectively based on the high-static-low-dynamic-stiffness (HSLDS). This model consists of a simple configuration connecting a vertical spring and a pair of oblique springs. The restoring force of the isolation system is approximated to linear and cubic stiffness by applying the Maclaurin series expansion. The dynamic equations of the SDOF and 2DOF are established for the harmonic force excitation. The frequency-amplitude response equation of the SDOF is obtained by employing the harmonic balance method (HBM) and is demonstrated in the classical Runge-Kutta method. The solution stability is ensured by applying the Floquet theory. Effects on the frequency response curves (FRCs) for the damping ratio and excitation amplitude are explored and discussed. The force transmissibility (FT) is defined to evaluate the vibration suppression capability. Effects on the FT of the SDOF and 2DOF for the excitation amplitude, mass ratio, and damping ratio are investigated. An experimental investigation of the SDOF is carried out to evaluate the actual attenuation performance in comparison with the equivalent linear system (ELS). The simulation and experimental results show that the HSLDS system with harmonic force excitation demonstrates hardening stiffness with multi-valued solutions. The occurrence of jump phenomenon is observed and explained by the stiffness variation. The system response and resonance frequency are affected by the excitation amplitude and damping ratio. The HSLDS system outperforms the ELS in a low frequency range if an appropriate mass is mounted. It is excited by a proper force and owns a suitable damper, which offers a theoretical guidance for the design and application of a novel HSLDS isolator

Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation

Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model using unlabeled target data and the knowledge of a well-trained source domain model. Most previous SFUDA works focus on inferring semantics of target data based on the source knowledge. Without measuring the transferability of the source knowledge, these methods insufficiently exploit the source knowledge, and fail to identify the reliability of the inferred target semantics. However, existing transferability measurements require either source data or target labels, which are infeasible in SFUDA. To this end, firstly, we propose a novel Uncertainty-induced Transferability Representation (UTR), which leverages uncertainty as the tool to analyse the channel-wise transferability of the source encoder in the absence of the source data and target labels. The domain-level UTR unravels how transferable the encoder channels are to the target domain and the instance-level UTR characterizes the reliability of the inferred target semantics. Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i)the source knowledge calibration module that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii)the target semantics calibration module that calibrates the unreliable semantics. With the help of the calibrated source knowledge and the target semantics, the model adapts to the target domain safely and ultimately better. We verified the effectiveness of our method using experimental results and demonstrated that the proposed method achieves state-of-the-art performances on the three SFUDA benchmarks. Code is available at https://github.com/SPIresearch/UTR

DopNet:A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets

The work presented in this paper aims to distinguish between armed or unarmed personnel using multi-static radar data and advanced Doppler processing. We propose two modified Deep Convolutional Neural Networks (DCNN) termed SCDopNet and MC-DopNet for mono-static and multi-static micro- Doppler signature (μ-DS) classification. Differentiating armed and unarmed walking personnel is challenging due to the effect of aspect angle and channel diversity in real-world scenarios. In addition, DCNN easily overfits the relatively small-scale μ-DS dataset. To address these problems, the work carried out in this paper makes three key contributions: first, two effective schemes including data augmentation operation and a regularization term are proposed to train SC-DopNet from scratch. Next, a factor analysis of the SC-DopNet are conducted based on various operating parameters in both the processing and radar operations. Thirdly, to solve the problem of aspect angle diversity for μ-DS classification, we design MC-DopNet for multi-static μ- DS which is embedded with two new fusion schemes termed as Greedy Importance Reweighting (GIR) and `21-Norm. These two schemes are based on two different strategies and have been evaluated experimentally: GIR uses a “win by sacrificing worst case” whilst `21-Norm adopts a “win by sacrificing best case” approach. The SC-DopNet outperforms the non-deep methods by 12.5% in average and the proposed MC-DopNet with two fusion methods outperforms the conventional binary voting by 1.2% in average. Note that we also argue and discuss how to utilize the statistics of SC-DopNet results to infer the selection of fusion strategies for MC-DopNet under different experimental scenarios

Effects of depth of straw returning on maize yield potential and greenhouse gas emissions

Appropriate straw incorporation has ample agronomic and environmental benefits, but most studies are limited to straw mulching or application on the soil surface. To determine the effect of depth of straw incorporation on the crop yield, soil organic carbon (SOC), total nitrogen (TN) and greenhouse gas emission, a total of 4 treatments were set up in this study, which comprised no straw returning (CK), straw returning at 15 cm (S15), straw returning at 25 cm (S25) and straw returning at 40 cm (S40). The results showed that straw incorporation significantly increased SOC, TN and C:N ratio. Compared with CK treatments, substantial increases in the grain yield (by 4.17~5.49% for S15 and 6.64~10.06% for S25) were observed under S15 and S25 treatments. S15 and S25 could significantly improve the carbon and nitrogen status of the 0-40 cm soil layer, thereby increased maize yield. The results showed that the maize yield was closely related to the soil carbon and nitrogen index of the 0-40 cm soil layer. In order to further evaluate the environmental benefits of straw returning, this study measured the global warming potential (GWP) and greenhouse gas emission intensity (GHGI). Compared with CK treatments, the GWP of S15, S25 and S40 treatments was increased by 9.35~20.37%, 4.27~7.67% and 0.72~6.14%, respectively, among which the S15 treatment contributed the most to the GWP of farmland. GHGI is an evaluation index of low-carbon agriculture at this stage, which takes into account both crop yield and global warming potential. In this study, GHGI showed a different trend from GWP. Compared with CK treatments, the S25 treatments had no significant difference in 2020, and decreased significantly in 2021 and 2022. This is due to the combined effect of maize yield and cumulative greenhouse gas emissions, indicating that the appropriate straw returning method can not only reduce the intensity of greenhouse gas emissions but also improve soil productivity and enhance the carbon sequestration effect of farmland soil, which is an ideal soil improvement and fertilization measure

Structure-Aware Feature Fusion for Unsupervised Domain Adaptation

Unsupervised domain Adaptation (UDA) aims to learn and transfer generalized features from a labelled source domain to a target domain without any annotations. Existing methods only aligning high-level representation but without exploiting the complex multi-class structure and local spatial structure. This is problematic as 1) the model is prone to negative transfer when the features from different classes are misaligned; 2) missing the local spatial structure poses a major obstacle in performing the fine-grained feature alignment. In this paper, we integrate the valuable information conveyed in classifier prediction and local feature maps into global feature representation and then perform a single mini-max game to make it domain invariant. In this way, the domain-invariant feature not only describes the holistic representation of the original image but also preserves mode-structure and fine-grained spatial structural information. The feature integration is achieved by estimating and maximizing the mutual information (MI) among the global feature, local feature and classifier prediction simultaneously. As the MI is hard to measure directly in high-dimension spaces, we adopt a new objective function that implicitly maximizes the MI via an effective sampling strategy and a discriminator design. Our STructure-Aware Feature Fusion (STAFF) network achieves the state-of-the-art performances in various UDA datasets

A Two-DOF Active-Passive Hybrid Vibration Isolator Based on Multi-Line Spectrum Adaptive Control

In order to effectively control the low-frequency vibration of ship machinery, based on the improved multi-line spectrum adaptive control algorithm, a two-degree-of-freedom (two-DOF) active-passive hybrid vibration isolator composed of an electromagnetic actuator, rubber spring, and the hydraulic device is proposed. The dynamic model of the two-DOF vibration isolation system is established and the main control force demand of the vibration isolation system at different damping forces is analyzed. By introducing the improved wavelet packet decomposition algorithm with the Hartley block least mean square algorithm to the filter-x least mean square (FxLMS) algorithm, an improved wavelet packet Hartley block filter-x least mean square (IWPHB-FxLMS) algorithm is established. The experimental results show that the IWPHB-FxLMS algorithm has better control performance. Compared with the traditional FxLMS algorithm, the IWPHB-FxLMS control algorithm improves the convergence speed by 91.7% and the line spectrum power spectrum attenuation by 58.1%. The active-passive hybrid vibration isolator is based on multi-line spectrum adaptive control and can achieve good control effects under the excitation of multi-frequency line spectrum and constant frequency line spectrum