3,3',4,4'-联苯四胺改性邻苯二甲腈树脂及其复合材料性能

采用3,3',4,4'-联苯四胺(LBS)与联苯型邻苯二甲腈树脂(BPh)进行预聚反应制备改性联苯型邻苯二甲腈树脂预聚物(BLBS)。利用红外光谱法、差示扫描量热仪、旋转流变仪和热重分析对BLBS的固化行为、流变性能和耐温性能进行研究。结果表明,BLBS最低可在240℃前开始固化,但仍需要较高的温度才能固化完全;BLBS-3固化物在氮气和空气氛围中的质量损失5%时的温度(Td5)分别为549.4℃和555.6℃。石英纤维增强BLBS复合材料(QF/BLBS)具有优异的力学性能和耐热性能,玻璃化转变温度(T_(g))大于500°C,室温弯曲强度和层间剪切强度分别为711.5 MPa和48.5 MPa,400℃热处理2 h后弯曲强度和层间剪切强度分别为680.5 MPa和31.3 MPa。

Modeling and analysis of the ocean dynamic with Gaussian complex network

The techniques for oceanographic observation have made great progress in both space-time coverage and quality, which make the observation data present some characteristics of big data. We explore the essence of global ocean dynamic via constructing a complex network with regard to sea surface temperature. The global ocean is divided into discrete regions to represent the nodes of the network. To understand the ocean dynamic behavior, we introduce the Gaussian mixture models to describe the nodes as limit-cycle oscillators. The interacting dynamical oscillators form the complex network that simulates the ocean as a stochastic system. Gaussian probability matching is suggested to measure the behavior similarity of regions. Complex network statistical characteristics of the network are analyzed in terms of degree distribution, clustering coefficient and betweenness. Experimental results show a pronounced sensitivity of network characteristics to the climatic anomaly in the oceanic circulation. Particularly, the betweenness reveals the main pathways to transfer thermal energy of El Niño–Southern oscillation. Our works provide new insights into the physical processes of ocean dynamic, as well as climate changes and ocean anomalies.

Strengthened Initialization of Adaptive Cross-Generation Differential Evolution

Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest algorithms.In order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant operator.In this paper,an enhanced version is proposed,namely SIACGDE.It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor.These improvements make the direction of crossgeneration mutation more clearly and the ability of searching more efficiently.The experiments show that the new algorithm has better diversity and improves convergence to a certain extent.At the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.

Single depth image 3D face reconstruction via domain adaptive learning

1 Introduction In this paper,we propose a novel domain-adaptive reconstruction method that effectively leverages deep learning and synthetic data to achieve robust 3D face reconstruction from a single depth image.The method applies two domain-adaptive neural networks for predicting head pose and facial shape,respectively.Both networks undergo training with a customized domain adaptation strategy,using a combination of auto-labeled synthetic and unlabeled real data.

Learning conditional photometric stereo with high-resolution features

Photometric stereo aims to reconstruct 3D geometry by recovering the dense surface orientation of a 3D object from multiple images under differing illumination.Traditional methods normally adopt simplified reflectance models to make the surface orientation computable.However,the real reflectances of surfaces greatly limit applicability of such methods to real-world objects.While deep neural networks have been employed to handle non-Lambertian surfaces,these methods are subject to blurring and errors,especially in high-frequency regions(such as crinkles and edges),caused by spectral bias:neural networks favor low-frequency representations so exhibit a bias towards smooth functions.In this paper,therefore,we propose a self-learning conditional network with multiscale features for photometric stereo,avoiding blurred reconstruction in such regions.Our explorations include:(i)a multi-scale feature fusion architecture,which keeps high-resolution representations and deep feature extraction,simultaneously,and(ii)an improved gradient-motivated conditionally parameterized convolution(GM-CondConv)in our photometric stereo network,with different combinations of convolution kernels for varying surfaces.Extensive experiments on public benchmark datasets show that our calibrated photometric stereo method outperforms the state-of-the-art.

A survey on visual analysis of ocean data

A major challenge in analysis of huge amounts of ocean data is the complexity of the data and the inherent complexity of ocean dynamic processes.Interactive visual analysis serves as an efficient complementary approach for the detection of various phenomena or patterns,and correlation exploring or comparing multiple variables in researchers daily work.Firstly,this paper presents a basic concept of the ocean data produced from numerous measurement devices or computer simulations.The characteristics of ocean data and the related data processing techniques are also described.Secondly,the main tasks of ocean data analysis are introduced.Based on the main analysis tasks in the field of oceanography,the survey emphasizes related interactive visualization techniques and tools from four aspects:visualization of multiple ocean environmental elements and multivariate analysis,ocean phenomena identification and tracking,patterns or correlation discovery,ensembles and uncertainties exploration.Finally,the opportunities are discussed for future studies.

Deep reinforcement learning based worker selection for distributed machine learning enhanced edge intelligence in internet of vehicles

Nowadays,Edge Information System(EIS)has received a lot of attentions.In EIS,Distributed Machine Learning(DML),which requires fewer computing resources,can implement many artificial intelligent applications efficiently.However,due to the dynamical network topology and the fluctuating transmission quality at the edge,work node selection affects the performance of DML a lot.In this paper,we focus on the Internet of Vehicles(IoV),one of the typical scenarios of EIS,and consider the DML-based High Definition(HD)mapping and intelligent driving decision model as the example.The worker selection problem is modeled as a Markov Decision Process(MDP),maximizing the DML model aggregate performance related to the timeliness of the local model,the transmission quality of model parameters uploading,and the effective sensing area of the worker.A Deep Reinforcement Learning(DRL)based solution is proposed,called the Worker Selection based on Policy Gradient(PG-WS)algorithm.The policy mapping from the system state to the worker selection action is represented by a deep neural network.The episodic simulations are built and the REINFORCE algorithm with baseline is used to train the policy network.Results show that the proposed PG-WS algorithm outperforms other comparation methods.