一种用于电气设备状态监测的新式FBG传感器北大核心CSCD

为了监测绕组变压器的静态应力场和发生短路等故障时的动态应力变化,设计了一种用于电气设备状态监测的新式FBG传感器。该传感器由聚醚醚酮材料封装的FBG构成,通过内部圆锥形空腔结构实现将轴向应力集中于FBG敏感位置。通过仿真对不同压力强度下传感器结构的应力场部分及形变趋势进行了计算与分析,论证了设计的合理性。实验分别对静态载荷和动态冲击进行测试,结果显示,在静态压载测试中,当100 N相似文献   

Biological applications of amide and amino acid containing synthetic macrocycles

ABSTRACT

Amino acid derived macrocycles with elaborate well-defined stereochemistry are a unique class of compounds that have been isolated from natural sources. Macrocycles like cyclosporine, octreotide, and valinomycin have been used in multiple applications, like drugs or ion sensors. Chemists have long been fascinated by the unique molecular recognition capabilities of these macrocycles and tried to design synthetic analogs with similar functions. This article is focused on reviewing current research on amide and amino acid containing macrocycles that have been developed in research laboratories for biological recognition, specifically for anion sensing, ion transport, carbohydrate sensing, and peptide sensing.     

Throughput-based fair bandwidth allocation in OBS networks

Fair bandwidth allocation (FBA) has been studied in optical burst switching (OBS) networks, with the main idea being to map the max-min fairness in traditional IP networks to the fair-loss probability in OBS networks. This approach has proven to be fair in terms of the bandwidth allocation for differential connections, but the use of the ErlangB formula to calculate the theoretical loss probability has made this approach applicable only to Poisson flows. Furthermore, it is necessary to have a reasonable fairness measure to evaluate FBA models. This article proposes an approach involving throughput-based-FBA, called TFBA, and recommends a new fairness measure that is based on the ratio of the actual throughput to the allocated bandwidth. An analytical model for the performance of the output link with TFBA is also proposed.     

A Noise-Aware Real-Time Processing Approach for Electroencephalogram Signal Classification

Electroencephalogram (EEG) signal processing has emerged as a critical problem for biometric applications due to its real-time requirement. While compressive sensing is an efficient method for signal compression, its application in EEG signal processing is limited due to its noise unawareness during transmission and time-consuming reconstruction procedure. In this paper, we propose a noise-aware sparse Bayesian learning approach with block structure (NA-BSBL) to achieve higher efficiency on data compression, reconstruction and classification. By applying novel structure for parameter and introducing the Mahalanobis Distance, our approach achieves an almost 20% reconstruction performance lift and 10% accuracy lift under noise condition. For further application of reconstructed EEG signal, we extract both the spatial and frequency domain features for classification. Experimental results show that the proposed approach can achieve 94% classification accuracy with 16% speed up compared with the conventional approach.     

Hexanuclear 3d − 4f metal–organic cages assembled from a carboxylic acid‐functionalized tris‐triazamacrocycle for highly selective fluorescent sensing of picric acid

Two Ln^III ions are sandwiched by dinuclear Co^II building blocks derived from a tris‐triazamacrocyclic ligand bearing pendant carboxylic acid functionality, 1,3,5‐tris((4,7‐bis(2‐carboxyethyl)‐1,4,7‐triazacyclonon‐1‐yl)methyl)‐benzene (H₆L), giving rising to two nanoscale heterometallic metal–organic cages formulated as [Co₄Ln₂(LH_2.5)₂(H₂O)₄]·(ClO₄)₆·NO₃·nH₂O [Ln = Dy, n = 12 ( 1 ); Ln = Yb, n = 9 ( 2 )], whose internal cavity accommodates a guest NO₃^? anion. Their hexanuclear cage‐like architectures are maintained both in solution and solid states as confirmed by mass spectrum as well as X‐ray diffraction experiments. These two cages display ligand‐based fluorescence emissions and therefore both were chosen to be operated as fluorescent chemosensors for the detection of nitroaromatic compounds. Attractively, these metal–organic cages allow highly selective and sensitive detection of picric acid (PA) over other nitroaromatics in solution and suspension, and the fluorescence resonance energy transfer (FRET) between the cage probes and PA is mainly responsible for the remarkable detection efficiency.     

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.     

Computational modelling of multi-material energetic materials and systems

ABSTRACT

In this paper, we present a systematic roadmap for developing a robust and parallel multi-material reactive hydrodynamic solver that integrates historically stable algorithms with new and current modern methods to solve explosive system design problems. The Ghost Fluid Method and Riemann solvers were used to enforce appropriate interface boundary conditions. Improved performance in terms of computational work and convergence properties was achieved by modifying a local node sorting strategy that decouples ghost nodes, allowing us to set material boundary conditions via an explicit procedure, removing the need to solve a coupled system of equations numerically. The locality and explicit nature of the node sorting concept allows for greater levels of parallelism and lower computational cost when populating ghost nodes. Non-linear numerical issues endemic to the use of real Equations of State in hydro-codes were resolved by using more thermodynamically consistent forms allowing us to accurately resolve large density gradients associated with high energy detonation problems at material interfaces. Pre-computed volume tables were implemented adding to the robustness of the solver base.     

一种新的磁共振图像处理流水线的设计与实现

磁共振图像的重建、后处理及可视化是磁共振成像（MRI）系统的重要组成部分.本文开发了一个新的用于磁共振图像重建、后处理及可视化的开源框架YAP（Yet Another Pipeline），利用此框架可以方便地构建图像处理流水线.与现有的一些其他开源框架相比，本文开发的框架具有如下特点：（1）采用基于接口的设计，可使用基于接口的插件对流水线的功能进行扩展；（2）允许用户使用编写脚本的方式构建图像处理流水线，编辑与修改流水线都很方便；（3）支持带有分支结构的流水线，便于流水线的构建与调试.目前，该框架已经在商用系统中获得了应用.     

Compressively sampled light field reconstruction using orthogonal frequency selection and refinement

This paper considers the compressive sensing framework as a way of overcoming the spatio-angular trade-off inherent to light field acquisition devices. We present a novel method to reconstruct a full 4D light field from a sparse set of data samples or measurements. The approach relies on the assumption that sparse models in the 4D Fourier domain can efficiently represent light fields. The proposed algorithm reconstructs light fields by selecting the frequencies of the Fourier basis functions that best approximate the available samples in 4D hyper-blocks. The performance of the reconstruction algorithm is further improved by enforcing orthogonality of the approximation residue at each iteration, i.e. for each selected basis function. Since sparsity is better preserved in the continuous Fourier domain, we propose to refine the selected frequencies by searching for neighboring non-integer frequency values. Experiments show that the proposed algorithm yields performance improvements of more than 1 dB compared to state-of-the-art compressive light field reconstruction methods. The frequency refinement step also significantly enhances the visual quality of reconstruction results of our method by a 1.8 dB average.     

Highly sensitive and selective detect of p‐arsanilic acid with a new water‐stable europium metal–organic framework

The p‐arsanilic acid (p‐ASA), as an aromatic organoarsenic compounds, had received considerable concerns for their potential toxicity and carcinogenic properties. It was essential to detect p‐ASA with a facile method. In this paper, an europium based fluorescent metal–organic framework (MOF) [Eu₂(clhex)·2H₂O)]·H₂O ( BUC‐69 ) was successfully prepared under hydrothermal conditions with 1,2,3,4,5,6‐cyclohexanehexacarboxylic acid (H₆clhex) as organic linker. BUC‐69 displayed superior fluorescence capability to achieve selective and sensitive detection toward p‐ASA in water, which presented the first example of a MOF‐based sensor to detect p‐ASA. BUC‐69 showed excellent chemical stability in solutions under pH ranging from 4 to 12, which makes it be a potential sensor both in acidity and alkalinity condition. Significantly, BUC‐69 performed well in fluorescent sensing of p‐ASA at a low concentration (10^?6 M) in the simulated wastewater prepared with real lake water, and the results were comparable to the values detected by Inductively Coupled Plasma Optical Emission Spectrometer (ICP‐OES). The corresponding mechanism of fluorescent sensing toward p‐ASA with BUC‐69 was proposed and affirmed.