Magnetism in FeNiW disordered alloys: Experiment and theory

We report here an experimental study of magnetization of FeNiW alloys at different compositions. We have studied variation of magnetization with temperature (at low external fields) and magnetic field (at low temperatures). The alloy shows para to ferromagnetic transitions across the composition range. We do not find any indication of the spin-glass phase. We have supplemented the experimental work with theoretical analysis using the first-principles tight-binding linear muffin-tin orbitals based augmented space recursion method. Our theoretical estimates of magnetic moment and Curie temperatures agree well with experiment. Our mean-field phase analysis also does not indicate the possibility of a spin-glass phase.     

High-temperature hysteresis and magnetization reversal in nanocomposite FeCo+MnO

The hysteretic behavior of mechanically alloyed nanocomposites FeCo+MnO was studied at high temperatures. These composites present an unusual high and thermally stable coercivity, compared to FeCo milled at equal conditions. Coercivity enhancement was observed in hysteresis loops obtained between room temperature and 750 K. It is attributed to the isolation of the FeCo ferromagnetic particles by the paramagnetic MnO (T_N=120 K). The M_rev(M_irr)_H curves are clearly linear for the composite, indicating that coherent rotation is the reversal mechanism in these materials.     

Structural,magnetic and optical properties of diluted magnetic semiconductor (DMS) phase of Ni modified CuO nanoparticles

Control on the size of copper oxide (CuO) in the nano range is a highly motivating approach to study its multifunctional nature. The present investigation reports a sol-gel derived Ni doped CuO nanoparticles (Cu_1-xNi_xO). Rietveld refinement of the XRD spectra confirms the formation of single monoclinic phase of Cu_1-xNi_xO nanoparticles having crystallite size within the range of 19–21 nm. Raman spectra show the presence of characteristics Raman active modes and vibrational bands in the Cu_1-xNi_xO samples that corroborate the monoclinic phase of the samples as revealed by refinement of XRD data. The estimated band gap of pure CuO is found to be ∼1.43 eV, which decreases with the increase of dopant concentration into CuO matrix. This result is in line with estimated crystallite size. Magnetization curves confirm the weak ferromagnetic nature of Cu_1-xNi_xO nanoparticles which reveal the DMS phase. This weak magnetic nature may be induced in the samples due to the exchange interaction between the localized magnetic d-spins of Ni ions and carriers (holes or electrons) from the valence band of pristine CuO lattice. Replacement of Cu⁺² by Ni⁺² ions into the host CuO lattice induces the magnetization. The quantified value of squareness ratio (S < 0.5) confirms the inter-grain magnetic interactions in the Cu_1-xNi_xO nanoparticles which is also the reason of weak induced magnetization.     

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.     

基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

一维金属光栅的光学反射吸收

利用RCWA(rigid coupled-wave analysis)方法研究了一维金属光栅的反射特性,考察了 瑞利反常、表面等离激元驻波共振和几何共振三种共振吸收机理,分析了这三种机理的相互作用,如表面等离激元驻波共振和几何共振可以形成混合模式. 在反射式复合金属光栅中,确认了第四种共振形式,即相位共振. 数值计算表明相位共振对光学吸收的影响有两种形式: 当光栅周期大于一个波长时,相位共振导致尖锐的吸收峰,峰位在几何共振吸收峰一侧;当光栅周期小于一个波长时,相位共振导致混合模式的共振吸收峰发生劈裂. 对一维金属光栅反射特性的研究增加了对金属光栅共振吸收模式及其相互作用的认识. 关键词： 一维金属光栅 瑞利反常 表面等离激元 相位共振     

The magnetic properties of one-dimensional spin-1 ferromagnetic Heisenberg model in a magnetic field within Callen approximation

The effect of magnetic field h on the magnetic properties of the one-dimensional spin-1 ferromagnetic Heisenberg model is studied by the double-time Green’s function method. The magnetization and susceptibility are obtained within the Callen approximation. The zero-field susceptibility is as a decreasing function of the temperature T. The magnetization m increases in the whole field region, but the susceptibility maximum χ(T_m) decreases. The position T_m of the susceptibility maximum is both solved analytically and fits well to be a power law T_m∼h^γ at low fields and to be linear increasing at high fields. The height χ(T_m) decreases as a power law χ(T_m)∼h^−β with h increasing. The exponents (γ,β) obtained in our results agree with the other theoretical results. Our results are roughly in agreement with the results obtained in the experiment of Ni(OH)(NO₃)H₂O.     

Study of domain structure and magnetization reversal after thermal treatments in Fe40Co38Mo4B18 microwires

We have studied the effect of thermal treatment on the magnetic domain structure and magnetic reversal process of amorphous and nanocrystalline Fe₄₀Co₃₈Mo₄B₁₈ microwires. The domain structure and the magnetization reversal of amorphous FeCoMoB microwires reflect the complex stress distribution introduced by the glass coating. Hence, the thickness of radial domain structure decreases with temperature and the temperature dependence of the switching field presents a discontinuous behavior. After nanocrystallization, the domain structure of FeCoMoB microwire is almost constant within the temperature range 10-400 K and the switching field decreases almost linearly with temperature mostly because of the decrease of saturation magnetization.     

A new ferromagnetic hysteresis model for soft magnetic composite materials

A new ferromagnetic hysteresis model for soft magnetic composite materials based on their specific properties is presented. The model relies on definition of new anhysteretic magnetization based on the Cauchy-Lorentz distribution describing the maximum energy state of magnetic moments in material. Specific properties of soft magnetic composite materials (SMC) such as the presence of the bonding material, different sizes and shapes of the Fe particles, level of homogeneity of the Fe particles at the end of the SMC product treatment, and achieved overall material density during compression, are incorporated in both the anhysteretic differential magnetization susceptibility and the irreversible differential magnetization susceptibility. Together they form the total differential magnetization susceptibility that defines the new ferromagnetic hysteresis model. Genetic algorithms are used to determine the optimal values of the proposed model parameters. The simulated results show good agreement with the measured results.