An integrated method based on energy concentration for evaluating normally distributed spectra in the visible region

This work proposes a method to scientifically quantify the quality of normally distributed visible spectra from the viewpoint of energy concentration: high transmittance, narrow spectral width, and small deviation from the target wavelength in the target region. In the proposed method, the spectral width of the spectrum is compared with that of a reference spectrum before its deviation from the target wavelength is examined. After extracting the maximum transmittance, a performance index of the spectrum is obtained in a percentile value. The effectiveness of the proposed method was analytically proven by surface plasmon resonance-generated spectra with five design features and five variations in each feature. The results indicated that the proposed method not only substantially reflected the predictions made by intuitive visual inspections, but also avoided misleading and ambiguous results evaluated by existing full-width-at-half-maximum (FWHM) method, figure-of-merit (FOM) method, and coordinates on the chromaticity diagram. In addition to the numerical analysis, the experimentally obtained spectra of phosphors in white light emitting diodes were also evaluated. The results proved that the proposed method can successfully highlight the scaled performance difference among the spectra, which is not supported by existing FWHM and FOM methods by simultaneously considering the aforementioned three characteristics of a spectrum.     

Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework

As one of the representative unsupervised data augmentation methods, generative adversarial networks (GANs) have the potential to solve the problem of insufficient samples in fault diagnosis of rotating machinery. However, the existing unsupervised GANs are usually incapable of simultaneously generating multi-mode fault samples and have some shortcomings such as mode collapse and gradient vanishing. To overcome these deficiencies, a supervised model called modified auxiliary classifier GAN (MACGAN) designed with new framework is proposed in this paper. Firstly, a new ACGAN framework is developed by adding an independent classifier to improve the compatibility between the classification and discrimination. Secondly, the Wasserstein distance is introduced in the new loss functions to overcome mode collapse and gradient vanishing. Finally, to achieve stable training, a spectral normalization is used to replace the weight clipping to constrain the weight parameters of discriminator. The proposed method is applied to fault diagnosis of bearing and gear. Compared with the existing GANs, the proposed method can more efficiently generate multi-mode fault samples with higher qualities, which can be used to assist the training of deep learning-based fault diagnosis models with high accuracy and good stability.     

轧机传动系统关键零部件疲劳仿真方法研究

轧机传动系统的是轧机的重要组件,其强度直接影响到轧机的正常运行。针对轧机传动系统疲劳损伤问题,分析了载荷谱构成,认为载荷来源主要是轧制冲击载荷与扭振载荷;讨论了轧机多工况处理方法,提出了一种基于多体动力学仿真提取载荷放大系数(TAF),利用Matlab软件编制扭振载荷谱及仿真疲劳载荷谱的一般流程;以某钢铁集团现役轧机为例,基于Fatigue Porcess进行疲劳仿真分析,结果证明了该方法的可行性,为轧机传动系统关键零部件或相似机械产品的疲劳损伤位置和疲劳寿命的预测提供了参考。     

基于动态频谱接入的载波聚合技术研究与实现

以LTE-Advanced系统为研究背景,提出了一种基于动态频谱接入技术的载波聚合方案(DSA-CA)。该方案的主要思想是使支持载波聚合技术的eNB根据小区的平均负载自适应地调节聚合的载波数量,从而达到合理利用频谱的目的。通过对基于动态频谱接入技术的载波聚合方案在LTE-Advanced 系统中的性能进行仿真研究,结果表明:DSA-CA方案与传统载波聚合方案(CA)相比,可使系统性能在一定程度上得到提高,尤其当一个小区处于高负载、另一个小区处于低负载时,DSA-CA方案的优势更加明显,此时高负载小区系统吞吐量得到明显提高,低负载小区的频谱效率也显著上升。     

Stochastic characterization of the spectrum sharing game in ad-hoc networks

This work focuses on infrastructure-less ad hoc wireless networks where multiple transmitter/receiver pairs share the same radio resources (spectrum); transmitters have to choose how to split a total power budget across orthogonal spectrum bands with the goal to maximize their sum rate under cumulative interference from concurrent transmissions. We start off by introducing and characterizing the non-cooperative game among transmitter/receiver pairs when the network topology is deterministically given. The corresponding Nash equilibria are derived, highlighting their dependency on the topological parameters (distances between wireless nodes, propagation model, and background noise power). The analysis is then extended to the case of random network topologies drawn from a given spatial stochastic process. Tools of stochastic geometry are leveraged to derive a statistical characterization of the equilibria of the spectrum sharing game. Finally, a distributed algorithm is proposed to let the players of the spectrum sharing game converge to equilibria conditions. Numerical simulations show that the proposed algorithm drives the users to stable points that are close to the equilibria of the game requiring limited information exchange among nodes.     

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

The present research proposes a combined framework that evaluates remaining capacity, material behavior, ions concentration of remaining metals, and current rate of chemical reactions of spent Li‐ion batteries accurately. Voltage, temperature, internal resistance, and capacity were studied during charging and discharging cycles. Genetic programming was applied on the obtained data to develop a model to predict remaining capacity. The results of experimental work and those estimated from model were found to be correlated, confirming the validation of model. Materials structure and electrochemical behavior of electrodes during cycles were studied by cyclic voltammetry, scanning electron microscopy, and energy dispersion spectrum.     

Enhanced fatigue behavior of a glass fiber reinforced hybrid particles modified epoxy nanocomposite under WISPERX spectrum load sequence

Two types of glass fiber reinforced plastic (GFRP) composites were fabricated viz., GFRP with neat epoxy matrix (GFRP-neat) and GFRP with hybrid modified epoxy matrix (GFRP-hybrid) containing 9 wt.% of rubber microparticles and 10 wt.% of silica nanoparticles. Fatigue tests were conducted on both the composites under WISPERX load sequence. The fatigue life of the GFRP-hybrid composite was about 4–5 times higher than that of GFRP-neat composite. The underlying mechanisms for improved fatigue performance are discussed. A reasonably good correlation was observed between the experimental fatigue life and the fatigue life predicted under spectrum loads.     

Cyclo-energy detector for spectrum sensing in cognitive radio

In this paper, a cyclo-energy detector is proposed for the spectrum sensing problem based on the cyclostationary signal analysis. The proposed detector can determine the existence of primary user by estimating the primary user's received power according to the cyclostationarity features of the secondary user. By using this detector, the secondary user does not need to stop transmitting signals when sensing the frequency spectrum. Simulation results are presented to confirm the performance of the proposed detector.     

Volume-based method for spectrum sensing

It is recently shown that algorithms derived from random matrix theory (RMT) can provide superior performance for spectrum sensing, which corresponds to the task of detecting the presence of primary users in cognitive radio. The essence of the RMT-based methods is to utilize the distribution of extremal eigenvalues of the received signal sample covariance matrix (SCM), namely, the Tracy–Widom (TW) distribution. Although the TW distribution is quite useful in spectrum sensing, computationally demanding numerical evaluation is required because it does not have an explicit closed-form expression. In this paper, we devise two novel volume-based detectors by exploiting the determinant of the SCM or volume to distinguish between the signal-presence and signal-absence cases. With the use of RMT, we accurately produce the theoretical decision threshold for one of the detectors under the Gaussian noise assumption. Simulation results are included to illustrate the effectiveness of the volume-based detectors.     

基于四元极谐变换矩与显著特征的图像鲁棒哈希算法

为了增强哈希序列对各种几何变换攻击的鲁棒性,设计基于四元极谐变换矩与显著特征的图像鲁棒哈希算法。引入线性插值与自适应Wiener滤波器,实现初始图像的预处理;计算预处理图像的颜色向量角度,并基于Fourier变换,得到其对应的幅度信息,以获取两个不同的频谱;计算两个频谱的残差,确定图像中的局部显著性区域;通过LBP算子,提取显著特征;基于四元极谐变换(Quaternion Polar Harmonic Transform,QPHT),获取预处理图像的QPHT矩;联合显著特征与QPHT矩,形成过渡哈希数组。引入Logistic映射,定义加密函数,实现对过渡哈希数组的加密,输出最终的哈希序列,以增强其抗碰撞性能。测量源图像与可疑图像之间的哈希序列所对应的l₂范数距离,并将其与优化阈值比较,对图像的真实性做出判断。在多种几何变换攻击下完成测试,输出数据显示:较当前准确性较高的哈希方法而言,该算法具有更理想的鲁棒性与识别准确率。