Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

Maximization of quadruple phase boundary for alkaline membrane fuel cell using non-stoichiometric α-MnO2 as cathode catalyst

Oxygen can only be reduced at the quadruple phase boundary (catalyst, carbon support, ionomer and oxygen) of the cathode catalyst layer with non-conducting electrocatalyst. To maximize the quadruple phase boundary sites is crucial to increase the peak power density of each membrane electrode assembly. The quadruple phase boundary is depending on the ratio of catalyst, carbon support and ionomer. The loading of catalyst layer is also crucial to the fuel cell performance. In this study, non-stoichiometric α-MnO₂ manganese dioxide nanorod material has been synthesized and the ratios of carbon, ionomer and catalyst loadings were optimized in alkaline membrane fuel cell. In total, ten membrane electrode assemblies have been manufactured and tested. Taguchi design method has been applied in order to understand the effect of each parameter. The conclusion finds out the ionomer has more influence on the alkaline membrane fuel cell peak power performance than carbon and loading.     

求解约束优化问题的自适应人工蜂群算法

针对约束优化问题，提出一种自适应人工蜂群算法。算法采用反学习初始化方法使初始种群均匀分布于搜索空间。为了平衡搜索过程中可行个体和不可行个体的数量，算法使用自适应选择策略。在跟随蜂阶段，采用最优引导搜索方程来增强算法的开采能力。通过对13个标准测试问题进行实验并与其他算法比较，发现自适应人工蜂群算法具有较强的寻优能力和较好的稳定性。     

车门铰链系统与车门下沉刚度的相关性

针对某型车门下沉问题,通过台架试验获得车门、铰链和车身等各单因素下沉量和车门绞链系统整体下沉量,对单因素下沉量与系统整体下沉刚度进行线性拟合分析,得到车门铰链系统各单因素与系统下沉刚度的相关度排序.对前、后车门分别选取相关度较高的单因素进行优化,最终改进方案的仿真和试验结果证明该方案可有效地提升车门下沉刚度.采用定量分析法可快速找出影响下沉刚度的敏感因素,并能够快速生成优化方案,为新车型设计提供参考.     

基于PMU的电力系统监控体系结构优化设计

文中提出了一种利用有限数量的相量测量单元(PMU)和相量数据集中器(PDC)设计最优监控结构的方法。通过在大量的设定值场景下,使电力系统可观测性曲线的期望值最大化,同时使通信基础设施成本最小化,最终确定PMU和PDC的最佳位置。提出了一种非线性动态扩展卡尔曼滤波(EFK)状态观测器。这种状态观测器可以将暂态行为转换为由代数微分方程描述的广域电力系统,而无需非线性反演技术。最后以IEEE-5电力系统为例,说明了该方法的有效性。     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

三维颗粒物质的能量最小化模拟方法

颗粒物质是大量宏观颗粒的集合,广泛存在于自然界,日常生活和工业生产中.颗粒物质的运动表现出非常复杂的现象,如堵塞(jamming)等.颗粒物质体系的定量研究仍是一个巨大的挑战.本文采用能量最小化方法对颗粒物质进行准静态模拟.对一些典型的非线性优化方法,如共轭梯度法,拟牛顿法等,通过预条件来提高这些方法的效率.这些预条件方法基于颗粒体系的几何结构和能量函数来构建.通过对一些典型的准静态过程,如压缩和剪切试验的数值模拟,观察到对三维颗粒体系,预条件方法可以有40%左右的效率提升.     

Targeting S1PRs as a Therapeutic Strategy for Inflammatory Bone Loss Diseases—Beyond Regulating S1P Signaling

As G protein coupled receptors, sphingosine-1-phosphate receptors (S1PRs) have recently gained attention for their role in modulating inflammatory bone loss diseases. Notably, in murine studies inhibiting S1PR2 by its specific inhibitor, JTE013, alleviated osteoporosis induced by RANKL and attenuated periodontal alveolar bone loss induced by oral bacterial inflammation. Treatment with a multiple S1PRs modulator, FTY720, also suppressed ovariectomy-induced osteoporosis, collagen or adjuvant-induced arthritis, and apical periodontitis in mice. However, most previous studies and reviews have focused mainly on how S1PRs manipulate S1P signaling pathways, subsequently affecting various diseases. In this review, we summarize the underlying mechanisms associated with JTE013 and FTY720 in modulating inflammatory cytokine release, cell chemotaxis, and osteoclastogenesis, subsequently influencing inflammatory bone loss diseases. Studies from our group and from other labs indicate that S1PRs not only control S1P signaling, they also regulate signaling pathways induced by other stimuli, including bacteria, lipopolysaccharide (LPS), bile acid, receptor activator of nuclear factor κB ligand (RANKL), IL-6, and vitamin D. JTE013 and FTY720 alleviate inflammatory bone loss by decreasing the production of inflammatory cytokines and chemokines, reducing chemotaxis of inflammatory cells from blood circulation to bone and soft tissues, and suppressing RANKL-induced osteoclast formation.     

Thermodynamic modelling of the ternary Bi-Ga-Te system for potential application in thermoelectric materials

Thermoelectric materials have drawn widespread attention because they can enable the direct conversion between electric and thermal energy. Over the years, different materials such as skutterudites, clathrates, intermetallic alloys, eutectic alloys, chalcogenides have been explored for Thermoelectric (TE) applications. Amongst the eutectic alloys, the Bi-Ga-Te system exhibits promising potential as a TE material. Accordingly, in this study, we performed the thermodynamic optimization and critical evaluation of binary Bi–Ga, Bi–Te, Ga–Te, and ternary Bi-Ga-Te systems using the CALPHAD method. It is observed that the Ga–Te system shows asymmetric liquid solution properties with strong negative enthalpy of mixing, whereas the Bi–Te liquid exhibits the symmetric regular solution behavior. Moreover, the Bi–Ga liquid solution has a positive enthalpy of mixing. Therefore, Modified Quasichemical Model (MQM) using pair approximation was utilized to describe the diversified thermodynamic properties of liquid solution in sub-binaries by taking into account the Short-Range Ordering (SRO). By merging the binary optimization results with a proper interpolation method, the liquid solution properties and phase diagram information in the Bi-Ga-Te ternary system were also reproduced successfully without any adjustable ternary parameter. Several ternary eutectic compositions were suggested for designing TE alloy with enhanced properties using the developed database.     

Discharge coefficient for vertical sluice gate under submerged condition using contraction and energy loss coefficients

A novel method is suggested for the determination of flow discharge in vertical sluice gates with considerably small bias. First, in order to derive an equation for the discharge coefficient, energy-momentum equations are implemented to define the physical realization of the phenomenon. Afterward, the discharge coefficient is presented in terms of contraction and energy loss coefficients. Subsequently, discharge coefficient, contraction, and energy loss coefficients were determined through an implicit optimization technique on the data. Data analysis illustrated that there is a meaningful power relationship between the contraction and energy loss coefficients. Thereafter, dimensional analysis is performed and an explicit best-fit regression equation is developed for defining the energy loss coefficient. The obtained equations for contraction and energy loss coefficients were then used in the computation of the discharge coefficient and determination of the flow discharge in the vertical sluice gate. The performance of the developed approach is validated against the selected benchmarks existing in the literature.