The isothermal reduction kinetics of chromium-bearing vanadium–titanium magnetite sinter

The reduction of chromium-bearing vanadium–titanium magnetite sinter (CVTMS) by CO was investigated at 1123–1223?K. The reduction degree increased with increasing temperature. The isothermal reduction kinetics of CVTMS was analysed, according to Sharp analysis and ln–ln analysis, the kinetic mechanism of reduction process for all samples in different basicity can be represented as f(α)?=?1.61(1?α)[?ln(1?α)]^1–1/1.61. The reaction activation energy of all samples (R?=?1.9, 2.1, 2.3, obtained according to the components of the materials burdening used in sintering process) at different reaction degrees were calculated by the model-free method. And the pre-exponential factors of reaction also were calculated by the mathematical method. The rate controlling step for the reduction process under the present reduction condition was chemical reaction.     

Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

Fault isolation is known to be a challenging problem in machinery troubleshooting. It is not only because the isolation of multiple faults contains considerable number of uncertainties due to the strong correlation and coupling between different faults, but often massive prior knowledge is needed as well. This paper presents a Bayesian network-based approach for fault isolation in the presence of the uncertainties. Various faults and symptoms are parameterized using state variables, or the so-called nodes in Bayesian networks (BNs). Probabilistically causality between a fault and a symptom and its quantization are described respectively by a directed edge and conditional probability. To reduce the qualitative and quantitative knowledge needed, particular considerations are given to the simplification of Bayesian networks structures and conditional probability expressions using rough sets and noisy-OR/MAX model, respectively. By adopting the simplified approach, symptoms under multiple-fault are decoupled into the ones under every single fault, while the quantity of the conditional probabilities is simplified into the linear form of the faults quantity. Prior knowledge needed in Bayesian network-based diagnostic model is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model. The computational efficiency is improved accordingly in the simplified BN model, after eliminating the redundant symptoms. The fault isolation methodology is illustrated through an example of diesel engine fuel injection system to verify the developed model.     

固体碳直接还原钢铁厂含锌固废的理论与实践

 钢铁厂内的含锌固废数量巨大，同时富含铁、碳等元素，是可循环再利用的二次资源。通过固体碳直接还原ZnO和FeO的热力学分析，发现在非标准状态下，ZnO的还原条件要优于FeO。为此，在实验室条件下设计了含锌固废在不同温度和时间下的还原试验，发现在还原温度为1 423 K、还原时间为15 min以上时，脱锌率可达到90%，而铁的金属化率需要在1 573 K时才能接近90%；在还原温度低于1 423 K时，还原时间对脱锌率的影响较大；在还原温度高于1 473 K时，还原温度对脱锌率影响较小，同时脱锌率可达到95%。这些结果对国内当前处理含锌固废主要工艺(回转窑、转底炉)的操作制度设置具有重要的参考价值，也为如何选择合理的工艺路线，以低投入、低运营成本实现含锌固废的再利用提供了理论依据。     

Evolution of properties of parts during MIM and sintering of recycled oxide particles

Metal injection moulding (MIM) is an established process for high volume production of complex shaped metallic parts using commercially available feedstocks. The characteristics of parts after moulding, debinding, and sintering cannot be simply predictable from raw materials because the properties get altered with the process parameters and the corresponding levels of porosity during processing steps. In this study, physical properties, microstructure, and mechanical properties of the MIM parts have been characterised to understand the evolution of strength during various steps in MIM processing. Feedstocks with different binder loading show a considerable difference in physical as well as mechanical characteristics. During sintering of parts which have solid loading of grinding sludge, simultaneous in situ reduction and densification takes place, whereas only densification occurs in carbonyl iron parts. It is, therefore, possible to make complex shaped parts of different levels of porosity from downgraded shop floor metallic waste.     

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.     

A practical data-flow verification scheme for business processes

Data in business processes becomes more and more important. Current standard languages for process modeling like BPMN 2.0 which include the data flow reflect this. Ensuring the correctness of the data flow in processes is challenging. Model checking, i.e., verifying properties of process models, is a well-known technique to this end. An important part of model checking is the construction of the state space of the model. However, state-space explosion typically is in the way of an effective verification. We study how to overcome this problem in our context by means of reduction. More specifically, we propose a reduction on the level of the process model. To our knowledge, this is new for the data-flow analysis of processes. The core of our approach are so-called regions of the process model that are relevant for the verification of properties describing the data flow. Non-relevant regions are candidates for reduction of the process model, yielding a smaller state space. Our evaluation shows that our approach works well on industrial process models.     

Self‐similarity analysis of vehicle driver's electrodermal activity

This paper characterizes stress levels via a self‐similarity analysis of the electrodermal activity (EDA) collected in a real‐world driving context. To characterize the EDA richness over scales, the fractional Brownian motion (FBM) process and its corresponding exponent H, estimated via a wavelet‐based approach, are used. Specifically, an automatic scale range selection is proposed in order to detect the linearity in a log scale diagram. The procedure is applied to the EDA signals, from the open database drivedb, originally captured on the foot and the hand of the drivers during a real‐world driving experiment, designed to evoke different levels of arousal and stress. The estimated Hurst exponent H offers a distinction in stress levels when driving in highway versus city, with a reference to restful state of minimal stress level. Specifically, the estimated H values tend to decrease when the driving environmental complexity increases. In addition, the estimated H values on the foot EDA signals allow a better characterization of the driving task than that of hand EDA. The self‐similarity analysis was applied to various physiological signals in literature but not to the EDA so far, a signal which was found to correlate most with human affect. The proposed analysis could be useful in real‐time monitoring of stress levels in urban driving spaces, among other applications.     

Tunable Fe/N co-doped 3D porous graphene with high density Fe-Nx sites as the efficient bifunctional oxygen electrocatalyst for Zn-air batteries

Nitrogen-doped transition metal materials display promising potential as bifunctional electrocatalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Herein, Fe/N co-doped three-dimensional (3D) porous graphene (FeN-3D-PG) is prepared via a template method using sodium alginate as the carbon source and low polymerization degree melamine resin as the nitrogen source. The low polymerization degree melamine resin can form complexes with Fe³⁺ in the aqueous solution and further forms high density Fe-N_x active sites during pyrolysis. Meanwhile, the formed 3D porous structure efficiently promotes the uniform distribution of Fe-N_x active sites. The FeN-3D-PG catalyst exhibits pH-independent ORR activity. For OER, the catalyst possesses a low over potential (370 mV at 10 mA cm⁻²) in alkaline electrolyte. The Zn-air batteries (ZABs) using FeN-3D-PG as cathode exhibits a power density up to 212 mW cm⁻², a high specific capacity of 651 mAh g⁻¹, and the charge-discharge stability of 80 h. This work provides new sight to transition metal materials based ZABs with excellent performance.     

Template-assisted polymerization-pyrolysis derived mesoporous carbon anchored with Fe/Fe3C and Fe−NX species as efficient oxygen reduction catalysts for Zn-air battery

Constructing highly efficient and durable non-noble metal modified carbon catalysts for oxygen reduction reaction (ORR) in the whole pH range is essential for energy conversion devices but still remains a challenge. Herein, the Fe/Fe₃C nanoparticles and Fe-N_X species anchored on the interconnected mesoporous carbon materials are fabricated through an economical and facile template-assisted polymerization-pyrolysis strategy. The catalyst exhibits unique features with the electronic interaction between Fe/Fe₃C and Fe−N_X, large specific surface area and high mesoporous structure as well as nitrogen doping in porous carbon skeletons, which can effectively catalyze ORR over the full pH range. In an alkaline electrolyte, the optimized catalyst displays favorable ORR performance with positive onset potential (E_onset = 0.91 V), half-wave potential (E_1/2 = 0.83 V), long-term cycles stability and methanol tolerance, exceeding those for the commercial Pt/C. Furthermore, the prepared catalyst could be directly assembled into the alkaline Zn−air battery that exhibits the open-circuit voltage of 1.44 V, high power density of 96.0 mW cm⁻² and long-term durability. Therefore, the template-assisted polymerization-pyrolysis strategy provides a promising route for designing high-performance non-noble metal decorated ORR electrocatalysts.     

基于Marr小波改进的SIFT算法的遥感影像配准

〖HTH〗通讯作者〖HTSS〗:[ZK（]金〓燕(1991-)，女，山东济宁人，硕士研究生，主要从事图形图像处理，图像与视觉信息计算方面的研究。E\|mail:jyan0529@163.com。[ZK）] 〖ZW)〗〖HT〗 〖AM〗〖HT5SS〗〖MM(〗〖ZZ(S〗〖HT5”〗〖SX(B〗第34卷〓第3期〖〗2019年6月〖SX)〗[KG0.2mm]〖KG7*3〗〖HT〗〖SX(B〗遥〓感〓技〓术〓与〓应〓用〖〗〖WT5，6〗REMOTE SENSING TECHNOLOGY AND APPLICATION〖SX)〗〓〓〓〓〖KG6*2〗〖WT5”BX〗〖SX(B〗Vol.34〓No.3〖〗Jun.2019〖WT〗〖SX)〗〖ZZ)〗〖MM)〗〖HT〗 〖HT2H〗〖JZ(〗〖WTHZ〗〖STHZ〗 基于Marr小波改进的SIFT算法的遥感影像配准 〖STBZ〗〖WTBZ〗〖HT4K〗 张海涛，金〓燕，刘万军 〖HT5K〗 (辽宁工程技术大学 软件学院，辽宁 葫芦岛〓125105) 〖JZ)〗〖ＨＴ５Ｈ〗〖GK2!2〗摘要〖HTK〗: [KG(0.1mm]针对遥感图像配准方法中错误匹配点对过多、配准效率低和其他性能，提出了一种基于小波的遥感图像配准方法。首先，利用尺度空间理论下的Marr小波对参考图像和待配准图像进行特征提取，然后利用欧氏距离对参考图像和待配准图像的特征点进行初配准，再根据随机采样一致法，对初配准结果进行精配准。为了验证方法的有效性，选择无人机实时航拍图像、不同时相变化遥感图像以及遥感不同高度的遥感图像。实验结果表明：该方法与SIFT(Scale Invariant Feature Transform)算法以及其他改进SIFT算法相比可以有效剔除错误匹配点对，提高了配准精度，同时提高配准效率两倍以上。该方法可以应用于不同遥感数据源，能够有效地提高配准精度，降低配准时间。[KG)] 〖ＨＴＨ〗关〓键〓词〖ＨＴ５Ｋ〗: 遥感图像配准;Marr小波;欧氏距离;随机采样一致法 〖ＨＴＨ〗中图分类号〖HTSS〗:TP79〓〓〖HTH〗文献标志码〖HTSS〗:A〓〓〖HTH〗文章编号〖HTSS〗:1004 0323(2019)03 0622 08 〖HK〗〖HT5SS〗