A generalized autocovariance least-squares method for Kalman filter tuning

This paper discusses a method for estimating noise covariances from process data. In linear stochastic state-space representations the true noise covariances are generally unknown in practical applications. Using estimated covariances a Kalman filter can be tuned in order to increase the accuracy of the state estimates. There is a linear relationship between covariances and autocovariance. Therefore, the covariance estimation problem can be stated as a least-squares problem, which can be solved as a symmetric semidefinite least-squares problem. This problem is convex and can be solved efficiently by interior-point methods. A numerical algorithm for solving the symmetric is able to handle systems with mutually correlated process noise and measurement noise.     

Grain size effects on cyclic fatigue and crack-growth resistance behaviour of partially stabilized zirconia

Cyclic fatigue crack growth and crack-resistance behaviour was studied in partially stabilized zirconia (PSZ) with three different cubic-phase grain sizes following sub-eutectoid heat treatments. Raman spectroscopy was used to determine the extent of phase transformation around the cracks for both cyclic and monotonic loading conditions. All tests were on long, through thickness cracks using compact-tension specimens. Predictions of crack-tip shielding were made following determination of toughening parameters using crackresistance data. It was found that the dominant factors affecting cyclic fatigue-crack growth were the level of crack-tip shielding, as a result of phase transformation, and the intrinsic toughness of the material. Grain size did not appear to significantly affect fatigue crack-growth behaviour.     

基于SURF-HOG与显著性特征的红外可见光图像配准融合北大核心CSCD

针对现有红外与可见光图像配准不精确,边缘及细节纹理缺失,融合时间较长,不能突出重点目标等不足,提出一种基于SURF-HOG描述符与红外显著性特征的红外与可见光图像融合方法。首先,在红外与可见光图像配准阶段,在SURF(Speed-Up Robust Features,SURF)框架内构建基于HOG(Histogram of Oriented Gradient,HOG)的特征点描述符,并通过NNDR(Nearest Neighbor Distance Ratio,NNDR)进行红外与可见光图像的特征点匹配;其次,在显著特征提取阶段,先通过四叉树算法对源红外图像分解,然后通过贝塞尔插值法重建红外图像背景,接着分别对红外图像中的背景及目标进行自适应抑制以提取目标红外显著性特征;最后,结合已配准的可见光图像与重建后的红外图像以获取最终融合结果。实验结果表明,所提方法对不同场景下的红外与可见光图像具有较高的配准精度,不同场景下的融合结果不但主观视觉上具有显著的目标特征,同时背景纹理和边缘细节清晰,整体对比度适宜,运行时间最短,并且在客观评价指标上也取得了较好的效果。     

中国电力中长期市场分时段交易价格形成机制及模型

电力中长期市场分时段交易是中国电力市场改革的重要举措之一。针对当前电力中长期市场分时段交易价格形成机制不合理、不完善的问题,提出了中国电力中长期市场分时段交易价格形成机制及其模型。首先,分析影响中国电力中长期分时段交易价格形成的因素,提出电力中长期交易的4种典型场景;然后,综合考虑生产成本和用户效用,探索性地提出适用于中长期分时段交易的系统平均成本定价、用户失负荷价值定价、系统边际成本定价和发电企业失负荷价值定价4种成本定价机制,构建了基于成本定价的分时段交易定价模型;最后,以某省实际数据为例进行测算,验证了模型的有效性。     

Comprehensively Strengthened Metal-Oxygen Bonds for Reversible Anionic Redox Reaction

Introducing anionic redox in layered oxides is an effective approach to breaking the capacity limit of conventional cationic redox. However, the anionic redox reaction generally suffers from excessive oxidation of lattice oxygen to O₂ and O₂ release, resulting in local structural deterioration and rapid capacity/voltage decay. Here, a Na_0.71Li_0.22Al_0.05Mn_0.73O₂ (NLAM) cathode material is developed by introducing Al³⁺ into the transition metal (TM) sites. Thanks to the strong Al–O bonding strength and small Al³⁺ radius, the TMO₂ skeleton and the holistic TM–O bonds in NLAM are comprehensively strengthened, which inhibits the excessive lattice oxygen oxidation. The obtained NLAM exhibits a high reversible capacity of 194.4 mAh g^-1 at 20 mA g^-1 and decent cyclability with 98.6% capacity retention over 200 cycles at 200 mA g⁻¹. In situ characterizations reveal that the NLAM experiences phase transitions with an intermediate OP4 phase during the charge–discharge. Theoretical calculations further confirm that the Al substitution strategy is beneficial for improving the overlap between Mn 3d and O 2p orbitals. This finding sheds light on the design of layered oxide cathodes with highly reversible anionic redox for sodium storage.     

Unveiling the Synergy of Interfacial Contact and Defects in α-Fe2O3 for Enhanced Photo-Electrochemical Water Splitting

Photo-electrochemical (PEC) water splitting is a promising method for converting solar energy into clean energy, but the mechanism of improving PEC efficiency through the interfacial contact and defect strategy remains highly controversial. Herein, reduced graphene oxide (rGO) and oxygen vacancies are introduced into α-Fe₂O₃ nanorod (NR) arrays using a simple spin-coating method and acid treatment. The resultant oxygen vacancy–α-Fe₂O₃/rGO-integrated system exhibits a higher photocurrent, four times than the pristine α-Fe₂O₃. It is well evidenced that the electronic interface interaction between α-Fe₂O₃ and rGO is boosted with the oxygen vacancies, facilitating electron transfer from α-Fe₂O₃ to rGO. Moreover, the oxygen vacancies not only create interband states in α-Fe₂O₃ that can trap photogenerated holes and thus facilitate charge separation but significantly also strengthen the adsorption of oxidative intermediates and reduce the energy barrier of rate-determining step during oxygen evolution reaction (OER). This study demonstrates an rGO–oxygen vacancy synergistic interfacial contact and defect modification approach to design semiconducting photocatalysts for high-efficiency solar energy capture and conversion. The generated principle is expected to be extendable to another material system.     

基于平面电容的油水多界面检测方法研究

含水量是衡量原油质量的重要指标,在原油的生产和储运过程中油水混合液分界面不断变化,因此全过程都要用到高精度传感器对其进行界面检测。基于电容边缘效应设计了一种侵入式平面电容传感器,其主要结构由基板和平面电极阵列两部分组成。运用有限元软件建立8电极阵列传感器模型,对不同电极工作时的电场分布进行研究,分析了平面电容传感器的检测灵敏度和成像精度。并且,研究了电极宽度、长度和相邻间距对传感器灵敏场分布的影响。对介电分布进行图像重建,使设计的平面电容阵列传感器可以检测3个分界面的高度,且经过尺寸参数优化,提高了传感器的成像精度。实验证明,运用平面电容阵列检测油水界面的方法具有可行性和有效性。     

Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.     

Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems

This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.

     

Mesoporous NiS2 Nanospheres Anode with Pseudocapacitance for High‐Rate and Long‐Life Sodium‐Ion Battery

It is of great importance to exploit electrode materials for sodium‐ion batteries (SIBs) with low cost, long life, and high‐rate capability. However, achieving quick charge and high power density is still a major challenge for most SIBs electrodes because of the sluggish sodiation kinetics. Herein, uniform and mesoporous NiS₂ nanospheres are synthesized via a facile one‐step polyvinylpyrrolidone assisted method. By controlling the voltage window, the mesoporous NiS₂ nanospheres present excellent electrochemical performance in SIBs. It delivers a high reversible specific capacity of 692 mA h g^?1. The NiS₂ anode also exhibits excellent high‐rate capability (253 mA h g^?1 at 5 A g^?1) and long‐term cycling performance (319 mA h g^?1 capacity remained even after 1000 cycles at 0.5 A g^?1). A dominant pseudocapacitance contribution is identified and verified by kinetics analysis. In addition, the amorphization and conversion reactions during the electrochemical process of the mesoporous NiS₂ nanospheres is also investigated by in situ X‐ray diffraction. The impressive electrochemical performance reveals that the NiS₂ offers great potential toward the development of next generation large scale energy storage.