Construction of Pd-doped RuO_(2) nanosheets for efficient and stable acidic water oxidation

RuO_(2) has been considered a potential alternative to commercial IrO_(2) for the oxygen evolution reaction(OER)due to its superior intrinsic activity.However,its inherent structure dissolution in acidic environments restricts its commercial applications.In this study,we report a novel Pd-doped ruthenium oxide(Pd–RuO_(2))nanosheet catalyst that exhibits improved activity and stability through a synergistic effect of Pd modulation of Ru electronic structure and the two-dimensional structure.The catalyst exhibits excellent performance,achieving an overpotential of only 204 mVat a current density of 10 mA cm^(-2).Impressively,after undergoing 8000 cycles of cyclic voltammetry testing,the overpotential merely decreased by 5 mV.The PEM electrolyzer with Pd0.08Ru0.92O_(2) as an anode catalyst survived an almost 130 h operation at 200 mA cm^(-2).To elucidate the underlying mechanisms responsible for the enhanced stability,we conducted an X-ray photoelectron spectroscopy(XPS)analysis,which reveals that the electron transfer from Pd to Ru effectively circumvents the over-oxidation of Ru,thus playing a crucial role in enhancing the catalyst's stability.Furthermore,density functional theory(DFT)calculations provide compelling evidence that the introduction of Pd into RuO_(2) effectively modulates electron correlations and facilitates the electron transfer from Pd to Ru,thereby preventing the overoxidation of Ru.Additionally,the application of the two-dimensional structure effectively inhibited the aggregation and growth of nanoparticles,further bolstering the structural integrity of the catalyst.

Variational Reconstruction and Simulation Experiments of Sea Surface Wind Field for Ocean Data Buoy

The sea surface wind field is an important physical parameter in oceanography and meteorology.With the continuous refinement of numerical weather prediction,air-sea interface materials,energy exchange,and other studies,three-dimensional(3D)wind field distribution at local locations on the sea surface must be measured accurately.The current in-situ observation of sea surface wind parameters is mainly achieved through the installation of wind sensors on ocean data buoys.However,the results obtained from this single-point measurement method cannot reflect wind field distribution in a vertical direction above the sea surface.Thus,the present paper proposes a theoretical framework for the optimal inversion of the 3D wind field structure variation in the area where the buoy is located.The variation analysis method is first used to reconstruct the wind field distribution at different heights of the buoy,after which theoretical analysis verification and numerical simulation experiments are conducted.The results indicate that the use of variational methods to reconstruct 3D wind fields is significantly effective in eliminating disturbance errors in observations,which also verifies the correctness of the theoretical analysis of this method.The findings of this article can provide a reference for the layout optimization design of wind measuring instruments in buoy observation systems and also provide theoretical guidance for the design of new observation buoys in the future.

Distributed Control of Multiple-Bus Microgrid With Paralleled Distributed Generators

A microgrid is hard to control due to its reduced inertia and increased uncertainties. To overcome the challenges of microgrid control, advanced controllers need to be developed.In this paper, a distributed, two-level, communication-economic control scheme is presented for multiple-bus microgrids with each bus having multiple distributed generators(DGs) connected in parallel. The control objective of the upper level is to calculate the voltage references for one-bus subsystems. The objectives of the lower control level are to make the subsystems' bus voltages track the voltage references and to enhance load current sharing accuracy among the local DGs. Firstly, a distributed consensusbased power sharing algorithm is introduced to determine the power generations of the subsystems. Secondly, a discrete-time droop equation is used to adjust subsystem frequencies for voltage reference calculations. Finally, a Lyapunov-based decentralized control algorithm is designed for bus voltage regulation and proportional load current sharing. Extensive simulation studies with microgrid models of different levels of detail are performed to demonstrate the merits of the proposed control scheme.

Adaptive Decentralized Output-Constrained Control of Single-Bus DC Microgrids

A single-bus DC microgrid can represent a wide range of applications. Control objectives of such systems include high-performance bus voltage regulation and proper load sharing among multiple distributed generators(DGs) under various operating conditions. This paper presents a novel decentralized control algorithm that can guarantee both the transient voltage control performance and realize the predefined load sharing percentages. First, the output-constrained control problem is transformed into an equivalent unconstrained one. Second, a two-step backstepping control algorithm is designed based on the transformed model for bus-voltage regulation. Since the overall control effort can be split proportionally and calculated with locally-measurable signals, decentralized load sharing can be realized. The control design requires neither accurate parameters of the output filters nor load measurement. The stability of the transformed systems under the proposed control algorithm can indirectly guarantee the transient bus voltage performance of the original system. Additionally, the high-performance control design is robust, flexible, and reliable. Switch-level simulations under both normal and fault operating conditions demonstrate the effectiveness of the proposed algorithm.

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

基于数据扩充及风力发电机组功率曲线分段回归的自适应监测方法

准确的功率曲线可以反映风机的运行状况,在风力发电机组的监测中至关重要。然而,一些新安装的风力发电机组由于没有足够的训练数据来拟合准确的功率曲线,从而导致监测结果不佳。本文提出了一种基于数据扩充的分段回归方法实现对风电机组的自适应监测,该方法可分为离线建模阶段和在线监测阶段。在离线建模时,首先,设计了一种新的映射函数,通过将其他数据集的数据映射到目标数据集上,从而实现对目标数据集的扩充,进而使得目标风力发电机组有足够的数据用于模型训练。然后,设计了一种分段建模策略,将扩充后数据的信息精炼到少量样本中,再进行功率曲线拟合,可以以较低的计算复杂度准确拟合出功率曲线。在线监测时,可依据功率曲线模型对功率进行预测,最后将预测结果与实际功率进行比较,从而实现对运行状态的监测。此外,我们提出一种增量学习策略,以利用新的数据实时更新模型提高预测和监测的准确度。实验采用真实风电数据,结果表明,所提监测方法能够在数据不足的情况下能准确地发现异常行为,监测准确率可达92.77%。

Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.

Minimax entropy-based co-training for fault diagnosis of blast furnace

Due to the problems of few fault samples and large data fluctuations in the blast furnace(BF)ironmaking process,some transfer learning-based fault diagnosis methods are proposed.The vast majority of such methods perform distribution adaptation by reducing the distance between data distributions and applying a classifier to generate pseudo-labels for self-training.However,since the training data is dominated by labeled source domain data,such classifiers tend to be weak classifiers in the target domain.In addition,the features generated after domain adaptation are likely to be at the decision boundary,resulting in a loss of classification performance.Hence,we propose a novel method called minimax entropy-based co-training(MMEC)that adversarially optimizes a transferable fault diagnosis model for the BF.The structure of MMEC includes a dual-view feature extractor,followed by two classifiers that compute the feature's cosine similarity to representative vector of each class.Knowledge transfer is achieved by alternately increasing and decreasing the entropy of unlabeled target samples with the classifier and the feature extractor,respectively.Transfer BF fault diagnosis experiments show that our method improves accuracy by about 5%over state-of-the-art methods.

可见、近红外光谱和深度学习CNN-ELM算法的煤炭分类

煤是工业的主要能源,煤的品质对工业和环境起决定性作用。在使用煤的过程中,如果不能准确确定煤的品种,有可能对生产效率、环境污染、经济损失等会造成重大的影响。传统的煤分类,主要依靠人工方法和化学分析方法,这些方法的缺点是高成本和耗费时间。如何快速准确确定煤的品质很重要。因此,提出深度学习、极限学习机-ELM算法和可见、红外光谱联合建立煤矿分类模型。首先,从抚顺、伊敏和河南夹津口煤矿区采取不同煤样品,并使用美国Spectra Vista公司的SVC HR-1024地物光谱仪测得光谱数据。然后利用深度学习的卷积神经网络-CNN提取光谱特征,并采用ELM算法对光谱数据建立分类模型。最后,为进一步提高分类精度,引入粒子群算法。通过全新定义惯性权重和加速系数的取值范围来改进粒子群算法,并使用改进粒子群算法优化CNN-ELM网络。实验结果表明,和PCA特征提取方法比较,CNN网络能够更好的提取光谱特征,CNN-ELM分类模型有良好的分类效果;改进ELM分类模型的分类精度高于基础ELM和SVM分类模型。与传统的化学分析方法和人工方法相比,此方法在经济、速度、准确性方面均具有无可比的优势。

基于支持向量机和遗传算法的非线性模型预测控制（英文）

This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

Online process monitoring for complex systems with dynamic weighted principal component analysis

Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process

In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.

Novel chiral thioureas for highly enantioselective Michael reactions of malonates to nitroalkenes

Highly efficient Michael addition reactions of malonates to nitroalkenes catalyzed by novel chiral thioureas derived from optically pure BINOL and amino acids are reported. Various trans-nitroalkenes reacted with malonates affording the desired products in up to 95% yield with excellent enantioselectivities （up to 97% ee）.

机器学习下的基于退化特征提取和时间平滑分析的非平稳工业过程在线故障预测

故障退化预测是预估过程劣化和故障发生的时间,已被认为是维护策略中的一个关键组成部分。故障退化过程通常是缓慢变化的,可以用自回归模型来建模。然而,工业过程往往表现出典型的非平稳特性,这就给故障退化信息的获取和非平稳过程的建模带来了挑战。针对上述问题,本文提出了一种新的故障退化建模和在线故障预测策略。首先,提出一种面向故障退化的慢特征分析(FDSFA)算法提取故障退化方向,并沿该方向提取候选故障退化特征。然后,利用趋势评估算法来选择主要的故障退化特征。其次,结合主要的故障退化特征及其稳定性加权因子,计算关键故障退化因子来表征故障退化趋势。针对过程非平稳特性,建立了带时序平滑正则项的时变回归模型。在更新策略的基础上,通过对预测误差的分析和建模,进一步建立了在线故障预测模型。最后,通过一个实际的工业案例验证了所提方法的预测性能。

Spinning from Nature:Engineered Preparation and Application of High-Performance Bio-Based Fibers

Many natural fibers are lightweight and display remarkable strength and toughness.These properties originate from the fibers’hierarchical structures,assembled from the molecular to macroscopic scale.The natural spinning systems that produce such fibers are highly energy efficient,inspiring researchers to mimic these processes to realize robust artificial spinning.Significant developments have been achieved in recent years toward the preparation of high-performance bio-based fibers.Beyond excellent mechanical properties,bio-based fibers can be functionalized with a series of new features,thus expanding their sophisticated applications in smart textiles,electronic sensors,and biomedical engineering.Here,recent progress in the construction of bio-based fibers is outlined.Various bioinspired spinning methods,strengthening strategies for mechanically strong fibers,and the diverse applications of these fibers are discussed.Moreover,challenges in reproducing the mechanical performance of natural systems and understanding their dynamic spinning process are presented.Finally,a perspective on the development of biological fibers is given.

Capacity Analysis on Distributed Antenna System with Imperfect CSI over Rayleigh Fading Channel

Considering that perfect channel state information(CSI)is hard to obtain in practice,the capacity of downlink distributed antennas system(DAS)with imperfect CSI is analyzed over Rayleigh fading channel.Based on the performance analysis,using the probability density function and numerical calculation,an accurate closedform expression of ergodic capacity of downlink DAS under imperfect CSI is derived.It includes the one under perfect CSI as a special case.This theoretical expression can provide good performance evaluation for downlink DAS for both perfect and imperfect CSI due to its accuracy.Simulation results indicate that the theoretical analysis agrees well with the corresponding simulation,and the capacity can be increased effectively by decreasing the estimation error and/or path loss.

Adaptive Energy Efficient Power Allocation Scheme for DAS with Multiple Receive Antennas

Energy efficiency(EE)of downlink distributed antenna system(DAS)with multiple receive antennas is investigated over composite Rayleigh fading channel that takes the path loss and lognormal shadow fading into account.Our aim is to maximize EE which is defined as the ratio of the transmission rate to the total consumed power under the constraints of the maximum transmit power of each remote antenna.According to the definition of EE,the optimized objective function is formulated with the help of Lagrangian method.By using the Karush-KuhnTucker(KKT)conditions and numerical calculation,considering both the static and dynamic circuit power consumptions,an adaptive energy efficient power allocation(PA)scheme is derived.This scheme is different from the conventional iterative PA schemes based on EE maximization since it can provide closed-form expression of PA coefficients.Moreover,it can obtain the EE performance close to the conventional iterative scheme and exhaustive search method while reducing the computation complexity greatly.Simulation results verify the effectiveness of the proposed scheme.

Nanocarbon-Enhanced 2D Photoelectrodes:A New Paradigm in Photoelectrochemical Water Splitting

Solar-driven photoelectrochemical(PEC)water splitting systems are highly promising for converting solar energy into clean and sustainable chemical energy.In such PEC systems,an integrated photoelectrode incorporates a light harvester for absorbing solar energy,an interlayer for transporting photogenerated charge carriers,and a co-catalyst for triggering redox reactions.Thus,understanding the correlations between the intrinsic structural properties and functions of the photoelectrodes is crucial.Here we critically examine various 2D layered photoanodes/photocathodes,including graphitic carbon nitrides,transition metal dichalcogenides,layered double hydroxides,layered bismuth oxyhalide nanosheets,and MXenes,combined with advanced nanocarbons(carbon dots,carbon nanotubes,graphene,and graphdiyne)as co-catalysts to assemble integrated photoelectrodes for oxygen evolution/hydrogen evolution reactions.The fundamental principles of PEC water splitting and physicochemical properties of photoelectrodes and the associated catalytic reactions are analyzed.Elaborate strategies for the assembly of 2D photoelectrodes with nanocarbons to enhance the PEC performances are introduced.The mechanisms of interplay of 2D photoelectrodes and nanocarbon co-catalysts are further discussed.The challenges and opportunities in the field are identified to guide future research for maximizing the conversion efficiency of PEC water splitting.

One-Variable Attack on the Industrial Fault Classification System and Its Defense

Recently developed fault classification methods for industrial processes are mainly data-driven.Notably,models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data patterns.However,these data-driven models are vulnerable to adversarial attacks;thus,small perturbations on the samples can cause the models to provide incorrect fault predictions.Several recent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarial samples.This paper proposes a black-box attack method with an extreme constraint for a safe-critical industrial fault classification system:Only one variable can be perturbed to craft adversarial samples.Moreover,to hide the adversarial samples in the visualization space,a Jacobian matrix is used to guide the perturbed variable selection,making the adversarial samples in the dimensional reduction space invisible to the human eye.Using the one-variable attack(OVA)method,we explore the vulnerability of industrial variables and fault types,which can help understand the geometric characteristics of fault classification systems.Based on the attack method,a corresponding adversarial training defense method is also proposed,which efficiently defends against an OVA and improves the prediction accuracy of the classifiers.In experiments,the proposed method was tested on two datasets from the Tennessee–Eastman process(TEP)and steel plates(SP).We explore the vulnerability and correlation within variables and faults and verify the effectiveness of OVAs and defenses for various classifiers and datasets.For industrial fault classification systems,the attack success rate of our method is close to(on TEP)or even higher than(on SP)the current most effective first-order white-box attack method,which requires perturbation of all variables.