Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

Hidden capacity,concealment,security,and robustness are essential indicators of hiding algorithms.Currently,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the algorithms.In practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret image.An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms.In this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet transform.The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding network.During the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret image.These lowresolution secret images are combined using densemodules to obtain a high-quality secret image.The experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various attacks.The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical applications.In conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms.Its ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various applications.Further research and experimentation can explore the algorithm’s performance under different scenarios and expand its potential applications.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Robustness Study and Superior Method Development and Validation for Analytical Assay Method of Atropine Sulfate in Pharmaceutical Ophthalmic Solution

Background: The robustness is a measurement of an analytical chemical method and its ability to contain unaffected by little with deliberate variation of analytical chemical method parameters. The analytical chemical method variation parameters are based on pH variability of buffer solution of mobile phase, organic ratio composition changes, stationary phase (column) manufacture, brand name and lot number variation;flow rate variation and temperature variation of chromatographic system. The analytical chemical method for assay of Atropine Sulfate conducted for robustness evaluation. The typical variation considered for mobile phase organic ratio change, change of pH, change of temperature, change of flow rate, change of column etc. Purpose: The aim of this study is to develop a cost effective, short run time and robust analytical chemical method for the assay quantification of Atropine in Pharmaceutical Ophthalmic Solution. This will help to make analytical decisions quickly for research and development scientists as well as will help with quality control product release for patient consumption. This analytical method will help to meet the market demand through quick quality control test of Atropine Ophthalmic Solution and it is very easy for maintaining (GDP) good documentation practices within the shortest period of time. Method: HPLC method has been selected for developing superior method to Compendial method. Both the compendial HPLC method and developed HPLC method was run into the same HPLC system to prove the superiority of developed method. Sensitivity, precision, reproducibility, accuracy parameters were considered for superiority of method. Mobile phase ratio change, pH of buffer solution, change of stationary phase temperature, change of flow rate and change of column were taken into consideration for robustness study of the developed method. Results: The limit of quantitation (LOQ) of developed method was much low than the compendial method. The % RSD for the six sample assay of developed method was 0.4% where the % RSD of the compendial method was 1.2%. The reproducibility between two analysts was 100.4% for developed method on the contrary the compendial method was 98.4%.

Doping-enhanced robustness of anomaly-related magnetoresistance in WTe_(2±α)flakes

We study systematically the negative magnetoresistance(MR)effect in WTe_(2±α)flakes with different thicknesses and doping concentrations.The negative MR is sensitive to the relative orientation between electrical-/magnetic-field and crystallographic orientation of WTe_(2±α).The analysis proves that the negative MR originates from chiral anomaly and is anisotropic.Maximum entropy mobility spectrum is used to analyze the electron and hole concentrations in the flake samples.It is found that the negative MR observed in WTe_(2±α)flakes with low doping concentration is small,and the high doping concentration is large.The doping-induced disorder obviously inhibits the positive MR,so the negative MR can be more easily observed.In a word,we introduce disorder to suppress positive MR by doping,and successfully obtain the negative MR in WTe_(2±α)flakes with different thicknesses and doping concentrations,which indicates that the chiral anomaly effect in WTe_(2)is robust.

Adversarial Attack-Based Robustness Evaluation for Trustworthy AI

Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles.The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI.Although various adversarial attack and defense methods are actively being studied,there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks.An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall.Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks.In this paper,a Sophisticated Adversarial Robustness Score(SARS)is proposed for AI model robustness evaluation.SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process.This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators.Moreover,the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks.This paper proposed using SARS,calculated based on adversarial attacks,to identify data groups with robustness vulnerability and improve robustness through adversarial training.Through SARS,it is possible to evaluate the level of robustness,which can help developers identify areas for improvement.To validate the proposed method,experiments were conducted using a malware dataset.Through adversarial training,it was confirmed that SARS increased by 70.59%,and the recall reduction rate improved by 64.96%.Through SARS,it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types.In addition,it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training.

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure.

Research on the model of high robustness computational optical imaging system

Computational optical imaging is an interdisciplinary subject integrating optics, mathematics, and information technology. It introduces information processing into optical imaging and combines it with intelligent computing, subverting the imaging mechanism of traditional optical imaging which only relies on orderly information transmission. To meet the high-precision requirements of traditional optical imaging for optical processing and adjustment, as well as to solve its problems of being sensitive to gravity and temperature in use, we establish an optical imaging system model from the perspective of computational optical imaging and studies how to design and solve the imaging consistency problem of optical system under the influence of gravity, thermal effect, stress, and other external environment to build a high robustness optical system. The results show that the high robustness interval of the optical system exists and can effectively reduce the sensitivity of the optical system to the disturbance of each link, thus realizing the high robustness of optical imaging.

Influence of High-Robustness Polycarboxylate Superplasticizer on the Performances of Concrete Incorporating Fly Ash and Manufactured Sand

Using ethylene glycol monovinyl polyoxyethylene ether,2-acrylamido-2-methylpropane sulfonic acid(AMPS)and acrylic acid as the main synthetic monomers,a high robustness polycarboxylate superplasticizer was prepared.The effects of initial temperature,ratio of acid to ether,amount of chain transfer agent,and synthesis process on the properties of the superplasticizer were studied.The molecular structure was characterized by GPC(Gel Permeation Chromatography)and IR(Infrared Spectrometer).As shown by the results,when the initial reaction temperature is 15℃,the ratio of acid to ether is 3.4:1 and the acrylic acid pre-neutralization is 15%,The AMPS substitution is 10%,the amount of chain transfer agent is 8%,and the performance of the synthesized superplasticizer is the best.Compared with commercially available ordinary polycarboxylate superplasticizer in C30 concrete prepared with manufactured sand and fly ash,the bleeding rate decreases by 52%,T50 decreases by 1.2 s,and the slump time decreases by 1.1 s.In C60 concrete prepared with fly ash and river sand,the bleeding rate decreases by 46%,T50 decreases by 0.8 s,and the slump time decreases by 3.2 s.

智能反射面辅助的多天线通信系统鲁棒安全资源分配算法

为了解决蜂窝通信系统中因窃听者、障碍物阻挡和信道不确定性导致安全性低和传输质量差的问题,该文提出一种智能反射面(IRS)辅助的多天线通信系统鲁棒安全资源分配算法。首先,考虑合法用户的安全速率约束、最大发射功率约束和IRS相移约束,基于有界信道不确定性,建立了一个联合优化基站主动波束、IRS被动波束的鲁棒资源分配问题。然后,利用S-程序、连续凸近似、交替优化和罚函数等方法对含参数摄动的原非凸问题进行转换,得到可直接求解的确定性凸优化问题。最后,提出一种基于迭代的鲁棒能效最大化算法。仿真结果表明,该文算法具有较好的能效和较强的鲁棒性。

高渗透率下基于并网逆变器阻抗重塑的锁相环设计方法

针对锁相环、电网阻抗与并网逆变器相互耦合所引发的系统稳定性下降问题。首先,建立考虑电网阻抗的锁相环控制结构模型,通过分析锁相环闭环传递函数可知,电网阻抗会使锁相环系统产生右半平面闭环极点,严重影响锁相环与逆变器系统的稳定性。其次,通过分析逆变器系统输出阻抗,说明锁相环所引入的负阻抗是逆变器系统稳定裕度下降的主要原因。鉴于此,该文提出一种新型锁相环设计方法,理论分析表明,所提方法既能够保证高渗透率下锁相环具有高鲁棒性,又能够对逆变器系统输出阻抗进行重塑,有效拓宽系统对电网阻抗的适应范围。最后,通过仿真与实验验证所提新型锁相环设计方法的有效性。

可信机器学习综述

机器学习技术不断发展,在许多领域都有广泛的应用并展现出超出人类本身的能力。但机器学习方法利用不当或决策存在偏差,反而会损害人们的利益,特别是在一些敏感安全需求高的领域,如金融、医疗等,人们越来越重视机器学习的可信研究。目前,机器学习技术普遍存在一些缺点,如对代表性不足的群体存在偏见、缺乏用户隐私保护、缺乏模型可解释性、容易受到威胁攻击等。这些缺点降低了人们对机器学习方法的信任。尽管研究者已针对这些不足进行了深入探索,但缺乏一个整体的框架与方法系统地提供机器学习的可信分析。因此本文针对机器学习的公平性、可解释性、鲁棒性与隐私4个要素归纳总结了现阶段主流的定义、指标、方法与评估,然后讨论了各要素之间的关系,并结合机器学习全生命周期构建了一个可信机器学习框架。最后,给出了一些目前可信机器学习领域亟待解决的问题与面临的挑战。

智能反射面辅助的抗干扰安全通信系统鲁棒资源分配算法

为了解决恶意干扰攻击、窃听和不完美信道状态信息造成的通信质量降低和安全性差等问题,该文提出一种智能反射面(IRS)辅助的抗干扰安全通信系统鲁棒资源分配算法。首先,基于合法用户的最小安全速率约束、最大发射功率约束和IRS相移约束,在非法节点不完美信道状态信息、干扰器波束成形向量未知的情况下,构建了一个联合优化基站的波束成形向量、人工噪声的协方差矩阵和IRS的相移矩阵的鲁棒资源分配问题。其次,为了求解该非凸问题,利用交替优化、Cauchy-Schwarz不等式、连续凸逼近和泰勒级数展开等方法,将原问题转化为易于求解的凸优化问题。仿真结果表明,与传统算法相比所提算法能有效提高系统安全性、降低功率开销、提高抗干扰裕度,且在一定信道误差范围内能够减低约35%的保密中断概率,具有较强的鲁棒性。

新能源电力系统不确定优化调度方法研究现状及展望

风电场和光伏电站出力的不确定性给电力系统优化调度带来很大技术挑战。文中主要介绍了考虑新能源不确定性的电力系统优化调度方法的研究现状及后续研究方向展望。首先,重点论述了各种不确定优化调度(UOD)方法,包括随机优化方法、鲁棒优化方法、随机鲁棒优化结合方法和基于人工智能技术的方法。其中,随机优化方法包括场景法、机会约束规划法和近似动态规划法;鲁棒优化方法包括传统鲁棒优化法和分布鲁棒优化法;随机鲁棒优化结合方法包括采样鲁棒优化法和分布鲁棒机会约束规划法。然后,介绍了每一种方法的优化模型形式、模型的转化和求解原理及其优缺点。最后,对UOD的后续重点研究方向进行展望,包括兼顾多个目标的UOD问题及多目标不确定优化方法、输配系统UOD问题及分布式不确定优化方法、考虑稳定性约束的UOD问题及含常微分方程约束的不确定优化方法、考虑管道传输动态的综合能源系统UOD问题及含偏微分方程约束的不确定优化方法。

基于融合注意力机制LSTM网络的地下水位自适应鲁棒预测

地下水水位是旱天污水管网地下水入渗量的重要影响因素,快速精准地预测地下水水位能有效提升旱天污水管网地下水入渗量估算准确度,辅助优化管网病害治理与维护策略。针对目前城市复杂水文预测存在的准确度低、灵敏度低、泛化能力弱等问题,本文提出了一种新的鲁棒自适应水位预测算法。首先,对水文数据进行预处理,解决了数据时间跨度大、噪声多、缺失及异常、非平稳等问题。其次,针对不同输入特征对预测指标的影响,在模型训练阶段提出一种新的空间变量注意机制,可快速识别与水位关联的关键变量,并对输入特征赋予不同的影响权重。然后,针对不同序列长度对预测效果的影响,还设计了自适应时间注意力机制,帮助网络自适应地找出与不同时间序列长度预测指标相关的编码器隐藏状态,以更好地捕捉时间上的依赖关系。在此基础上,以上下文向量作为输入,提出一种融合注意力机制的长短时记忆网络水文预测算法。最后,通过意大利Petrignano水文数据验证了所提算法的有效性,并与GRU、Elman、LSTM、VA–LSTM和S–LSTM等方法进行预测性能比较。结果表明,基于融合注意力机制的LSTM网络在面临大规模、噪点多的复杂数据时有优于其它几种算法的预测效果,表明该算法具有强自适应性和鲁棒性。本文研究结果可以为市政排水策略合理调整、及时控制提供参考。

部分分布式电源提供辅助服务的主动配电网快速鲁棒优化

经逆变器接入配电网的分布式电源提供有功与无功辅助服务是确保配电网安全经济运行的重要手段。该文同时计及了储能系统、可投切电容电抗器、有载调压变压器分接头、静止无功补偿器调节能力,以储能与网络损耗及弃风弃光最小为目标函数,计及运行约束,基于支路潮流方程构建了部分分布式电源提供辅助服务的多时段二阶段混合整数二阶锥鲁棒优化模型,提出了一种新颖的基于割平面的主、次问题二阶段直接交替迭代求解方法。不同于现有列与约束生成(columns and constraints generation,CCG)算法,该方法求解主问题时无需增加新的变量与约束条件,求解次问题时,只需针对每个时段进行求解,因此极大降低了求解复杂度与计算机内存。若求解结果不满足二阶锥精确凸松弛条件,则构建二阶段混合整数序列二阶锥鲁棒优化模型,依然能够快速求解,且可恢复出原问题的精确解。最后,采用2个仿真实例验证了所提出方法的性能。IEEE 123节点配电网的仿真结果表明,该方法计算速度是CCG算法的12~22倍。该方法可为含高比例间歇性分布式电源配电网鲁棒优化运行提供实时快速分析与求解工具,提高新能源就地消纳能力。

含静止无功发生器的直驱风电并网系统稳定性分析及振荡抑制策略

风电并网系统稳定性分析及振荡抑制策略大多集中于风电场本身,针对含静止无功发生器(SVG)的直驱风电并网系统研究较少。基于特征值灵敏度筛选出对含SVG的直驱风电并网系统稳定性较显著的控制通道,然后基于独立通道分析设计理论将并网系统由多输入多输出耦合系统等效拆分为多个单输入单输出控制通道,在定性分析并网系统稳定性的同时定量评估不同控制通道间的交互作用程度。为抑制控制通道间的交互作用,提出了基于H∞鲁棒控制的次同步振荡抑制策略。通过电磁暂态仿真,验证了基于独立通道分析设计理论分析并网系统稳定性的正确性以及基于H∞鲁棒控制的次同步振荡抑制策略的有效性。

永磁同步电机交直轴电流解耦控制方法综述

该文归纳整理永磁同步电机交直轴电流解耦控制方法,分析各种解耦方法的思想来源、脉络体系和演变过程,论证不同解耦方法的解耦本质和内在联系。从整体和局部视角出发,建立解耦问题的思维架构,概括出对角化解耦、抗干扰解耦和逆系统解耦3类方法。3类方法依次体现出模型依赖性降低、鲁棒性增强、算法复杂度提高的趋势,呈现出从“模型论”向“控制论”过渡的技术发展路线。讨论参数不确定条件下各种解耦控制方法的鲁棒性,指出将解耦方法与先进控制算法、扰动观测技术、系统辨识方法以及人工智能方法相结合,并充分利用已知的模型信息,有利于提升交直轴电流解耦效果,增强系统鲁棒性,减弱单一观测器、控制器、滤波器等的设计难度与参数整定要求,有助于实现系统全局最优。最后,对永磁同步电机交直轴电流解耦问题的关键技术、应用与发展做出展望。

货币政策对企业劳动投资效率的影响研究

基于2010—2020年我国A股上市公司的相关数据,对货币政策与企业劳动投资效率之间的关系进行探讨。研究发现,在货币政策紧缩时,企业劳动投资效率会有明显的提高,会计稳健性的提高以及自由现金流量的减少是紧缩性货币政策影响企业劳动投资效率的主要作用机制。进一步研究发现,高质量的公司治理水平会抑制紧缩性货币政策对企业劳动投资效率的影响。非效率劳动投资分组检验结果表明,紧缩性货币政策对企业劳动投资效率的促进效应,更多地表现为抑制劳动投资过度。

基于分布鲁棒优化的车-站-网日前能量管理与交易

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