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.

Dynamic Hypergraph Modeling and Robustness Analysis for SIoT

The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.

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.

Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins

This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built,where thermal,structural,and multidisciplinary knowledge could be integrated for visualization.To implement visualized computing,the membership function of fuzzy decision-making was optimized using a genetic algorithm.Transient thermodynamic,structural statics,and flow field analyses were conducted,especially for glass fiber composite materials,which have the characteristics of high strength,corrosion resistance,temperature resistance,dimensional stability,and electrical insulation.An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP.Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution.A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT.Moreover,manufacturability was verified based on a thermal-solid coupled finite element analysis.The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.

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.

考虑源荷不确定性及用户响应行为的电力系统低碳经济调度

电力系统是未来实现碳中和目标的重要领域,随着源-荷不确定性的加剧,基于确定性模型的低碳经济调度方法无法准确描述不确定因素对碳排放的影响。针对上述问题,该文首先从系统层面构建考虑不确定性的低碳鲁棒优化模型,利用概率模型对源荷不确定因素进行建模,并采用机会约束对目标满足期望的显著性水平进行描述,通过最大化不确定因素的置信水平得出风险规避策略下的鲁棒调度方案。接着从用户层面构建基于事件驱动的用户低碳响应模型,根据系统层计算结果,通过设定事件触发的碳排放阈值定义碳排放超额事件,并以价格形式引导用户的低碳用能行为。最后通过算例分析,验证所提模型能有效量化评估系统的低碳经济调度不确定性水平,充分发挥用户侧降碳能力,实现源荷双侧低碳目标的协同。

基于计算机视觉与街景图像的城市街道绿化泛类结构量化分析与分布机制研究

在全球城市化和环境压力加剧的背景下,对城市街道绿化泛类结构(urban street greening general structure,USGGS)的量化是加强城市区域碳汇、缓解城市热岛效应以应对全球气候变化的重要前提。通过量化与分析不同城市的USGGS,探究其与城市建成环境之间的关系。使用改进的DeepLabV3+神经网络模型,对天津、杭州、深圳的城市全景街景图像进行语义分割,并结合细粒度数据量化USGGS,使用Robust回归模型分析USGGS与城市功能属性POI的关系。研究显示,天津的USGGS主要由单乔木和乔-灌结构组成,与商业属性和生活属性的POI紧密相关;而杭州和深圳则展现出包括草本植物在内的多样化USGGS与休闲文化设施的POI更强的关联性。通过对3个城市USGGS的量化、分析与比较,为城市绿色基础设施规划和管理奠定了一定的数据基础,同时基于城市街景图像对USGGS的分析也为城市碳汇计算与城市热环境研究提供了新的视角。

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。

计及故障维修与网络重构的灾后配电网综合调度策略

当前,灾后配电网负荷恢复与故障维修多是独立考虑,缺乏有效联动,且没有合理计及包含故障维修时间及新能源出力的不确定性在内的双重因素影响。为此,提出一种灾后配电网维修恢复联合调度策略。首先,提出灾后配电网两阶段求解框架,第一阶段求解故障维修计划及网络重构问题,第二阶段解决移动式储能系统参与下的灾后配电网源荷协同调度问题。其次,构建移动式储能系统与维修人员的调度模型,量化分析移动式储能系统时空转移模型及能量模型,同时基于维修人员的时空转移特性,建立故障维修及网络重构模型。在此基础上,以恢复运行成本最小化为目标,分别采用离散鲁棒与连续鲁棒处理故障维修时间与新能源出力的不确定性,构建灾后配电网综合调度模型。最后,采用改进IEEE 33节点配电网进行仿真测试。结果表明,所提调度策略能够显著降低负荷失电率,减少停电损失。此外,两阶段算法在显著提升求解速度的同时能够保证求解精度,具有可行性和合理性。

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。

金融强国的核心要素、建设短板与发展建议

加快建设金融强国是党中央重大战略决策部署,是全面建成社会主义现代化强国的重要任务。金融强国应当具备强大的货币、强大的中央银行、强大的金融机构、强大的国际金融中心、强大的金融监管、强大的金融人才队伍等关键核心金融要素。我国已建成全球第二大金融体系,已是金融大国,但是距离金融强国要求仍有短板,包括资产扩张驱动发展模式亟待改革、金融市场资源配置功能亟需加强、金融体系国际化水平总体偏低、国际金融公共产品提供能力较弱。加快建设金融强国,下一步建议进一步加强党的领导、完善市场机制、强化金融监管、夯实基础条件、做强主权货币、扩大金融开放、保障金融稳定。

考虑移动氢能存储的港口多能微网两阶段分布鲁棒优化调度

为有效应对海上风电固有的间歇及波动性给港口多能微网带来的不确定性风险,提出一种考虑移动氢能存储的港口多能微网两阶段分布鲁棒优化调度模型。首先,结合Wasserstein距离实现风电出力概率分布模糊集的精确刻画,并通过非参数核密度估计拟合海上风电预测误差概率分布,获得不同置信水平下风电出力区间及场景。其次,分析氢能船舶、汽车等移动氢能存储资源对间歇性风电出力的能源存储潜力,并结合用能心理、交通属性差异,将两者分别建模为激励型、价格型需求响应,实现港口移动氢能存储灵活性资源的高效聚合。再次,针对含移动氢能存储的港口多能微网,构建基于概率分布模糊集的日前-日内两阶段分布鲁棒优化调度模型,并运用线性决策规则与强对偶理论将其转换为混合整数线性规划模型求解。最后,基于海上风电实测数据进行仿真验证。结果证明,移动氢能存储可显著提升港口多能微网的低碳灵活性,所提模型在兼顾港口多能微网经济性的同时,可进一步保证风电不确定性风险下的鲁棒性。

基于分布鲁棒优化的车-站-网日前能量管理与交易

为考虑上级电网电价、光伏出力等多重多层级不确定性对车-站-网互动博弈模型的影响,且充分体现配电网主动管理技术支撑效果,文中提出了一种基于分布鲁棒优化的车-站-网能量管理与交易方法。首先,针对主动配电网内多元主体能量管理与交易问题,建立了配电网运营商、充电站和电动汽车的日前市场互动框架。其次,融合主动网络管理技术和网络约束,在配电网运营商与聚合了电动汽车的多个充电站之间构建了以多主体各自利益最大为目标的双层Wasserstein分布鲁棒互动博弈模型。然后,提出了结合Karush-Kuhn-Tucker条件、对偶原理和大M法的化简方法以解决多层级不确定性造成的求解难题,将双层Wasserstein分布鲁棒模型转化为单层混合整数二阶锥规划模型,并利用商业求解器YALMIP/GUROBI进行了求解。最后,通过算例仿真验证了所提模型和方法的有效性。

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。