Journal of The Franklin Institute-engineering and Applied Mathematics最新文献_第2页

Robust Rao-type tests for step-stress accelerated lifetests with interval-censored data and Weibull lifetime distributions

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-18 DOI: 10.1016/j.jfranklin.2025.107650

Narayanaswamy Balakrishnan , María Jaenada , Leandro Pardo

{"title":"Robust Rao-type tests for step-stress accelerated lifetests with interval-censored data and Weibull lifetime distributions","authors":"Narayanaswamy Balakrishnan , María Jaenada , Leandro Pardo","doi":"10.1016/j.jfranklin.2025.107650","DOIUrl":"10.1016/j.jfranklin.2025.107650","url":null,"abstract":"<div><div>Many engineering products are highly reliable in the present highly competitive market, often exhibiting long mean lifetimes to failure. This makes experimental testing both time-intensive and challenging. Accelerated life-tests are commonly used to induce early failures by subjecting products to higher-than-normal stress conditions, enabling enough failures to be observed for accurate statistical analysis. Additionally, censored data is a common challenge in reliability studies. Specifically, interval-censored data arises when continuous monitoring of devices is impractical or infeasible due to technical constraints or budget limitations. Statistical inference in such situations is often based on the likelihood function of the model. However, likelihood-based methods can be highly sensitive to outliers, which may result in biased or unreliable estimates. To address this issue, minimum density power divergence techniques can be used as a robust alternative. These methods extend traditional likelihood-based approach and have demonstrated appealing performance in reliability inference. In this paper, we develop robust restricted estimators based on the density power divergence for step-stress accelerated life-tests under Weibull distributions with interval-censored data and use these restricted estimators to generalize the Rao Score test for testing composite null hypotheses, including testing the significance of stress factors contributing degradation of the devices. We present the theoretical asymptotic properties of the estimators and also associated test statistics, along with numerical analyses that support the robustness of the proposed estimators and tests of hypotheses.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 7","pages":"Article 107650"},"PeriodicalIF":3.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integration of adaptive projection BFGS and inertial extrapolation step for nonconvex optimization problems and its application in machine learning

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-17 DOI: 10.1016/j.jfranklin.2025.107652

Gonglin Yuan , Yuehan Yang , Yong Li , Xiong Zhao , Zehong Meng

{"title":"Integration of adaptive projection BFGS and inertial extrapolation step for nonconvex optimization problems and its application in machine learning","authors":"Gonglin Yuan , Yuehan Yang , Yong Li , Xiong Zhao , Zehong Meng","doi":"10.1016/j.jfranklin.2025.107652","DOIUrl":"10.1016/j.jfranklin.2025.107652","url":null,"abstract":"<div><div>With the rapid development of machine learning and large data technologies, large-scale optimization problems become more and more common, and traditional optimization algorithms face the challenges of computational complexity and memory consumption. In this paper, we combine the adaptive projection BFGS method with the inertial extrapolation technique, and propose a BFGS algorithm combining inertial extrapolation and adaptive projection, which enhances the search capability of the algorithm, ensures that there is a suitable descent direction in each iteration, and reduces the oscillation in the iteration. Under certain mild conditions and weak Wolfe–Powell (WWP) conditions, the algorithm demonstrates both global convergence and superlinear convergence rates for nonconvex unconstrained optimization problems. In addition, to solve large-scale optimization problems, we propose an inertial extrapolation adaptive projection BFGS algorithm based on variance reduction, which performs well in dealing with large-scale datasets and offers new insights to address the limitations of traditional algorithms. Finally, we evaluate their performance and efficiency using classical numerical experiments and applications in machine learning models.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 7","pages":"Article 107652"},"PeriodicalIF":3.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

H∞ state estimation for delayed neural networks via variable-augmented-based free-weighting matrices method

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-17 DOI: 10.1016/j.jfranklin.2025.107647

Xu-Kang Chang, Yong He

{"title":"H∞ state estimation for delayed neural networks via variable-augmented-based free-weighting matrices method","authors":"Xu-Kang Chang, Yong He","doi":"10.1016/j.jfranklin.2025.107647","DOIUrl":"10.1016/j.jfranklin.2025.107647","url":null,"abstract":"<div><div>The issue of <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> state estimation for neural networks with time-varying delays is investigated in this study. Firstly, an augmented Lyapunov-Krasovskii functional (LKF) with two delay-product-type terms is constructed to enhance the consideration of system state, delay, and delay derivative information. Furthermore, for taking into account more effective information, the LKF is augmented with both single and double integral variables. Accordingly, the LKF derivative becomes a higher-order term that exhibits nonlinear characteristics of the time delay. To solve the nonlinear problem, a variable-augmented-based free-weighting-matrices (VAFWMs) approach is employed to transform the nonlinear term into a linear form and provides more freedom in obtaining less-conservative results. Consequently, two improved <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> state estimation criteria are derived. Lastly, a numerical example is provided to demonstrate the merits and effectiveness of the presented methods. Meanwhile, to prove the practical viability of the presented methods, this study extends the methods to a real-world quadruple-tank process system.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 7","pages":"Article 107647"},"PeriodicalIF":3.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quaternion kernel partial least squares regression algorithms

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-15 DOI: 10.1016/j.jfranklin.2025.107651

José Domingo Jiménez-López, Rosa María Fernández-Alcalá, Jesús Navarro-Moreno, Juan Carlos Ruiz-Molina

引用次数: 0

Quantized integral sliding mode control for dynamic positioning vessel with time-varying delay and thruster faults

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-15 DOI: 10.1016/j.jfranklin.2025.107645

Jia-Bin Wang , Li-Ying Hao , Fang-Xu Li , Xin Yang , Xiu-Ning Yu

引用次数: 0

WDTM-CL: Efficient Wavelet-based dual Transformer model with contrastive learning for spectral reconstruction from RGB images

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-15 DOI: 10.1016/j.jfranklin.2025.107646

Jiang Zhu, Van Kwan Zhi Koh, Bihan Wen, Zhiping Lin

{"title":"WDTM-CL: Efficient Wavelet-based dual Transformer model with contrastive learning for spectral reconstruction from RGB images","authors":"Jiang Zhu, Van Kwan Zhi Koh, Bihan Wen, Zhiping Lin","doi":"10.1016/j.jfranklin.2025.107646","DOIUrl":"10.1016/j.jfranklin.2025.107646","url":null,"abstract":"<div><div>Spectral reconstruction from RGB images presents a significant challenge due to its ill-posedness. Existing Transformer-based methods for this task are usually computationally inefficient, as their complexity increases quadratically with the spatial resolution of the input. Furthermore, existing methods focused more on preserving local image structures, while their global context and non-local correlations are not exploited explicitly, resulting in degraded hyperspectral image (HSI) reconstruction. To tackle these issues, we propose an efficient Wavelet-based dual Transformer model (WDTM) with contrastive learning (CL) dubbed WDTM-CL for spectral reconstruction in this paper. Our WDTM-CL incorporates a dual attention mechanism adept at capturing both the non-local spatial similarities and the global spectral correlations within HSI. Wavelets are used for signal decomposition, to preserve the essential details of the feature maps, enabling effective multi-head self-attention learning and improving computational efficiency. Finally, we employ a patch-wise contrastive loss for hyperspectral data, to ensure structural fidelity in the reconstructed HSI by promoting patch-wise consistency with the ground truth HSI. This strategy captures the spectral and spatial information more accurately. Extensive experimental validation across a range of benchmark datasets shows that our proposed WDTM-CL achieves state-of-the-art performance in spectral reconstruction tasks.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 7","pages":"Article 107646"},"PeriodicalIF":3.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event-triggered memory sliding mode load frequency control of power system with BESSs against frequency-based deception attacks

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-15 DOI: 10.1016/j.jfranklin.2025.107644

Siwei Qiao , Xinghua Liu , Shiping Wen , Gaoxi Xiao , Badong Chen , Shuzhi Sam Ge

{"title":"Event-triggered memory sliding mode load frequency control of power system with BESSs against frequency-based deception attacks","authors":"Siwei Qiao , Xinghua Liu , Shiping Wen , Gaoxi Xiao , Badong Chen , Shuzhi Sam Ge","doi":"10.1016/j.jfranklin.2025.107644","DOIUrl":"10.1016/j.jfranklin.2025.107644","url":null,"abstract":"<div><div>An event-triggered memory sliding mode load frequency control (LFC) approach is proposed to address the cyber-security issue of multi-area power systems with battery energy storage systems (BESSs). To reveal the impact of cyber-attacks on the security and stability of multi-area power systems with BESSs, a frequency-based deception attack is considered as a contamination of the communication network. Attackers are assumed to determine the time of cyber-attacks by monitoring the operation status of the power systems with BESSs and designing the attack released condition with historical frequency. An event-triggered mechanism is investigated to reduce the communication pressure. In order to improve the triggering performance, a Lyapunov-based term is introduced in the triggering scheme. To ensure the stability of the power system under deception attacks, a memory sliding mode control (SMC) method is proposed in which the sliding mode surface is designed with a more effective structure. Based on integral inequality and Lyapunov’s theorem, the stability conditions of the multi-area power systems with BESSs under historical frequency-based deception attacks are obtained by linear matrix inequality (LMI). The effectiveness of the proposed approach is discussed and confirmed by numerical simulations.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 7","pages":"Article 107644"},"PeriodicalIF":3.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust online identification method for biofabrication processes with multiple unknown disturbances

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-15 DOI: 10.1016/j.jfranklin.2025.107643

Yixuan Chu, Xiaojing Ping, Shunyi Zhao, Fei Liu

引用次数: 0

Distributed model-free deep reinforcement learning control for unknown nonlinear multi-agent systems

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-03-14 DOI: 10.1016/j.jfranklin.2025.107636

Wenxiao Hu , Chenglong Du , Fanbiao Li , Xinmin Chen , Weihua Gui

引用次数: 0

Event-triggered control for output regulation of probabilistic Boolean control networks under dimension reduction method