Journal of The Franklin Institute-engineering and Applied Mathematics最新文献_第6页

Energy-harvesting recursive filtering for delayed systems with amplify-and-forward relays: Cooperative communication manner 带放大和前向中继的延迟系统的能量收集递归滤波：合作通信方式

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-11 DOI: 10.1016/j.jfranklin.2024.107265

{"title":"Energy-harvesting recursive filtering for delayed systems with amplify-and-forward relays: Cooperative communication manner","authors":"","doi":"10.1016/j.jfranklin.2024.107265","DOIUrl":"10.1016/j.jfranklin.2024.107265","url":null,"abstract":"<div><p>The recursive filtering problem is explored for a class of nonlinear time-delayed systems with amplify-and-forward (AF) relay and energy-harvesting (EH) technology. In order to enhance the signal quality of the remote transmission in wireless communication, the relay under the AF protocol is introduced that posing the merits of low complexity, less energy consumption and fast transmission rate. Meanwhile, to further alleviate the fading and improve the robustness of the signals, the cooperative communication manner is employed, which is different from the commonly encountered two-hop one. In the cooperative communication, the maximal ratio combining (MRC) method is utilized to make sure that the signal-to-noise ratio of the combined output can reach theoretical maximum. In addition, in order to achieve sustainable power supply in harsh environments, the EH sensors and relays are deployed, which can collect energy from external environments with certain probabilities. The aim of this paper is to develop a proper filter so as to reduce the impacts caused by the channel fading and insufficient energy. Furthermore, a minimized upper bound of the filtering error covariance is gained through appropriately designing the filter parameters. Then, the exponential boundedness is analyzed with regard to filtering error. Finally, a numerical example is exhibited to test the validity of the proposed filtering scheme.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230143","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

A randomized algorithm based approach to fault detection for uncertain systems subject to deception attack 基于随机算法的受欺骗攻击不确定系统故障检测方法

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-07 DOI: 10.1016/j.jfranklin.2024.107264

引用次数: 0

Feasible minimum distance feedback-based-navigation for a differential drive robot in an environment with obstacles 基于最小距离反馈的差分驱动机器人在有障碍物环境中的可行导航

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-06 DOI: 10.1016/j.jfranklin.2024.107253

引用次数: 0

Harnessing topological machine learning in Raman spectroscopy: Perspectives for Alzheimer’s disease detection via cerebrospinal fluid analysis 在拉曼光谱中利用拓扑机器学习：通过脑脊液分析检测阿尔茨海默病的前景

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-06 DOI: 10.1016/j.jfranklin.2024.107249

{"title":"Harnessing topological machine learning in Raman spectroscopy: Perspectives for Alzheimer’s disease detection via cerebrospinal fluid analysis","authors":"","doi":"10.1016/j.jfranklin.2024.107249","DOIUrl":"10.1016/j.jfranklin.2024.107249","url":null,"abstract":"<div><p>The cerebrospinal fluid of 21 subjects who received a clinical diagnosis of Alzheimer’s disease (AD) as well as of 22 pathological controls has been collected and analysed by Raman spectroscopy (RS). We investigated whether the Raman spectra could be used to distinguish AD from controls, after a preprocessing procedure. We applied machine learning to a set of topological descriptors extracted from the spectra, achieving a high classification accuracy of 86%. Our experimentation indicates that RS and topological analysis may be a reliable and effective combination to confirm or disprove a clinical diagnosis of Alzheimer’s disease. The following steps will aim at leveraging the intrinsic interpretability of the topological data analysis to characterize the AD subtypes, e.g. by identifying the bands of the Raman spectrum relevant for AD detection, possibly increasing and/or confirming the knowledge about the precise molecular events and biological pathways behind the Alzheimer’s disease.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0016003224006707/pdfft?md5=0c005e25e9077747b7e71e8f934493b5&pid=1-s2.0-S0016003224006707-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stability and L1-gain analysis of switched positive systems with unstable subsystems and sector nonlinearities 具有不稳定子系统和扇形非线性的开关正系统的稳定性和 L1 增益分析

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-06 DOI: 10.1016/j.jfranklin.2024.107255

引用次数: 0

Predefined-time self-structuring neural network H∞ cooperative control for multirobot systems with prescribed performance and input quantization 用于具有规定性能和输入量化的多机器人系统的预定义时间自构造神经网络 H∞ 协同控制

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-05 DOI: 10.1016/j.jfranklin.2024.107242

{"title":"Predefined-time self-structuring neural network H∞ cooperative control for multirobot systems with prescribed performance and input quantization","authors":"","doi":"10.1016/j.jfranklin.2024.107242","DOIUrl":"10.1016/j.jfranklin.2024.107242","url":null,"abstract":"<div><p>In this paper, a predefined-time adaptive command filter <em>H<sub>∞</sub></em> controller with a self-adjusting performance function is proposed for multirobot systems. It guarantees that the tracking error meets the desired performance requirements and solves the vulnerability problem that arises in traditional prescribed performance. First, an asymmetric tan-type barrier Lyapunov function is introduced to establish asymmetric barrier constraints under input saturation and input quantization. Second, a prescribed performance with self-adjustment is introduced in the asymmetric tan-type barrier Lyapunov function, which limits the position error and changes the performance envelope based on its state. Third, a predefined-time adaptive command filter is introduced to address the \"complexity explosion\" issue and improve the convergence speed. Fourth, a predefined-time self-structuring neural network is introduced to fit the model uncertainty and time-varying disturbances, and a predefined-time <em>H<sub>∞</sub></em> control strategy is designed to address the strong sudden disturbances. Finally, some simulation examples are provided to test the validity of the above algorithms.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163117","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

Sketch to Chinese paintings: A three-stage progressive generation network via enhancing sketch 从素描到中国画通过增强素描的三阶段渐进生成网络

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-05 DOI: 10.1016/j.jfranklin.2024.107246

{"title":"Sketch to Chinese paintings: A three-stage progressive generation network via enhancing sketch","authors":"","doi":"10.1016/j.jfranklin.2024.107246","DOIUrl":"10.1016/j.jfranklin.2024.107246","url":null,"abstract":"<div><p>With the proposal and wide application of Generative Adversarial Networks (GAN), sketch-based image generation has gradually become a research hotspot. Because of its unique artistic characteristics, Chinese painting has attracted more and more people to engage in research in the field of sketch-based Chinese painting. Most existing researches on sketch generation of Chinese paintings tend to extract edge maps from mature Chinese paintings and train generative models. When edge maps are extracted from sketches with sparse lines as input for generation, the quality of the generated Chinese painting is poor. This paper proposes a three-stage progressive Chinese painting generation network based on sketch. By the reduction and enhancement networks, our model converts the input sketch into types of sketches with different line richness. Each stage is used to learn to generate different Chinese painting information, realizing the progressive generation of Chinese painting through three connected generation networks. The experimental results show that our model can generate better-quality Chinese paintings and perform better in generating Chinese paintings from sketches.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151879","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

Adaptive boundary feedback control of a rigid–flexible system under unknown time-varying disturbance 未知时变扰动下刚柔系统的自适应边界反馈控制

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-05 DOI: 10.1016/j.jfranklin.2024.107256

引用次数: 0

A semidefinite programming approach for robust elliptic localization 稳健椭圆定位的半有限编程方法

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-04 DOI: 10.1016/j.jfranklin.2024.107237

{"title":"A semidefinite programming approach for robust elliptic localization","authors":"","doi":"10.1016/j.jfranklin.2024.107237","DOIUrl":"10.1016/j.jfranklin.2024.107237","url":null,"abstract":"<div><p>This short communication addresses the problem of elliptic localization with outlier measurements. Outliers are prevalent in various location-enabled applications, and can significantly compromise the positioning performance if not adequately handled. Instead of following the common trend of using <span><math><mi>M</mi></math></span>-estimation or adjusting the conventional least squares formulation by integrating extra error variables, we take a different path. Specifically, we explore the worst-case robust approximation criterion to bolster resistance of the elliptic location estimator against outliers. From a geometric standpoint, our method boils down to pinpointing the Chebyshev center of a feasible set, which is defined by the available bistatic ranges with bounded measurement errors. For a practical approach to the associated min–max problem, we convert it into the convex optimization framework of semidefinite programming (SDP). Numerical simulations confirm that our SDP-based technique can outperform a number of existing elliptic localization schemes in terms of positioning accuracy in Gaussian mixture noise.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0016003224006586/pdfft?md5=0d8c1767740ff4f6c4393f10bd009bd9&pid=1-s2.0-S0016003224006586-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts 具有输出量化和丢包功能的马尔可夫开关神经网络的采样数据控制

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-03 DOI: 10.1016/j.jfranklin.2024.107252

{"title":"Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts","authors":"","doi":"10.1016/j.jfranklin.2024.107252","DOIUrl":"10.1016/j.jfranklin.2024.107252","url":null,"abstract":"<div><p>This paper explores sampled-data control for Markovian switching neural networks (MSNNs) with dynamic output quantization and packet dropouts. The primary goal is to construct a multi-mode, quantized sampled-data controller that ensures stochastic stability and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> disturbance-reduction performance of the closed-loop MSNN. A Bernoulli-distributed random variable with uncertain probability is introduced to characterize the incidence of packet dropouts. To describe potential mode inconsistencies that may occur between the MSNN and controller, an exponential hidden Markov model is employed. Furthermore, the quantizer’s dynamic scaling factor is intentionally built as a piecewise function to avoid the potential division-by-zero problem. A sufficient condition for stochastic stability and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> disturbance-reduction performance is proposed, utilizing a mode- and time-dependent Lyapunov-type functional and several stochastic analysis tools. Then, through decoupling nonlinearities, a numerically efficient approach for determining the desired controller gains and parameter range associated with the dynamic scaling factor is developed. In order to facilitate comparisons, the situation with no uncertainty in the probability of packet dropouts is studied, and both analysis and design approaches are offered. Finally, two simulation examples are provided to validate the effectiveness and applicability of the developed approaches.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0016003224006732/pdfft?md5=2d6e96716cca42090f58b16df10764bf&pid=1-s2.0-S0016003224006732-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0