IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-15 DOI: 10.1007/s40747-024-01728-1

Minghao Gao, Pengfei Yang, Hailong Tan, Qi Li

{"title":"Protocol-based set-membership state estimation for linear repetitive processes with uniform quantization: a zonotope-based approach","authors":"Minghao Gao, Pengfei Yang, Hailong Tan, Qi Li","doi":"10.1007/s40747-024-01728-1","DOIUrl":"https://doi.org/10.1007/s40747-024-01728-1","url":null,"abstract":"This paper is concerned with the zonotopic state estimation problem for a class of linear repetitive processes (LRPs) with weighted try-once-discard protocols (WTODPs) subject to uniform quantization. In such a system, the process disturbance and measurement noise are generally assumed to be unknown but bounded in certain zonotopes. The measurement data are uniformly quantized prior to entering the network. In order to effectively curb data collision, a WTODP is considered, based on which only the selected sensor is allowed to transmit the data through network. The aim of this paper is to find a zonotope that covers all possible states consistent with the system model and WTODP-based measured outputs. By using the zonotope properties, a zonotope containing all possible states is first constructed whose size is then minimized by designing an appropriate correlation matrix. Moreover, a sufficient condition is offered for the existence of an upper bound on the size of this zonotope. At last, we valid the efficacy of the developed estimation approach via an illustrate example.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"36 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-15 DOI: 10.1007/s40747-024-01764-x

Sherihan Aboelenin, Foriaa Ahmed Elbasheer, Mohamed Meselhy Eltoukhy, Walaa M. El-Hady, Khalid M. Hosny

{"title":"A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer","authors":"Sherihan Aboelenin, Foriaa Ahmed Elbasheer, Mohamed Meselhy Eltoukhy, Walaa M. El-Hady, Khalid M. Hosny","doi":"10.1007/s40747-024-01764-x","DOIUrl":"https://doi.org/10.1007/s40747-024-01764-x","url":null,"abstract":"Recently, scientists have widely utilized Artificial Intelligence (AI) approaches in intelligent agriculture to increase the productivity of the agriculture sector and overcome a wide range of problems. Detection and classification of plant diseases is a challenging problem due to the vast numbers of plants worldwide and the numerous diseases that negatively affect the production of different crops. Early detection and accurate classification of plant diseases is the goal of any AI-based system. This paper proposes a hybrid framework to improve classification accuracy for plant leaf diseases significantly. This proposed model leverages the strength of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT), where an ensemble model, which consists of the well-known CNN architectures VGG16, Inception-V3, and DenseNet20, is used to extract robust global features. Then, a ViT model is used to extract local features to detect plant diseases precisely. The performance proposed model is evaluated using two publicly available datasets (Apple and Corn). Each dataset consists of four classes. The proposed hybrid model successfully detects and classifies multi-class plant leaf diseases and outperforms similar recently published methods, where the proposed hybrid model achieved an accuracy rate of 99.24% and 98% for the apple and corn datasets.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"77 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 bi-subpopulation coevolutionary immune algorithm for multi-objective combinatorial optimization in multi-UAV task allocation

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-15 DOI: 10.1007/s40747-024-01720-9

Xi Chen, Yu Wan, Jingtao Qi, Zipeng Zhao, Yirun Ruan, Jun Tang

{"title":"A bi-subpopulation coevolutionary immune algorithm for multi-objective combinatorial optimization in multi-UAV task allocation","authors":"Xi Chen, Yu Wan, Jingtao Qi, Zipeng Zhao, Yirun Ruan, Jun Tang","doi":"10.1007/s40747-024-01720-9","DOIUrl":"https://doi.org/10.1007/s40747-024-01720-9","url":null,"abstract":"With the development of Unmanned Aerial Vehicle (UAV) technology towards multi-UAV and UAV swarm, multi-UAV cooperative task allocation has more and more influence on the success or failure of UAV missions. From the operational research point of view, such problems belong to high-dimensional combinatorial optimization problems, which makes the solving process face many challenges. One is that the discrete and high-dimensional decision variables make the quality of the solution obtained with acceptable time not guaranteed. Second, the desired solution of real missions often needs to satisfy multiple objective functions, or a set of solutions for decision-making. Therefore, this paper constructs a Multi-objective Combinatorial Optimization in Multi-UAV Task Allocation Problem (MCOTAP) model, and proposes a Bi-subpopulation Coevolutionary Immune Algorithm (BCIA). The two coevolutionary mechanisms improve the lower limit of population diversity, and the evolutionary strategy pool integrating multiple strategies and the adaptive strategy selection mechanism enhance the local search ability in the late evolution. In the experiments, BCIA competes fairly with the mainstream multi-objective evolutionary algorithms (MOEAs), multi-objective immune algorithms (MOIAs) and the recently proposed multi-UAV mission planning algorithms. The experimental results on different test problems (including several multi-objective combinatorial optimization benchmark problems and the proposed MCOTAP model) show that BCIA has superior performance in solving multi-objective combinatorial optimization problems (MCOPs). At the same time, the effectiveness of each design component of BCIA has been comprehensively verified in the ablation study.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"25 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Optimization of high-dimensional expensive multi-objective problems using multi-mode radial basis functions

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-15 DOI: 10.1007/s40747-024-01737-0

Jiangtao Shen, Xinjing Wang, Ruixuan He, Ye Tian, Wenxin Wang, Peng Wang, Zhiwen Wen

{"title":"Optimization of high-dimensional expensive multi-objective problems using multi-mode radial basis functions","authors":"Jiangtao Shen, Xinjing Wang, Ruixuan He, Ye Tian, Wenxin Wang, Peng Wang, Zhiwen Wen","doi":"10.1007/s40747-024-01737-0","DOIUrl":"https://doi.org/10.1007/s40747-024-01737-0","url":null,"abstract":"Numerous surrogate-assisted evolutionary algorithms are developed for multi-objective expensive problems with low dimensions, but scarce works have paid attention to that with high dimensions, i.e., generally more than 30 decision variables. In this paper, we propose a multi-mode radial basis functions-assisted evolutionary algorithm (MMRAEA) for solving high-dimensional expensive multi-objective optimization problems. To improve the reliability, the proposed algorithm uses radial basis functions based on three modes to cooperate to provide the qualities and uncertainty information of candidate solutions. Meanwhile, bi-population based on competitive swarm optimizer and genetic algorithm are applied for better exploration and exploitation in high-dimensional search space. Accordingly, an infill criterion based on multi-mode of radial basis functions that comprehensively considers the quality and uncertainty of candidate solutions is proposed. Experimental results on widely-used benchmark problems with up to 100 decision variables demonstrate the effectiveness of our proposal. Furthermore, the proposed method is applied to the structure optimization of the blended-wing-body underwater glider (BWBUG) and gets impressive solutions.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-15 DOI: 10.1007/s40747-024-01745-0

Zan Yang, Chen Jiang, Jiansheng Liu

{"title":"Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization","authors":"Zan Yang, Chen Jiang, Jiansheng Liu","doi":"10.1007/s40747-024-01745-0","DOIUrl":"https://doi.org/10.1007/s40747-024-01745-0","url":null,"abstract":"This paper proposes a surrogate-assisted dynamic population optimization algorithm (SDPOA) for the purpose of solving computationally expensive constrained optimization problems, in which the population is dynamically updated based on the real-time iteration information to achieve targeted searches for solutions with different qualities. Specifically, the population is dynamically constructed by simultaneously considering the real-time feasibility, convergence, and diversity information of all the previously evaluated solutions. The evolution strategies adapted to dynamic populations are designed to arrange targeted search resources for individuals with different potentials. Specifically, for mutation, targeted base solution selection for the top 2 and other center points is designed for emphasizing the exploitation in promising regions; for selection, the search sources arranged on the best and other population individuals are adaptively adjusted with the iteration progresses; for constraint handling, the diversity of infeasible solutions is integrated into the original constraint-domination principle to avoid the locality of only using constraint violation to rank infeasible solutions. For accelerating the convergence, the sparse local search is designed based on update state of the current best solution in which two excellent but non adjacent individuals are used to provide valuable guidance information for local search. Therefore, SDPOA strikes a balance between feasibility, diversity, and convergence. Empirical studies demonstrate that the SDPOA achieves the best performance among all the compared state-of-the-art algorithms, and the SDPOA can obtain new structures with smaller compliance in the design of polyline-based core sandwich structures.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"93 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 temporal-difference learning via deep neural network function approximation: a non-asymptotic analysis

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-15 DOI: 10.1007/s40747-024-01757-w

Guoyong Wang, Tiange Fu, Ruijuan Zheng, Xuhui Zhao, Junlong Zhu, Mingchuan Zhang

引用次数: 0

A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-15 DOI: 10.1007/s40747-024-01768-7

Ming Jiang, Zhiwei Liu, Yan Xu

{"title":"A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network","authors":"Ming Jiang, Zhiwei Liu, Yan Xu","doi":"10.1007/s40747-024-01768-7","DOIUrl":"https://doi.org/10.1007/s40747-024-01768-7","url":null,"abstract":"Traffic prediction plays an increasingly important role in intelligent transportation systems and smart cities. Both travelers and urban managers rely on accurate traffic information to make decisions about route selection and traffic management. Due to various factors, both human and natural, traffic data often contains missing values. Addressing the impact of missing data on traffic flow prediction has become a widely discussed topic in the academic community and holds significant practical importance. Existing spatiotemporal graph models typically rely on complete data, and the presence of missing values can significantly degrade prediction performance and disrupt the construction of dynamic graph structures. To address this challenge, this paper proposes a neural network architecture designed specifically for missing data scenarios—graph convolutional recurrent ordinary differential equation network (GCRNODE). GCRNODE combines recurrent networks based on ordinary differential equation (ODE) with spatiotemporal memory graph convolutional networks, enabling accurate traffic prediction and effective modeling of dynamic graph structures even in the presence of missing data. GCRNODE uses ODE to model the evolution of traffic flow and updates the hidden states of the ODE through observed data. Additionally, GCRNODE employs a data-independent spatiotemporal memory graph convolutional network to capture the dynamic spatial dependencies in missing data scenarios. The experimental results on three real-world traffic datasets demonstrate that GCRNODE outperforms baseline models in prediction performance under various missing data rates and scenarios. This indicates that the proposed method has stronger adaptability and robustness in handling missing data and modeling spatiotemporal dependencies.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"52 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-15 DOI: 10.1038/s42256-024-00972-x

Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian

{"title":"Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning","authors":"Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian","doi":"10.1038/s42256-024-00972-x","DOIUrl":"https://doi.org/10.1038/s42256-024-00972-x","url":null,"abstract":"Accurately predicting battery lifetime in early cycles holds tremendous value in real-world applications. However, this task poses significant challenges due to diverse factors influencing complex battery capacity degradation, such as cycling protocols, ambient temperatures and electrode materials. Moreover, cycling under specific conditions is both resource-intensive and time-consuming. Existing predictive models, primarily developed and validated within a restricted set of ageing conditions, thus raise doubts regarding their extensive applicability. Here we introduce BatLiNet, a deep learning framework tailored to predict battery lifetime reliably across a variety of ageing conditions. The distinctive design is integrating an inter-cell learning mechanism to predict the lifetime differences between two battery cells. This mechanism, when combined with conventional single-cell learning, enhances the stability of lifetime predictions for a target cell under varied ageing conditions. Our experimental results, derived from a broad spectrum of ageing conditions, demonstrate BatLiNet’s superior accuracy and robustness compared to existing models. BatLiNet also exhibits transferring capabilities across different battery chemistries, benefitting scenarios with limited resources. We expect this study could promote exploration of cross-cell insights and facilitate battery research across comprehensive ageing factors.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"24 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 generalized diffusion model for remaining useful life prediction with uncertainty

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-15 DOI: 10.1007/s40747-024-01773-w

Bincheng Wen, Xin Zhao, Xilang Tang, Mingqing Xiao, Haizhen Zhu, Jianfeng Li

{"title":"A generalized diffusion model for remaining useful life prediction with uncertainty","authors":"Bincheng Wen, Xin Zhao, Xilang Tang, Mingqing Xiao, Haizhen Zhu, Jianfeng Li","doi":"10.1007/s40747-024-01773-w","DOIUrl":"https://doi.org/10.1007/s40747-024-01773-w","url":null,"abstract":"Forecasting the remaining useful life (RUL) is a crucial aspect of prognostics and health management (PHM), which has garnered significant attention in academic and industrial domains in recent decades. The accurate prediction of RUL relies on the creation of an appropriate degradation model for the system. In this paper, a general representation of diffusion process models with three sources of uncertainty for RUL estimation is constructed. According to time-space transformation, the analytic equations that approximate the RUL probability distribution function (PDF) are inferred. The results demonstrate that the proposed model is more general, covering several existing simplified cases. The parameters of the model are then calculated utilizing an adaptive technique based on the Kalman filter and expectation maximization with Rauch-Tung-Striebel (KF-EM-RTS). KF-EM-RTS can adaptively estimate and update unknown parameters, overcoming the limits of strong Markovian nature of diffusion model. Linear and nonlinear degradation datasets from real working environments are used to validate the proposed model. The experiments indicate that the proposed model can achieve accurate RUL estimation results.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Time optimal trajectory planning of robotic arm based on improved sand cat swarm optimization algorithm

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-01-15 DOI: 10.1007/s10489-024-06124-3

Zhenkun Lu, Zhichao You, Binghan Xia

{"title":"Time optimal trajectory planning of robotic arm based on improved sand cat swarm optimization algorithm","authors":"Zhenkun Lu, Zhichao You, Binghan Xia","doi":"10.1007/s10489-024-06124-3","DOIUrl":"10.1007/s10489-024-06124-3","url":null,"abstract":"<div>In order to address the issue of automatic charging for electric vehicles, a hanging automatic charging system was proposed, with a particular focus on the time-optimal trajectory planning of the robotic arm within the system. Additionally, a multi-strategy improved Sand Cat Swarm Optimization Algorithm (YSCSO) was put forth as a potential solution. The 0805A six-axis manipulator was selected as the research object, and a kinematic model was constructed using the D-H parameter method. The 5-7-5 polynomial interpolation function was proposed and solved to construct the motion trajectory of the robotic arm joint. The cubic chaos-refraction inverse learning, introduced to initialize the population based on the sand cat swarm algorithm SCSO, balances the relationship between the elite pool weighted guided search behavior and the spiral Lévy flight predation behavior through the use of a dynamic nonlinear sensitivity range. Furthermore, the vigilance behavior mechanism of the sand cat was increased to improve the overall optimization performance of the algorithm. The proposed method was applied to 36 benchmark functions of global optimization, and the improvement strategy, convergence behavior, population diversity, exploration, and development of the algorithm were experimentally analyzed. The results demonstrated that the proposed method exhibited superior performance, with 80.86% of the test results significantly different from those of the comparison algorithm. Three constrained mechanical design optimization problems were employed to assess the algorithm’s practicality in engineering applications. Subsequently, the algorithm was applied to the optimal trajectory planning of a robotic arm, resulting in a significant reduction in the optimized joint motion time, a smooth and continuous kinematic curve devoid of abrupt changes, and a 42.72% reduction in motion time. These findings further substantiate the theoretical feasibility and superiority of the algorithm in addressing engineering challenges.</div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

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