Complex & Intelligent Systems最新文献_第2页

Robust underwater object tracking with image enhancement and two-step feature compression 基于图像增强和两步特征压缩的鲁棒水下目标跟踪

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-17 DOI: 10.1007/s40747-024-01755-y

Jiaqing Li, Chaocan Xue, Xuan Luo, Yubin Fu, Bin Lin

引用次数: 0

Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination Mape：利用多源对抗扰动消除防御可转移的对抗攻击

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-17 DOI: 10.1007/s40747-024-01770-z

Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang

{"title":"Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination","authors":"Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang","doi":"10.1007/s40747-024-01770-z","DOIUrl":"https://doi.org/10.1007/s40747-024-01770-z","url":null,"abstract":"Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model and its output information, transferable adversarial attacks exhibit a notably high stealthiness and detection difficulty, making them a significant focus of defense. In this work, we propose a deep learning defense known as multi-source adversarial perturbations elimination (MAPE) to counter diverse transferable attacks. MAPE comprises the single-source adversarial perturbation elimination (SAPE) mechanism and the pre-trained models probabilistic scheduling algorithm (PPSA). SAPE utilizes a thoughtfully designed channel-attention U-Net as the defense model and employs adversarial examples generated by a pre-trained model (e.g., ResNet) for its training, thereby enabling the elimination of known adversarial perturbations. PPSA introduces model difference quantification and negative momentum to strategically schedule multiple pre-trained models, thereby maximizing the differences among adversarial examples during the defense model’s training and enhancing its robustness in eliminating adversarial perturbations. MAPE effectively eliminates adversarial perturbations in various adversarial examples, providing a robust defense against attacks from different substitute models. In a black-box attack scenario utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 95.1% on CIFAR-10 and over 71.5% on Mini-ImageNet, demonstrating state-of-the-art performance.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"86 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987981","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

Enhancing navigation performance in unknown environments using spiking neural networks and reinforcement learning with asymptotic gradient method 利用尖峰神经网络和渐近梯度强化学习增强未知环境下的导航性能

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-17 DOI: 10.1007/s40747-024-01777-6

Xiaode Liu, Yufei Guo, Yuanpei Chen, Jie Zhou, Yuhan Zhang, Weihang Peng, Xuhui Huang, Zhe Ma

{"title":"Enhancing navigation performance in unknown environments using spiking neural networks and reinforcement learning with asymptotic gradient method","authors":"Xiaode Liu, Yufei Guo, Yuanpei Chen, Jie Zhou, Yuhan Zhang, Weihang Peng, Xuhui Huang, Zhe Ma","doi":"10.1007/s40747-024-01777-6","DOIUrl":"https://doi.org/10.1007/s40747-024-01777-6","url":null,"abstract":"Achieving accurate and generalized autonomous navigation in unknown environments poses a significant challenge in robotics and artificial intelligence. Animals exhibits superlative navigation capabilities by combining the representation of internal neurals and sensory cues of self-motion and external information. This paper proposes a brain-inspired navigation method based upon the spiking neural networks (SNN) and reinforcement learning, integrated with a lidar system that serves as the local environment explorer, by which realizes high performance of obstacle avoidance and target arrival in mapless circumstances. An asymptotic gradient method is introduced to optimize the backpropagation during training, which facilitates the improvement of model robustness. The results of our experiments conducted on the Gazebo platform showcase how our approach effectively improves navigation performance in various intricate environments. Our approach yielded a higher success navigation rate ranging from 2% to 5%, depending on the SNN timesteps. Considering the inherent lower computational cost of SNN, this work contributes to advancing the fusion of SNN and reinforcement learning techniques for energy-efficient autonomous navigation tasks in real-world mapless scenarios.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"30 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987982","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

DMR: disentangled and denoised learning for multi-behavior recommendation 多行为推荐的去纠缠和去噪学习

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-16 DOI: 10.1007/s40747-024-01778-5

Yijia Zhang, Wanyu Chen, Fei Cai, Zhenkun Shi, Feng Qi

{"title":"DMR: disentangled and denoised learning for multi-behavior recommendation","authors":"Yijia Zhang, Wanyu Chen, Fei Cai, Zhenkun Shi, Feng Qi","doi":"10.1007/s40747-024-01778-5","DOIUrl":"https://doi.org/10.1007/s40747-024-01778-5","url":null,"abstract":"In recommender systems, leveraging auxiliary behaviors (e.g. view, cart) to enhance the recommendation in the target behavior (e.g. purchase) is crucial for mitigating the sparsity issue inherent in single-behavior recommendation. This has given rise to the multi-behavior recommendation (MBR). Existing MBR task faces two primary challenges. First, the irrelevant auxiliary behaviors that do not align with the target behavior, can negatively impact the prediction accuracy for user preference in the target behavior. Second, these methods typically learn coarse-grained user preferences, failing to model the consistency and distinctiveness among multiple behaviors at a fine-grained level. To address these issues, we propose a disentangled and denoised model for multi-behavior recommendation (DMR), which employs user preferences reflected in the target behavior to guide the learning of user and item embeddings in auxiliary behaviors. Specifically, we first design a disentangled graph convolutional network, modeling the fine-grained user preference under multiple behaviors in view of item attribute domains. We also propose a denoised contrastive learning strategy, where we align the user preferences in multiple behaviors by reducing the influence of noisy data existing in auxiliary behaviors. Experimental results on two real-world datasets show the proposal can improve the performance of MBR models effectively, which achieves on average 3.12% on the Retailrocket dataset and 3.28% on the Beibei dataset over the performance of state-of-the-art baselines. Extensive experiments also demonstrate our model’s competitive performance for fine-grained preference learning and denoised learning.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"45 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987108","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

Protocol-based set-membership state estimation for linear repetitive processes with uniform quantization: a zonotope-based approach 基于协议的均匀量化线性重复过程的集合成员状态估计：一种基于区顶的方法

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 基于卷积神经网络和视觉转换器的植物叶片病害检测与分类混合框架

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 多无人机任务分配多目标组合优化的双亚群协同进化免疫算法

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 基于多模径向基函数的高维昂贵多目标问题优化

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 基于代理辅助动态种群进化优化的计算昂贵约束问题

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 基于深度神经网络函数逼近的自适应时间差学习：非渐近分析

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