Complex & Intelligent Systems最新文献

Unmanned Aerial Vehicles (UAVs) have become important in an extensive range of fields such as surveillance, environmental monitoring, agriculture, infrastructure inspection, commercial applications, and many others. Ensuring stable flight and precise control of UAVs, especially in adverse weather conditions or turbulent environments, presents significant challenges. Developing control systems that can adapt to these environmental factors while ensuring safe and reliable operation is a main motivation. Considering the challenges, first, an adaptive model is identified using the input/output data sets. New adaptation laws are obtained for dynamic parameters. Then, a Type-3 (T3) Fuzzy Logic System (FLS) is used to compensate for the error of dynamic identification. T3-FLS is tuned by a sliding mode control (SMC) strategy. The robustness is analyzed considering the adaptation error using the SMC approach. The main idea is that the basic dynamics of UAVs are taken into account, and adaptation laws are designed to enhance the modeling accuracy. On the other hand, an optimized T3-FLS with SMC is introduced to eliminate the adaption errors and ensure robustness. Several simulations show that known parameters converge under uncertainty, and the stability is kept, well. Also, output signals follow the desired trajectories under dynamic perturbations, identification errors, and uncertainties.

The impact of electromagnetic radiation generated by signal transmission base stations and power stations to meet the needs of communication equipment and energy consumption on the environment has caused people concerns. Monitoring and prediction of electric and magnetic fields have become critical tasks for researchers. In this paper, we propose a granularity data method based on T–S (Takagi–Sugeno) fuzzy model, named fuzzy rule-based model, which utilizing finite rules that are determined by the deviations between the predicted values and the true values after the data goes through a granulation-degranulation mechanism, to predict the intensity of power frequency electric field and electromagnetic field. A series of experiments show that fuzzy rule-based models have better robustness and higher prediction accuracy in comparison with several existing prediction models. The improvement of the performance of the fuzzy rule-based model quantified in terms of Root Mean Squared Error is 20.86%, 51.91%, 62.28%, 65.10%, and 71.92% in comparison with that of the Ridge model, Lasso model, and a family of support vector machine model with different kernel functions, including linear kernel (SVM-linear), radial basis function (SVM-BRF), polynomial kernel (SVM-polynomial) respectively, on the electromagnetic field testing data, and 37.42%, 55.16%, 58.79%, 59.28%, 64.27% lower than that of the Ridge model, Lasso model, SVM-linear model, SVM-BRF model and SVM-polynomial model on the power frequency electric field testing data.

A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.

To overcome the challenges in underwater object detection across diverse marine environments—marked by intricate lighting, small object presence, and camouflage—we propose an innovative solution inspired by the human retina's structure. This approach integrates a cone-rod cell module to counteract complex lighting effects and introduces a reparameterized multiscale module for precise small object feature extraction. Moreover, we employ the Wise Intersection Over Union (WIOU) technique to enhance camouflage detection. Our methodology simulates the human eye's cone and rod cells' brightness and color perception using varying sizes of deep and ordinary convolutional kernels. We further augment the network's learning capability and maintain model lightness through structural reparameterization, incorporating multi-branching and multiscale modules. By substituting the Complete Intersection Over Union (CIOU) with WIOU, we increase penalties for low-quality samples, mitigating the effect of camouflaged information on detection. Our model achieved a MAP_0.75 of 72.5% on the Real-World Underwater Object Detection (RUOD) dataset, surpassing the leading YOLOv8s model by 5.8%. Additionally, the model's FLOPs and parameters amount to only 10.62 M and 4.62B, respectively, which are lower than most benchmark models. The experimental outcomes affirm our design's efficacy in addressing underwater object detection's various disturbances, offering valuable technical insights for related oceanic image processing challenges.

Multi-view subspace clustering (MVC) intends to separate out samples via integrating the complementary information from diverse views. In MVC, since the structural information in the graph is crucial to the graph learning, most of the existing algorithms construct the superficial graph from the original data by directly measuring the similarity between the first-order complementary nearest neighbors. However, the information provided by the superficial graph structure would be influenced by contaminated or absent samples. To address this problem, in the proposed method, the higher-order complementary neighbor graphs are exploited to discover the latent structural information between the samples, and fusing the latent structural information across different orders to achieve the MVC. Specifically, the higher-order neighbor graphs under different views are leveraged to estimate the missing samples. Then, to integrate the neighbor graphs of different orders, the multi-order neighbor diffusion fusion is proposed. Nevertheless, the above problem of diffusion fusion is an intractable non-convex issue. Thus, to address it, the multi-order neighbor diffusion fusion is considered as a combination problem of the solution under different order, and the heuristic algorithm is leveraged to solve it. In this way, not only the data representation under different view and also the neighbor structure under different order can be diffused under a joint optimization framework, thus the consistency and integral information among various perspectives and orders can be utilized effectively and simultaneously. Experiments on both incomplete and complete multi-view dataset demonstrate the convincingness of the high-order neighborhood structure based subspace clustering scheme by comparing with the existing approaches.

Satellite swarm networks have occupied a prominent position in many modern applications due to their low cost, simplicity of design, and flexibility. Reliability is an influential factor in the design of satellite networks with different structures. Usually, small satellites are based on COST components, which may reduce continues operability due to the lack of using backup system on board the sagecraft. Any failure in one subsystem means a complete loss of the function and data stored in this subsystem; hence the need for a reliable and applicable solution for this matter is a crucial topic. Using the redundancy strategy in satellite swarm networks increases reliability and availability. Blockchain is characterized by using a distributed ledger which enables the database to be replicated across nodes in the network and results in increasing transparency, security, and trust. This paper suggests adoption of blockchain technology in distributed multi-satellite mission swarm networks to provide a high level of reliability and availability of the entire system; the blockchain is usually used to secure system transactions in multilayer approach by storage of the key parameters in more than one node; here we suggest the adoption of this approach not only to secure satellite network transaction, but also to increase system reliability so that failure of one node can be recovered by other nodes. We compared this approach with similar traditional networks that do not use blockchain. The results show a higher reliability efficiency of 95.3% for applying blockchain technology compared to 64.3% without the use of blockchain, as well as a higher availability of 99% compared to 91%.

Saturated information load is defined as the information received by a unmanned aerial vehicle (UAV) node in a swarm network reaches the overload limit of its processing capability. When a UAV swarm performs a mission in an uncertain and adversarial complex environment, overloading of UAVs will lead to information diversion, which may cause other UAVs to experience overloading and diversion as well, affecting the transmission efficiency and robustness of the entire swarm network, which in turn affects the information sensing ability, execution ability, and coordination ability of the swarm in performing the mission. Therefore, this paper proposes a saturated information load-based UAV swarm network topology modelling method, which sets the saturated information load of the nodes in the network model in order to reasonably allocate network resources and optimise the network topology. In addition, through robustness experiments of complex networks and comparative analysis of different saturated information loads and three typical modelling methods, the saturated information load-based network structure modelling method has outstanding advantages and performance in terms of network connectivity, network communication efficiency, and destruction resistance.

The window strategy, known for its flexibility and efficiency, is extensively used in dynamic path planning. To further enhance the performance of the Automated Guided Vehicles (AGVs) sorting system, the two processes of AGV movement and path planning can be executed concurrently based on the window strategy. Nonetheless, difficulties in matching the computing time of the planning server with the moving time of AGVs may cause delays or reduced path optimality. To address the problem, this paper proposes an optimal time reuse strategy. The proposed solution controls computing time by managing path length for each planning instance, ensuring alignment with the moving time of AGVs to maximize path optimality and avoid delays. To achieve this, two aspects need to be considered. Firstly, on a systemic level, we control the entry rate of AGVs by adjusting the replanning period, thus avoiding congestion caused by excessive AGVs and maintaining high system efficiency. Secondly, we reversely control the computing time by adjusting the path length that needs to be planned for each single planning, so that it matches the moving time of AGVs. Simulation results show that our method outperforms existing top-performing methods, achieving task completion rates 1.64, 1.57, and 1.12 times faster across various map sizes. This indicates its effectiveness in synchronizing planning and movement times. The method contributes significantly to dynamic path planning methodologies, offering a novel approach to time management in AGV systems.

Increasing attention has been given to the utilization of swarm intelligent optimization algorithms to facilitate cooperative target search of unmanned aerial vehicle swarm (UAVs). However, there exist common issues associated with swarm intelligent optimization algorithms, which are low search efficiency and easy to trap in local optima. Simultaneously, the concentrated initial positioning of UAVs increase the probability of collisions between UAVs. To address these issues, this paper proposes a reinforced robotic bean optimization algorithm (RRBOA) aimed at enhancing the efficiency of UAVs for cooperative target search in unknown environments. Firstly, the algorithm employs a region segmentation exploration strategy to enhance the initialization of UAVs, ensuring a uniform distribution of UAVs to avoid collisions and the coverage capability of UAVs search. Subsequently, a neutral evolution strategy is incorporated based on the spatial distribution pattern of population, which aims to enhance cooperative search by enabling UAVs to freely explore the search space, thus improving the global exploration capability of UAVs. Finally, an adaptive Levy flight strategy is introduced to expand the search range of UAVs, enhancing the diversity of UAVs search and then preventing the UAVs search from converging to local optima. Experimental results demonstrate that RRBOA has significant advantages over other methods on nine benchmark simulations. Furthermore, the extension testing, which focuses on simulating pollution source search, confirms the effectiveness and applicability of RRBOA

The current paradigm of joint detection and tracking still requires a large amount of instance-level trajectory annotation, which incurs high annotation costs. Moreover, treating embedding training as a classification problem would lead to difficulties in model fitting. In this paper, we propose a new self-supervised multi-object tracking based on the real-time joint detection and embedding (JDE) framework, which we termed as self-supervised multi-object tracking (SS-MOT). In SS-MOT, the short-term temporal correlations between objects within and across adjacent video frames are both considered as self-supervised constraints, where the distances between different objects are enlarged while the distances between same object of adjacent frames are brought closer. In addition, short trajectories are formed by matching pairs of adjacent frames using a matching algorithm, and these matched pairs are treated as positive samples. The distances between positive samples are then minimized for futher the feature representation of the same object. Therefore, our method can be trained on videos without instance-level annotations. We apply our approach to state-of-the-art JDE models, such as FairMOT, Cstrack, and SiamMOT, and achieve comparable results to these supevised methods on the widely used MOT17 and MOT20 challenges.