Complex & Intelligent Systems最新文献

Multi-attribute group decision-making is an important research field in decision science, and its theories and methods have been widely applied to engineering, economics and management. However, as the information embedded volume and complexity of decision-making expand, the diversity and heterogeneity of decision-making groups present significant challenges to the decision-making process. In order to effectively address these challenges, this paper defines the concept of spherical Z-number, which is a fuzzy number that takes into account a wide range of evaluation and its reliability. Additionally, a group decision-making model in a spherical Z-number environment is proposed. First, an objective phased tracking method is used to determine expert weights, maintain the consistency in decision-making group evaluations. The gained and lost dominance score method is combined with prospect theory to integrate expert psychological behavior when facing risks. The proposed method considers both group utility and individual regret, and balances the gains and losses of various options in the decision-making process. Finally, in response to the 3R principle, the model is employed to address the shared e-bike recycling supplier selection problem and to assess the viability of the decision-making outcomes. The results demonstrate that the model is robust in the context of varying parameter configurations. Moreover, the correlation coefficients between its ranking outcomes and those of alternative methodologies are all above 0.77, and its average superiority degree is 1.121, which is considerably higher than that of other methods. Consequently, the model's effectiveness and superiority are substantiated.

In Automated Fiber Placement (AFP), the substantial structure of the placement robot, the variable mold shapes, and the limited free space pose significant challenges for planning collision-free robot transitions. The task involves planning a collision-free path within the robot's high-dimensional configuration space. Informed RRT* is a common approach for such problems but often struggles with efficiency and path quality in environments with large informed sampling spaces influenced by obstacles. To address these issues, this paper proposes an improved Informed RRT* algorithm with a Local Knowledge Acceleration sampling strategy (LKA-Informed RRT*), aimed at enhancing planning efficiency and adaptability in complex obstacle settings. An Adaptive Sampling Control (ASC) rate is introduced, measuring the algorithm’s convergence speed, guides the algorithm to switch between informed and local sampling adaptively. The proposed local sampling method uses failure nodes from the exploration process to estimate obstacle distributions, steering sampling toward regions that expedite path convergence. Experimental results show that LKA-Informed RRT* significantly outperforms state-of-the-art algorithms in convergence efficiency and path cost. Compared to the original Informed RRT*, the proposed method reduces planning time by about 60%, substantially boosting efficiency for collision-free transitions in complex environments.

Network traffic intrusion detection technology plays an important role in host and platform security. At present, machine learning and deep learning methods are often used for network traffic intrusion detection. However, the imbalance of relevant data sets will cause the model algorithm to learn the features of the majority categories and ignore the features of the minority categories, which will seriously affect the precision of network intrusion detection models. The number of samples of the attacks is much less than the number of normal samples. The classification performance on unbalanced data sets is poor and can not identify the minority attack samples well. To solve these problems, this paper proposed the resampling mechanism, which used random undersampling for the majority categories samples and K-Smote oversampling for the minority categories samples, so as to generate a more balanced data set and improve the model's detection rate for the minority categories. This paper proposed the Self-Attention with Gate (SAG) and BiGRU network model for intrusion detection on the CICIDS2017 data set, which can fully extract high-dimensional information from data samples and improve the detection rate of network attacks. The Self-Attention with Gate mechanism (SAG) based on the Transformer performed the feature extraction, filtered the irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. Compared to the experimental results of other algorithms, such as Random Forest (RF) and BiGRU, it can be found that the overall classification precision for the SAG-BiGRU model in this paper is much higher and also has a good effect on the NSL-KDD data set.

This paper proposes a robust image encryption algorithm that utilizes a Novel three-dimensional (3D) Chaotic map and an Enhanced Logistic Sine System (ELSS). We leverage the unpredictability of 3D chaotic dynamics alongside the complexity of ELSS and DNA Sequence to forge a formidable image encryption scheme. Firstly, the image pixels are converted from decimal to hexadecimal notation and sorted in a 1D pixel array carrying a unique sequence of three channels of the RGB image. Secondly, the secret key is appended to XOR, the values with that 1-D pixels array. Thirdly, values are sorted by performing the binary right shift operation and encoded into DNA. Fourthly, a novel chaotic map is used to perform scrambling operations. Lastly, a novel enormous keyspace ELSS is used to perform efficient Border and Cross-Border (B &CB) pixel exchange, further enhancing the encryption quality of the proposed algorithm. Comprehensive security analysis proved that the proposed algorithm exhibits remarkable resilience against powerful known and chosen plaintext attacks and other prevalent cryptanalysis attacks, including differential attacks and exhaustive key search attacks. Henceforth, the proposed algorithm’s superior security and low computational cost make it an ideal choice for real-time secure image communication across various platforms, including satellite, multimedia, and military communications.

The linguistic q-rung orthopair fuzzy ((L^{q}ROF)) set serves as a useful way of presenting uncertain information by offering more space for decision experts. In the present research, we first link the concepts of Hamacher t-norm and t-conorm with the frame of (L^{q}ROF) numbers to develop and analyze the innovative (L^{q}ROF) Hamacher operations. Then, following the proposed Hamacher’s norm operations, a series of aggregation operators including (L^{q}ROF) weighted averaging, (L^{q}ROF) ordered weighted averaging, (L^{q}ROF) hybrid averaging, (L^{q}ROF) weighted geometric, (L^{q}ROF) ordered weighted geometric, (L^{q}ROF) hybrid geometric operators are investigated. Some interesting aspects of these AOs are also presented. We further develop evaluation based on distance from average solution (EDAS) approach in light of the newly outlined concepts to cope with (L^{q}ROF) decision-making problems where the weight information of criteria is fully unknown, ultimately, the practicality of the framed approach is demonstrated through an empirical case, and a detailed analysis is carried out to showcase the methodology dominance.

The influence maximization problem that has drawn a great deal of attention from researchers aims to identify a subset of influential spreaders that can maximize the expected influence spread in social networks. Existing works on the problem primarily concentrate on developing non-adaptive policies, where all seeds will be ignited at the very beginning of the diffusion after the identification. However, in non-adaptive policies, budget redundancy could occur as a result of some seeds being naturally infected by other active seeds during the diffusion process. In this paper, the adaptive seeding policies are investigated for the intractable adaptive influence maximization problem. Based on deep learning model, a novel approach named graph convolutional networks with self-attention mechanism (ATGCN) is proposed to address the adaptive influence maximization as a regression task. A controlling parameter is introduced for the adaptive seeding model to make a tradeoff between the spreading delay and influence coverage. The proposed approach leverages the self-attention mechanism to dynamically assign importance weight to node representations efficiently to capture the node influence feature information relevant to the adaptive influence maximization problem. Finally, intensive experimental findings on six real-world social networks demonstrate the superiorities of the adaptive seeding policy over the state-of-the-art baseline methods to the conventional influence maximization problem. Meanwhile, the proposed adaptive seeding policy ATGCN improves the influence spread rate by up to 7% in comparison to the existing state-of-the-art greedy-based adaptive seeding policy.

In this paper, based on facial landmark approaches, the possible vulnerability of ensemble algorithms to the FGSM attack has been assessed using three commonly used models: convolutional neural network-based antialiasing (A_CNN), Xc_Deep2-based DeepLab v2, and SqueezeNet (Squ_Net)-based Fire modules. Firstly, the three individual deep learning classifier-based Facial Emotion Recognition (FER) classifications have been developed; the predictions from all three classifiers are then merged using majority voting to develop the HEM_Net-based ensemble model. Following that, an in-depth investigation of their performance in the case of attack-free has been carried out in terms of the Jaccard coefficient, accuracy, precision, recall, F1 score, and specificity. When applied to three benchmark datasets, the ensemble-based method (HEM_Net) significantly outperforms in terms of precision and reliability while also decreasing the dimensionality of the input data, with an accuracy of 99.3%, 87%, and 99% for the Extended Cohn-Kanade (CK+), Real-world Affective Face (RafD), and Japanese female facial expressions (Jaffee) data, respectively. Further, a comprehensive analysis of the drop in performance of every model affected by the FGSM attack is carried out over a range of epsilon values (the perturbation parameter). The results from the experiments show that the advised HEM_Net model accuracy declined drastically by 59.72% for CK + data, 42.53% for RafD images, and 48.49% for the Jaffee dataset when the perturbation increased from A to E (attack levels). This demonstrated that a successful Fast Gradient Sign Method (FGSM) can significantly reduce the prediction performance of all individual classifiers with an increase in attack levels. However, due to the majority voting, the proposed HEM_Net model could improve its robustness against FGSM attacks, indicating that the ensemble can lessen deception by FGSM adversarial instances. This generally holds even as the perturbation level of the FGSM attack increases.

In this paper, a multi-fault tolerant controller considering actuator saturation is proposed. Based on the adaptive dynamic programming(ADP) algorithm, the fault tolerant control of the reconfigurable manipulator with sensor and actuator faults are carried out. Firstly, combined with the state space expression, the nonlinear transformation of sensor fault is performed by adopting the differential homeomorphism principle. An improved cost function is constructed based on the fault estimation function obtained by the fault observer, and combined with hyperbolic tangent function to deal with input constraint problem. Then, an evaluation neural network (NN) is established and the Hamilton–Jacobi–Bellman (HJB) equation is solved by online strategy iterative algorithm. Furthermore, based on Lyapunov theorem, the stability of reconfigurable manipulator systems with multi-fault are proved. Lastly, the simulation studies are used to certify the effectiveness of the presented fault tolerant control (FTC) scheme.

This paper addresses the challenge of large-scale electric vehicle (EV) charging scheduling during peak demand periods, such as holidays or rush hours. The growing EV industry has highlighted the shortcomings of current scheduling plans, which struggle to manage surge large-scale charging demands effectively, thus posing challenges to the EV charging management system. Deep reinforcement learning, known for its effectiveness in solving complex decision-making problems, holds promise for addressing this issue. To this end, we formulate the problem as a Markov decision process (MDP). We propose a deep Q-learning (DQN) based algorithm to improve EV charging service quality as well as minimizing average queueing times for EVs and average idling times for charging devices (CDs). In our proposed methodology, we design two types of states to encompass global scheduling information, and two types of rewards to reflect scheduling performance. Based on this designing, we developed three modules: a fine-grained feature extraction module for effectively extracting state features, an improved noise-based exploration module for thorough exploration of the solution space, and a dueling block for enhancing Q value evaluation. To assess the effectiveness of our proposal, we conduct three case studies within a complex urban scenario featuring 34 charging stations and 899 scheduled EVs. The results of these experiments demonstrate the advantages of our proposal, showcasing its superiority in effectively locating superior solutions compared to current methods in the literature, as well as its efficiency in generating feasible charging scheduling plans for large-scale EVs. The code and data are available by accessing the hyperlink: https://github.com/paperscodeyouneed/A-Noisy-Dueling-Architecture-for-Large-Scale-EV-ChargingScheduling/tree/main/EV%20Charging%20Scheduling.

Language models and other recent machine learning paradigms blur the distinction between generative and discriminative tasks, in a continuum that is regulated by the degree of pre- and post-supervision that is required from users, as well as the tolerated level of error. In few-shot inference, we need to find a trade-off between the number and cost of the solved examples that have to be supplied, those that have to be inspected (some of them accurate but others needing correction) and those that are wrong but pass undetected. In this paper, we define a new Supply-Inspect Cost Framework, associated graphical representations and comprehensive metrics that consider all these elements. To optimise few-shot inference under specific operating conditions, we introduce novel algorithms that go beyond the concept of rejection rules in both static and dynamic contexts. We illustrate the effectiveness of all these elements for a transformative domain, data wrangling, for which language models can have a huge impact if we are able to properly regulate the reliability-usability trade-off, as we do in this paper.