Complex & Intelligent Systems

Similarity measures are very effective and meaningful tool used for evaluating the closeness between any two attributes which are very important and valuable to manage awkward and complex information in real-life problems. Therefore, for better handing of fuzzy information in real life, Ullah et al. (Complex Intell Syst 6(1): 15–27, 2020) recently introduced the concept of complex Pythagorean fuzzy set (CPyFS) and also described valuable and dominant measures, called various types of distance measures (DisMs) based on CPyFSs. The theory of CPyFS is the essential modification of Pythagorean fuzzy set to handle awkward and complicated in real-life problems. Keeping the advantages of the CPyFS, in this paper, we first construct an example to illustrate that a DisM proposed by Ullah et al. does not satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Then, combining the 3D Hamming distance with the Hausdorff distance, we propose a new DisM for CPyFSs, which is proved to satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Moreover, similarly to some DisMs for intuitionistic fuzzy sets, we present some other new complex Pythagorean fuzzy DisMs. Finally, we apply our proposed DisMs to a building material recognition problem and a medical diagnosis problem to illustrate the effectiveness of our DisMs. Finally, we aim to compare the proposed work with some existing measures is to enhance the worth of the derived measures.

Extractive approaches have been the mainstream paradigm for identifying overlapping entity–relation extraction. However, limited by their inherently methodological flaws, which hardly deal with three issues: hierarchical dependent entity–relations, implicit entity–relations, and entity normalization. Recent advances have proposed an effective solution based on generative language models, which cast entity–relation extraction as a sequence-to-sequence text generation task. Inspired by the observation that humans learn by getting to the bottom of things, we propose a novel framework, namely GenRE, Generative multi-turn question answering with contrastive learning for entity–relation extraction. Specifically, a template-based question prompt generation first is designed to answer in different turns. We then formulate entity–relation extraction as a generative question answering task based on the general language model instead of span-based machine reading comprehension. Meanwhile, the contrastive learning strategy in fine-tuning is introduced to add negative samples to mitigate the exposure bias inherent in generative models. Our extensive experiments demonstrate that GenRE performs competitively on two public datasets and a custom dataset, highlighting its superiority in entity normalization and implicit entity–relation extraction. (The code is available at https://github.com/lovelyllwang/GenRE ).

In this paper, we present HoloSLAM which is a novel solution to landmark detection issues in the simultaneous localization and mapping (SLAM) problem in autonomous robot navigation. The approach integrates real and virtual worlds to create a novel mapping robotic environment employing a mixed-reality technique and a sensor, namely Microsoft HoloLens. The proposed methodology allows the robot to interact and communicate with its new environment in real-time and overcome the limitations of conventional landmark-based SLAMs by creating and placing some virtual landmarks in situations where real landmarks are scarce, non-existent, or hard to be detected. The proposed approach enhances the robot’s perception and navigation capabilities in various robot environments. The overall process contributes to the robot’s more accurate understanding of its environment; thus, enabling it to navigate with greater efficiency and effectiveness. In addition, the newly implemented HoloSLAM offers the option to guide the robot to a specific location eliminating the need for explicit navigation instructions. The open-source framework proposed in this paper can benefit the robotics community by providing a more reliable, realistic, and robust mapping solution. The experiments show that the Ellipsoidal-HoloSLAM system is accurate and effectively overcomes the limitations of conventional Ellipsoidal-SLAMs, providing a more precise and detailed mapping of the robot’s environment.

Sensor technology is developing rapidly and up to date. The lifetime of the Wireless Sensor Network (WSN) has also attracted many researchers, and the location of the Base Station (BS) plays a crucial role in prolonging the lifetime. The energy consumption in the WSN occurs during transmission of data and selection of cluster-head nodes. A reasonable location of the BS prolongs the lifetime of the WSN. This study proposes a Levy Flight Edge Regeneration Black Hole algorithm (LEBH) to speed up convergence and enhance optimization capabilities. The performance of LEBH and other heuristic algorithms are compared on CEC 2013. The result shows that the LEBH outperforms other heuristics in most cases. In this study, the energy consumption and WSN models are simulated. Subsequently, the proposed LEBH is combined with relocation technology to change the location of the BS to prolong the lifetime. Simulation results show LEBH-BS prolongs the lifetime of the WSN better than random-base and static-base stations and other heuristic algorithms in most cases.

A T-spherical uncertain linguistic set (TSULS) is not only an expanded form of the T-spherical fuzzy set and the uncertain linguistic set but can also integrate the quantitative judging ideas and qualitative assessing information of decision-makers. For the description of complex and uncertain assessment data, TSULS is a powerful tool for the precise description and reliable processing of information data. However, the existing multi-attribute border approximation area comparison (MABAC) method has not been studied in TSULS. Thus, the goal of this paper is to extend and improve the MABAC method to tackle group decision-making problems with completely unknown weight information in the TSUL context. First, the cross-entropy measure and the interactive operation laws for the TSUL numbers are defined, respectively. Then, the two interactive aggregation operators for TSUL numbers are developed, namely T-spherical uncertain linguistic interactive weighted averaging and T-spherical uncertain linguistic interactive weighted geometric operators. Their effective properties and some special cases are also investigated. Subsequently, a new TSULMAGDM model considering the DM’s behavioral preference and psychology is built by integrating the interactive aggregation operators, the cross-entropy measure, prospect theory, and the MABAC method. To explore the effectiveness and practicability of the proposed model, an illustrative example of Sustainable Waste Clothing Recycling Partner selection is presented, and the results show that the optimal solution is h3. Finally, the reliable, valid, and generalized nature of the method is further verified through sensitivity analysis and comparative studies with existing methods.

With the rapid advancement and growth of computer networks, there have been greater and greater demands for remote user password authentication protocols. In current ages, smartcard-based authentication protocol has formed the standard with their incredibly insubstantial, user-friendly equipment and low-cost apps. In this study, we proposed an effective robust authentication protocol using the conformable chaotic map, where a conformable calculus is a branch of newly appearing fractional calculus. It has a magnificent property, because it formulates using a controller term. We shall also offer formal proof of smooth execution of the proposed authenticated protocol. Our new protocol is more secure as compared to several comparable protocols.

This study aims to model a workforce-planning problem of pilot roles which include captain and first officer in an airline company and to make an efficient plan having maximal utilization of minimum workforce requirements. To tackle this problem, a mixed integer programming based a new mathematical model is proposed. The model considers different conditions such as employing pilots with different skill types, resignations, retirements, holidays of pilots, transitions between different skills regarding needs of the demands during the planning horizon. The application of the proposed approach is investigated using a case study with real-world data from an airline company in Turkey. The results show that a company can use transitions instead of new employment and this is a more suitable medium-term production and human resource planning decision.

Detecting text within medical images presents a formidable challenge in the domain of computer vision due to the intricate nature of textual backgrounds, the dense text concentration, and the possible existence of extreme aspect ratios. This paper introduces an effective and precise text detection system tailored to address these challenges. The system incorporates an optimized segmentation module, a trainable post-processing method, and leverages a vision-language pre-training model (oCLIP). Specifically, our segmentation head integrates three essential components: the Feature Pyramid Network (FPN) module, which combines a residual structure and channel attention mechanism; the Efficient Feature Enhancement Module (EFEM); and the Multi-Scale Feature Fusion with RSEConv (MSFM-RSE), designed specifically for multi-scale feature fusion based on RSEConv. By introducing a residual structure and channel attention mechanism into the FPN module, the convolutional layers are replaced with RSEConv layers that employ a channel attention mechanism, further augmenting the representational capacity of the feature maps. The EFEM, designed as a cascaded U-shaped module, incorporates a spatial attention mechanism to introduce multi-level information, thereby enhancing segmentation performance. Subsequently, the MSFM-RSE adeptly amalgamates features from various depths and scales of the EFEM to generate comprehensive final features tailored for segmentation purposes. Additionally, a post-processing module employs a differentiable binarization strategy, allowing the segmentation network to dynamically determine the binarization threshold. Building on the system’s improvement, we introduce a vision-language pre-training model that undergoes extensive training on various visual language understanding tasks. This pre-trained model acquires detailed visual and semantic representations, further reinforcing both the accuracy and robustness in text detection when integrated with the segmentation module. The performance of our proposed model was evaluated through experiments on medical text image datasets, demonstrating excellent results. Multiple benchmark experiments validate its superior performance in comparison to existing methods. Code is available at: https://github.com/csworkcode/VLDBNet .

The service robots of space station in-cabin have attracted more and more attention. The space in-cabin robot with a pneumatic continuum arm is studied in this paper. It could be safer, more efficient and more flexible than the space rigid robot. However, the coupling motion of the moving base and the pneumatic continuum continuous arm brings a new challenge for controlling the end-effector to track the desired path. In this paper, a new control method based on the zeroing neural network (ZNN) is developed to solve the high-precision kinematics trajectory tracking control problem of unknown models. The real-time Jacobian matrix of the in-cabin robots with a pneumatic continuum arm is estimated by the input–output information when the parameter and the structure of the kinematic model are unknown. Moreover, this paper also employs a modified activation function power-sigmoid activation function (PSAF) to improve the robustness. In addition, it is proved through the Lyapunov stability theory that the proposed control approach is convergent and stable. Finally, the simulation results are given to show the effectiveness and robustness of the proposed control method for space in-cabin robots with a pneumatic continuum arm.

A DP-TABU algorithm is proposed which can effectively solve the multi-line scheduling problem of single Deport (SD-ML-VSP). The multi-line regional coordinated dispatch of the single-line deport of the bus is to solve the problems of idle low-peak vehicles and insufficient peak capacity in single-line scheduling. The capacity of multiple lines at the same station is adjusted to realize resource sharing such as timetables, vehicles, and drivers. Shared capacity such as bus departure intervals and bus schedules. Taking the regional scheduling of multiple lines at the same station as the service object, a vehicle operation planning model based on the objective of optimal public transportation resources (minimum bus and driver costs) is established to optimize the vehicle dispatching mode of multiple lines. We applied this algorithm to the three lines S105, S107, and S159 of Zhengzhou Public Transport Corporation, and the results proved that the algorithm is effective. Through comparison with manual scheduling and simulated annealing algorithm, the advantages of DP-TABU algorithm in performance optimization and robustness are further verified.