Complex & Intelligent Systems最新文献

Abstract

In dealing with the real-world optimization problems, a decision-maker has to frequently face the ambiguity and hesitancy due to various uncontrollable circumstances. Rough set theory has emerged as an indispensable tool for representing this ambiguity because of its characteristic of incorporating agreement and understanding of all the involved specialists and producing more realistic conclusions. This paper studies an application of the rough set theory for a multi-objective non-linear programming problem that originates for the management of solid wastes. Municipal solid waste management is a global problem that affects every country. Because of the poor waste management system in many nations, the bulk of municipal solid waste is disposed of in open landfills with no recovery mechanism. Hence, an effective and long term waste management strategy is the demand of the day. This research offers an incinerating, composting, recycling, and disposing system for the long-term management of the municipal solid waste. A model for the municipal solid waste management with the goal of minimizing the cost of waste transportation, cost of waste treatment and maximizing the revenue generated from various treatment facilities is developed under rough interval environment. To tackle the conflicting nature of different objectives, an approach is proposed that gives the optimistic and pessimistic views of the decision-maker for optimizing the proposed model. Also, the biasness/preference of the decision-maker for a specific objective is handled by establishing the respective non-linear membership and non-membership functions instead of the linear ones. Finally, to demonstrates the practicality of the proposed methodology, a case study is solved and the obtained Pareto-optimal solution has been compared to those obtained by the existing approaches.

Internet of Things devices generate a large number of heterogeneous workloads in real-time that require specific application to tackle, and the inability to communicate between devices and communication base stations due to complex scenarios is a thorny issue. Service caching play a key role in managing specific-request workload from devices, and unmanned aerial vehicles with computation and communication functions can effectively solve communication barrier between devices and ground base stations. In addition, the joint optimization of workload offloading and service cache placement is a key issue. Accordingly, we design an unmanned aerial vehicle-enabled mobile edge computing system with multiple devices, unmanned aerial vehicles and edge servers. The proposed framework takes into account the randomness of workload arrival, the time-varying nature of channel states, the limitations of the hosting service caching, and wireless communication blocking. Furthermore, we designed workload offloading and service caching hosting decision-making optimization problems to minimize the long-term weighted average latency and energy consumption costs. To tackle this joint optimization problem, we propose a request-specific workload offloading and service caching decision-making scheme based on the medley deep reinforcement learning scheme. To this end, the proposed scheme is decomposed into two-stage optimization subproblems: the workload offloading decision-making problem and the service caching hosting selection problem. In terms of the first subproblem, we model each device as a learning agent and propose the workloads offloading decision-making scheme based on multi-agent deep deterministic policy gradient. For the second subproblem, we present the decentralized double deep Q-learning scheme to tackle the service caching hosting policy. According to the comprehensive experimental results, the proposed scheme is able to converge rapidly on various parameter configurations and whose performance surpasses the other four baseline learning algorithms.

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.

The detection of anomalies in high-dimensional time-series has always played a crucial role in the domain of system security. Recently, with rapid advancements in transformer model and graph neural network (GNN) technologies, spatiotemporal modeling approaches for anomaly detection tasks have been greatly improved. However, most methods focus on optimizing upstream time-series prediction tasks by leveraging joint spatiotemporal features. Through experiments, we found that this modeling approach not only risks the loss of some original anomaly information during data preprocessing, but also focuses on optimizing the performance of the upstream prediction task and does not directly enhance the performance of the downstream detection task. We propose a spatiotemporal anomaly detection model that incorporates an improved attention mechanism in the process of temporal modeling. We adopt a heterogeneous graph contrastive learning approach in spatio modeling to compensate for the representation of anomalous behavioral information, thereby guiding the model through thorough training. Through validation on two widely used real-world datasets, we demonstrate that our model outperforms baseline methods. We also explore the impact of multivariate time-series prediction tasks on the detection task, and visualize the reasons behind the benefits gained by our model.

Accurate and stable estimation of the state of health (SOH), which is one of the critical indicators to characterize the ability of lithium-ion (Li-ion) batteries to store and release energy, is critical in the stable driving of electric vehicles. In this paper, a novel SOH estimation method based on the aging factors of battery, which combines convolutional neural network (CNN), wavelet neural network (WNN), and wavelet long short-term memory (WLSTM) named CNN–WNN–WLSTM, is designed. The proposed CNN–WNN–WLSTM estimation scheme inherits both the fast convergence and robust stability of the WNN, as well as the ability of long short-term memory neural network (LSTM) to extract the time series features of the data; moreover, using CNN can make the proposed algorithm extract the data features from the original battery data automatically, and the WNN–WLSTM is then adopted to produce the final SOH estimation by exploiting the features from the CNN. To further speed and achieve global optimization, the RMSprop optimizer, instead of the usually used Adagrad optimizer, is chosen as the solver of the CNN–WNN–WLSTM network. Experimental results on data set from the NASA Ames Prognostics Center of Excellence show that the proposed algorithm can be commendably used for Li-ion battery health management by quantitative comparison with other commonly used machine learning methods, such as back-propagation neural network, WNN, LSTM, WLSTM, convolutional neural network–long short-term memory neural network (CNN–LSTM), and Gaussian process regression.

A 3D transformable model can be transformed into different shapes through folding operations to suit different needs, such as a table or a chair in daily life. Furthermore, the features of foldable structure and flat components allow it to be folded into a smaller stack for compact storage when not in use. To this end, this study applies a new foldable modular chain structure and proposes a novel method of constructing 3D models into 3D shapes based on this structure and guiding the transformation between shapes. For the construction of the model, that is, to find a module chain path that constructs the model shape, the divide-and-conquer method is adopted. The model is first divided into multiple units, and then the search for the linearly connected module sub-path is executed for each unit. This involves three major steps: unit-based segmentation of the model, search for the unit tree structure that can form the target 3D shape, and search for the modular chain path based on the unit tree. The experimental cases demonstrate the application of the square modular chain in the fields of furniture and toys and prove the effectiveness of the method in constructing and transforming the foldable chain-type modular configurations of the input 3D models.

Abstract

Visual sentiment analysis is in great demand as it provides a computational method to recognize sentiment information in abundant visual contents from social media sites. Most of existing methods use CNNs to extract varying visual attributes for image sentiment prediction, but they failed to comprehensively consider the correlation among visual components, and are limited by the receptive field of convolutional layers as a result. In this work, we propose a visual semantic correlation network VSCNet, a Transformer-based visual sentiment prediction model. Precisely, global visual features are captured through an extended attention network stacked by a well-designed extended attention mechanism like Transformer. An off-the-shelf object query tool is used to determine the local candidates of potential affective regions, by which redundant and noisy visual proposals are filtered out. All candidates considered affective are embedded into a computable semantic space. Finally, a fusion strategy integrates semantic representations and visual features for sentiment analysis. Extensive experiments reveal that our method outperforms previous studies on 5 annotated public image sentiment datasets without any training tricks. More specifically, it achieves 1.8% higher accuracy on FI benchmark compared with other state-of-the-art methods.

Various external factors that interfere with traffic flow, such as weather conditions, traffic accidents, incidents, and Points of Interest (POIs), need to be considered in performing traffic forecasting tasks. However, the current research methods encounter difficulties in effectively incorporating these factors with traffic characteristics and efficiently updating them, which leads to a lack of dynamics and interpretability. Moreover, capturing temporal dependence and spatial dependence separately and sequentially can result in issues, such as information loss and model errors. To address these challenges, we present a Knowledge Representation learning-actuated spatial–temporal graph neural network (KR-STGNN) for traffic flow prediction. We combine the knowledge embedding with the traffic features via Gated Feature Fusion Module (GFFM), and dynamically update the traffic features adaptively according to the importance of external factors. To conduct the co-capture of spatial–temporal dependencies, we subsequently propose a spatial–temporal feature synchronous capture module (ST-FSCM) combining dilation causal convolution with GRU. Experimental results on a real-world traffic data set demonstrate that KR-STGNN has superior forecasting performances over diverse prediction horizons, especially for short-term prediction. The ablation and perturbation analysis experiments further validate the effectiveness and robustness of the designed method.

Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments. To address this issue, this paper presents a novel machine-learning-based approach for radar signal sorting. Our method utilizes SemHybridNet, a Semantically Enhanced Hybrid CNN-Transformer Network, for the classification of semantic information in two-dimensional radar pulse images obtained by converting the original radar data. SemHybridNet incorporates two innovative modules: one for extracting period structure features, and the other for ensuring effective integration of local and global features. Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field.

3D object detection is a critical task in the fields of virtual reality and autonomous driving. Given that each sensor has its own strengths and limitations, multi-sensor-based 3D object detection has gained popularity. However, most existing methods extract high-level image semantic features and fuse them with point cloud features, focusing solely on consistent information from both sensors while ignoring their complementary information. In this paper, we present a novel two-stage multi-sensor deep neural network, called the adaptive learning point cloud and image diversity feature fusion network (APIDFF-Net), for 3D object detection. Our approach employs the fine-grained image information to complement the point cloud information by combining low-level image features with high-level point cloud features. Specifically, we design a shallow image feature extraction module to learn fine-grained information from images, instead of relying on deep layer features with coarse-grained information. Furthermore, we design a diversity feature fusion (DFF) module that transforms low-level image features into point-wise image features and explores their complementary features through an attention mechanism, ensuring an effective combination of fine-grained image features and point cloud features. Experiments on the KITTI benchmark show that the proposed method outperforms state-of-the-art methods.