查看更多>>摘要：Detecting defects on aircraft impeller surfaces is challenging due to the thin and fragile structure of certain defects, as well as their varying scale and geometry. To address these two challenges, we propose the Geometric Spatial Constraints Network (GSCNet) for precise impeller defect detection. First, we develop an automatic image acquisition equipment to capture high-quality data of impeller surface defects. Subsequently, we introduce GSCNet, which comprises two main components: Rich Semantic Information Representation (RSIR) and Spatial Correlation Awareness (SCA) to detect surface defects. Within RSIR, we propose a geometric-constraints-guided, deformable-convolution-based module named Slender Partial Convolution (SPC), along with a Multi-Geometric Feature Fusion (MFF) module. SPC captures the features of tubular structures without redundant information by aligning the convolution kernel shape with slender defects, while MFF facilitates the fusion of various semantic features, thereby enhancing the ability to extract semantic information. In SCA, we introduce a novel attention mechanism that captures inherent spatial correlation to enhance the high-similarity defects classification capability by modeling representative spatial information. Finally, we design a similarity-enhanced loss function to further improve the detection of multiple geometric defects simultaneously, as it alleviates the scale sensitivity of IoU-based loss. Comparative experiments demonstrate that our framework outperforms all representative detection models, achieving 83.2% mAP on the AISD dataset, which surpasses the second-best model by 3.8%. The first set of ablation experiments confirms the effectiveness of each module within the framework. The second set of ablation experiments on the NEU-SEG and MT datasets validates the feature extraction and plug-and-play capability of RSIR. The generalization ability of GSCNet is further demonstrated on the NEU-DET and GC10-DET datasets.
查看更多>>摘要：Redundancy design is a widely used technique for enhancing system reliability across various industries, including aerospace and manufacturing. Consequently, the redundancy allocation problem (RAP) has attracted considerable attention in the field of reliability engineering. The RAP seeks to determine an optimal redundancy scheme for each subsystem under resource constraints to maximize system reliability. However, existing RAP models and exact algorithms are predominantly confined to simple 1-out-of-n subsystems or single optimization strategies, thereby limiting the optimization potential and failing to adequately address the engineering requirements. This paper introduces a model and an exact algorithm for RAP with k-out-of-n subsystems and heterogeneous components under mixed and K-mixed redundancy strategies. The model employs a continuous time Markov chain method to calculate subsystem reliability exactly. A dynamic programming (DP) algorithm based on super component and sparse node strategies is designed to obtain the exact solution for RAP. Numerical experiments confirm that all benchmark test problems reported in the literature are exactly solved by the proposed DP. The experiment results demonstrate that the proposed RAP model offers high flexibility and potential for reliability optimization. Additionally, owing to the generality of the problem considered, the proposed DP also exactly solves other RAP models with 1-out-of-n subsystems and simplified redundancy strategies, which provides a more generalized framework for redundancy optimization. Finally, the research's applicability in reliability engineering is validated through an optimization case study of a natural gas compressor pipeline system.
查看更多>>摘要：This study addresses the challenge of real-time quality monitoring in batch operation by emphasizing the significance of within-batch quality estimation. While data-driven machine learning models are easy to construct, they often lack reliability and interpretability when dealing with sparse quality data. Conversely, first-principles models (FPMs) are interpretable but struggle with accuracy and adaptability to changing conditions. To overcome these issues, a three-phase reinforced incomplete cyber-physical ensemble plus error compensation learning (RICPE-P-ECL) method is proposed. This method enhances the adaptability of the incomplete cyber-physical model (IncompCPM), which relies on partially-available FPMs, for online quality prediction under varying conditions. The innovation in RICPE-P-ECL lies in its ensemble design and error compensation strategy. Phase 1 constructs IncompCPMs to predict quality for each operating condition, creating base models for ensemble learning. Phase 2 combines these IncompCPMs, with real-time information assigning weights to each model. Phase 3 involves an error compensation agent that adjusts the real-time ensemble prediction, addressing the limitations of FPMs and sparse data. The method is evaluated using a fed-batch bioreactor as the process model, and the results demonstrate that RICPE-P-ECL outperforms traditional data-driven models such as semi-supervised latent dynamic variational autoencoder and semi supervised dual attentioned latent dynamic complementary state space model, achieving R~2 values close to 1 for real-time within-batch quality prediction across five new testing conditions.
查看更多>>摘要：This paper underscores the utilization of the Circular Intuitionistic Fuzzy (CIF) framework to enhance the Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA) methodology, emphasizing its practical relevance through an application to hybrid cloud services. The CIF framework incorporates membership and non-membership components accompanied by a radius, forming a deformable circular structure within an intuitionistic fuzzy interpretation triangle. The study utilizes geometric mean techniques to maintain consistency in CIF evaluative ratings and importance levels while reducing the impact of outliers. By incorporating upper and lower importance levels and parameterized CIF scoring functions, the methodology ensures balanced weight determination. Refined radius operations further enhance CIF data analysis, improving the methodology's comprehensiveness. The enhanced CIF MAIRCA approach balances theoretical and real-world evaluations, harmonizes criteria, and computes aggregate disadvantage gap measures to rank alternatives, with smaller gaps indicating better options. This research illustrates the real-world effectiveness of the developed methodology through a hybrid cloud services case study. By exploring various parameter configurations, it highlights the approach's robustness, adaptability, and ability to ensure stability and reliability in complex real-world scenarios. To extend the utility of the enhanced CIF MAIRCA methodology to other decision-making scenarios, this study applies it to a vendor evaluation case. Comparative analyses with other models highlight its strengths in managing uncertainty, adaptability, and precision, affirming its value as a reliable decision-support tool.
Hao LiuMingkai LiJack C.P. ChengChimay J. Anumba...
103187.1-103187.12页
查看更多>>摘要：Accurate construction cost estimation at early stages is critical to enable project stakeholders to make financial decisions (e.g., set up the project budget). However, the heavy reliance on cost engineers' subjective experience and manual effort in practice makes the estimation an error-prone and time-consuming process. To this end, this study proposes a novel hypergraph deep learning-based framework to predict the actual costs of construction projects accurately and efficiently at early stages. It starts with a systematic hypergraph formulation incorporating construction cost factors and their interrelationships. A hypergraph deep learning model is then developed based on the formulated hypergraph for end-to-end construction cost prediction. Afterwards, model interpretation is undertaken to reveal the cost factor importance from the model training results in a quantitative manner. The framework is validated using an actual construction cost dataset of school projects. The results show high accuracy in cost prediction without human intervention and meaningful interpretations of cost factor importance for better understanding of construction cost patterns.
查看更多>>摘要：A safe and efficient decision-making system is crucial for autonomous vehicles. However, the complexity of driving environments often limits the effectiveness of many rule-based and machine learning approaches. Reinforcement learning (RL), with its robust self-learning capabilities and adaptability to diverse environments, offers a promising solution. Despite this, concerns about safety and efficiency during the training phase have hindered its widespread adoption. To address these challenges, we propose a novel RL framework, Simple to Complex Collaborative Decision (S2CD), based on the Teacher-Student Framework (TSF) to facilitate safe and efficient knowledge transfer. In this approach, the teacher model is first trained rapidly in a lightweight simulation environment. During the training of the student model in more complex environments, the teacher evaluates the student's selected actions to prevent suboptimal behavior. Besides, to enhance performance further, we introduce an RL algorithm called Adaptive Clipping Proximal Policy Optimization Plus (ACPPO+), which combines samples from both teacher and student policies while utilizing dynamic clipping strategies based on sample importance. This approach improves sample efficiency and mitigates data imbalance. Additionally, Kullback-Leibler (KL) divergence is employed as a policy constraint to accelerate the student's learning process. A gradual weaning strategy is then used to enable the student to explore independently, overcoming the limitations of the teacher. Moreover, to provide model interpretability, the Layer-wise Relevance Propagation (LRP) technique is applied. Simulation experiments conducted in highway lane-change scenarios demonstrate that S2CD significantly enhances training efficiency and safety while reducing training costs. Even when guided by suboptimal teachers, the student consistently outperforms expectations, showcasing the robustness and effectiveness of the S2CD framework.
查看更多>>摘要：Deep mining abnormal information from operation data is a crucial step in fault diagnosis of equipment, and it holds significant importance for ensuring the efficient operation of rotating machinery. The nonlinear dynamics methods represented by multivariate multiscale entropy have shown good application effects in quantifying the fault characteristics of rotating machinery using multiple sensor signals. However, these methods essentially belong to the category of data-level fusion, which suffers from drawbacks such as poor real-time performance, limited capability to handle only similar types of sensors, and significant influence from sensor information. This paper develops a novel tool named enhanced hierarchical Poincare plot index (EHPPI), for extracting anomaly information from multi-source signals via feature-level fusion. Firstly, the Poincare plot index is extended to create the EHPPI, allowing for the extraction of information from signals at various frequency scales. Subsequently, EHHPI is utilized to extract information from all channel signals. Ultimately, we concatenate the information extracted from all channels by EHPPI to form features and integrate them with random forests to identify faults in rotating machinery. The EHPPI and other popular nonlinear dynamics metrics are applied in different scenarios, such as simulation faults, experimental bench faults, and real machine faults, whose results strongly prove its advantages. The EHPPI has a favorable effect on improving the operational efficiency of rotating machinery.
查看更多>>摘要：In this paper, a hybrid two-way fluid-solid interaction method (HTFSIM) is proposed to overcome the limitations of conventional two-way fluid-solid interaction method (CTFSIM) in simulating intermittent fluid domains, providing a more detailed understanding of the flow pulsation mechanism of peristaltic pumps. The HTFSIM distinguishes between intermittent and continuous fluid domains based on the peristaltic pump's operating principle. By combining point cloud 3D reconstruction of hyper-elastic structures from finite element calculations with traditional two-way fluid-solid coupling, the flow in these domains is calculated separately and then superimposed to capture the flow fluctuations of the peristaltic pump cycle. Comparison of computational and experimental results with the CTFSIM demonstrates that the HTFSIM achieves higher computational accuracy and efficiency. Furthermore, the results regarding the contribution of individual rollers to the flow rate indicate that the flow rate variation caused by Roller 2 follows an asymmetric sinusoidal distribution, which influences the upper limit of the peristaltic pump outlet flow rate. Meanwhile, the reflux induced by Roller 1 affects the lower limit of the outlet flow rate. These findings are crucial for understanding the mechanism behind the flow pulsations in peristaltic pumps.
查看更多>>摘要：Effective manufacturing in flexible job shops often requires collaboratively organizing production and logistics activities. This necessitates a thorough exploration of corresponding collaborative scheduling problem. However, extant studies remain relatively preliminary, not only neglecting the inevitable disturbances in real-world but also failing to satisfy the essential need for collaboration, that is, to simultaneously optimize both activities' objectives. Therefore, this study proposes a novel production-logistics collaborative scheduling problem for dynamic flexible job shops, in which the common yet underappreciated disturbance of logistics equipment breakdowns is meticulously considered, and two typical objectives individually pursued by two activities are optimized simultaneously. To solve the proposed problem, a nested-hierarchical deep reinforcement learning method is developed. In this method, a new nested-hierarchical framework that rationally deploys multiple agents is designed to facilitate the required multi-objective optimization while ensuring the practicality of decision-making process. Based on this framework, appropriate state features, actions, and reward functions are devised for each agent, and a training mechanism based on multi-agent proximal policy optimization is proposed to train agents effectively. Experiments in an aviation component production shop are conducted to confirm the effectiveness of proposed method and problem.
查看更多>>摘要：Drone-supported last-mile humanitarian logistics systems play a crucial role in efficiently delivering essential relief items during disasters. In contrast to conventional truck-based transportation methods, drones provide a versatile and rapid transportation alternative. They are capable of navigating challenging terrain and bypassing damaged infrastructure. However, establishing an effective drone-supported last-mile humanitarian logistics system faces various challenges. This study introduces a novel approach to address these challenges by proposing a drone-supported last-mile humanitarian logistics system planning (DLHLSP) problem. The DLHLSP problem involves decision-making for both pre-disaster and post-disaster phases, taking into account the unique characteristics of drone-based delivery operations and uncertain demands. In the pre-disaster phase, decisions include determining drone-supported relief facility locations, drone deployment strategies, and drone visit schedules to disaster sites. Post-disaster decisions focus on inventory management, relief item procurement, and drone-based delivery operations. To capture the demand uncertainty in chaotic disaster environment, we establish a multistage stochastic programming model incorporating nonanticipativity constraints to make decisions at each stage without knowledge of the demand information in future time periods. Next, we employ the Benders decomposition algorithm to obtain exact solutions. Furthermore, we perform numerical experiments to verify the exact algorithm using randomly generated numerical instances. The results show that the algorithm significantly outperforms the Gurobi solver and could solve the problem of practical scale. Finally, the study validates the proposed model based on a case study of the Lushan earthquake in China and provides several managerial implications and insights. Overall, this research contributes to the field of humanitarian logistics by offering a comprehensive framework for the planning of drone-supported last-mile humanitarian logistics systems.
NETL
NSTL
Elsevier