Applied Soft Computing最新文献

{"title":"Fault diagnosis of helicopter accessory gearbox under multiple operating conditions based on feature mode decomposition and multi-scale convolutional neural networks","authors":"Anping Wan ,&nbsp;Zengzhen Zhu ,&nbsp;Khalil AL-Bukhaiti ,&nbsp;Xiaomin Cheng ,&nbsp;Xiaosheng Ji ,&nbsp;Jinglin Wang ,&nbsp;Tianmin Shan","doi":"10.1016/j.asoc.2025.113403","DOIUrl":"10.1016/j.asoc.2025.113403","url":null,"abstract":"<div><div>The expanding global civil helicopter market has intensified the need for advanced fault diagnosis techniques in helicopter engines. Traditional fault classification methods, such as Variational Mode Decomposition (VMD), have limitations in decomposing complex signals and separating different signal components, which can impede accurate fault feature extraction and compromise diagnostic accuracy and reliability. This paper introduces a novel fault diagnosis method that combines Feature Mode Decomposition (FMD) with a Multi-Scale Convolutional Neural Network (MCNN) to address these challenges. The approach begins by collecting signals from helicopter accessory gearboxes under simulated ground operation conditions. The FMD technique is applied to decompose the gear vibration signals, and the decomposed signals from different sensors are reconstructed and normalized. This preprocessed data is then fed into the MCNN network at various scales, enabling simultaneous extraction and fusion of multi-scale features. The final fault classification is performed using a <span><math><mi>softmax</mi></math></span> classifier. Experimental results demonstrate the efficacy of the proposed method in extracting fault features. Under the given conditions, a fault diagnosis accuracy of up to 100 % was achieved for helicopter accessory gearboxes, marking a significant improvement of 3.1 % compared to VMD-based methods. This enhancement in accuracy represents a substantial advancement in aviation safety and reliability. The study showcases the superior performance of FMD in decomposing complex mechanical signals, particularly its ability better to capture both periodic and impulsive characteristics of fault signals. The integration of MCNN allows for more effective multi-sensor data processing, enhancing the model's capacity to detect and classify faults across various scales and conditions. The FMD-MCNN approach improves the accuracy and efficiency of fault diagnosis and demonstrates significant potential for practical application in aviation maintenance technology.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113403"},"PeriodicalIF":7.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in EEG-based emotion recognition: Challenges, methodologies, and future directions","authors":"Jichi Chen ,&nbsp;Yuguo Cui ,&nbsp;Chunfeng Wei ,&nbsp;Kemal Polat ,&nbsp;Fayadh Alenezi","doi":"10.1016/j.asoc.2025.113478","DOIUrl":"10.1016/j.asoc.2025.113478","url":null,"abstract":"<div><div>Emotion recognition plays a pivotal role in affective computing and human-computer interaction, especially in the fields of mental health care, auxiliary medicine, and intelligent system design. As a non-invasive and time-sensitive neural signal, electroencephalogram (EEG) has become an important means of emotion recognition research. However, due to its susceptibility to noise and individual differences, EEG-based emotion recognition still faces major challenges. This review systematically summarizes the latest progress in EEG-based emotion recognition, sorts out the research paradigm of EEG-based emotion recognition, including public datasets, signal preprocessing techniques, feature extraction methods, and recognition models, and focuses on the end-to-end modeling advantages of deep learning methods in this field in recent years. Through a comparative analysis of representative literature, this study concludes that although deep learning models have promoted the development of this field, their generalization ability, interpretability, and applicability in real-world scenarios are still limited. In addition, current EEG datasets are often limited by small sample size, lack of diversity, and inconsistent labeling standards. In summary, future research should focus on cross-subject recognition techniques, small sample learning strategies, and the development of real-time, deployable emotion recognition systems. These directions are expected to bridge the gap between academic research and practical applications and further promote the advancement of EEG-based emotion recognition technology.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113478"},"PeriodicalIF":7.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constraint Consensus assisted Evolutionary Algorithm for large-scale constrained optimization","authors":"Noha Hamza ,&nbsp;Saber Elsayed ,&nbsp;Ruhul Sarker ,&nbsp;Daryl Essam","doi":"10.1016/j.asoc.2025.113383","DOIUrl":"10.1016/j.asoc.2025.113383","url":null,"abstract":"<div><div>Large-scale constrained optimization problems present significant challenges due to their large number of variables and many constraints. Improper handling of these constraints can lead to suboptimal or infeasible solutions. Many existing approaches overlook this aspect. In this paper, we integrate a constraint-objective cooperative coevolution framework with a Constraint Consensus method, known as DBmax (Maximum Direction-based Method), into differential evolution. In this framework, a problem is decomposed into a number of smaller subproblems (subcomponents) using the Recursive Differential Grouping technique, where interactive variables are allocated to one subproblem. By assessing the impact of each group on the objective function and constraint violation, the most suitable group is selected for evolution. Subsequently, the DBmax method is applied adaptively to the infeasible solutions within the chosen group for improving their feasibility. The algorithm was evaluated on 12 test problems, with the experimental results consistently demonstrating its effectiveness by outperforming existing state-of-the-art methods, in terms of the solution’s feasibility and quality.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113383"},"PeriodicalIF":7.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random-key algorithms for optimizing integrated Operating Room Scheduling","authors":"Bruno Salezze Vieira ,&nbsp;Eduardo Machado Silva ,&nbsp;Antônio Augusto Chaves","doi":"10.1016/j.asoc.2025.113368","DOIUrl":"10.1016/j.asoc.2025.113368","url":null,"abstract":"<div><div>Efficient surgery room scheduling is essential for hospital efficiency, patient satisfaction, and resource utilization. This study addresses the challenge as a combinatorial optimization problem that incorporates multi-room scheduling, equipment scheduling, and complex availability constraints for rooms, patients, and surgeons, facilitating rescheduling and enhancing operational flexibility. To solve such a problem, we introduce multiple algorithms based on a Random-Key Optimizer (RKO), coupled with relaxed formulations to compute lower bounds efficiently, rigorously tested on literature and new, real-world-based instances. The RKO approach decouples the problem from the solving algorithms through an encoding/decoding layer, making it possible to use the same solving algorithms to multiple room scheduling problems case studies from multiple hospitals, given the particularities of each place, even other optimization problems. Among the possible RKO algorithms, we design the heuristics Biased Random-Key Genetic Algorithm with <span><math><mi>Q</mi></math></span>-Learning, Simulated Annealing, and Iterated Local Search for use within an RKO framework, employing a single decoder function. The proposed heuristics, complemented by the lower-bound formulations, provided optimal gaps for evaluating the effectiveness of the heuristic results. Our results demonstrate significant lower- and upper-bound improvements for the literature instances, notably in proving one optimal result. Our strong statistical analysis shows the effectiveness of our implemented heuristic search mechanisms. Furthermore, the best-proposed heuristic efficiently generates schedules for the newly introduced instances, even in highly constrained scenarios. This research offers valuable insights and practical solutions for improving surgery scheduling processes, delivering tangible benefits to hospitals by optimizing resource allocation, reducing patient wait times, and enhancing overall operational efficiency.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113368"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiagent reinforcement learning with evolution for multitarget tracking by unmanned aerial vehicle swarm","authors":"Keming Jiao ,&nbsp;Jie Chen ,&nbsp;Bin Xin ,&nbsp;Li Li ,&nbsp;Yulong Ding ,&nbsp;Zhixin Zhao ,&nbsp;Yifan Zheng","doi":"10.1016/j.asoc.2025.113463","DOIUrl":"10.1016/j.asoc.2025.113463","url":null,"abstract":"<div><div>Multitarget tracking has immense potential in both military and civilian applications. For an unmanned aerial vehicle (UAV) swarm, a critical challenge in multitarget tracking is how to coordinate multiple unmanned aerial vehicles to continuously and accurately track multiple targets. This paper considers the cooperative decision-making problem for multitarget tracking by multiple unmanned aerial vehicles with limited sensing range in a dynamic environment. Meanwhile, a multiagent advantage actor critic with evolution, named MAA2CE, is proposed to learn the cooperative tracking policies for a UAV swarm. During training, each UAV is viewed as an agent with a policy network, making decisions based on its own information and policy. The prioritized experience replay is adopted to take full advantage of the valuable experience for learning. Considering the tracking performance of agents, the high performance agent can replicate its own network parameters to the other agents with a certain probability by network evolution. The experimental results demonstrate that the proposed algorithm is superior to three peer algorithms in learning efficiency, can acquire better collaborative tracking policies, and significantly improves the collaborative multitarget tracking proficiency of the UAV swarm.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113463"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral image classification using Uniform Manifold Approximation and Projection with fusion deep learning network","authors":"Chen Lou ,&nbsp;Mohammed A.A. Al-qaness ,&nbsp;Sike Ni ,&nbsp;Dalal Al-Alimi ,&nbsp;Robertas Damaševičius ,&nbsp;Saeed Hamood Alsamhi","doi":"10.1016/j.asoc.2025.113371","DOIUrl":"10.1016/j.asoc.2025.113371","url":null,"abstract":"<div><div>Hyperspectral images (HSI) are crucial for remote sensing applications as they provide detailed spectral information that accurately identifies and analyzes various materials and land features. HSI classification faces challenges due to large data volumes and high dimensionality. Dimensionality reduction techniques help address these problems by simplifying model computation, reducing redundancy, and improving feature selection. However, traditional methods struggle to capture nonlinear structures and local–global relationships in hyperspectral data. We propose a new multi-feature fusion classification model called the Uniform Manifold Approximation and Projection (UMAP) and Simple Attention Module (SimAM) mechanism fusion network (UMAPSAMFN). The main workflow of the model consists of several steps. The network uses UMAP to map high-dimensional HSI data into a low-dimensional space while maintaining a local and global structure. The feature data are sent to the convolutional neural network (CNN) and graph convolutional network (GCN) modules to capture pixel-level features and contextual information. These data are separately processed through multi-head attention modules to enhance the ability to represent feature information. Finally, the processed data are jointly fed into a fusion module with an attention mechanism to boost feature information and achieve deep modeling results. The experimental results on three benchmark HSI datasets show that UMAPSAMFN consistently exhibits the highest classification accuracy, with overall classification accuracies of 93.28%, 96.13%, and 97.73% on the Indian Pines, Pavia University, and Salinas datasets, respectively.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113371"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel wind power interval prediction method based on neural ensemble search and dynamic conformalized quantile regression","authors":"Jianming Hu ,&nbsp;Yuwen Deng ,&nbsp;Jinxing Che","doi":"10.1016/j.asoc.2025.113476","DOIUrl":"10.1016/j.asoc.2025.113476","url":null,"abstract":"<div><div>Wind power interval prediction plays a crucial role in delivering accurate estimations of the potential range of wind power generation, enhancing the stability and reliability of the power system. In this study, a novel method named Conformalized Quantile Regression with Neural Ensemble Search (NESCQR) for wind power interval prediction is proposed. The NESCQR algorithm combines Neural Ensemble Search (NES) and dynamic conformalized quantile regression. The NES employs a forward selection strategy to identify an optimal subset of models, aiming to minimize prediction errors and consequently produce tighter prediction intervals (PIs). Meanwhile, the dynamic conformalization process allows the model to effectively adapt to temporal variations in the data, significantly improving its robustness. Experiments on four real datasets show that the proposed NESCQR algorithm can obtain extremely narrow prediction intervals while ensuring valid coverage rate, and effectively alleviate the quantile crossing phenomenon, providing reliable and effective help for decision-makers.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113476"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient and privacy-preserving deep inference towards cloud–edge collaborative","authors":"Yulong Wang ,&nbsp;Guoxin Zhong ,&nbsp;Yubing Duan ,&nbsp;Yunchang Cheng ,&nbsp;Mingyong Yin ,&nbsp;Run Yang","doi":"10.1016/j.asoc.2025.113381","DOIUrl":"10.1016/j.asoc.2025.113381","url":null,"abstract":"<div><div>The cloud–edge collaborative inference approach splits deep neural networks (DNNs) into two parts to run collaboratively on resource-constrained edge devices(AIoT devices) and cloud servers, aiming at minimizing inference latency and protecting data privacy for AIoT computing system. However, despite not exposing the raw input data from edge devices directly to the cloud, state-of-the-art attacks can still target collaborative inference to reconstruct the raw private data from exposed local models’ intermediate outputs, introducing serious privacy risks. In this paper, we propose a secure privacy inference framework for cloud–edge collaboration system towards AIoT network, called CIS (<u>C</u>ollaborative <u>I</u>nference <u>S</u>hield), which supports adaptively partitioning the network according to dynamically changing network bandwidth and fully releases the computational power of edge devices. To mitigate the influence introduced by private perturbation, CIS provides a way to achieve differential privacy protection by adding refined noise to the intermediate layer feature maps offloaded to the cloud. Meanwhile, given a total privacy budget, the budget is reasonably allocated by the size of the feature graph rank generated by different convolution filters, making cloud inference robust to the perturbed data, thus effectively trading-off between privacy and availability. Finally, we construct a real cloud–edge collaborative inference computing scenario to verify the effectiveness of inference latency and model partitioning on resource-constrained edge devices. Furthermore, the state-of-the-art cloud–edge collaborative reconstruction attack is utilized to evaluate the practical availability of the end-to-end privacy protection mechanism provided by CIS.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113381"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SLB-Mamba: A vision Mamba for closed and open-set student learning behavior detection","authors":"Zhifeng Wang ,&nbsp;Longlong Li ,&nbsp;Chunyan Zeng ,&nbsp;Shi Dong ,&nbsp;Jianwen Sun","doi":"10.1016/j.asoc.2025.113369","DOIUrl":"10.1016/j.asoc.2025.113369","url":null,"abstract":"<div><div>By effectively analyzing the learning behaviors of smart classroom students in the classroom, the interaction between teaching and learning can be significantly improved, thereby enhancing the quality of education. However, current traditional analysis of students’ classroom behavior mainly focuses on closed-set behavior detection in a single scenario. In the face of complex and open real classroom environments, obtaining meaningful behavior representations in small and densely populated complex scenarios while achieving good performance in both closed and open environments remains a major challenge. To address these challenges, this study introduces a new method called SLB-Mamba to detect students’ learning behaviors in both closed-set and open-set scenarios. The SLB-Mamba network offers high computational efficiency and flexibility in deployment and practical applications. Firstly, an Attention calculation method Reward-Weighted Attention (RWA) based on the concept of benefit value was designed to enhance the feature extraction ability of the backbone network. Additionally, the Vision State Space Feature Pyramid Network (VSSFPN) structure built through State Space Model (SSM) can effectively integrate cross-scale features. The effectiveness of SLB-Mamba has been validated through rigorous testing and evaluation on real classroom data of smart classrooms, and it has been compared with state-of-the-art (SOTA) methods. The experimental results show that SLB-Mamba achieved mean Average Precision (mAP) scores of 93.79% and 92.2% on the SLB-K12 and SCSB datasets, respectively, with the Absolute Open-Set Error (A-OSE) values of 163 and 289. These findings highlight the significant advantages of the proposed method in improving detection accuracy and efficiency in both closed-set and open-set scenarios, thereby extending the applicability of the educational assessment framework. The source code of this study is publicly available at <span><span>https://github.com/CCNUZFW/SLB-Mamba</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113369"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep convolutional generative adversarial network accelerated optimization algorithm for parameter optimization of permanent magnet synchronous generator controllers","authors":"Linfei Yin,&nbsp;Haomiao Li,&nbsp;Yongzi Ye,&nbsp;Fang Gao","doi":"10.1016/j.asoc.2025.113458","DOIUrl":"10.1016/j.asoc.2025.113458","url":null,"abstract":"<div><div>In permanent magnet synchronous generators (PMSG), optimized rotor-side controller (RSC) parameters improve the power coefficient. Aiming at the traditional intelligent optimization algorithms since the long optimization time and insufficient global search capability, this work proposes adaptive differential evolution variants with linear population size reduction (L-SHADE) for constrained optimization with Levy flights (COLSHADE) accelerated by using deep convolutional generative adversarial network (DCGAN). The DCGAN-COLSHADE converts the parameters of the PMSG controllers into pictures and utilizes the DCGAN alternative algorithmic iterative process to speed up the COLSHADE iterative process and accomplish a broader and deeper global optimization problem. The PMSG simulation results utilizing the maximum power point tracking strategy verify the DCGAN-COLSHADE can obtain globally optimal solutions and higher system stability. The fitness function value of DCGAN-COLSHADE is 3.96 % smaller than the comparison algorithm; the average computation time is 79.28 % less than the particle swarm optimization (PSO), 80.35 % less than the moth flame optimization (MFO), 80.75 % less than the whale optimization algorithm (WOA), 80.52 % less than gray wolf optimization (GWO) and 77.96 % less than COLSHADE. In addition, the results of rapid control prototype (RCP) hardware experiments validate the feasibility and effectiveness of the algorithm.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113458"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}