计算机科学最新文献_第5页

Thrust force assessment of a MFC-actuated tail-like robotic fish using Unsteady Panel Method

IF 3.1 3区计算机科学

Mechatronics Pub Date : 2025-03-05 DOI: 10.1016/j.mechatronics.2025.103308

Maíra M. da Silva , Emanuel A.R. Camacho , André R.R. Silva , Flávio D. Marques

{"title":"Thrust force assessment of a MFC-actuated tail-like robotic fish using Unsteady Panel Method","authors":"Maíra M. da Silva , Emanuel A.R. Camacho , André R.R. Silva , Flávio D. Marques","doi":"10.1016/j.mechatronics.2025.103308","DOIUrl":"10.1016/j.mechatronics.2025.103308","url":null,"abstract":"<div><div>Fish-like robots are used in various fields, such as environmental monitoring and underwater exploration. These devices are designed to emulate the motion of a real fish. They can have flexible bodies to mimic body/caudal-based locomotion patterns or fins to mimic median/paired fin-based locomotion patterns. Standard propulsion methods include oscillating fins, flapping tails, and body undulations. This work investigates a robotic fish with a flexible tail actuated by a Macro-Fiber Composite (MFC) pair in a bi-morph configuration. This device is designed to mimic body/caudal-based locomotion patterns; therefore, it should present propulsion capabilities due to its body undulations. These propulsion capabilities are assessed using the Unsteady Panel Method for different sinusoidal inputs. This method requires the device’s kinematics, which is derived using an analytical model based on the Euler–Bernoulli beam theory, considering the electro-mechanical coupling of the actuators. The mean thrust force derived using the Unsteady Panel Method is compared with the actual mean thrust acquired during an experimental campaign. The experimental and numerical results indicated that higher thrust forces can be achieved when the device is excited in its second resonance frequency. These results are in line with Lighthill’s findings.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"107 ","pages":"Article 103308"},"PeriodicalIF":3.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of reservoir water levels via an improved attention mechanism based on CNN − LSTM

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-05 DOI: 10.1007/s10489-025-06393-6

Haoran Li, Lili Zhang, Yunsheng Yao, Yaowen Zhang

{"title":"Prediction of reservoir water levels via an improved attention mechanism based on CNN − LSTM","authors":"Haoran Li, Lili Zhang, Yunsheng Yao, Yaowen Zhang","doi":"10.1007/s10489-025-06393-6","DOIUrl":"10.1007/s10489-025-06393-6","url":null,"abstract":"<div><p>Water level prediction is crucial for flood control scheduling and water resource management. The application of various deep learning methods to water level prediction in reservoirs is limited. Accurate water level prediction aids in optimizing reservoir operation strategies, ensuring flood safety downstream and meeting water supply demands. To achieve accurate predictions, a new structure based on a convolutional neural network − long short-term memory (CNN − LSTM) model is proposed, which incorporates a self-attention mechanism and a local attention mechanism in an SL − CNN − LSTM coupled model. Using the Three Gorges Reservoir head area in China as a case study, hydrometeorological data from three points in the reservoir's head area and upstream water level characteristics are used as input variables. Data collected every six hours from 2008 to 2021 were used, with the model trained and tested at an 8:2 ratio. The study revealed that a two-layer CNN configuration performed best in most models. The SL − CNN − LSTM-2 model achieved the best performance across all the metrics, with an R<sup>2</sup> of 0.9988, an MAE of 0.2767, an RMSE of 0.3404, and a MAPE of 0.1717, particularly for extreme water level predictions with minimal residuals, validating its strong ability to balance long- and short-term dependencies. Additionally, the model effectively extracts features and captures critical information in time series data, balancing learning capacity and computational efficiency. The research results are highly important for water resource management in large reservoirs, providing reliable technical support for flood control scheduling and water resource optimization.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Industrial Informatics Information for Authors

IF 11.7 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2025-03-05 DOI: 10.1109/TII.2025.3542522

引用次数: 0

A heuristic distributed and no-wait method for solving multiagent task allocation problems with coupled temporal constraints

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-03-05 DOI: 10.1016/j.swevo.2025.101898

Wei Cui , Yanxiang Feng , Ye Cao , Xiaoling Li , Yikang Yang

{"title":"A heuristic distributed and no-wait method for solving multiagent task allocation problems with coupled temporal constraints","authors":"Wei Cui , Yanxiang Feng , Ye Cao , Xiaoling Li , Yikang Yang","doi":"10.1016/j.swevo.2025.101898","DOIUrl":"10.1016/j.swevo.2025.101898","url":null,"abstract":"<div><div>Temporal constraints, primarily arising from engagement rules and requiring tasks to be performed in a specific order, are critical in task allocation problems (TAPs). However, existing allocation methods often fall short of handling temporal constraints. This paper proposes a heuristic distributed and no-wait algorithm, called the Temporal-Constraints Performance Impact (TC-PI) algorithm, for solving multi-agent TAPs with temporal constraints. By requiring each agent either travels to or immediately executes its assigned task, the TC-PI eliminates unnecessary waiting time and effectively reduces the <em>average task completion time</em>. The proposed algorithm consists of three phases. Firstly, each agent sequentially adds tasks to its task list while ensuring temporal constraints are satisfied. Secondly, conflicts where multiple agents select the same task are resolved through local communication. Finally, any remaining conflicts caused by temporal constraints are further addressed. To maintain task order and minimize completion time, task significance is redefined by incorporating temporal relationships among tasks. A penalty mechanism prevents infinite task reallocation cycles, enhancing system robustness and avoiding deadlocks. Simulation results demonstrate that TC-PI effectively resolves temporal conflicts, achieves no-wait task allocations, and flexibly handles dynamic task arrivals.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101898"},"PeriodicalIF":8.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551982","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}

引用次数: 0

Dual-stage dynamic hierarchical attention framework for saliency-aware explainable diabetic retinopathy grading

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-03-05 DOI: 10.1016/j.engappai.2025.110364

Shilpa Elsa Abraham, Binsu C. Kovoor

{"title":"Dual-stage dynamic hierarchical attention framework for saliency-aware explainable diabetic retinopathy grading","authors":"Shilpa Elsa Abraham, Binsu C. Kovoor","doi":"10.1016/j.engappai.2025.110364","DOIUrl":"10.1016/j.engappai.2025.110364","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR) is a serious complication of diabetes, which leads to severe vision impairment if left untreated. For effective treatment, it is crucial to detect the disease early and grade it accurately. In recent years, convolutional neural networks have shown promising results in automated DR grading, yet their black-box nature challenges their interpretability. To address this, we propose a novel deep learning framework leveraging hierarchical attention refinement to dynamically highlight lesion salient regions in retinal images. The proposed model employs a Residual Network-18 backbone network to capture basic semantic feature representation from retinal fundus images, followed by a channel-wise and spatial weighed attention encoding to generate an initial saliency representation. This is further enhanced with a hierarchical cross attention mechanism to produce enriched saliency maps. Concurrently, these saliency maps guide the final decision-making process, thereby enhancing interpretability and assisting in accurate DR grading. Experimental results validate the efficacy of the proposed model in enhancing the performance of DR grading across multiple evaluation metrics. Further, quantitative and qualitative analysis of the generated saliency maps demonstrated substantial enhancements in pinpointing lesion areas within fundus images, leading to enhanced explainability and diagnostic accuracy of the model’s predictions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"148 ","pages":"Article 110364"},"PeriodicalIF":7.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Convolutional neural networks for advanced adhesive joints application patterns

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-05 DOI: 10.1007/s10489-025-06340-5

Kiro Scholtes, Florian Flaig, Marvin Kaufmann, Frank Guido Lehne, Till Vallée, Holger Fricke, Michael Müller

{"title":"Convolutional neural networks for advanced adhesive joints application patterns","authors":"Kiro Scholtes, Florian Flaig, Marvin Kaufmann, Frank Guido Lehne, Till Vallée, Holger Fricke, Michael Müller","doi":"10.1007/s10489-025-06340-5","DOIUrl":"10.1007/s10489-025-06340-5","url":null,"abstract":"<div><p>Adhesive bonding is a widely used joining technique across various industries. Achieving uniform adhesive coverage over the entire surface without the formation of air pockets is crucial for creating strong and durable joints. Simultaneously, it is essential to minimise waste caused by material leakage at the edges. However, generating an optimal adhesive pattern to achieve the desired adhesive distribution after compression remains a challenge, as fluids tend to spread in a circular manner, while industry-relevant target geometries are typically non-circular. This paper investigates the application of Convolutional Neural Networks (CNNs) to optimise adhesive application patterns by utilising a simplified simulation model known as the Partially Filled Gaps Model (PFGM) to generate extensive training data. The CNN is trained to predict fluid distribution outcomes based on initial adhesive application patterns and addresses the inverse problem of determining an optimal application pattern to achieve a desired target distribution after compression. Two training approaches are introduced: a basic inverse model that utilizes a straightforward input–output data exchange, and a more advanced strategy that incorporates a forward model to improve accuracy. The forward model predicts the final distribution, enabling better refinement of the initial application patterns. The results demonstrate that the CNN-based approach is highly effective in generating optimal application patterns for adhesive bonds. Its primary advantage, compared to alternative methods, lies in its ability to achieve precise results within a short computation time. However, a significant drawback is the limited flexibility in accommodating variations in parameters.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06340-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Combined Model WAPI Indoor Localization Method Based on UMAP

IF 1.7 4区计算机科学

International Journal of Communication Systems Pub Date : 2025-03-05 DOI: 10.1002/dac.70034

Jiasen Zhang, Xiaoxun Yang, Wei Zhu, Dongjie Wu, Jiashan Wan, Na Xia

{"title":"A Combined Model WAPI Indoor Localization Method Based on UMAP","authors":"Jiasen Zhang, Xiaoxun Yang, Wei Zhu, Dongjie Wu, Jiashan Wan, Na Xia","doi":"10.1002/dac.70034","DOIUrl":"https://doi.org/10.1002/dac.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid advancement of the Internet, indoor localization technology has gained increasing importance across various fields. However, the complexity of indoor environments presents significant challenges for achieving precise positioning using GPS or BeiDou systems. As a result, there is a growing demand for innovative localization methods that deliver high accuracy, improved security, and cost-effectiveness. In this study, a dataset comprising 9291 fingerprints collected from a building was processed and split into training and test sets in a 7:3 ratio. To facilitate feature extraction, four algorithms—UMAP, LDA, PCA, and SVD—were employed. Subsequently, six machine learning models (KNN, Random Forest, ANN, SVM, GBDT, and XgBoost) were trained on the training set and evaluated on the test set to compare their performance with different feature extraction algorithms. The objective was to identify the most effective feature extraction method. Model performance was assessed using three metrics: average error, coefficient of determination, and accuracy. Finally, a stacking ensemble model was developed, incorporating the six models as primary learners and selecting the five models with superior predictive performance as secondary learners. This approach aimed to enhance the localization accuracy. UMAP feature extraction significantly improved the prediction accuracy of the indoor localization model, whereas the stacking ensemble model, combining KNN, GBDT, XgBoost, ANN, Random Forest, and SVM as primary learners and Random Forest as the secondary learner, achieved the highest localization accuracy, with an error of approximately 1.48 m.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Helping to Choose a Robust Alternative: A Sensitivity Analysis and a Software Tool for Multi-Criteria Decision-Making

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-03-05 DOI: 10.1111/exsy.70031

Pavel Novoa-Hernández, David A. Pelta, Carlos Cruz Corona

{"title":"Helping to Choose a Robust Alternative: A Sensitivity Analysis and a Software Tool for Multi-Criteria Decision-Making","authors":"Pavel Novoa-Hernández, David A. Pelta, Carlos Cruz Corona","doi":"10.1111/exsy.70031","DOIUrl":"https://doi.org/10.1111/exsy.70031","url":null,"abstract":"<div>\u0000 \u0000 <p>Multicriteria decision-making (MCDM) often involves evaluating or ranking alternatives on multiple attributes, a process that is far from trivial due to flexible preferences and uncertainty in the criteria importance. The recently proposed <b>W</b>eightless, <b>I</b>nterval-<b>B</b>ased <b>A</b>pproach (WIBA) tackles these issues by relying on an ordering of the criteria (according to their relevance) instead of explicit weights and using interval scores to evaluate alternatives. Although originally proposed for selecting solutions of interest in the context of multi-objective and many objective optimization problems, it can be adapted to rank such solutions. However, the robustness of WIBA rankings has not been studied, and sensitivity analysis approaches based on perturbations of the weights cannot be applied. Furthermore, there is no friendly environment for exploring WIBA properties. This paper addresses these gaps by (1) introducing a novel local sensitivity analysis technique to explore how small perturbations in the order of criteria affect rankings, and (2) presenting WIBApp, a freely available visual software tool that implements WIBA features, including the proposed sensitivity analysis. Using a case study on the selection of technical universities, the paper first illustrates WIBA's flexibility and utility in real-world decision scenarios, enabling decision makers to effectively deal with uncertainty and complexity, and second shows how WIBApp simplifies data management, enhances analysis and facilitates comparisons among rankings. By advancing the theoretical foundations of WIBA and providing a practical implementation, this work contributes to providing decision makers with a robust framework for handling multi-criteria problems, enhancing the reliability of rankings and supporting informed decisions.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feasibility Analysis for Deployment of Free Space Optical Links in Urban Coastal Environments

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-03-05 DOI: 10.1002/ett.70097

Lakshmi Priya Isanaka, Meenakshi Murugappa

{"title":"Feasibility Analysis for Deployment of Free Space Optical Links in Urban Coastal Environments","authors":"Lakshmi Priya Isanaka, Meenakshi Murugappa","doi":"10.1002/ett.70097","DOIUrl":"https://doi.org/10.1002/ett.70097","url":null,"abstract":"<div>\u0000 \u0000 <p>Optical Wireless Communication (OWC) is a highly congruous communication system for the emerging Fifth Generation (5G) and Sixth Generation (6G) communication environments. The authors have discussed the efficacy of a terrestrial Free Space Optics (FSO) link in the coastal urban environments. Performance metrics such as received signal power and Link Margin (LM) are determined and hence used to judge the effectuation of the FSO model under consideration. Fog-Evoked Signal Degradation (FESD) is the indispensable contributor to atmospheric attenuation. Various models have been taken into account for the computation of FESD considering four consecutive months (November to February) for six consecutive years from 2018 to 2023. Mathematical analysis has been carried out from the real-time measured visibility data and wind speed values. Also, the altitude of the location has been considered for computing the scattering and Turbulence-Induced Attenuation (TIA). The LM is derived uniquely for summer and winter seasons to determine the feasibility for the establishment of the FSO link. For the purpose of the performance analysis, all three of the optical window wavelengths 850, 1300, and 1550 nm have been taken into account. Both the simulation results and mathematical estimates are used to compute the effective maximum achievable link range, link availability, and minimum visibility requirement for the specific geographic location under consideration.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Eye Blinking Feature Processing Using Convolutional Generative Adversarial Network for Deep Fake Video Detection

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-03-05 DOI: 10.1002/ett.70083

Dipesh Ramulal Agrawal, Farha Haneef

{"title":"Eye Blinking Feature Processing Using Convolutional Generative Adversarial Network for Deep Fake Video Detection","authors":"Dipesh Ramulal Agrawal, Farha Haneef","doi":"10.1002/ett.70083","DOIUrl":"https://doi.org/10.1002/ett.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>Deepfake video detection is one of the new technologies to detect Deepfakes from video or images. Deepfake videos are majorly used for illegal actions like spreading wrong information and videos online. Hence, deepfake video detection techniques are used to detect videos as real. Several deepfake detection methods have been introduced to detect Deepfakes from videos, but some techniques have limitations and low accuracy in predicting the video as real or fake. This paper introduces advanced deepfake detection techniques, such as converting the video into frames, pre-processing the frames, and using feature extraction and classification techniques. Pre-processing of frames using the sequential adaptive bilateral wiener filtering (SABiW) removes the noise from frames and detects the face using the 2D Haar discrete wavelet transform (2D-Haar). Then, the features are extracted from a pre-processed image with a depthwise separable residual network (DSRes). Finally, the video is classified using the Convolutional attention advanced generative adversarial network (Con-GAN) model as a deepfake video or original video. The Mud ring optimization algorithm is used to detect the weight coefficients of the network. Then, the overall performance of the proposed model is compared with other existing models to describe their superiority. The proposed method uses four datasets, which are FaceForensics++, Celeb DF v2, WildDeepfake, and DFDC. The performance of the proposed model provides a high accuracy rate of 98.91% and a precision of 98.32%. The proposed model provides better performance and efficient detection by detecting Deepfakes.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0