Engineering Applications of Artificial Intelligence最新文献

{"title":"Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach","authors":"Reda Ghanem ,&nbsp;Ismail M. Ali ,&nbsp;Shadi Abpeikar ,&nbsp;Kathryn Kasmarik ,&nbsp;Matthew Garratt","doi":"10.1016/j.engappai.2024.109522","DOIUrl":"10.1016/j.engappai.2024.109522","url":null,"abstract":"<div><div>Algorithms using swarming collective motion can solve coverage problems in unknown environments by reacting to unknown obstacles in real-time when they are encountered. However, these algorithms face two key challenges when deployed on real robots. First, hand-tuning efficient collective motion parameters is both time-consuming and difficult. Second, predicting the time required for a swarm to solve a particular problem is not straightforward. This paper introduces a novel evolutionary framework to address both problems by proposing a methodology that autonomously tunes collective motion parameters for coverage problems while predicting the time required for real robots to complete the task. Our approach utilizes a simulation–optimization framework that employs a genetic algorithm to optimize the parameters of a frontier-led swarming algorithm. Results indicate that the optimized parameters are transferable to real robots, achieving 100% coverage while maintaining 84% connectivity between them. Compared to state-of-the-art swarm methods, our system reduced turnaround time by 50% and 57% in different environments while maintaining collective motion. It also achieved a 55% reduction in turnaround time on average across five scenarios compared to budget-constrained path planning, with a 10% increase in coverage. Furthermore, our framework outperformed both hand-tuned and learned collective motion approaches, reducing turnaround time by 73% in non-collective motion scenarios and by 63% while maintaining 85% connectivity in collective motion scenarios. This approach effectively combines the adaptability of swarm behavior with the predictive reliability of planning methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578470","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}

{"title":"Diophantine spherical vague sets and their applications for micro-technology robots based on multiple-attribute decision-making","authors":"Murugan Palanikumar ,&nbsp;Nasreen Kausar ,&nbsp;Željko Stević ,&nbsp;Sarfaraz Hashemkhani Zolfani","doi":"10.1016/j.engappai.2024.109447","DOIUrl":"10.1016/j.engappai.2024.109447","url":null,"abstract":"<div><div>We introduce the concept of Diophantine spherical vague set approach to multiple-attribute decision-making. The Spherical vague set is a novel expansion of the vague set and interval valued spherical fuzzy set. Three new concepts have been introduce such as Diophantine spherical vague weighted averaging operator, Diophantine spherical vague weighted geometric operator, generalized Diophantine spherical vague weighted averaging operator and generalized Diophantine spherical vague weighted geometric operator. We provide a numerical example to show how Euclidean distance and Hamming distance interact. Applications of the Diophantine spherical vague number include idempotency, boundedness, commutativity and monotonicity in algebraic operations. They can determine the optimal option and are more well-known and reasonable. Our goal was to identify the optimal choice by comparing expert opinions with the criteria. As a result, the model’s output was more accurate as well as in the range of the natural number <figure><img></figure>. The weighted averaging distance and weighted geometric distance operators are distance measure that is based on aggregating model. By comparing the models under discussion with those suggested in the literature, we hoped to show their worth and reliability. It is possible to find a better solution more quickly, simply, and practically. Our objective was to compare the expert evaluations with the criteria and determine which option was the most suitable. Because they yield more precise solutions, these models are more accurate and more related to models with <figure><img></figure>. To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. This investigation yielded some intriguing results.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578474","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}

{"title":"Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information","authors":"Arun Sarkar ,&nbsp;Ömer Faruk Görçün ,&nbsp;Fatih Ecer ,&nbsp;Tapan Senapati ,&nbsp;Hande Küçükönder","doi":"10.1016/j.engappai.2024.109483","DOIUrl":"10.1016/j.engappai.2024.109483","url":null,"abstract":"<div><div>In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic <span><math><mrow><mi>q</mi></mrow></math></span>-rung orthopair fuzzy (DHL<em>q</em>-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank <span><math><mrow><mi>t</mi></mrow></math></span>-norms and <span><math><mrow><mi>t</mi></mrow></math></span>-conorms are defined for DHL<em>q</em>-ROF numbers. A wide range of generalized aggregation operators, such as DHL<em>q</em>-ROF Frank weighted averaging, DHL<em>q</em>-ROF Frank weighted geometric, DHL<em>q</em>-ROF Frank generalized weighted averaging, and DHL<em>q</em>-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne &amp; Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578468","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}

{"title":"Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning","authors":"Durgesh Choudhary,&nbsp;Rabindra Nath Mahanty,&nbsp;Niranjan Kumar","doi":"10.1016/j.engappai.2024.109585","DOIUrl":"10.1016/j.engappai.2024.109585","url":null,"abstract":"<div><div>The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572598","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}

{"title":"Real-time joint recognition of weather and ground surface conditions by a multi-task deep network","authors":"Diego Gragnaniello ,&nbsp;Antonio Greco ,&nbsp;Carlo Sansone ,&nbsp;Bruno Vento","doi":"10.1016/j.engappai.2024.109543","DOIUrl":"10.1016/j.engappai.2024.109543","url":null,"abstract":"<div><div>Climate change and the occurrence of intense and unexpected weather events highlighted the need for real-time weather warning systems, especially in smart roads and isolated scenarios like rural areas. In this work, we propose to jointly recognize the weather and the ground surface conditions using existing video surveillance systems. Previous works separately tackled these two tasks even if they are correlated to each other. We propose a convolutional neural network with shared weights in the lower layers and two separate classification branches on top to exploit the correlation between the tasks and, at the same time, learn diverse high-level features for each task. Moreover, the network architecture implements attention mechanisms allowing the classification branches to focus on diverse image regions. The method is versatile and allows us to train the network on partially labeled data. The experimental analysis on real data demonstrate the effectiveness of the proposed method on both tasks, confirmed by the accuracy comparison with existing methods for the recognition of weather and ground surface conditions. The multi-task solution improves the inference speed (50 frames per second) and reduces the required memory (less than 1 GB) with respect to a system with two different single-task approaches; these results confirm that the proposed solution is ready for video surveillance applications to support smart cities.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572734","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}

{"title":"Cross-attention interaction learning network for multi-model image fusion via transformer","authors":"Jing Wang ,&nbsp;Long Yu ,&nbsp;Shengwei Tian","doi":"10.1016/j.engappai.2024.109583","DOIUrl":"10.1016/j.engappai.2024.109583","url":null,"abstract":"<div><div>Current image fusion techniques often fail to adequately consider the inherent correlations among different modalities, resulting in suboptimal integration of multi-modal information. Drawing inspiration from inter-modal interactions, this paper introduces a cross-attention interaction learning network, CrossATF, leveraging the transformer architecture. The cornerstone of CrossATF resides in a generator network equipped with dual encoders. The multi-modal encoder incorporates two transformer modules of comparable computational complexity, alongside a meticulously designed cross-modal transformer. This architectural choice empowers the model to effectively extract modality-specific features while simultaneously integrating complementary features from diverse modalities. Furthermore, an auxiliary encoder is enlisted to encode features from the entire input image, thereby enhancing the model's comprehensive understanding of the image. Significantly, the loss function is tailored to selectively preserve a more targeted set of information from the source images, endowing the network with heightened feature extraction capabilities. Comprehensive experimental results across various datasets substantiate the promising performance of the proposed approach when compared to both task-specific methodologies and unified fusion frameworks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572597","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}

{"title":"Engineering applications of artificial intelligence a knowledge-guided reinforcement learning method for lateral path tracking","authors":"Bo Hu ,&nbsp;Sunan Zhang ,&nbsp;Yuxiang Feng ,&nbsp;Bingbing Li ,&nbsp;Hao Sun ,&nbsp;Mingyang Chen ,&nbsp;Weichao Zhuang ,&nbsp;Yi Zhang","doi":"10.1016/j.engappai.2024.109588","DOIUrl":"10.1016/j.engappai.2024.109588","url":null,"abstract":"<div><div>Lateral Control algorithms in autonomous vehicles often necessitates an online fine-tuning procedure in the real world. While reinforcement learning (RL) enables vehicles to learn and improve the lateral control performance through repeated trial and error interactions with a dynamic environment, applying RL directly to safety-critical applications in real physical world is challenging because ensuring safety during the learning process remains difficult. To enable safe learning, a promising direction is to make use of previously gathered offline data, which is frequently accessible in engineering applications. In this context, this paper presents a set of knowledge-guided RL algorithms that can not only fully leverage the prior collected offline data without the need of a physics-based simulator, but also allow further online policy improvement in a smooth, safe and efficient manner. To evaluate the effectiveness of the proposed algorithms on a real controller, a hardware-in-the-loop and a miniature vehicle platform are built. Compared with the vanilla RL, behavior cloning and the existing controller, the proposed algorithms realize a closed-loop solution for lateral control problems from offline training to online fine-tuning, making it attractive for future similar RL-based controller to build upon.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572736","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}

{"title":"A new spatiotemporal long-term prediction method for Continuous Annealing Processes","authors":"Wenshuo Song ,&nbsp;Weihua Cao ,&nbsp;Yan Yuan ,&nbsp;Kang-Zhi Liu","doi":"10.1016/j.engappai.2024.109514","DOIUrl":"10.1016/j.engappai.2024.109514","url":null,"abstract":"<div><div>Accurately and consistently predicting the heating state is essential to maintain stable operation in continuous annealing processes (CAPs). However, long-term prediction biases often arise due to unmodeled dynamics associated with high-dimensional, time-varying, and strongly coupled variables. This study introduces a spatiotemporal-based forecast model designed to extend the prediction horizon while significantly reducing bias accumulation. The model leverages the spatial characteristics derived from classified process parameters by analyzing the internal structure and dynamics of the process. Additionally, it captures the temporal features of each parameter type through deep learning techniques that preserve and learn from historical data, enabling the model to account for the autocorrelation of multiple variables, including the output, and their correlation with the output. We conducted experiments with real process data, confirming the model’s accuracy and consistency in real-world settings. Additionally, ablation experiments validated the need to integrate both temporal and spatial features for long-term prediction accuracy. Compared to existing methods, the proposed model significantly reduces prediction bias and enhances forecast robustness.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572737","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}

{"title":"Autonomous smart palm tree harvesting with deep learning-enabled date fruit type and maturity stage classification","authors":"Jawad Yousaf ,&nbsp;Zainab Abuowda ,&nbsp;Shorouk Ramadan ,&nbsp;Nour Salam ,&nbsp;Eqab Almajali ,&nbsp;Taimur Hassan ,&nbsp;Abdalla Gad ,&nbsp;Mohammad Alkhedher ,&nbsp;Mohammed Ghazal","doi":"10.1016/j.engappai.2024.109506","DOIUrl":"10.1016/j.engappai.2024.109506","url":null,"abstract":"<div><div>This work proposes an innovative autonomous system based on intelligent deep-transfer learning for the sustainable harvesting of palm trees. The machine learning-based autonomous robot detects and captures the date fruit bunches on palm trees in the natural farm environment using the lightweight you only look once (YOLO)v8 algorithm. Five different types of fruit bunches are further classified using a deep transfer learning system based on the type (Khalas, Barhi, Sullaj, Meneifi, and Naboot Saif) and the maturity stage of date fruit (Immature, Khalal, Khalal with Rutab, Pre-Tamar, and Tamar) for their efficient, faster, and accurate harvesting. Five deep convolutional neural network (CNN) models, the Alex Krizhevsky network (AlexNet), the visual geometry group (VGG-16), the residual network (ResNet-50), Inception-v3 and Efficient Net, were trained on around 12,000 images at the bunch level for the two classification tasks. The findings of various performed experiments suggested that the VGG-16 network outperforms the compared models with maximum achieved testing accuracies of 98.89% and 98.17% for date type and maturity stage classification, respectively. The obtained testing accuracies of AlexNet, ResNet-50, Efficient Net, and Inception-v3 models are 97.33%, 97.87%, 98.39%, 96.61% 98%, 93%, and 86.5% for both date type/maturity stage predictions. These obtained accuracies are superior than the state-of-the-art legacy models. Autonomous robotic vehicle front and top cameras are used to localize the quick response (QR)-labeled palm trees using canny edge detection and hough transformation, and date bunch detection and capturing using the trained YOLOv8 algorithm. The robotic vehicle transfers all captured images using Firebase after the completion of the farm journey. The developed and integrated front-end user interface (UI) provides ease to farmers for the two classification tasks of the retrieved images, along with the harvesting decision for each image. The use of proposed sustainable smart harvesting robots to classify and analyze date bunches in the natural environment can significantly improve the yield and global supply chain of this fruit.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572596","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}

{"title":"A robust accent classification system based on variational mode decomposition","authors":"Darshana Subhash ,&nbsp;Jyothish Lal G. ,&nbsp;Premjith B. ,&nbsp;Vinayakumar Ravi","doi":"10.1016/j.engappai.2024.109512","DOIUrl":"10.1016/j.engappai.2024.109512","url":null,"abstract":"<div><div>State-of-the-art automatic speech recognition models often struggle to capture nuanced features inherent in accented speech, leading to sub-optimal performance in speaker recognition based on regional accents. Despite substantial progress in the field of automatic speech recognition, ensuring robustness to accents and generalization across dialects remains a persistent challenge, particularly in real-time settings. In response, this study introduces a novel approach leveraging Variational Mode Decomposition (VMD) to enhance accented speech signals, aiming to mitigate noise interference and improve generalization on unseen accented speech datasets. Our method employs decomposed modes of the VMD algorithm for signal reconstruction, followed by feature extraction using Mel-Frequency Cepstral Coefficients (MFCC). These features are subsequently classified using machine learning models such as 1D Convolutional Neural Network (1D-CNN), Support Vector Machine (SVM), Random Forest, and Decision Trees, as well as a deep learning model based on a 2D Convolutional Neural Network (2D-CNN). Experimental results demonstrate superior performance, with the SVM classifier achieving an accuracy of approximately 87.5% on a standard dataset and 99.3% on the AccentBase dataset. The 2D-CNN model further improves the results in multi-class accent classification tasks. This research contributes to advancing automatic speech recognition robustness and accent-inclusive speaker recognition, addressing critical challenges in real-world applications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572735","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}