IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-15 DOI: 10.1016/j.engappai.2024.109606

Sathishkumar Moorthy , Sachin Sakthi K.S. , Sathiyamoorthi Arthanari , Jae Hoon Jeong , Young Hoon Joo

{"title":"Hybrid multi-attention transformer for robust video object detection","authors":"Sathishkumar Moorthy , Sachin Sakthi K.S. , Sathiyamoorthi Arthanari , Jae Hoon Jeong , Young Hoon Joo","doi":"10.1016/j.engappai.2024.109606","DOIUrl":"10.1016/j.engappai.2024.109606","url":null,"abstract":"<div><div>Video object detection (VOD) is the task of detecting objects in videos, a challenge due to the changing appearance of objects over time, leading to potential detection errors. Recent research has addressed this by aggregating features from neighboring frames and incorporating information from distant frames to mitigate appearance deterioration. However, relying solely on object candidate regions in distant frames, independent of object position, has limitations, as it depends heavily on the performance of these regions and struggles with deteriorated appearances. To overcome these challenges, we propose a novel Hybrid Multi-Attention Transformer (HyMAT) module as our main contribution. HyMAT enhances relevant correlations while suppressing flawed information by searching for an agreement between whole correlation vectors. This module is designed for flexibility and can be integrated into both self- and cross-attention blocks to significantly improve detection accuracy. Additionally, we introduce a simplified Transformer-based object detection framework, named Hybrid Multi-Attention Object Detection (HyMATOD), which leverages competent feature reprocessing and target-background embeddings to more effectively utilize temporal references. Our approach demonstrates state-of-the-art performance, as evaluated on the ImageNet video object detection benchmark (ImageNet VID) and the University at Albany DEtection and TRACking (UA-DETRAC) benchmarks. Specifically, our HyMATOD model achieves an impressive 86.7% mean Average Precision (mAP) on the ImageNet VID dataset, establishing its superiority and practicality for video object detection tasks. These results underscore the significance of our contributions to advancing the field of VOD.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109606"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659096","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

A timestamp-based Nesterov’s accelerated projected gradient method for distributed Nash equilibrium seeking in monotone games 基于时间戳的涅斯捷罗夫加速投影梯度法，用于单调博弈中的分布式纳什均衡寻求

IF 2.1 3区计算机科学

Systems & Control Letters Pub Date : 2024-11-15 DOI: 10.1016/j.sysconle.2024.105966

Nian Liu , Shaolin Tan , Ye Tao , Jinhu Lü

引用次数: 0

Artificial intelligent pancreas for type 1 diabetic patients using adaptive type 3 fuzzy fault tolerant predictive control 利用自适应 3 型模糊容错预测控制为 1 型糖尿病患者提供人工智能胰腺

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-15 DOI: 10.1016/j.engappai.2024.109627

Arman Khani , Peyman Bagheri , Mahdi Baradarannia , Ardashir Mohammadzadeh

{"title":"Artificial intelligent pancreas for type 1 diabetic patients using adaptive type 3 fuzzy fault tolerant predictive control","authors":"Arman Khani , Peyman Bagheri , Mahdi Baradarannia , Ardashir Mohammadzadeh","doi":"10.1016/j.engappai.2024.109627","DOIUrl":"10.1016/j.engappai.2024.109627","url":null,"abstract":"<div><div>In this paper, the design methodology of artificial intelligent pancreas is presented. Accurate regulation of blood glucose levels in type 1 diabetic patients is of great importance in the presence of possible faults caused by sensor measurements. Regulation of blood glucose levels using a type 3 fuzzy predictive controller in type 1 diabetic patients in the presence of sensor faults is considered. The proposed structure includes a main control structure and a virtual dynamic, in which the main structure includes a fuzzy identifier, predictive controller, and an adaptive compensator, and the virtual structure is used to identify the sensor faults. Glucose is unknown in the dynamics of type 1 diabetes and is estimated on-line using a type 3 fuzzy system. Also, Lyapunov stability analysis is used to design the adaptive compensator to ensure the stability of the closed-loop system. The proposed methodology is evaluated based on Bergman’s minimum model for different patients under various parametric uncertainties and disturbances.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109627"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659143","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

Analysis of computer communication networks based on evaluation of domination and double domination for interval-valued T-spherical fuzzy graphs and their applications in decision-making problems 基于区间值 T 球形模糊图的支配和双重支配评价的计算机通信网络分析及其在决策问题中的应用

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-15 DOI: 10.1016/j.engappai.2024.109650

Sami Ullah Khan , Fiaz Hussain , Tapan Senapati , Shoukat Hussain , Zeeshan Ali , Domokos Esztergár-Kiss , Sarbast Moslem

{"title":"Analysis of computer communication networks based on evaluation of domination and double domination for interval-valued T-spherical fuzzy graphs and their applications in decision-making problems","authors":"Sami Ullah Khan , Fiaz Hussain , Tapan Senapati , Shoukat Hussain , Zeeshan Ali , Domokos Esztergár-Kiss , Sarbast Moslem","doi":"10.1016/j.engappai.2024.109650","DOIUrl":"10.1016/j.engappai.2024.109650","url":null,"abstract":"<div><div>This research introduces the Interval-Valued T-Spherical Fuzzy Graph (IVTSFG), a novel extension of fuzzy graph theory designed to address imprecision in decision-making processes, network analysis, and Computer Communication Networks (CCNs). Integrating four types of membership degrees-membership, non-membership, abstinence, and hesitancy-the IVTSFG framework significantly enhances the ability to model and analyze complex systems with uncertain data. The study explores the theories of domination and double domination within the context of IVTSFGs, presenting new methods for evaluating network resilience and optimization. Key findings include the development of innovative techniques for applying domination and double domination in IVTSFGs, demonstrating improved performance in managing CCNs. Comparative analysis with existing fuzzy graph models highlights the advantages of IVTSFGs, particularly in capturing nuanced relationships within network structures. The research provides practical examples and empirical comparisons, showcasing the framework's effectiveness in various decision-making scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109650"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659146","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

AI in the Organizational Nexus: Building Trust, Cementing Commitment, and Evolving Psychological Contracts 组织关系中的人工智能：建立信任、巩固承诺和发展心理契约

IF 5.9 3区管理学

Information Systems Frontiers Pub Date : 2024-11-15 DOI: 10.1007/s10796-024-10561-3

Muhammad Farrukh Moin, Abhishek Behl, Justin Zuopeng Zhang, Amit Shankar

{"title":"AI in the Organizational Nexus: Building Trust, Cementing Commitment, and Evolving Psychological Contracts","authors":"Muhammad Farrukh Moin, Abhishek Behl, Justin Zuopeng Zhang, Amit Shankar","doi":"10.1007/s10796-024-10561-3","DOIUrl":"https://doi.org/10.1007/s10796-024-10561-3","url":null,"abstract":"<p>Since the Industrial Revolution, significant technological advancements have revolutionized various manual processes and workflows entrenched for decades. Artificial Intelligence (AI) offers similar transformative potential across diverse industrial and social domains. The rapid pace of change in the AI-driven digital age presents unprecedented opportunities and challenges for sustained progress. Given the potentially profound impact of AI, this study seeks to explore its disruptive effects and challenges within organizational contexts. Drawing on the Social Exchange Theory, this research examines the relationship between psychological contract (PC) fulfillment and organizational commitment, with trust acting as a mediator and AI acceptance as a moderator. Data were collected from the service industry using a time-lagged design. The findings indicate that PC fulfillment positively influences workers’ trust and organizational commitment. Furthermore, AI acceptance attenuates the direct and indirect positive effects of PC fulfillment on job-related outcomes. This study offers valuable insights into building and maintaining trust and fostering a committed workforce amidst the digitalization era. It underscores the importance of fulfilling promissory expectations in fostering trust and commitment. Additionally, it sheds light on the disruptive effects of AI technology on critical job outcomes, emphasizing the societal and industrial implications, the future of work, and avenues for further advancements in AI technology.</p>","PeriodicalId":13610,"journal":{"name":"Information Systems Frontiers","volume":"9 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Group cohesion and passive dynamics of a pair of inertial swimmers with three-dimensional hydrodynamic interactions. 具有三维流体动力相互作用的一对惯性游泳者的群体凝聚力和被动动力学。

IF 3.1 3区计算机科学

Bioinspiration & Biomimetics Pub Date : 2024-11-15 DOI: 10.1088/1748-3190/ad936d

Mohamed Niged Mabrouk, Daniel Floryan

引用次数: 0

WTC3D: An Efficient Neural Network for Noncontact Pulse Acquisition in Internet of Medical Things WTC3D：医疗物联网中用于非接触式脉搏采集的高效神经网络

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2024-11-15 DOI: 10.1109/tii.2024.3485749

Changchen Zhao, Pengcheng Cao, Meng Hu, Bin Huang, Huiling Chen, Jing Li

引用次数: 0

Lyapunov-Assisted Decentralized Dynamic Offloading Strategy based on Deep Reinforcement Learning 基于深度强化学习的李亚普诺夫辅助分散动态卸载策略

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-15 DOI: 10.1109/jiot.2024.3498839

Jingjing Wang, Hui Zhang, Xu Han, Jiaxiang Zhao, Jiangzhou Wang

引用次数: 0

Corrigendum to "Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning" [Neural Networks Volume 178, October (2024), 1-11/106414]]. 海德拉：用于参数高效微调的多头低阶自适应》[《神经网络》第 178 卷，10 月（2024 年），1-11/106414]]更正。

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-15 DOI: 10.1016/j.neunet.2024.106878

Sanghyeon Kim, Hyunmo Yang, Younghyun Kim, Youngjoon Hong, Eunbyung Park

引用次数: 0

A dual knowledge embedded hybrid model based on augmented data and improved loss function for tool wear monitoring 基于增强数据和改进损失函数的双知识嵌入式混合模型，用于刀具磨损监测

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2024-11-15 DOI: 10.1016/j.rcim.2024.102901

Xiaohui Fang , Qinghua Song , Jing Qin , Zhenyang Li , Haifeng Ma , Zhanqiang Liu

{"title":"A dual knowledge embedded hybrid model based on augmented data and improved loss function for tool wear monitoring","authors":"Xiaohui Fang , Qinghua Song , Jing Qin , Zhenyang Li , Haifeng Ma , Zhanqiang Liu","doi":"10.1016/j.rcim.2024.102901","DOIUrl":"10.1016/j.rcim.2024.102901","url":null,"abstract":"<div><div>Tool wear monitoring (TWM) is essential for enhancing the machining accuracy of intelligent manufacturing systems and ensuring the consistency and reliability of products. The complex and dynamic processing environment demands higher real-time monitoring and generalization ability of TWM. Traditional data-driven models lack guided training in physical processes and are limited by the amount of samples with wear labels. To guide the model to capture the underlying physical mechanism and enhance compliance with the law of tool wear, a dual knowledge embedded hybrid model based on augmented data and improved loss function for TWM is proposed in this paper. The second training data source is obtained by constructing the mapping relationship between cutting force and tool wear, which effectively complements and enhances the physical characteristics between the data and addresses the issue of insufficient labeled data in actual network training. Subsequently, a structure integrating serial convolution, parallel convolution, bidirectional gated recurrent unit (BiGRU) and attention mechanism is developed to extract the spatial and temporal features in time series data. Moreover, Based on the physical law of tool wear, an improved loss function with physical constraints is proposed to improve the physical consistency of the model. The experimental results show that the model prediction RMSE error is reduced by 12.67% after augmented data compared to a single data source, and the RMSE error of the prediction result is reduced by 25.16% at most after the improvement of the loss function. The model has high prediction accuracy within short training epochs and good real-time performance. The proposed approach provides a modeling strategy with low computational resource requirements based on the fusion of physical and data information.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102901"},"PeriodicalIF":9.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637642","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

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