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.2024.102894

Yi Wu , Xiaohui Jia , Tiejun Li , Jinyue Liu

{"title":"A real-time collision avoidance method for redundant dual-arm robots in an open operational environment","authors":"Yi Wu , Xiaohui Jia , Tiejun Li , Jinyue Liu","doi":"10.1016/j.rcim.2024.102894","DOIUrl":"10.1016/j.rcim.2024.102894","url":null,"abstract":"<div><div>Due to the structural resemblance of redundant dual-arm robots to human arms, they are widely employed to replace humans in open operational environments. Addressing safety concerns related to the autonomous operations of redundant dual-arm robots in open environments, this paper proposes a real-time collision avoidance method. Firstly, an avoidance direction adjustment algorithm is designed based on the avoidance function method, providing a collision avoidance formulation for the robot control point. Secondly, an obstacle classification algorithm is devised to categorize obstacles into robot body obstacles and end-effector obstacles, and the collision avoidance strategy of redundant dual-arm robots is designed. Subsequently, a collision avoidance penalty factor is introduced based on the proximity between the end-effector and the target point, ensuring the convergence of the joint velocity. Finally, a novel collision avoidance formulation for redundant manipulators is presented, further extended under dual-arm coordinated tasks. Numerical simulations and physical experiments demonstrate that the proposed method can achieve self-collision avoidance for redundant dual-arm robots and dynamic/static obstacle avoidance in dual-arm coordinated tasks, with smooth collision avoidance maneuvers. The research results provide safety guidelines for autonomous operations of redundant dual-arm robots in open operational environments.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102894"},"PeriodicalIF":9.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643208","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

Patients' cognitive and behavioral paradoxes in the process of adopting conflicting health information: A dynamic perspective 患者在接受相互矛盾的健康信息过程中的认知和行为悖论：动态视角

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-15 DOI: 10.1016/j.ipm.2024.103939

Yan Jin , Di Zhao , Zhuo Sun , Chongwu Bi , Ruixian Yang , Shengli Deng

{"title":"Patients' cognitive and behavioral paradoxes in the process of adopting conflicting health information: A dynamic perspective","authors":"Yan Jin , Di Zhao , Zhuo Sun , Chongwu Bi , Ruixian Yang , Shengli Deng","doi":"10.1016/j.ipm.2024.103939","DOIUrl":"10.1016/j.ipm.2024.103939","url":null,"abstract":"<div><div>Diversified access to health information has increased the likelihood of encountering conflicting health messages, making it more difficult for patients to adopt information rationally. Prior research has primarily focused on the outcomes of patients' information adoption and responded to concerns by exploring the influences that led to these outcomes, overlooking a crucial aspect. Specifically, patients' cognitive and behavioral responses are continuously fluctuating during the process of information adoption. A total of 336 subjects (valid sample) participated in this study. A combination of situational experiments, grounded theory, and questionnaires was employed to develop a model of patients' adoption of conflicting health information. The concept of \"trans-theory\" was introduced to explain how patients' cognitive and behavioral responses changed at different segments of adoption. In contrast to prior studies viewing information adoption as a whole, we propose that the process can be divided into four distinct segments: information attention, comprehension, evaluation, and decision. Moreover, the sequential influence of information, ability, psychological, and environmental factors in the adoption process produces three common paradoxes in patients' cognitive and behavioral responses, affecting their ability to make rational adoption decisions. This study explores the dynamics of information adoption from the patient's perspective, providing novel insights into the study of conflicting health information adoption and offering guidance for designing more effective interventions for facilitating rational adoption by patients. Additionally, it can help the healthcare system better understand patients' cognitive and behavioral responses to deliver more effective healthcare services.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103939"},"PeriodicalIF":7.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Word-Sequence Entropy: Towards uncertainty estimation in free-form medical question answering applications and beyond 词序熵：在自由格式医学问题解答应用及其他应用中实现不确定性估计

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.2024.109553

Zhiyuan Wang , Jinhao Duan , Chenxi Yuan , Qingyu Chen , Tianlong Chen , Yue Zhang , Ren Wang , Xiaoshuang Shi , Kaidi Xu

{"title":"Word-Sequence Entropy: Towards uncertainty estimation in free-form medical question answering applications and beyond","authors":"Zhiyuan Wang , Jinhao Duan , Chenxi Yuan , Qingyu Chen , Tianlong Chen , Yue Zhang , Ren Wang , Xiaoshuang Shi , Kaidi Xu","doi":"10.1016/j.engappai.2024.109553","DOIUrl":"10.1016/j.engappai.2024.109553","url":null,"abstract":"<div><div>Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative inequality introduces a large number of irrelevant words and sequences within the generated set for uncertainty quantification (UQ), which can lead to biases. This paper proposes Word-Sequence Entropy (<em>WSE</em>), which calibrates uncertainty at both the word and sequence levels based on semantic relevance, highlighting keywords and enlarging the generative probability of trustworthy responses when performing UQ. We compare <em>WSE</em> with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs), and demonstrate that <em>WSE</em> exhibits superior performance in accurate UQ under two standard criteria for correctness evaluation. Additionally, in terms of the potential for real-world medical QA applications, we achieve a significant enhancement (e.g., a 6.36% improvement in model accuracy on the COVID-QA dataset) in the performance of LLMs when employing responses with lower uncertainty that are identified by <em>WSE</em> as final answers, without requiring additional task-specific fine-tuning or architectural modifications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109553"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659129","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

Attention based network for fusion of polarimetric and contextual features for polarimetric synthetic aperture radar image classification 基于注意力的网络，用于融合偏振和上下文特征，进行偏振合成孔径雷达图像分类

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.2024.109665

Maryam Imani

{"title":"Attention based network for fusion of polarimetric and contextual features for polarimetric synthetic aperture radar image classification","authors":"Maryam Imani","doi":"10.1016/j.engappai.2024.109665","DOIUrl":"10.1016/j.engappai.2024.109665","url":null,"abstract":"<div><div>Polarimetric synthetic aperture radar (PolSAR) images containing polarimetric, scattering and contextual features are useful radar data for ground surface classification. Appropriate feature extraction and fusion by using a small set of available labeled samples is an important and challenging task. Several transformers with self-attention mechanism have recently achieved great success for PolSAR image classification. While almost all methods just exploit the self-attention features from the PolSAR cube, the feature fusion method proposed in this work, which is called attention based scattering and contextual (ASC) network, utilizes the polarimetric self-attention beside two cross-attention blocks. The cross-attention blocks extract the polarimetric-scattering dependencies and polarimetric-contextual interactions, individually. The proposed ASC network uses three inputs: the PolSAR cube, the scattering feature maps obtained by clustering of the entropy-alpha features, and the segmentation maps obtained by a super-pixel generation algorithm. The features extracted by self- and cross-attention blocks are fused together, and the residual learning improves the feature learning. While transformers and attention-based networks usually need large training sets, the proposed ASC network shows high efficiency with relatively low number of training samples in various real and synthetic PolSAR images. For example, in the Flevoland PolSAR image containing 15 classes acquired by AIRSAR in L-band, with using 100 training samples per class (less than 1% of labeled samples), the ASC network achieves the overall accuracy of 99.51, which is statistically preferred than the self-attention-based network according to the McNemars test.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109665"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659356","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

Discovering data spaces: A classification of design options 发现数据空间：设计方案分类

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-11-15 DOI: 10.1016/j.compind.2024.104212

Anna Gieß , Thorsten Schoormann , Frederik Möller , Inan Gür

引用次数: 0

Design of STAR-RIS Assisted Uplink NOMA for Maximum Fairness 设计 STAR-RIS 辅助上行链路 NOMA 以实现最大公平性

IF 6.8 2区计算机科学

IEEE Transactions on Vehicular Technology Pub Date : 2024-11-15 DOI: 10.1109/tvt.2024.3498842

Noureen Khan, Hamza Ahmed Qureshi, Luiggi Cantos, Mubasher Ahmed Khan, Muhammad Rehman, Jinho Choi, Yun Hee Kim

引用次数: 0

Synchronization Method for Wireless Power Transfer System by Detecting Voltage Transient on a Sensor Inductor 通过检测传感器电感器上的瞬态电压实现无线电力传输系统同步的方法

IF 7.7 1区工程技术

IEEE Transactions on Industrial Electronics Pub Date : 2024-11-15 DOI: 10.1109/tie.2024.3488320

Xiaosheng Wang, C. Q. Jiang, Jiayu Zhou, Weisheng Guo, Yuanshuang Fan, Liping Mo

引用次数: 0

Adaptive selection of spectral–spatial features for hyperspectral image classification using a modified-CBAM-based network 使用基于修改后 CBAM 的网络，为高光谱图像分类自适应选择光谱空间特征

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-11-15 DOI: 10.1016/j.neucom.2024.128877

He Fu , Cailing Wang , Zhanlong Chen

{"title":"Adaptive selection of spectral–spatial features for hyperspectral image classification using a modified-CBAM-based network","authors":"He Fu , Cailing Wang , Zhanlong Chen","doi":"10.1016/j.neucom.2024.128877","DOIUrl":"10.1016/j.neucom.2024.128877","url":null,"abstract":"<div><div>Convolutional neural networks (CNNs) have demonstrated strong capabilities in hyperspectral image (HSI) classification. However, it is still a challenge to adaptively adjust the size of the receptive fields (RFs) of CNNs base on the information of different scales in HSI to achieve adaptive selection of spectral–spatial features. In the paper, we modify the convolutional block attention module (CBAM) and propose a modified-CBAM-based network (MCNet) to adaptively select spectral–spatial features for HSI classification. In particular, the modified CBAM not only enables the model to adjust its RF size according to the information of different scales in HSI, but also enables the model to achieve a joint focus on important spectral and spatial features. This is very important to adaptively select more descriptive and discriminative spectral–spatial features. The proposed MCNet is compared with currently popular methods on Indian Pines, Kennedy Space Center, University of Pavia, and Botswana HSI datasets. The results show that MCNet has better classification results than other methods on overall accuracy, average accuracy, and Kappa.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"615 ","pages":"Article 128877"},"PeriodicalIF":5.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652655","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

The role of mobile edge computing in advancing federated learning algorithms and techniques: A systematic review of applications, challenges, and future directions 移动边缘计算在推进联合学习算法和技术中的作用：对应用、挑战和未来方向的系统回顾

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-15 DOI: 10.1016/j.compeleceng.2024.109812

Amir Masoud Rahmani , Shtwai Alsubai , Abed Alanazi , Abdullah Alqahtani , Monji Mohamed Zaidi , Mehdi Hosseinzadeh

{"title":"The role of mobile edge computing in advancing federated learning algorithms and techniques: A systematic review of applications, challenges, and future directions","authors":"Amir Masoud Rahmani , Shtwai Alsubai , Abed Alanazi , Abdullah Alqahtani , Monji Mohamed Zaidi , Mehdi Hosseinzadeh","doi":"10.1016/j.compeleceng.2024.109812","DOIUrl":"10.1016/j.compeleceng.2024.109812","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) and Federated Learning (FL) have recently attracted considerable interest for their potential applications across diverse domains. MEC is an architecture for distributed computing that utilizes computational capabilities near the network edge, enabling quicker data processing and minimizing latency. In contrast, FL is a method in the field of Machine learning (ML) that allows for the simultaneous involvement of multiple participants to collectively train models without revealing their raw data, effectively tackling concerns related to security and privacy. This systematic review explores the core principles, architectures, and applications of FL within MEC and vice versa, providing a comprehensive analysis of these technologies. The study emphasizes FL and MEC's unique characteristics, advantages, and drawbacks, highlighting their attributes and limitations. The study explores the complex architectures of both technologies, showcasing the cutting-edge methods and tools employed for their implementation. Aside from examining the foundational principles, the review explores the depths of the internal mechanisms of FL and MEC, offering a valuable in-depth of their architecture understanding and the fundamental principles and processes that facilitate their operation. At last, the concluding remarks and future research directions are provided.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109812"},"PeriodicalIF":4.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655564","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

An ensemble reinforcement learning-assisted deep learning framework for enhanced lung cancer diagnosis 用于增强肺癌诊断的集合强化学习辅助深度学习框架

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-11-15 DOI: 10.1016/j.swevo.2024.101767

Richa Jain, Parminder Singh, Avinash Kaur

{"title":"An ensemble reinforcement learning-assisted deep learning framework for enhanced lung cancer diagnosis","authors":"Richa Jain, Parminder Singh, Avinash Kaur","doi":"10.1016/j.swevo.2024.101767","DOIUrl":"10.1016/j.swevo.2024.101767","url":null,"abstract":"<div><div>Lung cancer ranks among the most lethal diseases, highlighting the necessity of early detection to facilitate timely therapeutic intervention. Deep learning has significantly improved lung cancer prediction by analyzing large healthcare datasets and making accurate decisions. This paper proposes a novel framework combining deep learning with integrated reinforcement learning to improve lung cancer diagnosis accuracy from CT scans. The data set utilized in this study consists of CT scans from healthy individuals and patients with various lung stages. We address class imbalance through elastic transformation and employ data augmentation techniques to enhance model generalization. For multi-class classification of lung tumors, five pre-trained convolutional neural network architectures (DenseNet201, EfficientNetB7, VGG16, MobileNet and VGG19) are used, and the models are refined by transfer learning. To further boost performance, we introduce a weighted average ensemble model “DEV-MV”, coupled with grid search hyperparameter optimization, achieving an impressive diagnostic accuracy of 99.40%. The integration of ensemble reinforcement learning also contributes to improved robustness and reliability in predictions. This approach represents a significant advancement in automated lung cancer detection, offering a highly accurate, scalable solution for early diagnosis.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101767"},"PeriodicalIF":8.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658391","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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