检索结果-内蒙古大学图书馆

Interval Type-2 Fuzzy Risk Evaluation and Prevention for Parallel Breast Cancer Treatment System

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第2期10卷 673-685页

作者： Mo, Hong Hu, Haihong Hu, Jinhui Li, Yuanyuan Wang, Xiao Wang, Fei-Yue Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha 410114 Peoples R China Hunan Univ Chinese Med Affiliated Hosp 1 Changsha 410208 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha 410015 Peoples R China Univ Chinese Acad Sci Ctr China Econ & Social Secur Beijing 100190 Peoples R China

Breast cancer (BC) seriously threatens women's health. The establishment of a risk evaluation model cancer is conducive to early screening and prevention of BC. In this article, a new method of the evaluation and the framework of prevention for BC are proposed by synthesizing interval type-2 fuzzy sets (IT2 FSs), two-level fuzzy comprehensive evaluation, and parallel control (Artificial societies, Computational experiments, and Parallel execution, ACP). At the same time, 12 risk factors were selected as indicators to evaluate the risk level of BC, and then, according to the evaluation results, the correspondingly prevention strategies and intervention measures for different risk levels were discussed. Then, the prevention strategies are analyzed and selected in the computational experiment module of the artificial system, and the patient indexes are monitored in real time by the parallel mechanism between the actual and the artificial system, to feedback, adjust, and optimize the prevention scheme in time. This parallel BC prevention process can achieve dynamic closed-loop control effects. A new effective way for BC prevention was presented, which is of great significance for reducing its incidence rate and developing new medical means.

关键词： History Fuzzy control complex systems Training Real-time systems Medical diagnostic imaging Industries ACP breast cancer (BC) fuzzy comprehensive evaluation interval type-2 fuzzy sets (IT2 FSs)

来源：评论

学校读者我要写书评

暂无评论

Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

arXiv

引用

arXiv 2024年

作者： Chen, Zhuo Ou, Ni Jiang, Jiaqi Luo, Shan Robot Perception Lab Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-lab.led tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

TransForce: Transferable Force Prediction for Vision-based Tactile Sensors with Sequential Image Translation

arXiv

引用

arXiv 2024年

作者： Chen, Zhuo Ou, Ni Zhang, Xuyang Luo, Shan The Robot Perception Lab Centre for Robotics Research Department of Engineering King's College London LondonWC2R 2LS United Kingdom The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force lab.ls. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force lab.ls is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

来源：评论

学校读者我要写书评

暂无评论

Multi-Heterogeneity Public Goods Evolutionary Game on Lattice

SSRN

引用

SSRN 2022年

作者： Xie, Kai Liu, Xingwen Wang, Huazhang Jiang, Yulian Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Sichuan Chengdu610225 China College of Electrical Engineering Southwest Minzu University Sichuan Chengdu610225 China

The topic of how cooperation emerges and develops in the real world has been intensively studied. Many mechanisms and models have been proposed to explain this complex phenomenon with the idealized assumption that all players are homogeneous or have little heterogeneity. This paper investigates the evolution of the public goods game with multiple heterogeneities. Roughly speaking, the quantity of investment (by a cooperator), punishment (received by a defector), or cost (paid by a punisher) is not fixed but depends on specific evolutionary situation. Moreover, heterogeneity is also introduced in expected payoff of each player for the strategy updating rule. Multi-heterogeneity makes the considered model more realistic. The simulation experiment shows that the combination of modified punishment mechanism and strategy updating rule can promote cooperation density in both the multi-heterogeneity and homogeneity models. © 2022, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evolution of cooperation in Snowdrift game with Multi-memory mechanism

Evolution of cooperation in Snowdrift game with Multi-memory...

引用

第42届中国控制会议

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab.of Complex Systems & Intelligent Control Key Lab.of Electronic Information of State Ethnic Affairs CommissionCollege of Electrical EngineeringSouthwest Minzu University Engineering Research Center for Tibetan Information Processing School of Information Science and TechnologyTibet University

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural *** memory can greatly influence the emergence of cooperative behavior,memory-based research on evolutionary games has proliferated in recent *** the present paper,inspired by the well-known "Stanford marshmallow experiment"(SME),we proposed a multi-memory mechanism in Snowdrift game(SDG) on spatial lattice networks whose core lies in what follows:Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single *** simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary *** specifically,as memory length increases to an intermediate length,the cooperation level increases as *** mechanism with short memory length,the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative *** findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Online Progressive Instance-Balanced Sampling for Weakly Supervised Vibration Damper Detection

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Chen, Minghao Tian, Yunong Li, Zhishuo Li, En Liang, Zize Inst Automat Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Academyof Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Engn Lab Ind Vis & Intelligent Equipment Technol Beijing 100190 Peoples R China

The detection of vibration dampers is important for power systems. Deep learning-based damper detection needs massive annotations, which are lab.r-intensive and time-consuming. Therefore, weakly supervised object detection (WSOD) is considered. Part domination is the main problem in weakly supervised vibration damper detection. Current WSOD methods neglect that overwhelming negative instances exist in each image during the training phase, which would mislead the training and make detection results stuck in the most discriminative parts of objects. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this article. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module, combining random sampling and intersection over union (IoU)-balanced sampling, progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances to make the network focus on positive instances. Extensive experimental results on the vibration damper, pattern analysis, statistical modelling and computational learning visual object classes (PASCAL VOC) 2007, and PASCAL VOC 2012 datasets demonstrate that the proposed method can significantly improve the baseline, which is also comparable to many existing methods. In addition, compared with the baseline, the proposed method requires no extra network parameters, and the supplementary training overheads are small.

关键词： Shock absorbers Vibrations Object detection Proposals Training Sampling methods Convolutional neural networks Instance balance multiple instance learning (MIL) progressive sampling vibration damper detection weakly supervised object detection (WSOD)

来源：评论

学校读者我要写书评

暂无评论

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for intelligent Vehicles

引用

IEEE TRANSACTIONS ON intelligent VEHICLES 2022年第3期7卷 417-440页

作者： Hu, Zhongxu Lou, Shanhe Xing, Yang Wang, Xiao Cao, Dongpu Lv, Chen Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 637460 Singapore Cranfield Univ Ctr Autonomous & Cyber Phys Syst Cranfield MK43 0AL Beds England Chinese Acad Sci Inst Automat Qingdao Acad Intelligent Ind Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system

关键词： Vehicles Digital twins Data models Behavioral sciences Safety Autonomous vehicles Reliability Driver digital twin human-centric deisgn intelligent vehicles human-machine interactions cyber-physical systems

来源：评论

学校读者我要写书评

暂无评论

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

引用

Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

来源：评论

学校读者我要写书评

暂无评论

Measurement Invariance Investigation for Performance of Deep Learning Architectures

引用

IEEE ACCESS 2022年 10卷 78070-78087页

作者： Chen, Dewang Lu, Yuqi Hsu, Chih-Yu Fujian Univ Technol Sch Transportat Fuzhou 350118 Fujian Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Fujian Univ Technol Intelligent Transportat Syst Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Fujian Peoples R China

Models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Multi-class classification is a classification task where each image is assigned to one and only one lab.l. Confusion matrix, Precision, Recall, and F1 Score are popular performance metrics. The common sense is that the performance of any architecture is dependent on the sizes of data set. The goodness of the architecture of deep learning models for different data sets is critical issue. This paper implements Pearson correlation coefficient and the multivariate linear regression method to assess the accuracy of deep learning architectures with five performance indicators. The five performance indicators are training loss rate, robustness, training time, number of model parameters and computation complexity. There are five image datasets used to test four deep learning models: Alexnet, GoogLeNet, ResNet, MobileNet to obtain the values of accuracy and other five indicators. The most important contribution of the article is to show that the accuracy indicator related to training loss rate and training time indicators are not dependent on the selection of the data group. According to the definition of Measurement Invariance (MI), the measurement invariance is demonstrated by the linear regression analysis and inner product of the unit normal vector of the linear regression planes.

关键词： Convolutional neural networks Training Licenses Predictive models Diseases Deep learning Neural networks Deep learning model multivariate linear regression Pearson correlation coefficient measurement invariance performance indicators

来源：评论

学校读者我要写书评

暂无评论

Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

引用

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2023年 80卷

作者： Li, Wenhao Zhang, Haiou Wang, Guilan Xiong, Gang Zhao, Meihua Li, Guokuan Li, Runsheng Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn Wuhan 430074 Hubei Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent M Cloud Comp Ctr Donggguan 523808 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Wuhan Natl Lab Optoelect Wuhan 430074 Hubei Peoples R China

Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.

关键词： Wire and arc additive manufacturing Defect detection Online Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：