检索结果-内蒙古大学图书馆

Opening Doors and Drawers by a UR5 Robot with Force Control 1

32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Dukić, Jana Vulić, Lukrecia Šimundić, Valentin Pejić, Petra Cupec, Robert Faculty of Electrical Engineering Computer Science and Information Technology Osijek J. J. Strossmayer University of Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

Robots capable of automatically opening and closing articulated objects such as doors and drawers have broad potential applications in households, healthcare facilities, and industrial environments. Drawers can be opened and closed by pulling or pushing along the axis of translation, while doors can be opened by rotating around the axis of rotation. When a robot’s motion path deviates from the ideal trajectory required for opening and closing, its action can cause various damages due to excessive lateral forces. In this paper, we address the problem of force control in robotic manipulation of doors and drawers. The goal of force control is to minimize lateral forces when opening and closing doors and drawers. At the same time, position control is performed in the direction of the ideal trajectory. A force-torque sensor attached to a robot can monitor all forces in 3D space. We conduct experiments on opening and closing the doors and drawers using a UR5 robot with and without force control, and analyze the force response while monitoring the success of the actions performed. With this study, we provide insight into the usefulness of force control in opening and closing doors and drawers by a robot. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Multi-layer Graph Convolution Neural Network for Human Behavior Recognition Incorporating Spatial Attention 2

Multi-layer Graph Convolution Neural Network for Human Behav...

引用

2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Wei, Yingzi Yin, Suyu Zhang, Yuheng Fu, Bin Shenyang Ligong University College of Information Science and Engineering Shenyang China Soochow University School of Computer Science and Technology Suzhou China

ISBN: (纸本)9798350368888

Aiming at the problem that the low accuracy in recognizing human behavior in complex environments using existing graph convolution related algorithms , this paper proposed a human body behavior recognition algorithm based on improved graph convolution network. The algorithm alternately used GCN and TCN to extract human features by stacking multi-layer spatiotemporal graph convolutions, and utilized spatiotemporal attention mechanism to enhance spatiotemporal feature extraction. This algorithm used OpenPose to extract human joint information, passed it into an improved spatiotemporal convolutional network model, and finally classified and recognized actions through Softmax. The algorithm was validated on the NTU-RGB+D *** results show that the algorithm can be effective in human behavior recognition ,with recognition accuracy of 88.9%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Novel IoT Application Recommendation System Using Metaheuristic Multi-Criteria Analysis

引用

computer Systems Science & engineering 2021年第5期37卷 149-158页

作者： Mohammed Hayder Kadhim Farhad Mardukhi Department of Computer Engineering and Information Technology Razi UniversityKermanshahIran

There are a variety of Internet of Things(IoT)applications that cover different aspects of daily *** of these applications has different criteria and sub-criteria,making it difficult for the user to *** requires an automated approach to select IoT applications by considering *** paper presents a novel recommendation system for presenting applications on the ***,using the analytic hierarchy process(AHP),a multi-layer architecture of the criteria and sub-criteria in IoT applications is *** architecture is used to evaluate and rank IoT *** a result,finding the weight of the criteria and subcriteria requires a metaheuristic *** this paper,a sequential quadratic programming algorithm is used to find the optimal weight of the criteria and sub-criteria *** the best of our knowledge,this is the first study to use an analysis of metaheuristic criteria and sub-criteria to design an IoT application recommendation *** evaluations and comparisons in the experimental results section show that the proposed method is a comprehensive and reliable model for the construction of an IoT applications recommendation system.

关键词： Internet of Things smart objects recommendation system multicriteria analysis sequential quadratic programming

来源：评论

学校读者我要写书评

暂无评论

Recognition of Wh-Question Sign Gestures in Video Streams using an Attention Driven C3D-BiLSTM Network 2

Recognition of Wh-Question Sign Gestures in Video Streams us...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Dey, Arnab Biswas, Samit Le, Dac-Nhuong Dept. of Computer Science and Technology Indian Institute of Engineering Science and Technology Howrah Shibpur711103 India Faculty of Information Technology Haiphong University Haiphong180000 Viet Nam

Human sign language is a visual and gestural means of communication used by people with hearing impairments to interact with others. It has the potential to enable interaction between individuals who struggle with verbal communication and those who lack the essential proficiency to comprehend sign language. Wh-questions constitute a substantial portion of daily sign language interactions. Automatic recognition of Wh-question words in sign language, encompassing interrogative words, has received limited attention in gesture recognition. In this context, a novel dataset named the American Question Sign Video dataset (AQSVd) has been introduced as a significant contribution. Recognizing Wh-question signs in video streams empowers individuals to effectively convey messages through hand movements and gestures, fostering inclusivity and accessibility in communication for deaf and hearing-impaired individuals. The paper proposes a Deep Convolutional-3D BiLSTM Multi-head Attention network for recognizing American Wh-Question word sign gestures in video streams. Incorporating Multi-head attention in the proposed model enhances its ability to capture intricate spatial and temporal features, essential for accurate gesture recognition. The proposed model is thoroughly evaluated across a range of datasets, such as INCLUDE50, WLASL-100, and our AQSVd dataset, achieving remarkable results. Specifically, our model demonstrated an outstanding 98.91% validation accuracy on the AQSVd dataset. The C3D-BiLSTM MHAttention model outperforms other state-of-the-art models, demonstrating its superiority in sign language recognition tasks. The proposed dataset and C3D-BiLSTM Multi-head Attention model contribute significantly to this field, offering potential benefits in education, human-robot interaction, and overall communication for the hearing-impaired community. © 2024 Elsevier B.V.. All rights reserved.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

引用

computers, Materials & Continua 2022年第3期70卷 6041-6055页

作者： Shobhit Verma Nonita Sharma Aman Singh Abdullah Alharbi Wael Alosaimi Hashem Alyami Deepali Gupta Nitin Goyal Computer Science&Engineering Department Dr.B.R.Ambedkar National Institute of TechnologyJalandharIndia Computer Science&Engineering Department Lovely Professional UniversityJalandharIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia

This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis *** addition to forecasting the occurrences of conjunctivitis incidences,the proposed model also improves performance by using the ensemble *** rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December ***-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets.A stacked generalization ensemble model based on Auto-ARIMA(Autoregressive Integrated Moving Average),NNAR(Neural Network Autoregression),ETS(Exponential Smoothing),HW(Holt Winter)is proposed and applied on the *** analysis is conducted on the collected dataset of conjunctivitis disease,and further compared for different performance *** result shows that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error),MAPE(Mean Absolute Percentage Error),ACF1(Auto Correlation Function)of the proposed ensemble is decreased *** the RMSE,for instance,error values are reduced by 39.23%,9.13%,20.42%,and 17.13%in comparison to Auto-ARIMA,NAR,ETS,and HW model *** research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA,NAR,ETS,and HW model applied discretely.

关键词： Disease prediction stack ensemble neural network autoregression exponential smoothing auto-ARIMA holt winter

来源：评论

学校读者我要写书评

暂无评论

Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

引用

computers, Materials & Continua 2022年第11期73卷 4393-4406页

作者： Vasumathi Devi Majety N.Sharmili Chinmaya Ranjan Pattanaik ELaxmi Lydia Subhi R.M.Zeebaree Sarmad Nozad Mahmood Ali S.Abosinnee Ahmed Alkhayyat Department of Computer Science Engineering Vignan’s Nirula Institute of Technology and Science for WomenGuntur522005India Department of Computer Science Engineering Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnam530048India Department of Computer Science and Engineering Ajay Binay Institute of Technology CuttackOdisha750314India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Computer Technical Engineering Department College of Technical EngineeringThe Islamic UniversityNajaf54001Iraq College of Technical Engineering The Islamic UniversityNajafIraq

Histopathology is the investigation of tissues to identify the symptom of *** histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining *** investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s *** the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data *** this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)*** proposed EHCDLHIC technique initially performs Weiner filtering based noise removal *** the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are *** the classification process,the bidirectional gated recurrent unit(BGRU)model can be *** the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the *** validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is *** experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

关键词： Histopathological image classification machine learning deep learning handcrafted features bacterial foraging optimization

来源：评论

学校读者我要写书评

暂无评论

Trajectory Planning of Rechargeable UAVs for AoS Minimization in IoT Networks 16

Trajectory Planning of Rechargeable UAVs for AoS Minimizatio...

引用

16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Liu, Yunchao Lin, Suling Gong, Jie School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9798350390643

Freshness of information is a critical issue for real-time Internet of Things (IoT) applications. Unmanned aerial vehicles (UAVs), due to their high flexibility and low cost, are widely deployed in time-sensitive IoT scenarios. In this article, we explore the trajectory planning problem in rechargeable-UAV-aided IoT Networks. To accurately measure the freshness of information, we introduce Age of information (AoI) and Age of Synchronization (AoS). AoI is utilized to plan the UAVs' trajectories for accessing sensors (SNs) and reduce the average AoS of the system. UAVs can recharge at charging stations to maintain energy above a certain threshold. We formulate a Markov decision process with the objective of minimizing the average AoS under the energy constraint. To adjust the dynamic environment, we propose a multi-agent deep reinforcement learning method to jointly optimize the UAVs' flight trajectories and battery recharging. Simulation results demonstrate that the proposed algorithm can significantly reduce the total average AoS under the energy constraint. © 2024 IEEE.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

引用

Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

来源：评论

学校读者我要写书评

暂无评论

EPCA—Enhanced Principal Component Analysis for Medical Data Dimensionality Reduction

引用

SN computer Science 2023年第3期4卷 243页

作者： Vinutha, M.R. Chandrika, J. Krishnan, Balachandran Kokatnoor, Sujatha Arun Department of Information Science and Engineering Malnad College of Engineering Hassan 573202 India Department of Computer Science and Engineering School of Engineering and Technology CHRIST (Deemed to be University) Bangalore 560074 India

Innovations in technology from the last one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and thereby enhanced the classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique per

关键词： Accuracy Artificial neural networks Liver cirrhosis Machine learning Medical data Principal component analysis Singular value decomposition Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Enhanced face recognition using rank-level fusion with multi-feature vectors and interval type-3 fuzzy set 3

Enhanced face recognition using rank-level fusion with multi...

引用

3rd IEEE Applied Signal Processing Conference, ASPCON 2023

作者： Ghosh, Manas Sing, Jamuna Kanta Rcc Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350325881

In the near past, interval type-3 fuzzy set theory has been introduced as a more effective means of addressing uncertainty in decision-making than its fuzzy set counterparts. This paper suggests an approach to face recognition which integrates multiple feature vectors and interval type-3 fuzzy sets for fuzzy rank-level fusion. Use of multiple feature vectors resolves inter-class resemblances while incorporating interval type-3 fuzzy membership function in generating fuzzy rank reduces the uncertainty due to intra-class divergences. The output of the classifier is regarded as confidence factor. For each feature vector, the fuzzy ranks are generated by formulating an interval type-3 fuzzy membership function for each of top-ordered H classes based on confidence factors utilizing all training feature vectors and those of the training samples of the respective class. Multiple fuzzy ranks, built upon interval type-3 fuzzy set, are generated by employing of multiple feature vectors. To determine the final score of a class, summation of the fuzzy rank complements is combined with the complement of arithmetic mean of respective classifier's output. Final scores are evaluated to conclude test image's class. Two publicly accessible face databases are used to evaluate the method and it has shown that the solution, advocated here, exhibits an improved effectiveness over its existing related counterparts. © 2023 IEEE.

关键词： Face recognition

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：