检索结果-内蒙古大学图书馆

7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Addo, Daniel Al-Antari, Mugahed A. Zhou, Shijie Sarpong, Kwabena Butun, Ertan Talo, Muhammed Abualkebash, Humam Ukwuoma, Chiagoziem C. School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Sejong University DaeyangAI Center Department of Artificial Intelligence Seoul Korea Republic of Firat University Department of Computer Engineering Elaziǧ Turkey Firat University Department of Software Engineering Elazig Turkey Kocaeli University Department of Mechatronics Engineering Kocaeli Turkey The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China

ISBN: (纸本)9798350342154

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (ViT). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile ViT is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling

arXiv

引用

arXiv 2024年

作者： Wei, Ruofeng Li, Bin Chen, Kai Ma, Yiyao Liu, Yunhui Dou, Qi Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

Scale-aware monocular depth estimation poses a significant challenge in computer-aided endoscopic navigation. However, existing depth estimation methods that do not consider the geometric priors struggle to learn the absolute scale from training with monocular endoscopic sequences. Additionally, conventional methods face difficulties in accurately estimating details on tissue and instruments boundaries. In this paper, we tackle these problems by proposing a novel enhanced scale-aware framework that only uses monocular images with geometric modeling for depth estimation. Specifically, we first propose a multiresolution depth fusion strategy to enhance the quality of monocular depth estimation. To recover the precise scale between relative depth and real-world values, we further calculate the 3D poses of instruments in the endoscopic scenes by algebraic geometry based on the image-only geometric primitives (i.e., boundaries and tip of instruments). Afterwards, the 3D poses of surgical instruments enable the scale recovery of relative depth maps. By coupling scale factors and relative depth estimation, the scale-aware depth of the monocular endoscopic scenes can be estimated. We evaluate the pipeline on in-house endoscopic surgery videos and simulated data. The results demonstrate that our method can learn the absolute scale with geometric modeling and accurately estimate scale-aware depth for monocular scenes. Code is available at: https://***/med-air/MonoEndoDepth © 2024, CC BY.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Indoor Hydroponics Robot System for Automated Seeding and Logistics

Indoor Hydroponics Robot System for Automated Seeding and Lo...

引用

International Conference on Control and automation (ICCA)

作者： Hon Kit Hui Jimmy H. M. Lee Anthony K. W. Sum Darwin Lau Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In recent years, indoor hydroponics farming has been a promising new mode to ensure stable and increased density food production regardless of weather and location. However, the lack of development of hydroponic robot systems limit the automation of smaller space indoor environments where automated hydroponic mechatronic racks are not suitable nor economical. This work presents an automated robot system for exactly this purpose. It comprises of: 1) a seeding end-effector for 6 degree-of-freedom robot arms to plant small seeds; 2) a mobile robot tray logistics robot; and 3) an automated software to schedule and operate the system. The novel robot system has been implemented and in operation for over 2 years at the Controlled Environment Hydroponic Research and Development Centre in Hong Kong, showing its efficacy to minimise human labour and increase efficiency.

关键词： Schedules automation Hydroponics Software Seeds (agriculture) Task analysis Robots

来源：评论

学校读者我要写书评

暂无评论

Unknown-input-observer-based approach to dynamic event-triggered fault estimation for Markovian jump systems with time-varying delays

引用

Science China(Information Sciences) 2022年第3期65卷 138-151页

作者： Xiaoting DU Lei ZOU Zhongyi ZHAO Yezheng WANG Maiying ZHONG College of Electrical Engineering and Automation Shandong University of Science and Technology Department of Computer Science Brunel University London

This paper investigates the unknown-input-observer-based fault estimation problem for a class of discrete-time-delay Markovian jump systems under the dynamic event-triggered transmission scheme. The dynamic event-triggered mechanism is used to decide whether the current information should be transmitted to the estimator or not to save the limited communication resources. This study aims to design an event-based fault estimator such that the estimation error is exponentially ultimately bounded in the mean square *** adopting the Lyapunov-Krasovskii functional approach, sufficient conditions are obtained to guarantee the existence of the desired estimator to achieve the prescribed performance requirement. The estimator gains are derived based on the convex optimization technique. A numerical example is provided to illustrate the effectiveness of the developed estimator design scheme.

关键词： fault estimation unknown input observer Markovian jump systems dynamic event-triggered mechanism time-varying delays

来源：评论

学校读者我要写书评

暂无评论

Crop and Weeds Classification in Aerial Imagery of Sesame Crop Fields Using a Patch-Based Deep Learning Model-Ensembling Method 2

Crop and Weeds Classification in Aerial Imagery of Sesame Cr...

引用

2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Moazzam, S. Imran Khan, Umar S. Nawaz, Tahir Qureshi, Waqar S. National University of Sciences and Technology National Centre of Robotics and Automation Department of Mechatronics Engineering Islamabad Pakistan Technological University Dublin Department of Computer Science Dublin Ireland

ISBN: (纸本)9781665498197

Sesame (Sesamum indicum L.) is an important commercial and food crop, and its yields is limited by many insects, pests, diseases, and weeds. Autonomous aerial agrochemicals spray application on sesame fields using a drone aims to save crop from these yield limiting factors and in addition agrochemicals application quantity and site could be controlled, and human health is expected to be protected. For accurate and selective spray application, autonomous systems would need some parameters to distinguish between crop, weed and background. In this research an aerial sesame field dataset has been collected with the focus to classify patch areas of sesame and weeds present in the field. Dataset was captured using Agrocam. We have developed a patch image-based classification approach along with a novel SesameWeedNet convolutional neural network (CNN) inspired by the layer's configuration of VGG networks and depth-wise convolutions of the MobileNet. The small model contains 6 convolutional layers, and it runs faster and accurately on small patch images. Our approach breaks 1920×1080-pixel images into smaller patch images of size 45×45 pixels. After that, these small patch images are fed to a relatively small CNN for training, validation, and finally for classification. Patch based model ensemble and dataset grouping are two major parts in our methodology. Our system recommends the dataset grouping according to vegetation present in the images to enhance classification results. We have achieved accuracy up to 96.7% with our proposed method. We have tested our system under sunlight variation, in wet and dry soil conditions and at different growth stages. To the best of our knowledge, no attempt has been made to classify and treat crop and weeds in sesame fields at the post-emergence stage previously. In this research we have made the contribution of aerial sesame-weed dataset and a complete deep learning-based approach to classify weeds in sesame fields under variable lighti

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

引用

computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

引用

Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Simplified Temporal Consistency Reinforcement Learning

arXiv

引用

arXiv 2023年

作者： Zhao, Yi Zhao, Wenshuai Boney, Rinu Kannala, Juho Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland Department of Computer Science Aalto University Finland

Reinforcement learning (RL) is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation. To improve sample efficiency, recent work focuses on model-based RL which interleaves model learning with planning. Recent methods further utilize policy learning, value estimation, and, self-supervised learning as auxiliary objectives. In this paper we show that, surprisingly, a simple representation learning approach relying only on a latent dynamics model trained by latent temporal consistency is sufficient for high-performance RL. This applies when using pure planning with a dynamics model conditioned on the representation, but, also when utilizing the representation as policy and value function features in model-free RL. In experiments, our approach learns an accurate dynamics model to solve challenging high-dimensional locomotion tasks with online planners while being 4.1× faster to train compared to ensemble-based methods. With model-free RL without planning, especially on high-dimensional tasks, such as the Deep-mind Control Suite Humanoid and Dog tasks, our approach outperforms model-free methods by a large margin and matches model-based methods’ sample efficiency while training 2.4× faster. © 2023, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

引用

24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology Research Academy 2024.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Resilient source seeking with robot swarms

Resilient source seeking with robot swarms

引用

IEEE Conference on Decision and Control

作者： Antonio Acuaviva Jesus Bautista Weijia Yao Juan Jimenez Hector Garcia de Marina Department of Computer Architecture and Automation Universidad Complutense de Madrid Spain Department of Computer Engineering Automation and Robotics Research Centre for Information and Communication Technologies (CITIC-UGR) University of Granada Granada Spain School of Robotics Hunan University

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We present a solution for locating the source, or maximum, of an unknown scalar field using a swarm of mobile robots. Unlike relying on the traditional gradient information, the swarm determines an ascending direction to approach the source with arbitrary precision. The ascending direction is calculated from field strength measurements at the robot locations and their relative positions concerning the swarm centroid. Rather than focusing on individual robots, we focus the analysis on the density of robots per unit area to guarantee a more resilient swarm, i.e., the functionality remains even if individuals go missing or are misplaced during the mission. We reinforce the algorithm’s robustness by providing sufficient conditions for the swarm shape so that the ascending direction is almost parallel to the gradient. The swarm can respond to an unexpected environment by morphing its shape and exploiting the existence of multiple ascending directions. Finally, we validate our approach numerically with hundreds of robots. The fact that a large number of robots with a generic formation always calculate an ascending direction compensates for the potential loss of individuals.

关键词： Sufficient conditions Shape Focusing Position measurement Robustness Mobile robots Robots

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：