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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Guo, Feng Guo, Xiwang Wang, Jiacun Wang, Weitian Cao, Jinrui Bigaume, Imane Liaoning Petrochemical University Information And Control Engineering Fushun China Monmouth University Computer Science And Software Eng. Wes Long BranchNJ United States Montclair State University School Of Computing Montclair United States New Jersey City University Computer Science Department Jersey City United States

ISBN: (纸本)9798350315790

In the remanufacturing process, the efficient disassembly of end-of-life (EOL) products assumes paramount significance for recycling endeavors. This work introduces a novel disassembly model aiming at enhancing disassembly efficiency within a hybrid disassembly line through the strengths of humanrobot collaboration. In order to optimize the generation of disassembly sequences, this work proposes an improved fireworks optimization algorithm (IFW) that combines uniform mutation and two-point crossover methods. Afterwards, the model's validity and algorithmic accuracy are substantiated using the CPLEX solver (a commercial solver). Furthermore, a comparative analysis and evaluation against alternative algorithms, such as salp swarm algorithm (SSA) and whale optimization algorithm (WOA), demonstrate a better stability of the proposed algorithm in the human-robot collaborative hybrid disassembly line balancing problem. © 2024 IEEE.

关键词： Optimization

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HG3-NeRF: Hierarchical Geometric, Semantic, and Photometric Guided Neural Radiance Fields for Sparse View Inputs

arXiv

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arXiv 2024年

作者： Gao, Zelin Dai, Weichen Zhang, Yu College of Control Science and Engineering Zhejiang University China School of Computer Science Hangzhou Dianzi University China

Neural Radiance Fields (NeRF) have garnered considerable attention as a paradigm for novel view synthesis by learning scene representations from discrete observations. Nevertheless, NeRF exhibit pronounced performance degradation when confronted with sparse view inputs, consequently curtailing its further applicability. In this work, we introduce Hierarchical Geometric, Semantic, and Photometric Guided NeRF (HG3-NeRF), a novel methodology that can address the aforementioned limitation and enhance consistency of geometry, semantic content, and appearance across different views. We propose Hierarchical Geometric Guidance (HGG) to incorporate the attachment of Structure from Motion (SfM), namely sparse depth prior, into the scene representations. Different from direct depth supervision, HGG samples volume points from local-to-global geometric regions, mitigating the misalignment caused by inherent bias in the depth prior. Furthermore, we draw inspiration from notable variations in semantic consistency observed across images of different resolutions and propose Hierarchical Semantic Guidance (HSG) to learn the coarse-to-fine semantic content, which corresponds to the coarse-to-fine scene representations. Experimental results demonstrate that HG3-NeRF can outperform other state-of-the-art methods on different standard benchmarks and achieve high-fidelity synthesis results for sparse view inputs. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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AI-Based Maize and Weeds Detection on the Edge with CornWeed Dataset 18

AI-Based Maize and Weeds Detection on the Edge with CornWeed...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Iqbal, Naeem Manss, Christoph Scholz, Christian Konig, Daniel Igelbrink, Matthias Ruckelshausen, Arno Dfki Plan-based Robot Control Osnabrueck Germany Dfki Marine Perception Oldenburg Germany University of Applied Sciences Osnabrueck Faculty of Engineering and Computer Science Osnabrueck Germany

ISBN: (纸本)9788396744784

Artificial intelligence (AI) is used more heavily in agricultural applications. Yet, the lack of wireless-fidelity (Wi-Fi) connections on agricultural fields makes AI cloud services unavailable. Consequently, AI models have to be processed directly on the edge. In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection. Thus, this paper presents the CornWeed data set, which has been recorded on farm machines, showing labelled maize crops and weeds for plant detection. The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as frames per second (FPS) metrics for the considered networks on multiple edge devices. Moreover, for the FPS analysis, the detection algorithms are converted to open neural network exchange (ONNX) and TensoRT engine files as they could be used as future standards for model exchange. © 2023 Polish Information Processing Society.

关键词： Data acquisition

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LMI-Based Non-Iterative Design Strategies for Guaranteed Cost Static Output Feedback control for Linear Systems 5

LMI-Based Non-Iterative Design Strategies for Guaranteed Cos...

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5th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2023

作者： Oya, Hidetoshi Nakagawa, Takuya Nagai, Shunya Nakano, Kazushi Tokyo City University Department of Computer Science Tokyo Japan Tokyo City University Graduate School of Integrative Science and Engineering Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9798350325041

This paper proposes a non-iterative LMI-based design strategy for guaranteed cost static output feedback controllers for linear systems. The proposed design approach is based on the well-known necessary and sufficient condition for the existence of the static output feedback stabilizing control for linear systems. In this paper, for linear systems we show the non-iterative LMI-based design procedure for static output feedback controllers which minimize the upper bound of a given quadratic cost function, and the effectiveness of the proposed static output feedback controller with guaranteed cost is presented through a simple numerical example. © 2023 IEEE.

关键词： Linear systems

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Eye-gesture-based multi-context interaction 18

Eye-gesture-based multi-context interaction

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Huazhen Wu, Shiqian Zhang, Hanyuan Gao, Zheng Wuhan University of Science Nd Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China Wuhan University of Science Nd Technology Key Laboratory of Metallurgical Equipment and Control Technology Wuhan China

ISBN: (纸本)9798350312201

Eye gestures have been acknowledged as a crucial real-time input channel for human-computer interaction, especially for people with disabilities who lack fine motor skills. However, existing eye-gesture-based interactions mainly rely on gaze duration, which can cause visual fatigue and is not user-friendly for those with disabilities. To address these issues, we propose acceptable interactive paths based on eye gestures in multiple contexts, such as reading and selection, to assist impaired individuals in interacting efficiently and naturally in their daily work. In the selection context, cursor movement is accomplished by fixation position, while single and double clicks of the left mouse button are executed through blink frequency. In the reading context, page scrolling is controlled by the vertical coordinates of the fixation position. In experiments, we set up reading and selection contexts to evaluate and compare the constructed interaction paths with the existing system. The experimental results demonstrate that the interaction paths proposed in this paper are efficient and natural. © 2023 IEEE.

关键词： Mammals

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Bicheng Guo Conghao Zhou Haoyu Liu Shibo He Jiming Chen Xuemin Sherman Shen College of Control Science and Engineering Zhejiang University China Department of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework.

关键词： Continuing education Training Deep learning Adaptation models Multi-access edge computing Computational modeling Data models Global communication Convergence

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Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

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Neural Regeneration Research 2024年第7期19卷 1553-1558页

作者： Sihong Huang Jungong Han Hairong Zheng Mengjun Li Chuxin Huang Xiaoyan Kui Jun Liu Department of Radiology The Second Xiangya HospitalCentral South UniversityChangshaHunan ProvinceChina Department of Computer Science Aberystwyth UniversityAberystwythCeredigionUK Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenGuangdong ProvinceChina School of Computer Science and Engineering Central South UniversityChangshaHunan ProvinceChina Department of Radiology Quality Control Center of Hunan ProvinceChangshaHunan ProvinceChina Clinical Research Center for Medical Imaging of Hunan Province ChangshaHunan ProvinceChina

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly *** resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain *** approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and ***,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional *** that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is *** the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain *** this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy ***-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network *** the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention *** than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 ***,better cognitive function was associated with more efficient ***,our data indicated that small-worldness in the acute sta

关键词： cognitive function cross-section follow-up functional connectivity graph theory longitudinal study mild traumatic brain injury prediction small-worldness structural connectivity subnetworks whole brain network

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Making children read more and better by integrating natural language processing and gamification techniques into mobile applications 24

Making children read more and better by integrating natural ...

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Voicu, George Catalin Ciolofan, Sorin N. Dragoicea, Monica Manager Emag Bucharest Romania Politehnica University of Bucharest Department of Computer Science Bucharest Romania Politehnica University of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

It is well known that nowadays children tend to spend lot of time playing games on mobile devices at the expense of reading books. The current research aims to explore ways to design and develop a mobile application, attractive for children, that uses voice recognition technology and gamification techniques to help children read more and better and to track the progress of reading. The application is implemented with Flutter, Dart and Firebase and is addressed to children, the books being made available in encyclopedia format. By integrating technologies such as NLP and gamification the application aims to improve reading fluency among children, but also to give them the opportunity to accumulate new and exciting information in the same time. © 2023 IEEE.

关键词： Human computer interaction

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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Optimal denial-of-service attack scheduling for remote state estimation via sequential observations 43

Optimal denial-of-service attack scheduling for remote state...

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43rd Chinese control Conference, CCC 2024

作者： Yang, Xinxin Ni, Yuqing Chen, Xiaomeng Chen, Shan Liu, Fei Jiangnan University The Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Wuxi214122 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong

ISBN: (纸本)9789887581581

This study investigates the optimal allocation problem of attack energy in multi-system remote state estimation for Cyber-Physical Systems (CPSs). We consider a scenario where there are M independent systems, each equipped with a remote sensor monitoring system. The local estimates from these systems are sent to a remote estimator through a packet loss channel. The objective of the attacker is to degrade the communication channel between the sensors and the remote estimator. Due to capacity limitations, the attacker is restricted to attacking at most one out of M channels. We incorporate interference from other sensors or interference sources into the communication model based on the SINR (Signal-to-Interference-plus-Noise Ratio) model. It should be noted that the same attack power can lead to different packet loss rates in different network environments. From the attacker's perspective, the goal is to find the optimal attack strategy that maximizes the average estimation error of the remote estimator. Given the uncertainties of wireless network environments, we employ the sequentially-observed Markov decision process (SOMDP) framework to solve the problem. By observing the wireless channels, we gather additional information and incorporate it into the decision-making policy, resulting in better performance than using a standard Markov decision process (MDP). The simulation results support our theoretical findings. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Markov processes

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