检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Song, Shanshan Huangfu, Bingwen Guo, Jiani Liu, Jun Cui, Junhong Shen, Xuemin The College of Computer Science and Technology Jilin University Changchun130012 China The School of Electronic and Information Engineering Beihang University Beijing100191 China Shenzhen Institute for Advanced Study UESTC Shenzhen China The College of Computer Science and Technology Jilin University Changchun China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs’ environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks’ demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation;(ii) greatly reduce the training time of AI algorithms;(iii) more rapidly obtain network status for multi-task scheduling at a low cost. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

ECG-LLM: Leveraging Large Language Models for Low-Quality ECG Signal Restoration

ECG-LLM: Leveraging Large Language Models for Low-Quality EC...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Longfei Liu Guosheng Cui Cheng Wan Dan Wu Ye Li Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electrocardiography (ECG) signals are often plagued by various types of noise, which substantially undermines the precision of subsequent analysis. This paper presents ECG-LLM, an innovative model designed to address the challenges posed by low-quality ECG signals. Leveraging the capabilities of Large Language Models (LLMs), ECG-LLM forecasts and imputes missing values in 12-lead ECG data. ECG-LLM adapts the auto-regressive characteristics of LLMs, utilizes textual markers for timestamp information, and employs a strategy of freezing Transformer layers while training new embedding and projection layers. We used ECG datasets from multiple sources for our experiments. Experimental results demonstrate that ECG-LLM significantly outperforms state-of-the-art time series forecasting models, achieving a Mean Squared Error (MSE) of 0.644 and a Mean Absolute Error (MAE) of 0.456 for a forecast length of 64. Additionally, after using ECG-LLM to restore the electrocardiogram, the disease recognition accuracy of the baseline model was improved. Our findings highlight the feasibility and effectiveness of applying LLMs to ECG signal processing, offering a new perspective for medical signal analysis and providing a potential new approach for signal preprocessing in healthcare. Code is available at https://***/dragonlfy/ECG-LLM.

关键词： Training Adaptation models Large language models Biological system modeling Medical services Electrocardiography Predictive models Transformers Forecasting Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Distributed Quantized Detection of Sparse Signals Under Byzantine Attacks

arXiv

引用

arXiv 2023年

作者： Quan, Chen Han, Yunghsiang S. Geng, Baocheng Varshney, Pramod K. The Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 United States The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China The Department of Computer Science University of Alabama at Birmingham BirminghamAL35294 United States

This paper investigates distributed detection of sparse stochastic signals with quantized measurements under Byzantine attacks. Under this type of attack, sensors in the networks might send falsified data to degrade system performance. The Bernoulli-Gaussian (BG) distribution in terms of the sparsity degree of the stochastic signal is utilized for modeling the sparsity of signals. Several detectors with improved detection performance are proposed by incorporating the estimated attack parameters into the detection process. First, we propose the generalized likelihood ratio test with reference sensors (GLRTRS) and the locally most powerful test with reference sensors (LMPTRS) detectors with adaptive thresholds, given that the sparsity degree and the attack parameters are unknown. Our simulation results show that the LMPTRS and GLRTRS detectors outperform the LMPT and GLRT detectors proposed for an attack-free environment and are more robust against attacks. The proposed detectors can achieve the detection performance close to the benchmark likelihood ratio test (LRT) detector, which has perfect knowledge of the attack parameters and sparsity degree. When the fraction of Byzantine nodes are assumed to be known, we can further improve the system's detection performance. We propose the enhanced LMPTRS (E-LMPTRS) and enhanced GLRTRS (E-GLRTRS) detectors by filtering out potential malicious sensors with the knowledge of the fraction of Byzantine nodes in the network. Simulation results show the superiority of proposed enhanced detectors over LMPTRS and GLRTRS detectors. © 2023, CC BY-NC-SA.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Technologies for Modulation of Visible Light and their Applications

arXiv

引用

arXiv 2024年

作者： Park, Sanghyo Notaros, Milica Mohanty, Aseema Kim, Donggyu Notaros, Jelena Mouradian, Sara Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical & Computer Engineering Tufts University MedfordMA02155 United States Department of Electrical & Computer Engineering University of Washington SeattleWA98195 United States

Control over the amplitude, phase, and spatial distribution of visible-spectrum light underlies many technologies, but commercial solutions remain bulky, require high control power, and are often too slow. Active integrated photonics for visible light promises a solution, especially with recent materials and fabrication advances. In this review, we discuss three growing application spaces which rely on control of visible light: control and measurement of atomic quantum technologies, augmented-reality displays, and measurement and control of biological systems. We then review the commercial dynamic surfaces and bulk systems which currently provide visible-light modulation and the current state-of-the-art integrated solutions. Throughout the review we focus on speed, control power, size, optical bandwidth, and technological maturity when comparing technologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Light modulation

来源：评论

学校读者我要写书评

暂无评论

SimiDTR: Deep Trajectory Recovery with Enhanced Trajectory Similarity 28th

SimiDTR: Deep Trajectory Recovery with Enhanced Trajectory ...

引用

28th International Conference on Database Systems for advanced Applications, DASFAA 2023

作者： Zhang, Yupu Deng, Liwei Zhao, Yan Chen, Jin Xie, Jiandong Zheng, Kai University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China Department of Computer Science Aalborg University Aalborg Denmark Cloud Database Innovation Lab of Cloud BU Huawei Technologies Co. Ltd. Chengdu China

ISBN: (纸本)9783031306365

The pervasiveness of GPS-equipped smart devices and the accompanying deployment of sensing technologies generates increasingly massive amounts of trajectory data that cover applications such as personalized location recommendation and urban transportation planning. Trajectory recovery is of utmost importance for incomplete trajectories (resulted from the constraints of devices and environment) to enable their completeness and reliability. To achieve effective trajectory recovery, we propose a novel trajectory recovery framework, namely Deep Trajectory Recovery with enhanced trajectory Similarity (SimiDTR), which is capable of contending with the complex mobility regularity found in trajectories in continuous space. In particular, we design a rule-based information extractor to extract the spatial information related to an incomplete trajectory, which is then fed into a deep model based on attention mechanism to generate a tailored similar trajectory for the incomplete trajectory. Finally, we use a deep neural network model to recover the incomplete trajectory with the blessing of its similar trajectory. An extensive empirical study with real data offers evidence that the framework is able to advance the state of the art in terms of effectiveness for trajectory recovery, especially in scenes with sparse trajectory data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recovery

来源：评论

学校读者我要写书评

暂无评论

Explainable Symbolic Regression Model for Tool Wear Diagnosis 9

Explainable Symbolic Regression Model for Tool Wear Diagnosi...

引用

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Mishra, Debasish Han, Seulki Pattipati, Krishna R. Bollas, George M. University of Connecticut Department of Electrical & Computer Engineering 371 Fairfield Way StorrsCT06269 United States Pratt & Whitney Institute of Advanced Systems Engineering University of Connecticut 159 Discovery Dr. StorrsCT06269 United States University of Connecticut Department of Chemical & Biomolecular Engineering 159 Discovery Dr. StorrsCT06269 United States

ISBN: (纸本)9798350311402

In precision machining, predicting the tool health can improve productivity, job quality, and reduce machine downtime and energy consumption. While deep learning (DL) algorithms have garnered recent interest, they lack the physics understanding associated with machining processes. To address this limitation, we present a symbolic regression framework for tool wear diagnostics. The method explores analytical symbolic expressions using health indicators and cutting settings. Tool health indicators are computed from wavelet subspaces of vibration signals by applying a distance metric to the wavelet coefficients. These indicators are strongly correlated with tool wear measurements, making them suitable for tool wear diagnostics. We applied the developed framework to the IEEE PHM 2010 data, which comprises three sets of run-to-failure machining tests conducted with three tools. The framework predicted tool wear with an R2 of 0.947 and a mean absolute error (MAE) of 0.006 mm across test sets. The results demonstrate the effectiveness of the symbolic regression approach for tool wear diagnostics, showcasing the richness of information in the indicators and the quality of the developed model for tool wear estimation. © 2023 IEEE.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

Development of a digital twin environment for smart collision avoidance algorithms for mobile robots using reinforcement learning

Development of a digital twin environment for smart collisio...

引用

SICE Annual Conference

作者： Natsumi Matsumoto Kazuyuki Kobayashi Tomoyuki Ohkubo Kaiqiao Tian Nashwan J Sebi Ka C Cheok Cai Changqing Graduate School of Science and Engineering Hosei University Tokyo Japan Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Electrical & Computer Engineering Department School of Engineering & Computer Science Oakland University Rochester USA School of Electrical Engineering and Information Technology Changchun Institute of Technology China

This paper describes a study on building a digital twin environment using point cloud data for mobile robots. A digital twin environment for mobile robots is a method to reproduce almost the same environment in a simulation space as if it were a twin, using data from an actual natural environment. In fact, in the development of mobile robots, experiments on public urban area are conducted, however, they are incredibly time-consuming and labor-intensive, so it would be helpful if a realistic simulation environment could be created. This study focuses on building a digital twin environment using point-cloud-data for mobile robots. In order to verify the effectiveness of this approach, we constructed the environment using data from a real mobile robot that has been driven. The navigation of the mobile robot was performed in the constructed simulation environment, and it was possible to realize a navigation run in the driving environment that was almost the same as the run of the real mobile robot.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CONTEXT-AWARE LLM-BASED SAFE CONTROL AGAINST LATENT RISKS

arXiv

引用

arXiv 2024年

作者： Luu, Quan Khanh Deng, Xiyu Van Ho, Anh Nakahira, Yorie School of Materials Science Japan Advanced Institute of Science and Technology Ishikawa Nomi Japan Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

It is challenging for autonomous control systems to perform complex tasks in the presence of latent risks. Motivated by this challenge, this paper proposes an integrated framework that involves Large Language Models (LLMs), stochastic gradient descent (SGD), and optimization-based control. In the first phase, the proposed framework breaks down complex tasks into a sequence of smaller subtasks, whose specifications account for contextual information and latent risks. In the second phase, these subtasks and their parameters are refined through a dual process involving LLMs and SGD. LLMs are used to generate rough guesses and failure explanations, and SGD is used to fine-tune parameters. The proposed framework is tested using simulated case studies of robots and vehicles. The experiments demonstrate that the proposed framework can mediate actions based on the context and latent risks and learn complex behaviors efficiently. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via Bézier Curve Modelling

arXiv

引用

arXiv 2024年

作者： Liu, Shuaixin Li, Kunqian Ding, Yilin Xu, Kuangwei Jiang, Qianli Wu, Q.M. Jonathan Song, Dalei College of Engineering Ocean University of China Qingdao266404 China Institute for Advanced Ocean Study Ocean University of China Qingdao266100 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

We introduce a novel vision-based framework for in-situ trunk identification and length measurement of sea cucumbers, which plays a crucial role in the monitoring of marine ranching resources and mechanized harvesting. To model sea cucumber trunk curves with varying degrees of bending, we utilize the parametric Bézier curve due to its computational simplicity, stability, and extensive range of transformation possibilities. Then, we propose an end-to-end unified framework that combines parametric Bézier curve modeling with the widely used You-Only-Look-Once (YOLO) pipeline, abbreviated as TISC-Net, and incorporates effective funnel activation and efficient multi-scale attention modules to enhance curve feature perception and learning. Furthermore, we propose incorporating trunk endpoint loss as an additional constraint to effectively mitigate the impact of endpoint deviations on the overall curve. Finally, by utilizing the depth information of pixels located along the trunk curve captured by a binocular camera, we propose accurately estimating the in-situ length of sea cucumbers through space curve integration. We established two challenging benchmark datasets for curve-based in-situ sea cucumber trunk identification. These datasets consist of over 1,000 real-world marine environment images of sea cucumbers, accompanied by Bézier format annotations. We conduct evaluation on SC-ISTI, for which our method achieves mAP50 above 0.9 on both object detection and trunk identification tasks. Extensive length measurement experiments demonstrate that the average absolute relative error is around 0.15. The new benchmarks, source code, and pre-trained models are available on the project home page: https://***/OUCVisionGroup/TISC-Net. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

引用

computers, Materials & Continua 2021年第12期69卷 2863-2882页

作者： Jagannath Paramguru Subrat Kumar Barik Ajit Kumar Barisal Gaurav Dhiman Rutvij HJhaveri Mohammed Alkahtani Mustufa Haider Abidi KIIT University BhubaneswarOdishaIndia Department of Electrical Engineering CETBhubaneswarOdishaIndia Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Computer Science&Engineering Pandit Deendayal Petroleum UniversityGandhinagarIndia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in *** non-linearity are added to make the fossil fuel-based power systems more *** order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic *** this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum *** conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in *** adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches.

关键词： Economic load dispatch valve point loading industry 4.0 prohibited operating zones ramp rate limit oscillatory particle swarm optimization

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：