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arXiv 2024年

作者： Yang, Ning Chen, Shuo Zhang, Haijun Berry, Randall The Institute of Automation Chinese Academy of Sciences Beijing100190 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW72BX United Kingdom The Department of Computing and Communication Engineering Beijing University of Science and Technology Beijing100083 China The Department of Electrical and Computer Engineering Northwestern University Chicago60208 United States

Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement learning (RL) methodologies within MEC networks promotes a deeper understanding of mobile user behaviors and network dynamics, thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks, initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore, it outlines various RL strategies employed in offloading, caching, and communication within MEC networks. Finally, it explores open issues linked with software and hardware platforms, representation, RL robustness, safe RL, large-scale scheduling, generalization, security, and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Case Study of the Implementation Difficulties in the Pilot Low Cost Concentrating Solar Power Systems Deployment in Sub-Saharan Africa

A Case Study of the Implementation Difficulties in the Pilot...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sankar Babu Potluri Garima Goswami Annu Yadav P G. Ravivarman P. Gajendran S. A. Tiwaskar Department of EEE S V R Engineering College Nandyal India Department of EE Teerthankar Mahaveer University Moradabad India School of COMPUTER Science and Applications IIMT University Meerut India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Computer Engineering Vishwakarma Institute of Information Technology Pune Maharashtra India

Among those most important resources for a particular country's expansion and expansion was electrical power. According to the most recent sustainability the growth paper, the continent continues to have a significant electrical power deficit. sunlight concentration appears to be a viable solution to make up the difference. This is due to the fact the majority of focusing solar energy plant elements are easily accessible and reasonably priced on their African marketplace, and authorized locals can construct the structures. Within Africa south of the Sahara, steer 50 nm-concentrating solar energy plants are being put into operation with promising outcomes that might be grew and utilized to generate large amounts of electrical power. One notable challenge was identified during an evaluation of a prototype focusing photovoltaic plant within the channel-region: the monitoring system's inability to lower the daily costs required to operate the monitoring control structure and enhance the numerous special machines focusing conduct. This essay emphasizes the current energy landscape and African study of concentrated sunlight is currently progressing. To demonstrate current study strategy about the development of inexpensive monitoring devices in regards to mathematical difficulty, energy use, and special machine alignment precision, the article also provides an in-depth examination of the most recent solar monitoring structures for essential receiver networks.

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Modulating spectral reflectance and its impact on thematic mapping: An analysis on glacier facies using very high-resolution satellite imagery 14

Modulating spectral reflectance and its impact on thematic m...

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Earth Resources and Environmental Remote Sensing/GIS Applications XIV 2023

作者： Wankhede, Sagar F. Pandit, Prashant H Jawak, Shridhar D Luis, Alvarinho J Balakrishna, Keshava Department of Civil Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Gogol Goa Margao403602 India Department of Information Engineering and Computer Science University of Trento Trento38123 Italy Institute for Earth Observation Eurac Research Viale Druso 1 Bolzano39100 Italy SIOS Knowledge Centre Svalbard Science Centre P.O. Box 156 Longyearbyen SvalbardN-9171 Norway Ministry of Earth Sciences Government of India Headland Sada Goa Vasco-da-Gama403804 India

ISBN: (纸本)9781510666979

Analysis of the spectral properties of glacier surface facies representing glaciological zones is an established method for characterizing facies. This necessitates a reliable estimation of spectral reflectance from image pixels using appropriate atmospheric correction methods. Improving spatial resolution by pansharpening may improve visual detection of facies, but the translation of visual perception to algorithmic classification is unclear. In this study, we analyzed the changes in spectral reflectance by comparing one model-based and two image-based methods of atmospheric corrections using very high-resolution (VHR) WorldView-2/3 satellite data for selected glaciers in Svalbard and the Himalayas. Two pansharpening methods were then applied followed by 12 machine learning and three object-based image analysis (OBIA) rule sets. A combination of wavelength ranges representing traditional and recent spectrums were tested using four different spectral band combinations. In total, we compared 8100 surface facies maps to understand the impact of every image processing routine and mapping method on the final thematic categorization of facies. Broad conclusions suggest that VHR data does not require shortwave infrared ranges;OBIA is more accurate and less sensitive to image processing variations;machine learning is more efficient but readily influenced by image processing. A spatial-spectral information OBIA ruleset yielded the highest overall accuracy (0.86). Segmentation parameters were found to be consistent across study regions and may be transferable outside the current study. This study demonstrates the need for testing image processing induced variations for cryospheric applications and may be assessed on open satellite data for long-Term standardized mapping of glacier surface facies. © 2023 SPIE. All rights reserved.

关键词： Image analysis

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Pre-trained Language Model based Medical Named Entity Recognition

Pre-trained Language Model based Medical Named Entity Recogn...

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2021 China Automation Congress, CAC 2021

作者： Cheng, Zheng Wu, Jiaju Ji, Bin Liu, Huijun School of Automation University of Electronic Science and Technology of China Mianyang China Institute of Computer Application China Academy of Engineering Physics Mianyang China College of Civil Aviation Nanjing University of Aeronautics and Astronautic Nanjing China College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781665426473

Medical named entity recognition is an important part of structuring Chinese electronic medical records and construction of medical knowledge graph. The CCKS2019 conference organized a medical named entity recognition evaluation task to extract six types of medical entities from unstructured Chinese electronic medical records. Based on the data set of this evaluation task, pre-trained language model based entity recognition approaches are studied First, select the BiLSTM-CRF model based on random initialized word embedding as the baseline system;secondly, apply wordlvec to the baseline system;thirdly, apply ELMo to the baseline system. Experimental results show that the pre-trained language model is comparable to the best approach of this evaluation task, and the context-related pre-trained language model performs better. © 2021 IEEE

关键词： Knowledge graph

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Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

arXiv

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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The Impact of Advanced Neural Network Architectures for Extracting Elusive Information

The Impact of Advanced Neural Network Architectures for Extr...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Renu Kachhoria Love Mittal Prakash Divakaran Vinay Singh N. Thangarasu S. Rajarajeswari Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Department of Coputing & Information Technology Usha Martin University Ranchi India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai

Advanced Neural community Architectures, consisting of Convolution Neural Networks (CNNs), have emerged as powerful solutions to demanding situations in extracting complicated and elusive information from large, high-dimensional datasets. CNNs have effective feature extraction abilities that may be used to identify subtle patterns, traits, and relationships in those datasets. but, effective architectures must be tailored to the particular nature of the hassle that allows you to maximize the potential of these fashions. CNNs were used effectively in some of domain names to discover styles in facts that could otherwise remain hidden. As an example, CNNs have been used inside the medical domain to quickly and accurately become aware of cardiologic patterns in electrocardiograms. Similarly, they have been used within the security domain to perceive capability threats earlier than they occur. In each domain names, the capability to extract cognitive facts from numerous datasets speedy and appropriately has saved lives and avoided failures. The exercise of the use of superior neural architectures to permit statistics extraction is complex and requires thoughtful layout. The structure must recall the traits of the project, which include the records kind, the range of dimensions concerned, and the amount of noise or outliers, to determine which strategies are maximum suitable for every specific utility.

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Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

arXiv

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arXiv 2024年

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence Anhui University Hefei230031 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Computer Engineering Indiana University Purdue University IndianapolisIN46202 United States

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. Copyright © 2024, The Authors.

关键词： Forecasting

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A Weather-Informed Traffic Forecasting System Constructed Using the SAE Method

A Weather-Informed Traffic Forecasting System Constructed Us...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Vivek Deshpande V.J. Vijayalakshmi Siddharth Sriram S. Sathya Tusha Manita D Shah Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India School of Management – Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Access to accurate traffic data is critical for drivers to effectively plan routes and reduce congestion. Traffic patterns, including vehicle speeds and volumes, may be analysed with the use of monitors set up along roads. The vast majority of currently available traffic prediction systems make use of historical traffic data. But, there are two issues with people: (1) they just used traffic statistics to make forecasts, and (2) they didn't consider the volume of data or the time it would take to analyses it. Yet, the weather has a significant effect in predicting how busy the roads will be. In this research, we provide a WTPS, or weather-based traffic predictive model, which uses both traffic and weather information to make accurate predictions. The experimental findings demonstrate that WTPS beats the state-of-the-art methods in terms of prediction accuracy in both dry and wet conditions. In order to handle high volumes of traffic, WTPS was developed using big data concepts.

关键词： Accuracy Roads Urban areas Weather forecasting Traffic control Predictive models Prediction algorithms Forecasting Monitoring Vehicles

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Exploring Text-Guided Single Image Editing for Remote Sensing Images

arXiv

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arXiv 2024年

作者： Han, Fangzhou Si, Lingyu Dong, Hongwei Zhang, Lamei Chen, Hao Du, Bo The Department of Information Engineering Harbin Institute of Technology Harbin China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China The Hubei Luojia Laboratory National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. During the generation stage, consistency in content and details between the original and edited images must be maintained, while in the editing stage, controllability and accuracy of the edits should be ensured. For natural images, these challenges can be tackled by training generative backbones on large-scale benchmark datasets and using text guidance based on vision-language models (VLMs). However, these previously effective approaches become less viable for RSIs due to two reasons: First, existing generative RSI benchmark datasets do not fully capture the diversity of remote sensing scenarios, particularly in terms of variations in sensors, object types, and resolutions. Consequently, the generalization capacity of the trained backbone model is often inadequate for universal editing tasks on RSIs. Second, the large spatial resolution of RSIs exacerbates the problem in VLMs where a single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics when using text to guide RSI editing. To solve above problems, this paper proposes a text-guided RSI editing method that is controllable but stable, and can be trained using only a single image. It adopts a multi-scale training approach to preserve consistency without the need for training on extensive benchmark datasets, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability in the text-guided editing process. High-quality RSI editing results can be achieved by first applying multi-scale training to the target image, followed by text-guided fine-tuning using RSI pre-trained VLMs and PE. Experi

关键词： Semantics

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A Structured Architecture Development for 6G technology for the Accurate Communication

A Structured Architecture Development for 6G Technology for ...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Ankit Verma P. Bagyalakshmi R. Pavaiyarkarasi Ramy Riad Al-Fatlawy G Rajalakshmi B Arunkumar Department of Electronics & Communication Engineering IES College Of Technology Bhopal M. P India Department of Mechanical Engineering Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of ECE R.M.K. Engineering College RSM Nagar Tamilnadu Department of computers Techniques engineering College of technical engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer Science Engineering Karpagam Academy of Higher Education

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Following the worldwide pandemic, the next age will bring with it never-before-seen possibilities as well as problems due to the revolutionary merging of the biological, digital, and physical domains of human experience. In order to seamlessly link these realms, a whole new infrastructure is needed to envision 6 G, a dramatic paradigm change. Breaking down architecture into its component parts of platform, works, orchestration, and specialization yields new ideas. Within a heterogeneous cloud architecture, the 6 G landscape that is being envisioned embraces an attitude of openness, scalability, flexibility, and platform agnosticism. Extremely specific architecture supports the complex decomposition of converged applications as well as services into micro-services with serverless operations. This innovative framework also has an open resource orchestration architecture. Important characteristics of these architectural possibilities are explained, with a focus on how they might be adjusted to the constantly changing technology landscapes of the future. At the airinterface leveled 6 G plans to use sub-Terahertz spectrum, use state-of-the-art technologies for sharing spectrum, optimize air-interface design using $\mathrm{AI} / \mathrm{ML}$ methods, combine radio sensing and communication, and meet strict latency, reliability, while synchronization requirements. As this thorough review explains, to fully use these radio technology breakthroughs, 6 G network design advances must occur concurrently.

关键词： 6G mobile communication Cloud computing Software architecture Scalability computer architecture Biology System-on-chip

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