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arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning

arXiv

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arXiv 2024年

作者： Huan, Ouwen Yang, Yang Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, a multi-modal data based semisupervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Accurate Solutions to Optimal Control Problems via a Flexible Mesh and Integrated Residual Transcription

arXiv

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arXiv 2024年

作者： Nita, Lucian Kerrigan, Eric C. Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom Department of Aeronautics Imperial College London LondonSW7 2AZ United Kingdom

We propose joining a flexible mesh design with an integrated residual transcription in order to improve the accuracy of numerical solutions to optimal control problems. This approach is particularly useful when state or input trajectories are non-smooth, but it may also be beneficial when dynamics constraints are stiff. Additionally, we implement an initial phase that will ensure a feasible solution is found and can be implemented immediately in real-time controllers. Subsequent iterations with warm-starting will improve the solution until optimality is achieved. Optimizing over the mesh node locations allows for discontinuities to be captured exactly, while integrated residuals account for the approximation error in-between the nodal points. First, we numerically show the improved convergence order for the flexible mesh. We then present the feasibility-driven approach to solve control problems and show how flexible meshing and integrated residual methods can be used in practice. The presented numerical examples demonstrate for the first time the numerical implementation of a flexible mesh for an integrated residual transcription. The results show that our proposed method can be more than two times more accurate than conventional fixed mesh collocation for the same computational time and more than three times more accurate for the same problem size. © 2024, CC BY.

关键词： Mesh generation

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On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications

arXiv

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arXiv 2023年

作者： Tan, Chenjiao Cao, Qian Li, Yiwei Zhang, Jielu Yang, Xiao Zhao, Huaqin Wu, Zihao Liu, Zhengliang Yang, Hao Wu, Nemin Tang, Tao Ye, Xinyue Chai, Lilong Liu, Ninghao Li, Changying Mu, Lan Liu, Tianming Mai, Gengchen Department of Geography University of Georgia AthensGA United States School of Computing University of Georgia AthensGA United States Department of Landscape Architecture and Urban Planning Department of Computer Science and Engineering Texas A&M University College StationTX United States School of Electrical and Computer Engineering University of Georgia AthensGA United States Department of Poultry Science University of Georgia AthensGA United States Department of Agricultural and Biological Engineering University of Florida GainesvilleFL United States School of Architecture Tsinghua University Beijing China

The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision. This paper explores the capabilities of GPT-4V in the realms of geography, environmental science, agriculture, and urban planning by evaluating its performance across a variety of tasks. Data sources comprise satellite imagery, aerial photos, ground-level images, field images, and public datasets. The model is evaluated on a series of tasks including geo-localization, textual data extraction from maps, remote sensing image classification, visual question answering, crop type identification, disease/pest/weed recognition, chicken behavior analysis, agricultural object counting, urban planning knowledge question answering, and plan generation. The results indicate the potential of GPT-4V in geo-localization, land cover classification, visual question answering, and basic image understanding. However, there are limitations in several tasks requiring fine-grained recognition and precise counting. While zero-shot learning shows promise, performance varies across problem domains and image complexities. The work provides novel insights into GPT-4V’s capabilities and limitations for real-world geospatial, environmental, agricultural, and urban planning challenges. Further research should focus on augmenting the model’s knowledge and reasoning for specialized domains through expanded training. Overall, the analysis demonstrates foundational multimodal intelligence, highlighting the potential of multimodal foundation models (FMs) to advance interdisciplinary applications at the nexus of computer vision and language. © 2023, CC0.

关键词： Agriculture

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A Generative Design Approach for the Optimization of Multi-Mw Offshore Direct-Drive Wind Turbine Electrical Generator Supporting Structures Using Modal Analyses

SSRN

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SSRN 2023年

作者： Gonzalez-Delgado, Daniel Jaen-Sola, Pablo Oterkus, Erkan School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom Naval Architecture and Marine Engineering Department University of Strathclyde 100 Montrose St GlasgowG4 0LZ United Kingdom

Generative design techniques together with the rapid development of additive manufacturing represent a revolution in the field of structural optimization processes. In this study, a static structural and modal analysis were integrated to drive a generative design optimization process for a 3 MW direct-drive offshore wind turbine electrical generator rotor structure. This novel optimization approach implements an automated fittest-for-purpose process including a static structural analysis and a modal analysis as the input for the optimization strategy algorithm, allowing to explore a wide range of non-conventional topologies. If compared with the simple generator rotor disk structure, the results obtained using this innovative method achieved over 7% of weight reduction and 39% increase in the generator operational range with the consequent growth in the wind turbine energy capture capability. Moreover, this approach generates a vast amount of structural analysis information, crucial at an early stage of the development of large-scale projects for a cost-effective scheme. © 2023, The Authors. All rights reserved.

关键词： Offshore wind turbines

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Enhanced Lung Cancer Detection Using CNNs for Accurate and Efficient CT Image Classification

Enhanced Lung Cancer Detection Using CNNs for Accurate and E...

引用

computing, Sciences and Communications (ICCSC), International Conference on

作者： Mohammad Bdair E D Kanmani Ruby Prachi Juyal Rakheeba Taseen A. Abirami A Soujanya School of Architecture Computing & Engineering University of East London London UK Department of ECE Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Allied Science (Mathematics) Graphic Era (Deemed to be University) Dehradun Uttarakhand India School of computer science and Engineering Presidency university Bangalore India Department of Mathematics Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering CVR College of Engineering Ibrahimpatnam (M) Telangana India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are prone to errors, which produces uneven outcomes. The research provides a unique deep learning-based method for classifying lung nodules from CT images using convolutional neural networks (CNNs). By automating feature extraction and categorization, the suggested technique greatly increases diagnostic precision. The result shows superior performance over existing systems is demonstrated by evaluation measures such as AUC-ROC, accuracy, sensitivity, and specificity. With a training duration of 12 hours, an AUC-ROC of 0.95, accuracy of 92%, sensitivity of 94%, specificity of 90%, and inference speed of 0.5 seconds per scan, the results demonstrate notable improvements in diagnostic efficiency and accuracy. Deep learning has the potential to revolutionize the diagnosis of lung cancer and enhance patient care, as demonstrated by the study.

关键词： Training Accuracy Lungs Computed tomography Lung cancer Sensitivity and specificity Feature extraction Data models Stability analysis Convolutional neural networks

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Boosting the Restoring Performance of Deduplication Data by Classifying Backup Metadata

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ACM/IMS Transactions on Data Science 2021年第2期2卷 1–16页

作者： Yang, Ru Deng, Yuhui Zhou, Yi Huang, Ping Department of Computer Science Jinan University Guangzhou510632 China Department of Computer Science Jinan University China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China TSYS School of Computer Science Columbus State University Doimukh Georgia College of Engineering Temple University United States

Restoring data is the main purpose of data backup in storage systems. The fragmentation issue, caused by physically scattering logically continuous data across a variety of disk locations, poses a negative impact on the restoring performance of a deduplication system. Rewriting algorithms are used to alleviate the fragmentation problem by improving the restoring speed of a deduplication system. However, rewriting methods give birth to a big sacrifice in terms of deduplication ratio, leading to a huge storage space waste. Furthermore, traditional backup approaches treat file metadata and chunk metadata as the same, which causes frequent on-disk metadata accesses. In this article, we start by analyzing storage characteristics of backup metadata. An intriguing finding shows that with 10 million files, the file metadata merely takes up approximately 340 MB. Motivated by this finding, we propose a Classified-Metadata based Restoring method (CMR) that classifies backup metadata into file metadata and chunk metadata. Because the file metadata merely takes up a meager amount of space, CMR maintains all file metadata in memory, whereas chunk metadata are aggressively prefetched to memory in a greedy manner. A deduplication system with CMR in place exhibits three salient features: (i) It avoids rewriting algorithms' additional overhead by reducing the number of disk reads in a restoring process, (ii) it increases the restoring throughput without sacrificing the deduplication ratio, and (iii) it thoroughly leverages the hardware resources to boost the restoring performance. To quantitatively evaluate the performance of CMR, we compare our CMR against two state-of-the-art approaches, namely, a history-aware rewriting method (HAR) and a context-based rewriting scheme (CAP). The experimental results show that compared to HAR and CAP, CMR reduces the restoring time by 27.2% and 29.3%, respectively. Moreover, the deduplication ratio is improved by 1.91% and 4.36%, respectively. © 2

关键词： Metadata

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Flexible ConvNext Block Based Multi-task Learning Framework for Liver MRI Images Analysis

Flexible ConvNext Block Based Multi-task Learning Framework ...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dong Sui Weifeng Liu Maozu Guo Yang Li Lei Zhang Gongning Luo Kuanquan Wang School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Diagnostic Radiology and Nuclear Medicine University of Maryland Baltimore MD USA Bio-Computing Research Center Harbin Institute of Technology Harbin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Liver cancer is the second leading cause of death all over the world in the 2020s’, and the incidence rate has been growing on a global scale and become a serious threat to human life. Early diagnosis of liver cancer from medical images can allow the patients to receive better treatment and achieve good outcomes. Although medical imaging approaches have made significant progress over the past decades, there are still great demands to reconstruct the network structure for the adaptation to downstream tasks. It remains a great challenge for liver tumor identification from MRI images. Recently, self-attention mechanism based transformer models can capture long-range dependencies, which make them perform well on many medical image analysis tasks. Such as Segformer and TransUNet, since lacking the translation in-variance and inductive bias of CNNs, they are still needed large-scale training to fill the gap, especially in the field of medical image analysis domain. In this study, we incorporate a novel flexible Condeathblock as a feature extractor to perform liver images analysis and propose a new analytical framework CXNet to extract discriminative multi-scale visual representations. The experiments results demonstrated our framework outperforms the state-of the-art models on three datasets, including a publicly available liver segmentation dataset as well as two in-house liver tumor classification/segmentation datasets. On the 3DIRCADb dataset, our CXNet outperforms the UNet model by 2.82%, 2.73%, and 4.46% in terms of the Jaccard similarity coefficient, Dice coefficient, and accuracy, and outperforms the TransUNet model by 8.49%, 5.35%,which 3.62%, respectively. Code is available at https://***/SPECTRELWF/CXNet

关键词： Image segmentation Analytical models Liver cancer Image analysis Magnetic resonance imaging Liver Feature extraction

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CSI-Based Human Activity Recognition using Convolutional Neural Networks 11

CSI-Based Human Activity Recognition using Convolutional Neu...

引用

11th International Conference on Computer engineering and Knowledge, ICCKE 2021

作者： Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shahid Beheshti University G. C. Cognitive Telecommunication Research Group Department of Electrical Engineering Tehran Iran Standard Research Institute Faculty of Technology and Engineering Department of Electrical Engineering Alborz31745-139 Iran University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom

ISBN: (纸本)9781665402088

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as "walk", "run", and "sleep". These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks. © 2021 IEEE.

关键词： Channel state information

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