检索结果-内蒙古大学图书馆

Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants

引用

Neural Computing and Applications 2024年第19期36卷 11643-11657页

作者： Costa, Emanoel L. R. Braga, Taiane Dias, Leonardo A. de Albuquerque, Édler L. Fernandes, Marcelo A. C. InovAI Lab - IMD/nPITI Federal University of Rio Grande do Norte Natal59078-900 Brazil Federal Institute of Education Science and Technology of Bahia Salvador40301-015 Brazil Warwick Manufacturing Group University of Warwick CoventryCV4 7AL United Kingdom Department of Industrial Processes and Chemical Engineering Federal Institute of Education Science and Technology of Bahia Salvador40301-015 Brazil Department of Computer and Automation Engineering Federal University of Rio Grande do Norte Natal59078-900 Brazil

In order to address the growing problem of air pollution, it is necessary to implement innovative regulations and practical solutions to reduce and control its impact. Numerous studies have recommended using multivariate statistical methods to identify the connections and characteristics of atmospheric pollutants, which can provide valuable information about their generation, dispersion, and contribution to the deterioration of air quality. This study thoroughly examines the air quality in Salvador, Bahia, using the Self-Organizing Maps (SOM) technique. The data used in the analysis spans from 2011 to 2016 and comes from air quality monitoring stations. The dataset includes hourly measurements of pollutants such as SO2, CO, O3, particulate matter, and meteorological data (wind speed, ambient temperature, relative humidity, the standard deviation of wind direction, rainfall, and wind direction). The SOM analysis successfully identifies significant clusters, revealing associations between high concentrations of specific pollutants and environmental variables. For example, clusters with elevated SO2 concentrations are observed in areas that suggest the presence of local sources of pollution. The validation of the results using Principal Component Analysis strengthens the findings. These findings are essential for developing air quality management policies, as they highlight areas of concern and offer insights for mitigation strategies. This study demonstrates the effectiveness of the SOM technique in environmental analysis and emphasizes the importance of domain knowledge in comprehensively interpreting air pollution patterns. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Self organizing maps

来源：评论

学校读者我要写书评

暂无评论

Drone-Assisted Load Distribution Framework for Traffic Optimization in IoT Networks

Drone-Assisted Load Distribution Framework for Traffic Optim...

引用

International Communication Systems and Networks and Workshops, COMSNETS

作者： Priyanka Soni Omkar Manohar Mense Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms.

关键词： Base stations Smart cities Scalability Smart homes Internet of Things Servers Relays Optimization Drones Strain

来源：评论

学校读者我要写书评

暂无评论

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

引用

computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

来源：评论

学校读者我要写书评

暂无评论

AI and data-driven infrastructures for workflow automation and integration in advanced research and industrial applications

AI and data-driven infrastructures for workflow automation a...

引用

2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Forni, Tommaso Vozza, Mario Le Piane, Fabio Lorenzoni, Andrea Baldoni, Matteo Mercuri, Francesco DAIMON Lab CNR-ISMN via P. Gobetti 101 Bologna40129 Italy Department of Control and Computer Engineering Polytechnic University of Turin Corso Castelfidardo 34/d Turin10138 Italy Department of Computer Science and Engineering University of Bologna Viale del Risorgimento 2 Bologna40136 Italy

The use of AI and data-driven technologies and infrastructures for innovation and development of advanced research and industrial applications requires a strong degree of integration across a broad range of tools, disciplines and competences. In spite of a huge disruptive potential, the role of AI for research and development in the context of industrial applications is often hampered by the lack of consolidated and shared practices for transforming domain-specific processes for generating knowledge into added value. These issues are particularly striking for small-medium enterprises (SMEs), which must adopt clear and effective policies for implementing successful technology transfer paths for innovation. The activities of the DAIMON lab of the CNR-ISMN focus on the design, development, implementation and application of integrated modelling, data-driven and AI methods and infrastructures for innovation in hi-tech applications. Our approach is based on the development of horizontal platforms, which can be applied to a broad range of vertical use-cases. Namely, we target the realisation of high-throughput workflows, related to specific domains and use cases, which are able to collect and process simulations and/or physical data and information. The implementation of an interoperable integration framework is a prerequisite for further application of AI tools for predictivity and automation. With a strong focus on the development of key enabling technologies (KETs), such as advanced materials, the approach pursued is extended to a broad range of application fields and scenarios of interest in industry, including electronic and ICT, advanced and sustainable manufacturing, energy, mobility. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

Dynamics and Computational Principles of Echo State Networks: A Mathematical Perspective

arXiv

引用

arXiv 2025年

作者： Singh, Pradeep Kumar, Ashutosh Ghosh, Sutirtha Hrishit, B.P. Raman, Balasubramanian Machine Intelligence Lab Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

Reservoir computing (RC) represents a class of state-space models (SSMs) characterized by a fixed state transition mechanism (the reservoir) and a flexible readout layer that maps from the state space. It is a paradigm of computational dynamical systems that harnesses the transient dynamics of high-dimensional state spaces for efficient processing of temporal data. Rooted in concepts from recurrent neural networks, RC achieves exceptional computational power by decoupling the training of the dynamic reservoir from the linear readout layer, thereby circumventing the complexities of gradient-based optimization. This work presents a systematic exploration of RC, addressing its foundational properties such as the echo state property, fading memory, and reservoir capacity through the lens of dynamical systems theory. We formalize the interplay between input signals and reservoir states, demonstrating the conditions under which reservoirs exhibit stability and expressive power. Further, we delve into the computational trade-offs and robustness characteristics of RC architectures, extending the discussion to their applications in signal processing, time-series prediction, and control systems. The analysis is complemented by theoretical insights into optimization, training methodologies, and scalability, highlighting open challenges and potential directions for advancing the theoretical underpinnings of *** Codes 37N35, 37D45, 93C10, 93C35, 93C40, 93C55 © 2025, CC BY.

关键词： State space methods

来源：评论

学校读者我要写书评

暂无评论

Musculoskeletal Risk Modeling Using Sensors and Machine Learning

Musculoskeletal Risk Modeling Using Sensors and Machine Lear...

引用

2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Vora, H. Davoudi Kakhki, F. Moghadam, A. Kyrarini, M. Machine Learning & Safety Analytics Lab Santa Clara University Santa ClaraCA95053 United States Department of Computer Science and Engineering Santa Clara University Santa ClaraCA95053 United States Department of General Engineering Santa Clara University Santa ClaraCA95053 United States Department of Technology College of Engineering San Jose State University San JoseCA95192 United States Department of Electrical & Computer Engineering Santa Clara University Santa ClaraCA95053 United States

ISBN: (纸本)9798350386097

This study develops a predictive modeling approach for occupational ergonomic risk in manual lifting tasks to enhance real-time biomechanical risk assessment, aimed at mitigating occupational musculoskeletal disorders (MSDs). Wearable Electromyography (EMG) sensors were used to collect data on muscle activity of eight upper extremity muscles across ten participants (n=10) engaged in both low-risk and high-risk tasks for MSDs. A machine learning model was developed and optimized for lifting task risk classification. The model shows high accuracy, precision, and recall values in classifying low and high-risk lifting tasks. This approach significantly surpasses traditional occupational risk assessment methods, which often rely on historical data and are reactive rather than proactive. The integration of wearable sensors with machine learning provides precise risk classification and facilitates the implementation of effective safety interventions across various occupational settings. This strategy has potential to improves safety planning and can contribute to substantial reduction in the occurrence and severity of MSDs, with ultimate goal of enhancing overall workplace safety and health. © 2024 IEEE.

关键词： Electromyography

来源：评论

学校读者我要写书评

暂无评论

Adversarial Learning Based Knowledge Distillation on 3D Point Clouds

Adversarial Learning Based Knowledge Distillation on 3D Poin...

引用

IEEE Workshop on Applications of computer Vision (WACV)

作者： Sanjay S J Akash J Sreehari Rajan Dimple A Shajahan Charu Sharma Machine Learning Lab IIIT Hyderabad India Department of Computer Science TKM College of Engineering Kollam India

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The significant improvements in point cloud representation learning have increased its applicability in many real-life applications, resulting in the need for lightweight, better-performing models. One widely proposed efficient method is knowledge distillation, where a lightweight model uses knowledge from large models. Very few works exist on distilling the knowledge for point clouds. Most of the work focuses on cross-modal-based approaches that make the method expensive to train. This paper proposes PointKAD, an adversarial knowledge distillation framework for point cloud-based tasks. PointKAD includes adversarial feature distillation and response distillation with the help of discriminators to extract and distill the representation of feature maps and logits. We conduct extensive experimental studies on both synthetic (ModelNet40) and real (ScanObjectNN) datasets to show that PointKAD achieves state-of-the-art results compared to the existing knowledge distillation methods for point cloud classification. Additionally, we present results on the part segmentation task, highlighting the efficacy of the PointKAD framework. Our experiments further reveal that PointKAD is capable of transferring knowledge across different tasks and datasets, show-casing its versatility. Furthermore, we demonstrate that PointKAD can be applied to a cross-modal training setup, achieving competitive performance with cross-modal-based point cloud methods for classification.

关键词： Point cloud compression Training Representation learning Deep learning Image segmentation computer vision Three-dimensional displays Feature extraction Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Neurological Care: Multi-label Classification Framework for Brain Disease Detection Using MRI

Enhancing Neurological Care: Multi-Label Classification Fram...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Shahriar Arefin Zummon Md. Jalal Uddin Chowdhury Department of Computer Science and Engineering Leading University Sylhet Bangladesh DeepNet Research and Development Lab Sylhet Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Brain diseases, including traumas, tumors, haemorrhages, and other neurological abnormalities, present significant risks to human health and may result in life-threatening effects. Traditional healthcare often encounters challenges in the concurrent identification of various disorders, hence limiting the therapeutic efficacy of identifying brain diseases using MRI analysis. This paper presents a multiple-label classification model tailored for brain disease diagnosis using a large-scale MRI dataset. Our approach employs multi-label classification, a methodology previously unexplored in this context, to uniquely address the challenge of identifying multiple diseases from a single MRI scan. Comprehensive experiments rigorously evaluated the proposed model, achieving state-of-the-art performance with an overall accuracy of 98.43%, surpassing existing models in terms of both diagnostic precision and computational efficiency. The results demonstrate the potential for more accurate, rapid, and efficient brain disease diagnosis, ultimately improving patient management. In addition to advancing the field of medical image analysis, our work paves the way for future research that aims to further enhance automated systems in healthcare diagnostics.

关键词： Accuracy Magnetic resonance imaging Computational modeling Multi label classification Brain modeling Transformers Medical diagnosis Medical diagnostic imaging Diseases Tumors

来源：评论

学校读者我要写书评

暂无评论

Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

引用

computers, Materials & Continua 2021年第8期68卷 2363-2376页

作者： Muhammad Usman Ghani Khan Zubaira Naz Javeria Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Usman Tariq Al-khawarizmi Institute of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan UniversityRiyadhSaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),*** treatment methods for ADHD are either psycho-stimulant medications or cognitive *** traditional methods,namely therapy,need a large number of visits to hospitals and include *** could be used for the effective treatment of *** could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain *** paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial *** proposed system consists of a puzzle game where its complexity increases with each *** signals were acquired using the Neurosky headset;then sent the signals to the designed gaming *** neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation *** results show that this game improves player overall performance from 74%to 98%by playing each game level.

关键词： Neurogaming brain-computer interface attention deficit hyperactivity disorder electroencephalography spatial memory cognitive skills attention level

来源：评论

学校读者我要写书评

暂无评论

Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial Pruning

arXiv

引用

arXiv 2024年

作者： Li, Jianhao Sun, Tianyu Zhang, Xueqian Wang, Zhongdao Feng, Bailan Zhao, Hengshuang The Department of Computer Science and Engineering Beihang University China The Department of Electronic Engineering Tsinghua University China Noah’s Ark Lab. Beijing China The Department of Computer Science University of Hong Kong Hong Kong

This paper studies point cloud perception within outdoor environments. Existing methods face limitations in recognizing objects located at a distance or occluded, due to the sparse nature of outdoor point clouds. In this work, we observe a significant mitigation of this problem by accumulating multiple temporally consecutive LiDAR sweeps, resulting in a remarkable improvement in perception accuracy. However, the computation cost also increases, hindering previous approaches from utilizing a large number of LiDAR sweeps. To tackle this challenge, we find that a considerable portion of points in the accumulated point cloud is redundant, and discarding these points has minimal impact on perception accuracy. We introduce a simple yet effective Gumbel Spatial Pruning (GSP) layer that dynamically prunes points based on a learned end-to-end sampling. The GSP layer is decoupled from other network components and thus can be seamlessly integrated into existing point cloud network architectures. Without incurring additional computational overhead, we increase the number of LiDAR sweeps from 10, a common practice, to as many as 40. Consequently, there is a significant enhancement in perception performance. For instance, in nuScenes 3D object detection and BEV map segmentation tasks, our pruning strategy improves several 3D perception baseline methods. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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：