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Advancements in AI-Powered Robotic Cleaning systems: Autonomous Path Planning, Predictive Maintenance, and Cleanliness Assessment Frameworks 3

Advancements in AI-Powered Robotic Cleaning Systems: Autonom...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jones Sarah, A. Nandhakumar, D. Kumar, Atchitha Magesh Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Artificial Intelligence (AI) is transforming robotic cleaning systems to be more efficient, safe, and adapt in various environments, including public spaces and industrial facilities. The novelty of these systems is the use of advanced sensing and deep learning to identify areas that need cleaning, plan paths, and save resources all while meeting high cleanliness standards required in sensitive areas like hospitals and pharmaceutical facilities. The objective is how recent developments in path planning, dirt detection, and predictive maintenance improve these systems, which reduce costs and perform without human supervision. Though these systems work well, challenges remain in deciding on cleaning paths for complex spaces, correctly detecting various types of dirt, adapting to changing environments, and maintaining to prevent breakdowns. © 2025 ieee.

关键词： Predictive maintenance

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An OPC UA Client/Gateway-Based Digital Twin Architecture of a SCADA System with Embedded System Connections

An OPC UA Client/Gateway-Based Digital Twin Architecture of ...

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ieee/ASME international conference on advanced intelligent Mechatronics (AIM)

作者： Abdelsattar, Ahmad Park, Edward J. Marzouk, Amr Simon Fraser Univ Sch Mechatron Syst Engn Surrey BC V3T 0A3 Canada

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

A Supervisory Control and data acquisition (SCADA) system employed to monitor system processes became one of the fundamental components of factory automation. With the emergence of digital twin concepts in Industry 4.0, this paper proposes an architecture to merge SCADA systems with key facets of digital twin with a proof-of-concept demonstration on a FESTO MPS processing station. We propose an OPC UA client-based architecture that employs Ignition software, which has built-in OPC UA modules, in order to create a digital twin of the station's sensors and actuators. Monitoring and controlling of the system was enhanced by connecting mobile devices to the network and adding a Raspberry Pi controller connected with traditional microcontrollers. The demonstrated implementation shows that the proposed client-based architecture for digital twins and devices is feasible. Interoperability allows to connect devices of interest with low-latency, low CPU overhead utilization, and minimal added hardware and software extensions.

关键词： PI control Microcontrollers SCADA systems Process control Computer architecture Software Mobile handsets

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Design and Simulation of Tandem Fixed-wing Unmanned Aerial systems 2

Design and Simulation of Tandem Fixed-wing Unmanned Aerial S...

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2nd international conference on Signal Processing and intelligent computing, SPIC 2024

作者： Li, Xu Yin, Jinglin Wang, Xin Yuan, Yong Shenyang Ligong University College of Equipment Engineering Shenyang China Co. Ltd. Chongqing China

ISBN: (纸本)9798350368888

This paper focuses on the 3D model construction of a tandem-stage fixed-wing UAV and its kinematic simulation analysis. Through an in-depth understanding of the flight principle of a tandem-stage fixed-wing UAV, the equations of motion of this type of UAV are first constructed. Then the flight process of the UAV is exhaustively simulated and analysed using advanced kinematic analysis software. Under the set initial conditions, the dynamic relationship between the lift force and the flight speed and flight time of the UAV during the flight process was obtained, and the relationship between the thrust paddle centre-of-mass angular velocity amplitude, angular acceleration amplitude and the flight time was also explored, and the rule of change between the flight speed, lift force and flight time of the UAV was further *** concluding data from these simulation analyses not only provide an important reference basis for the design optimisation of the tandem fixed-wing UAV, but also lay a solid theoretical foundation for subsequent experimental verification and practical application. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Hybrid Deep Learning and Feature Engineering for Early Detection of Melanoma Skin Cancer Using Multimodal Imaging data

Hybrid Deep Learning and Feature Engineering for Early Detec...

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2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Packiamary, M. Muthukumaravel, A. Bharath Institute Of Higher Education And Research Department Of Computer Applications Tamilnadu Chennai India Bharath Institute Of Higher Education And Research Faculty Of Arts And Science Tamilnadu Chennai India

ISBN: (纸本)9798350364828

Skin cancer, particularly melanoma, is a severe public health concern that requires improved early diagnosis to enhance treatment results. Existing systems depend on manual assessment and limited imaging, which leads to insufficient results. To address these problems, the study provides an innovative strategy for early melanoma diagnosis utilizing multimodal imaging data that leverages Hybrid Deep Learning and Feature Engineering. The hybrid approach combines automated deep learning features for pattern extraction with autoencoder features, and their fusion is provided as SVM input, yielding exact melanoma diagnosis output. The integrative method intends to overcome system constraints by increasing sensitivity and specificity in identifying early melanoma features. The experimental results reveal that the proposed system outperforms existing systems in terms of accuracy (94.5%), precision (93.7%), recall (93.4%), and F1 (94.2%). These results highlight the system's improved diagnostic accuracy and its potential for prompt and reliable melanoma detection, which addresses the limitations of existing systems. © 2024 ieee.

关键词： Diagnosis

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Research on Maintenance and Fault Prediction Model of intelligent Building Driven by Internet of Things 3

Research on Maintenance and Fault Prediction Model of Intell...

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3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Li, Xin Gao, Lingyu Liu, Yuding Harbin Huade University Heilongjiang Harbin150025 China

ISBN: (纸本)9798331519032

With the rapid development of Internet of Things (IoT) technology, its application in the field of intelligent buildings is increasingly extensive, which significantly improves the energy efficiency management and fault prediction capabilities of intelligent buildings. The purpose of this paper is to discuss the construction and optimization method of maintenance and fault prediction model of intelligent building driven by IoT. Through in-depth analysis of sensor data in intelligent buildings, statistical characteristics, time series characteristics, frequency characteristics and correlation characteristics are extracted, and the running state and potential faults of building systems are fully captured. On this basis, this paper proposes a fault prediction model based on ensemble learning, which combines random forest and AdaBoost algorithm to effectively improve the prediction accuracy and model stability. The experimental results show that the model performs well in fault prediction tasks in typical intelligent building scenes, with high accuracy, recall and F1 score, and its performance is stable under different data sets. In addition, by introducing L1 and L2 regularization, the model complexity is effectively controlled and the risk of over-fitting is reduced. The research in this paper not only provides a new solution for the maintenance and fault prediction of intelligent buildings, but also provides a useful reference for the future development of intelligent building management systems. © 2024 ieee.

关键词： Prediction models

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Domain-Adaptive Point Cloud Semantic Segmentation from Urban to Off-Road Scenes Based on Knowledge-Augmented Deep Learning 27

Domain-Adaptive Point Cloud Semantic Segmentation from Urban...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Xu, Fengyu Xiao, Yongxiong Mei, Jilin Hu, Yu Fu, Qiang Research Center for Novel Computing Sensing and Intelligent Processing Zhejiang Lab Hangzhou China Institute of Computing Technology The Research Center for Intelligent Computing Systems Beijing China

ISBN: (纸本)9798331505929

Domain-adaptive point cloud semantic segmentation (PCSS) is crucial for high-level autonomous driving. However, supervised deep learning methods are often constrained by training data and suffer from poor generalization in unknown environments. To address these challenges, we propose a domain-adaptive PCSS approach leveraging knowledge-augmented deep learning (KADL). Specifically, we introduce three strategies: (1) point cloud data augmentation based on the compact bird's-eye view (CBEV) map, which is a novel point cloud organization method;(2) implicit knowledge augmentation based on knowledge distillation;(3) explicit knowledge augmentation based on attribution analysis and network modulation. For experimental validation, we utilize two distinct datasets, namely the urban dataset SemanticKITTI and the off-road dataset RELLIS-3D, which are involved in the training and testing phases, respectively. Additionally, we have added road labels to the RELLIS-3D dataset, which originally lacked a road category. To our knowledge, this work is the first to investigate domain-adaptive PCSS from urban to off-road scenes. The experimental results demonstrate that our method is effective and has promising performance. The code and data are available at https://***/xfy0032/kadlpcss © 2024 ieee.

关键词： Semantic Segmentation

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Archigraphs As A Promising Concept For The Development Of data And Knowledge Storage systems

Archigraphs As A Promising Concept For The Development Of Da...

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2024 ieee international conference on Big data, Bigdata 2024

作者： Sukhobokov, Artem A. Vetoshkin, Artem A. Mironova, Alexandra R. Zenger, Anna C. Baklikov, Vitaly Sap America Inc. Newtown SquarePA United States Bauman Moscow State Technical University Dep. Information Processing and Management Systems Moscow Russia Ozon Marketplace Kazakhstan Llp Almaty Kazakhstan Skyward Inc. North BethesdaMD United States

ISBN: (纸本)9798350362480

The article describes one of the types of complex graphs - an archigraph, which, unlike conventional graphs with only vertices and edges, can contain an arbitrary number of different types of elements and has advanced means of structuring and encapsulating its individual parts. Due to these properties, an archigraph can become a good conceptual model for creating new generations of data and knowledge storage systems. This is especially relevant for systems that store a large number of elements, or systems which elements are diverse in their characteristics. The transition to the use of the archigraph model is demonstrated for the file systems, database management systems, data warehouses, blockchain platforms, data lake management systems and various architectures derived from data lakes: data Lakehouse, data Space, data Fabric, data Mesh, in which data Lake is an integral component. Moreover, we show the possibility of expanding the basic capabilities of an archigraph by making its elements more complex and ensuring work with sets and logical operations. Such advanced archigraphs are proposed to be used in constructing knowledge lakes used by intelligent systems. An approximate composition of the Knowledge Lake elements we show in this work. © 2024 ieee.

关键词： data warehouses

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Parallel computing Techniques for Accelerating Machine Learning Algorithms on Big data

Parallel Computing Techniques for Accelerating Machine Learn...

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2023 international conference on Power Energy, Environment and intelligent Control, PEEIC 2023

作者： Mishra, Rahul Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350357769

In the era of Big data, the computational demands of machine learning (ML) algorithms have grown exponentially, necessitating the development of efficient parallel computing techniques. This research paper delves into the exploration and evaluation of advanced parallel computing methodologies tailored for accelerating ML algorithms when applied to vast datasets. We commence by providing a comprehensive overview of the existing parallelization paradigms, highlighting their strengths and limitations in the context of ML. Subsequently, we introduce novel techniques that harness the combined power of distributed systems, multicore processors, and graphics processing units (GPUs) to achieve significant speed-ups. Experimental results, derived from processing multi-terabyte datasets, demonstrate that our proposed methods can achieve up to a tenfold increase in computational efficiency compared to traditional approaches. Furthermore, we present a detailed analysis of the scalability, fault-tolerance, and resource utilization of our techniques, providing insights for practitioners aiming to deploy ML algorithms on large-scale data infrastructures. This work not only paves the way for faster ML computations but also offers a blueprint for the next generation of scalable, parallel ML systems. © 2023 ieee.

关键词： Big data Distributed systems Machine Learning Acceleration Parallel computing Scalability

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A Roadmap towards intelligent Operations for Reliable Cloud computing systems 23

A Roadmap towards Intelligent Operations for Reliable Cloud ...

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23rd ieee international conference on data Mining (ieee ICDM)

作者： Huo, Yintong Lee, Cheryl Liu, Jinyang Yang, Tianyi Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9798350381641

The increasing complexity and usage of cloud systems have made it challenging for service providers to ensure reliability. This paper highlights two main challenges, namely internal and external factors, that affect the reliability of cloud microservices. Afterward, we discuss the data-driven approach that can resolve these challenges from four key aspects: ticket management, log management, multimodal analysis, and the microservice resilience testing approach. The experiments conducted show that the proposed data-driven AIOps solution significantly enhances system reliability from multiple angles.

关键词： AIOPS roadmap

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Analysis of user Music Behavior and Emotion Technology in intelligent Music System Design 2

Analysis of user Music Behavior and Emotion Technology in In...

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2nd ieee international conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Wu, Mingwei Minjiang University Fujian Fuzhou China

ISBN: (纸本)9798331504960

With the popularity of smart music systems, users' music behaviors and emotional needs are receiving increasing attention. Understanding users' emotional responses in music selection and experience can help improve personalized recommendation and user experience, and thus promote the development of intelligent music technology. However, most existing studies focus on technical implementation and lack in-depth analysis of user behavior and emotion. In this paper, we use a combination of data analysis to collect a large amount of data on users' music behavior and emotional responses in different scenarios. By using an emotional computing model to categorize users' music preferences and combining it with machine learning algorithms, the relationship between users' emotional states and music choices is explored in depth. The results of the study show that there is a significant correlation between users' music choices and their emotional states, and there is a clear preference trend between specific emotions and specific music genres. intelligent music system design should pay more attention to the user's emotional experience, and optimize the recommendation algorithm through a deeper understanding of the user's behavioral patterns, so as to provide a more personalized music experience. © 2024 ieee.

关键词： Music

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