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Dynamic data Collection of AUV Based on Deep Reinforcement Learning

Dynamic Data Collection of AUV Based on Deep Reinforcement L...

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IEEE international conference on Signal processing, Communications and computing (ICSPCC)

作者： Yongqi Tang Lianyou Jing Wentao Shi Chengbing He School of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Ocean Institute Northwestern Polytechnical University Taicang China School of Marine Science and Technology Northwestern Polytechnical University Xi'an China

The Underwater Internet of Things (IoUT) shows great potential in enabling a smart ocean. Underwater sensor network (UWSN) is the main form of IoUT, but it faces the problem of reliable data transmission. To address these issues, this paper considers the use of autonomous underwater vehicles (AUV) as mobile collectors to build a reliable dynamic data collection system, while using Value of Information (VoI) as a primary metric to measure data quality. This paper first builds a realistic model to characterize the behavior of AUV and sensor nodes and challenging environments. Then a method based on deep reinforcement learning is used to dynamically plan the AUV's navigation route by jointly considering the location of nodes, the value of node data information and the state of AUV, with the goal of maximizing the data collection efficiency of AUV. The simulation results show that the dynamic data collection scheme is superior to the traditional path planning scheme which only considers the node location, and can greatly improve the efficiency of AUV data collection.

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8th international Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

8th International Congress on Artificial Intelligence and Pa...

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8th international Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

ISBN: (纸本)9783031495519

The proceedings contain 39 papers. The special focus in this conference is on Artificial Intelligence and Pattern Recognition. The topics include: Echo State Networks for the Prediction of Chaotic Systems;weighted t-Distributed Stochastic Neighbor Embedding for Projection-Based Clustering;Evaluation of XAI Methods in a FinTech Context;towards Automatic Principles of Persuasion Detection Using Machine Learning Approach;indirect Condition Monitoring of the Transmission Belts in a Desalination Plant by Using Deep Learning;a Novel Method for Filtering a Useful Subset of Composite Linguistic Summaries;harnessing Key Phrases in Constructing a Concept-Based Semantic Representation of Text Using Clustering Techniques;a Comparative Study of Deep Learning Methods for Brain Magnetic Resonance Image Reconstruction;enhancing Spanish Aspect-Based Sentiment Analysis Through Deep Learning Approach;oversampling Method Based Covariance Matrix Estimation in High-Dimensional Imbalanced Classification;a Hybrid Approach for Spanish Emotion Recognition Applying Fuzzy Semantic processing;a Knowledge-Based User Feedback Classification Approach for Software Support;A Metaphorical Text Classifier to Compare the Use of RoBERTa-Large, RoBERTa-Base and BERT-Base Uncased;Improvements to the IntiGIS Model Related to the Clustering of Consumers for Rural Electrification;diagnosis of Alzheimer Disease Progression Stage from Cross Sectional Cognitive data by Deep Neural Network;Robust MCU Oriented KWS Model for Children Robotic Prosthetic Hand Control;detection of Malicious Bots Using a Proactive Supervised Classification Approach;hybrid Selection of Breast Cancer Risk Factors in Cuban Patients;good Negative Sampling for Triple Classification;polarity Prediction in Tourism Cuban Reviews Using Transformer with Estimation of Distribution algorithms;multivariate Cuban Consumer Price Index database, Statistic Analysis and Forecast Baseline Based on Vector Autoregressive;SqueezerFaceNet: Reducing a Small

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A Study on the Effect of Antenna Array Parameters in Optimizing the Radiation Pattern

A Study on the Effect of Antenna Array Parameters in Optimiz...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Archisman Banerjee Sankarshan Pradhan Ayan Dutta Bitan Misra School of Electronics Engineering KIIT-Deemed University Bhubaneswar India HCL Technologies Kolkata India Department of Computer Science and Engineering Techno International New Town Kolkata India

In this work, aconcentric elliptical array of antennas is optimized with the help of three relatively new and efficient meta-heuristic algorithms; namely, Teaching Learning Based Optimization (TLBO), Multi-Verse Optimization (MVO), and Grey Wolf Optimization(GWO). This work demonstrates a detailed study of the effect of various antenna array contro lvariables for the improvemen to normalized power patterns generated by elliptical array *** objective of this study is to reduce Side Lobe Level (SLL) value to the possible amount and simultaneously reach the desired First Null BeamWidth (FNBW) value. A number of cases are studied and the outcomes are exhibited in a desegregated way with the best optimal values of eccentricities, semi-major axis, and inter-element spacing. An investigation is performed to explore theperformance of the algorithms based on the common antenna parameters such as the number of elements, obtained SLL, and obtained FNBW. Additionally, the statistical specifications of all the algorithms are examined in terms of mean, standard deviation, best objective function, and time complexity. T-test iscarried out at the end of this study, separately on each acquired data set to authenticate the result.

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A New Era of Land Cover Land Use Categorization Using Remote Sensing and GIS of Big data

A New Era of Land Cover Land Use Categorization Using Remote...

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international conference on Contemporary computing and Informatics (IC3I)

作者： V Kiranmai A Somayajula Deepika Ghai Sandeep Kumar Lovely Professional University Phagwara Punjab India Department of CSE Koneru Lakshmaiah Educational Foundation Vaddeswaram India

Big data is the present era in which we find ourselves. Using spatial data, including auxiliary geospatial datasets and remotely sensed satellite images, has become prevalent in land cover and land use mapping (LCLU). Moreover, deep learning and machine learning algorithms have recently been innovated, offering new prospects for LCLU mapping. But problems often arise when it comes to using big geospatial data. This article summarizes research advancements in remote sensing (RS), machine learning (ML), deep learning (DL), and geographic information big data for LCLU classification. We analyzed the advantages, disadvantages, and potential future directions of LCLU mapping utilizing big geospatial data. Further research is needed to improve the LCLU process at larger scales.

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An Approach to Mine data for Predicting Forest Fires Using Support Vector Machines and Gini Index for Feature Selection

An Approach to Mine Data for Predicting Forest Fires Using S...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Prathimesh Kunal Groresh Shingari Deepika Sharma Computer science engineering Chandigarh University Mohali India

Increasing temperatures regularly cause dry vegetation in wooded regions to burn due to sun radiation, which in turn sparks forest fires. Predicting and mapping fire susceptibility in fire-prone forests worldwide is a critical research focus. This paper presents a data mining approach for forest fire prediction. The maps that arise from evaluating the Gini index's performance in identifying significant features serve as a basis for firefighting tactics and regulations that are more successful in promoting sustainable forest resource management. The proposed model utilizes Support Vector Machine for regression and achieves an improved accuracy of approximately 87% compared to previous research. This study emphasizes the importance of enhancing traditional algorithms by prioritizing key features to ensure consistent results and optimize predictive models.

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Multi-node Intelligent of Power Supply Chain under Green and Low-Carbon Goals with AI Simulation

Multi-node Intelligent of Power Supply Chain under Green and...

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： Jiangdong Fan Chang Liu Junping Ge Zhan Gao Jianfeng Wu Weiyong Zhang State Grid Zhejiang Electric Power Co. LTD. Hangzhou Zhejiang Jinhua Power Supply Company State Grid Zhejiang Electric Power Co. LTD. Jinhua Zhejiang China

Multi-node intelligent of power material supply chain under green and low-carbon goals with AI simulation is studied. This study combines the supply chain operation performance evaluation with the enterprise process monitoring system to fully mobilize the physical support role of the data center is considered and then form a comprehensive operation performance evaluation system. The multi-node intelligent of power material supply chain is implemented with the KNN and the data mining algorithms. The models are designed based on the multi-core cluster systems with different node computing, communication and storage capabilities, the Kubernetes is selected as the model, and VMI is selected as the tool to finish the work of bad data identification. Through the verification on the AI simulation, the performance is validated. It can be seen from the experiment that the model error is between [0], [6], which belongs to the low or medium levels which are acceptable.

关键词： Performance evaluation Process monitoring data centers Power supplies Computational modeling Supply chains data models

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Application of LoRa Network for data Transmission in Intelligent Smart Grid Systems 8th

Application of LoRa Network for Data Transmission in Intelli...

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8th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Soham, K. Dhaval, V. Dhanamjayulu, C. Rafikiran, Shaik Hussaian Basha, C.H. Prashanth, V. School of Electrical Engineering VIT University Vellore632014 India Tirupati India Nitte Meenakshi Institute of Technology Karnataka Bengaluru560064 India

ISBN: (纸本)9789819976218

As new ways to generate renewable energy cost effectively are being developed, high load devices such as electric vehicles (EVs) are becoming common. EVs require high current and voltage for charging, due to the large size of the batteries and the requirement for fast charging. This has made intelligent smart grids necessary, as traditional grids are incapable of dealing with the disturbances in the grid produced by the load required by these devices. Our paper proposes an intelligent smart grid which is interconnected using LoRa network and uses a long short-term memory (LSTM)-based neural network for processing, forecasting and predicting the disturbances produced in the network. The LoRa modules will be used in a mesh configuration;this will remove the need for a repeater and also give us greater network coverage. A LoRa access point will be placed, which will collect data from the individual modules and send them to the power station. Here, the LSTM algorithm will be applied on the data collected thereby doing the necessary predictions. The use of LoRa has make the system independent of cellular networks and due to its low power consumption, and efficiency have been improved. LSTM algorithm is capable of dealing with not only individual data but also complete time series. It can also deal with missing data, which makes it much more beneficial. Overall, our proposed system can greatly benefit both consumers and electricity companies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting Long short-term memory LoRa Neural networks Smart grid

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Automated Detection of Rust Defects from 3D Point Cloud data Through Machine Learning 1

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32nd international conference on The Digital Transformation in the Graphic Engineering, INGEGRAF 2023

作者： Fernández, Antonio Pernas, Cristina Álvarez, Marcos X. Díaz-Vilariño, Lucía CINTECX Universidade de Vigo GeoTech Group Vigo36310 Spain NTNU Department of Computer Science Ametyst-bygget A226 Gjøvik2815 Norway

ISBN: (数字)9783031516238

ISBN: (纸本)9783031516221

We introduce a method for automatic corrosion detection based on the application of machine learning techniques to 3D point cloud data generated by a LIDAR sensor. In our approach a point is assigned one of the considered class labels (healthy, stain, weld or rust) by processing its feature vector with a cascade of three binary classifiers. The effectiveness of the proposed system is demonstrated through a case study on three different bulkheads in the hold of a merchant ship. The experimental results show that the corrosion detection rate is improved by combining colour and local geometry features. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Brain Tumor Classification using Transfer Learning on Augmented data and Visual Explanation using Grad-CAM

Brain Tumor Classification using Transfer Learning on Augmen...

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international conference on ent computing and Control Systems (ICICCS)

作者： K. Vijaya Kumar Mudit Baid Karthik Menon Dept of Computational Intelligence SRM Institute of Science and Technology Chennai India

Brain tumors are irregular growths of cells within the brain, and some of them can be cancerous. MRI is the typical diagnostic tool for identifying brain tumors. MRI images can reveal any anomalous tissue regeneration in the brain. Applying novel algorithms to MRI images, predicting a tumor becomes much faster and more accurate to help deliver treatments to patients. These predictions also help radiologists make quick decisions. In the proposed work to enhance diversity in the dataset, data augmentation techniques are utilized. The presence of brain tumors is detected using VGG19 and InceptionV3 neural networks, with the performance visualized through Grad CAM.

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A Chinese-European Standard Protocol Converter for Charging Piles

A Chinese-European Standard Protocol Converter for Charging ...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Xiaolin He Lvming He Kanghua Luo Zhaoyun Zhang Dongguan University of Technology SchoolofElectronn gineering&Intell Dongguan China

Aiming at the problem of inconsistent charging standards and restricted import and export of electric vehicles in various countries today, this essay propose a Chinese-European Standard Protocol Converter for Charging Piles, which enables two-way communication between GB/T 27930 digital communication protocol for Chinese standard piles and ISO 15118 digital communication protocol for European standard vehicles. The converter is based on commercially available charging conversion modules, with the STM32F413 chip and the new highly integrated MSE102X carrier chip, to build a series of sub-functional module circuits, providing new ideas for future updates to the charging converter. This device offers great data processing capability, low power consumption, and high charging efficiency.

关键词： Protocols Power demand ISO Standards Europe Digital communication Electric vehicles data processing

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