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IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model

Measurement: Sensors 2023年 29卷

作者： Nithyashri, J. Poluru, Ravi Kumar Balakrishnan, S. Ashok Kumar, M. Prabu, P. Nandhini, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpet District Kattankulathur603203 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria Bengaluru560029 India Department of Computer Science and Engineering Karpagam Academy of Higher Education Tamilnadu Coimbatore India

This research proposes an IoT based technique for predicting rainfall forecast in coastal regions using a deep reinforcement learning model. The proposed technique utilizes Long Short-Term Memory (LSTM) networks to capture the temporal dependencies between the rainfall data collected from the coastal regions and the prediction model parameters. The proposed technique is evaluated on a dataset of rainfall data collected from the coastal regions of India and compared to traditional methods of rainfall forecasting. The accuracy and reliability of these models are evaluated by comparing them to prior models. Precipitation in coastal locations may be predicted with an average accuracy of 89% using the suggested model, as shown by the results. The suggested framework is computationally efficient and can be trained with little input. The results of this research give strong evidence that the proposed model is an effective tool for coastal precipitation forecasting. © 2023 The Authors

关键词： Internet of things

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Digital Twin for mobility: simulation integrated approach for demand matrices calibration with empirical data

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Transportation Research Procedia 2025年 86卷 516-523页

作者： Anna Laura Pala Giovanni Felici Vincenza Torrisi Davide Donato Russo Alessio Salvatore Pierfrancesco Leonardi Giuseppe Stecca Matteo Ignaccolo Giuseppe Inturri National Research Council Institute of Systems Analysis and Computer Science ”Antonio Ruberti” Rome Italy DIAG Sapienza Rome Italy Department of Electric Electronic and Computer Engineering Catania Italy f Department of Bioscience and Territory University of Molise Department of Enterprise Engineering University of Rome ”Tor Vergata” Italy Department of Civil Engineering and Architecture Catania Italy

A Digital Twin for Mobility (DTmob) is based on the digital representation of the real-world transport system. A crucial element underlying the development of such systems is an accurate simulation of traffic flows within the road network, which is generally based on transport demand data in the form of Origin-Destination (O-D) matrices. Estimating demand matrices accurately is a complex challenge, as they are hindered by incomplete data, inaccuracies in transport models and temporal-spatial variability. In this paper we present an integrated methodological approach for the calibration of O-D matrices via the implementation of an iterative calibration procedure incorporating a traffic survey dataset, followed by the assignment procedure performed by a macrosimulation model. This study addresses this issue by employing Nielsen’s Single Path Matrix Estimation Method (SPME) jointly with the macrosimulation tool PTV Visum, developing a script in Python to achieve their integration. We tested it on the real-case study of Catania urban road network (Italy). Several scenarios have been analyzed and evaluated through the GEH statistic as KPI, also providing results with Ordinary Least Square (OLS) method as benchmark. Obtained results prove the validity of our integrated framework as the threshold in terms of the difference between estimated and counted flows was satisfied in all performed analyses. Research findings lay the basis for investigate further calibration algorithms and for exploring the use of real-time data in line with DTmob requirements.

关键词： Digital Twin transport modelling traffic simulation OD matrix calibration

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A Comparison Diagnosis Algorithm for Conditional Fault Local Diagnosis of Multiprocessor systems 25th

A Comparison Diagnosis Algorithm for Conditional Fault Loca...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan City Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9789811995811

An efficient diagnosis is very important for a multiprocessor system. In this paper, we present a (α, β) -trees combination S(u, X, α, β) and give some conclusions about the local diagnosis. Based on the (α, β) -trees combination, we give a conditional fault local diagnosis algorithm to identify the fault or fault-free status of each processor correctly under the MM ∗ model when the number of faulty nodes does not exceed α+ 2 β- 3 and every node has at least one fault-free neighboring node. According to our results, a connected network with a (α, β) -trees combination S(u, X, α, β) for a node u is conditionally locally (α+ 2 β- 3 ) -diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+ αβ2). As an application, we show that our algorithm can identify all the faulty nodes of n-dimensional star graph Sn if the faulty node number does not exceed 3 n- 8. Compared with existing algorithms, our algorithm allows more faulty node in a multiprocessor system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

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Exploring the Impact of Vibrotactile Feedback on Boom Lift Safety Training: A Study Design

Exploring the Impact of Vibrotactile Feedback on Boom Lift S...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Minseok Kang Chae Heon Lim Seul Chan Lee Changgu Kang Kangsoo Kim Department of Electrical and Software Engineering University of Calgary Department of Industrial and Systems Engineering Gyeongsang National University Department of Computer Science Gyeongsang National University

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

In this paper, we present our research plan to investigate how various locations of vibrotactile feedback influence user experience and risk perception within a virtual reality (VR) based safety training simulation. We describe our planned user study where the study participants experience a construction boom lift ride (or operation) in an immersive VR environment. During the virtual lift ride, participants perform stage-maintenance tasks, such as assembling light bulbs and electrical lines, which involve certain safety risks—for example, hitting electrical lines and structures during the lift operation. In the study, the vibrotactile feedback condition is varied in three levels: (1) No-Vibration, (2) Controller-Vibration, and (3) Floor-Vibration. We plan to collect and analyze both subjective and objective data related to general user experience, sense of presence, and risk perception to examine the effectiveness of the safety training. Further details of our study and the boom lift ride simulation are described, and future plans are discussed.

关键词： Training Ethics Three-dimensional displays Conferences Virtual reality User interfaces User experience

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Dissipative Avoidance Feedback for Reactive Navigation Under Second-Order Dynamics

arXiv

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arXiv 2024年

作者： Smaili, Lyes Tang, Zhiqi Berkane, Soulaimane Hamel, Tarek Department of Computer Science and Engineering University of Quebec in Outaouais GatineauQC Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden Department of Electrical Engineering Lakehead University ON Canada France

This paper addresses the problem of autonomous robot navigation in unknown, obstacle-filled environments with second-order dynamics by proposing a Dissipative Avoidance Feedback (DAF). Compared to the Artificial Potential Field (APF), which primarily uses repulsive forces based on position, DAF employs a dissipative feedback mechanism that accounts for both position and velocity, contributing to smoother and more natural obstacle avoidance. The proposed continuously differentiable controller solves the motion-to-goal problem while guaranteeing collision-free navigation by using the robot’s state and local obstacle distance information. We show that the controller guarantees safe navigation in generic n-dimensional environments and that all undesired ω-limit points are unstable under certain controlled curvature conditions. Designed for real-time implementation, DAF requires only locally measured data from limited-range sensors (e.g., LiDAR, depth cameras), making it particularly effective for robots navigating unknown workspaces. Simulations in 2D and 3D environments are conducted to validate the theoretical results and to showcase the effectiveness of our approach. © 2024, CC BY.

关键词： Feedback

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Transformative Technology in Poultry Management: 3D CNNs for Broiler Chicken Weight Prediction

Transformative Technology in Poultry Management: 3D CNNs for...

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Cognitive Robotics and Intelligent systems (ICC - ROBINS), International Conference on

作者： Anuprabha K Vishnu Kumar Kaliappan Gangadhar Baniekal Hiremath Department of Computer Science Engineering KPR Institute of Engineering and Technology Coimbatore Tamilnadu India Wasp Autonomous Systems Pvt Ltd Ballari Karnataka India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

In global food production, broiler chickens hold significant importance, which are traditionally managed through manual labour for both meat and egg production. However, the poultry farming industry has increasingly turned to automation to address challenges associated with manual labour and enhance efficiency. This research introduces a novel methodology for estimating chicken weight utilizing top-view videos as input and leveraging 3D Convolutional Neural Networks (CNNs). Accurate weight assessment is a critical aspect of poultry farming, influencing feeding strategies and resource allocation. Traditional manual weighing methods are laborious and time-intensive, necessitating the exploration of automated alternatives. The methodology involves dataset collection, preprocessing, and employing 3D CNNs for effective feature extraction. 3D CNN architecture, adept at capturing spatial and temporal features, consists of convolutional and pooling layers, followed by flatten and dense layers for weight prediction. The model achieves a remarkable accuracy of 95%, as evidenced in the experimental results. Experimental validation showcases the potential of this method as a non-invasive and efficient tool for estimating chicken weight. It contributes to the growing field of automated livestock management in agriculture.

关键词： Three-dimensional displays Sociology Production Manuals Feature extraction Robustness Convolutional neural networks

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Redundant Weighted Clustered Scheme with Dynamic Weights Adjustment for Damaged S-UAV

Redundant Weighted Clustered Scheme with Dynamic Weights Adj...

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IEEE International Conference on Communications (ICC)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Evangelos K. Markakis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science University of Cyprus Cyprus Department of Electrical & Electronic Engineering Science University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Electrical and Computer Engineering Hellenic Mediteranean University Estavromenos Heraklion Greece

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Swarm of UAVs (S-UAVs) is the term used to describe an unplanned gathering of un-manned aerial vehicles (UAVs) that collaborate to complete predefined missions. Any UAV has a risk of being damaged in crisis situations like fire. Clustering is one of the most often used routing algorithms in S-UAVs. The clustering scheme divides the UAVs into clusters, with a cluster head (CH) and cluster members (CM) in each cluster. The CH plays a critical role within inter-cluster communication, and because of this, its selection is an ongoing area of study. A clustered weighted scheme with redundancy and dynamic weight adjustment is proposed in this paper. The selection of the principal CH, redundant CHs, and CMs is based on a weighted formula composed of distance, speed, and rewarding index. Whenever the primary CH is damaged, the redundant CH takes its place immediately. After the first clustering process, the proposed scheme dynamically and autonomously adjusts the weights to optimize the UAV role selection. According to the outcomes of the carried-out simulation, this is a promising scheme that reduces data loss in a crisis-case scenario and optimizes the time delay through the dynamic adjustments of the weights.

关键词： Delay effects Redundancy Clustering algorithms Routing Autonomous aerial vehicles Indexes Vehicle dynamics

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

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Networking and Communications (ICNWC), International Conference on

作者： S. Jhansi Ida K. Balasubadra Skandarsini R R Lakshmi Narayanaa T Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India Department of Information Technology RMD Engineering College Thiruvallur India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model’s training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model’s versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model’s adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities.

关键词： Training Adaptation models Sequential analysis Credit cards Feature extraction Harmonic analysis Real-time systems

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Multi-Task Learning for Material Property Prediction

Multi-Task Learning for Material Property Prediction

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International Conference on Machine Learning and Applications (ICMLA)

作者： C. M. A. Rahman Nishat B. Alam Amr S. El-Wakeel JuHyeong Ryu P. K. Gyawali Lane Department of Computer Science and Electrical Engineering (LCSEE) West Virginia University Department of Industrial and Management Systems Engineering (IMSE) West Virginia University

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Material property prediction is a critical task within material science and other related fields where the identification of advanced materials with dynamic characteristics is essential for developing innovative technologies, improving product performance, and driving scientific discoveries. The search for suitable candidate materials aimed at specific applications is traditionally conducted through time-consuming experimental methods. Conversely, computational approaches, such as density functional theory (DFT) calculations, necessitate the resolution of complex mathematical equations and significant computational resources. Machine learning (ML) models have significantly transformed this field by automating the tedious process of material searching within extensive search spaces. However, these models are unable to incorporate diverse features of varying dimensions for different materials and properties. Recently, graph convolutional neural networks (GCNNs), have been employed to analyze the complex structures of materials, thereby efficiently predicting their associated properties. In this work, we demonstrate the potential of multi-task learning (MTL) in conjunction with the GCNNs in material property prediction. Our findings suggest that the MTL framework integrated into the GCNN architecture such as Crystal Graph Convolutional Neural Network (CGCNN) and its advanced variant Orbital Graph Convolutional Neural Network (OGCNN) can enhance the generalization and efficacy of property predictions. Notably, we achieved a maximum improvement of 7.95 % in one of our setups, underscoring the potential of these models to handle multiple property predictions with considerable effectiveness.

关键词： Space vehicles Photonic band gap Computational modeling Machine learning computer architecture Predictive models Multitasking Mathematical models Convolutional neural networks Material properties

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Hybrid Approach of TabNet and Transformer-XGBoost for Predicting Traffic Flow in Smart Cities

Hybrid Approach of TabNet and Transformer-XGBoost for Predic...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Shreekant Salotagi Yagnesh Challagundla Sudeep Mungara Mude Nagarjuna Naik Computer Science & Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Computer Science & Engineering Dayananda Sagar University Bangalore India Department of Computer science Herbert Wertheim College of Engineering University of Florida Gainesville Florida USA Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions.

关键词： Measurement Hands Adaptation models Accuracy Smart cities Transportation Tail Predictive models Transformers Junctions

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