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Control of Software-Defined Networks of Unmanned Aerial Vehicles using Distributed Deep Learning 3

Control of Software-Defined Networks of Unmanned Aerial Vehi...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Hauider Abbas, Syed Guru Vimal Kumar, M. Lekha, D. Geethamahalakshmi, G. Sesha Vidhya, S. Deepak, A. Integral University Cse Lucknow India Veltech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Cse India Rmk College of Engineering and Technology Cse India Easwari Engineering College Ramapuram Chennai India Rmk College of Engineering and Technology Ece Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Ece India

ISBN: (纸本)9781665462167

There are a variety of civilian, public, and military applications that might be developed for drones. Because they come equipped with their own communications infrastructure, they may be remotely controlled from a distance. Unmanned Aerial Vehicles (UAVs) are gaining popularity for its utilization in a range of activities due to their low cost, versatility, ease of deployment, and the ability to replace manually-operated aircraft in many situations. These vehicles are capable of performing a wide range of activities, such as monitoring, managing crowds, providing wireless coverage, and surveillance. Unmanned Aerial Vehicles (UAVs), often known as drones have the ability to offer solutions that are not only trustworthy but also economical for addressing a wide range of real-time challenges. With the inherent characteristics such as mobility, flexibility, and compatibility in terms of communications, UAVs are able to provide a wide range of services. The ability to monitor a particular area and the flexibility to react to changing demands for services proves the effectiveness of deploying Unmanned Aerial Vehicles (UAVs). As a result, deep learning, also known as DL, is utilized in an increasingly broad manner to overcome the challenges that UAVs face in terms of connectivity and resource utilization. © 2023 IEEE.

关键词： Drones

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Neural Network-Based Adaptive Tracking Control for Marine Lifting Arm System 17th

Neural Network-Based Adaptive Tracking Control for Marine Li...

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17th international conference on Intelligent robotics and applications

作者： Wang, Kai Ma, Xin He, Xin Zhang, Lei Yang, Xue Shandong Univ Minist Educ Engn Res Ctr Intelligent Unmanned Syst Ctr RobotSch Control Sci & Engn 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Marine Grp Ltd 7000 Jingshi Rd Jinan 250022 Peoples R China Shandong Marine Grp Technol Co Ltd 300 Changjiang Rd Yantai 264006 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

The marine lifting arm system is a novel type of lifting equipment for the dismantling of large offshore platforms. In practical application, the marine lifting arm system is commonly subject to unexpected disturbances such as persistent vessel-induced disturbances, uncertain dynamics, and physical constraints, posing significant challenges for controller system design. This paper proposes a neural network (NN) based adaptive tracking control method with state constraints for the marine lifting arm system to effectively compensate for vessel motions induced by wave disturbances. First, the dynamic model of a marine lifting system is established by employing Lagrange's method. Subsequently, a nonlinear controller is developed based on the dynamic model to compensate for 3-DOF vessel motions, including heave, roll, and pitch motion. By constructing constrained terms, the nonlinear controller effectively restricts the lifting arm's motion within the predefined range, ensuring the safety of lifting operations even under adverse sea conditions. The neural network is employed to estimate parameter/structure uncertainties and the nonlinear input dead zones for lifting arm systems. In particular, the designed update law of the neural network considers physical constraints, thereby further ensuring that the lifting arm operates within the safe range. The theoretical stability of the proposed control system is rigorously proven using the Lyapunov technique. Finally, the practicality and effectiveness of the proposed method are verified by the hardware experiments on a self-built offshore lifting system.

关键词： Marine lifting system Tracking control Nonlinear controller State constraint

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Leveraging AI Algorithms for Energy Efficiency: A Smart Energy System Perspective 3

Leveraging AI Algorithms for Energy Efficiency: A Smart Ener...

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3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Dong, Jie Gao, Jie Yu, Jinyun Kong, Linling Jiang, Nian Wu, Qian State Grid Zhejiang Shaoxing Power Supply Company China Technology Co. Ltd. Hangzhou Branch China Technology Co. Ltd. China

ISBN: (纸本)9781643684444

The rapid progress of artificial intelligence (AI) algorithms has opened up new opportunities for optimizing energy consumption and promoting sustainable practices in intelligent energy systems. Artificial intelligence algorithms can analyze energy usage patterns and user behavior patterns, further providing support for load balancing, demand side management, and power grid stability optimization calculations, and ultimately providing recommendations for energy-saving practices. This article explores the application of artificial intelligence algorithms in various stages of energy management and optimization from the above three aspects, discusses the models and implementation steps of mainstream artificial intelligence algorithms in each stage, and provides the challenges of utilizing artificial intelligence algorithms in energy systems in the conclusion. © 2023 The Authors.

关键词： Energy utilization

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Dynamic Task Subspace Ensemble for Class-Incremental Learning 1

引用

3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Zhang, Weile He, Yuanjian Cong, Yulai Sun Yat Sen Univ Shenzhen 518107 Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

Deep-learning models are expected to continually learn new concepts without forgetting old ones in real-world applications with shifting data distributions. However, the notorious catastrophic forgetting often occurs. Recently, methods based on task subspace modeling have been developed to address this issue by gradually adding new subspaces to learn new concepts. In this paper, we reveal that such task-subspace-modeling methods may suffer from the inter-task confusion issue, leading to degraded performance in challenging class-incremental learning settings. Concerning addressing the forgetting issue of deep learning models, we propose a two-stage framework called Dynamic tAsk Subspace Ensemble (DASE), the first stage of which involves the dynamic expansion of the extractor network for memory efficiency, while the second stage delivers dynamic learning and aggregation of diverse features. To further enhance the discriminative capacity of the aggregated features for both historical and new classes, we also introduce new feature-enhancement techniques. Experimental results demonstrate that our method achieves state-of-the-art CIL performance on natural image datasets (CIFAR-100 and ImageNet) and Synthetic Aperture Radar image datasets (MSTAR and OpenSARShip).

关键词： class-incremental learning inter-task confusion dynamic task subspace ensemble memory-efficient

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Deep Learning and Machine Learning Techniques Applied to Speaker Identification on Small Datasets 3rd

Deep Learning and Machine Learning Techniques Applied to Spe...

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3rd international conference on Optimization, Learning Algorithms and applications (OL2A)

作者： Manfron, Enrico Teixeira, Joao Paulo Minetto, Rodrigo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Associate Lab Sustainabil & Technol SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana BR-80230901 Curitiba Parana Brazil

ISBN: (纸本)9783031530357;9783031530364

In this study, we explore the capabilities of speaker recognition technology for biometric authentication developing speaker recognition-based access control systems and serving as a resource for future research and improvements in secure and efficient speaker identification solutions. We focused on developing and evaluating machine learning and deep learning models for speaker identification. The models were trained and tested on private datasets with 32 speakers and public datasets with 1251 to 6112 speakers. The Gaussian Mixture Model performed well with our private datasets, with 93,10%, and 95% accuracy in correctly identifying the speakers. The Multilayer Perceptron achieved a peak accuracy of 93.33% on the Framed Trim private dataset. The VGGM model, after initial training on larger datasets, achieved an accuracy of 90.34% and 98.33% on our private datasets. At last, the model ResNet50 slightly outperformed the other models on two versions of our private dataset, achieving accuracies of 97.93% and 100%.

关键词： Speaker Identification Convolutional Neural Network Deep Learning

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Design of Bionic robotic Hand Gesture Recognition System Based on Machine Vision 3

Design of Bionic Robotic Hand Gesture Recognition System Bas...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Li, Hui Guo, Haoqi Gannan University of Science and Technology Jiangxi Ganzhou China Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9781665459112

Gesture recognition technology is a natural human-computer interaction method with a wide range of applications, among which using bionic robotic hands to achieve gesture recognition is more meaningful and valuable for research. To improve the accuracy and the speed of gesture recognition, and also to facilitate the deployment of gesture recognition algorithm on edge device, a bionic robotic hand gesture recognition system is designed based on machine vision. Firstly, a bionic robot with six degrees of freedom based on Raspberry Pi 3B+ is chosen as the development platform of the system, including five finger degrees of freedom and one wrist degree of freedom;Secondly, a gesture recognition method is proposed based on a combination of feature extraction model, Inception v3 network, and migration learning, and it is trained and tested on a self-built gesture dataset. The results show that the method can accurately recognize gesture types with an average accuracy of 0.92;Finally, the trained model is deployed on a Raspberry Pi 3B+ bionic manipulator and tested on a physical system, and the average recognition speed is 0.17s after the system is stabilized, which shows that the system can effectively recognize and imitate hand gesture. © 2022 IEEE.

关键词： Degrees of freedom (mechanics)

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Exploring the Privacy Horizons: A Survey on HCI & HRI 24th

Exploring the Privacy Horizons: A Survey on HCI & HRI

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24th international conference on Computational Science and Its applications (ICCSA)

作者： Saporito, Antonio Tabari, Parinaz De Rosa, Mattia Fuccella, Vittorio Costagliola, Gennaro Univ Salerno Dept Informat Via Giovanni Paolo II I-84084 Fisciano SA Italy

ISBN: (纸本)9783031653179;9783031653186

With the constant evolution of technology and the rise of Human-Computer Interaction (HCI) and Human-robot Interaction (HRI), Human-technology Interaction (HTI) is becoming more important in everyday life, covering several routines closely linked to technology. In this survey, we aim to examine the important domain of user privacy in the contexts of HCI and HRI. Additionally, we analyze privacy issues, ranging from data collection to ethical and legal implications. Through a detailed analysis, we also highlight challenges and strategies for designing interactive systems that prioritize end-user privacy. This survey aims to provide a clear and comprehensive overview of the challenges and opportunities of integrating privacy into interactive systems. Finally, we will present the different strategies and techniques to be used, such as encryption and anonymization, to improve the level of privacy protection.

关键词： Human-Computer Interaction HCI Human-robot Interaction HRI Human-technology Interaction HTI User Privacy Ethics Artificial intelligence

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robotics Arm Controller with Fuel Cell-Based Stepper Motor Drive Using Double-Ended Forward Converter 3rd

Robotics Arm Controller with Fuel Cell-Based Stepper Motor D...

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3rd international conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2024

作者： Kanchana, K. Chintamani, B. Kavitha, S. Priya, R. Karpaga Sinthia, P. Malathi, M. Department of Electrical and Electronics Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819737444

robotics is a rapidly evolving technology essential in many industries and a significant pillar for all the following technologies. Selecting the best motor for the ideal robot is a problematic issue if one is developing a robot, especially for industry. Several criteria for arms control, location, and angular and linear movements must be considered when choosing electrical motors for industrial robots. Choosing the suitable motor is a crucial decision in the design of robots that requires careful consideration of factors such as the robot’s weight, wheel size, and intended use. Torque, acceleration, and speed must all be considered during the design stage. Industrial robots typically use a range of motor types for various functions. The proposed work uses a stepper motor for high-torque robotic applications and more precise angular movement. The double-ended forward converter is used to operate the stepper motor. For current regulation, loss minimization, and low-power applications, the suggested converter is strongly recommended. This converter increases functionality and has buck. The technology’s source is also mentioned: fuel cells. A mathematical model of a double-ended forward converter is presented in this paper. The suggested converter’s prototype model’s outcomes are compared and verified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Stepping motors

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Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 IEEE.

关键词： K-means clustering

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