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Wireless Sensor Network-Driven IoT Solution for Advanced Borewell Rescue robotics 4

Wireless Sensor Network-Driven IoT Solution for Advanced Bor...

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4th IEEE international conference on Emerging Techniques in Computational intelligence, ICETCI 2024

作者： Meera, V. Mercia, Sharon Nesa PadmaPriya, J. Murugavalli, S. Panimalar Engineering College Artificial Intelligence And Data Science Chennai City Campus Chennai India Panimalar Engineering College Computer Science Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai City Campus Chennai India

ISBN: (纸本)9798350387063

The fact that deaths from bore wells persist in India is highly alarming, particularly when young people are involved. Since 2009, there have been more than 40 documented child deaths, and the National Disaster Response Force (NDRF) reported a worrisome 70% failure rate for rescue efforts. The solution must be found quickly and effectively. In response to this humanitarian problem, this paper has proposed a novel design for a robotics device. This proposed system is a creative illustration of how human interaction and technology may be used in tandem to swiftly evaluate the victims and guarantee their safe release. The main element of this solution is a skillfully designed robotic arm that smoothly integrates Internet of Things (IoT) devices (Rasp Pi or Arduino) and is powered by high-precision servo motors. Among the many features it offers are an advanced APR (Auto Playback Recorder) system, an infrared-based CCTV camera, a strong LED lighting system, advanced sensors (Passive Infrared, Ultrasonic Distance Measurement, Heartbeat, Temperature, and Gas), and a dual-functional mask. Artificial intelligence (AI) is another tactic that will be used in the rescue operation. This paper discusses the pressing need for safe, efficient bore well rescues and offers a comprehensive analysis of the methodology. This project uses cutting edge technology and human collaboration to stop more catastrophes and increase the speed and effectiveness of life-saving procedures in emergency situations. In an effort to contribute to the creation of a more secure and safe future, this research is presented as a critical first step in lowering the death toll related to Indian bore wells. © 2024 IEEE.

关键词： Wireless sensor networks

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Enhanced Activity Recognition Through Joint Utilization of Decimal Descriptors and Temporal Binary Motions 16th

Enhanced Activity Recognition Through Joint Utilization of D...

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16th international conference on Computational Collective intelligence (ICCCI)

作者： Gnouma, Mariem Yahia, Samah Ejbali, Ridha Zaied, Mourad Res Team Intelligent Machines Gabes Tunisia Modelisat Anal & Control Syst Gabes Tunisia Univ Gabes Natl Engn Sch Gabes St Omar Ibn El Khattab Gabes 6029 Zrig Tunisia

ISBN: (纸本)9783031708183;9783031708190

Human Action Recognition (HAR) has been a prominent area of research within machine learning over the last few decades. Its applications span domains such as visual surveillance, robotics, and pedestrian detection. Despite the numerous techniques introduced by computer vision researchers to address HAR, persistent challenges include dealing with redundant features and computational cost. This paper specifically addresses the challenge of silhouette-based human activity recognition. While previous research on silhouette-based HAR has predominantly focused on recognition from a singular perspective, the aspect of view invariance has often been overlooked. This paper presents a novel framework that aims to achieve view-invariant Human Action Recognition. The proposed approach integrates a pre-processing stage based on the extraction of multiple 2D Differential History Binary Motions (DHBMs) from spatio-temporal frames capturing human motion. These multi-batch DHBMs are then used to capture and analyse human behaviour using the Decimal Descriptor pattern (DDP) approach. This strategy enhances the extraction of intricate details from image data, contributing to a more robust HAR methodology. The selected features are processed by the Sparse Stacked Auto-encoder (SSAE), a representative of deep learning methods, to provide effective detection of human activity. The subsequent classification is performed using Softmax. The experiments are conducted on publicly available datasets, namely IXMAS and KTH. The results of the study demonstrate the superior performance of our methodology compared to previous approaches, achieving higher levels of accuracy.

关键词： Human activities recognition Differential History of Binary Motion Decimal Descriptor pattern Deep Learning

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Stacked Multi-Head Cross Attention for Image Recognition 24

Stacked Multi-Head Cross Attention for Image Recognition

引用

1st international conference on Image Processing Machine Learning and pattern Recognition

作者： Liu, Shuaichen Du, Xiao Wu, Tieru Jilin Univ Sch Artificial Intelligence Changchun Jilin Peoples R China Jilin Univ Sch Math Changchun Jilin Peoples R China

ISBN: (纸本)9798400707032

Long-range dependency plays a critical role in extracting intricate image features particularly in tasks involving image recognition. In previous study, the significance of long-range positional dependencies has been proved in both image classification and image segmentation. Based on this, we introduce a Multi-Head Cross Attention module, namely MHCA, along with four different operators, which are designed to capture and integrate contextual information at every pixel position within feature maps, spanning both horizontal and vertical directions, with parallel fashion, thus can transfer information and share weights across multiple heads of features. Moreover, by stacking our module twice, forming MHCA(2) layer, the whole context of each pixel in feature can be captured, with more lighter computation burden than general full connection or Non-local networks, and it is designed to be seamlessly plugged into existing network architectures. By replacing specific convolution layer in convolutional networks with a MHCA(2) layer, we construct MHCA network. Through extensive experiments upon various datasets, we demonstrate the validity of our approach. Furthermore, comparative analysis with similar methodologies highlight the superior performance of our method.

关键词： Cross Attention Image Classification Image Segmentation Long-range Dependency Lightweight Architecture

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A Bi-LSTM and AutoEncoder Based Framework for Multi-step Flight Trajectory Prediction 8

A Bi-LSTM and AutoEncoder Based Framework for Multi-step Fli...

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8th international conference on Control and robotics Engineering (ICCRE)

作者： Wu, Han Liang, Yan Zhou, Bin Sun, Hao Northwestern Polytech Univ Sch Automat Xian Peoples R China

ISBN: (纸本)9798350345650

Trajectory prediction (TP) is a key component in the route planning for civil aircraft. Most existing methods obtain multi-step TP via iterating the one-step TP model, which generally generates large cumulative error due to deviate from the original evolutionary pattern. To improve the situation, this paper proposes a multi-step TP framework with three modules: the Bi-directional Long Short-Term Memory Network (Bi-LSTM) based multi-step TP module, AutoEncoder based multi-step TP module, and voting fusion module. In the Bi-LSTM based multistep TP method, to avoid the forgetting of evolutionary characteristics, the Bi-LSTM is designed to directly extract the mapping relationship between input of historical trajectory fragments and output of multi-step labels via data- driven method. In the AutoEncoder based multi-step TP module, the Bi-LSTM is deigned to learn mapping relationship between the input and core evolutionary features from output labels extracted via the encoder, and then the decoder is adopted to reconstruct predictions by outputs from Bi-LSTM. Third, the voting method was used to fuse the per-dimension predictions from the above two modules and further to refine multi-step predictions. The proposed multi-step TP framework is applied to real flight trajectory prediction of civil aircraft and outperforms multiple deep learning methods in the terms of RMSE and MAE.

关键词： multi-step trajectory prediction AutoEncoder BiLSTM time series analysis

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Tumor Segmentation Algorithm for Liver CT Images Based on Generative Adversarial Networks 6

Tumor Segmentation Algorithm for Liver CT Images Based on Ge...

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6th IEEE international conference on pattern Recognition and Artificial intelligence, PRAI 2023

作者： Zhen, Hu Jinzhi, Zhou Lili, Guo Li, Gong Southwest University of Science and Technology Sichuan Provincial Key Laboratory of Robotics for Special Environment Mianyang China

ISBN: (纸本)9798350325485

An automatic segmentation algorithm based on a generative adversarial network was proposed to solve the problems of insufficient segmentation, fuzzy segmentation, boundary ambiguity, and low segmentation accuracy in the existing liver tumor CT image segmentation methods. The algorithm first performs liver pre-segmentation on CT images to reduce the influence of irrelevant information. Secondly, the generation network of GAN uses Dual Attention Unet (DAUnet), which introduces a dual attention mechanism in the jump connection to enhance the characteristics of liver tumors. Finally, the prediction accuracy of tumor images is improved by GAN-generated adversarial training, and a mixed loss function is introduced in the training process. Finally, experimental results on LiTS data show that the proposed algorithm's Dice similarity coefficient (DSC) value reaches 76.15%, 3.63% higher than that of the Unet algorithm. DAUnet can effectively improve the performance of tumor segmentation in liver images by generating adversarial training. © 2023 IEEE.

关键词： Generative adversarial networks

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Control Design Based on EKF via Pigeon-Inspired Optimization with pattern Search for UAV Close Formation Flight

Control Design Based on EKF via Pigeon-Inspired Optimization...

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2023 IEEE international conference on robotics and Biomimetics, ROBIO 2023

作者： Yuan, Guangsong Duan, Haibin Luo, Delin State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical Engineering Beijing100083 China Xiamen University School of Aerospace Engineering Xiamen361102 China

ISBN: (纸本)9798350325706

An unmanned aerial vehicle (UAV) close formation controller based on extended kalman filter (EKF) has been designed in this paper. The Newton-Raphson iterative method is designed for extremum location estimation, namely extremum seeking control (ESC). To facilitate extremum position estimation, the performance function is approximated using first and second-order Taylor expansions. The EKF is still utilized to estimate the gradient and Hessian matrix of the parameterized performance function. Compared with traditional estimation strategy, this allows a faster convergence and smooth command reference for close formation controller. The pigeon-inspired optimization (PIO) with pattern search (PS) is proposed to optimize these free parameters of the EKF. The simulation results show that the proposed PIO with pattern search has strong optimization performance. The EKF optimized by the algorithm enables the controller to converge well to the optimal relative position. © 2023 IEEE.

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Automated Vision-based Bolt Sorting by Manipulator for Industrial Applications 20

Automated Vision-based Bolt Sorting by Manipulator for Indus...

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IEEE 20th international conference on Automation Science and Engineering (CASE)

作者： Prakash, Surya S. K. Shukla, Amit Pandya, Naisarg Jha, Shirish Shekhar IIT Mandi Sch Mech & Mat Engn Suran Himachal Prades India IIT Mandi Chairperson Ctr Artificial Intelligence & Robot Suran Himachal Prades India IIT Mandi Ctr Artificial Intelligence & Robot Suran Himachal Prades India

ISBN: (纸本)9798350358513;9798350358520

In industrial automation, nuts and bolts are key components that facilitate assembly processes. Estimating their sizes and sorting them accordingly would be a significant advancement for automation in industrial settings. This paper introduces a distinct approach by amalgamating deep learning based segmentation with classical vision for automated bolt sorting in unstructured industrial environment. We evaluated advanced deep learning algorithms PPLiteSeg(STDC I&II), OCRNet, and DeepLabV3+ on our custom bolt dataset. DeepLabV3+ achieved the best performance over the other models in accurate bolt segmentation, with a remarkable mean Intersection over Union (mIoU) of 0.95. Custom bolt dataset is prepared considering various environmental conditions and included both new and aged bolts. We present a comparative analysis of the segmentation results for each learning model mentioned. To estimate bolt size, we integrated the segmented binary image with depth information from Intel RealSense D415 camera. Through a series of experiments, we successfully localized bolts (both position and orientation) relative to robot's base and sorted them according to their size by using Kinova Gen3 Lite robotic arm. The proposed approach has the potential to significantly advance the automation for assembly lines and warehouse management within industries, particularly when dealing with small objects.

关键词： Robotic arms

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A Passivity-based Approach on Relocating High-Frequency Robot Controller to the Edge Cloud

A Passivity-based Approach on Relocating High-Frequency Robo...

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IEEE international conference on robotics and Automation (ICRA)

作者： Chen, Xiao Sadeghian, Harnid Chen, Lingyun Troebinger, Mario Swirkir, Abadalla Naceri, Abdeldjallil Haddadin, Sami Tech Univ Munich Munich Inst Robot & Machine Intelligence Munich Germany Univ Isfahan Fac Engn Esfahan *** Iran Omar Al Mukhtar Univ OMU Ctr Tactile Internet Human Loop CeTI Albaida Libya Omar Al Mukhtar Univ OMU Dept Elect & Elect Engn Albaida Libya

ISBN: (纸本)9798350323658

As robots become more and more intelligent, the complexity of the algorithms behind them is increasing. Since these algorithms require high computation power from the onboard robot controller, the weight of the robot and energy consumption increases. A promising solution to tackle this issue is to relocate the expensive computation to the cloud. In this pioneering work, the possibility of relocating a state-of-the-art nonlinear control is investigated. To this end, the Unified Force-Impedance Controller (UFIC) is relocated to a remote location and high frequency feedback loop is established by including the remote controller in the loop. Passivity analysis is used to ensure the stability of the whole system, comprising the robot in interaction with the environment, the communication channel, as well as the remote controller. The instability associated with the communication channel is resolved by Time Domain Passivity Approach (TDPA). The performance of the proposed framework is experimentally evaluated on a robot arm in interaction with the environment. The results illustrate the stability of the system to a time-varying delay of up to 50 +/- 10ms.

关键词： Energy utilization

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analysis of the application of AI Intelligent System in Land Use Control 24

Analysis of the application of AI Intelligent System in Land...

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3rd international Symposium on robotics, Artificial intelligence and Information Engineering, RAIIE 2024

作者： Yang, Xianhong Wu, Yue Yang, Kun Lei, Jianming The Natural Resources Technology Information Center of Guizhou Province Guiyang550004 China School of Geography and Environment School of Karst Science Guizhou Normal University Guiyang550001 China

ISBN: (纸本)9798400718311

With the acceleration of urbanization and the continuous growth of population, land resources are increasingly tight, and land use control has become an important means to ensure the sustainable development of cities and the rational utilization of land resources. Traditional land use control methods often rely on manual judgment and decision-making, and have problems such as low efficiency and strong subjectivity. The emergence of AI intelligent systems provides a new solution for land use control. AI technology, with its strong data processing capabilities and predictive analysis capabilities, shows great potential in the field of land use control. This paper introduces the technical support system of land use control, analyzes the current situation and necessity of land use control system in China, and then discusses the application of AI intelligent system in land use control. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Artificial intelligence

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Fabrication and analysis of Pneumatically Actuated Soft Robotic Gripper with Negative Pressure

Fabrication and Analysis of Pneumatically Actuated Soft Robo...

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2025 international conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility, ADMMS 2025

作者： Sreejith, Warriar J.S. Sadique, Anwar George, Boby Government Engineering College Thrissur India

Soft-bending actuators are gaining considerable attention in robotics for handling delicate objects and adapting to complex shapes, making them ideal for biomimetic robots. Soft pneumatic actuators (SPAs) are preferred in soft robotics because to their safety and compliance characteristics. Using negative pressure for actuation, it enhances stability by reducing the risk of sudden or unintended movements, crucial for delicate handling and consistent performance. Negative pressure actuation is more energy-efficient, safe and are less prone to leakage, increasing reliability and durability. This paper involves development of a new soft pneumatic actuator design by comparing various designs and to determine its performance parameters. This paper depicts on designing, and fabricating flexible soft pneumatic actuators working under negative pressure for soft robotic applications. The material used for fabrication was liquid silicone rubber and uniaxial tensile tests were conducted to characterise the properties of materials used to fabricate the soft actuator. The design process begins with conceptualizing the gripper's geometry and layout, considering factors such as material properties, actuation mechanisms etc. Finite element analysis is then employed to evaluate the performance and behavior of the gripper under different loading conditions in negative pressure. Moulds were manufactured using rapid prototyping machine for manufacturing soft pneumatic actuators. Experimental studies were conducted and compared with simulation results. © 2025 SAE international. All Rights Reserved.

关键词： Tensile testing

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