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IEEE International Conference on robotics and Automation (ICRA)

作者： Kangjie Zhou Pengying Wu Yao Su Han Gao Ji Ma Hangxin Liu Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper proposes an informative trajectory planning approach, namely, adaptive particle filter tree with sigma point-based mutual information reward approximation (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.

关键词： Accuracy Target tracking Trajectory planning Robot sensing systems Real-time systems Particle filters Trajectory

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Probabilistic Visibility-Aware Trajectory Planning for Target Tracking in Cluttered Environments

Probabilistic Visibility-Aware Trajectory Planning for Targe...

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American Control Conference (ACC)

作者： Han Gao W. Pengying S. Yao Kangjie Zhou M. Ji Hangxin Liu Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI)

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Target tracking has numerous significant civilian and military applications, and maintaining the visibility of the target plays a vital role in ensuring the success of the tracking task. Existing visibility-aware planners primarily focus on keeping the target within the limited field of view of an onboard sensor and avoiding obstacle occlusion. However, the negative impact of system uncertainty is often neglected, rendering the planners delicate to uncertainties in practice. To bridge the gap, this work proposes a model predictive control (MPC)-based trajectory planner for real-time visibility-aware and safe target tracking in the presence of system uncertainty. For more accurate target motion prediction, we introduce the concept of belief-space probability of detection (BPOD) to measure the predictive visibility of the target under stochastic robot and target states. An Extended Kalman Filter variant incorporating BPOD is developed to predict target belief state under uncertain visibility within the planning horizon. To reach real-time trajectory planning, we propose a computationally efficient algorithm to uniformly calculate both BPOD and the chance-constrained collision risk by utilizing linearized signed distance function (SDF), and subsequently solve the MPC problem by sequential convex programming. Extensive simulation results with benchmark comparisons show the capacity of the proposed approach to robustly maintain the visibility of the target under high system uncertainty. The practicality of the proposed trajectory planner is validated by real-world experiments.

关键词： Uncertainty Target tracking Trajectory planning Stochastic systems Simulation Stochastic processes Robot sensing systems

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Segmentation of digital rock images using texture analysis and deep network

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Arabian Journal of Geosciences 2023年第7期16卷 1-10页

作者： Manzoor, Shahzad Qasim, Tehreem Bhatti, Naeem Zia, Muhammad Department of Electronics Quaid i Azam University Islamabad Pakistan Department of Robotics and Artificial Intelligence SZABIST Islamabad Pakistan

Image segmentation is an important part of the standard digital rock physics (DRP) workflows. In this paper, we present ground-truthing of digital rock images using texture analysis. We propose a deep learning–based approach for automated segmentation which is validated using the extracted ground-truth. To generate the ground-truth, we utilize the a priori knowledge of minerals and pores. We perform texture based iterative intensity thresholding to calculate the porosity and mineral composition. The ground-truth is generated for the berea sandstone image dataset and the grosmont carbonate image dataset. Results obtained from the experiments conform to the porosity and composition ranges provided in the literature. For the purpose of automated segmentation, we use a SegNet deep network which performs pixel-wise segmentation of digital rock images into pores and minerals. We train and test the SegNet using 1024 images of berea sandstone dataset ground-truthed using iterative intensity thresholding. We utilize a split of 90% and 10% for training and testing, respectively. Experimental outcomes show that the SegNet-based segmentation results for the minerals such as clay, ankerite, and zircon are better than the existing methods.

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Effect of Brass Content and Speed of Cut on Machining Outcomes of Copper–2% Silver Alloy

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Journal of The Institution of Engineers (India): Series D 2024年 1-7页

作者： Prashanth, Mrudula Prasad, N. J. Krishna Kumar, B. S. Ajay Satish, N. Department of Industrial Engineering and Management Bangalore Institute of Technology Bengaluru Visveswaraya Technological University Belgaum India Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Mechanical Engineering Bangalore Institute of Technology Bengaluru Visveswaraya Technological University Belagavi Karnataka India Department of Robotics and Artificial Intelligence Bangalore Institute of Technology Bengaluru Visveswaraya Technological University Belagavi India

Copper is one of the ductile materials which is difficult to machine, but extensively used in variety of applications such as electrical, thermal and nuclear. Turning is one of the predominantly used lathe operation, and the current work focuses on turning different alloys of copper at various compositions of alloying elements to investigate the effect of the machining parameter, i.e. speed of cut on cutting force, thrust force, unit power consumption and machinability which marks the effect as machining outcomes. The work also focuses on the effect of the alloys formed out of varying compositions of brass on the machining outcomes of copper–2wt% silver alloy. The work concludes on, the forces exerted on tool, unit power consumption and machinability to be least at 9% brass addition to copper silver alloy. © The Institution of Engineers (India) 2024.

关键词： Brass

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Measuring Semantic Similarity of Persian Sentences Using ParsBERT Model

Measuring Semantic Similarity of Persian Sentences Using Par...

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Conference on artificial intelligence and robotics and Asia-Pacific International Symposium

作者： Ali Zareshahi MohammadAli Javadzade Esmaeel Bastami Department of Artificial Intelligence and Robotics Faculty and Research Institute of Artificial Intelligence and Cognitive Sciences Imam Husain (AS) University Tehran Iran Imam Husain (AS) University Tehran Iran

Semantic similarity is a subset of natural language processing in artificial intelligence that examines the similarity between two texts in terms of meaning. It has various applications, such as plagiarism detection, document similarity checks, encyclopedias, question-answering systems, search engines, and chatbots. To address the need for updated semantic similarity models, we used the ParsBERT language model as a base model. It is trained on Persian data and converts tokenized sentences into meaningful vectors. Through pooling layers, optimized vectors are extracted, resulting in 768-dimensional vectors for each sentence. Cosine similarity is then used to calculate the similarity between the two vectors. The results show that the proposed model achieved a Pearson correlation coefficient of approximately 0.82, surpassing previous models. The best coefficient achieved by previous implementations was around 0.77, using a combination of FastText text embeddings and a CNN model. The proposed model’s higher coefficient indicates its superior performance compared to previous models.

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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1716-1729页

作者： Weiliang Zuo Jingmin Xin Changnong Liu Nanning Zheng Akira Sano IEEE the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence the National Engineering Research Center for Visual Information and Applicationsand the Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’an 710049China the Data Center China Construction BankBeijing 100033China the Department of System Design Engineering Keio UniversityYokohama 223-8522Japan

Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still *** paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost *** establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is *** the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.

关键词： Capon beamformer direction-of-arrival(DOA)estimation large-sample mean-squared-error(MSE) subspace-based methods uniform linear array

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iDrone: IoT-Enabled Unmanned Aerial Vehicles for Detecting Wildfires Using Convolutional Neural Networks

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SN Computer Science 2022年第3期3卷 1-13页

作者： Sethuraman, Sibi Chakkaravarthy Tadkapally, Gaurav Reddy Mohanty, Saraju P. Subramanian, Anitha School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States School of Electronics Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India

The rise of global temperatures, over the past few decades, has disrupted the usual balance of nature. As a result of increasing temperatures, wildfires have destroyed millions of acres of land, thousands of structures, and homes. The pollution and toxic gases produced by the wildfires are carried out to thousands of miles, thus threatening the lives all around the world. Most wildfires occur due to anthropogenic factors, which cannot be predicted solely based on climate conditions. Henceforth, to detect wildfires before escalating, we propose iDrone, which is a wildfire detection system equipped with an end-to-end CNN image classification model: XtinguishNet, trained on a wildfire imagery dataset to detect the possible flames or smokes in an image. In addition, our approach also acquires the weather data and the intensity of the fire. Contrasting with existing wildfire detection systems, our proposed solution is a fusion of the Internet of Things (IoT) and Deep Learning, aiming to provide a one-stop solution for all the needs required to minimize the damage caused by wildfires. When validated and tested using various benchmark datasets, video surveillance, iDrone acquired a high accuracy of 98.36% with the least computational power. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Computer vision Deep learning Forest fires iDrone Wildfires

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Tackling Privacy Heterogeneity in Federated Learning 21

Tackling Privacy Heterogeneity in Federated Learning

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Xu, Ruichen Zhang, Ying-Jun Angela Huang, Jianwei The Department of Information Engineering The Chinese University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of HongKong Shenzhen518172 China

ISBN: (纸本)9783903176553

Differentially private federated learning enables clients with privacy concerns to collaboratively train a model while preserving their privacy. Clients’ locally available data and maximum tolerable privacy budgets will affect their contributions to the training performance. To date, existing studies focus on homogeneous privacy budgets and thus there lack systematic studies regarding the impact of clients’ diverse privacy budgets (privacy heterogeneity). This paper represents the first step toward filling this gap. Through rigorous convergence analysis, we illustrate that the influence of privacy protection on training loss is affected by client selection probabilities. In addition, client selection and privacy protection together induce a non-vanishing training error in federated learning. Our analysis then shows that the non-vanishing training error is a convex function of client selection probabilities and thus allows us to formulate privacy-aware client selection as a convex optimization problem. Numerical results demonstrate that the privacy-aware client selection strategy can significantly improve learning performance. For example, compared with unbiased selection, the privacy-aware client selection strategy decreases the test loss by up to 63% on MNIST convolutional neural network classifier. © 2023 IFIP.

关键词： Budget control

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Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images

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Data intelligence 2023年第4期5卷 1008-1032页

作者： Abdullahi Umar Ibrahim Ayse Gunnay Kibarer Fadi Al-Turjman Department of Biomedical Engineering Near East UniversityNicosiaMersin 10Turkey Research Center for Al and loT Faculty of EngineeringUniversity of KyreniaMersin 10Turkey Artificial Intelligence Engineering Dept. Al and Robotics InstituteNear East UniversityMersin 1Turkey

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis *** can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to *** challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high *** this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet *** study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle *** classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 *** classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 *** result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.

关键词： Tuberculosis Deep Learning Pretrained AlexNet Chest X-ray Microscopic slide

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3D-modeling techniques for object recognition based on point cloud data

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JMST Advances 2024年第3期6卷 329-340页

作者： Kang, Chang Ho Kim, Sun Young Department of Artificial Intelligence and Robotics Sejong University Seoul Republic of Korea School of Mechanical Engineering Kunsan National University Gunsan Republic of Korea

This study examines 3D-modeling techniques for object recognition using point cloud data and evaluates the performance of key deep learning-based semantic segmentation techniques. This study compared AI structure for point clouds such as PointNet, PointNet++, PointCNN, and DGCNN (dynamic graph CNN). The comparison confirmed that each technique offers different approaches to effectively manage the characteristics and spatial structure of point cloud data. Notably, DGCNN demonstrated high segmentation performance through dynamic graph convolutional networks. This study provides essential foundational data that can contribute to advancing disaster response technology and is expected to significantly aid disaster response agencies in making quick and accurate decisions.

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