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IEEE International Conference on Robotic Computing (IRC)

作者： Maryam Khazaei Pool Carlos Diaz Alvarenga Marcelo Kallmann Department of Computer Science and Engineering University of California Merced CA USA Amazon Robotics

ISBN: (纸本)9781665472616

Path smoothing is an important operation in a number of path planning applications. While several approaches have been proposed in the literature, a lack of simple and effective methods with quality-based termination conditions can be observed. In this paper we propose a deterministic shortcut-based smoothing method that is simple to be implemented and achieves user-specified termination conditions based on solution quality, overcoming one of the main limitations observed in traditional random-based approaches. We present several benchmarks demonstrating that our method produces higher-quality results when compared to the traditional random shortcuts approach.

关键词： Smoothing methods Optimization methods Benchmark testing Path planning Complexity theory Robots

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Tac-VGNN: A Voronoi Graph Neural Network for Pose-Based Tactile Servoing

Tac-VGNN: A Voronoi Graph Neural Network for Pose-Based Tact...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Wen Fan Max Yang Yifan Xing Nathan F. Lepora Dandan Zhang Department of Engineering Mathematics and Bristol Robotics Laboratory University of Bristol U.K. Department of Computer Science University of Bristol U.K.

Tactile pose estimation and tactile servoing are fundamental capabilities of robot touch. Reliable and precise pose estimation can be provided by applying deep learning models to high-resolution optical tactile sensors. Given the recent successes of Graph Neural Network (GNN) and the effectiveness of Voronoi features, we developed a Tactile Voronoi Graph Neural Network (Tac-VGNN) to achieve reliable pose-based tactile servoing relying on a biomimetic optical tactile sensor (TacTip). The GNN is well suited to modeling the distribution relationship between shear motions of the tactile markers, while the Voronoi diagram supplements this with area-based tactile features related to contact depth. The experiment results showed that the Tac-VGNN model can help enhance data interpretability during graph generation and model training efficiency significantly than CNN-based methods. It also improved pose estimation accuracy along vertical depth by 28.57% over vanilla GNN without Voronoi features and achieved better performance on the real surface following tasks with smoother robot control trajectories. For more project details, please view our website: https://***/view/tac-vgnn/home

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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Computational Intelligence Router of Machine and Artificial Intelligence Learning for the Expansion of Agriculture's Manufacturing Sector 3

Computational Intelligence Router of Machine and Artificial ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Raman, Ramakrishnan Alanya-Beltran, Joel Akram, Shaik Vaseem Shah, Heli Sanjay, P.C. Ponnusamy, R. Symbiosis Institute of Business Management Pune Maharashtra Pune India Universidad Tecnológica Del Perú Peru Uttaranchal University Uttaranchal Institute of Technology India Parul University Mechatronics & Robotics Pit India India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350399264

The economic sector includes agriculture as a major player. Around the world, the primary issue and hot topic is agriculture automation. As a result of the population boom, there is a huge increase in the need for both jobs and food. Farmers employed traditional techniques, which included insufficient to meet these needs. This led to the introduction of new automated techniques. These innovative techniques supplied the world's food needs while giving billions of people access to jobs. An agricultural revolution has been sparked by artificial intelligence. With the use of this technology, crop yields are now more resistant to many conditions like population increase, climate change, job troubles, and food security concerns. The primary goal of this study is to evaluate the numerous ways artificial intelligence is being used in agriculture, including the use of sensors and other tools built into drones and robots for weeding, spraying, and irrigation. The excessive use of pesticides, herbicides, water, and other resources is reduced by these technologies, and the soil fertility is maintained. They also aid in the effective use of labor, which raises productivity and quality. In order to provide a concise summary of the current state of agricultural automation, including the use of drones and robots for weeding, this study analyses the research of several researchers. Along with two automated weeding strategies, the various ways of soil water sensing are described. This study discusses the use of drones as well as the numerous spraying and crop monitoring techniques that may be utilized with them. © 2023 IEEE.

关键词： Drones

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Walking-age estimator based on gait parameters

Walking-age estimator based on gait parameters

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Tomohito Kuroda Shogo Okamoto Yasuhiro Akiyama Department of Computer Science Tokyo Metropolitan University Hino Japan Dept. Mechanical Engineering and Robotics Shinshu University Ueda Japan

Human gait motions differ depending on age. We estimated peoples’ age using kernel regression analysis with reported height and weight and representative gait parameters as explanatory variables. The samples were Japanese women aged in their 20s-70s represented in an open database. The estimated ages exhibited a correlation coefficient of 0.65 with the actual ages, and the mean absolute error was 17.7 years. Estimation errors were significantly greater for older adults than for young people. Given that the method involves gait parameters that can be measured using wearable devices, such as inertial measurement units, this technique provides an accessible way for people to independently monitor their physical health in daily life.

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Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance

arXiv

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arXiv 2023年

作者： Fiedler, David Čertický, Michal Alonso-Mora, Javier Pěchouček, Michal Čáp, Michal Department of Computer Science Faculty of Electrical Engineering CTU in Prague Czech Republic Department of Cognitive Robotics 3ME TU Delft Netherlands

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57 % compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20 % reduction in vehicle distance driven and 5 % lower average passenger travel delay compared to a system that uses insertion heuristic. © 2023, CC BY-NC-SA.

关键词： Vehicle routing

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CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting

CNN-Based Task State Estimation for Safer Automation of Oxy-...

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IEEE International Conference on Automation science and engineering (CASE)

作者： James Akl Shreedhar Kodate Berk Calli Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Data Science Program Worcester Polytechnic Institute Worcester MA USA

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our ‘Oxy-fuel Cutting Task State’ (OCTS) dataset is available at ***/10.5281/zenodo.7734951.

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Advanced Receiver Operating Characteristic Curve Analysis to Identify Outliers in Binary Machine Learning Classifications for Precision Medicine

Advanced Receiver Operating Characteristic Curve Analysis to...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Namdar, Khashayar Khalvati, Farzad Department of Diagnostic & Interventional Radiology The Hospital for Sick Children TorontoON Canada SickKids Research Institute Neurosciences & Mental Health Research Program TorontoON Canada Department of Medical Imaging University of Toronto TorontoON Canada Institute of Medical Science University of Toronto TorontoON Canada University of Toronto Department of Computer Science TorontoON Canada Department of Mechanical and Industrial Engineering University of Toronto TorontoON Canada Vector Institute TorontoON Canada

ISBN: (纸本)9798350351552

The Receiver Operating Characteristic (ROC) curve is a critical tool for binary classification analysis in medicine, with the Area Under the ROC Curve (AUROC) serving as a widely accepted metric to evaluate the performance of binary classifiers. This study conducts a comprehensive review of the ROC curve with a focus on its utility in outlier identification. We introduce a novel scoring method to rank actual positives and actual negatives within a test set, according to their impact on AUROC degradation. We bridge the scoring system with the ROC curve analysis to quantify each data point's contribution to AUROC loss. Furthermore, we introduce the IMICS ROC Analyzer, a graphical user interface-based software, embedded with our innovative algorithms. Through the use of an open-source prostate cancer dataset, we illustrate the application of our algorithms for practical outlier detection in binary classification tasks. The IMICS ROC Analyzer enhances the field of precision medicine by allowing for measuring an individual's contributions (be it patients, lesions, or samples) to the overall AUROC, thus facilitating confidence measurement of Machine Learning (ML) classifiers for individual cases of interest in a cohort. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine

arXiv

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arXiv 2023年

作者： Kalaria, Dvij Lin, Qin Dolan, John M. Robotics Institute Carnegie Mellon University United States Electrical Engineering and Computer Science Department Cleveland State University United States

Autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times. Autonomous race cars require highly accurate perception, state estimation, planning and precise application of controls. What makes it even more challenging is the accurate identification of vehicle model parameters that dictate the effects of the lateral tire slip, which may change over time, for example, due to wear and tear of the tires. Current works either propose model identification offline or need good parameters to start with (within 15-20% of actual value), which is not enough to account for major changes in tire model that occur during actual races when driving at the control limits. We propose a unified framework which learns the tire model online from the collected data, as well as adjusts the model based on environmental changes even if the model parameters change by a higher margin. We demonstrate our approach in numeric and high-fidelity simulators for a 1:43 scale race car and a full-size car. © 2023, CC BY.

关键词： Control system synthesis

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A Game-Theoretic Approach for Pricing and Determining Quality Levels of Cybersecurity Products Under an Exogenous Information-Sharing program 19th

A Game-Theoretic Approach for Pricing and Determining Qualit...

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19th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2022

作者： Rasti-Barzoki, Morteza Altmann, Jörn Egger, Bernhard Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute of Engineering Research College of Engineering Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031293146

Price and quality level of products are two important decisions of any business. This paper provides equilibrium solutions for these decisions of two players for a cybersecurity ecosystem, including a solution provider and an information provider. We assume that end users join a cybersecurity ecosystem based on the prices and qualities of the solution provider’s and information provider’s products;so that, the increasing of the prices or/and decreasing the qualities will reduce the number of end-users of that ecosystem. Also, it is assumed that there is an exogenous information-sharing program under which the effect of quality of the information provider’s product on the cybersecurity level depends on the level of information-sharing. Under this information-sharing program, the equilibrium solutions of prices and qualities of these two players are given. Also, some results and insights are given with a numerical example. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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