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International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)

作者： Muhammad Nurul Puji Prawito Prajitno Warsito Purwo Taruno Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Electronic and Instrumentation Physics of The Department of Physics Universitas Indonesia Depok Indonesia C-TECH LABS Edwar Technology Tangerang Indonesia

ISBN: (数字)9798350389920

ISBN: (纸本)9798350389937

Magnetic Induction Tomography (MIT) is a low-resolution imaging technique used to reconstruct the magnetic permeability distribution of an object. COMSOL Simulation of VMIT (Volumetric Magnetic Induction Tomography) system has been developed by using 8 coils as transmitter and 8 coils as receiver. The existence of sine excitation signal in the transmitter coil cause magnetic field changing, it causing emf at the receiver coil. The magnitude of the magnetic field induction is also influenced by the permeability of the medium between the transmitter and receiver coils. COMSOL and MATLAB simulation of VMIT 8 coil systems indicate that, by using ILBP algorithms, ball-shaped object with ferromagnetic materials (iron) can be reconstructed to produce a similar image. The sensitivity matrix significantly influences image reconstruction in MIT, leading to distorted elliptical images of objects, particularly when positioned at the top center, as the matrix shows homogeneity mainly in the middle area.

关键词： Coils Microwave antennas Transmitters Magnetic resonance imaging Receivers Tomography Radar antennas Permeability Magnetic fields Image reconstruction

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A Novel Adaptive Actor-Critic Reinforcement Learning Controller for Constrained Robots

A Novel Adaptive Actor-Critic Reinforcement Learning Control...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Luis Pantoja-Garcia Rodolfo Garcia-Rodriguez Vicente Parra-Vega Robotics and Advanced Manufacturing Department Research Center for Advanced Studies (CINVESTAV) Saltillo Coah Mexico Aeronautical Engineering Program and Postgraduate Program in Aerospacial Engineering Universidad Politecnica Metropolitana de Hidalgo Tolcayuca Mexico

ISBN: (纸本)9781665494090

Force-position control for robot manipulators has become a basic control regime for advanced applications such as interaction or cooperative tasks that imply contact to environment or external object. Thus, for many applications environmental information is evaluated continuously to generate adequate control actions to complete the task. There has been proposed techniques such as adaptive or intelligent control to cover some of these requirements, however it seems insufficient given the uncertainty involved when dealing with the environment. The emergence of deep learning has recently renewed interest in these tasks from the perspective of reinforcement learning algorithms. In this paper, an actor-critic scheme is proposed for simultaneous control of contact force and joint positions while moving along an environmental rigid surface. The critic stage evaluates information of the environment through an extended error manifold, trying to minimize the cumulative future rewards by a neural network. The actor stage, which receives environment information from the critic stage, is used to approximate inverse robot dynamics. Together with two continuous orthogonal sliding PID controllers gives rise to simultaneous tracking of position and force, with stability analysis. Numerical simulations show the performance of the proposed approach under several conditions.

关键词： Upper bound Uncertainty Force Reinforcement learning Robot sensing systems Stability analysis Trajectory

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Stress Classification Using a Low-Cost Optical Fiber Physiological Sensor: A Preliminary Study

Stress Classification Using a Low-Cost Optical Fiber Physiol...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： María Gaitán-Padilla Marcela Múnera Carlos A. Cifuentes Maxwell E. Monteiro Maria J. Pontes Camilo A. R. Diaz Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Bristol Robotics Laboratory University of the West of England Bristol UK Instituto Federal do Espirito Santo (IFES) Serra Brazil

The classification and measurement of stress through physiological sensors have been studied because of their high impact on mental health and well-being. Low-cost optical fiber wearable sensors, can monitor signals and be adaptive systems. Therefore, the present study sought to evaluate a lowcost optical fiber physiological sensor developed for stress classification while monitoring physiological signals. Five volunteers participated in a stress-induced test, where pulse and respiratory measurements were correctly performed with the optical fiber sensor during both stress and relaxation. The data obtained were used to find the best machine-learning classification algorithm for stress detection: a Bagged Decision Tree Classifier with an accuracy of 88%, and a precision of 91%. Thus, the proposed sensor can effectively monitor pulse and respiratory signals for stress classification. Based on these results, a study with more volunteers will be performed.

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AllWeather-UsableWearable Dual Energy Harvester for Outdoor Sustainable Operation

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SusMat 2025年第1期5卷 159-170页

作者： Yeongju Jung Jiyong Ahn Ji-Seok Kim Junhyuk Bang Minwoo Kim Seongmin Jeong Jinwoo Lee Seonggeun Han Il-Kwon Oh Seung Hwan Ko Applied Nano and Thermal Science Lab Department of Mechanical EngineeringSeoul National UniversitySeoulRepublic of Korea National Creative Research Initiative for Functionally Antagonistic Nano-Engineering Department of Mechanical EngineeringSchool of Mechanical and Aerospace EngineeringKorea Advanced Institute of Science and Technology(KAIST)DaejeonRepublic of Korea Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoulRepublic of Korea Device Solutions Research Samsung ElectronicsHwaseong-siRepublic of Korea Institute of Engineering Research/Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National UniversitySeoulRepublic of Korea Interdisciplinary Program in Bioengineering Seoul National UniversitySeoulRepublic of Korea

Sustainable power sources for outdoor wearable electronics are essential for the continuous operation of wearable ***,the current lack of engineering design that can harvest energy regardless of weather conditions presents a significant *** this regard,this study introduces a wearable,breathable all-weather usable dual energy harvester(AWuDEH)that can seamlessly generate electrical energy regardless of weather *** this study,the AWuDEH integrated with the thermoelectric generator and the droplet-based electricity generator is *** AWuDEH,especially engineered with a bi-functional top substrate for radiative cooling and electrification,achieves sustainable energy harvesting outdoors,thereby addressing the conventional challenge associated with the necessity for separate energy harvesters tailored to outdoor usage contingent on weather *** device reaches a maximum power output of 14.6μW cm^(-2)under simulated sunny conditions and generates a much more enhanced thermoelectric power of 74.78μW cm^(-2)and a droplet-based electric power of 256.25 mW m^(-2)in rainy *** proof,this study developed self-powered wearable electronics capable of acquiring physiological signals in simulated outdoor *** study presents a promising advancement in wearable technology,offering a potent solution for sustainable energy harvesting independent of weather conditions.

关键词： all-weather usable droplet-based electricity generator hybrid architecture outdoor sustainable operation thermoelectric generator

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Water Quality Index (WQI) Prediction Using Machine Learning Algorithms

Water Quality Index (WQI) Prediction Using Machine Learning ...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Kunyanuth Kularbphettong Phanu Waraporn Nareenart Raksuntorn Rujijan Vivhivanives Chanyapat Sangsuwon Chongrag Boonseng Computer Science Program Suan Sunandha Rajabhat University Bangkok Thailand Robotics Engineering Program Suan Sunandha Rajabhat University Bangkok Thailand Faculty of Science and Technology Suan Sunandha Rajabhat Universit Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Water resources used by human activities ranges typically from personal and household, agricultural, industrial, recreational to environmental pursuits. The effects of these water utilizations are actually of great concerns by many due to various threats created by human functions and the nature itself, for instance, climate change, pollution, scarcity, and even conflicts. To mitigate these threats, implementation of water quality management based on recognized standards and guidelines not only will they provide solid framework and benchmark used in relation to the assessment of the water quality but will also enable the identification of corresponding classification indicated by the water quality index (WQI) pertinent and relevant to the surface water dataset. This paper aims at applying selected predictive modeling techniques that are highly optimized for use in semi-automating the work of the water quality classification (WQC) and the water quality index (WQI) that subsequently can be used in assisting the planning, problem-solving and/or decision-making processes. The preliminary results obtained are quite satisfactory as follow: predicting WQI using neural network model (NN) outperforms both the Multiple Linear Regression (MLR) and the Support Vector Machine (SVM) based on a mean absolute error (MAE) lower than the two models and predicting WQC using SVM, and ANN models based on accuracy score with SVM returns a favorable accuracy score higher than two others.

关键词： Water resources Water quality Indexes Classification algorithms Support vector machines Linear regression Climate change Predictive models Environmental monitoring Quality assessment Surface treatment

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Deep Learning-Based Approach for Identification and Compensation of Nonlinear Distortions in Parametric Array Loudspeakers

arXiv

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arXiv 2024年

作者： Li, Mengtong Zhuang, Tao Chen, Kai Zhong, Jia-Xin Lu, Jing Key Laboratory of Modern Acoustics Nanjing University Nanjing210008 China NJU-Horizon Intelligent Audio Lab Horizon Robotics Beijing100094 China Graduate Program in Acoustics The Penn sylvania State University University ParkPA16802 United States

Compared to traditional electrodynamic loudspeakers, the parametric array loudspeaker (PAL) offers exceptional directivity for audio applications but suffers from significant nonlinear distortions due to its inherent intricate demodulation process. The Volterra filter-based approaches have been widely used to reduce these distortions, but the effectiveness is limited by its inverse filter’s capability. Specifically, its pth-order inverse filter can only compensate for nonlinearities up to the pth order, while the higher-order nonlinearities it introduces continue to generate lower-order harmonics. In contrast, this paper introduces the modern deep learning methods for the first time to address nonlinear identification and compensation for PAL systems. Specifically, a feedforward variant of the WaveNet neural network, recognized for its success in audio nonlinear system modeling, is utilized to identify and compensate for distortions in a double sideband amplitude modulation-based PAL system. Experimental measurements from 250 Hz to 8 kHz demonstrate that our proposed approach significantly reduces both total harmonic distortion and intermodulation distortion of audio sound generated by PALs, achieving average reductions to 4.55% and 2.47%, respectively. This performance is notably superior to results obtained using the current state-of-the-art Volterra filter-based methods. Our work opens new possibilities for improving the sound reproduction performance of PALs. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Leveraging Structure from Motion to Localize Inaccessible Bus Stops

arXiv

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arXiv 2022年

作者： Panigrahi, Indu Bu, Tom Mertz, Christoph Robotics Institute Summer Scholars Program Carnegie Mellon University PittsburghPA15213 United States The Department of Computer Science Princeton University NJ08544 United States The Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

The detection of hazardous conditions near public transit stations is necessary for ensuring the safety and accessibility of public transit. Smart city infrastructures aim to facilitate this task among many others through the use of computer vision. However, most state-of-the-art computer vision models require thousands of images in order to perform accurate detection, and there exist few images of hazardous conditions as they are generally rare. In this paper, we examine the detection of snow-covered sidewalks along bus routes. Previous work has focused on detecting other vehicles in heavy snowfall or simply detecting the presence of snow. However, our application has an added complication of determining if the snow covers areas of importance and can cause falls or other accidents (e.g. snow covering a sidewalk) or simply covers some background area (e.g. snow on a neighboring field). This problem involves localizing the positions of the areas of importance when they are not necessarily visible. We introduce a method that utilizes Structure from Motion (SfM) rather than additional annotated data to address this issue. Specifically, our method learns the locations of sidewalks in a given scene by applying a segmentation model and SfM to images from bus cameras during clear weather. Then, we use the learned locations to detect if and where the sidewalks become obscured with snow. After evaluating across various threshold parameters, we identify an optimal range at which our method consistently classifies different categories of sidewalk images correctly. Although we demonstrate an application for snow coverage along bus routes, this method can extend to other hazardous conditions as well. Code for this project is available at https://***/ind1010/SfM_for_ BusEdge. Copyright © 2022, The Authors. All rights reserved.

关键词： Snow

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Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction

arXiv

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arXiv 2023年

作者： Choi, Hwan-Soo Jeong, Jongoh Cho, Young Hoo Yoon, Kuk-Jin Kim, Jong-Hwan Robot Intelligence Technology Lab School of Engineering KAIST Daejeon Korea Republic of dSPECTER Seoul Korea Republic of Visual Intelligence Lab Robotics Program Dept. of Mechanical Engineering KAIST Daejeon Korea Republic of

Sensor fusion approaches for intelligent self-driving agents remain key to driving scene understanding given visual global contexts acquired from input sensors. Specifically, for the local waypoint prediction task, single-modality networks are still limited by strong dependency on the sensitivity of the input sensor, and thus recent works therefore promote the use of multiple sensors in fusion in feature level in practice. While it is well known that multiple data modalities encourage mutual contextual exchange, it requires global 3D scene understanding in real-time with minimal computation upon deployment to practical driving scenarios, thereby placing greater significance on the training strategy given a limited number of practically usable sensors. In this light, we exploit carefully selected auxiliary tasks that are highly correlated with the target task of interest (e.g., traffic light recognition and semantic segmentation) by fusing auxiliary task features and also using auxiliary heads for waypoint prediction based on imitation learning. Our RGB-LIDAR-based multi-task feature fusion network, coined Cognitive TransFuser, augments and exceeds the baseline network by a significant margin for safer and more complete road navigation in the CARLA simulator. We validate the proposed network on the Town05 Short and Town05 Long Benchmark through extensive experiments, achieving up to 44.2 FPS real-time inference time. © 2023, CC BY.

关键词： Semantics

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Multimodal Feature Selection for Detecting Mothers' Depression in Dyadic Interactions with their Adolescent Offspring 17

Multimodal Feature Selection for Detecting Mothers' Depressi...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Bilalpur, Maneesh Hinduja, Saurabh Cariola, Laura A. Sheeber, Lisa B. Alien, Nick Jeni, Laszlo A. Morency, Louis-Philippe Cohn, Jeffrey F. Intelligent Systems Program University of Pittsburgh Pittsburgh United States Department of Psychology University of Pittsburgh Pittsburgh United States Clinical and Health Psychology University of Edinburgh Edinburgh United Kingdom Oregon Research Institute Oregon United States Department of Psychology University of Oregon United States Robotics Institute Carnegie Mellon University United States Language Technology Institute Carnegie Mellon University United States

ISBN: (纸本)9798350345445

Depression is the most common psychological disorder, a leading cause of disability world-wide, and a major contributor to inter-generational transmission of psychopathol-ogy within families. To contribute to our understanding of depression within families and to inform modality selection and feature reduction, it is critical to identify interpretable features in developmentally appropriate contexts. Mothers with and without depression were studied. Depression was defined as history of treatment for depression and elevations in current or recent symptoms. We explored two multimodal feature selection strategies in dyadic interaction tasks of mothers with their adolescent children for depression detection. Modalities included face and head dynamics, facial action units, speech-related behavior, and verbal features. The initial feature space was vast and inter-correlated (collinear). To reduce dimension-ality and gain insight into the relative contribution of each modality and feature, we explored feature selection strategies using Variance Inflation Factor (VIF) and Shapley values. On an average collinearity correction through VIF resulted in about 4 times feature reduction across unimodal and multimodal features. Collinearity correction was also found to be an optimal intermediate step prior to Shapley analysis. Shapley feature selection following VIF yielded best performance. The top 15 features obtained through Shapley achieved 78 % accuracy. The most informative features came from all four modalities sampled, which supports the importance of multimodal feature selection. © 2023 IEEE.

关键词： Feature Selection

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Trajectory Learning and Demonstration of a Robot Manipulator Based-on Sensor Fusion from a Hand-held Tool

Trajectory Learning and Demonstration of a Robot Manipulator...

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International Automatic Control Conference

作者： Kai-Tai Song Szu-Chen Ou Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Yang Ming Chiao Tung University Hsinchu Taiwan ROC Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan ROC

ISBN: (数字)9798350354904

ISBN: (纸本)9798350354911

This paper presents a programming system for a robot manipulator using a hand-held demonstration tool. In this system, a user holds the self-designed tool to demonstrate a desired motion path, and the robot arm replicates the learned trajectory. The path-demonstration tool, including optical tags (OT) and an IMU, is designed to collect trajectory data from hand motion. Sensor data fusion allows the system to track accurately the demonstrated trajectory. A calibration procedure is proposed to transform the trajectory from tool’s coordinate system to robot’s coordinate system. A cubic spline-based trajectory planning method is proposed to ensure smooth reproduction of the learned trajectory in both time and space. Experimental results show that the robot is able to replicate squared and curved trajectories collected from the path-demonstration tool. The average position error is within 2.435 mm in XYZ directions and the average orientation error is within 0.65 degrees.

关键词： Trajectory planning Tracking Robot kinematics Transforms Sensor fusion programming Robot sensing systems Manipulators Trajectory Splines (mathematics)

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