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IEEE Latin American Robotics Symposium, LARS

作者： Iure Rosa L. Oliveira Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa MG Brazil

DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorithm, state representation, and training procedure. In this paper we explore various cutting-edge DRL algorithms, such as policy-, value-, and actor-critic-based approaches. Our results demonstrate the effectiveness of the ranging sensor approach, which achieves robust navigation policies capable of generalizing to unseen virtual environments with a high success rate. We combine Behavior Cloning with Imitation Learning to expedite the training process, leveraging expert demonstrations and reinforcement learning. Our methodology enables faster training while enhancing the learning efficiency and performance of the robot, obtaining better results in terms of crash and success rate, and being able to reach a cumulative reward of approximately 12000.

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Interferometry Among Strongly Driven Quasiparticles with Distinct Dynamical and Geometrical Phases

Interferometry Among Strongly Driven Quasiparticles with Dis...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Seamus O'Hara Joe Costello Qi Tian Qile Wu Ken West Loren Pfeiffer Mark Sherwin Liang Wu Department of Physics and Institute of Terahertz Science and Technology University of California Santa Barbara Santa Barbara CA Intelligence Community Post-Doctoral Research Fellowship Program Philadelphia PA Department of Physics University of Pennsylvania Philadelphia PA Department of Electrical and Computer Engineering Princeton University Princeton NJ

ISBN: (纸本)9798350369311

In this proceeding, we present polarization data of sidebands emitted from strongly driven quasiparticles, demonstrate the dependence of these data on various parameters of the external driving field, and describe the data using an analytical expression, dependent on driving field parameters and the effective Hamiltonian of the system. We propose using this method on spin-momentum locked systems, such as altermagnets, to observe and reconstruct sidebands which are influenced by the topology of individual bands.

关键词： Electro-optic effects Amplitude modulation Topology Interferometry

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Q-Learning based Local Path Planning for UAVs with Different Priorities

Q-Learning based Local Path Planning for UAVs with Different...

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IEEE Latin American Robotics Symposium, LARS

作者： Kevin B. de Carvalho O. B. Hiago Batista Leonardo A. Fagundes-Júnior Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa MG Brazil

Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context of unmanned autonomous vehicles. In many applications, the agent may have to react to environment shifting. Algorithms such as geometric and dynamic programming as well as techniques such as artificial potential fields have been employed to tackle this local planning problem. In recent years, machine learning has gained more evidence in many research fields due to its flexibility and generalization capabilities. In this study, we propose a Q-learning-based approach to local planning, which weighs three crucial factors- path length, safety, and energy consumption- that can be freely adjusted by the user to suit its application’s needs. The performance of the proposed method was tested in simulated static and dynamic scenarios as well as benchmarked with a baseline approach. The results show that it can perform well in both kinds of environments without struggling with the commom pitfalls of other local planning algorithms.

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ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS

arXiv

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arXiv 2024年

作者： Lee, Jonghyun Jung, Dahuin Lee, Saehyung Park, Junsung Shin, Juhyeon Hwang, Uiwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Division of Digital Healthcare Yonsei University Korea Republic of AIIS ASRI INMC and ISRC Seoul National University Korea Republic of

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output’s entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://***/DeYO/. Copyright © 2024, The Authors. All rights reserved.

关键词： Entropy

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Smartphone Application As An Air Quality Monitor Using Raspberry Pi for Reducing Air Pollution 2

Smartphone Application As An Air Quality Monitor Using Raspb...

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2nd International Conference on Innovative and Creative Information Technology, ICITech 2021

作者： Siagian, Pandapotan Fernando, Erick Computer Engineering Study Program Del Institute of Technology Faculty of Informatics and Electrical Engineering North Sumatera22381 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781728197463

Good air quality is very important in human health. However, air pollution has been polluted everywhere, whether in the house, in cities. This is caused by a growing industry that produces air pollution. Room air pollution and various cities will contribute to air pollution, which will cause problems for human health. This work measures the carbon monoxide level using the MQ-7 sensor and measures the air's carbon dioxide level with the CCS811 sensor and MH-Z19 sensor. All three sensors are connected to the NodeMCU ESPS266, and we call it a NodeMCU module. Tests are carried out in three rooms in the house, and then three NodeMCU modules are used so that the three modules will send data alternately. Raspberry Pi3 (RPi3) functions as a server that collects data and acquires information data from sensors. Data from the CCS811 (TCOV) sensor, MH-Z19 (co2), will be processed first with fuzzy logic rules to make decisions about CO2, which are right, moderate, nasty conditions. The process is carried out on the Raspberry Pi 3 server with MQTT using publication, customer, fuzzy logic, and broker services. The value of the sensor's measurement results can be accessed via a smartphone and provides good air control values. © 2021 IEEE.

关键词： Air quality

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Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone 5

Long Short Term Memory Networks for Stroke Activity Recognit...

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5th IEEE International Conference on Information Technology, Information Systems and electrical engineering, ICITISEE 2021

作者： Siagian, Pandapotan Fernando, Erick Del Institute Of Technology Faculty Of Informatics And Electrical Engineering Computer Engineering Study Program North Sumatera 22381 Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

Aims this study to analyze rehabilitation exercises using sensor data embedded in smartphones is widely used to recognize human activities regularly to gain a better understanding of human behavior. However, it is rarely used to measure accelerators of patient movement in hospital wards. This paper proposes an optimized neural network-based feature extraction approach based on the Long Short Term Memory technique to recognize human activity using data from a tri-axial accelerometer. The activity to be measured is the Range of Motion for patient rehabilitation, and rehabilitation exercises are one of the most important steps for recovery. The results of the softmax regression trained on the dataset set have 1, 076, 105 records and 12 class attributes (class distribution). The experimental results on the SAR dataset show that our Long Short Term Memory-based approach is practical and successful, with an accuracy of 89.74%. © 2021 IEEE.

关键词： Smartphones

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Active Battery Management System for Electric Longboard

Active Battery Management System for Electric Longboard

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： David Bailey Kaleb Hutto Peter Wallace Adel El-Shahat Jason Qian Hala M. Abdel Mageed Dept. of Electrical and Computer Engineering Southern University Statesboro Georgia USA Energy Program School of Engineering Technology Purdue University West Lafayette USA High Voltage Department National Institute of Standards (NIS) Giza Egypt

Emphasizing the focus on recycling and sustainability is one of the hot topics all over the world. In this work, a long, non-electrical board was transformed into an electrical board. Hence, the battery management system was created to effectively balance 18,650 cells during charge and discharge cycles as well as a wireless remote control to control the electrical long panel. To do that, the skateboard has been fitted with a brushless DC motor to drive a single wheel located on the rear truck to allow the user to use controllers for sending analog signals to drive the motor with variable speed outputs. A better battery management system has been designed to make the power system more affordable, and ecological. Moreover, hot swap battery system using 18650 interchanged Lithium-Ion battery cells has been built. Besides, it is proven that this system is more robust and reliable than the ordinary two-piece AA battery system. The new proposed system has the capability of having a large battery grid that can be rearranged to satisfy the user's needs. It allows the user to change between the speed function and the torque function.

关键词： Wireless communication Brushless DC motors Capacitors Battery management systems Wheels Transportation Discharges (electric)

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VRGrip: Developing a Grip Strength Training Platform Integrating a Wireless E-Textile Forearm Band with an Adaptive 3D VR Game Environment

VRGrip: Developing a Grip Strength Training Platform Integra...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Lohith Chatragadda Aiden Fletcher Matthew J. Delmonico Kunal Mankodiya Dhaval Solanki Interdisciplinary Neuroscience Program University of Rhode Island Kingston RI USA Department of Kinesiology University of Rhode Island Kingston RI USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Neurodegenerative disease is a growing global problem. Many of these diseases such as Parkinson's disease can cause grip strength weakness. In this work, we focused on developing an e-textile based EMG acquisition system interfaced with an adaptive VR game engine for detecting hand squeeze actions. We prototyped and performed preliminary tests on a grip detection system consisting of: 1) an e-textile forearm band (E-band), 2) a portable data acquisition module (DAM), and 3) an adaptive 3D VR game engine. Digital health solutions such as our VRGrip system widen the scope of in-home rehabilitation and assessment, and we aimed to make the first step in creating a system for grip strength rehabilitation. To validate our concept, we performed preliminary tests. These tests revealed that the Bluetooth communication protocol between DAM and the game engine was reliable, that the adaptive game engine successfully tailors the game's difficulty level to the user's performance, and that the system was reliable at detecting hand squeezes (F=0.96).

关键词： Wireless communication Training Adaptive systems Three-dimensional displays Protocols Dams Games

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Wearable Active Vibration Sensing for Mid-Activity Knee Health Assessment

Wearable Active Vibration Sensing for Mid-Activity Knee Heal...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Goktug C. Ozmen Christopher J. Nichols Lan Lan Emily Moise Christopher Sugino Alper Erturk Omer T. Inan School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA BioEngineering Graduate Program Georgia Institute of Technology Atlanta GA USA Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA USA Coulter Department of Biomedical Engineering School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Non-invasive vibration measurements from the knee offer a convenient and affordable alternative to benchtop or biomechanics lab joint health monitoring systems. Recently, joint acoustic emissions (JAEs) measured from the knee were shown to be an indicator of knee health. However, the origin of JAEs is still not fully understood, which limits its acceptance and use by clinical experts. In this proof-of-concept study, rather than relying on the movements of the knee and corresponding frictional rubbing of internal surfaces to produce vibrations, we propose using an active vibration sensing approach with a known vibration source interrogating the knee. We aim to elucidate the linkage between knee vibration characteristics and structural changes in the joint following injuries. We measured tibial vibration responses of two participants using a laser vibrometer system to quantify the frequency band where the most repeatable tibial vibration measurement can be taken. Subsequently, a custom-designed wearable system measured mid-activity tibial vibration characteristics from four participants (five healthy knees and three knees with prior acute injury) during unloaded knee flexion-extensions. An active sensing knee health score was defined as the ratio of the changes in low- to high-frequency response during flexion-extension. Since changes in the boundary of tibia would alter low-frequency response more than high frequency response, we found that increased knee laxity with acute injuries resulted in an increased active sensing knee health score. Our findings demonstrate the potential of active vibration sensing as an interpretable, computationally inexpensive alternative to JAEs for wearable knee health assessment.

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Gramian Angular Field and Recurrence Plots as Feature engineering Techniques on Residential Appliances Labeling: A Comparative Analysis

Gramian Angular Field and Recurrence Plots as Feature Engine...

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IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America)

作者： J. S. Corrêa D. L. Cavalca R. A. S. Fernandes Department of Electrical Engineering Federal University of São Carlos São Carlos Brazil Graduate Program in Computer Science Federal University of São Carlos São Carlos Brazil

The demand for electricity has increased rapidly and, for this reason, there is a need to efficiently use it. In this way, the identification of residential appliances enables such use for consumers and is crucial for demand response programs. Due to the variety of appliances in homes and their dynamic behavior, the search for patterns that explain and allow the correct labeling of temporal windows becomes a challenging task, since a window may contain more than one appliance. In this sense, the present paper proposes the transformation of time-series into images, using Gramian angular field and recurrence plots. The dataset composed of images was submitted to the labeling process, considering the use of convolutional neural networks. A comparative analysis was performed using the UK-DALE dataset. The results demonstrated the effectiveness of the proposed feature engineering stage, since the labeling task reached F1-scores until 94 %.

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