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Data Alignment Design for Robotic programming by Demonstration Based on IMU and Optical Tracker

IEEE Transactions on Instrumentation and Measurement 2023年 72卷 1页

作者： Song, Kai-Tai Li, Bing-Yi Ou, Szu-Chen National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Graduate Degree Program of Robotics Hsinchu300093 Taiwan

This study proposes a data alignment design for sensor fusion of an optical target (OT) and an inertial measurement unit (IMU) to track the trajectory of a handheld tool for robot programming by demonstration (PbD). A novel method is proposed for coordinate and temporal alignment between the OT and the IMU sensor to increase tracking accuracy. The IMU measurements are aligned with the corresponding angular velocities and accelerations derived from the OT to calibrate the IMU-OT relationship. Global and local optimizations are combined to reduce the computation time for the temporal alignment and coordinate calibration. Experimental results show that the proposed method provides improved calibration results with shorter calculation time compared with the two existing methods. The sensor fusion experiments verify the accuracy of trajectory tracking of 1.3 mm using the calibration result, an improvement from 1.4 mm of the data alignment using ruler measurement. Integrated PbD experiments using a 6-DOF manipulator were carried out to evaluate the proposed method. The manipulator imitated the hand motion of a bottle transfer task with a trajectory error of 0.66 mm. Practical robotic experiments demonstrate that the method is suitable for robot programming applications. © 1963-2012 IEEE.

关键词： Calibration

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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UAV-Based Optimization for Fruit Counting in Greenhouses

UAV-Based Optimization for Fruit Counting in Greenhouses

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： João Vítor R. Vasconcelos Thayron M. Hudson André G. dos Santos Martin Saska Alexandre S. Brandão Department of Electrical Engineering Núcleo de Especialização em Robótica Graduate Program on Computer Science Federal Univer-sity of Vicosa Vicosa MG Brazil Graduate Program on Computer Science Federal University of Vicosa Vicosa MG Brazil Department of Cybernetics Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a solution for counting fruit in agricultural greenhouses using Unmanned Aerial Vehicles (UAV s). Initially, a heuristic based on Simulated Annealing was used to optimize the UAV's trajectory, ensuring efficient coverage of the beds. Next, digital image processing (DIP) techniques were implemented to count the fruit, including depth segmentation, application of bounding boxes, color filtering, and element counting. The DIP accuracy was evaluated in multiple scenarios and the results indicate high reliability in fruit counting, with the potential to optimize agricultural operations and provide valuable information to producers. Possible future improvements could include further refinements in image processing to increase the accuracy of counting other fruits. Ultimately, this work contributes to the advancement of automation in agriculture by offering a viable and efficient solution for counting fruit in greenhouses using UAV s.

关键词： Image segmentation Accuracy Image color analysis Greenhouses Simulated annealing Autonomous aerial vehicles Trajectory

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DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion

arXiv

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arXiv 2024年

作者： Lee, Donggyu Park, Juhyeon Moon, Taesup The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of The Department of Electrical and Computer Engineering Automation and Systems Research Institute Institute of New Media and Communications Interdisciplinary Program in Artificial Intelligence Artificial Intelligent Institute Seoul National University Seoul08826 Korea Republic of

While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI) to AD based on MRI images, there remains a critical gap in research regarding the group robustness of the diagnosis. Although numerous studies pointed out that deep learning-based classifiers may exhibit poor performance in certain groups by relying on unimportant attributes, this issue has been largely overlooked in the early diagnosis of MCI to AD conversion. In this paper, we present the first comprehensive investigation of the group robustness in the early diagnosis of MCI to AD conversion using MRI images, focusing on disparities in accuracy between groups, specifically sMCI and pMCI individuals divided by age. Our experiments reveal that standard classifiers consistently underperform for certain groups across different architectures, highlighting the need for more tailored approaches. To address this, we propose a novel method, dubbed DEAL (DEcoupled classifier with Adaptive Linear modulation), comprising two key components: (1) a linear modulation of features from the penultimate layer, incorporating easily obtainable age and cognitive indicative tabular features, and (2) a decoupled classifier that provides more tailored decision boundaries for each group, further improving performance. Through extensive experiments and evaluations across different architectures, we demonstrate the efficacy of DEAL in improving the group robustness of the MCI to AD conversion prediction. © 2024, CC BY-NC-SA.

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Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study

IEEE Transactions on Energy Markets, Policy and Regulation

引用

IEEE Transactions on Energy Markets, Policy and Regulation 2024年第1期2卷 40-51页

作者： Ferrando, Robert Pagnier, Laurent Mieth, Robert Liang, Zhirui Dvorkin, Yury Bienstock, Daniel Chertkov, Michael University of Arizona Program in Applied Mathematics and Department of Mathematics TucsonAZ85721 United States Department of Industrial and Systems Engineering at Rutgers University PiscatawayNJ08854 United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218-2625 United States Elec. and Comp. Eng. and Ralph o'Connor Sustainable Energy Institute Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218-2625 United States Columbia University IEOR New YorkNY10027 United States

This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels. © 2023 IEEE.

关键词： Costs

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Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships 26th

Uncertainty Quantification Using Subsampling for Assembly-F...

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26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Rachtman, Eleonora Sarmashghi, Shahab Bafna, Vineet Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego La JollaCA United States Department of Electrical and Computer Engineering UC San Diego La JollaCA United States Department of Computer Science and Engineering UC San Diego La JollaCA United States

Computing the distance between two genomes, often without using alignments or even access to assembled sequences, is fundamental to many downstream analyses. © 2022, The Author(s), under exclusive license to Spri... 详细信息

ISBN: (纸本)9783031047480

关键词： Uncertainty analysis

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Outage Performance Analysis of Terahertz Band Drone-to-Ground Communications

Outage Performance Analysis of Terahertz Band Drone-to-Groun...

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Akhtar Saeed Ozgur Gurbuz Fatih Alagoz Department of Electrical Engineering DHA Suffa University Karachi Pakistan Electronics Engineering Program Sabanci University Istanbul Turkiye Department of Computer Engineering Bogazici University Istanbul Turkiye

Recent studies have shown the potential of em-ploying the Terahertz (THz) band for 6G non-terrestrial communications including drones. This paper assesses the outage performance of the THz band Drone-to-Ground (D2G) vertical communications up to 100 m altitude at four practically feasible THz transmission windows (TWs): f 1 = 0.140 THz; [0.110-0.170] THz, f 2 = 0.300 THz; [0.275-0325] THz, f 3 = 0.850 THz; [0.790-0.910] THz, and f 4 = 0.935 THz; [0.930-03940] THz. We consider vertical D2G communication, where realistic THz-based absorption gains of the four THz TWs at the drone altitudes are obtained via the International Telecommunications Union (ITU) model using the Standard Weather Profile. Additionally, multipath (MP) fading and beam misalignment (BM) fading are incorporated into the overall channel gain for assessing the outage performance. Numerical results reveal that under severe BM fading and Rayleigh fading, THz band D2G communication employing 60 dBi total antenna gains at $f_{1},\ f_{2},\ f_{3}$, and f 4 can be reliably established up to the drone altitudes: 76.9 m, 59.2m, 34.4 m, and 43.2 m, and up to 85.1 m, 60.3m, 36.9m, 45.5m under the severe BM fading and the presence of a strong Line-of-Sight component, truly showcasing the achievable altitude and range limits for the THz band D2G communications among 6G non-terrestrial cognitive networks.

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Local Sequential MCMC for Data Assimilation with Applications in Geoscience

arXiv

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arXiv 2024年

作者： Ruzayqat, Hamza Knio, Omar Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

This paper presents a new data assimilation (DA) scheme based on a sequential Markov Chain Monte Carlo (SMCMC) DA technique [36] which is provably convergent and has been recently used for filtering, particularly for high-dimensional non-linear, and potentially, non-Gaussian state-space models. Unlike particle filters, which can be considered exact methods and can be used for filtering non-linear, non-Gaussian models, SMCMC does not assign weights to the samples/particles, and therefore, the method does not suffer from the issue of weight-degeneracy when a relatively small number of samples is used. We design a localization approach within the SMCMC framework that focuses on regions where observations are located and restricts the transition densities included in the filtering distribution of the state to these regions. This results in immensely reducing the effective degrees of freedom and thus improving the efficiency. We test the new technique on high-dimensional (d ∼ 104 − 105) linear Gaussian model and non-linear shallow water models with Gaussian noise with real and synthetic observations. For two of the numerical examples, the observations mimic the data generated by the Surface Water and Ocean Topography (SWOT) mission led by NASA, which is a swath of ocean height observations that changes location at every assimilation time step. We also use a set of ocean drifters' real observations in which the drifters are moving according the ocean kinematics and assumed to have uncertain locations at the time of assimilation. We show that when higher accuracy is required, the proposed algorithm is superior in terms of efficiency and accuracy over competing ensemble methods and the original SMCMC *** Codes 62M20, 60G35, 60J20, 94A12, 93E11, 65C40 © 2024, CC BY.

关键词： Markov chains

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A Nanostructure-Electrode-Based Electrochemical Sensor for Detection of Lactate Dehydrogenase

A Nanostructure-Electrode-Based Electrochemical Sensor for D...

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IEEE Nanotechnology Materials and Devices Conference (NMDC)

作者： Sajid Uchayash Sunney Que Md Fazlay Rubby Long Que Department of Electrical and Computer Engineering Iowa State University Ames IA USA Ames High School and worked partly on this project through the NSF outreach program at ISU

ISBN: (数字)9798331541439

ISBN: (纸本)9798331541446

This paper reports on the development of nanostructure-electrode-based electrochemical sensors for detecting lactate dehydrogenase (LDH). Using an ultrathin nanopore template, gold nanostructured electrodes were fabricated. An on-chip specific chemical surface functionalization process was developed for the sensors for detecting LDH for the first time. Experiments showed the sensitivity has been improved, compared to the electrochemical sensors with plain electrodes. It has been found that at least ~5U/mL (~61 nM) of LDH can be readily detected.

关键词： Electrodes Chemical sensors Gold Sensitivity Nanoscale devices Sensors System-on-chip Surface treatment Nanotechnology Chemicals

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Mitigating electrical Losses Through a programmable Smart Energy Advanced Metering Infrastructure System

Mitigating Electrical Losses Through a Programmable Smart En...

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International Conference on Prognostics and Health Management, PHM

作者： Daniel O. Williams Zhaojun Steven Li Afsaneh Ghanavati Industrial Engineering Department Western New England University Springfield MA USA Electrical and Computer Engineering Program School of Engineering Wentworth Institute of Technology Boston MA USA

With the current alarming exponential increase in global energy demand chiefly due to population growth, electrification, and the issues associated with fossil generation, utilities are reinvesting their returns in alternative ways of clean power generation. Although, finding alternative ways to provide clean energy and to advance the power grid are of the main interest globally, many countries face power theft as a frequent problem. in Ghana, power losses in the distribution system cost the nation over a billion Ghana Cedis in the country's total annual revenue, of which power theft plays a predominant role. This paper presents an electricity theft mitigation technique through a programmable smart energy meter. The proposed method is such that interruptions are added to the smart energy meters in order to detect input signals from an added current sensor placed at the terminal point of the service line, from where in-between the sensor and the meter, illegal connections are made. The proposed Advanced Metering Infrastructure (AMI) system will provide smart services, including calculating consumed energy in kWh and generating a bill sent to the utility station. After which, the AMI system will disconnect the power supply from the meter.

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