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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Tan, Xianglong Liu, Zhikang Yu, Chen Schwertfeger, Sören Rosendo, Andre Hamlyn Centre Imperial College London United Kingdom University of York Department of Computer Science United Kingdom School of Information Science and Technology ShanghaiTech University China Worcester Polytechnic Institute Robotics Engineering United States

ISBN: (纸本)9781665481090

The ability of acquiring energy form environments is greatly beneficial for robot to function in uncertain environments. In this work, we present a robot capable of drawing circuits with conductive ink while also rearranging the visual world to receive maximum energy from a power source. A range of circuit drawing tasks is designed to simulate real-world scenarios, including avoiding physical obstacles and regions that would discontinue drawn circuits. We adopt the state-of-the-art Transporter networks for pick-and-place manipulation from visual observation. We conduct experiments in both simulation and real-world settings, and our results show that, with a small number of demonstrations, the robot learns to rearrange the placement of objects (removing obstacles and bridging areas unsuitable for drawing) and to connect a power source with a minimum amount of conductive ink. As autonomous robots become more and more involved in our daily lives, our proposed approach brings a novel way for machines to keep themselves functional by rearranging their surroundings to create their own electric circuits. © 2022 IEEE.

关键词： Timing circuits

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Exploring Anatomical Similarity in Cardiac-Gated Spect Images for A Deep Learning Network

Exploring Anatomical Similarity in Cardiac-Gated Spect Image...

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IEEE International Conference on Image Processing

作者： Xirang Zhang Yongyi Yang P. Hendrik Pretorius Michael A. King Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL USA Department of Radiology University of Massachusetts Medical School Worcester MA USA

Motion compensation is effective for reducing motion blur in cardiac gated imaging. In this work, we investigate the potential benefit of incorporating an anatomical similarity measure in training a deep learning (DL) network for motion compensation on cardiac gated SPECT images, which are known to suffer from limited data counts and exhibit image intensity distortion (due to partial-volume effect) associated with cardiac motion. In this similarity measure we utilize the spatial image gradient to characterize the correspondence of boundary points on the left-ventricular wall between two gate frames. In the experiment we demonstrated this approach on a set of 197 clinical acquisitions, and the results show that with the proposed approach the DL network can improve the anatomical similarity among the gate frames upon motion compensation.

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Assessment of Deep-Learning Based Motion Compensation on Detection of Perfusion Defects in Cardiac-Gated SPECT Images

Assessment of Deep-Learning Based Motion Compensation on Det...

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IEEE International Symposium on Biomedical Imaging

Motion compensation is an effective approach for noise suppression and motion blur reduction in cardiac-gated SPECT imaging. In this work, we investigate the potential benefit of applying motion compensation with a deep learning (DL) network for assessment of perfusion defects in gated images. In addition to evaluating motion-compensation accuracy on clinical acquisitions, we also conduct a receiver-operating characteristic (ROC) study to assess the detection performance of perfusion detects when DL motion compensation is used to generate the perfusion images. For this task we use a clinical model observer on a set of hybrid studies generated from clinical acquisitions in which the perfusion defects are introduced as ground truth. The results in the experiments demonstrate that DL motion compensation can yield higher detection accuracy than conventional ungated studies.

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Chinese E-Romance: Analyzing and Visualizing 7.92 Million Alibaba Valentine's Day Purchases

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Data and Information Management 2021年第4期5卷 363-371页

作者： Wang, Yongzhen Liu, Xiaozhong Ju, Yingnan Börner, Katy Lin, Jun Sun, Changlong Si, Luo WISE Lab Institute of Science of Science and S&T Management Faculty of Humanities and Social Sciences Dalian University of Technology Dalian China Computer Science Department Worcester Polytechnic Institute WorcesterMA United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Alibaba DAMO Academy Hangzhou China

The days that precede Valentine's Day are characterized by extensive gift shopping activities all across the globe. In China, where much shopping takes place online, there has been an explosive growth in e-commerce sales during Valentine's Day over the recent years. This exploratory study investigates the extent to which each product category and each shopper group can exhibit romantic love within China's e-market throughout the 2 weeks leading up to 2019 Valentine's Day. Massive data from Alibaba, the biggest e-commerce retailer worldwide, are utilized to formulate an innovative romance index (RI) to quantitatively measure e-romantic values for products and shoppers. On this basis, millions of shoppers, along with their millions of products purchased around Valentine's Day, are analyzed as a case study to demonstrate their love consumption and romantic gift-giving. The results of the analysis are then illustrated to help understand Chinese e-romance based on the perspectives of different product categories and shopper groups. This empirical information visualization also contributes to improving the segmentation, targeting, and positioning of China's e-market for Valentine's Day. © 2021 Yongzhen Wang et al., published by Sciendo

关键词： Sales

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Safe Control for Nonlinear Systems under Faults and Attacks via Control Barrier Functions

arXiv

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arXiv 2022年

作者： Zhang, Hongchao Li, Zhouchi Clark, Andrew The Electrical and Systems Engineering Department McKelvey School of Engineering Washington University in St. Louis St. LouisMO63130 United States The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Safety is one of the most important properties of control systems. Sensor faults and attacks and actuator failures may cause errors in the sensor measurements and system dynamics, which leads to erroneous control inputs and hence safety violations. In this paper, we improve the robustness against sensor faults and actuator failures by proposing a class of Fault-Tolerant Control Barrier Functions (FT-CBFs) for nonlinear systems. Our approach maintains a set of state estimators according to fault patterns and incorporates CBF-based linear constraints for each state estimator. We then propose a framework for joint safety and stability by integrating FT-CBFs with Control Lyapunov Functions. With a similar philosophy of utilizing redundancy, we proposed High order CBF-based approach to ensure safety when actuator failures occur. We propose a sum-of-squares (SOS) based approach to verify the feasibility of FT-CBFs for both sensor faults and actuator failures. We evaluate our approach via two case studies, namely, a wheeled mobile robot (WMR) system in the presence of a sensor attack and a Boeing 747 lateral control system under actuator failures. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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A Feasibility Study of Increasing Temporal Resolution in Cardiac-gated SPECT Acquisitions

A Feasibility Study of Increasing Temporal Resolution in Car...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： X. Zhang Y. Yang J. G. Brankov M. N. Wernick P. H. Pretorius M. A. King Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago Chicago Illinois United States of America Department of Radiology University of Massachusetts Medical School Worcester Massachusetts United States of America

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

In cardiac-gated studies in myocardial perfusion imaging with SPECT the number of gate intervals is typically kept to be small as a trade-off between motion blur and noise level in the reconstructed images. In this study we investigate the effect of increasing the number of gate intervals by quantifying the noise level and functional measurements based on a set of clinical acquisitions. To suppress the increased noise owing to lowered data counts with more gate intervals used, we employ a deep learning (DL) denoising network pre-trained on ungated cardiac SPECT images derived from reduced-dose studies. In the experiment we evaluated the gated images from 20 clinical acquisitions where the number of gate intervals was doubled from conventional 8 gates. The quantitative results indicate that with 16 gates the gated images can more accurately capture functional measurements near the ED or ES phase compared to 8 gates, while the increased image variability caused by lowered data counts can be effectively suppressed with DL denoising.

关键词： Semiconductor device measurement Phase measurement Semiconductor detectors Noise reduction Noise Logic gates Myocardium

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Trust-Number: Trust-Based Modeling to Handle Decision-Making Problems

SSRN

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SSRN 2023年

作者： Ghoushchi, Saeid Jafarzadeh Mardani, Abbas Martínez, Luis Faculty of Industrial Engineering Urmia University of Technology Urmia Iran Business School Worcester Polytechnic Institute WorcesterMA01609-2280 United States Computer Science Department University of Jaén Jaén Spain

Fuzzy sets play an effective role in dealing with the uncertainty and ambiguity of the input data in real-world decision-making problems. Nevertheless, the effectiveness of fuzzy sets becomes unreliable and even more uncertain when the input data come from untrustworthy sources. Therefore, a new measurement could be considered based on the data degree of trust to reduce the deviation of unreliable information in fuzzy decision-making problems. The main aim of this study is to introduce a new information modeling, called Trust Numbers (T-Numbers), which models variations and deviations associated with triangular fuzzy numbers and their application to decision-making. Besides, it introduces new operations on T-numbers to develop a decision model based on this theory. The performance of this model has been analyzed by its implementation in two case studies and by comparing the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) and its T-numbers extension (T-TOPSIS). According to the obtained results, it can be concluded that T-numbers can be applied to classical fuzzy numbers facing uncertain and distrust information available © 2023, The Authors. All rights reserved.

关键词： Decision making

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Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape

arXiv

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arXiv 2024年

作者： Huang, Chao Zang, Wenshuo Pinciroli, Carlo Li, Zhi Jane Banerjee, Taposh Su, Lili Liu, Rui College of eronautics and Engineering Kent State University KentOH44240 United States Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States Industrial Engineering University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States Electrical and Computer Engineering Northeastern University 360 Huntington Ave BostonMA02115 United States

Compared with single robots, Multi-Robot Systems (MRS) can perform missions more efficiently due to the presence of multiple members with diverse capabilities. However, deploying an MRS in wide real-world environments is still challenging due to uncertain and various obstacles (e.g., building clusters and trees). With a limited understanding of environmental uncertainty on performance, an MRS cannot flexibly adjust its behaviors (e.g., teaming, load sharing, trajectory planning) to ensure both environment adaptation and task accomplishments. In this work, a novel joint preference landscape learning and behavior adjusting framework (PLBA) is designed. PLBA efficiently integrates real-time human guidance to MRS coordination and utilizes Sparse Variational Gaussian Processes with Varying Output Noise to quickly assess human preferences by leveraging spatial correlations between environment characteristics. An optimization-based behavior-adjusting method then safely adapts MRS behaviors to environments. To validate PLBA's effectiveness in MRS behavior adaption, a flood disaster search and rescue task was designed. 20 human users provided 1764 feedback based on human preferences obtained from MRS behaviors related to {"task quality", "task progress", "robot safety"}. The prediction accuracy and adaptation speed results show the effectiveness of PLBA in preference learning and MRS behavior adaption. © 2024, CC BY-NC-ND.

关键词： Multipurpose robots

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Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help?

arXiv

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arXiv 2025年

作者： Han, Keqi Su, Yao He, Lifang Zhan, Liang Plis, Sergey Calhoun, Vince Yang, Carl Dept. of Computer Science Emory University AtlantaGA United States Dept. of Computer Science Worcester Polytechnic Institute WorcesterMA United States Dept. of Computer Science and Engineering Lehigh University BethlehemPA United States Dept. of Electrical and Computer Engineering Univ. of Pittsburgh PittsburghPA United States TReNDS Center GSU Georgia Tech Emory University AtlantaGA United States

Functional brain connectome is crucial for deciphering the neural mechanisms underlying cognitive functions and neurological disorders. Graph deep learning models have recently gained tremendous popularity in this field. However, their actual effectiveness in modeling the brain connectome remains unclear. In this study, we re-examine graph deep learning models based on four large-scale neuroimaging studies encompassing diverse cognitive and clinical outcomes. Surprisingly, we find that the message aggregation mechanism, a hallmark of graph deep learning models, does not help with predictive performance as typically assumed, but rather consistently degrades it. To address this issue, we propose a hybrid model combining a linear model with a graph attention network through dual pathways, achieving robust predictions and enhanced interpretability by revealing both localized and global neural connectivity patterns. Our findings urge caution in adopting complex deep learning models for functional brain connectome analysis, emphasizing the need for rigorous experimental designs to establish tangible performance gains and perhaps more importantly, to pursue improvements in model interpretability. © 2025, CC BY.

关键词： Contrastive Learning

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Advancements in air quality monitoring: a systematic review of IoT-based air quality monitoring and AI technologies

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Artificial Intelligence Review 2025年第9期58卷 1-67页

作者： Garcia, Antony Saez, Yessica Harris, Itamar Huang, Xinming Collado, Edwin Facultad de Ingeniería Eléctrica Universidad Tecnológica de Panamá Avenida Universidad Tecnológica de Panamá Panamá Panama Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester USA Centro de Estudios Multidisciplinarios en Ciencias Ingeniería y Tecnología AIP (CEMCIT AIP) Panamá Panama Facultad de Ingeniería Mecánica Universidad Tecnológica de Panamá Avenida Universidad Tecnológica de Panamá Panamá Panama

Air quality monitoring is a critical component of environmental management, especially in developing countries where pollution levels are a growing concern. This review systematically analyzes recent advances in Internet-of-Things (IoT)-based air quality monitoring systems and highlights the impact of artificial intelligence (AI) technologies. A comprehensive selection process was used to identify 220 relevant studies using databases such as Google Scholar, Scopus, and ResearchRabbit. Following a selection process based on specific eligibility criteria, a total of 147 studies were chosen for a detailed analysis. These studies present notable advances in the application of artificial intelligence to improve data accuracy, predictive capabilities, and real-time analysis in air quality monitoring systems. A key contribution of this review is the proposal of a classification framework for AI techniques in air quality monitoring, organized into five main application areas: data imputation, sensor calibration, anomaly detection, air quality index (AQI) estimation, and short-term forecasting. The review takes an in-depth look at the different uses of these technologies in both urban and industrial settings, presenting successful case studies that showcase their effectiveness in addressing pressing air quality issues. Additionally, the paper identifies research gaps in the literature, particularly related to data quality, system scalability, and integration challenges in AI-driven IoT systems. The insights provided aim to guide researchers and practitioners in selecting appropriate AI techniques and system architectures, inform the design of more reliable and scalable air quality monitoring frameworks, and support future efforts to mitigate air pollution through data-driven decision-making.

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