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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Sirisomboon, Panmanas Sripinyowanich Jongyingcharoen, Jiraporn Junto, Apiwat Phanomsophon, Thitima Dachoupakan Sirisomboon, Cheewanun Rerngsamran, Panan Pokhrel, Dharma Raj Sharma, Sneha Posom, Jetsada Pornchaloempong, Pimpen Terdwongwarakul, Anupun Chongstitvatana, Prabhas School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Agricultural Engineering Bangkok Thailand School of Engineering and Innovation Rajamangala University of Technology Tawan-Ok Department of Agricultural Engineering Chon Buri Thailand School of Agricultural Technology King Mongkut's Institute of Technology Ladkrabang Office of Administrative Interdisciplinary Program on Agricultural Technology Bangkok Thailand Chulalongkorn University Faculty of Science Department of Microbiology Bangkok Thailand University of Melbourne Department of Infrastructure Engineering Victoria Australia Khon Kaen University Faculty of Engineering Department of Agricultural Engineering Khon Kaen Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Food Engineering Bangkok Thailand Kasetsart University Faculty of Engineering at Kanpangsan Department of Agricultural Engineering Thailand Chulalongkorn University Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350385601

This research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emissivity of mango fruit. The result showed that in the controlled environment of the packaging house in factory, the emissivity was increased (the mango emitted more energy) when the surface temperature was lower and the emissivity of mango is 0.71-0.84 and in the uncontrolled environment of open-air packaging yard in the mango orchard, the different transpiration rates of mango effected mainly by ambient temperature and relative humidity make the morning condition emissivity of 0.44-0.66 and the afternoon condition of 0.72-0.98. There was the effect of the different measured positions on the fruits where the physiology was different and the fluctuated environment in the latter condition made the wide range of emissivity of mangoes. This indicated the shortcoming of thermal imaging of horticultural product if the correct emissivity varied and difficult to set in the thermal camera setting, hence the inaccurate thermogram to be obtained. © 2024 IEEE.

关键词： Fruits

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Amico: Message Exchange Service for Elderly People

Amico: Message Exchange Service for Elderly People

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Atsushi Isozaki Kazuki Fukae Kenichi Arai Toru Kobayashi Masahide Nakamura Computer and Information Science Program Graduate School of Engineering Nagasaki University Nagasaki Japan Division of Electrical Engineering and Computer Science Graduate School of Engineering Nagasaki University Nagasaki Japan Center of Mathematical and Data Sciences Kobe University Hyogo Japan

ISBN: (纸本)9781665492331

This study is related to a system that enables elderly people to communicate interactively with young people who use existing message exchange services by simply speaking to an avatar on a tablet PC, without having to perform complicated operations of the tablet PC. On the other hand, the young people side can bring in elderly people who could not previously participate in existing message exchange services groups. The effectiveness of the system was verified through demonstration experiments with two households consisting of elderly people in their 80s.

关键词： Economics Avatars Older adults Usability Smart phones Consumer electronics Commercialization

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Persuasive Smart Bin Technology for Sustainable Behavior: A Case Study of Recycling

Persuasive Smart Bin Technology for Sustainable Behavior: A ...

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International Conference on computer Supported Cooperative Work in Design

作者： Emilly Marques da Silva Daniel Schneider Claudio Miceli António Correia Postgraduate Program in Informatics Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

This Participatory Action Design Research (PADR) study explores how persuasive technologies and the Internet of Things (IoT) can be used to encourage participation in recycling activities. By involving primary school students in the design process of a sociotechnical solution through participatory methods, we aim to create intelligent and persuasive applications that effectively promote sustainable behaviors in the school environment. To address the objectives of this investigation, the PADR methodology was adapted to the context of persuasive technologies by incorporating some critical activities, such as defining target behaviors. We argue that applying a participatory action design research approach involving users in all the stages of the design process of persuasive technologies for sustainability can lead to engaging and intuitive solutions tailored to the specific needs of users in real-world contexts.

关键词： Federated learning Recycling Internet of Things Sustainable development

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Learning Goal-based Movement via Motivational-based Models in cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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DOMINO++: Domain-Aware Loss Regularization for Deep Learning Generalizability 26th

DOMINO++: Domain-Aware Loss Regularization for Deep Learning...

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26th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2023

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu Gainesville United States Torch Technologies LLC ShalimarFL United States Center for Cognitive Aging and Memory McKnight Brain Institute UF Gainesville United States Department of Clinical and Health Psychology College of Public Health and Health Professions UF Gainesville United States Department of Neuroscience College of Medicine UF Gainesville United States United States Air Force Research Laboratory Eglin Air Force Base ValparaisoFL United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering UF Gainesville United States Department of Computer Information and Science and Engineering Herbert Wertheim College of Engineering UF Gainesville United States Department of Mechanical and Aerospace Engineering Herbert Wertheim College of Engineering UF Gainesville United States

ISBN: (纸本)9783031439001

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model’s training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++’s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Magnetic resonance

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Camera-based Motion Correction for PET/MR Brain Imaging

Camera-based Motion Correction for PET/MR Brain Imaging

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2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Fisher, Jonathan Groll, Andrew Kim, Chris C. Innes, Derek Levin, Craig S. Stanford University Department of Electrical Engineering CA United States Molecular Imaging Program Stanford University Department of Radiology CA United States Stanford University Department of Computer Science CA United States Stanford University Physics Electrical Engineering and Bioengineering Department of Radiology CA United States

ISBN: (纸本)9781665488723

In this work, we evaluated the performance of a camera-based rigid body motion correction solution in PET studies. We compared the image quality obtained by reconstructing a static phantom scan to those obtained by reconstructing a moving phantom scan with and without applying motion correction. Our suggested solution uses a camera to track a 3D marker attached to the object to be imaged (in this work, a resolution phantom). This data was incorporated in the multiple acquisition frames (MAF) image reconstruction algorithm to compensate for motion. While we got multiple spatially shifted replicas of the resolution phantom in the motion study's reconstructed image when motion correction was not applied, the motion-corrected image was comparable to the static image, with some degradation in spatial resolution (3.2-4.2 vs. 2.8-3.2 mm) and lower peak to valley ratio (indicating lower contrast to noise ratio). The context of the results presented is in PET/MR imaging, but in principle, this motion compensation method could be used in any type of medical imaging modality. © 2022 IEEE.

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic Computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

arXiv

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arXiv 2024年

作者： Zhang, Xiran Abdulah, Sameh Cao, Jian Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Statistics Program King Abdullah University of Science and Technology Saudi Arabia Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics The University of Houston HoustonTX United States

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of- Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n3) and space complexity of O(n2), mainly due to a Cholesky factorization operation for an n × n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on largescale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing the

关键词： Statistics

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Multi-Domain Emotion Recognition Enhancement: A Novel Domain Adaptation Technique for Speech-Emotion Recognition

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 528-541页

作者： Ammar Amjad Sucharita Khuntia Hsien-Tsung Chang Li-Chia Tai Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan Taiwan Department of Physical Medicine and Rehabilitation Chang Gung Memorial Hospital Taoyuan Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

As artificial intelligence advances, speech-emotion recognition (SER) has become a critical research area. Traditional SER methods typically rely on homogeneous domain data for training and testing. This practice requires adaptation when confronted with real-world data's heterogeneous linguistic, methodological, and speaker-related attributes. These variances can degrade the accuracy and generalization of SER models. To address this gap, we introduce a novel domain adaptation technique, multi domain emotion recognition enhancement (MDERE), which utilizes a non-negative matrix to reduce the inflexibility of the conventional binary label matrix for source domain data. This process yields a label matrix that better adapts to the nuances of the source labels while preserving their original structure. This framework refines SER methods by fine-tuning a transformation matrix for enhanced emotion discrimination. Elastic net regularization, which combines L1 and L2 penalties, enriches the transformation matrix, selectively emphasizing relevant features to enhance the robustness of emotion detection. The framework constructs customized similarity and dissimilarity graphs to reconcile the differences between source and target domains, enabling nuanced cross-domain data analysis. Extensive testing on multiple cross-domain SER tasks has shown that MDERE substantially improves recognition accuracy, surpassing several state-of-the-art algorithms. These results demonstrate MDERE's ability to effectively align domain variations enhances the generalizability of SER systems.

关键词： Emotion recognition Speech processing Speech recognition Training Adaptation models Testing Sparse matrices Linear regression Accuracy Vectors

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Curriculum-Based Reinforcement Learning for an Effective Multi-Agent Path Planning Algorithm in Warehouse Scenarios

Curriculum-Based Reinforcement Learning for an Effective Mul...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Iure Rosa L. De Oliveira Kevin B. De Carvalho Alexandre S. Brandão Graduate Program in Computer Science Universidade Federal de Viçosa Viçosa Brazil Electrical Engineering Department Universidade Federal de Vicosa Viçosa Brazil

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training.

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