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New Version of HEAT: Calibration and visualization Tool for Thermal Imagers on Hayabusa2 and Hera

New Version of HEAT: Calibration and Visualization Tool for ...

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IEEE conference on Aerospace

作者： Ramon Vilardell Belles Hirohide Demura Ryuji Konno Toko Mori Kengo Kakazu Takehiko Arai Hiroki Senshu Naoya Sakatani Tatsuaki Okada University of Aizu Aizu-Wakamatsu Japan Maebashi Institute of Technology Maebashi Japan Chiba Instiute of Technology Chiba Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara Japan

The Hayabusa2/Hera exploration Assistant for Thermal Imagers (HEAT) is a powerful tool for understanding asteroid thermal characteristics. HEAT serves as a versatile solution for the calibration and conversion of data from thermal infrared imagers, TIR and TIRI, on the Hayabusa2 and Hera spacecraft, targeting asteroids 162173 Ryugu and 65803 Didymos, along with its moon Dimorphos. HEAT enables the production of brightness temperature images and the thermal inertia of the asteroid’s surface. In response to evolving mission needs, HEAT has undergone significant enhancement. Python-based wrapping is introduced to HEAT to improve usability and accommodate the expansion of the capabilities of TIRI, which has seven bands, a mode closed shutter, and 3D visualization capabilities. The development tool prioritizes user-friendliness, cross-platform compatibility, and efficient data handling, making it an indispensable resource for asteroid research. By implementing thread parallelization utilizing OpenMP, we substantially reduced the calculation time for the thermal data. The architecture-independent nature of our parallelization approach ensures compatibility with various computational resources, contributing to the scalability and adaptability of HEAT for future missions. Moreover, the 3D and 2D visualization address the need for improved data interpretation and analysis. Users now have access to an easy-to-use 3D visualization and crater detection tool, empowering them to explore and manipulate 3D models. This tool also incorporates advanced features, such as Laplacian smoothing and dynamic color mapping, ensuring that the visual representation of celestial bodies’ thermal behavior is accurate and insightful. The future goals of HEAT are to develop customized algorithms for detecting depressions and improve graphical user interfaces for data analysis. This promises to further improve the usability of HEAT.

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Unveiling Multivariate EEG Features: A Novel Approach to Enhancing ADHD Diagnosis Through visual and Auditory Attention Tests

Unveiling Multivariate EEG Features: A Novel Approach to Enh...

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2023 International conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Fan, Zuo-Cian Lin, Ro-Wei Tsai, Ching-Shu Chou, Wen-Jiun Li, Chia-Jung Huang, Zih-Jun Shih, Bin-Yu Wang, Liang-Jen Ko, Li-Wei Department of Biological Science & Technology Hsinchu300 Taiwan National Yang Ming Chiao Tung University International Ph.D. Program in Interdisciplinary Neuroscience Hsinchu300 Taiwan Kaohsiung Chang Gung Memorial Hospit Department of Child and Adolescent Psychiatry National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Hsinchu300 Taiwan

ISBN: (纸本)9798350305791

We sought to revolutionize the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) by pioneering an innovative approach that seamlessly integrated auditory and visual tests with electroencephalogram (EEG) coherence analysis, forming the foundation for our Support Vector Machine (SVM) models. Our primary objective was to achieve precise differentiation between individuals with ADHD and those without, aiming to reshape the landscape of ADHD diagnosis and early intervention. We constructed an SVM model based on CATA coherence, incorporating a carefully selected set of 180 features, and another grounded in CPT coherence with 70 features. Astonishingly, our exploration uncovered that a model incorporating CATA's coherence and Power Spectral Density (PSD) as features achieved an astounding accuracy rate of 97.7%, using only half the number of features. This revelation hints at untapped potential for multivariate advancements in the realm of ADHD classification. The landscape of ADHD diagnosis and intervention is perpetually evolving, most notably with the FDA's approval of game therapy for ADHD. Leveraging the transformative power of our findings, we envision the development of user-friendly game therapies for in-home use, empowering both clinicians and parents alike. This marks a paradigm shift in the realm of ADHD intervention, offering objective, data-driven tools for the early management of ADHD. Gamified therapy not only enhances accessibility but also fosters engagement and compliance, all of which are pivotal in the long-term care of individuals with ADHD. In summary, our study represents a significant advancement in our comprehension and classification of ADHD. By fusing auditory and visual tests with EEG coherence analysis, fortified by state-of-the-art machine learning techniques, we have achieved an unprecedented level of accuracy in identifying individuals with ADHD. Our findings, aligned with the FDA-approved game therapy, offer a glimpse into a

关键词： Electroencephalography

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Chinese Painting Algorithm: A Study of Scene Characterization by Chromatographic Multiple analysis and Handwriting Co-construction

Chinese Painting Algorithm: A Study of Scene Characterizatio...

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IEEE/WIC/ACM International Joint conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Weng, Ziyang Yan, Weixin Hu, Yidan Weng, Zhimo Wuhan Univ Sch Informat Management Wuhan Peoples R China Shenzhen Museum Contemporary Art & Urban Planning Art Technol Lab Shenzhen Peoples R China Zhongnan Univ Econ & Law China Korea Inst New Media Wuhan Peoples R China

ISBN: (纸本)9781665494021

The understanding of scene representation is a deep knowledge service structure strategy arising from the increasing scale of data and the need for complex logic solving. This study proposes a modeling improvement method based on the fusion of complex feature data and exploration behavior trajectory extremes, which effectively utilizes the artistic feature study of the interplay between the unique colorant mixture attachment features of Chinese painting and complex handwriting features as the orientation region, realizes the classification constraint of colorant data through multispectral detection, and characterizes the handwriting as the behavior law, realizes the parametric extraction and then couples the solution encoding to complete the improvement of the algorithm. Since all scenes in Chinese painting are recorded in the bearer medium with handwriting characteristics after mixing Chinese brushes and colorants, the computational model of Chinese painting algorithm proposed in this paper starts from the processing of representation hierarchical structure and painting behavior of various scenes deposited to realize the principle of describing their material deposition goals and information exchange functions. The experimental analysis shows that i. deep knowledge understanding achieves the derivation of sparse feature validity, ii. the coverage calculation obtained by drawing on technological means can vividly describe the implicit characteristics of handwriting behavior, and iii. the improved modeling process has more humanized perceptual habits and enhances the accuracy and robustness of service domain requirements.

关键词： drawing multispectral reflectography Imaging spectroscopy Non-invasive diagnostics

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Research on Early Warning of Drug Crime Behaviors Based on data-driven Method 24

Research on Early Warning of Drug Crime Behaviors Based on D...

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Proceedings of the 2024 4th International conference on Big data, Artificial Intelligence and Risk Management

作者： Shaobing Wu Changmei Wang Institute of Information Security Key Laboratory of Intelligent Drug Control Yunnan Police College Kunming Yunnan China Education Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Yunnan Normal University Kunming Yunnan China

ISBN: (纸本)9798400709753

The identification of target suspects in images and videos from multi-source heterogeneous data is a difficult task in criminal investigation. In criminal scene analysis during law enforcement, images and videos from surveillance applications and security systems play an important role in providing visual files for the scene. With the rise of big data, artificial intelligence, the Internet of Things, blockchain, and cyberspace security, it is particularly important to improve the application of computer technology in drug crime cases and achieve data-driven drug crime behavior prediction. This paper builds police subject-specific databases such as drug addicts, drug traffickers, key personnel, and fugitives, and achieves drug crime behavior prediction and warning based on identity information, facial recognition and storage and picture comparison using a combination of Python and OpenCV. It provides a useful exploration for combating Drug Crime.

关键词： data-driven

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Impact analysis of Different Production History Input Methods on Test Well Interpretation Results for Africa Low Productivity Swabbing Wells 1

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13th International Field exploration and Development conference, IFEDC 2023

作者： Wang, Rui-Feng Qu, Dong-Meng Zhao, Guo-Liang Jinlin-Zhang Ren, Bai-Chuan Zimo-Lin RIPED CNPC Beijing China Beijing ShineTech Oil Technology Ltd. Beijing China

ISBN: (数字)9789819704798

ISBN: (纸本)9789819704781

Some blocks in Africa use swabbing for well testing due to low productivity characteristics. As such, the bottom hole flowing pressure does not decrease continuously, but changes in a jaggies pattern with the alternation of swabbing and recovery, which creates great uncertainties in the production history input during well test interpretation and thus affects the accuracy of results. There are three common production history input methods: a. Average production method: the entire production history is averaged and input;b. Swabbing cycle method: the production of each swabbing cycle is input separately;c. Swabbing section and slug flow section method: Input the swabbing section and slug flow section separately for each swabbing cycle. After employing actual well testing data in several Africa blocks, the impact of those methods on well test interpretation has been analyzed with following insights (i) the starting point of the PBU and the rate before PBU have significant effects on the interpretation results;(ii) the impact of three production history input methods on permeability results is negligible (iii) the skin factor obtained by the Swabbing section and slug flow section method is generally appropriate, but the workload of this method is significantly heavier;(iv) use Swabbing section and slug flow section method for the 3–5 swabbing sections before the PBU, while the previous production section uses the Average production method input, can greatly reduce the workload while ensuring the accuracy of the interpretation. Based on the above insights, well test interpretation was conducted on the data of multiple swabbing wells in several blocks in Africa. The practice shows that the interpretation accuracy can be improved without significantly increasing workload, thus making the interpretation results matched better with the actual field characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Well testing

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Transforming Asset Maintenance and Optimisation Through Autonomous Operations

Transforming Asset Maintenance and Optimisation Through Auto...

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Offshore Technology conference, OTC 2022

作者： Pierson, Nicholas Clarke, Richard Mikhailov, Oleg Wood plc Exploration Robotics Technologies Inc

ISBN: (纸本)9781613998526

Autonomous Operations is about utilising the latest available fit-for-purpose technology in operations, maintenance, inspection, and asset optimisation. It involves collecting operating data such as visual, thermal, low-and high-frequency acoustic, FLIR (Forward Looking InfraRed), and gas-sensing through devices mounted on autonomous robots or drones, possibly mixed with stand-alone fixed devices. This "mobile sensor" approach is ideal for existing infrastructure where adding enhanced sensing capabilities with an IoT solution is cost prohibitive. It is flexible: if a site is modified, the robots can be given a new route to monitor. It is also future proof as sensors can be easily upgraded as technology advances. Autonomous robot inspection is enabled by a software platform designed for full processing "at Edge" as a typical robotics inspection generates tens of gigabytes of data per hour. data pre-processing, construction of detailed 3D temperature, vibration amplitude and gas concentration maps, identification of anomalies, their classification by criticality and communication to operators is done by computers at the facility. Time lapse analysis is used to process the data either in-situ, or remotely, depending on site connectivity. Reports are automatically generated indicating whether the site is functioning within normal working conditions, or an anomaly has been detected. The electronic reports contain the 3D model and are viewable on mobile devices providing the field engineer with detailed diagnostic data literally at their fingertips. The process builds and maintains the living digital twin without having to expend engineering hours to update 3D models. This process not only removes the need for people, which reduces cost and safety risk, but also improves reliability by increasing the available data set and reducing inspection intervals. By utilising data analytics and artificial intelligence, the anomalies detected can be risk ranked, prioritised and sche

关键词： Robots

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Knowledge-based Graph exploration analysis on Customer's Preference of Insurance Services 10

Knowledge-based Graph Exploration Analysis on Customer's Pre...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Chen, Yingfan Kongjit, Chalermpon Media and Technology Chiang Mai University College of Arts Chiang Mai Thailand

ISBN: (纸本)9798331543273

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics. © 2025 IEEE.

关键词： Knowledge graph

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Fault Detection in Seismic data Using Graph Attention Network 33rd

Fault Detection in Seismic Data Using Graph Attention Networ...

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33rd International conference on database and Expert Systems Applications (DEXA)

作者： Palo, Patitapaban Routray, Aurobinda Singh, Sanjai Kumar Indian Inst Technol Dept Elect Engn Kharagpur 721302 W Bengal India Oil & Nat Gas Corp Ltd Dehra Dun 248003 Uttarakhand India

ISBN: (纸本)9783031124266;9783031124259

This paper presents a natural attention-based approach for automated fault detection in seismic data. Fault analysis in seismic data is important for drilling and exploration in the oil and natural gas industries. The seismic fault is a perceptual phenomenon, and manual fault detection is still practiced in various industries. The convolutional neural network (CNN) is the most commonly used method in the newly conducted research for automated fault detection. However, our paper uses a graph attention network (GAT) based approach. We first extract 2D patches centered around the points of concern. Next, we present these extracted patches in the graph domain using the k-nearest neighbor graph. The graph representation of patches is connectional in the graph domain based on seismic amplitude similarity. Then, we apply GAT to classify the faults. Both the training and testing sets contain both synthetic and real data. The proposed methodology gives good accuracy when applied to field data.

关键词： Graph attention network (GAT) Graph neural network (GNN) Seismic fault detection

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Calibration of a Multi-Residential Building Energy Model - Part ii: Calibration Using Surrogate-Based Optimization 17

Calibration of a Multi-Residential Building Energy Model - P...

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17th conference of International-Building-Performance-Simulation-Association (IBPSA)

作者： Van Long Le Marguerite, Charlotte Beauthier, Charlotte de Ghelin, Olivier Fontaine Goffaux, Cecile de Moffarts, Loic Cenaero Gosselies Belgium Thomas & Piron Batiment Wierde Belgium

ISBN: (纸本)9781775052029

The present study deals with a calibration of a white-box Building Energy Model (BEM) by using an optimization technique. The studied building is a multi-residential building which consists of 41 apartments having from 1 to 3 bedrooms. The energy consumption data are collected by digitalmeters installed for each building apartment. As the calibrated energy model will be used for the purpose of energy load prediction as well as for fault detection and diagnosis (FDD) at building and apartment levels, the energy model considers many parameters. Even after a reduction step, i.e., selecting the most influent parameters by a sensitivity analysis (i.e. work presented in a separate paper: Part I - Cluster-Based Sensitivity analysis), the total number of parameters for the optimization-based calibration is still large. Moreover, the white-box energy model simulation is quite expensive in terms of computational time. Therefore, the meta-modeling technique (e.g., Radial Basis Function network, Kriging model) is employed to reduce the computational cost of the white-box energy model simulation. The evolutionary algorithm, implemented within the Cenaero's in-house design space exploration and multi-disciplinary optimization platform, called Minamo, is used to figure out the global optimum of the constrained optimization problem.

关键词： Calibration

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AutoGaze: A Very Initial exploration in A SAM2-based Pipeline for Automated Eye-Object Interaction analysis in First-Person Videos

AutoGaze: A Very Initial Exploration in A SAM2-based Pipelin...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Qing Zhang Yifei Huang Jun Rekimoto The University of Tokyo Sony CSL Kyoto

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents a novel automated workflow for analyzing eye-tracking data in first-person videos. Our system uses the Segment Anything Model 2 (SAM2) to segment and track objects, correlating them with gaze information to provide a detailed understanding of visual attention and hand-object interactions. The pipeline outputs a structured JSON file containing rich information about gaze behavior, object interactions, and their temporal relationships. We demonstrate the system’s effectiveness in a life science operational context and compare its performance with a multimodal Large Language Model (LLM) for automated annotation. Our findings highlight the potential of combining advanced computer vision techniques with LLMs for comprehensive and scalable analysis of gaze behavior in real-world scenarios.

关键词： visualization Solid modeling Three-dimensional displays Pipelines Gaze tracking Virtual reality User interfaces Life sciences Trajectory Videos

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