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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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HUMAP: Hierarchical Uniform Manifold Approximation and Projection

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IEEE Transactions on visualization and Computer Graphics 2024年 PP卷 PP-PP页

作者： Marcilio-Jr, Wilson E. Eler, Danilo M. Paulovich, Fernando v. Martins, Rafael M. São Paulo State University Brazil Netherlands Linnaeus University Sweden

Dimensionality reduction (DR) techniques help analysts to understand patterns in high-dimensional spaces. These techniques, often represented by scatter plots, are employed in diverse science domains and facilitate similarity analysis among clusters and data samples. For datasets containing many granularities or when analysis follows the information visualization mantra, hierarchical DR techniques are the most suitable approach since they present major structures beforehand and details on demand. This work presents HUMAP, a novel hierarchical dimensionality reduction technique designed to be flexible on preserving local and global structures and preserve the mental map throughout hierarchical exploration. We provide empirical evidence of our technique's superiority compared with current hierarchical approaches and show a case study applying HUMAP for dataset labelling. © 1995-2012 IEEE.

关键词： Dimensionality Reduction Hierarchical exploration Manifold Learning

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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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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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4th International conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Wu, Shaobing Wang, Changmei Institute of Information Security Key Laboratory of Intelligent Drug Control Yunnan Police College Yunnan Kunming China Education Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Yunnan Normal University Yunnan Kunming 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. © 2024 Copyright held by the owner/author(s).

关键词： data-driven drug crime behavior prediction identity recognition

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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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Comparative analysis of Methadone’s Impact on Gut Microbiome: A Cross-Population Study Between the United States and China 1

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International conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2023

作者： Bidar, Hasnae Elomari, Fatima Chakib, Soukaina Sehli, Sofia Alidrissi, Najib Rhalem, Wajih Ghazal, Hassan Laboratory of Genomics Bioinformatics and Digital Health School of Medicine Mohammed VI University Sciences and Health Casablanca Morocco Faculty of Medicine and Pharmacy of Rabat Mohammed V University Rabat Morocco Addictology Service Ar-Razi University Psychiatric Hospital Rabat-Salé University Hospital Center Salé Morocco Department of Surgery Faculty of Medicine Mohammed VI University of Sciences and Health Casablanca Morocco Mohammed V University in Rabat Rabat Morocco Moroccan Society of Digital Health Rabat Morocco Royal Institute of Sports Royal Institute for Managerial Training in Youth and Sport Department of Sports Sciences Laboratory of Sports Sciences and Performance Optimization Salé10102 Morocco

ISBN: (数字)9783031523854

ISBN: (纸本)9783031523847

Background: Methadone, a synthetic opioid, is frequently employed as an alternative treatment for opioid use disorder (OUD) and the management of chronic pain. However, its prolonged usage comes with a notable drawback: adverse effects on the gut microbiome. The utilization of methadone has been associated with substantial modifications in both the composition and functionality of the gut microbiome. These alterations encompass shifts in bacterial diversity and abundance, which can potentially exert significant influences on gut health and the intricate gut-brain axis. This article undertakes a meticulous examination, conducting a comparative analysis of findings from two distinct studies conducted in the United States and China. Through this approach, it sheds light on the multifaceted impact of methadone on the gut microbiome, elucidating the potential ramifications of these effects on addiction and overall health. The overarching objective is to provide readers with a comprehensive understanding of how methadone influences the gut microbiome, underscoring the imperative necessity for further exploration in this promising yet understudied realm. Methods: We obtained gut microbiome data from two sources: the study conducted by Li et al. (2020), accessible under accession number PRJEB36803, and the study by Cruz- Lebrón et al. (2021). These datasets were used to examine two distinct cohorts of individuals undergoing methadone maintenance treatment (MMT) for opioid use disorder (OUD). The first cohort included 24 patients from the Cleveland Treatment Center and Case Western Reserve University in USA. The second cohort comprised 16 MMT patients from Honghe Prefecture, Yunnan Province, China, with ages ranging from 29 to 58 years. To analyze the raw 16S rRNA amplicon sequence data, we employed the EzBioCloud metagenomics platform. Results: Both studies demonstrated that methadone use can lead to alterations in gut microbiome composition and potentially impact metabolic

关键词： RNA

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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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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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Development Effect and Adaptability analysis of Three Kinds of Horizontal Wells in Sudongnan Gas Field, Ordos Basin 11th

Development Effect and Adaptability Analysis of Three Kinds ...

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11th International Field exploration and Development conference, IFEDC 2021

作者： Zhang, Zheng-tao Wang, Xing-yan Fei, Shi-xiang Zhang, Hang Cui, Yue-hua Liu, Xue-ling Wang, Zhun-bei Research Institute of Petroleum Exploration and Development PetroChina Changqing Oilfield Company Xi’an China National Engineering Laboratory of Low Permeability Oil and Gas Field Exploration and Development Xi’an China Gas Production Plant 1 of PetroChina Changqing Oilfield Company Xi’an China

ISBN: (纸本)9789811921483

Horizontal well is the principal development method of Sudongnan tight sandstone gas field in Ordos Basin. At present, three types of horizontal wells, i.e. the horizontal track well, the ladder track well and high slope track well, are primarily used. However, systematic work has not been carried out on the development effect, selection standard and applicability of these three types of horizontal wells. In this paper, based on drilling data, logging data, gas test and production data of 537 horizontal wells in sudongnan gas field, geological analysis, geological modeling, numerical simulation are adopted to comprehensively analyze the development effect of three types of trajectory horizontal Wells with different (effective) sand body. Furthermore, the quantitative criteria of horizontal well trajectory type selection based on three factors of single gas layer thickness (Hg), vertical reserve concentration (Φ) and vertical gas bearing interval (Hv) are proposed: ① when one main gas layer is developed, the thickness of gas reservoir is Hg > 5 m, the vertical reserves concentration is Φ > 55%, and the vertical gas bearing interval is Hv > 20 m, the horizontal track should be selected;② when two or more gas layers are developed, the thickness of single gas layer is Hg > 3 m, the vertical reserves concentration of single layer is 30% v ≤ 20 m, the ladder track should be selected;③ when three or more gas layers are developed, the thickness of single gas layer is Hg v ≤ 15 m, the high slope track should be selected. The establishment of this standard has reference and guiding significance for horizontal well development trajectory optimization of Upper Paleozoic tight sandstone gas reservoirs in Sudongnan gas field and Ordos Basin. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Horizontal wells

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Rebalancing Techniques for Asynchronously Distributed EEG data to Improve Automatic Seizure Type Classification 57

Rebalancing Techniques for Asynchronously Distributed EEG Da...

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57th Annual conference on Information Sciences and Systems, CISS 2023

作者： McCallan, Niamh Davidson, Scot Ng, Kok Yew Biglarbeigi, Pardis Finlay, Dewar Lan, Boon Leong McLaughlin, James Nibec School of Engineering Ulster University BelfastBT15 1ED United Kingdom Department of Ecse and Aep Monash University Bandar Sunway47500 Malaysia School of Science and Engineering University of Dundee DD1 4HN United Kingdom

ISBN: (纸本)9781665451819

Epilepsy, a nervous system disorder, is charac-terised by unprovoked, unpredictable, and recurrent seizures. To diagnose epileptic seizures, electroencephalography (EEG) is frequently used in medical settings. Effective automated detection and classification strategies are needed because visual analysis and interpretation of EEG signals consume time and call for specialised expertise. The main objective of this paper is to examine the effectiveness of multiple rebalancing techniques to address the problem of asynchronously distributed data, specifically employing random resampling, synthetic minority oversampling technique (SMOTE), and adaptive synthetic sampling approach for imbalanced learning (ADASYN), for seizure type classification. The model utilises both frequency information using variational mode decomposition (vMD), and phase information by extracting the phase locking value (PLv) across 19 common EEG channels found in the Temple University Hospital EEG Seizure Corpus (TUSZ) v1.5.2 dataset. The random subspace k-nearest neighbour (RSkNN) ensemble classifier is used for seizure type classification of five classes - complex partial seizures (CPSZ), simple partial seizures (SPSZ), absence seizures (ABSZ), tonic clonic seizures (TCSZ), and tonic seizures (TNSZ) - to determine the performance of each rebalancing techniques, with the highest accuracy and weighted F1 score of 96.28% and 0.964, respectively using SMOTE with two nearest neighbours. © 2023 IEEE.

关键词： Electroencephalography

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