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14th International Conference on Synchrotron Radiation Instrumentation, SRI 2021

作者： Vijay Kartik, S. Sprung, Michael Westermeier, Fabian Barty, Anton Deutsches Elektronen-Synchrotron DESY Notkestraße 85 Hamburg22607 Germany

With the advent of next-generation X-ray detectors with large sensor areas and high sampling rates, photon science experiments are now having to deal with a data explosion. We present a scalable, near real-time data processing toolkit developed to address this challenge at PETRA III, which is currently in use at the coherence applications beamline P10 and at the macromolecular crystallography beamline P11. this toolkit runs automated analysis pipelines on high-volume data concurrently with data acquisition, thus providing quick feedback during experiments at the beamlines. Named mento (Maxwell-Enhanced Near real-Time Online analysis), the toolkit leverages the computing resources of the in-house HPC cluster 'Maxwell' to enhance analysis performance, and additionally takes advantage of the available distributed data storage to provide analysis results directly back to the experimenter with minimal delay. mento works seamlessly with the experiment control mechanisms used at P10, P11, and other PETRA III beamlines, and ensures that analysis on the HPC cluster is triggered automatically during data acquisition at the beamline, and that results are available for visualization at the beamline control hutch even though the analysis itself is performed remotely. mento thus helps the human-in-the-loop concentrate on novel science aspects of the experiment, without needing to manage the computational workload in a high data-rate regime. mento has been used with both in-house and commercial analysis software to achieve speedups of 50x-100x relative to the existing analysis pipelines, thus proving its utility for different kinds of experiments at PETRA III. the source code for mento is available at https://***/fs-sc/mento. © Published under licence by IOP Publishing Ltd.

关键词： data acquisition

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A decision support system for DM algorithm selection based on module extraction 14

A decision support system for DM algorithm selection based o...

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14th International symposium on Intelligent Systems

作者： Man, T. Zhukova, N. A. thaw, A. M. Abbas, S. A. ITMO Univ Kronverkskiy Prospekt 49 St Petersburg 197101 Russia St Petersburg Electrotech Univ LETI Ulitsa Prof Popova 5 St Petersburg 197022 Russia Russian Acad Sci St Petersburg Inst Informat & Automat 14 Ya Liniya Bo 39 St Petersburg 199178 Russia

data mining techniques are needed in various fields. However, most data researchers do not have sufficient knowledge and experience. Due to the significant number of algorithms and parameters for data analysis, intuition-based decisions can't lead to optimal solutions. Ontology is widely adopted to build knowledge-driven decision support systems since it is suited to encapsulate the concepts and relationships of terms associated with various domains. It is suitable for capturing knowledge by computers, allowing sharing and reusing it whenever necessary. All concepts and relationships about data analysis can be described using Web Ontology Language (OWL). Its reasoning mechanism is vital in any knowledge-based system. Ontology can be reasoned to recommend suitable solutions for a specific analysis task by considering the data characteristics and task requirements. In the previous work, we have developed a system that describes comprehensive knowledge and logical internal relationships about data mining We found that the reasoning and query complexity is high in the large size ontology, so this paper focuses on the modeling and implementation of an ontology-based decision support system for data mining algorithm selection that contains a new sub-ontology extraction method. Extracting effective content can significantly reduce the size of the ontology. this extraction method can improve query efficiency on the premise of ensuring the quality of information. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： Decision support system data analysis ontology module extraction

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Insights from hydronic heating systems in 259 commercial buildings

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ENERGY AND BUILDINGS 2024年 321卷

作者： Raftery, Paul Singla, Rupam Cheng, Hwakong Paliaga, Gwelen Univ Calif Berkeley Ctr Built Environm Berkeley CA 94720 USA TRC 436 14th St Suite 1020 Oakland CA 94612 USA Taylor Engineers 1080 Marina Village Pkwy Alameda CA 94501 USA

this paper analyzes hydronic heating systems in 259 buildings across 56 US organizations. the dataset comprises 120,000,000 measurements taken by building automation systems from 2014 to 2024 and for a typical building contains measured supply and return water temperature, flow rate, output power, system state and outdoor temperature spanning 2.2 years (15-minute interval). Pump and boiler data are available for smaller subsets of buildings. analysis shows that many assumptions about these systems do not align with real-world performance. Systems operate far more frequently than expected - 81% of hours annually for the median building. Loads are lower and more skewed than expected, and oversizing is common even accounting for redundancy requirements. Load distributions indicate that relatively small equipment could serve a surprisingly large percentage of annual loads. Notably, equipment sized to 30% of the maximum measured load could serve up 84% of the total heating energy consumption for the median building. analysis of key parameters, such as supply water temperature, show that functional resets are rare and that manual overrides are common, often persisting for months or years. Return water temperatures and other data show that boilers operate less efficiently than expected. these findings offer valuable insights for operators, designers, owners, and policymakers, highlighting that substantial energy savings opportunities are commonplace and can be captured by modifying design and operation based on how these systems actually operate. the paper includes the code used for analysis and visualization as well as an open-source dataset of 216 buildings.

关键词： Hydronic heating systems heating hot water (HHW) systems Heating ventilation and air conditioning HVAC Controls Energy efficiency Open-source measured data

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Exploring Key Factors Affecting Students' Adaptability in Online Learning

Exploring Key Factors Affecting Students' Adaptability in On...

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IEEE International symposium on Information (IT) in Medicine and Education, ITME

作者： Yingying Fan Hong Wang Yue Sun School of Information Science and Engineering Shandong Normal University Jinan China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Under the impact of the COVID-19 pandemic, traditional classroom teaching has been obstructed, making online learning the primary choice for education. Students' adaptability to online learning environments significantly affects their learning outcomes and overall quality of education. this study aims to identify the key factors affecting students' adaptability to online learning. We first preprocess the data by creating dummy variables to make the data suitable for subsequent statistical analysis. Secondly, a quartile relation matrix is used to visualize the Pearson relation coefficients between various variables. then, the R package IsingFit is employed for model fitting, and a psychometric network model is used to visualize the network relationships, resulting in an undirected relation graph. the experimental results indicate that family economic status, home address, online learning equipment, and the usability of the school's student management system are key factors affecting student adaptability. the results highlight the necessity of improving home learning conditions, providing technical support, and optimizing school management to enhance the online learning experience for students.

关键词： Economics COVID-19 Adaptation models Learning management systems visualization Correlation Pandemics Education Web and internet services data visualization

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Acceleration of Gravitation Field analysis for Asteroids by GPU Computation 14

Acceleration of Gravitation Field Analysis for Asteroids by ...

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14th IEEE International symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC)

作者： Kono, Fumiya Nakasato, Naohito Hirata, Naru Matsumoto, Koji Univ Aizu Aizu Wakamatsu Fukushima Japan Natl Astron Observ Japan Oshu Iwate Japan

ISBN: (纸本)9781665438605

Researches with explorations by space probes for asteroids have been performed actively to approach to the origin of the solar system and life. One of methods toward the goal is analyzing structure of solar system bodies by numerical simulation. GFandSlope is a code which calculates the gravitation field, slope, and attraction of given model data for small solar system bodies. When we use the existing sequential computation code, it is inevitable to take large time to analyze high resolution models with different initial conditions. this work achieved to compute several thousands faster than the previous by GPU implementation, which will also boost researches in the field of space science. this paper presents the evaluation of our GPU codes for fast gravitation field analysis and discusses numerical precision in floating point operations on the GPU for practical application.

关键词： Gravitation Field Asteroids N-body problem GPU CUDA OpenCL

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Visual analysis of Industrial Multivariate Time Series 21

Visual Analysis of Industrial Multivariate Time Series

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14th International symposium on Visual Information Communication and Interaction, VINCI 2021

作者： Musleh, Maath Chatzimparmpas, Angelos Jusufi, Ilir Linnaeus University Vaxjo Sweden

ISBN: (纸本)9781450386470

the recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. the ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. We investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field. © 2021 ACM.

关键词： visualization

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Visual analysis of Traditional Chinese Medicine for Menstrual Disorders from 2013 to 2023 Based on CiteSpace

Visual Analysis of Traditional Chinese Medicine for Menstrua...

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IEEE International symposium on Information (IT) in Medicine and Education, ITME

作者： Yuting Wang Yumeng Li Yang Zhang College of Medical Information Changchun University of Chinese Medicine

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

OBJECTIVE: this study aims to visually analyze the use of Traditional Chinese medicine (TCM) for the treatment of menstrual disorder symptoms from 2013 to 2023, using Citespace software, with the objective of understanding the field's developmental trajectory and frontier hotspots. MEthODS: Relevant literature was retrieved from two major databases: China National Knowledge Infrastructure (CNKI) and WANGFANG data (WanFang) and a database was constructed. the data were cleaned using NoteExpress and the annual publication count was statistically analyzed using Microsoft Excel, which was imported into Citespace to map authors, institutions, and keywords (co-occurrence, clustering, and emergent words) for visualization and analysis. RESULTS: the study totally included 815 articles after data cleaning according to the inclusion criteria, with an average annual publication volume of 74. the publication volume in most years fluctuated slightly around the mean, while there were some years with significant fluctuations. the Centrality of any author and institution was 0.00, indicating a lack of communication between authors, institutions and institutions, with only a few exceptions, such as researchers from Changchun University of Chinese Medicine (CCUCM) and the Guang'anmen Hospital of the China Academy of Traditional Chinese Medicine (GAMHCATCM), Beijing University of Chinese Medicine (BJUCM) and GAMHCATCM, with the highest number of publications accumulating 5 articles, but no core group of authors has been formed yet. the main theme of the literature in this field is to validate the clinical efficacy of Chinese herbal medicine in treating menstrual disorders, with decoction as the dosage form, usually taken orally after decoction or by enema. Acupuncture or combination with Western medicine may be used to enhance therapeutic effects at times. In recent years, the research hotspots are TCM compounding and TCM decoction.

关键词： visualization Fluctuations databases Hospitals Education data visualization Software Trajectory Spreadsheet programs Information technology

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A Wide-Band High-Speed Sample and Hold in 0.35µm CMOS Technology

A Wide-Band High-Speed Sample and Hold in 0.35µm CMOS Techn...

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IEEE Latin American symposium on Circuits and Systems (LASCAS)

作者： Mateus Bernardino Moreira Hervé Lapuyade François Rivet Yann Deval Bordeaux INP UMR CNRS 5218 IMS Laboratory Univ. Bordeaux Talence France

the design of a wide-band high-speed Sample and Hold (S&H) is presented herein. A new topology is proposed to address the stringent large bandwidth high-speed requirements of fast data transmissions. In a high-speed communication system, the fast operation of the analog to digital converter (ADC) is required, presenting a great challenge for the S&H design. the main contribution of the proposed design is to minimize the impact of the sampling frequency induced by the switch position. the designed S&H operates with a sampling frequency of 800MHz, an input signal of 80MHz, and a power consumption of 3.05mW. the circuit occupies an area of $0.0572mm^{2}$ in the $0.35\mu \mathrm{m}$ CMOS process.

关键词： Time-frequency analysis Power demand Circuits and systems Bandwidth Switches Topology Frequency measurement

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Enhancing Forest Fire Management in Malaysia with a Power BI Dashboard: Leveraging VIIRS Hotspots and Environmental data

Enhancing Forest Fire Management in Malaysia with a Power BI...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Yee Jian Chew Shih Yin Ooi Ying Han Pang Jin Kang Hoi Faculty of Information Science and Technology Multimedia University Melaka Malaysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Addressing the growing threat of forest fires intensified by climate change and human activities, this study leverages the Microsoft Power BI dashboard to enhance forest fire management in Malaysia. Utilizing the VIIRS 375 m Active Fire product data from NASA's Fire Information for Resource Management System (FIRMS) alongside meteorological variables (wind speed, precipitation, and temperature) from the NASA Langley Research Center (LaRC) POWER Project, the dashboard harnesses Power Bl's capabilities to offer a user-friendly interface that dynamically visualizes data on fire occurrences, environmental conditions, and trend analyses. this tool is aimed at supporting proactive forest fire management strategies, thereby safeguarding Malaysia's natural landscapes and communities by facilitating improved monitoring, analysis, and predictive forecasting of fire events. through the integration of satellite-based observations and environmental data from NASA's initiatives, the dashboard is designed to offer critical insights and effective forest fire mitigation efforts in Malaysia.

关键词： Temperature distribution Technological innovation Precipitation Wind speed NASA data visualization Forestry

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Speed and Torque Estimation in Induction Motor through the analysis of Stray Flux Signals

Speed and Torque Estimation in Induction Motor through the A...

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IEEE International symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

作者： Geovanni Díaz-Saldaña Israel Zamudio-Ramírez Óscar Ugalde Ugalde Roque A. Osornio-Ríos Jose A. Antonino-Daviu Facultad de Ingeniería Universidad Autónoma de Querétaro Querétaro México Instituto Tecnológico de la Energía Universitat Politècnica de València (UPV) Valencia Spain

Induction motors are elements of great importance within the industry due to the large number of applications they have; because of this, their monitoring is essential to ensure the correct performance of the tasks. Several methods have been created for the estimation of speed and torque in these machines, many of these methods use sensors that are invasive to the motor or require taking it off-line to perform a series of tests to obtain the necessary parameters for its estimation. the present research proposes a methodology for the estimation of speed and torque in induction motors through the analysis of stray flux signals obtained in a non-invasive manner and the information provided by the manufacturer on the data plate of the machine. Preliminary results show a speed estimation with an error of less than 0.2% and a methodological proposal for torque estimation, as well as clear prospectives to improve the methodology to obtain better results in torque estimation and to generalize the method by testing the algorithm with different machines.

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