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Humanitarian Technology conference (KHI-HTC), ieee Karachi Section

作者： Rida Zehra Sumaira Khan Daniyal Lightwala S.M. Hur Rizvi Electrical Engineering Habib University Karachi Pakistan ECE Habib University Karachi Pakistan

With the increased penetration of distributed energy resources, it is crucial for distribution network operators to perform analysis to ensure grid stability. Technological advances in the recent years, have made it possible for the development of such tools, some of which are licensed, while others are opensourced. PSS Sincal, CYME to name a few, are commercial softwares which require license, limiting their adoption. Whereas, tools like GridLab-D and OpenDSS are open-sourced tools used to model, simulate and perform analysis of a distribution network. This paper, presents a python-based open-source DNTool, which provides automated modeling as well as visualization of the results. The developed tool uses OpenDSS, through Python interface, to model, solve and analyze the distribution network, whereas the visualization is done on PyQt5. Modeling of the network is performed in OpenDSS, through the data of the components in the distribution network provided in the form of a CSV File. The tool uses Snapshot Analysis Method to solve and perform analysis on the distribution network. The results are then visualized using PyQt5. The features of DN-Tool are exhibited for ieee-13 bus distribution system.

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Proposing Big Data Architecture for Addressing Dropout Problem in MOOC Platforms

Proposing Big Data Architecture for Addressing Dropout Probl...

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2024 International conference on Advanced Technologies for Communications, ATC 2024

作者： Nguyen, Anh-Khoa Nguyen, Dinh-Khoa Vo, Khoa Tan Nguyen, Thu Nguyen-Hoang, Tu-Anh Dinh, Ngoc-Thanh Nguyen, Hong-Tri University of Information Technology Faculty of Information Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam The Industrial University of Ho Chi Minh City Viet Nam Aalto University Finland

ISBN: (纸本)9798350353983

Accessing academic knowledge on Massive Open Online Courses (MOOCs) has made learning more convenient with flexible schedules and a vast array of course options. The common weakness of these platforms, however, lies in the difficulty of controlling learners' behaviour. Noone can know for certain whether the level of engagement during learning is sufficient for learners to fully grasp the knowledge, or whether learners may fail to complete the courses they have enrolled in, leading to dropout behaviour. In this study, we also ap-plied an artificial intelligence model to predict whether current students can complete the course, enabling quick detection of dropout behaviour and timely preventive measures. Based on the proposed architecture, a real-time monitoring, analysis, and management application system for learner behavior can be developed. This empowers course managers to detect which learners might drop out of which courses, enabling timely alerts to learners for adjusting their study plans or, on a broader scale, restructuring the organization of courses with excessively high dropout rates. To maximize scalability with the increasing volume of MOOC data and applicability across different MOOC platforms, our architecture will be built on the Microsoft Azure Cloud computing service, utilizing modern and renowned big data technologies to perform tasks ranging from streaming data collection, batch data processing, distributed processing of large-scale data, to storing data in any format with the latest data lakehouse storage architecture, and real-time data visualization and anomaly detection through integrated models. © 2024 ieee.

关键词： Cloud computing architecture

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A Progressive Approach for Uncertainty visualization in Diffusion Tensor Imaging

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COMPUTER GRAPHICS FORUM 2021年第3期40卷 411-422页

作者： Siddiqui, Faizan Hollt, Thomas Vilanova, Anna Delft Univ Technol Delft Netherlands Eindhoven Univ Technol Eindhoven Netherlands

Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance imaging technique that, combined with fiber tracking algorithms, allows the characterization and visualization of white matter structures in the brain. The resulting fiber tracts are used, for example, in tumor surgery to evaluate the potential brain functional damage due to tumor resection. The DTI processing pipeline from image acquisition to the final visualization is rather complex generating undesirable uncertainties in the final results. Most DTI visualization techniques do not provide any information regarding the presence of uncertainty. When planning surgery, a fixed safety margin around the fiber tracts is often used;however, it cannot capture local variability and distribution of the uncertainty, thereby limiting the informed decision-making process. Stochastic techniques are a possibility to estimate uncertainty for the DTI pipeline. However, it has high computational and memory requirements that make it infeasible in a clinical setting. The delay in the visualization of the results adds hindrance to the workflow. We propose a progressive approach that relies on a combination of wild-bootstrapping and fiber tracking to be used within the progressive visual analytics paradigm. We present a local bootstrapping strategy, which reduces the computational and memory costs, and provides fiber-tracking results in a progressive manner. We have also implemented a progressive aggregation technique that computes the distances in the fiber ensemble during progressive bootstrap computations. We present experiments with different scenarios to highlight the benefits of using our progressive visual analytic pipeline in a clinical workflow along with a use case and analysis obtained by discussions with our collaborators.

关键词： CCS Concepts • Human-centered computing → Visual analytics Scientific visualization

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visualization of Data Cubes for Anomaly Detection in Network Traffic Data Streams 11

Visualization of Data Cubes for Anomaly Detection in Network...

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11th ieee International conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ahlers, Volker Laue, Tim Wellermann, Nils Heine, Felix Univ Appl Sci & Arts Hannover Hannover Germany

ISBN: (纸本)9781665426053

For anomaly-based intrusion detection in computer networks, data cubes can be used for building a model of the normal behavior of each cell. During inference an anomaly score is calculated based on the deviation of cell metrics from the corresponding normality model. A visualization approach is shown that combines different types of diagrams and charts with linked user interaction for filtering of data.

关键词： network security data cubes multidimensional analysis user interfaces information visualization

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Study on Array Electrode System for visualization Measurement in Air-Solid Two-Phase Flow 4

Study on Array Electrode System for Visualization Measuremen...

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4th International conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2022

作者： Li, Lipin Hu, Yuexin Zhu, Jianbo Shi, Ruixue Wang, Hongli Song, Jincheng Shi, Zhipeng Xi'an Shiyou University Shanxi Key Laboratory of Measurement and Control Technology for Oiland Gas Wells Xi'an China The Fourth Gas Production Plant of Changqing Oilfield Company Thousand Gas Well Evaluation and Potential Engineering Project Team of Changqing Oilfield Company Xi'an China The First Gas Production Plant of Changqing Oilfield Company Jingbian China The Third Oil Transportation Division of Changqing Oilfield Company Yinchuan China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

In power, chemical, pharmaceutical, metallurgical, and other industrial production, the visual information of gas-solid two-phase flow in closed pipelines is of great significance to the safety, economy, and efficiency of the production process. According to the basic principle of electrical capacitance tomography (ECT), we analyzed and designed a 12-electrode capacitance sensor, realized the data acquisition circuit with capacitance digital conversion (CDC) chip as the core and ARM chip as the control platform, and developed the upper computer software based on QT. We have tested the visualization system, and the results show that the system has significant stability, and the measurement results are clear and accurate. © 2022 ieee.

关键词： Two phase flow

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Exploring Eye Gaze Patterns in Three Dimensions: An Innovative visualization Dashboard

Exploring Eye Gaze Patterns in Three Dimensions: An Innovati...

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Contemporary Computing and Communications (InC4), ieee International conference on

作者： Akshay S Yashwanth M V Manohar N Tian Jipeng Amudha J Department of Computer Science Amrita Vishwa Vidyapeetham School of Computing Mysore Karnataka India Department of Computer Science Computer Science College Zhongyuan University of Technology China Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Bengaluru Karnataka India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Eye tracking is a method of measuring and analyzing the movements of the eyes as they move and focus on different objects in the visual field. This technique has been used in a wide range of fields, including psychology, neuroscience and human-computer interaction. Eye tracking provides valuable insights into how individuals process visual information, including attentional biases, cognitive processing, and decision-making. Traditionally, eye tracking has relied on licensed software and specialized hardware, making it costly and less accessible for some researchers and applications. However, with recent technological advancements and by leveraging the capabilities of webcams, researchers, and practitioners can now perform eye-tracking experiments without the need for expensive equipment. By utilizing webcams, researchers can conduct eye-tracking experiments in a cost-effective manner, making the method more accessible to a broader range of researchers and practitioners.

关键词： visualization Three-dimensional displays Neuroscience Webcams Data visualization Psychology Gaze tracking

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Chaos Game Representation of Audio Signals

Chaos Game Representation of Audio Signals

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ieee International Instrumentation and Measurement Technology conference (I2MTC)

作者： Cohen-McFarlane, Madison Dick, Kevin Green, James R. Goubran, Rafik Carleton Univ Inst Data Sci Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781728195391

Audio measurements are fundamental to daily life and have been used to perform a variety of tasks including the classification of human sounds (e.g. talking or coughing) and environmental acoustic monitoring. Audio visualization methods have been introduced to represent both the time- and frequency-domain information of a recording. In this paper we introduce Chaos Game Representation (CGR) to investigate possible reoccurring local and global patterns within audio measurements to supplement current audio visualization methods and for possible use in the training and evaluation of learning algorithms. A major challenge of the application of CGR within audio-space is quantization. Here, we leverage the non-uniform mu-law (mu = 255) quantization as the basis for the first quantized audio CGR representation. We propose a 256-nodal arrangement of the quantized states from an audio measurement for playing the Chaos Game to generate visualizations that capture both local and global sequential time-series information. CGRs were generated for 287,756 individual audio measurements (pure sinusoids, linear & quadratic chirps, and ambient audio measurements from DCASE2018). A typology of visually observable patterns is discussed describing the relationship between the time-series audio signal and their resulting CGR visualizations. These images may be leveraged for training image-based classifiers for audio classification tasks.

关键词： Chaos Game Representation Audio Signal Processing Audio visualization

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Improving Architect-Specific Building Image Generation using Reinforced Data Processing

Improving Architect-Specific Building Image Generation using...

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ieee MIT Undergraduate Research Technology conference (URTC)

作者： Cherie Hong Yestin Arvin Gochuico Jiwon Shin Chaeyeon Ha Lenin Marquez Youngjin Yoo Jin-Kook Lee School of Information University of Texas at Austin Austin United States College of Arts & Sciences University of Miami Miami United States College of Science & Technology Yonsei University Seoul South Korea College of Human Ecology Yonsei University Seoul South Korea College of Engineering California State Polytechnic University Pomona Pomona United States

ISBN: (数字)9798331531003

ISBN: (纸本)9798331531010

This paper investigates the innovative potential of fine-tuning generative Artificial Intelligence (AI) models through Reinforced Data Processing (RDP) for architectural visualization, focusing on the styles of three renowned architects. The study examines how the varying levels of detail in textual descriptions within image-text pairs impact model performance and evaluates these effects using a domain-specific RDP approach. The findings demonstrate that utilizing RDP models outperforms the base model in generating architect-specific images, offering a practical and creative tool for architects. This study underscores the potential of integrating generative AI into architectural design through RDP, offering substantial support for creative work.

关键词： visualization Generative AI Image synthesis Buildings Data visualization Data processing Diffusion models Data models Creativity Testing

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Research on Electric Power Big Data Analysis System under Smart Grid and information visualization 2

Research on Electric Power Big Data Analysis System under Sm...

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2nd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2023

作者： Li, Rui Shi, Pengbo Li, Mingkai Ding, Ning Lu, Xiangyu Liu, Yuexiao Yuan, Mingmin Yang, Guanghua State Grid Beijing Electric Power Research Institute Beijing100075 China

ISBN: (纸本)9798350339765

Aiming at the problem of poor real-time control effect in the traditional power marketing remote real-time fee control system, a big data-based power marketing remote real-time fee control system design is proposed. First, the overall system architecture consisting of a channel collaborative operation platform, a channel collaborative management platform, a risk management and control platform, and a data sharing application platform is given. The key description includes user behaviour analysis, business behaviour analysis, operation planning, operation execution, operation release, and operation monitoring. Operation report, operation assistance and other main function design. Build a data distributed structure model to realize data collection and sample combination. According to the correlation test results of DC voltage harmonics and voltage fluctuations, the remote real-time cost control information characteristics of power marketing are extracted to realize the adaptive grouping of data. The application of this system in Chinese power companies not only improves the analysis capabilities of power companies' marketing operations, but also greatly improves the accuracy of forecasts. This is important for promoting the sustainable development of Chinese power companies and responding to national policies. Prompt and guiding significance. © 2023 ieee.

关键词： Big data

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MANet: A Deep Learning Framework for Multi-Cancer Microarray Analysis, Classification, and visualization

MANet: A Deep Learning Framework for Multi-Cancer Microarray...

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2024 International conference on Frontiers of information Technology, FIT 2024

作者： Younis, Haseeb Azeem, Muhammad Ronan, Isabel Minghim, Rosane School of Computer Science University College Cork Cork Ireland School of Nursing and Midwifery University College Cork Cork Ireland

ISBN: (纸本)9798331510503

Machine learning (ML) methods have been used much more frequently in recent years to extract gene expression data from microarray studies, especially in cancer research. Even after the continued interest in applying ML to scientific cancer research, there is still no universal approach for categorizing cancer microarray data. A system is needed that can detect and classify a normal profile and a cancer profile, specifying the type of cancer. Due to the variance and high dimensionality of microarray data, it is difficult to extract the relevant descriptors and provide insights that can be helpful in identifying cancer types and stages. In this paper, we proposed MANet: a methodology using cancer microarray data based on Deep Learning (DL) to classify 13 different types of cancers as well as normal profiles. To implement this methodology, we used a Curated Microarray Database (CuMiDa) that has 78 datasets for different types of cancers. Due to the diverse feature vectors for each dataset, we used Principal Component Analysis (PCA) for uniform feature engineering. Our single model has the capability to learn the patterns, cluster instances into their corresponding classes and classify the cancer. We also used the Uniform Manifold Approximation and Projection (UMAP) to visualise the instance separation on original data. Additionally, this UMAP visualises segregation done by our methodology in low dimensions. Using the proposed methodology, we achieved approximately 80% average accuracy, precision, recall, and F1 Score for 14 classes using a single model. © 2024 ieee.

关键词： Lung cancer

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