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检索条件"任意字段=Proceedings - 3rd International Conference on Distributed Computing Systems."

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Dynamic Partitioning of 3D Scenes into User-Bounding Boxes for Immersive Metaverse Experience

Dynamic Partitioning of 3D Scenes into User-Bounding Boxes f...

引用

Disruptive Technologies (ICDT), international conference on

作者： Deep Kumar Sneha Chauhan Lalit Kumar Awasthi Department of CSE National Institute of Technology Uttarakhand India Department of CSIT KIET Group of Institutions Ghaziabad NCR India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The partitioning of 3-dimensional environments into user-specific bounding boxes, accompanied by immersive audio and visual experiences, presents a significant challenge in virtual and augmented reality systems. This study examines methods for the adaptive segmentation and management of virtual environments to improve user interaction and immersion. The system utilizes spatial partitioning algorithms such as octrees, Voronoi diagrams, and bounding volume hierarchies to dynamically allocate regions of 3D space according to the needs of individual users. Immersive audio experiences utilize directional sound rendering via Head-Related Transfer Functions and dynamic audio zones, whereas adaptive visual rendering incorporates techniques like level of detail, frustum culling, and real-time lighting adjustments. The proposed framework integrates AI-driven personalization, procedural content generation, and gesture-based interaction to establish context-aware and responsive environments. Synchronization among networked devices enables smooth transitions in multi-user environments, supported by edge computing and consensus algorithms. This research addresses the technical complexities of real-time dynamic partitioning and immersive multimodal experiences to enhance user engagement and redefine interaction paradigms in virtual spaces. The proposed solutions exhibit scalability and adaptability for use in gaming, training simulations, and collaborative metaverse environments.

关键词： Training Visualization Three-dimensional displays Metaverse Scalability Transfer functions Rendering (computer graphics) Real-time systems Synchronization Immersive audio

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Model of the peer‐to‐peer distributed system for securable information storage and processing without traffic prioritization (TheOoL project) 3

Model of the peer‐to‐peer distributed system for securable...

引用

3rd international Workshop on Modeling, Information Processing and computing, MIP: computing 2021

作者： Nenashev, Alexey V. Tolstenko, Alexandr Yu. Oleshko, Rostislav S. Samara State Technical University Index Samara Russia

The mathematical model "Peer-to-peer distributed system for securable information storage and processing in enterprise networks" is described hereinafter. It is a versatile distributed operating system designed for the protection of distributed computing and insulation of private networks without restricting the possibilities of effective interactions, cryptographic security, protection from unauthorized access with the application of biometry and an innovative protocol of data exchange for topology control based on distributed ledger technology. The modeling was performed with the purpose of evaluation of performance of the system depending on productivity of the hardware of its nodes and the network's telecommunications equipment. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Queueing networks

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A Lip-reading Recognition Method that Integrates a 3D Dual-stream Convolutional Neural Network 3

A Lip-reading Recognition Method that Integrates a 3D Dual-s...

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3rd international conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2024

作者： Huang, Zheng Xing Mamut, Mutellip Ibrahim, Ghalip Yadikar, Nurbiya Ubul, Kurban School of Computer Science and Technology Xinjiang University Wulumuqi China Library of Xinjiang University Xinjiang University Wulumuqi China The Equipment Department of Xinjiang University Xinjiang University Wulumuqi China

ISBN: (纸本)9798331534035

Lip reading refers to interpreting a speaker's content by observing lip movements and is widely applied in speech reconstruction, digital human modeling, and multimodal speech systems. A key challenge in this area lies in efficiently extracting temporal information. This study introduces a Temporal Residual Network to capture critical lip-reading features and proposes, for the first time, a method that uses temporal residuals to merge slow and fast dual-stream channels. Specifically, the slow-stream channel in the frontend network replaces the average pooling layer in the ResNet-18 backbone with a 3D convolution, while the fast-stream channel retains the pooling layer and incorporates channel-wise shifting via TSM. Two CNN streams with different channel capacities are utilized to extract static features from keyframes and dynamic information from non-keyframe sequences. Experimental results confirm the effectiveness of this approach. The model achieves 89.14% accuracy on the English LRW dataset, surpassing the baseline, and 58.45% on the Chinese LRW-1000 dataset, establishing a new benchmark in lip-reading technology. © 2024 IEEE.

关键词： Convolutional neural networks

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ISecIT 2021 - proceedings of the 3rd international conference on Information Security and Information Technologies, co-located with 1st international Forum "Digital Reality", DRForum 2021

ISecIT 2021 - Proceedings of the 3rd International Conferenc...

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3rd international Scientific and Practical conference on Information Security and Information Technologies, ISecIT 2021

The proceedings contain 43 papers. The topics discussed include: development of a method for detecting deviations in the nature of traffic from the elements of the communication network;method of recognition sarcasm in English communication with the application of information technologies;using the sum of real type functions to encrypt messages;method of calculation of information protection from clusterization ratio in social networks;modification of query processing methods in distributed databases using fractal trees;peculiar properties of creating a system of support to make anti- crisis decisions by experts of the situational center at the cyber protection object;the basic principles of the compact video frames representation technology, which are presented in a differential form in computer systems.and methodical approach to the development of a mathematical model for calculating the combat potentials of strike unmanned aircraft.

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Isolation Forest Based Anomaly Detection and Fault Localization for Solar PV System

Isolation Forest Based Anomaly Detection and Fault Localizat...

引用

international conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Shaharier Kabir Abu Shufian Md. Saniat Rahman Zishan Department of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Institute of Energy Technology Chittagong University of Engineering & Technology Chattogram Bangladesh

The decrease in fossil fuel reserves has prompted a global move toward distributed energy resources. For this reason, solar PV power generation has recently gained much attention as a feasible renewable energy source. However, large-scale generation is challenging if there are anomalies in individual solar PV panels. This will reduce the efficiency of the PV system and create a potential fire hazard. In this perspective, the anomaly detection technique discloses system anomalies accurately and effectively. Identified anomalies will localize the event for an improved generation. This paper addresses the performance analysis of using the isolation forest technique to identify anomalies in the PV system and the rule-based fault localization technique to identify defective panel events. In the developed model, the isolation forest technique found around 453 anomalies in 45,740 observations, and approximately six panels indicated a fault in the system. The accuracy score is found to be approximately 0.9886. The proposed fault detection method will help detect the faults in solar power systems.

关键词： Fault diagnosis Location awareness Training Renewable energy sources Fault detection Forestry Production

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3rd international conference on Adaptive Instructional systems. AIS 2021, Held as Part of the 23rd HCI international conference, HCII 2021

3rd International Conference on Adaptive Instructional Syste...

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3rd international conference on Adaptive Instructional systems. AIS 2021, Held as Part of the 23rd HCI international conference, HCII 2021

ISBN: (纸本)9783030778729

The proceedings contain 62 papers. The special focus in this conference is on Adaptive Instructional systems. The topics include: Dynamic Analytics for the Detection of Quality and Alignment Issues in an Online Hybrid Adaptive Instructional and Assessment System;enhancing Personalization by Integrating Top-Down and Bottom-Up Approaches to Learner Modeling;measuring and Integrating Facial Expressions and Head Pose as Indicators of Engagement and Affect in Tutoring systems.dynamic Cognitive Modeling for Adaptive Serious Games;using Principled Assessment Design and Item Difficulty Modeling to Connect Hybrid Adaptive Instructional and Assessment systems. Proof of Concept;early Prediction of At-Risk Students in a Virtual Learning Environment Using Deep Learning Techniques;predicting Literacy Skills via Stealth Assessment in a Simple Vocabulary Game;measuring Cognitive Load for Adaptive Instructional systems.by Using a Pressure Sensitive Computer Mouse;designing a Learner Model for Use in Training Analysts in a Social Media Practice Environment;helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations;helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations;individual Differences in the Relationship Between Emotion and Performance in Command-and-Control Environments;staying Ahead of the Curve: Selecting Students for Newly Arising Tasks;an Experiential Competency Application Framework;competency-Based Experiential-Expertise and Future Adaptive Learning systems.

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3rd international conference on Adaptive Instructional systems. AIS 2021, Held as Part of the 23rd HCI international conference, HCII 2021

3rd International Conference on Adaptive Instructional Syste...

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3rd international conference on Adaptive Instructional systems. AIS 2021, Held as Part of the 23rd HCI international conference, HCII 2021

ISBN: (纸本)9783030778569

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Optimizing Production and Inventory Management for Defective Items in Manufacturing systems

Optimizing Production and Inventory Management for Defective...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Khandakar Rabbi Ahmed Arafat Hossain MD Amaddus Bepary Asif Mahathir Mohammad Mustafizur Rahaman MD Arafat Dewan Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Business Data Analytics Central Michigan University Mt Pleasant USA Dept. of Business Administration and Management Southern California State University Los Angeles CA Dept. of Business Administration and Management Southern California State University USA Dept. of Cloud Engineering Westcliff University Irvine CA Dept. of Computer Science Los Angeles City College Los Angeles USA

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems.

关键词： Production Quality control Inventory management Real-time systems Fourth Industrial Revolution Random forests Optimization Bagging Manufacturing systems Defect detection

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CASTLE: A Cascaded Spatio-Temporal Approach for Long-lead Streamflow Forecasting

CASTLE: A Cascaded Spatio-Temporal Approach for Long-lead St...

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2023 IEEE international conference on Big Data, BigData 2023

作者： Zhuang, Yong Small, David L. Flynn, Patrick D Palash, Wahid Islam, Shafiqul Chen, Ping Ding, Wei Grand Valley State University School of Computing AllendaleMI United States Verisk Extreme Event Solutions Lafayette City Center BostonMA United States University of Massachusetts Boston Department of Computer Science BostonMA United States Integrated Sustainability 1600-400 3rd Avenue Southwest CalgaryABT2P 4H2 Canada Tufts University Department of Civil and Environmental Engineering BostonMA United States

ISBN: (纸本)9798350324457

Effective early warning systems.for extreme flood events in large river basins necessitate reliable long-lead streamflow forecasts. However, the inherent uncertainty within each phase of the weather system-rainfall prediction, runoff generation, and streamflow prediction-amplifies with each stage, rendering accurate long-lead streamflow estimations challenging. In response to this, our study introduces a novel deep-learning-based model, the Cascaded Spatio-Temporal Learning Deep Network (CASTLE). CASTLE synergistically integrates observed upstream precipitation, recent streamflow data, and short-term precipitation forecasts derived from a selection of quantitative climate models to produce an accurate streamflow estimate. Specifically, we employ deep residual architectures on both observed and forecasted precipitation data to model the cascading spatio-temporal processes, which begin with upstream rainfall, move to rainfall-runoff, and finally conclude with downstream discharge. Our aim is to identify hidden space-time patterns that can be used to forecast future downstream flow over extended periods. We assess CASTLE's efficacy by forecasting the downstream discharge of the Ganges River over a long lead time. Results show that our approach outperforms the current state-of-the-art streamflow forecasting models. © 2023 IEEE.

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Exploring Driver Physiological Response During Level 3 Conditional Driving Automation 3

Exploring Driver Physiological Response During Level 3 Condi...

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3rd IEEE international conference on Human-Machine systems. ICHMS 2022

作者： Gluck, Aaron Deng, Min Zhao, Yijin Menassa, Carol Li, Da Brinkley, Julian Kamat, Vineet Clemson University School of Computing ClemsonSC United States University of Michigan College of Engineering Ann ArborMI United States Clemson University Department of Civil Engineering ClemsonSC United States

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Nearly half of new vehicle production is expected to be autonomous, SAE international automation Level 5, by 2045, potentially improving safety, lessening traffic congestion, and lowering driver stress. However, many of these benefits will begin to be realized when vehicles reach SAE international Level 3, in which the occupant is allowed to perform secondary tasks instead of solely focusing on the task of driving. The ability to be engaged in non-driving tasks places critical importance on transferring the occupant's focus back to having complete control of the vehicle. Failure during a vital takeover situation can result in damages, injury, or even death. Therefore, it is imperative to understand what factors influence successful Level 3 vehicle takeovers. Our study investigates whether a driver's physiological data can help predict takeover performance. To accomplish this, we developed an SAE international Level 3 driving takeover simulation on a driving simulator. The simulation tracked the participant's interaction with the vehicle after receiving the takeover request (e.g., takeover reaction time, takeover success, and steering rotation), while physiological sensors tracked the participant's bodily responses (e.g., brain activity, skin conductivity, and heart rate). We have found potential physiological markers that may be used to develop a personalized takeover performance model. Further data analysis and testing will clarify what real-time physiological data results in the best takeover performance. © 2022 IEEE.

关键词： Autonomous vehicles

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