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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Sun, Li Hou, Jing Chapman, Richard Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States California State University San Marcos Department of Computer Science and Information System San MarcosCA92096 United States

ISBN: (纸本)9781665494274

Cell-free massive MIMO network is a promising solution to support 5G communication. However, as an ultra-dense network (UDN), it suffers from two major issues: power consumption and scalability, which hinders it to be widely used in practice. The increasing and time-varying user demand, the dynamic propagation environment, and the huge amount of access points (APs) make it a challenging task to address these issues. For this purpose, we propose a distributed solution to solve the access point activation (APA) problem in cell-free massive MIMO networks to reduce power consumption considering dynamic user demand. We leverage the user-centric characteristic and design a multi-agent deep reinforcement learning (MADRL) algorithm by which each AP independently decides whether it needs to be switched on or off. The simulation results show that the MADRL outperforms the centralized and the random strategies. This research demonstrates the ability of distributed learning in solving the APA problem in cell-free massive MIMO networks. © 2023 IEEE.

关键词： Reinforcement learning

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Systematic analysis of artificial intelligence in the era of industry 4.0

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Journal of Management Analytics 2023年第1期10卷 89-108页

作者： Weiru Chen Wu He Jiayue Shen Xin Tian Xianping Wang Department of Information Technology&Decision Science Old Dominion UniversityNorfolkVAUSA Department of Computer&Information Science Arkansas Tech UniversityRussellvilleARUSA Department of Engineering Technology SUNY Polytechnic InstituteUticaNYUSA College of Computing and Software Engineering Kennesaw State UniversityMariettaGAUSA Computer Information Technology&Graphics Department Purdue University NorthwestHammondINUSA

Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry *** study conducts a systematic literature review to study the state-of-the-art on AI in industry *** paper describes the development of industries and the evolution of *** paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry *** findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of *** the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.

关键词： Industry 4.0 AI deep learning neural network supervised learning unsupervised learning reinforced learning

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Joint Beamforming and Trajectory Optimization for UAV-Assisted Double IRS Secure Transmission System: A Deep Reinforcement Learning Approach

Joint Beamforming and Trajectory Optimization for UAV-Assist...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Qi, Yihao Su, Zhou Xu, Qichao Fang, Dongfeng Shanghai University School of Mechatronic Engineering and Automation Shanghai China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China California Polytechnic State University Department of Computer Science San Luis Obispo United States

ISBN: (纸本)9798350333336

Unmanned aerial vehicle (UAV) integrated with intelligent reflecting surface (IRS) has excellent potential to improve air-to-ground communication performance. However, the openness of the air-to-ground channel makes secure information transmission a challenging issue. In this paper, we first propose a UAV-assisted double IRS secure transmission system, where one IRS is carried by UAV, and the other is deployed near the base station. The collaborative beamforming gain provided by the cascaded reflection link overcomes the performance bottleneck of a single IRS in the air. Secondly, with the aim of maximizing the sum secrecy rate, a problem is formulated for jointly optimizing the trajectory of UAV and the phase shift of each IRS. As the formulated problem is a non-convex optimization problem and varies dynamically with the environment, a deep reinforcement learning-based secure transmission approach is presented to continuously achieve the optimal phase shift and trajectory of UAV in dynamic environments. Furthermore, we reduce the dimension of the action space by dividing the IRS into several sub-surfaces, each of which shares the same phase shift. The simulation results demonstrate that the secrecy rate of the UAV-assisted double IRS secure transmission system can be significantly improved by the proposed approach. © 2023 IEEE.

关键词： Reinforcement learning

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Kamen Shah Maria Pantoja Dept. Computer Science and Software Engineering California Polytechnic State University San Luis Obispo USA

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals.

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Genealogical Relationship Extraction from Unstructured Text Using Fine-Tuned Transformer Models

Genealogical Relationship Extraction from Unstructured Text ...

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International Conference on Semantic Computing

作者： Carloangello Parrolivelli Lubomir Stanchev Computer Science and Software Engineering Department California Polytechnic State University San Luis Obispo CA USA

The paper tackles the task of extracting genealogical relationships, such as "sibling-of", "parent-of", "child-of", and "spouse-of", from unstructured, free-form text. In order ...

The paper tackles the task of extracting genealogical relationships, such as "sibling-of", "parent-of", "child-of", and "spouse-of", from unstructured, free-form text. In order to solve the problem, we propose a three-stage pipeline consisting of Named Entity Recognition (NER), Coreference Resolution (CR), and Relationship Classification (RC). NER identifies tokens in the text that refer to people, such as proper nouns or nicknames, using the SpaCy software. CR maps multiple tokens representing pronouns to their antecedents. For example, CR could map "She", "His sister", and "Maria" to the antecedent "Maria Johnson". CR allows us to transform a genealogical relationship between two tokens, such as the sibling relationship between "him" and "his sister", to a relationship between the corresponding antecedents, for example, "Bob Johnson" and "Maria Johnson". Our novel algorithm for coreference resolution is based on the AllenNLP software. The last step is the RC, which classifies the relationship between two sets of tokens given adjacent context. We use the LUKE transformer deep-learning model to extract the genealogical relationships. The end-to-end pipeline can extract and correctly classify genealogical relationships from our hand-labeled testing set of Wikipedia documents with macro precision, recall, and F1 scores of 0.794, 0.616, and 0.676, respectively.

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SENTINEL: Scalable Edge Network for Telepresence and Integrated AI 1

SENTINEL: Scalable Edge Network for Telepresence and Integra...

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1st IEEE Conference on Telepresence, Telepresence 2024

作者： El-Hadedy, Mohamed Ung, James Kim, David Hwu, Wen-Mei He, Yutao Cheng, Benny California State Polytechnic University Department of Electrical and Computer Engineering Pomona United States University of Illinois Coordinated Science Laboratory Urbana-Champaign United States University of California Los Angeles United States Naval Surface Warfare Center Corona Division United States

ISBN: (纸本)9798331528249

This paper presents a research platform centered on an RC car with a hybrid system utilizing TurningPi and DeskPi6C, with multiple Raspberry Pi Compute Module 4 nodes for edge computing. The platform is designed to facilitate telepresence applications by integrating advanced computer vision (YOLO) and natural language processing (NLP) models (BERT), managed by a backend server. The system's NLP module achieves command processing latencies ranging from 1000 to 3000 μs in ideal scenarios, enabling near-instantaneous responses in telepresence scenarios. The YOLOv5s model, deployed on the edge, supports real-time visual feedback with an impressive inference speed of approximately 10 FPS. The system's scalability demonstrated through Kubernetes, ensures that YOLO inference speed significantly improves as more pods are added, up to the platform's 10-node capacity, thereby highlighting its potential for handling the increased workload. The system's efficiency and reliability are underscored by its power consumption and resource utilization metrics, which are critical for sustained telepresence operations. The central server maintains stable power usage at around 57.8 W, while the DeskPi Super 6C and Turing Pi nodes peak at 36.0 W and 20.7 W, respectively, under load. This ensures robust performance in remote environments, reassuring the audience of its suitability for sustained telepresence operations. This platform showcases the potential of integrating Kubernetes with edge computing to enhance telepresence applications, offering real-time AI processing and scalable operations. The research provides valuable insights into developing versatile, efficient systems for telepresence, enabling more prosperous and responsive remote interactions. © 2024 IEEE.

关键词： Natural language processing systems

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Critical Infrastructure Security Goes to Space: Leveraging Lessons Learned on the Ground

arXiv

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arXiv 2023年

作者： Ellis, Tim Hitaj, Briland Lindqvist, Ulf Shands, Deborah Tinnel, Laura DeBruhl, Bruce Computer Science Laboratory SRI International United States Computer Science and Software Engineering Department California Polytechnic State University United States

Space systems enable essential communications, navigation, imaging and sensing for a variety of domains, including agriculture, commerce, transportation, and emergency operations by first responders. Protecting the cybersecurity of these critical infrastructure systems is essential. While the space environment brings unique constraints to managing cybersecurity risks, lessons learned about risks and effective defenses in other critical infrastructure domains can help us to design effective defenses for space systems. In particular, discoveries regarding cybersecurity for industrial control systems (ICS) for energy, manufacturing, transportation, and the consumer and industrial Internet of Things (IoT) offer insights into cybersecurity for the space domain. This paper provides an overview of ICS and space system commonalities, lessons learned about cybersecurity for ICS that can be applied to space systems, and recommendations for future research and development to secure increasingly critical space systems. © 2023, CC BY.

关键词： Cybersecurity

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Teaching the Equivalent Rectangular Stress Block

Teaching the Equivalent Rectangular Stress Block

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Best Practices and Lessons Learned for Teaching Concrete Materials and Reinforced Concrete 2021

作者： Carroll, J. Chris Dymond, Benjamin Z. Behrouzi, Anahid A. The Department of Civil Computer and Electrical Engineering The School of Science and Engineering Saint Louis University United States The Department of Civil Engineering Construction Management and Environmental Engineering Northern Arizona University United States California Polytechnic State University United States

ISBN: (纸本)9781641952361

The equivalent rectangular stress block is the basis for determining the flexural strength of reinforced concrete members. Instructors commonly present the concept in two dimensions, contrary to its three-dimensional nature. Unfortunately, this can be particularly difficult for students to understand and visualize, especially for students with visual learning style preferences. This paper presents an overview of the equivalent rectangular stress block, select active learning methods, and four specific examples used by the authors to introduce the equivalent rectangular stress block in an undergraduate reinforced concrete design course. The first method focuses on understanding the terms associated with the equivalent rectangular stress block using a visual, hands-on learning activity;the second focuses on visualizing the internal forces and couple moment within a beam;the third uses a virtual three-dimensional model to derive the depth of the equivalent rectangular stress block;and the fourth illustrates the various stress blocks used in reinforced concrete flexure theory via physical three-dimensional models. Each description includes detailed instructions to create the resources and how to facilitate the related activities within a course. The overall goal is to provide educators with several examples that will help students better visualize the three-dimensional concept. © 2023 American Concrete Institute. All rights reserved.

关键词： Students

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Collaboration between UAVs and UGVs for Site-Specific Application of Chemicals 8

Collaboration between UAVs and UGVs for Site-Specific Applic...

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Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII 2023

作者： Bhandari, Subodh Raheja, Amar Renella, Noah Ramirez, Rick Uryeu, Dennis Samuel, Joshua Dept. of Aerospace Engineering California State Polytechnic University 3801 W. Temple Ave PomonaCA91768 United States Dept. of Computer Science California State Polytechnic University 3801 W. Temple Ave PomonaCA91768 United States Dept. of Electrical Engineering California State Polytechnic University 3801 W. Temple Ave PomonaCA91768 United States

ISBN: (数字)9781510661950

ISBN: (纸本)9781510661943

This paper presents the collaboration between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for site-specific application of chemical (herbicides, pesticides, and other chemicals). The paper shows and discusses the experimental plot used in the project, methods for collaboration between the UAVs and UGVs for detection and isolation of weeds and strawberry plants using machine learning and remote sensing techniques, and methods for sitespecific application of chemicals. The paper also discusses the method of communication between UAVs and UGVs for data sharing and hardware used in the project including UAVs, UGVs, sensors, and communication devices. Some experimental results are shown and discussed. © 2023 SPIE.

关键词： Remote sensing

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Improved Distributed Algorithms for Random Colorings

arXiv

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arXiv 2023年

作者： Carlson, Charlie Frishberg, Daniel Vigoda, Eric Department of Computer Science University of California Santa Barbara United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo United States

Markov Chain Monte Carlo (MCMC) algorithms are a widely-used algorithmic tool for sampling from high-dimensional distributions, a notable example is the equilibirum distribution of graphical models. The Glauber dynamics, also known as the Gibbs sampler, is the simplest example of an MCMC algorithm;the transitions of the chain update the configuration at a randomly chosen coordinate at each step. Several works have studied distributed versions of the Glauber dynamics and we extend these efforts to a more general family of Markov chains. An important combinatorial problem in the study of MCMC algorithms is random colorings. Given a graph G of maximum degree ∆ and an integer k ≥ ∆ + 1, the goal is to generate a random proper vertex k-coloring of G. Jerrum (1995) proved that the Glauber dynamics has O(n log n) mixing time when k > 2∆. Fischer and Ghaffari (2018), and independently Feng, Hayes, and Yin (2018), presented a parallel and distributed version of the Glauber dynamics which converges in O(log n) rounds for k > (2 + Ε)∆ for any Ε > 0. We improve this result to k > (11/6 − δ)∆ for a fixed δ > 0. This matches the state of the art for randomly sampling colorings of general graphs in the sequential setting. Whereas previous works focused on distributed variants of the Glauber dynamics, our work presents a parallel and distributed version of the more general flip dynamics presented by Vigoda (2000) (and refined by Chen, Delcourt, Moitra, Perarnau, and Postle (2019)), which recolors local maximal two-colored components in each step. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

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