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DRONECOM: Dynamic Relay Operations for Network Efficient Communication in Mines

DRONECOM: Dynamic Relay Operations for Network Efficient Com...

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international conference on control, Automation and systems ( ICCAS)

作者： Akash Patel Scott Fredriksson Samuel Nordström Emanuele Pagliari Ilias Tevetzidis Christoforos Kanellakis George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

As the routine operations are starting to become highly automated, it is crucial to develop autonomous solutions that are infrastructure independent. Achieving this is challenging due to the ever-changing landscape of mines, which complicates infrastructure development. In response, this paper introduces a robust framework employing drones to gather data from hard-to-access areas in mines and deliver the data back to the base station for routine monitoring purposes. these tasks include gathering data from operational vehicles (mine trucks, loaders etc.), as well as various sensors (e.g. monitoring rock bolts) and relaying the data to the mine’s base station for monitoring purposes. the proposed framework is based on autonomous navigation using a known point cloud map of the mine, proximity detection via Ultra WideBand (UWB) radios and the data transfer is accomplished through the IEEE 802.15.4 communication standard, operating in the 868 MHz ISM band, with the aim to guarantee long range operation. On the mission level, the drones act as data mules capable of autonomously extracting data from operating vehicles, storing the data onboard and eventually delivering the data to the base station, which is enabled through a Point and Click (PAC) autonomy framework based on global planning, reactive navigation, communication link and behavior management. the efficacy of this framework has been demonstrated through real-world experiments conducted at a test mine in Sweden, validating the overall architecture of the proposed solution.

关键词： Base stations Automation Navigation Fasteners Rocks Sensors Data mining Monitoring Drones Videos

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ADS Modeling and Safety Verification in Extreme Driving Scenarios

ADS Modeling and Safety Verification in Extreme Driving Scen...

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IEEE international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Ying Zhao Yi Zhu Li Zhao Zexin Li Chan Yin School of Computer Science and Technology Jiangsu Normal University Xuzhou China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Autonomous vehicles play a vital role in improving traffic efficiency and reducing traffic accidents. the spatio-temporal data in driving scenarios is complex and unpredictable, with uncertain interaction behavior. Despite efforts by developers to model road dynamics for evaluating Autonomous Driving systems (ADS), safety concerns under accidental and catastrophic road conditions have been overlooked. Firstly, to tackle this, this paper proposes extending feature variables for catastrophic scenarios using Domain-Specific Modeling Language (DSML). Introducing the Accidental and Catastrophic Automatic Driving Scenario Modeling Language (AC-ADSML) aims to accurately model ADS behavior in accidental and catastrophic road conditions. the AC-ADSML meta-model categorizes scenario variables and specifies abstract and concrete syntax along with operational semantics. Secondly, it is then transformed into Networks of Stochastic Hybrid Automata (NSHA) following ATL rules. the CARLA simulator is employed to replicate urban driving scenarios during natural disasters with PCTL scenario specifications. Model security can be verified using UPP AAL-SMC. Finally, an autonomous vehicle with adaptive cruise control is used as an example to verify the safety of the proposed model under different road condition parameters.

关键词： Analytical models Adaptation models Computational modeling Roads Disasters Semantics Syntactics Security Autonomous vehicles Meteorology

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How to Open science: Promoting Principles and Reproducibility Practices Within the Artificial Intelligence in Education Community 1

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Haim, Aaron Shaw, Stacy T. Heffernan, Neil T. Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (数字)9783031363368

ISBN: (纸本)9783031363351;9783031363368

Across the past decade, open science has increased in momentum, making research more openly available and reproducible. Artificial Intelligence (AI), especially within education, has produced effective models to better predict student outcomes, generate content, and provide a greater number of observable features for teachers. While completed, generalized AI models take advantage of available open science practices, models used during the actual research process are not made available. In this tutorial, we will provide an overview of open science practices and their benefits and mitigation within AI education research. In the second part of this tutorial, we will use the Open science Framework to make, collaborate, and share projects - demonstrating how to make materials, code, and data open. the final part of this tutorial will go over some mitigation strategies when releasing datasets and materials so other researchers may easily reproduce them. Participants in this tutorial will learn what the practices of open science are, how to use them in their own research, and how to use the Open science Framework. the website (https://***/) and associated resources can be found on an Open science Framework project (https://***/10.17605/***/yd9kr).

关键词： Open science Reproducibility Preregistration

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Using a Machine Learning Approach for Analysis of Polling systems with Correlated Arrivals 24th

Using a Machine Learning Approach for Analysis of Polling Sy...

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24th international conference on Distributed and computer and Communication Networks, DCCN 2021

作者： Vishnevsky, Vladimir Semenova, Olga Bui, D.T. Institute of Control Sciences of Russian Academy of Sciences Profsoyuznaya Street 65 Moscow117997 Russia Moscow Institute of Physics and Technology Institutskiy per. 9 Dolgoprudny Moscow Region 141701 Russia

ISBN: (纸本)9783030925062

the paper investigates stochastic polling systems using machine learning. M/M/1 and MAP/M/1-type polling systems with cyclic polling, as well as M/M/1-type polling systems with adaptive cyclic polling are considered. To train a machine model of a M/M/1-type polling system, we used the results of analytical calculations, and for other considered systems that do not allow exact analysis, we used the simulation results. Numerical examples are given, and it is shown that the results of machine learning are close enough to the results of analytical or simulation calculations. © 2021, Springer Nature Switzerland AG.

关键词： Correlated arrivals Machine learning Polling systems

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Algorithm for Path Planning of Mobile Robot 24

Algorithm for Path Planning of Mobile Robot

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24th international conference on Soft Computing and Measurements, SCM 2021

作者： Das, Nimai Chandra Uddin, Raihan Haque Zim, Md. Ziaul Skakun, Anastasiia D. Department of Automatic Control Systems Saint-Petersburg Electrotechnical University Leti Saint Petersburg Russia Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665439749

Research in Unmanned Surface Vehicles (USVs) and Autonomous Mobile Robots navigation has demonstrated some success in navigating flat indoor environments while avoiding obstacles. But in common resilience to unsafe conditions, unmanned surface vehicles (USVs) and autonomous mobile robots have wide applications in security reconnaissance, investigation of an obscure domain, and crisis reaction. Various examinations have been directed on the driving mechanism, motion planning, and trajectory tracking strategies for robots, yet restricted investigations have been led with respect to the obstacle detection and avoiding ability of robots. However, for little scale robots that contain sensitive surveillance sensors and can't afford to utilize heavy defensive shells, the nonappearance of obstacle avoidance solutions arrangements would leave the robot helpless before possibly risky hindrances. In this paper, we present an algorithm for obstacle detection and avoidance system has been developed for miniature Unmanned Surface Vehicles (USVs) and autonomous mobile robots. We show an investigation differentiating heading build rules and relative course directions dodging distinctive operators in multi-specialist conditions, estimating separation between the two robots. the integration of distance measurement and avoiding other robots or obstacles is our observation in a multi-robot environment, where obstacles are also available. In this paper, we have discussed two algorithms, one will try to avoid other robot or obstacles and another will try to measure the distance between main agents to another agent. It utilizes an algorithm so that the system is both compact and power efficient. the proposed system can detect not only the presence, but also the approaching direction of a ferromagnetic obstacle. therefore, an intelligent avoidance behavior can be generated by adapting the trajectory tracking method with the detection information. Design optimization is conducted to enhance

关键词： Multi agent systems

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Artificial Intelligence for Adaptive, Responsive, and Level-Compliant Interaction in the Vehicle of the Future (KARLI) 24th

Artificial Intelligence for Adaptive, Responsive, and Level-...

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24th international conference on Human-computer Interaction, HCI international, HCII 2022

作者： Diederichs, Frederik Wannemacher, Christoph Faller, Fabian Mikolajewski, Martin Martin, Manuel Voit, Michael Widlroither, Harald Schmidt, Eike Engelhardt, Doreen Rittger, Lena Hashemi, Vahid Sahakyan, Manya Romanelli, Massimo Kiefer, Bernd Fäßler, Victor Rößler, Tobias Großerüschkamp, Marc Kurbos, Andreas Bottesch, Miriam Immoor, Pia Engeln, Arnd Fleischmann, Marlis Schweiker, Miriam Pagenkopf, Anne Mathis, Lesley-Ann Piechnik, Daniela Fraunhofer IOSB Karlsruhe76131 Germany Continental Automotive GmbH Hannover30165 Germany Fraunhofer IAO Stuttgart70569 Germany Ford-Werke GmbH Köln50735 Germany Audi AG Ingolstadt85045 Germany paragon semvox GmbH Kirkel66459 Germany TWT GmbH Science & Innovation Stuttgart70565 Germany INVENSITY GmbH Wiesbaden65185 Germany studiokurbos GmbH Stuttgart70173 Germany Allround Team GmbH Köln50735 Germany Hochschule der Medien Stuttgart70569 Germany Universität Stuttgart IAT Stuttgart70174 Germany

ISBN: (纸本)9783031063930

the KARLI project consortium investigates and develops monitoring systems for drivers and other occupants with new artificial intelligence approaches, based on high quality labeled data that is collected in real vehicles. the project’s target applications are integrated in vehicles that enable various levels of automation and transitions of control. Level-compliant occupant behavior is assessed with AI algorithms and modulated with responsive and adaptive human machine interface (HMI) solutions. the project also targets the prediction and prevention of motion sickness in order to improve the user experience, enabling productivity and maintaining an adequate driver state. the user-centered approach is represented by defining five KARLI User Roles which specify the driving related behavior requirements for all levels of automation. the project results will be evaluated at the end of the project. the KARLI applications will be evaluated regarding user experience benefits and AI performance measures. the KARLI project is approaching two main challenges that are ambitious and have a high potential: First, raising and investigating the potential of AI for driver monitoring and driver-vehicle interaction, and second, accelerating the transfer from research to series production applications. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicles

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Evaluation of the Energy Pricing of Electric Vehicle Charging Stations using Convex Optimization Strategy

Evaluation of the Energy Pricing of Electric Vehicle Chargin...

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international conference on Open Source systems and Technologies (ICOSST)

作者： Muneeb Hassan Umar Jadoon Muhammad Shahzad Younis School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad-Pakistan

Due to exponential growth in transportation sector, our environment is facing a drastic change. the global warming is not only effecting our planet but also indirectly effecting humans and other living creatures. To cater this problem, Electric Vehicles (EVs) were introduced. EVs provide us a greener environment which reduces carbon and greenhouse emissions. But, increased demand of EVs also results in increased demand of power and Charging Stations (CSs). this paper mainly discusses the profit maximization of CSs owners by introducing the photovoltaic source (PV) and battery bank. the control scheme uses an algorithm involving Convex Optimization which ensures that profit of CS is maximized while the issues of grid overloading and load shedding are avoided along with optimized usage of all available resources. the addition of the PV source to the CSs, which helps the CSs owners to increase the profit and provide good rates during the peak hours. Generator is added to enhance user experience and at the same time it helps in avoiding load shedding. this problem also manages the random arrival and departure of the EVs during 24 hours which raises an issue of grid overloading. the simulation results show that the objectives were successfully achieved.

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Causal Graph Discovery for Explainable Insights on Marine Biotoxin Shellfish Contamination 24th

Causal Graph Discovery for Explainable Insights on Marine ...

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24th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Ribeiro, Diogo Ferraz, Filipe Lopes, Marta B. Rodrigues, Susana Costa, Pedro Reis Vinga, Susana Carvalho, Alexandra M. Lisbon1000-049 Portugal Lisbon1000-049 Portugal INESC-ID Instituto Superior Técnico Universidade de Lisboa Lisbon1000-029 Portugal Department of Mathematics NOVA SST Caparica2829-516 Portugal UNIDEMI Department of Mechanical and Industrial Engineering NOVA SST Caparica2829-516 Portugal IPMA - Instituto Portuguës do Mar e da Atmosfera Algés1495-165 Portugal Lisbon Unit for Learning and Intelligent Systems Lisbon Portugal

ISBN: (纸本)9783031482311

Harmful algal blooms are natural phenomena that cause shellfish contamination due to the rapid accumulation of marine biotoxins. To prevent public health risks, the Portuguese Institute of the Ocean and the Atmosphere (IPMA) regularly monitors toxic phytoplankton in shellfish production areas and temporarily closes shellfish production when biotoxins concentration exceeds safety limits. However, this reactive response does not allow shellfish producers to anticipate toxic events and reduce economic losses. Causality techniques applied to multivariate time series data can identify the variables that most influence marine biotoxin contamination and, based on these causal relationships, can help forecast shellfish contamination, providing a proactive approach to mitigate economic losses. this study used causality discovery algorithms to analyze biotoxin concentration in mussels Mytilus galloprovincialis and environmental data from IPMA and Copernicus Marine Environment Monitoring Service. We concluded that the toxins that cause diarrhetic and paralytic shellfish poisoning had more predictors than the toxins that cause amnesic poisoning. Moreover, maximum atmospheric temperature, DSP toxins-producing phytoplankton and wind intensity showed causal relationships with toxicity in mussels with shorter lags, while chlorophyll a (chl-a), mean sea surface temperature and rainfall showed causal associations over longer periods. Causal relationships were also found between toxins in nearby production areas, indicating a spread of biotoxins contamination. this study proposes a novel approach to infer the relationships between environmental variables to enhance decision-making and public health safety regarding shellfish consumption in Portugal. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Surface waters

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Multimodal Agentic Model Predictive control 25

Multimodal Agentic Model Predictive Control

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Proceedings of the 24th international conference on Autonomous Agents and Multiagent systems

作者： Saptarashmi Bandyopadhyay John (Jack) Cole Tom Goldstein David Jacobs Department of Computer Science University of Maryland College Park MD USA

ISBN: (纸本)9798400714269

control problems for autonomous AI agents, especially safety-critical applications such as autonomous vehicle control, require robust decision-making frameworks to ensure safe navigation in such complex and dynamic environments. this necessitates approaches such as Agentic Model Predictive control (MPC), which can anticipate future problems and plan for them accordingly. Recently, Multimodal Vision Language Models (VLMs), have emerged as a way to give a semantic meaning to a scene that draws on extremely large amounts of information and contextual understanding of the world. these models vary in a wide range of sizes, trading off speed with performance as they scale further and further. this paper introduces a novel framework that integrates MPC with Multimodal VLMs in order to enhance the ability of autonomous vehicles to navigate and respond to real-world scenarios. Leveraging the open-source Waymax library released by Waymo, along with Waymo Open Motion, Berkeley DeepDrive and NuScenes Datasets, our method uses Multimodal VLMs to detect and draw bounding boxes around important parts of the scene, such as pedestrians or other vehicles. these models are helpful for querying specific attributes of identified objects, such as telling if a vehicle is accelerating or decelerating, or by recognizing if a newly detected obstacle is on a collision course with the vehicle. By incorporating these and other semantic insights into an MPC framework, an autonomous vehicle can make more informed and more context aware decisions to mitigate the risk of a collision and safely navigate its surroundings. We evaluate our approach in diverse simulated environments using VLMs of different scales, demonstrating improvements in safety metrics compared to traditional MPC methods. the integration of VLMs with MPC represents a significant advancement in autonomous decision-making, and especially in dynamic and uncertain situations. Our approach paves the way for future research in using Mul

关键词： ai agents

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"A Fresh Squeeze on Data": Exploring Gender Differences in Self-efficacy and Career Interest in Computing science and Artificial Intelligence Among Elementary Students 24th

"A Fresh Squeeze on Data": Exploring Gender Differences in S...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Li, Shuhan Hasty, Annabel Wilson, Eryka Aliabadi, Roozbeh Columbia Univ New York NY 10025 USA Quest Acad Palatine IL 60067 USA Keiser Univ Ft Lauderdale FL 33309 USA ReadyAI Pittsburgh PA 15206 USA

ISBN: (纸本)9783031363351;9783031363368

Artificial intelligence (AI) is a growing field in both global job markets and educational spaces. this non-experimental quantitative study aims to explore how the educational program A Fresh Squeeze on Data affects students' self-efficacy and career choices and whether gender will differentiate the learning outcomes. Under the social cognitive career theory framework, this study designs questionnaires as data collection instrument. the results suggest that the program significantly improves students' comfortability with AI-related subjects but not for career interest or other measurements in self-efficacy. Unexpectedly, the program's effect is not divided by gender. Nevertheless, this study opens up conversations about assisting students from underrepresented backgrounds to envision success in AI courses and career pathways through an activity-driven curriculum. the paper also informs educators and researchers to devise culturally responsive pedagogy in teaching AI that empowers young girls before they develop a gendered career view.

关键词： K-12 Education AI Education Gender Equity Self-Efficacy Career Interest

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