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arXiv 2023年

作者： Lau, Nuno Reis, Luís Paulo Simões, David Kasaei, Mohammadreza Abreu, Miguel Silva, Tiago Resende, Francisco DETI/UA Electronics Telecommunications and Informatics Dep. University of Aveiro Portugal DEI/FEUP Informatics Engineering Dep. Faculty of Engineering The University of Porto Portugal IEETA Institute of Electronics and Telematics Engineering of Aveiro Portugal LIACC Artificial Intelligence and Computer Science Lab. University of Porto Portugal

The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. This paper also includes general information related to the design of our agent architecture. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Experimental Analysis of Gmapping SLAM Algorithm for Mobile Robot Indoor Navigation

Experimental Analysis of Gmapping SLAM Algorithm for Mobile ...

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Artificial Intelligence, Robotics and Control (AIRC), International Conference on

作者： Mohamed Reda Ahmed Abdulbaky Haitham El-Hussieny Mahmoud Magdy Mechanical Engineering Dept. The British University in Egypt (BUE) Cairo Egypt InnoVision Systems Company Cairo Egypt Mechatronics and Robotics Engineering Dep. Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt

ISBN: (数字)9798350385076

ISBN: (纸本)9798350385083

The main subject of this paper is the experimental analysis of LiDAR-SLAM-based indoor navigation for a mobile robot, considering the effect of the mobile robot's linear and angular velocities on the deviation that occurs in the resulting 2D map. Our objective in this paper is to utilize the Robotics Operating System (ROS) and the Gmapping method, which rely solely on LiDAR and IMU for simultaneous localization and mapping (SLAM). It was observed that as the velocity increases, the deviation of the map decreases. After experimenting with various velocities, it was determined that the optimum velocity is 1.0 m/s. This velocity is optimal for indoor navigation within the scope of our experimental environment, exhibiting the minimum deviation from the ground truth map.

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Reconfigurable Intelligent Surfaces and DF-relay Improved Spectral Efficiency in Cognitive Radio Networks 97

Reconfigurable Intelligent Surfaces and DF-relay Improved Sp...

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97th IEEE Vehicular Technology Conference, VTC 2023-Spring

作者： El Mettiti, Abderahmane Saber, Mohamed Chehri, Abdellah Chaibi, Hasna Badaoui, Abdel Saadane, Rachid Faculty of Sciences University Mohamed v Ehtp LRIT-GSCM Morocco SIRC/LAGeS Ehtp Laboratory Engineering System Casablanca Morocco Royal Military College Dep. Math. and Computer Science Kingston Canada Supmti Genuis Laboratory Rabat Morocco Mohammed v University Laboratory Lastimi Casablanca Morocco

ISBN: (纸本)9798350311143

Cognitive radio (CR) is considered a primary technology for spectrum usage efficiency and dynamic spectrum management. Spectrum sensing is an essential cognitive radio cycle function. Recently, reconfigurable intelligent surfaces (RIS) technology has emerged as a promising new enabler of 6G wireless communication, with the ability to control signal propagation and increase signal coverage and spectrum management. This paper investigates how integrating RISs with CR can improve spectral efficiency. First, we optimize the RIS parameters by identifying the optimal transmit powers and number of RIS antennas. Then, we compare the novel RIS technology with the traditional decode-and-forward (DF) relaying technology. Next, we investigate how adopting RIS can enhance spectral efficiency. Finally, we demonstrate how integrating the novel technology of reconfigurable intelligent surfaces with CR can significantly increase spectrum detection, the most crucial step in the cognitive radio process. Simulation studies indicate that RIS is more energy-efficient than SISO and DF-relay communications in various setups. Moreover, by boosting spectrum sensing capability in CR situations, RISs can significantly enhance the efficiency of frequency spectrum usage. © 2023 IEEE.

关键词： Cognitive radio

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Recurrent Penalized Splines for Real-Time Forecasting of the Parameters of Technical systems

Recurrent Penalized Splines for Real-Time Forecasting of the...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Elena Kochegurova Anastasiia Kaida Maria Galkina Information Systems and Technologies Dep. Tomsk Polytechnic University Tomsk Russian Federation School of Nuclear Science and Engineering Tomsk Polytechnic University Tomsk Russian Federation

Forecasting time series (TS) from their historical data is used in many preventive monitoring and decision support systems in industry. For systems operating in real-time, an urgent problem is to reduce forecasting time without compromising other quality indexes (accuracy and trend continuation). The purpose of this paper is to describe a forecasting model based on a recurrent penalized P-spline. The spline model has a compact computational scheme where the efficiency is achieved by using a short input of historical data a simple mathematical description. The model was compared to 11 most well-known forecasting algorithms. The performance of the proposed model compared quite well to the competitive forecasting algorithms. The model was used to forecast a real-world TS of a technological parameter of a boiler plant.

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Research Reviews on Network-based Congestion Control

Research Reviews on Network-based Congestion Control

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International Conference on Information Networking

作者： Yeunwoong Kyung Jaewook Lee Seokwon Jang Haneul Ko Seungmin Oh Jaehwan Lee Youngjun Kim Div. of Info. & Comm. Eng Kongju National University Cheonan Korea Dep. of Info. and Comm. Engineering Pukyong National University Busan Korea Mobile Core Network Research Section Electronics and Telecomm. Research Institute Daejeon Korea Dep. of Electronic Eng Kyung Hee University Yongin-si Korea Dep. of Computer Science and Eng Kongju National University Cheonan Korea School of Computer Science and Eng Kyungnam University Changwon Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Congestion control has been considered as a principal technology for efficient end-to-end traffic transmission. Previous studies on congestion control are mainly focused on the control of the transmission rate or window size at the host. However, such host-based congestion controls have difficulties on short time-scale variable network conditions and increased host complexity. To address this problem, network-based congestion controls that utilize network information have been considered which determine and control the congestion using the network information. In this paper, we review the network-based congestion control methods.

关键词： Data analysis 5G mobile communication Reviews Network architecture Complexity theory

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A Safety and Security Requirements Management Methodology in Reconfigurable Collaborative Human-Robot Application

A Safety and Security Requirements Management Methodology in...

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IEEE International Workshop on Factory Communication systems (WFCS)

作者： Ali M. Hosseini Clara Fischer Mukund Bhole Wolfgang Kastner Thilo Sauter Sebastian Schlund Institute of Computer Technology TU Wien Vienna Austria Institute of Management Science TU Wien Vienna Austria Institute of Computer Engineering TU Wien Vienna Austria Dep. of Integrated Sensor Systems Danube Univ. Krems Wiener Neustadt Austria

The current industry has to adapt to rapidly changing customers' needs. Reconfigurable manufacturing, therefore, provides capacity and functionality on demand which is essential for competitiveness in fast-changing markets. Furthermore, Industry 4.0 or even more so, Industry 5.0 emphasizes human-centred production with collaborative robots, Cobots, to create human-robot interactions. In such scenarios, safety and security are difficult to address due to the intrinsic features of reconfigurable manufacturing, like exposure to numerous requirements changes in a short period. As safety and security can conflict in different phases of the system life-cycle, one of the earliest activities to avoid conflicts is requirements engineering which can significantly diminish the cost and time of fixing issues compared to later phases like operation. This paper proposes a methodology for safety and security requirements interaction management, including conflict detection and resolution, and shows its applicability through a reconfigurable collaborative human-robot use case. Based on the proposed methodology, we detected and resolved two safety and security requirement conflicts.

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Camera-LiDAR Fusion based Object Segmentation in Adverse Weather Conditions for Autonomous Driving

Camera-LiDAR Fusion based Object Segmentation in Adverse Wea...

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International Baltic Electronics Conference, BEC

作者： Junyi Gu Mauro Bellone Artjom Lind Dep. of Mechanical and Industrial Engineering Tallinn University of Technology Tallinn Estonia FinEst Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Institute of Computer Science University of Tartu Tartu Estonia

ISBN: (数字)9798350365115

ISBN: (纸本)9798350365122

This paper presents the results of a series of machine learning experiments that focus on the domain adaption between different training datasets for autonomous driving. The experiments use a neural network model obtained through transfer learning from the Waymo Open Dataset and then tested with our custom dataset, which was recorded at the TalTech campus in different weather conditions. In this work, we developed a set of tools to extract and process the sensory data from the iseAuto shuttle. The camera and LiDAR sensors of the iseAuto shuttle were calibrated, and the point clouds data of the LiDAR sensor was projected onto the camera plane. We present, here, our publicly available iseAuto dataset which was used for a classification task under adverse weather and low illumination conditions. The iseAuto dataset contains 8000 frames of camera and LiDAR data for traffic object detection and segmentation. We provide ground truth annotations of 2400 frames, in which the object contours were manually labeled out. In addition, we provided the manual-labeled ground-truth segmentations of cars and humans in images, which can be used by the community to test the accuracy of the segmentation of their models. An additional focus of this paper is to demonstrate that with a few custom annotated data, using transfer learning and semi-supervised learning, it is possible to obtain reasonable accuracy on noisy real-world data. The current performance in vehicle segmentation ranges from 65% to 85% in intersection over union (IoU) and between 43% and 60% IoU for pedestrian segmentation in challenging scenarios such as nighttime and rainy weather.

关键词： Point cloud compression Laser radar Accuracy Transfer learning Object segmentation Semisupervised learning Cameras Sensors Autonomous vehicles Meteorology

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A Simplified Variant of Gödel's Ontological Argument

arXiv

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arXiv 2022年

作者： Benzmüller, Christoph Otto-Friedrich-Universität Bamberg AI Systems Engineering Bamberg Germany Freie Universität Berlin Dep. of Mathematics and Computer Science Berlin Germany

A simplified variant of Gödel's ontological argument is presented. The simplified argument is valid already in basic modal logics K or KT, it does not suffer from modal collapse, and it avoids the rather complex predicates of essence (Ess.) and necessary existence (NE) as used by Gödel. The variant presented has been obtained as a side result of a series of theory simplification experiments conducted in interaction with a modern proof assistant system. The starting point for these experiments was the computer encoding of Gödel's argument, and then automated reasoning techniques were systematically applied to arrive at the simplified variant presented. The presented work thus exemplifies a fruitful human-computer interaction in computational metaphysics. Whether the presented result increases or decreases the attractiveness and persuasiveness of the ontological argument is a question I would like to pass on to philosophy and theology. Copyright © 2022, The Authors. All rights reserved.

关键词： Human computer interaction

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Review of Cloud AI for Real-Time Fraud Detection

Review of Cloud AI for Real-Time Fraud Detection

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International Conference on Autonomous Robots and Agents, ICARA

作者： Gulnara A.Abitova Miras Abalkanov Gulnara A. Abitova Gulnur Shuteyeva Elvira Aitmukhanbetova Korlan Kulniyazova Dep. Computer Engineeering Astana IT University Astana Kazakhstan Dept. Electical Engineering and Computer Science State Univerity of New York at Binghamton Binghamton USA Dept. System Anslysis and Managemnt Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

The paper presents a comprehensive analysis of the integration of Cloud AI in real-time fraud detection, highlighting significant advancements and practical applications. It is identifying a key challenge such as data privacy and the need for scalable solutions, while also exploring the potential of innovative AI algorithms and strategic partnerships to enhance fraud detection capabilities. Through detailed case studies, the work is demonstrated the effective application of Cloud AI across various sectors. The study concludes that Cloud AI, despite not being a complete solution, plays a crucial role in strengthening fraud detection systems. Our findings suggest that strategic implementation of Cloud AI can lead to more adaptive and efficient systems, thereby significantly contributing to the economic sector's resilience against fraudulent activities. This paper underscores the transformative impact of Cloud AI in fraud detection, offering insights into its future potential and laying the groundwork for further advancements in this field.

关键词： Economics Training Data privacy Technological innovation Scalability Real-time systems Fraud

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Zero-Shot Classification by Large-Margin Distance Learning 10

Zero-Shot Classification by Large-Margin Distance Learning

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10th International Conference on computer and Knowledge engineering, ICCKE 2020

作者： Reza Zarei, Mohammad Taheri, Mohammad Shiraz University Dep. of Computer Science and Engineering Shiraz Iran

ISBN: (纸本)9781728185668

Zero-Shot Learning (ZSL) has increasingly attained a lot of attentions due to the scalability it provides to recognition models for classifying instances from new classes of which no training data have been seen before. This scalability is achieved by providing semantic information about new classes, which could be obtained remarkably easier, with a lower cost, in comparison with gathering a new training set. In other words, ZSL can be seen as a subset of transfer learning. In this paper, a distance function is learnt in order to discriminate the classes with a customized large-margin loss function. We also propose a nonlinear prototype learning approach by defining a theoretical-based, but simple mapping function to generate the class prototypes from associated semantic information. The instances are compared with the generated prototypes for classification considering the learnt distance function. The evaluations on five widely-used ZSL datasets, illustrate the effectiveness and superiority of the proposed method in comparison with the state-of-the-art ZSL approaches, despite of its simplicity. © 2020 IEEE.

关键词： Distance education

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