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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Neural Augmentation Based Panoramic High Dynamic Range Stitching

arXiv

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arXiv 2024年

作者： Zheng, Chaobing Xu, Yilun Chen, Weihai Wu, Shiqian Zhang, Sen Li, Zhengguo Institute of Robotics and Intelligent Systems School of Electronic Information Wuhan University of Science and Technology 947 Heping Avenue Wuhan430081 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Automation and Electrical Engineering University of Science and Technology Beiing Beijing100083 China Key Laboratory of Knowledge Auomalion for indusrial Proceses of Mimistry ofEducation University of Science and Techmology Beijing Beijing1083 China Shunde lnnovation School University of Science and Technology Beijing Foshan 528300 China VI Department Institute for Infocomm Research A*STAR 138632 Singapore

Due to saturated regions of inputting low dynamic range (LDR) images and large intensity changes among the LDR images caused by different exposures, it is challenging to produce an information enriched panoramic LDR image without visual artifacts for a high dynamic range (HDR) scene through stitching multiple geometrically synchronized LDR images with different exposures and pairwise overlapping fields of views (OFOVs). Fortunately, the stitching of such images is innately a perfect scenario for the fusion of a physics-driven approach and a data-driven approach due to their OFOVs. Based on this new insight, a novel neural augmentation based panoramic HDR stitching algorithm is proposed in this paper. The physics-driven approach is built up using the OFOVs. Different exposed images of each view are initially generated by using the physics-driven approach, are then refined by a data-driven approach, and are finally used to produce panoramic LDR images with different exposures. All the panoramic LDR images with different exposures are combined together via a multi-scale exposure fusion algorithm to produce the final panoramic LDR image. Experimental results demonstrate the proposed algorithm outperforms existing panoramic stitching algorithms. © 2024, CC0.

关键词： Image fusion

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Interplay Between Neural Computational Energy and Multimodal Processing in Robot-Robot Interaction

Interplay Between Neural Computational Energy and Multimodal...

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IEEE International Conference on Development and Learning, ICDL

作者： Murat Kirtay Verena V. Hafner Minoru Asada Erhan Oztop department of Cognitive Science and Artificial Intelligence Tilburg University Tilburg Netherlands Department of Computer Science Adaptive Systems Group Humboldt-Universität zu Berlin Berlin Germany Research Cluster of Excellence Science of Intelligence Berlin Symbiotic Intelligent Systems Research Center Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan International Professional University of Technology in Osaka Osaka Japan Ozyegin University Istanbul Turkey

Multimodal learning is an active research area that is gaining importance in human-robot interaction. Despite the obvious benefit of levering multiple sensors for perceiving the world, its neural computational cost has not been addressed in robotics, especially in Robot-Robot Interaction (RRI). This study addresses the role of computational cost in multimodal processing by considering robot-robot interaction in a sequential multimodal memory recall task. In this setting, the learner (Nao) robot receives auditory-only, visual-only, or audio-visual information from an instructor (Pepper) robot and the environment regarding previously learned memory items. The goal of the learner robot is to perform the interactive task with as low as possible neural computational cost. The learner robot has two cognitive modules: a multimodal auto-associative network that stands for the perceptual-cognitive processing of the robot and an internal reward mechanism that monitors the changes in neural energy incurred for two consecutive steps by the processing of the attended stimuli. The reward computed is used to build an action policy for minimizing the neural energy consumption over the sequential memory recall task. The experimental results show that having access to both auditory and visual information is beneficial not only for better memory recall but also for minimizing the cost of neural computation.

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Design and Implementation of a Two-Limbed 3T1R Haptic Device

Design and Implementation of a Two-Limbed 3T1R Haptic Device

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Long Kang Yang Yang Byung-Ju Yi PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Xi'an Modern Control Technology Research Institute Xi'an China School of Automation Nanjing University of Information Science and Technology Nanjing China Department of the Electronic Systems Engineering Hanyang University Ansan South Korea

This paper presents a haptic device with a simple architecture of only two limbs that can provide translational motion in three degrees of freedom (DOF) and one-DOF rotational motion. Actuation redundancy eliminates all forward-kinematic singularities and improves the motion-force transmission property. Thanks to the special structure of the kinematic chains, all actuators are close to the base and full gravity compensation is achieved passively by using springs. Force producibility analysis shows that this haptic device is able to produce long-term continuous force feedback of 15–30 N in each direction. By developing a prototype of the haptic device and a virtual three-dimensional simulator, a preliminary performance evaluation of the haptic device was conducted. In addition, a torque distribution algorithm considering a relaxed form of actuator-torque saturation was experimentally evaluated, and a comparison with other algorithms reveals that this algorithm offers several advantages.

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Signal Attenuation Modeling and Simulation for 5G and Beyond P2P Wireless Communication Networks in FR2 Bands: Comparison within M-Region in Nigeria 2

Signal Attenuation Modeling and Simulation for 5G and Beyond...

引用

2nd International Conference on Multidisciplinary Engineering and Applied science, ICMEAS 2023

作者： Alozie, Emmanuel Faruk, Nasir Abdulkarim, Abubakar Usman, Aliyu D. Olayinka Imam-Fulani, Yusuf Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Abdullahi Baba, Bashir Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Kafin Hausa Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Kampala Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

ITU-R categorized Nigeria into four rain regions (i.e., M, N, P, and Q) depending on the atmospheric conditions. Previous works that have conducted rain and attenuation modeling and simulations have assumed similar signal propagation behavior and parameters within locations categorized under the same region. This paper aims to explore these assumptions by conducting an extensive radio propagation simulations of point-to-point microwave links with a clear line-of-sight in order to estimate the total path loss (attenuation), excess path loss, and flat fade margins for each of the locations within the M-regions, i.e., Kano, Sokoto, and Adamawa. Results obtained showed that in all the locations, the loss monotonically increases with distance, with Sokoto having the highest level of signal losses and fading, while, Adamawa has the lowest. The deviation for both total loss, excess loss, and flat fading margin was found to be between 15 dB across the region. © 2023 IEEE.

关键词： Location

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Modeling and Simulation of Total Attenuation of P2P NLOS 5G and Beyond Wireless Communication systems Operating in FR2 Bands in Nigeria 2

Modeling and Simulation of Total Attenuation of P2P NLOS 5G ...

引用

2nd International Conference on Multidisciplinary Engineering and Applied science, ICMEAS 2023

作者： Alozie, Emmanuel Faruk, Nasir Abdulkarim, Abubakar Usman, Aliyu D. Imam-Fulani, Yusuf Olayinka Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Baba, Bashir Abdullahi Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Kansanga Kampala Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Akoka Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

Signal propagation in a particular region differs from another due to differences in atmospheric, climatic and environmental properties, distinct terrain and clutter features. Adequate analysis is essential to understand the radio propagation behavior in a particular region. The ITU-R designated four rain regions, M, N, P, and Q, for Nigeria representing the rain rate distribution and also provided further classifications based on ground conductivity, among other salient parameters. Based on these classifications, this paper utilized the EDX Signal Pro software® to model and simulate a typical Point-to-Point (P2P), Non-Line of Sight (NLOS) link scenarios for 5G networks. The objective was to estimate and compare the total loss, excess loss, and flat fade margins for each rain region in Nigeria. Results obtained from the comparison showed that signals propagating in region N experience the highest level of losses and fading, while, region Q has the least losses and fading. © 2023 IEEE.

关键词： 5G mobile communication systems

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Integrated Optimization of Charging Infrastructure, Renewable Energy and Battery Electric Bus Scheduling Subject to Power Distribution Network Constraints

SSRN

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SSRN 2024年

作者： Najafi, Arsalan Gao, Kun Parishwad, Omkar Tsaousoglou, Georgios Jin, Sheng Yi, Wen Department of Architecture and Civil Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Institute of Intelligent Transportation Systems College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Department of Building and Real Estate The Hong Kong Polytechnic University Hung Hom Hong Kong

The adoption of Battery Electric Buses (BEBs) in electric public transit systems presents a significant opportunity for advancing sustainable transportation. However, it also poses challenges to the coupled electric transport and energy systems, particularly concerning the safety and efficiency of Power Distribution Networks (PDN). This study introduces a comprehensive BEB-PDN framework designed for the joint optimization of charging infrastructure, charging scheduling, and integration of renewable energy resources, with a specific focus on their impacts on PDN. The complexity of the proposed model is primarily due to its extensive decision variables and nonlinear constraints. To address this, a decomposition approach is employed, linearizing the problem and dividing it into master and subproblems for efficient resolution. A case study in Skövde, Sweden demonstrates that without renewable energy integration, high charging demands from BEBs can lead to PDN safety violations, such as significant voltage drops at certain nodes. The incorporation of renewable energy sources with battery energy storage in the BEB transit system is shown to enhance the PDN's stability and safety while catering to the system's spatiotemporal charging demands. This study offers an optimization framework for the integrated planning of charging infrastructure, BEB systems, and renewable energy, providing empirical insights into their potential benefits. © 2024, The Authors. All rights reserved.

关键词： Electric buses

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Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Engineering Shenzhen MSU-BIT University Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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OCET: One-Dimensional Convolution Embedding Transformer for Stock Trend Prediction 1

引用

17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Yang, Peng Fu, Lang Zhang, Jian Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Shenzhen Securities Information Co. Ltd. Shenzhen China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Due to the strong data fitting ability of deep learning, the use of deep learning for quantitative trading has gradually sprung up in recent years. As a classical problem of quantitative trading, Stock Trend Prediction (STP) mainly predicts the movement of stock price in the future through the historical price information to better guide quantitative trading. In recent years, some deep learning work has made great progress in STP by effectively grasping long-term timing information. However, as a kind of real-time series data, short-term timing information is also very important, because stock trading is high-frequency and price fluctuates violently. And with the popularity of Transformer, there is a lack of an effective combination of feature extraction and Transformer in STP tasks. To make better use of short term information, we propose One-dimensional Convolution Embedding (OCE). Simultaneously, we introduce effective feature extraction with Transformer into STP problem to extract feature information and capture long-term timing information. By combining OCE and Transformer organically, we propose a noval STP prediction model, One-dimensional Convolution Embedding Transformer (OCET), to capture long-term and short-term time series information. Finally, OCET achieves a highest accuracy up to 0.927 in public benchmark FI-2010 When reasoning speed is twice that of SOTA models and a highest accuracy of 0.426 in HKGSAS-2020. Empirical results on these two datasets show that our OCET is significantly superior than other algorithms in STP tasks. Code are available at https://***/langgege-cqu/OCET. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Hybrid saturation restoration for LDR images of HDR scenes

arXiv

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arXiv 2021年

作者： Zheng, Chaobing Li, Zhengguo Wu, Shiqian the Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China the SRO Department Institute for Infocomm Research Singapore138632 Singapore

There are shadow and highlight regions in a low dynamic range (LDR) image which is captured from a high dynamic range (HDR) scene. It is an ill-posed problem to restore the saturated regions of the LDR image. In this paper, the saturated regions of the LDR image are restored by fusing model-based and data-driven approaches. With such a neural augmentation, two synthetic LDR images are first generated from the underlying LDR image via the model-based approach. One is brighter than the input image to restore the shadow regions and the other is darker than the input image to restore the high-light regions. Both synthetic images are then refined via a novel exposedness aware saturation restoration network (EASRN). Finally, the two synthetic images and the input image are combined together via an HDR synthesis algorithm or a multi-scale exposure fusion algorithm. The proposed algorithm can be embedded in any smart phones or digital cameras to produce an information-enriched LDR image. © 2021, CC0.

关键词： Restoration

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