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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Lin, Zheyuan Ji, Shanshan Wang, Wen Qin, Mengjie Yang, Rong Wan, Minhong Gu, Jason Li, Te Zhang, Chunlong Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350325706

The application of robots in social life, equipped with sensors and actuators and embedded with AI, assists people in all aspects. However, the first perspective of the robot horizon is heavily constrained, which weakens its performance. A joint tracking system is designed and built to deal with this, by integrating a surveillance system with the robot visual, providing a third perspective. This system takes one horizontal view and two top views from various directions as inputs and matches a person among the frames and in time sequence. In order to deal with the identity match with a huge visual feature gap, a special dataset is collected, simultaneously labeling identities from a mobile robot perspective and multiple indoor static surveillance monitors. The experiment shows that such match is a task worth exploring that can be better handled by training on our dataset than existing open source Re-identification (Re-id) datasets. Moreover, in the real scenario, this system improves the performance on issues like in and out of the robot's field of vision and heavy occlusion by people or objects. © 2023 IEEE.

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A zeroing neurodynamics‐based location method for nodes in underwater acoustic sensor network

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 661-669页

作者： Shuqiao Wang Xiujuan Du Tiantai Deng College of Computer Qinghai Provincial Key Laboratory of loTQinghai Normal UniversityXiningChina The State Key Laboratory of Tibetan Intelligent Information Processing and Application XiningChina The Department of Electronics and Electrical Engineering The University of SheffieldSheffieldEnglandUnited Kingdom

Zeroing neurodynamics methodology,which dedicates to finding equilibrium points of equations,has been proven to be a powerful tool in the online solving of problems with considerable *** this paper,a method for underwater acoustic sensor network(UASN)localisation is proposed based on zeroing neurodynamics methodology to preferably locate moving underwater nodes.A zeroing neurodynamics model specifically designed for UASN localisation is constructed with rigorous theoretical analyses of its *** proposed zeroing neurodynamics model is compatible with some localisation algorithms,which can be utilised to eliminate error in non‐ideal situations,thus further improving its ***,the effectiveness and compatibility of the proposed zeroing neurodynamics model are substantiated by examples and computer simulations.

关键词： artificial neural network internet of things underwater acoustic sensor network

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Examining the economic feasibility of hybrid Photovoltaic - Battery Energy Storage System in public building with flexible demand: Cyprus case study 3

Examining the economic feasibility of hybrid Photovoltaic - ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Chatzigeorgiou, Nikolas G. Makrides, George Theocharides, Spyros Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (纸本)9798350375923

Photovoltaic - Battery Energy Storage Systems (PVBESS) are a promising solution against technical bottlenecks of high PV penetration. Public buildings are suitable for installation, although currently underexploited, while high BESS costs limit technology diffusion. This paper assesses the economic feasibility of PV-BESS in public buildings with flexible demand, through simulations showcasing BESS subsidisation impact and self-sufficiency contribution. The results derived that smaller PV-BESS sizes are more attractive investments, as larger systems are characterised by reduced profitability, while BESS subsidisation enhances system profitability. Shifting a small portion of load demand improves economic feasibility by decreasing BESS size, while combining PV production, energy storage, and energy flexibility is the optimal solution for public buildings against electricity prices. Finally, a trade-off between economic feasibility and energy self-sufficiency is observed. The results can be utilised by policymakers to design strategies for BESS promotion, while professionals can perform accurate system sizing, thus avoiding excess system capacities. © 2024 IEEE.

关键词： Battery storage

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Data-driven Predictive Maintenance Alerting Routine for utility-scale Photovoltaic Systems 3

Data-driven Predictive Maintenance Alerting Routine for util...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Marangis, Demetris Livera, Andreas Makrides, George Georgiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Cyprus Efficient and Sustainable Energy Solutions PHAETHON Centre of Excellence for Intelligent Nicosia Cyprus

ISBN: (纸本)9798350375923

Predictive maintenance is crucial for optimizing the performance of photovoltaic (PV) systems by proactively addressing potential issues before they escalate. Even though many fault diagnosis methods have been proposed, a major challenge remains the lack of accurate predictive maintenance routines for utility-scale PV systems. The scope of this work is to address this fundamental challenge by proposing a data-driven predictive maintenance alerting routine (that leverages digital twin technologies and decision rules) for detecting and predicting PV system faults. The results demonstrated that the developed predictive maintenance routine yielded a sensitivity of 93.9% for PV utility-scale failure detection over a yearly test period. The findings of the predictive maintenance alerting model proved its capability to anticipate faults up to 10 days in advance, achieving a sensitivity of 83.3%. Finally, the predictive maintenance alerting system can be used by operation and maintenance teams for scheduling corrective actions and optimizing field activities. © 2024 IEEE.

关键词： Predictive maintenance

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of Systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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Understanding Approaching Behavior for a Wheelchair with a Robotic Arm: A Human Study for Improving Autonomous Navigation

Understanding Approaching Behavior for a Wheelchair with a R...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Sarathchandra, H.A. Harindu Y. Priyanayana, K.S. Jayasekara, A.G.B.P. Gopura, R.A.R.C. University of Moratuwa Intelligent Service Robotics Group Department of Electrical Engineering Moratuwa10400 Sri Lanka University of Moratuwa Bionics Laboratory Department of Mechanical Engineering Moratuwa10400 Sri Lanka

ISBN: (纸本)9798350337020

An intelligent wheelchair is a system that evolves its functions for the well-being of humankind in the present status of robotics. Due to the active evolution of these wheelchair systems wheelchair-mounted robotic arms can be caught as the next level of development. However, the current state of intelligence is far behind the expansion and this lack causes to add extra cognitive load on handicapped users. Most of the available hindrances to autonomous operation could certainly be achieved by replicating natural human behaviors. Under these circumstances understanding human cognition on positioning a wheelchair around a table will drastically ease the autonomous object manipulation tasks by these systems. Hence to understand natural human behavior, a human study with three sub-studies was designed and reveal the prominent factors behind human cognition. Results were analyzed statistically to identify the significance of the considered factors. In lite of the study, approaching and positioning of the wheelchair mainly depend on the object position, obstacle position, and obstacles configuration within the considered workspace. Further orientation mainly depends on the object's position and approaching direction. Besides, these outcomes would be extremely beneficial in synthesizing human cognition to build human-friendly mobile robots for uplifting users' lives as well. © 2023 IEEE.

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Investigations and Time Estimation on Federated Learning for Future Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17387-17398页

作者： Fukumoto, Shun Li, Ruidong Zeng, Kai Nan, Haihan Su, Zhou Kanazawa University Intelligent Computation and Network Laboratory Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan Kunming University of Science and Technology Faculty of Information Engineering Automation Kunming066000 China Kanazawa University Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan George Mason University Electrical and Computer Engineering Department FairfaxVA22030 United States Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work. © 2014 IEEE.

关键词： Stochastic systems

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Fundamental Limits of Multiple-Access Integrated Sensing and Communication Systems

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IEEE Transactions on Information Theory 2025年第6期71卷 4317-4341页

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： Signal distortion

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