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IEEE International Conference on Robotics and Biomimetics

作者： Zhihao Li Junjia Liu Zhuo Li Minghao Yu Tao Teng Shunbo Zhou Miao Li Tin Lun Lam Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong SAR Hong Kong Center for Logistics Robotics Hong Kong SAR Computing Technologies Co. Ltd. Shenzhen China Institute of Technological Sciences Wuhan University Wuhan China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

One of the grand goals in robotics is to develop a versatile robot capable of executing a wide array of tasks based on language instructions. These tasks often involve long-horizon manipulation due to the complexity of the real-world tasks and the high-dimensional action space of the dual-arm mobile robots. This work proposes a generalizable framework that combines a Large Language Model, a Visual-language Model, and symbolic planning to address long-horizon bimanual mobile manipulation tasks. We use the Large Language Model as the commonsense reasoner to break down the task into a set of subgoals and generate goal-oriented state queries. Subsequently, we fine-tune a Visual-language Model to obtain answers to queries. Finally, we propose a Planning Domain Definition Language translator that translates the subgoal and the goal-oriented states into a symbolic planning problem that can be solved by search-based planners. The experiments show that the proposed method has generalization ability in novel objects and instruction. It outperforms the baseline methods across all the configurations. Moreover, our approach exhibits competence in increasingly complex tasks, encompassing those that demand upward of 10 planning steps.

关键词： Translation Large language models Scalability Humanoid robots Detectors Search problems Generators Planning Complexity theory Mobile robots

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FOF-X: Towards Real-time Detailed Human Reconstruction from a Single Image

arXiv

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arXiv 2024年

作者： Feng, Qiao Liu, Yebin Lai, Yu-Kun Yang, Jingyu Li, Kun College of Intelligence and Computing Tianjin University Tianjin300350 China Department of Automation Tsinghua University Beijing100084 China School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom School of Electrical and Information Engineering Tianjin University Tianjin300072 China

We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image. Balancing real-time speed against high-quality results is a persistent challenge, mainly due to the high computational demands of existing 3D representations. To address this, we propose Fourier Occupancy Field (FOF), an efficient 3D representation by learning the Fourier series. The core of FOF is to factorize a 3D occupancy field into a 2D vector field, retaining topology and spatial relationships within the 3D domain while facilitating compatibility with 2D convolutional neural networks. Such a representation bridges the gap between 3D and 2D domains, enabling the integration of human parametric models as priors and enhancing the reconstruction robustness. Based on FOF, we design a new reconstruction framework, FOF-X, to avoid the performance degradation caused by texture and lighting. This enables our real-time reconstruction system to better handle the domain gap between training images and real images. Additionally, in FOF-X, we enhance the interconversion algorithms between FOF and mesh representations with a Laplacian constraint and an automaton-based discontinuity matcher, improving both quality and robustness. We validate the strengths of our approach on different datasets and real-captured data, where FOF-X achieves new state-of-the-art results. The code will be released for research purposes at https://***/faculty/likun/projects/FOFX/***. © 2024, CC BY.

关键词： Fourier series

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Generative AI-aided Reinforcement Learning for Computation Offloading and Privacy Protection in VR-based Multi-Access Edge computing

Generative AI-aided Reinforcement Learning for Computation O...

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Smart World Congress (SWC), IEEE

作者： Feiran You Hongyang Du Jiawen Kang Wei Ni Dusit Niyato Abbas Jamalipour School of Electrical and Computer Engineering The University of Sydney Sydney NSW Australia Data61 Commonwealth Scientific and Industrial Research Organization (CSIRO) Sydney NSW Australia Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a Data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users’ privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users’ privacy within the PPO centralized management framework.

关键词： Privacy Solid modeling Simulation Noise reduction Noise Virtual reality Reinforcement learning Diffusion models Protection Optimization

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Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

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Science China(Information Sciences) 2021年第5期64卷 151-162页

作者： Xiang ZHANG Shuping HE Vladimir STOJANOVIC Xiaoli LUAN Fei LIU Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil EngineeringUniversity of Kragujevac Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable LyapunovKrasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters U, G and V. Finally, the filter gains and the optimal index α*are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

关键词： dissipative filtering hidden Markov model finite-time boundedness Markov jump systems conic-type nonlinearity

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Learning-Based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

Learning-Based Sustainable Multi-User Computation Offloading...

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IEEE International Conference on Communications (ICC)

作者： Minrui Xu Dusit Niyato Jiawen Kang Zehui Xiong Mingzhe Chen School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA

In this paper, a novel paradigm of mobile edgequantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.

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LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics

arXiv

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arXiv 2025年

作者： Tang, Jialiang Chen, Shuo Gong, Chen Zhang, Jing Tao, Dacheng School of Computer Science and Engineering Nanjing University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore School of Intellgence Science and Technology Nanjing University China Department of Automation Institute of Image ProceProceedingssing and Pattern Recognition Shanghai Jiao Tong University China School of Computer Science Wuhan University China

Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Recent studies have revealed that Large Language Models (LLMs), with their powerful in-contextual modeling capabilities, hold significant potential for TSF. However, existing LLM-based methods usually perform suboptimally because they neglect the inherent characteristics of time series data. Unlike the textual data used in LLM pre-training, the time series data is semantically sparse and comprises distinctive temporal patterns. To address this problem, we propose LLM-PS to empower the LLM for TSF by learning the fundamental Patterns and meaningful Semantics from time series data. Our LLM-PS incorporates a new multi-scale convolutional neural network adept at capturing both short-term fluctuations and long-term trends within the time series. Meanwhile, we introduce a time-to-text module for extracting valuable semantics across continuous time intervals rather than isolated time points. By integrating these patterns and semantics, LLM-PS effectively models temporal dependencies, enabling a deep comprehension of time series and delivering accurate forecasts. Intensive experimental results demonstrate that LLM-PS achieves state-of-the-art performance in both short- and long-term forecasting tasks, as well as in few- and zero-shot settings. © 2025, CC BY.

关键词： Spatio-temporal data

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Design and Development of an Experimental Photovoltaic System

Design and Development of an Experimental Photovoltaic Syste...

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Energy Efficiency and Agricultural engineering (EE&AE), International Conference on

作者： Ventsislav Keseev Nicolay Mihailov Boris Evstatiev Tsvetelina Mladenova Vladislav Hinkov Nikolay Valov Department of Telecommunications University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electrical Power Engineering University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computer Systems and Technologies University of Ruse “Angel Kanchev” Ruse Bulgaria Center for Computing and Information Services University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Automation and Mechatronics University of Ruse “Angel Kanchev” Ruse Bulgaria

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

The world faces many challenges today and the distributed photovoltaic (PV) systems for local high-efficiency generation of electricity from sunlight seem to be one of the good solutions, but most of the population live in crowded urban environments, which creates a number of limiting factors. This study focuses on the design and implementation of an experimental photovoltaic installation in Ruse, Bulgaria, which allows the investigation of the impact of these factors on the efficiency of urban PV plants for optimization and education purposes. Initially, the goals towards the research facility are defined and the design and implementation of the installation is presented, including the orientation and positioning of the PV park, its schematic, and the key characteristics of the equipment used. Next, the first full year of operation of the research facility is analyzed by examining its energy generation profile. The winter months have about 6 times lower energy yields than the best summer ones and about 4-5 times lower on average compared to those for the other 3 seasons. This is due to the nearby buildings and trees, which create different shadows over the different seasons. The daily energy generation period during the winter is more than twice as short in comparison to that for the other seasons, while the peak daily winter energy yield is about $30 \%$ lower. The yearly energy yield per 1 kW peak installed PV panels is $1131.75 \mathrm{kWh} / \mathrm{kW}$, while the Global Solar Atlas suggests that the specific photovoltaic power output for Ruse is $1360.2 \mathrm{kWh} / \mathrm{kWp}$. The obtained exploitation results indicate that the experimental installation fully complies with the stated goals and allows investigating a number of urban scenarios.

关键词： Photovoltaic systems Azimuth Electricity Urban areas Buildings Production Solar power generation

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GraCo: Granularity-Controllable Interactive Segmentation

arXiv

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arXiv 2024年

作者： Zhao, Yian Li, Kehan Cheng, Zesen Qiao, Pengchong Zheng, Xiawu Ji, Rongrong Liu, Chang Yuan, Li Chen, Jie School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China -Preferred Program Peking University Shenzhen Graduate School China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Automation and BNRist Tsinghua University Beijing China

Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity output and multi-granularity output. The latter aims to alleviate the spatial ambiguity present in the former. However, the multi-granularity output pipeline suffers from limited interaction flexibility and produces redundant results. In this work, we introduce Granularity-Controllable Interactive Segmentation (GraCo), a novel approach that allows precise control of prediction granularity by introducing additional parameters to input. This enhances the customization of the interactive system and eliminates redundancy while resolving ambiguity. Nevertheless, the exorbitant cost of annotating multi-granularity masks and the lack of available datasets with granularity annotations make it difficult for models to acquire the necessary guidance to control output granularity. To address this problem, we design an any-granularity mask generator that exploits the semantic property of the pre-trained IS model to automatically generate abundant mask-granularity pairs without requiring additional manual annotation. Based on these pairs, we propose a granularity-controllable learning strategy that efficiently imparts the granularity controllability to the IS model. Extensive experiments on intricate scenarios at object and part levels demonstrate that our GraCo has significant advantages over previous methods. This highlights the potential of GraCo to be a flexible annotation tool, capable of adapting to diverse segmentation scenarios. The project page: https://***/GraCo. Copyright © 2024, The Authors. All rights reserved.

关键词： Pipelines

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Code Clone Detection Using Boosting Algorithms

Code Clone Detection Using Boosting Algorithms

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Advanced Research in computing (ICARC), International Conference on

作者： M.V. Thanoshan Kuhaneswaran Banujan B.T.G.S Kumara S. Prasanth Zhenni Li Incheon Paik Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technologies Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan

To increase programming productivity, developers often copy and paste the source code with or without changing it. However, they may also introduce significant downsides in the long run, including complicating the software and raising maintenance costs. The activity of duplicating the code is known as code cloning. They are classified into four types - Type-1, Type-2, Type-3, and Type-4. In this paper, the author presents a machine-learning approach for detecting code clones of all kinds except for Type-2. Abstract Syntax Trees are used to extract features from the methods. A distance combination approach combines two feature vectors of a pair of methods and their class labels. Once the dataset is finalised, a machine- learning approach is utilised to classify the clone type. Moreover, boosting classifiers like XGBoost, CatBoost, LightGBM, Gradient Boosting and AdaBoost are evaluated for the highest classification accuracy. From the results obtained, LightGBM outperformed all the other classifiers with the highest F1 score of 0.81. This study would motivate future researchers to focus on identifying the Type-2 clones and extracting novel features in determining the clone types.

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

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IEEE Network 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Through nonlinear mapping of semantic representations, Deep Learning (DL)-based semantic communications further enhance communication efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities allow attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we propose a novel Mixture-of-Experts (MoE)-based SemCom system, comprising a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 1986-2012 IEEE.

关键词： Vehicular ad hoc networks

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