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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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Swarm Intelligence Research:From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

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Machine Intelligence Research 2023年第1期20卷 121-144页

作者： Guo-Yin Wang Dong-Dong Cheng De-You Xia Hai-Huan Jiang Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Big Data and Intelligent Engineering Yangtze Normal UniversityChongqing 408100China

Swarm intelligence has become a hot research field of artificial *** the importance of swarm intelli-gence for the future development of artificial intelligence,we discuss and analyze swarm intelligence from a broader and deeper *** a broader sense,we are talking about not only bio-inspired swarm intelligence,but also human-machine hybrid swarm *** a deeper sense,we discuss the research using a three-layer hierarchy:in the first layer,we divide the research of swarm intelli-gence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence;in the second layer,the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence;and in the third layer,we re-view single-population,multi-population and human-machine hybrid models from different ***-population swarm intel-ligence is inspired by biological *** further solve complex optimization problems,researchers have made preliminary explor-ations in multi-population swarm ***,it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive in-telligent behavior that meets the needs of human *** have introduced human intelligence into computing systems and proposed human-machine hybrid swarm *** addition to single-population swarm intelligence,we thoroughly review multi-population and human-machine hybrid swarm intelligence in this *** also discuss the applications of swarm intelligence in optimization,big data analysis,unmanned systems and other ***,we discuss future research directions and key issues to be studied in swarm intelligence.

关键词： Swarm intelligence single-population multi-population human-machine hybrid multi-granularity

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A Survey of LLM datasets:From Autoregressive Model to AI Chatbot

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Journal of Computer Science & Technology 2024年第3期39卷 542-566页

作者：杜非马新建杨婧如柳熠罗超然王学斌姜海鸥景翔 National Key Laboratory of Data Space Technology and System Beijing 100195China Advanced Institute of Big Data Beijing 100195China Fu Foundation School of Engineering and Applied Science Columbia UniversityNY 10027U.S.A. School of Software and Microelectronics Peking UniversityBeijing 100091China CCF IEEE

Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant ***,public researchers meet some problems when developing LLMs given that most of the LLMs are produced by industries and the training details are typically *** datasets are an important setup of LLMs,this paper does a holistic survey on the training datasets used in both the pre-train and fine-tune *** paper first summarizes 16 pre-train datasets and 16 fine-tune datasets used in the state-of-the-art ***,based on the properties of the pre-train and fine-tune processes,it comments on pre-train datasets from quality,quantity,and relation with models,and comments on fine-tune datasets from quality,quantity,and *** study then critically figures out the problems and research trends that exist in current LLM *** study helps public researchers train and investigate LLMs by visual cases and provides useful comments to the research community regarding data *** the best of our knowledge,this paper is the first to summarize and discuss datasets used in both autoregressive and chat *** survey offers insights and suggestions to researchers and LLM developers as they build their models,and contributes to the LLM study by pointing out the existing problems of LLM studies from the perspective of data.

关键词： large language model(LLM) autoregressive model AI chatbot natural language processing(NLP)corpora OpenAI

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

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Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing 400065China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing400065China Chongqing Innovation Center of Industrial Big-Data Co.Lt Chongqing400700China

The lack of facial features caused by wearing masks degrades the performance of facial recognition *** occluded face recognition methods cannot integrate the computational resources of the edge layer and the device ***,previous research fails to consider the facial characteristics including occluded and unoccluded *** solve the above problems,we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature ***,the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face ***,a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain ***,a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load,computational power,bandwidth,and delay tolerance constraints of the *** method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition *** experimental results show that the proposed method achieves an average gain of about 21%in recognition latency,while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

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EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suitable for Natural

EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suit...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Yuzhe Chen, Jiahao Duan, Xiaodong Li, Zhuohui College of Mechanical and Electronic Engineering Dalian China College of Computer Science and Engineering Dalian China Seac Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (纸本)9781665410205

Weed detection is crucial for the healthy growth of crops, yet existing detection models struggle to perform high-accuracy real-time detection of weeds in natural environments on edge computing devices. This paper introduces EMPA-YOLO, a model designed for rapid, accurate, real-time weed detection on low-performance edge computing devices. It incorporates an efficient multi-scale convolutional structure, C3EMSC, and a lightweight, adaptive weight subsampling layer, LAWDS, into YOLOv5s. Additionally, a logical distillation algorithm, AlignSoftTarget, is proposed for knowledge distillation. Validation on a mixed dataset of crops and weeds showed that EMPA-YOLO improved mAP50 by 11.2%, reduced parameter count by 4.8M, decreased computational load by 8.7GFLOPs, and increased inference frame rate by 58% compared to the original YOLOv5s algorithm. When compared to YOLOv3, YOLOv5s, YOLOv6, YOLOv8s, and RT-DETR, inference speed improved by 89.2%, 60.8%, 77.5%, 76.5%, and 94.7%, respectively, with mean accuracy enhancements of 7.3%, 11.2%, 16.7%, 0.9%, and 1.2%. Real-world testing on edge computing devices met real-time detection requirements, proving its efficacy and practicality in weed detection. © 2024 IEEE.

关键词： edge computing knowledge distillation lightweight model model compress ion YOLO

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CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis

arXiv

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arXiv 2025年

作者： Zhang, Bohan Zhang, Xiaokang Zhang, Jing Yu, Jifan Luo, Sijia Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks. However, these methods rely heavily on the quality of candidate responses and are unable to produce correct answers when all candidates are incorrect. In this paper, we propose a novel inference scaling strategy, CoT-based Synthesizer, which leverages CoT reasoning to synthesize superior answers by analyzing complementary information from multiple candidate responses, even when all candidate responses are flawed. To enable a lightweight and cost-effective implementation, we introduce an automated data generation pipeline that creates diverse training data. This allows smaller LLMs trained on this data to improve the inference accuracy of larger models, including API-based LLMs. Experimental results across four benchmark datasets with seven policy models demonstrate that our method significantly enhances performance, with gains of 11.8% for Llama3-8B and 10.3% for GPT-4o on the MATH dataset. The corresponding training data and code are publicly available on the repository. Copyright © 2025, The Authors. All rights reserved.

关键词： data accuracy

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Parameter optimization of the field-road trajectory segmentation model based on the chaos sensing slime mould algorithm

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Soft Computing 2024年第19期28卷 11065-11132页

作者： Pan, Jiawen Guo, Zhou Wu, Caicong Zhai, Weixin College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China

Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study aims to enhance the performance of the field-road segmentation problem by introducing a novel parameter optimization strategy that relies on metaheuristic algorithms. The utilization of the metaheuristic algorithm offers precise and efficient techniques for determining parameter combinations in the field-road segmentation model. A novel enhanced optimization algorithm called the chaos sensing slime mould algorithm (CSSMA), aimed at determining out the optimal parameter combination from a large constrained solution space is proposed to further improve the segmentation performance of the model. First, to strengthen the exploration capability of the algorithm, a chaotic strategy is used to initialize the search agents. Furthermore, the CSSMA incorporates the beetle antennae search algorithm and the genetic algorithm to improve its exploitation potential. Ultimately, a proposed technique for updating control parameters in a nonlinear and dynamic manner aims to achieve a balance between exploration and exploitation. This strategy involves adaptively adjusting computing operations based on feedback from the optimization process. In order to assess the effectiveness of the CSSMA, the algorithm is compared to other metaheuristic algorithms using 23 standard benchmark functions. Furthermore, the CSSMA is utilized to address the practical implementation of the parameter optimization approach for field-road segmentation. The goal is to achieve the utmost accuracy in segmentation and compare it with the current method. Experimental results demonstrate the superior convergence and speed of CSSMA, along with its benefits in extracting optimal parameter structures. It surpasses prior techniques in handling the complexities of field-road trajectory processing problems in

关键词： Genetic algorithms

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Research on Pedestrian Gender Detection Based on A Lightweight Model 36

Research on Pedestrian Gender Detection Based on A Lightweig...

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36th Chinese Control and Decision Conference, CCDC 2024

作者： Yuling, Hu Xinyi, Wang Weiguang, Zou Beijing University Of Civil Engineering And Architecture Institute Of Electrical And Information Engineering China Beijing University Of Civil Engineering And Architecture Beijing Key Laboratory Of Intelligent Processing For Building Big Data Beijing China

ISBN: (纸本)9798350387780

In recent years, a variety of emergency events have been occurring in buildings in countries. For there are some differences between male and female in movement ability and evacuation behavior, gender has a non-negligible impact on evacuation and decision-making of emergency events. Thus, it is of great practical significance to detect the gender of pedestrians at entrances or exits of buildings in real time to grasp the proportion of different gender people in buildings and to formulate emergency warning or evacuation strategies scientifically. In response to the requirement of rapid emergency response, this paper proposes a pedestrian gender detection method based on a lightweight object detection model YOLOX-Tiny. In addition, considering the inaccurate pedestrian gender detection caused by the change of pedestrian posture and camera angle, the attention mechanism CBAM is introduced to enhance the ability of filtering a large amount of redundant information, obtaining key information and acquiring pedestrian gender characteristic information. Through the experiments of self-made pedestrian gender datasets, the results show that the detection accuracy of the improved model is increased by 1.8%, and the detection speed reaches 251FPS, which can meet both speed and accuracy of pedestrian gender detection in emergencies. © 2024 IEEE.

关键词： Object detection

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