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DEMA-3D TSP:An Enhanced Reinforcement Learning with DEMA Attention in Sequence Optimization for Safflower Picking Robot 认领 引用
1
作者 LI Menghao WANG Xiaorong +2 位作者 LIU Zihe DUAN Mengyu JIN Zhengyang 《智慧农业（中英文）》 CSCD 2026年第2期200-219,共20页
[Objective]There are several critical challenges in automated safflower harvesting,particularly the inefficiencies in path planning,suboptimal route quality,and limited decision-making capability under dynamic and com... [Objective]There are several critical challenges in automated safflower harvesting,particularly the inefficiencies in path planning,suboptimal route quality,and limited decision-making capability under dynamic and complex environments.To solve these issues,the problem was formulated as a three-dimensional traveling salesman problem and an enhanced reinforcement learning model named actor-critic reinforcement learning pointer network(AC-RL-PtrNet)was proposed,specifically designed for deployment on intelligent safflower picking robots in agricultural settings.[Methods]First,to address the inherent limitations of conventional attention mechanisms in dynamic environments with complex spatial structures,an enhanced attention module was proposed based on the dynamic exponential moving average framework.By combining multi-head attention,spatial distance encoding,and adaptive exponential smoothing,the improved design allowed the model to better capture long-range dependencies and spatial context among safflowers.Meanwhile,to minimize computational cost while preserving inference quality,a structured pruning approach was adopted,which selectively removed redundant connections in the long short-term memory gates and fully connected layers.In parallel,the critic network was redesigned to improve learning stability and accuracy.This was achieved through the inclusion of batch normalization,residual feature aggregation,and a multi-layer value estimation head,all of which contributed to a tighter actorcritic synergy during policy training.[Results and Discussions]To quantitatively assess the impact of each component,ablation experiments were conducted across various configurations.The results confirmed that each module contributed distinct benefits,while their combination yielded the highest improvements in both planning precision and inference efficiency.This coordinated actor-critic design effectively enhanced both trajectory quality and decision stability,which were critical in sequential robotic picking tasks.Experimental results also demonstrated that,compared with traditional swarm intelligence algorithms particle swarm optimization(PSO),ant colony optimization(ACO),and non-dominated sorting genetic algorithm,the proposed AC-RL-PtrNet model achieved a planning time improvement ranging from-2.63%to 61.87%on the 25-target dataset and from 22.93%to 59.1%on the 31-target dataset.Meanwhile,the optimized paths were significantly shortened across different planning instances,indicating robust generalization capability under varied problem scales.Furthermore,field experiments provided concrete validation of the model's practical applicability.When deployed on a mobile picking robot in real safflower fields,the AC-RL-PtrNet achieved a 9.56%reduction in path length and 5.43%time saved for a 25-target picking task,and a 20.17%path reduction and 29.70%time saving for a 31-target scenario involving a different safflower variety.Overall,these results all indicated that the proposed method exhibited significant advantages in enhancing path planning efficiency and optimizing path quality.[Conclusions]This study offers a practical solution for achieving efficient and robust automatic picking by safflower picking robots and provides new insights into solving 3D combinatorial optimization problems. 展开更多
关键词 dynamic exponential moving average mechanism structural pruning reinforcement learning 3D traveling salesman problem safflower picking robot
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A Lightweight Time-Indexed Secure Communication Framework with Intrusion Detection Modeling for Resource-Constrained UAV Swarm Networks 认领 引用
2
作者 Li-Woei Chen Kun-Lin Tsai +2 位作者 Fang-Yie Leu Chao-Tung Yang Wei-Zong Liang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1459-1491,共33页
Unmanned aerial vehicle(UAV)swarm networks are increasingly deployed in surveillance,disaster response,and intelligent transportation systems,where secure and efficient communication is critical under resource-constra... Unmanned aerial vehicle(UAV)swarm networks are increasingly deployed in surveillance,disaster response,and intelligent transportation systems,where secure and efficient communication is critical under resource-constrained environments.However,conventional public-key-based security mechanisms introduce excessive computational overhead,while standalone intrusion detection systems are insufficient to defend against dynamic and multi-vector attacks in swarm networks.To address these challenges,in this paper,a lightweight time-indexed secure communication framework with intrusion detection modeling(TSCID)is proposed for resource-constrained UAV swarm networks.The proposed TSCID integrates a time-indexed session key derivation mechanism with lightweight authenticated encryption to ensure confidentiality,integrity,replay resistance,and session-key isolation with low computational cost.A security and communication-overhead analysis under standard symmetric-key cryptographic assumptions is conducted to evaluate the practicality and lightweight characteristics of the proposed framework.To enhance resilience against network-level attacks,an intrusion detection module based on deep neural networks is incorporated and optimized through structured pruning,enabling real-time anomaly detection on edge-class UAV devices.Experimental results demonstrate that TSCID reduces communication latency by up to 35%and energy consumption by nearly 30%compared with conventional public-key-based security mechanisms,while the lightweight intrusion detection model achieves over 92%detection accuracy with less than 4%false positives.Analytical and experimental results confirm that the proposed framework provides an efficient and secure solution for real-time UAV swarm communication under strict resource constraints. 展开更多
关键词 UAV swarm networks time-indexed secure communication lightweight cryptography intrusion detection system structured pruning resource-constrained networks
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Can Causal Circuits Enable Efficient Spatial Reasoning for Geoinformatics in Large Language Models? 认领 引用
3
作者 Sahil Tripathi Manaswi Kulahara +1 位作者 Abdul Khader Jilani Saudagar Hatoon S.AlSagri 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1072-1095,共24页
Spatial reasoning,defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding.However,existing works such as Bidir... Spatial reasoning,defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding.However,existing works such as Bidirectional Encoder Representations from Transformers(BERT)-based spatial Question Answering(QA)models and neuro-symbolic models rely on dataset-specific patterns,leading to shortcut learning,where reliance on superficial lexical cues rather than true relational understanding.Recent Large Language Models(LLMs)-based works,including fine-tuning and Chain-of-Thought(CoT)prompting,partially alleviate shortcut learning but remain limited by non-causal reasoning,where predictions depend on spurious correlations rather than stable relational structure.To address these limitations,we propose Causal Inference and Reasoning via Compact sUbnetwork IdenTification(CIRCUIT-X),motivated by the hypothesis that spatial reasoning in LLMs is governed by compact causal parameter subsets(a.k.a causal circuits).CIRCUIT-X operates in two stages:(i)Causal Importance Estimation(Stage Ⅰ)via structured interventions to mitigate shortcut learning,and(ii)Minimal Circuit Discovery(Stage Ⅱ)via structured pruning to mitigate non-causal reasoning.Empirically,CIRCUIT-X achieves up to 91%accuracy on SPAtial Reasoning on Textual Question Answering(SPARTQA)and 87%on StepGame,outperforming State-of-the-Art(SOTA)methods while improving intervention robustness by up to+11%and causal consistency by up to+16%.Therefore,it retains up to 96%of full-model performance using only 3%–6%of active parameters,while demonstrating strong robustness under cross-domain transfer with improvements of up to+10%in accuracy and substantially higher intervention stability under distribution shifts. 展开更多
关键词 Causal circuits geoinformatics large language models parameter-efficient learning spatial reasoning structured pruning
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Pushing to the Limit:An Attention-Based Dual-Prune Approach for Highly-Compacted CNN Filter Pruning 认领 引用
4
作者 Yu-Chu Fang Wen-Zhong Li +2 位作者 Yao Zeng Qing-Ning Lu Sang-Lu Lu 《Journal of Computer Science & Technology》 SCIE EI CSCD 2025年第3期805-820,共16页
Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from s... Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from sharp performance drops when the pruning ratio is large,probably due to the unrecoverable information loss caused by aggressive pruning.In this paper,we propose a dual attention based pruning approach called DualPrune to push the limit of network pruning at an ultra-high compression ratio.Firstly,it adopts a graph attention network(GAT)to automatically extract filter-level and layer-level features from CNNs based on the roles of their filters in the whole computation graph.Then the extracted comprehensive features are fed to a side-attention network,which generates sparse attention weights for individual filters to guide model pruning.To avoid layer collapse,the side-attention network adopts a side-path design to preserve the information flow going through the CNN model properly,which allows the CNN model to be pruned at a high compression ratio at initialization and trained from scratch afterward.Extensive experiments based on several well-known CNN models and real-world datasets show that the proposed DualPrune method outperforms the state-of-the-art methods with significant performance improvement,particularly for model compression at a high pruning ratio. 展开更多
关键词 ultra-high compression dual attention based structured pruning inter-layer dependency layer collapse neural network compression
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