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NestLipGNN:A Hierarchical Graph Neural Network Framework with Nested Multi-Granularity Learning for Robust Visual Speech Recognition 认领 引用
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作者 Vinh Truong Hoang Nghia Dinh +5 位作者 Luu Quang Phuong Kiet Tran-Trung Ha Duong Thi Hong Bay Nguyen Van Hau Nguyen Trung Thien Ho Huong 《Computers, Materials & Continua》 SCIE EI 2026年第7期1287-1310,共24页
Visual speech recognition(VSR)aims to infer spoken content from visual observations of articulatory movements.Despite significant progress,it remains a challenging task in computer vision and speech processing.Its dif... Visual speech recognition(VSR)aims to infer spoken content from visual observations of articulatory movements.Despite significant progress,it remains a challenging task in computer vision and speech processing.Its difficulty arises from pronounced speaker-to-speaker variability,the presence of homophenes(phonemes that are visually indistinguishable),changes in illumination,and the intrinsically high-dimensional nature of spatiotemporal lip dynamics.In this work,we propose NestLipGNN,a graph-based framework that integrates Graph Neural Networks(GNNs)with a nested multi-granularity learning strategy for visual speech recognition.We construct dynamic lip graphs from facial landmarks to model both spatial relationships between lip regions and their temporal motion during speech articulation.The proposed nested learning architecture supports hierarchical feature extraction across several levels of linguistic abstraction,spanning phoneme-level articulatory units,viseme-level visual speech categories,and word-level semantic representations.We further introduce a Temporal Graph Attention mechanism(T-GAT)that adaptively reweights the importance of distinct lip regions over time.We also introduce a graph-based contrastive learning objective to improve the discrimination of visually similar speech patterns,directly confronting the challenge of homophene resolution.Experiments on the LRW,LRS2,LRS3,and GRID datasets show that NestLipGNN improves recognition accuracy compared with existing methods,obtaining 92.3%word-level accuracy on LRW and delivering a 2.1%absolute performance gain over prior methods.Comprehensive ablation analyses confirm the contribution of each architectural component. 展开更多
关键词 Visual speech recognition graph neural networks nested optimization hierarchical representation learning spatiotemporal modeling contrastive learning lip reading
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Optimization of Nesting Systems in Shipbuilding:A Review 认领 引用
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作者 Sari Wanda Rulita Gunawan Muzhoffar Dimas Angga Fakhri 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第1期152-175,共24页
This review article provides a comprehensive analysis of nesting optimization algorithms in the shipbuilding industry,emphasizing their role in improving material utilization,minimizing waste,and enhancing production ... This review article provides a comprehensive analysis of nesting optimization algorithms in the shipbuilding industry,emphasizing their role in improving material utilization,minimizing waste,and enhancing production efficiency.The shipbuilding process involves the complex cutting and arrangement of steel plates,making the optimization of these operations vital for cost-effectiveness and sustainability.Nesting algorithms are broadly classified into four categories:exact,heuristic,metaheuristic,and hybrid.Exact algorithms ensure optimal solutions but are computationally demanding.In contrast,heuristic algorithms deliver quicker results using practical rules,although they may not consistently achieve optimal outcomes.Metaheuristic algorithms combine multiple heuristics to effectively explore solution spaces,striking a balance between solution quality and computational efficiency.Hybrid algorithms integrate the strengths of different approaches to further enhance performance.This review systematically assesses these algorithms using criteria such as material dimensions,part geometry,component layout,and computational efficiency.The findings highlight the significant potential of advanced nesting techniques to improve material utilization,reduce production costs,and promote sustainable practices in shipbuilding.By adopting suitable nesting solutions,shipbuilders can achieve greater efficiency,optimized resource management,and superior overall performance.Future research directions should focus on integrating machine learning and real-time adaptability to further enhance nesting algorithms,paving the way for smarter,more sustainable manufacturing practices in the shipbuilding industry. 展开更多
关键词 Cutting plate Nesting algorithms Nesting optimization Shipbuilding efficiency Algorithmic optimization
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Optimal Multiperiodic Control for Inventory Coupled Systems: A Multifrequency Second-Order Test 认领 引用
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作者 Marek Skowron Krystyn Styczeń 《Open Journal of Optimization》 2016年第3期91-101,共11页
A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products b... A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products between the subsystems. Because of the flexible subsystems interactions, each of them can be operated with their own periods utilizing advantageously their dynamic properties. A multifrequency second-order test generalizing the p-test for single systems is described. It can be used to decide which kind of the operation (the static one, the periodic one or the multiperiodic one) will intensify the productivity of a complex system. An illustrative example of the multiperiodic optimization of a complex chemical production system is presented. 展开更多
关键词 Optimal Multiperiodic Control Complex Systems Inventory Interactions Nested Optimization Multifrequency Second-Order Test
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Optimal Capacity Configuration of Large-scale Energy Bases Considering External Multi-stochastic Scenarios and Interactive Multi-timescale Objectives 认领 引用
4
作者 Yini Wang Yang Hu +3 位作者 Yueli Zhao Yunzhi Li Fang Fang Jizhen Liu 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2025年第6期1990-2001,共12页
Optimal capacity configuration(OCC)of large-scale energy bases with multi-timescale operation characteristics presents a critical challenge.To address the problem,this study proposes an OCC approach of large-scale ene... Optimal capacity configuration(OCC)of large-scale energy bases with multi-timescale operation characteristics presents a critical challenge.To address the problem,this study proposes an OCC approach of large-scale energy bases considering external multi-stochastic scenarios and interactive multi-timescale objectives.Firstly,guided by the system theory,the nonlinear state-space description is presented for systematic analysis of a general large-scale energy base.Due to interactive multi-timescale objectives between annual and daily cumulative objectives,a nested optimization structure is established.Then,considering the external multi-stochastic scenarios caused by the variables such as wind speed,solar irradiance,electric load,and thermal load,a multi-step optimization strategy is proposed including pre-configuration in regular scenarios and re-configuration by introducing micro-incremental scenarios.The multi-step optimization strategy and nested optimization structure jointly achieve the OCC of the large-scale energy base.In each step,the nested optimization structure is executed once.Finally,while ensuring the balance between thermal supply and load demand,the imbalances between electric power supply and the load demand are eliminated,significantly showing the efficiency of the proposed OCC approach. 展开更多
关键词 Large-scale energy base optimal capacity configuration multi-timescale objective nested optimization multi-stochastic scenario
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