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Digital Triads as Next-Generation Mechatronic Systems for Sustainability-A Case Study

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2025年第1期30卷 679-691页

作者： Bi, Zhuming Mikkola, Aki Devpalli, Dilipkummar Luo, Chaomin Purdue Univ Ft Wayne Dept Civil & Mech Engn Ft Wayne IN 46805 USA Lappeenranta Univ Technol Sch Energy Syst Dept Mech Engn Lappeenranta 53850 Finland Altair Tech Ridge Austin TX 78753 USA Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39759 USA

With the rapid development of information technologies, networking, and artificial intelligence, mechatronics has continuously evolved for several generations from traditional mechatronics to digital twins (DT-I), cyber-physical systems (CPSs), Internet of Things (IoT), human CPSs, and to metaverse nowadays. This article examines such a development trend from a new perspective in enhancing the sustainability of products or systems. To fill the technical gaps of existing concepts in supporting sustainable developments of next-generation mechatronic systems, we propose the concepts of digital triads (DT-II) and Internet of DT-II things;these concepts aim to expand the capabilities of mechatronic systems in dealing with changes and uncertainties in dynamic business environments and prolonging systems' lifespans through technological integration. For example, DT-II is innovative in the sense that it extends the capabilities of CPS or DT-I by introducing a life model so that relevant data, models, and methods can be preserved and transferred when a product or system needs reconfiguring to satisfy new requirements. DT-II promotes concurrent engineering in decision-making supports in the digital and physical worlds so that a product or system can be reconfigured to satisfy new requirements optimally. This smoothens the transitions in sustainable manufacturing and prolongs the enterprise's lifespan. To showcase the use and significance of DT-II, a case study of DT-II for sustainable product development is developed to predict the performance of new products for a client company. The developed DT-II is fully verified and validated for use, and the proposed procedure is generalized and can be used to develop DT-II for other products and systems for enhanced sustainability.

关键词： Mechatronics Sustainable development Manufacturing Business computational modeling Internet of Things Uncertainty Artificial intelligence (AI) cyber-physical systems (CPSs) digital triads (DT-II) digital twins (DT-I) Internet of Digital Triad Things (IoDDT) Internet of Things (IoT) sustainability verification and validation (V&V)

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Integrated Strategy for Optimized Charging and Balancing of Lithium-Ion Battery Packs

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 4980-4991页

作者： Astudillo, Galo D. Beiranvand, Hamzeh Cecati, Federico Werlich, Christian Wuersig, Andreas Liserre, Marco Fraunhofer Inst Silicon Technol D-25524 Itzehoe SH Germany Univ Kiel Chair Power Elect D-24143 Kiel SH Germany

During fast charging of lithium-ion batteries (LIBs), cell overheating and overvoltage increase safety risks and lead to faster battery deterioration. Moreover, in conventional battery management systems (BMSs), the cell balancing, charging strategy, and thermal regulation are treated separately at the expense of faster cell deterioration. Hence, this article proposes an optimized fast charging and balancing strategy with electrothermal regulation of LIB packs. Therefore, the power dissipation constraints of passive balancing (PB) are introduced in the proposed integrated optimal framework, and cell balancing is achieved by bypassing the extra charging current. The electrothermal model of the cells, along with a battery pack formed by a string of cells, is implemented. Extensive experiments are carried out to identify the coefficients for the lithium-ion cell model, that is, Samsung-INR18650-20R, and the charging current trajectory as well as the balancing signals are generated with model predictive control (MPC). The pack-level simulations and experiments show that the proposed algorithm maintains the electrothermal boundaries throughout the charging process, increasing the safe charge acceptance of the battery pack.

关键词： Batteries Integrated circuit modeling Resistors Regulation computational modeling Aging Thermal resistance Predictive models Power dissipation Fast charging Battery pack electrothermal battery model fast charging nonlinear model predictive control (NMPC)

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Advanced Machine Learning Ensembles for Improved Precipitation Forecasting: The Modified Stacking Ensemble Strategy in China

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 4242-4254页

作者： Tang, Tiantian Wu, Yifan Li, Yujie Xu, Lexi Shi, Xinyi Zhao, Haitao Gui, Guan Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Design Inst Water Conservancy & Hydroelect Power Hangzhou 310000 Peoples R China China United Network Commun Corp Res Inst Beijing 100048 Peoples R China Univ Elect Sci & Technol China Quzhou 324003 Peoples R China Nanjing Great Informat Technol Co Ltd AI Res Ctr Nanjing 210000 Peoples R China

Accurate and reliable precipitation forecasting is vital for effective water resource management and disaster mitigation, especially in geographically diverse and climatically complex regions like China. This study proposes an advanced methodology for medium- and long-term hydrological forecasting by integrating multiple machine learning models through a modified stacking ensemble strategy (MSES). We developed and compared five deterministic precipitation forecasting models, including elastic net regression (ENR), support vector regression, random forest, extreme gradient boosting, and light gradient boosting to provide forecasts with lead times ranging from 0 to 5 months at a spatial resolution of 0.5(degrees). The MSES was then evaluated against the traditional Bayesian model averaging (BMA) approach. Our comprehensive evaluation, based on deterministic forecasting metrics such as the anomaly correlation coefficient (ACC), mean squared skill score (MSSS), and Graded Precipitation Score (Pg), demonstrated the MSES outperformed individual models and the BMA method. The MSES achieved ACC scores between 0.6 and 0.9 for lead time (LT) = 0 month, with an average of around 0.8 for LT = 2 months. The MSSS for MSES was above 0.5 in more than half of the evaluations, and the Pg score was consistently above 80, indicating high accuracy in precipitation magnitude prediction. These findings highlight the promise of advanced machine learning strategies like MSES in improving the accuracy and robustness of precipitation forecasts, addressing critical needs in water resource management and disaster mitigation in China.

关键词： Forecasting Precipitation Predictive models Machine learning Accuracy Radio frequency Training Disasters computational modeling Boosting Ensemble methods machine learning multimodel machine learning integration precipitation forecasting

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How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 3211-3222页

作者： Sun, Yuchang Kountouris, Marios Zhang, Jun Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Clear Water Bay Hong Kong Peoples R China Univ Granada Andalusian Res Inst Data Sci & Computat Intelligen Dept Comp Sci & Artificial Intelligence Granada 18012 Spain EURECOM F-06410 Biot France

Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed learning framework. In this work, we focus on cross-silo FL, where clients become the model owners after training and are only concerned about the model's generalization performance on their local data. Due to the data heterogeneity issue, asking all the clients to join a single FL training process may result in model performance degradation. To investigate the effectiveness of collaboration, we first derive a generalization bound for each client when collaborating with others or when training independently. We show that the generalization performance of a client can be improved by collaborating with other clients that have more training data and similar data distributions. Our analysis allows us to formulate a client utility maximization problem by partitioning clients into multiple collaborating groups. A hierarchical clustering-based collaborative training (HCCT) scheme is then proposed, which does not need to fix in advance the number of groups. We further analyze the convergence of HCCT for general non-convex loss functions which unveils the effect of data similarity among clients. Extensive simulations show that HCCT achieves better generalization performance than baseline schemes, whereas it degenerates to independent training and conventional FL in specific scenarios.

关键词： Training Data models Collaboration computational modeling Training data Servers Games Convergence Analytical models Mobile computing Federated learning generalization collaboration pattern hierarchical cluster

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DSDC-GCN: Decoupled Static-Dynamic Co-Occurrence Graph Convolutional Networks for Skeleton-Based Action Recognition

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2101-2117页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Qin, Zhiguang Geng, Ji Liu, Yi Choo, Kim-Kwang Raymond Univ Elect Sci & Technol China Network & Data Secur Key Lab Sichuan Prov Chengdu 610054 Peoples R China Southwestern Univ Finance & Econ Sch Comp & Artificial Intelligence Chengdu 610074 Peoples R China Baidu Online Network Technol Beijing Co Ltd Beijing 100102 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA

The existing approaches for skeleton-based action recognition based on graph convolutional networks (GCNs) primarily emphasize the construction of human skeletal structure by leveraging inherent connections. However, the static skeletal topology used across all action categories fails to capture discriminative relationships between joint pairs, while current graph structures struggle to model dynamic motion information, limiting their ability to represent both temporal and motion-specific dependencies. To address this limitation, we propose the decoupled static-dynamic co-occurrence graph convolution (DSDC-GConv), which specifically aims to learn and adapt the graph topology by refining the inter-frame and intra-frame joint dependencies through decomposed manner. Additionally, a multi-level context-aware module is proposed to comprehensively model the latent saliencies of multiple domains in skeletal sequences. This module refines the spatial nodes, temporal dynamics, channel-wise characteristics, and motional dependencies within the graph convolution block. Furthermore, a hierarchical densely connected temporal convolution is proposed to enhance the representation of local features through partial dense connections and enrich the temporal information during the convolution process. Findings from our evaluations on five large-scale benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, Kinetics Skeleton 400, Northwestern-UCLA, PKU-MMD) demonstrate the effectiveness and superiority of our proposed method over competing approaches, with an recognition accuracy of 93.0% and 97.1% on NTU RGB+D 60, 89.9% and 90.6% on NTU RGB+D 120, 38.6% and 63.4% on Kinetics Skeleton 400, 97.4% on Northwestern-UCLA, 97.6% and 63.6% on PKU-MMD.

关键词： Convolution Skeleton Dynamics Accuracy Topology Semantics Feature extraction Correlation computational modeling Adaptation models Action recognition 3D human skeleton graph convolutional network spatial-temporal representatives

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Sequential Causal Effect Estimation by Jointly modeling the Unmeasured Confounders and Instrumental Variables

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 910-922页

作者： Sun, Zexu He, Bowei Shen, Shiqi Wang, Zhipeng Gong, Zhi Ma, Chen Qi, Qi Chen, Xu Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Beijing Key Lab Big Data Management & Anal Methods Beijing 100872 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China Tencent Wechat Beijing 100160 Peoples R China

Sequential causal effect estimation has recently attracted increasing attention from research and industry. While the existing models have achieved many successes, there are still many limitations. Existing models usually assume the causal graphs to be sufficient, i.e., there are no latent factors, such as the unmeasured confounders and instrumental variables. However, in real-world scenarios, it is hard to record all of the factors in the observational data, which makes the causally sufficient assumptions not hold. Moreover, existing models mainly focus on discrete treatments rather than continuous ones. To alleviate the above problems, in this paper, we propose a novel Continous Causal Model by explicitly capturing the Latent Factors (called (CM)-M-2-LF for short). Specifically, we define a sequential causal graph by simultaneously considering the unmeasured confounders and instrumental variables. Second, we describe the independence that should be satisfied among different variables from the mutual information perspective and further propose our learning objective. Then, we reweight different samples in the continuous treatment space to optimize our model unbiasedly. Beyond the above designs, we also theoretically analyze our model's causal identifiability and unbiasedness. Finally, we conduct extensive experiments on both simulation and real-world datasets to demonstrate the effectiveness of our proposed model.

关键词： Instruments Estimation History Mutual information Mathematical models computational modeling Sun Reliability Predictive models Long short term memory Continuous treatment instrumental variables sequential causal effects unmeasured confounders

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Thompson Sampling and Proportional-Greedy Algorithm for Uncertain Coded Edge Computing

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4865-4876页

作者： Kong, Linglin Sung, Chi Wan Shum, Kenneth W. City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China

This paper investigates the allocation of coded computing tasks in edge networks with uncertain conditions. Specifically, it focuses on scenarios where the computing speed-related parameters of edge devices are heterogeneous and unknown. The challenge lies in mitigating the impact of stragglers, which involves identifying fast workers and distributing a reasonable workload among them. In this paper, we put the fast worker identification into a multi-armed bandit (MAB) framework and propose a Thompson sampling (TS)-based approach to tackle it. Then, our approach leverages the heterogeneity of computing speeds by formulating the task allocation problem to minimize the expected computing delay. We derive a lower bound for the delay and prove that our proposed algorithm minimizes this bound. Importantly, the time complexity of our algorithm is independent of the number of tasks to be assigned, which is typically large in practical scenarios. When our scheme is applied to solve the linear regression problem, simulation results show that it reduces the computing delay of a state-of-the-art method by more than 15% in two different scenarios.

关键词： Delays Vectors computational modeling Edge computing Resource management Decoding Optimization Load modeling computational efficiency Codes Coded edge computing distributed computing computation offloading straggler mitigation multi-armed bandits Thompson sampling

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A Mobile Image-Driven PM2.5 Estimation Framework Using Deep Learning Techniques

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IEEE ACCESS 2025年 13卷 16196-16207页

作者： Kamble, Anupam Aramkul, Somrawee Champrasert, Paskorn Chiang Mai Univ Fac Engn Data Sci Consortium Chiang Mai 50200 Thailand Chiang Mai Rajabhat Univ Fac Sci & Technol Dept Comp Chiang Mai 50300 Thailand Chiang Mai Univ Fac Engn OASYS Res Grp Chiang Mai 50200 Thailand

Particulate pollution (PM2.5) is an important concern in Asian countries owing to its health hazards. When planning outdoor activities, understanding the PM2.5 concentration measurement is essential. Because of the lower number of government-run Air Quality Monitoring Stations, other options for obtaining location-specific PM2.5 concentration values are sought. This paper proposes using photo image processing to estimate the PM2.5 concentration. This research aims to improve the efficacy and reduce the computational complexity of the PM2.5 concentration estimation process. The proposed Efficient PM2.5 estimation framework uses EfficientNet-B1 and BiLSTM to estimate PM2.5 concentrations. Met-EfficientNet-B1-BiLSTM was designed and implemented to incorporate the meteorological features - temperature, wind speed, and humidity to further improve the estimation accuracy. The EfficientNet-B1 neural network is applied in the image feature vector extraction process. EfficientNet-B1, with a resolution of 240x240 pixels, was determined to be the optimal variant of EfficientNet for a small dataset of images needed for the estimation of the PM2.5 concentration value. The BiLSTM was used for the regression of these image features with PM2.5 concentration values to obtain the estimated PM2.5 concentration. A dataset comprising HDR and non-HDR images was explicitly created for this study to compare the types of images that improve the accuracy of PM2.5 concentration estimation and the feature extraction process. The proposed Efficient PM2.5 estimation framework reduces computational complexity and outperforms ResNet-18-LSTM by improving the efficacy by 5.75% in MAE and 11.43% in SMAPE metrics. The proposed Efficient PM2.5 estimation framework demonstrates that the mobile image can be efficiently used for PM2.5 concentration estimation.

关键词： Estimation Accuracy Feature extraction Pollution measurement Bidirectional long short term memory Atmospheric modeling computational modeling Mortality Humidity Correlation PM2.5 CNNs EfficientNet ResNet MBConv RNNs LSTM BiLSTM HDR

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Token Masking Transformer for Weakly Supervised Object Localization

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 2059-2069页

作者： Xu, Wenhao Wang, Changwei Xu, Rongtao Xu, Shibiao Meng, Weiliang Zhang, Man Zhang, Xiaopeng Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Shandong Fundamental Res Ctr Comp SciNatl Superco Jinan 250013 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100045 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Weakly supervised object localization (WSOL) is both a promising and challenging task that aims to achieve object localization exclusively through image category labels for supervision. Visual transformers have recently been applied to WSOL, demonstrating significant success through the exploitation of long-range feature dependencies in self-attention mechanisms. However, the transformer-based approach suffers from the same partial activation problem as the CNN-based approach due to the use of the classification task to train self-attention map, i.e., only a few discriminative regions are assigned high attention response and thus the localization map does not cover the whole object. To alleviate this problem, we propose a plug-and-play Token Masking Transformer (TMT) method to help transformer-based WSOL methods to obtain a more complete localization map by dynamic discriminative token masking. Specifically, a batch-wise discriminative token selection strategy is first introduced to flexibly determine the tokens to be masked in each image. Then, we design a token masking transformer block to perform token masking and inspire the network to mine more object-related tokens. Besides, we also design an intermediate token activation loss to further improve the performance of TMT by imposing constraints on intermediate tokens. Extensive experiments demonstrate that our TMT can substantially improve the performance of existing transformer-based methods without increasing the computational cost, and achieves state-of-the-art performance on two mainstream benchmarks.

关键词： Transformers Location awareness Visualization Training Computer vision computational modeling Semantics Hands Computer architecture Image resolution Weakly supervised object localization visual transformer token selection

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Trustworthy Federated Fine-Tuning for Industrial Chains Demand Forecasting

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computational INTELLIGENCE 2025年第2期9卷 1441-1453页

作者： Huang, Guoquan Lin, Guanyu Ning, Li Xu, Yicheng Lim, Chee Peng Zhang, Yong Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518038 Peoples R China Swinburne Univ Technol Hawthorn Vic 3122 Australia

Demand forecasting is crucial for the robust development of industrial chains, given the direct impact of consumer market volatility on production planning. However, in the intricate industrial chain environment, limited accessible data from independent production entities poses challenges in achieving high performances and precise predictions for future demand. Centralized training using machine learning modeling on data from multiple production entities is a potential solution, yet issues like consumer privacy, industry competition, and data security hinder practical machine learning implementation. This research introduces an innovative distributed learning approach, utilizing privacy-preserving federated learning techniques to enhance time-series demand forecasting for multiple entities pertaining to industrial chains. Our approach involves several key steps, including federated learning among entities in the industrial chain on a blockchain platform, ensuring the trustworthiness of the computation process and results. Leveraging Pre-training Models (PTMs) facilitates federated fine-tuning among production entities, addressing model heterogeneity and minimizing privacy breach risks. A comprehensive comparison study on various federated learning demand forecasting models on data from two real-world industry chains demonstrates the superior performance and enhanced security of our developed approach.

关键词： computational modeling Predictive models Demand forecasting Data models Training Companies Machine learning Industries Federated learning Servers Industrial chains demand forecasting federated learning blockchain privacy-preserving

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