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Graph convolutional networks-based method for uncertainty quantification of building design loads

Building Simulation 2025年第2期18卷 321-337页

作者： Jie Lu Zeyu Zheng Chaobo Zhang Yang Zhao Chenxin Feng Ruchi Choudhary Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhouChina Energy Efficient Cities Initiative Department of EngineeringUniversity of CambridgeCambridgeUK Department of Energy Engineering Zhejiang UniversityHangzhouChina Department of the Built Environment Eindhoven University of TechnologyEindhoventhe Netherlands Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province Jiaxing Research InstituteZhejiang UniversityJiaxingChina Data-centric Engineering The Alan Turing InstituteLondonUK

Uncertainty quantification of building design loads is essential to efficient and reliable building energy planning in the design *** data-driven methods struggle to generalize across buildings with diverse shapes due to limitations in representing complex geometric *** tackle this issue,a graph convolutional networks(GCN)-based uncertainty quantification method is *** graph-based approach is introduced to represent building shapes by dividing them into blocks and defining their spatial relationships through nodes and *** method effectively captures complex building characteristics,enhancing the generalization *** approach leveraging GCN could estimate design loads by understanding the impact of diverse uncertain ***,a class activation map is formulated to identify key uncertain factors,guiding the selection of important design parameters during the building design *** effectiveness of this method is evaluated through comparison with four widely-used data-driven *** indicate that the mean absolute percentage errors(MAPE)for statistical indicators of uncertainty quantification are under 6.0%and 4.0%for cooling loads and heating loads,*** proposed method is demonstrated to quantify uncertainty in building design loads with outstanding generalization *** regard to time costs,the computation time of the proposed method is reduced from 331 hours to 30 seconds for a twenty-floor building compared to a conventional physics-based method.

关键词： building design loads uncertainty quantification data-driven model graph convolutional networks Monte Carlo simulation

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Recticoil for Efficient Compact Inductive Power Transfer

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IEEE Transactions on Power Electronics 2025年第7期40卷 10154-10164页

作者： Yi, Zixuan Qin, Yuanyuan Yang, Xue-Xia Shao, Yaoxia Zeng, Dan Li, Meiling Shanghai University School of Communication and Information Engineering Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai200444 China Hangzhou City University School of Information and Electrical Engineering Hangzhou310015 China

The MHz inductive power transfer (IPT) system has broad application prospects due to its high safety, longer transmission distance, and better tolerance for misalignment. However, the increase in frequency will lead to an increased loss of lumped components, such as inductors and capacitors, in peripheral circuits, thereby reducing efficiency. This is currently the bottleneck problem encountered by MHz IPT. To address this key challenge, researchers have tried to remove the lumped components in the peripheral circuit while ensuring the functions of the IPT system. One successful and beneficial attempt is the self-resonant coil, which replaces traditional lumped resonant capacitors with distributed capacitors formed by coil turn-to-turn capacitance, ensuring compensation for leakage inductance and eliminating reactive power. This innovative integration makes efficiency and integration greatly improved. In this article, the novel coil named Recticoil, on which the rectification function is integrated further, is proposed for efficient compact IPT. Recticoil can directly output dc power without additional compensation and rectification circuits. By analyzing a toroidal coil where two separate conductors generate a voltage differential, and incorporating two diodes, resulting in a dc output. Subsequently, the self-resonant coil replaces the toroidal coil to form Recticoil, and the equivalent circuits of Recticoil are given and analyzed. In addition, the analysis and test method of rectification efficiency is given. The experimental results show that the Recticoil achieves a rectification efficiency as high as 97.25% at 6.78 MHz, outperforming both the bridge rectifier and the Class E rectifier. Additionally, the compact construction of Recticoil eliminates the volume of traditional rectifiers. All of these advantages of Recticoil render it a promising candidate for applications in wearables and miniaturized devices. © 1986-2012 IEEE.

关键词： Equivalent circuits

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Evolution of vegetation phenology and its response to urbanisation in Shaanxi province, China: A fine-scale perspective

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Journal of Geographical Sciences 2025年第5期35卷 1024-1048页

作者： GAO Meiling MA Jingjing LI Zhenhong YANG Guijun College of Geological Engineering and Geomatics Chang'an University State Key Laboratory of Loess Science Key Laboratory of Ecological Geology and Disaster Prevention Ministry of Natural Resources Big Data Center for Geosciences and Satellites

Shaanxi province in China is rich in vegetation resources, making the study of vegetation phenology crucial for understanding climate change and ecological dynamics. Previous studies on Shaanxi have primarily focused on coarse-scale phenology, which results in the loss of essential spatial details due to the lack of finer-scale analyses. Moreover, the impact of rapid urbanisation on the phenology of various vegetation types in this region remains unclear. To address these gaps, we integrated Landsat and MODIS data to generate a daily 30 m NDVI sequence. Vegetation phenology across Shaanxi from 2000 to 2020 was then extracted and analysed, and the responses of different vegetation types to urbanisation were explored. Results indicated that accurate vegetation phenology can be obtained at a 30 m resolution, which is more precise than conventional coarse-scale investigations. The Start of Season(SOS) and End of Season(EOS) in the province advanced by-0.56 and-0.08 day/year, respectively, from 2000 to 2020. In general, the SOS gradually delays, and the EOS progressively advances with increasing latitude. However, anomalies were identified in regions south of 32.64°N and across the zone from 34.04°N to 37.56°N, which are primarily due to the effects of high elevation and rapid urbanisation, respectively. Further investigation into phenological differences among various vegetation types revealed that forests exhibit the earliest SOS, while grasslands present the latest. In addition, a significant negative correlation was found between the SOS difference and air temperature difference along the urban–rural gradient, with urbanisation having the most significant impact on cropland phenology. This study provides valuable insights into vegetation phenology and its response to urbanisation. These insights offer crucial information for ecological conservation and management strategies.

关键词： gap filling and Savitzky–Golay filtering Google Earth Engine spatiotemporal changes urbanisation vegetation phenology

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Predicting Lactobacillus delbrueckii ***-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method

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Science China(Life Sciences) 2025年第2期68卷 558-574页

作者： Shujuan Yang Mei Bai Weichi Liu Weicheng Li Zhi Zhong Lai-Yu Kwok Gaifang Dong Zhihong Sun Key Laboratory of Dairy Biotechnology and Engineering Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China Key Laboratory of Dairy Products Processing Ministry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Collaborative Innovative Center for Lactic Acid Bacteria and Fermented Dairy Products Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China College of Computer and Information Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot 010018China

Lactobacillus delbrueckii ***(***)and Streptococcus thermophilus(***)are commonly used starters in milk *** experiments revealed that *** interactions(Lb St I)substantially impact dairy product quality and *** biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations,an artificial intelligence-based method for screening interactive starter combinations is ***,in the current research on artificial intelligence based interaction prediction in the field of bioinformatics,most successful models adopt supervised learning methods,and there is a lack of research on interaction prediction with only a small number of labeled ***,this study aimed to develop a semi-supervised learning framework for predicting Lb St I using genomic data from 362 isolates(181per species).The framework consisted of a two-part model:a co-clustering prediction model(based on the Kyoto Encyclopedia of Genes and Genomes(KEGG)dataset)and a Laplacian regularized least squares prediction model(based on K-mer analysis and gene composition of all isolates datasets).To enhance accuracy,we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate Lb St I prediction results,which were verified through milk fermentation *** through milk fermentation experiments confirmed a high precision rate of 85%(17/20;validated with 20 randomly selected combinations of expected interacting isolates).Our data suggest that the biosynthetic pathways of cysteine,riboflavin,teichoic acid,and exopolysaccharides,as well as the ATP-binding cassette transport systems,contribute to the mutualistic relationship between these starter bacteria during milk ***,this finding requires further experimental *** presented model and data are valuable resources for academics an

关键词： Lactobacillus delbrueckii subsp.bulgaricus and Streptococcus thermophilus interaction prediction semi-supervised learning dairy starter artificial intelligence milk fermentation

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural Computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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DMC-Watermark: A backdoor richer watermark for dual identity verification by dynamic mask covering

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Applied Intelligence 2025年第8期55卷 1-15页

作者： Zhu, Yujia Wang, Ruoxi Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Advanced Computing Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

With the increasing use of neural networks, the importance of copyright protection for these models has gained significant attention. Backdoor watermarking is one of the key methods for protecting copyright. However, on the one hand, most existing backdoor watermarks are triggered by visual images, making them easily detectable, and therefore vulnerable to various attacks. On the other hand, it is difficult for these methods to carry information related to the creator’s identity which can easily lead to fraudulent claims of ownership. These factors contribute to the vulnerability and limitations of backdoor watermarking. In this paper, we propose DMC-Watermark, a backdoor richer watermarking method that uses dynamic mask-covered image structures as triggers. Leveraging the semantic preservation of image structure in transformation attacks, we select image structure as triggers. Furthermore, we convert the author-related information into an array of color information and apply it as a mask to the extracted image structures, allowing it to serve as a second layer of verification during the validation phase to resist fraudulent claims of ownership. The final trigger pattern, embedded with author-related image structures, is applied to the selected images in the trigger set, generating a final trigger set that is trained together with clean samples to produce a protected model. The experiments show that the proposed DMC-Watermark performs well in terms of fidelity, invisibility, undetectability, functionality, dual verification and robustness on three different datasets and four representative DNNs, and it has wide applicability and excellent results in high-resolution images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image watermarking

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Enhancing Continuous Cognitive Diagnosis with Fuzzy Strategy-Based Hybrid Genetic Algorithm 3rd

Enhancing Continuous Cognitive Diagnosis with Fuzzy Strateg...

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3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： He, Chenlong Hu, Xuegang Cao, Zhiyong Bu, Chenyang Luo, Wenjian Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819645053

Continuous cognitive diagnosis models (CDMs) are vital tools for assessing students’ mastery of knowledge points. However, traditional probability-based CDMs are prone to falling into local optima due to their use of single-point search methods, which can affect the accuracy of the models. To address this issue, we propose a hybrid genetic algorithm (HGA) enhanced with a fuzzy strategy to improve continuous cognitive diagnosis. This approach introduces the multidimensional item response theory (MIRT) as a local search operator to boost diagnostic precision. Additionally, considering the limitation on the number of local searches within a finite time, we introduce a fuzzy strategy that dynamically adjusts the number of local searches by evaluating the similarity between the current population and the elite set, thus balancing global and local search. Experimental results on three real-world datasets demonstrate that our method significantly outperforms six existing comparison models, validating the effectiveness of the fuzzy strategy and continuous CDM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Continuous cognitive diagnosis Educational data mining Evolutionary algorithm Local search

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Laplacian Sparse Dynamic Slow Feature Analysis for Process Monitoring and Fault Isolation

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IEEE Sensors Journal 2025年第9期25卷 15653-15667页

作者： Wu, Ping Xue, Yikai Ni, Yuxuan Pan, Haipeng Zhang, Xi He, Yuchen Gao, Jinfeng Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China China Jiliang University Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province Hangzhou314423 China

The process monitoring and fault isolation are essential for ensuring operational safety and maintaining product quality in the industrial processes. This study introduces a novel approach to process monitoring and fault isolation based on the Laplacian sparse dynamic slow feature analysis (LSDSFA). To simultaneously account for sparsity, dynamics, and global structural information, a new optimization framework for LSDSFA is proposed. This framework incorporates the n l1 norm and a Laplacian graph into dynamic slow feature analysis (SFA), with enhancements to process variable representation. The optimization problem is addressed using the alternating direction method of multipliers (ADMMs). The proposed method decomposes the process variable space into latent and residual subspaces. In addition, three monitoring statistics are developed to facilitate effective process monitoring, while fault isolation is achieved through a two-level contribution plot. Specifically, the intrinsic structure of the process data is captured by employing a graph Laplacian operator to explore intervariable correlations. Simultaneously, the incorporation of the graph Laplacian operator and l1 norm into dynamic SFA (DSFA) reduces variable redundancy, thereby exploiting the intrinsic structure of the process data more effectively and improving interpretability through the sparsity. Experimental evaluations conducted on the widely adopted Tennessee Eastman Process (TEP) benchmark and a real-world blast furnace ironmaking process (BFIP) demonstrate the superior performance of the proposed LSDSFA-based method in comparison with related techniques. © 2001-2012 IEEE.

关键词： Blast furnaces

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Surface-Continuous Scene Representation for Light Field Depth Estimation via Planarity Prior

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 5051-5066页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cui, Zhenglong Cong, Ruixuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou International Innovation Institute Zhejiang Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences China

Light field (LF) imaging captures both spatial and angular information of the real world, enabling precise depth estimation. However, images are merely discrete expressions of scenes. Limited by imaging technology, LF camera cannot capture the infinite rays emitted by scenes, leading to the discrete information storage (e.g. pixel). Consequently, previous deep learning methods have encountered challenges in accurately extracting depth information from LF images. In this paper, we investigate a surface-continuous scene representation using planarity prior and design PlaneNet, a Plane-based Network that successfully generates highly detailed depth maps for real scenes. Specifically, inspired by the plane assumption that real-world scenes generally yield piecewise smooth surfaces, we refine it to the pixel level for continuous surface approximation, which can overcome the limitations of discrete representation. Rather than explicitly parameterizing planes as multiple coefficients, we propose a novel plane regular sampling operator (PRSO), enabling the network to fit smooth depth surfaces easily. To explore the role of our theory at the feature level, we also introduce PRSO into the intermediate layers of PlaneNet. Experiments show that our method achieves state-of-the-art performance on both synthetic and real-world LF scenes, ranking 1st (MSE) on the HCI 4D Light Field benchmark. Furthermore, we explore the utilization of our representation in multiple LF depth estimation networks, and experiments demonstrate improved performance when surface-continuous representation is applied. © 1991-2012 IEEE.

关键词： Pixels

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Order Forecasting Method for Large-Scale Personalized Customization Model 16th

Order Forecasting Method for Large-Scale Personalized Custom...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Wang, Lijun Wu, Chaohang Chen, Xianzhong Li, Qing Yao, Qijia School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Science and Technology Beijing Beijing China

ISBN: (纸本)9789819617760

Based on the personalized customization model in bearing steel enterprises, it is studied in this paper how historical data can be fully utilized to predict orders in the face of multi-variety and small-batch order models.. In order to overcome the bottleneck of information exchange that cannot be obtained from pure historical data based on diverse small batch customer needs. A demand fusion mechanism based on local rules is proposed, taking fuzzy customer demand preferences as "individuals" and introducing historical data such as order area and external environmental factors. First, based on the Hegselmann-Krause (HK) model, a personalized customized customer demand preference prediction model is proposed to simulate and predict the evolution process of large-scale customer demand preferences. Secondly, according to the fuzzy rules, the customer's demand preference in the historical data is used as the center point to introduce the fuzzy individual, and the standard deviation is determined based on the order area. Finally, the effectiveness of the proposed method was verified through simulation results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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