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Automatic exploration and transfer design of associative rules in She Ethnic Clothing Coloration

Multimedia Tools and Applications 2025年第12期84卷 10269-10289页

作者： Cao, Jingwen Xu, Pinghua Wu, Siyi Jiang, Wenqing Lin, Ruibing Zhang, Liping School of Fashion Design & Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou310018 China Digital Intelligence Style and Creative Design Research Center Key Research Center of Philosophy and Social Sciences Zhejiang Sci-Tech University Zhejiang Hangzhou310018 China Key Laboratory of Silk Culture Heritage and Products Design Digital Technology Ministry of Culture and Tourism P.R.China Zhejiang Hangzhou310018 China School of Art & Design Zhejiang Sci-Tech University Zhejiang Hangzhou310018 China

Ethnic clothing, blending historical and contemporary influences, showcased a complex color artistry, inspiring modern design. In this study, image analysis was applied to dissect the color palette of She ethnic costumes, examining 250 images categorized into upper apparel, lower attire, and accessories. Techniques including color correction, cluster-based color extraction, and an enhanced Apriori algorithm were utilized to analyze color variations across different costume types and to establish definitive Colour combinations. The research revealed only minor differences in color usage among the categories. However, it was noted that binary color pairings in accessories and ternary combinations in lower garments displayed a rich diversity of color schemes. The application of a 0.2 minimum support threshold in the algorithm effectively distinguished regional color patterns. The algorithm's swift processing time of 0.032 s demonstrated its effectiveness in rapidly analyzing complex color *** study deepened the understanding of the intricacies in color aesthetics of ethnic costumes, offering fresh perspectives for innovative design. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Color

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LVFGen: Efficient Liberty Variation Format (LVF) Generation Using Variational Analysis and Active Learning 25

LVFGen: Efficient Liberty Variation Format (LVF) Generation ...

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34th ACM International Symposium on Physical design, ISPD 2025

作者： Zhou, Junzhuo Xia, Haoxuan Xing, Wei Lin, Ting-Jung Huang, Li He, Lei University of California Los Angeles United States The University of Sheffield Sheffield United Kingdom Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo China BTD Inc Ningbo China Engineering Research Center of Chiplet Design and Manufacturing of Zhejiang Province Ningbo China

ISBN: (纸本)9798400712937

As transistor dimensions shrink, process variations significantly impact circuit performance, signifying the need for accurate statistical circuit analysis. In digital circuit timing analysis, the Liberty Variation Format (LVF) has emerged as an industrial leading representation of timing distributions in cell libraries at 22 nm and below. However, LVF characterization relies on the Monte Carlo (MC) method, which requires excessive SPICE simulations of cells with process variations. Similar challenges also exist for uncertainty propagation and quantification in chip manufacturing and the broader scientific communities. To resolve this foundational challenge, this paper presents LVFGen, a novel method that reduces the simulation costs of MC while generate high-accuracy LVF library. LVFGen utilizes an active learning strategy based on variational analysis to identify process variation samples that impact timing distributions more significantly. Compared to the state-of-the-art Quasi-MC method, LVFGen demonstrates an overall 2.27× speedup in LVF library generation within an accuracy level of 5k-sample MC and a 4.06× speedup within a 100k-sample MC accuracy. © 2025 Copyright held by the owner/author(s).

关键词： Active learning

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A Coordinated Deformation Characterization Method for Diesel Engine Main Bearings Based on Clearance Margins 16th

A Coordinated Deformation Characterization Method for Diesel...

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

作者： Zhao, Xin Shi, Jianhua Wu, Ligang Shanxi Datong University Shanxi Datong037009 China Shanxi Province Engineering Research Center Based on Twin Operation and Control Technology for Digital Production Lines Shanxi Datong037009 China

ISBN: (纸本)9789819617760

The stiffness of the main bearings of a diesel engine is the research that has been focused on due to the fact that traditional evaluation indexes can hardly accurately characterize the coordinated deformation of the crankshaft and the bearing shell. Regarding the clearance margin that could judge whether motion interference occurs between the main bearing shell and the crankshaft journal after deformation as the evaluation index, the characterization method of for diesel engine main bearings based on the evaluation index and the numerical calculation method for the main bearing clearance are proposed. Experiments are designed and conducted to verify the feasibility and accuracy of the evaluation index. The results show that it is feasible to take the main bearing clearance as the evaluation index of the coordinated deformation. The relative errors between the calculation results and the measurement data of the centripetal radial deformation of the main bearing shell in the preload condition are less than 7%, indicating the accuracy of the computational method. This research provides theoretical basis for main bearing stiffness reliable design of HDP diesel engine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Crankshafts

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Abuttable Analog Cell Library and Automatic AMS Layout 25

Abuttable Analog Cell Library and Automatic AMS Layout

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34th ACM International Symposium on Physical design, ISPD 2025

作者： Zhou, Tianjia Chang, Cheng Huang, Li Gu, Jingyun Ji, Zexin Liu, Xiangyang Liang, Hailang Chen, Zhanfei Lin, Ting-Jung Wang, Song Bai, Na Li, Zhengping He, Lei University of California Los Angeles Los Angeles United States BTD Inc Ningbo China Ningbo University Ningbo China Anhui University Anhui China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo China Engineering Research Center of Chiplet Design and Manufacturing of Zhejiang Province Ningbo China

ISBN: (纸本)9798400712937

The state-of-the-art analog circuit design applies mainly a full-custom layout methodology. This demands high expertise and heavy manual workload. Additionally, neither can the resulting layout be re-used easily across different designs or different PDKs. Learning from digital standard cells, existing work has proposed analog stem cells that are abuttable. But stem cells have a fixed 2× area overhead of same-sized Pcells, limiting their wide application. In this paper we develop a new type of abuttable analog cells (called Acells) for transistors and passive elements. Acells are compatible with digital standard cells and can be abutted in all directions, enabling the use of automatic digital place and route (PnR) engines. We automate Acell generation and show that the average area ratio over same-sized Pcell is 1.49 for 65nm technology and 1.3 for 28nm technology, and is expected to decrease for more advanced technologies. We then use digital PnR to automatically generate layout of several analog and mixed-signal (AMS) circuits mainly in 28nm. Compared to Pcell-based manual layout, Acell-based layout obtains similar performance and its circuit-level layout area is about 2% higher for large scale AMS circuits in our experiments. © 2025 Copyright held by the owner/author(s).

关键词： Integrated circuit layout

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Sketch-SparseNet: Sparse convolution framework for sketch recognition

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Pattern Recognition 2025年 167卷

作者： Yang, Jingru Wang, Jin Zhou, Yang Lu, Guodong Sun, Yu Yu, Huan Fang, Heming Li, Zhihui He, Shengfeng Zhejiang University State Key Laboratory of Fluid Power & Mechatronic Systems Hangzhou Zhejiang310027 China Zhejiang University Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Hangzhou Zhejiang310027 China Robotics Research Center of Yuyao City Yuyao Ningbo Zhejiang315400 China Zhejiang Sealand Technology Co. Yuhang District Hangzhou Zhejiang311100 China Singapore Management University 178903 Singapore

In free-hand sketch recognition, state-of-the-art methods often struggle to extract spatial features from sketches with sparse distributions, which are characterized by significant blank regions devoid of informative content. To address this challenge, we introduce a novel framework for sketch recognition, termed Sketch-SparseNet. This framework incorporates an advanced convolutional component: the Sketch-Driven Dilated Deformable Block (SD3B). This component excels at extracting spatial features and accurately recognizing free-hand sketches with sparse distributions. The SD3B component innovatively bridges gaps in the blank areas of sketches by establishing spatial relationships among disconnected stroke points through adaptive reshaping of convolution kernels. These kernels are deformable, dilatable, and dynamically positioned relative to the sketch strokes, ensuring the preservation of spatial information from sketch points. Consequently, Sketch-SparseNet extracts a more accurate and compact representation of spatial features, enhancing sketch recognition performance. Additionally, we introduce the SmoothAlign loss function, which minimizes the disparity between the output features of parallel SD3B and CNNs, facilitating effective feature fusion. Extensive evaluations on the QuickDraw-414k and TU-Berlin datasets highlight our method's state-of-the-art performance, achieving accuracies of 79.52% and 85.78%, respectively. To our knowledge, this work represents the first application of a sparse convolution framework that substantially alleviates the adverse effects of sparse sketch points. The codes are available at https://***/kingbackyang/Sketch-SparseNet. © 2025

关键词： Character recognition

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Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices

arXiv

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arXiv 2025年

作者： Lin, Junyan Chen, Haoran Fan, Yue Fan, Yingqi Jin, Xin Su, Hui Fu, Jinlan Shen, Xiaoyu Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative Institute of Digital Twin EIT China Ocean University of China China Zhejiang Gongshang University China Genmo.ai Meituan Inc Engineering Research Center of Chiplet Design and Manufacturing of Zhejiang Province China NUS Singapore

Multimodal Large Language Models (MLLMs) have made significant advancements in recent years, with visual features playing an increasingly critical role in enhancing model performance. However, the integration of multi-layer visual features in MLLMs remains underexplored, particularly with regard to optimal layer selection and fusion strategies. Existing methods often rely on arbitrary design choices, leading to suboptimal outcomes. In this paper, we systematically investigate two core aspects of multi-layer visual feature fusion: (1) selecting the most effective visual layers and (2) identifying the best fusion approach with the language model. Our experiments reveal that while combining visual features from multiple stages improves generalization, incorporating additional features from the same stage typically leads to diminished performance. Furthermore, we find that direct fusion of multi-layer visual features at the input stage consistently yields superior and more stable performance across various configurations. We make all our code publicly available: https://***/EIT-NLP/Layer_Select_Fuse_for_MLLM. © 2025, CC BY.

关键词： Visual BASIC

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An Encoder-Based Framework for Privacy-Preserving Machine Learning 24th

An Encoder-Based Framework for Privacy-Preserving Machine L...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Jiayun Ren, Wei Zhang, Xianchao Zheng, Xianghan School of Computer Science China University of Geosciences Wuhan430074 China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China Ministry of Education Chengdu China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China College of Computer and Big Data Fuzhou University Fujian Fuzhou China School of Information and Intelligent Engineering Sanya College Hainan China

ISBN: (纸本)9789819615506

As a data-driven science, machine learning requires vast amounts of training data and computational resources. However, for highly privacy-sensitive data, it is crucial to protect the privacy of the data during both the training and utilization of machine learning models. In this paper, we propose a privacy-preserving machine learning approach using autoencoders and differential privacy mechanisms to safeguard data privacy while minimizing the impact on data availability. Specifically, we augment logistic regression and ResNet18 models with different architectures of autoencoders to perform data encryption? without compromising the machine learning tasks. Additionally, we employ differential privacy mechanisms to introduce gradient perturbations in the encoding part of the autoencoder, enhancing the algorithm’s security and further protecting data privacy. We also design the cosine similarity between the encoded and original data as a metric for evaluating data privacy, considering model performance, privacy budget, and data privacy collectively to balance data availability and privacy. Extensive experiments conducted on MNIST, CIFAR-10, PathMNIST, and BloodMNIST datasets demonstrate that for simple logistic regression models handling easily classifiable datasets, employing simple autoencoder structures can enhance classification accuracy, with significant performance impact after adding differential privacy. For ResNet18, utilizing convolutional autoencoders for data encryption generally has minimal impact on model classification performance and can even improve accuracy in most cases. Adding differential privacy has minor effects on model classification performance. Selecting appropriate model structures and privacy budgets for different usage scenarios can ensure both data availability and privacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Ordinal focal loss: A relative fashionability ranking learning method

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Information Processing and Management 2025年第5期62卷

作者： Ma, Ling Zou, Chuhang Guo, Ziyi Li, Tao Liu, Zheng Zou, Fengyuan Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology Ministry of Culture and Tourism Zhejiang Sci-Tech University Linping Campus Zhejiang Hangzhou310018 China Amazon 410 Terry Ave N SeattleWA98109 United States School of Fashion Design & Engineering Zhejiang Sci-Tech University Linping Campus Zhejiang Hangzhou310018 China Clothing Engineering Research Center of Zhejiang Province Zhejiang Sci-Tech University Linping Campus Zhejiang Hangzhou310018 China Zhejiang International Institute of Fashion Technology Zhejiang Sci-Tech University Linping Campus Zhejiang Hangzhou310018 China

Existing fashion recommendations often rely on natural language processing or content-based image retrieval, overlooking direct aesthetic assessments of fashion images. Given the subjectivity and complexity of this task, we propose treating fashionability as a relative attribute to rank paired clothing images. To address this ranking challenge, we propose Ordinal Focal Loss, which transforms the pairwise ranking problem into a multi-classification task, leveraging ordinal attributes to improve classification boundaries. Furthermore, in terms of fashion feature representation, we propose modeling not just individual items but also their combined effect as an outfit, providing a more holistic and nuanced fashion representation. We introduce the Fashionability3k dataset, comprising 3k image pairs (2398 ordered and 601 similar pairs) with objective relative fashion labels. Experiments on three datasets—our Fashionability3k and two public datasets—show that our method outperforms the baseline by nearly 1 % in ranking accuracy. In the user study, it achieved a 0.72 consistency with human subjective perception. Moreover, combining local and global visual features leads to additional performance gains, with an average improvement of 2.78 % in ordered pairs and 1.09 % in similar pairs. This is the first study to treat fashionability as an objective attribute for comparative analysis, validated through extensive experiments. © 2025 Elsevier Ltd

关键词： Content based retrieval

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Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey

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IEEE Internet of Things Journal 2025年第10期12卷 13384-13397页

作者： Zhang, Tao Kong, Fanyu Deng, Dongshang Tang, Xiangyun Wu, Xuangou Xu, Changqiao Zhu, Liehuang Liu, Jiqiang Ai, Bo Han, Zhu Deng, Robert H. Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui243002 China Minzu University of China School of Information Engineering Beijing100081 China Chongqing University College of Computer Science Chongqing400044 China Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Jiaotong University School of Electronic and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Singapore Management University Department of Information Systems Singapore Singapore

Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats such as malicious network reconnaissance, side-channel attacks, and Distributed Denial of Service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of Moving Target Defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this paper, we firstly provide a thorough analysis of the threats in the cloud-edge-terminal network. Then we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this paper is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network. © 2014 IEEE.

关键词： Cloud security

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Complicated deformation simulating on temperature-driven 4D printed bilayer structures based on reduced bilayer plate model

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Applied Mathematics and Mechanics(English Edition) 2021年第11期42卷 1619-1632页

作者： Junjie SONG Yixiong FENG Yong WANG Siyuan ZENG Zhaoxi HONG Hao QIU Jianrong TAN State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province Zhejiang UniversityHangzhou 310027China

The four-dimensional(4D) printing technology, as a combination of additive manufacturing and smart materials, has attracted increasing research interest in recent years. The bilayer structures printed with smart materials using this technology can realize complicated deformation under some special stimuli due to the material *** deformation prediction of bilayer structures can make the design process more rapid and thus is of great importance. However, the previous works on deformation prediction of bilayer structures rarely study the complicated deformations or the influence of the printing process on deformation. Thus, this paper proposes a new method to predict the complicated deformations of temperature-sensitive 4D printed bilayer structures,in particular to the bilayer structures based on temperature-driven shape-memory polymers(SMPs) and fabricated using the fused deposition modeling(FDM) technology. The programming process to the material during printing is revealed and considered in the simulation model. Simulation results are compared with experiments to verify the validity of the method. The advantages of this method are stable convergence and high efficiency,as the three-dimensional(3D) problem is converted to a two-dimensional(2D) *** simulation parameters in the model can be further associated with the printing parameters, which shows good application prospect in 4D printed bilayer structure design.

关键词： reduced bilayer plate model four-dimensional(4D)printing temperature-driven shape-memory polymer(SMP) bilayer structure complicated deformation simulating

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