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Edge-Cloud Cooperation-Driven intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xian Univ Posts & Telecommun Sch Comp Sci & Technol Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian Key Lab Big Data & Intelligent Comp Xian 710121 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Xidian Univ Guangzhou Inst Technol Adv Mfg Technol Innovat Ctr Guangzhou 510555 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Comp Integrated Mfg Syst State Key Lab Precis Elect Mfg Technol & Equipment Guangzhou 510006 Peoples R China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company's production data demonstrates its accuracy.

关键词： Production Sustainable development Real-time systems Cloud computing Manufacturing computer architecture Internet of Things Energy efficiency Process control Energy consumption Cyber-physical system (CPS) deep clustering network (DCN) edge-cloud cooperation improved gray relation analysis Industry 5.0 Internet of Things (IoT)

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ER-C3D: Enhancing R-C3-D Network With Adaptive Shrinkage and Symmetrical Multiscale for Behavior Detection

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IEEE TRANSACTIONS ON compUTATIONAL SOCIAL SYSTEMS 2024年第5期11卷 5997-6009页

作者： Huang, Zhong Tao, Mengyuan An, Ning Hu, Min Ren, Fuji Hefei Univ Technol Sch Comp Sci & Informat Hefei 230009 Peoples R China Anqing Normal Univ Sch Elect Engn & Intelligent Mfg Anqing 246011 Peoples R China Anhui Prov Key Lab Affect Comp & Adv Intelligent Hefei 230009 Peoples R China Anhui Prov Key Lab Affect Comp & Adv Sch Comp Sci & Informat Intelligent Interconnected Syst Lab Anhui Prov Hefei 230009 Peoples R China Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei 230009 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

Behavior detection receives considerable attention in real-life human-computer interaction, where the complexity of background information and the variable durations of movements are two major factors affecting the accuracy of behavior detection. To overcome the inadequacy of these factors, this article proposes an enhancing region convolutional 3-D (ER-C3D) network with adaptive shrinkage and symmetrical multiscale for behavior detection. The improved ER-C3D network includes a feature subnet, a proposal subnet, and a classification subnet. First, a 3D-RSST unit is constructed by embedding an adaptive shrinkage structure and a soft thresholding operation. Meanwhile, a residual adaptive shrinkage mechanism, composed of multiple cascaded 3D-RSST units with different parameters, is designed to reduce redundant information of video streams in the feature subnet. Second, a spatiotemporal symmetrical multiscale structure is substituted for the single-layer convolution and embedded into the proposal subnet. Specially, contextual symmetrical multiscale motion characteristics with different levels and granularities are acquired by expanding the spatiotemporal receptive field of candidate temporal proposals. Finally, a soft-nonmaximal suppression strategy is introduced to filter high-quality temporal proposals in the classification subnet. The experimental results on the THUMOS'14 and ActivityNet1.2 datasets indicate that the mAP@0.5 of the improved ER-C3D network reaches 39.4% and 42.2%, respectively, which is 10.5% and 15.4% higher than R-C3D. compared with related methods, the proposed method shows improvement in both the positional precision of behavioral boundary and the accuracy of behavioral classification.

关键词： Feature extraction Proposals Spatiotemporal phenomena Videos Adaptive systems data mining Convolution Behavior detection residual adaptive shrinkage mechanism soft-nonmaximal suppression (soft-NMS) spatiotemporal receptive field spatiotemporal symmetrical multiscale structure

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Multi-granularity visual-textual jointly modeling for aspect-level multimodal sentiment analysis

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JOURNAL OF SUPERcompUTING 2025年第1期81卷 1-28页

作者： Chen, Yuzhong Shi, Liyuan Lin, Jiali Chen, Jingtian Zhong, Jiayuan Dong, Chen Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Fujian Peoples R China Minist Educ Engn Res Ctr Big Data Intelligence Fuzhou Peoples R China Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou 350108 Fujian Peoples R China Fujian Media Grp Fuzhou 350002 Fujian Peoples R China

Aspect-level multimodal sentiment analysis aims to ascertain the sentiment polarity of a given aspect from a text review and its accompanying image. Despite substantial progress made by existing research, aspect-level multimodal sentiment analysis still faces several challenges: (1) Inconsistency in feature granularity between the text and image modalities poses difficulties in capturing corresponding visual representations of aspect words. This inconsistency may introduce irrelevant or redundant information, thereby causing noise and interference in sentiment analysis. (2) Traditional aspect-level sentiment analysis predominantly relies on the fusion of semantic and syntactic information to determine the sentiment polarity of a given aspect. However, introducing image modality necessitates addressing the semantic gap in jointly understanding sentiment features in different modalities. To address these challenges, a multi-granularity visual-textual feature fusion model (MG-VTFM) is proposed to enable deep sentiment interactions among semantic, syntactic, and image information. First, the model introduces a multi-granularity hierarchical graph attention network that controls the granularity of semantic units interacting with images through constituent tree. This network extracts image sentiment information relevant to the specific granularity, reduces noise from images and ensures sentiment relevance in single-granularity cross-modal interactions. Building upon this, a multilayered graph attention module is employed to accomplish multi-granularity sentiment fusion, ranging from fine to coarse. Furthermore, a progressive multimodal attention fusion mechanism is introduced to maximize the extraction of abstract sentiment information from images. Lastly, a mapping mechanism is proposed to align cross-modal information based on aspect words, unifying semantic spaces across different modalities. Our model demonstrates excellent overall performance on two datasets.

关键词： Aspect-level sentiment analysis Multimodal data Visual-textual feature fusion Multi-granularity Constituent tree

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Generalized Global Self-Optimizing Control for Chemical Processes Part I. The Existence of Perfect Controlled Variables and Numerical Design Methods

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2023年第37期62卷 15051-15069页

作者： Ye, Lingjian Cao, Yi He, Yuchen Zhou, Chenchen Su, Hongxin Tang, Xinhui Yang, Shuanghua Huzhou Univ Zhejiang Prov Key Lab Smart Management & Applicat Huzhou 313000 Peoples R China Zhejiang Univ Coll Chem & Biol Engn Hangzhou 310027 Peoples R China China Jiliang Univ Key Lab Intelligent Mfg Qual Big Data Tracing & A Hangzhou 310018 Peoples R China

Self-optimizing control (SOC) maintains the near-optimal operation of chemical processes by selecting appropriate controlled variables (CVs). However, most existing works still adopt linear combinations of measurements as the CVs, which inherit the invariant active-set assumption and leave the general achievable performance of the SOC unknown. In this series of works, we present the so-called generalized global SOC (g(2)SOC) approach. The g(2)SOC extends the concept of SOC to cover the entire operating space using general nonlinear CVs, without any restrictions on the active-set. In part I of this work, theoretical analysis of the g(2)SOC is introduced, where the existence of perfect global CVs is illustrated under proper technical conditions. Then, two numerical design methods for g(2)SOC are outlined: one is the regression-based approach, and the other is the optimization-based approach. These developments are illustrated through a numerical example and a CSTR case study, both of which demonstrate the superior performance of the g(2)SOC by using nonlinear CVs. In part II of this work, we deal with the algorithmic aspects of g(2)SOC, where efficient algorithms are developed for large-scale problems.

关键词： Algorithms Approximation Chemical structure Determinants Optimization

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Construction and validation of a DNN-based biological age and its influencing factors in the China Kadoorie Biobank

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GEROSCIENCE 2025年 1-12页

作者： Huang, Yushu Da, Lijuan Dong, Yue Li, Zihan Liu, Yuan Li, Zilin Wu, Xifeng Li, Wenyuan Zhejiang Univ Affiliated Hosp 2 Ctr Clin Big Data &Analyt Sch Med Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Sch Publ Hlth Dept Big Data Hlth Sci Hangzhou Zhejiang Peoples R China Northeast Normal Univ Sch Math & Stat Changchun Jilin Peoples R China Northeast Normal Univ KLAS Changchun Jilin Peoples R China Zhejiang Prov Key Lab Intelligent Prevent Med Hangzhou Zhejiang Peoples R China

Biological age is an important measure of aging that reflects an individual's physical health and is linked to various diseases. Current prediction models are still limited in precision, and the risk factors for accelerated aging remain underexplored. Therefore, we aimed to develop a precise biological age and assess the impact of socio-demographic and behavioral patterns on the aging *** utilized Deep Neural Networks (DNN) to construct biological age from participants with physical examinations, blood samples, and questionnaires data from the China Kadoorie Biobank (CKB) between June 2004 and December 2016. triangle age, calculated as the residuals between biological age and chronological age, was used to investigate the associations of age acceleration with diseases. Socio-demographics (gender, education attainment, marital status, household income) and lifestyle characteristics (body mass index [BMI], smoking, drinking, physical activity, and sleep) were also assessed to explore their impact on age acceleration. 18,261 participants aged 57 +/- 10 years were included in this study. The DNN-based biological age model has demonstrated accurate predictive performance, achieving a mean absolute error of 3.655 years. triangle age was associated with increased risks of various morbidity and mortality, with the highest associations found for circulatory and respiratory diseases, with hazard ratios of 1.033 (95% CI: 1.023, 1.042) and 1.078 (95% CI: 1.027, 1.130), respectively. Socio-demographics, including being female, lower education, widowed or divorced, and low household income, along with behavioral patterns, such as being underweight, insufficient physical activity, and poor sleep, were associated with accelerated aging. Our DNN model is capable of constructing a precise biological age using commonly collected data. Socio-demographics and lifestyle factors were associated with accelerated aging, highlighting that addressing modifiable risk factors can effecti

关键词： Deep learning Biological age Age acceleration Influencing factors Morbidity Mortality

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MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction

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METHODS 2024年 231卷 1-7页

作者： Tang, Xiwei Ma, Wanjun Yang, Mengyun Li, Wenjun Hunan First Normal Univ Sch Comp Sci Changsha Hunan Peoples R China Changsha Univ Sci & Technol Changsha Hunan Prov Key Lab Intelligent Proc Big Data Trans Changsha Hunan Peoples R China

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

关键词： Drug repurposing Drug target interaction Deep neural network Multi-scale learning

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Dynamic semantic-geometric guidance and structure transfer network for cross-scene hyperspectral image classification

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NEURAL NETWORKS 2025年 187卷 107374页

作者： Xu, Qin Wang, Shuke Wei, Jie Jiang, Bo Tao, Zhifu Luo, Bin Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Anhui Prov Key Lab Multimodal Cognit Computat Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Big Data & Stat Hefei 230601 Peoples R China

Recently, cross-scene hyperspectral image classification(HSIC) via domain adaptation is drawing increasing attention. However, most existing methods either directly align the source domain and target domain without fully mining of SD information, or perform the domain adaptation from semantic and structure aspects with simply characterization method which is sensitive to noise, resulting in the negative transfer and performance decline. To address these issues, in this paper, we propose a novel Dynamic Semantic-Geometric Guidance and Structure Transfer (DSGG-ST) network for cross-scene hyperspectral image classification task. The main aspects of DSGG-ST are twofold. On the one hand, the dynamic semantic-geometric guidance (DSGG) module is designed which consists of the semantic guidance component and geometric guidance component. The proposed DSGG module can align source and target domains under the dynamical guidance of the domain- invariance learning from the semantic and geometric perspectives. On the other hand, the graph attention learning-matching (GALM) module is developed for effectively transferring the structure information between the source domain and target domain. In this module, the graph attention network is adopted to encode the underlying complex structures, and the SeedGNN is exploited for efficient graph matching and alignment. Extensive experiments on three commonly used cross-scene HSI datasets demonstrate that the proposed DSGG-ST obtains a new SOTA performance on cross-scene HSIC, verifying the effectiveness of the proposed DSGG-ST.

关键词： Cross-scene classification Hyperspectral image classification Domain adaption Structure alignment

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An adversarial sample detection method based on heterogeneous denoising

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MACHINE VISION AND APPLICATIONS 2024年第4期35卷 96-96页

作者： Zhu, Lifang Liu, Chao Zhang, Zhiqiang Cheng, Yifan Jie, Biao Ding, Xintao Anhui Normal Univ Sch Comp & Informat Wuhu 241002 Anhui Peoples R China Anhui Prov Key Lab Ind Intelligence Data Secur Wuhu 241002 Anhui Peoples R China Anhui Engn Res Ctr Med Big Data Intelligent Syst Wuhu 241002 Anhui Peoples R China

Deep learning has been used in many computer-vision-based applications. However, deep neural networks are vulnerable to adversarial examples that have been crafted specifically to fool a system while being imperceptible to humans. In this paper, we propose a detection defense method based on heterogeneous denoising on foreground and background (HDFB). Since an image region that dominates to the output classification is usually sensitive to adversarial perturbations, HDFB focuses defense on the foreground region rather than the whole image. First, HDFB uses class activation map to segment examples into foreground and background regions. Second, the foreground and background are encoded to square patches. Third, the encoded foreground is zoomed in and out and is denoised in two scales. Subsequently, the encoded background is denoised once using bilateral filtering. After that, the denoised foreground and background patches are decoded. Finally, the decoded foreground and background are stitched together as a denoised sample for classification. If the classifications of the denoised and input images are different, the input image is detected as an adversarial example. The comparison experiments are implemented on CIFAR-10 and MiniImageNet. The average detection rate (DR) against white-box attacks on the test sets of the two datasets is 86.4%. The average DR against black-box attacks on MiniImageNet is 88.4%. The experimental results suggest that HDFB shows high performance on adversarial examples and is robust against white-box and black-box adversarial attacks. However, HDFB is insecure if its defense parameters are exposed to attackers.

关键词： Adversarial detection CAM Bilateral filter Bilinear interpolation

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StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography 38

StegFormer: Rebuilding the Glory of Autoencoder-Based Stegan...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Ke, Xiao Wu, Huanqi Guo, Wenzhong Fuzhou Univ Fujian Prov Key Lab Networking Comp & Intelligent Coll Comp & Data Sci Fuzhou 350116 Peoples R China Minist Educ Engn Res Ctr Big Data Intelligence Fuzhou 350116 Peoples R China

ISBN: (纸本)1577358872

Image hiding aims to conceal one or more secret images within a cover image of the same resolution. Due to strict capacity requirements, image hiding is commonly called large-capacity steganography. In this paper, we propose StegFormer, a novel autoencoder-based image-hiding model. StegFormer can conceal one or multiple secret images within a cover image of the same resolution while preserving the high visual quality of the stego image. In addition, to mitigate the limitations of current steganographic models in real-world scenarios, we propose a normalizing training strategy and a restrict loss to improve the reliability of the steganographic models under realistic conditions. Furthermore, we propose an efficient steganographic capacity expansion method to increase the capacity of steganography and enhance the efficiency of secret communication. Through this approach, we can increase the relative payload of StegFormer to 96 bits per pixel without any training strategy modifications. Experiments demonstrate that our StegFormer outperforms existing state-of-the-art (SOTA) models. In the case of single-image steganography, there is an improvement of more than 3 dB and 5 dB in PSNR for secret/recovery image pairs and cover/stego image pairs.

关键词： Steganography

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RESAkey GAN: enhancing color image encryption through residual self-attention generative adversarial networks

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PHYSICA SCRIPTA 2025年第3期100卷 035212-035212页

作者： Liu, Tongzhe Chen, Junyao Wu, Ximei Long, Bofeng Wang, Lujie He, Chenchen Deng, Xuan Deng, Hongwei Chen, Zhong Hengyang Normal Univ Coll Comp Sci & Technol Hengyang 421002 Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & App Hengyang 421002 Peoples R China

Color image encryption techniques have garnered significant attention due to their importance in safeguarding sensitive visual data. This paper introduces RESAkey GAN, a novel generative adversarial network that leverages the Residual Self-Attention mechanism to generate highly secure cryptographic keys for color image encryption. RESAkey GAN employs a redesigned architecture based on Wasserstein GAN with Gradient Penalty, addressing issues such as mode collapse and training instability. The network incorporates three novel loss functions tailored for image encryption tasks, enhancing key randomness, correlation, sensitivity, and visual appearance. Experiments conducted on the SIPI dataset demonstrate that RESAkey GAN generates private keys with excellent security, robust scrambling and diffusion performance, enabling effective encryption of diverse image scenarios using a simple XOR operation. Our approach mitigates concerns associated with traditional key generation algorithms and showcases the potential of deep learning in advancing color image encryption techniques.

关键词： color image encryption key generation generative adversarial network bit-xor diffusion

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