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SalFAU-Net: Saliency Fusion Attention U-Net for Salient Object Detection

Journal of Advances in information Fusion 2024年第2期19卷 109-119页

作者： Mulat, Kassaw Abraham Feng, Zhengyong Eshetie, Tegegne Solomon Hasen, Ahmed Endris School of Computer Science China West Normal University Sichuan Nanchong637002 China School of Electronic and Information Engineering China West Normal University Sichuan Nanchong637002 China Paul C. Lauterbur Research Center Shenzhen Institute of Advanced Technology Chinese Academy of Science Guangdong Shenzhen518055 China

Salient object detection (SOD) remains an important task in computer vision, with applications ranging from image segmentation to autonomous driving. Fully convolutional network-based methods have made remarkable progress in visual saliency detection over the last few decades. However, these methods have limitations in accurately detecting salient objects, particularly in challenging scenes with multiple objects, small objects, or objects with low resolutions. To address this issue, we proposed a Saliency Fusion Attention U-Net (SalFAU-Net) model, which incorporates an saliency fusion module into each decoder block of the attention U-net model to generate saliency probability maps from each decoder block. SalFAU-Net employs an attention mechanism to selectively focus on the most informative regions of an image and suppress nonsalient regions. We train SalFAU-Net on the DUTS dataset using a binary cross-entropy loss function. We conducted experiments on six popular SOD evaluation datasets to evaluate the effectiveness of the proposed method. The experimental results demonstrate that our method, SalFAU-Net, achieves competitive performance compared to other methods in terms of mean absolute error, F-measure, S-measure, and E-measure. © 2024 JAIF.

关键词： image segmentation

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Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

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computers, Materials & Continua 2024年第1期78卷 1363-1379页

作者： Kwok Tai Chui Brij B.Gupta Varsha Arya Miguel Torres-Ruiz School of Science and Technology Hong Kong Metropolitan UniversityHong Kong999077China Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Center for Advanced Information Technology Kyung Hee UniversitySeoul02447Korea Symbiosis Centre for Information Technology Symbiosis International UniversityPuneMaharashtra411042India Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon School of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Business Administration Asia UniversityTaichung41354Taiwan Center for Interdisciplinary Research University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science&Engineering Chandigarh UniversityChandigarh160036India Centro de Investigación en Computación Instituto Politécnico NacionalUPALM-ZacatencoMexico City07320Mexico

The visions of Industry 4.0 and 5.0 have reinforced the industrial *** have also made artificial intelligence incorporated as a major *** machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe *** learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two *** response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning *** is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative *** algorithm also adaptively adjusts the portion of training data to balance the learning rate and training *** proposed algorithm is evaluated and analyzed using ten benchmark *** with other algorithms from existing works,SA-ITL improves the accuracy of all *** studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

关键词： Deep learning incremental learning machine fault diagnosis negative transfer transfer learning

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Rethinking development and major research plans of Industrial Internet in China

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Fundamental Research 2024年第1期4卷 3-7页

作者： Chunxiao Jiang Yang Cong Jiming Chen Chenghong Wang Guozheng Wu Ruizhen Zhao Zhiheng Wang Bin Xiao Ting Chen Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Division III Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China University of Electronic Science and Technology of China Chengdu 611731China

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial *** this foundation,we outline the development and current status of the Industrial Internet in China and ***,we detail the avant-garde paradigms encompassed within the National Natural science Foundation of China(NSFC)’s“Future Industrial Internet Fundamental Theory and Key Technologies”research plan and its corresponding management *** research initiative endeavors to enhance interdisciplinary collaborations,aiming for a synergistic alignment of industry,academia,research,and practical *** primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial *** is poised to traverse the“first mile”,encompassing foundational research and pioneering innovations specific to the industrial internet,and seamlessly bridges to the“last mile”,ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market *** anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

关键词： Industrial Internet Academic connotation Major research plan Scientific problem Policy advice

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EPIMR:Prediction of Enhancer-Promoter Interactions by Multi-Scale ResNet on image Representation

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Big Data Mining and Analytics 2024年第3期7卷 668-681页

作者： Qiaozhen Meng Yinuo Lyu Xiaoqing Peng Junhai Xu Jijun Tang Fei Guo School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Aeronautical Information Service Center of the Civil Aviation Administration of China(AISC.ATMB.CAAC) Beijing 100015China Center for Medical Genetics&Hunan Key Laboratory of Medical Genetics School of Life SciencesCentral South UniversityChangsha 410038China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518000China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Prediction of enhancer-promoter interactions(EPIs)is key to regulating gene expression and diagnosing genetic *** to limited resolution,biological experiments perform not as well as expected while precisely identifying specific interactions,giving rise to computational biology *** EPI predictors have been developed,but their prediction accuracy still needs to be ***,we design a new model named EPIMR to identify enhancer-promoter ***,Hilbert Curve is utilized to represent sequences to images to preserve the position and spatial ***,a multi-scale residual neural network(ResNet)is used to learn the distinguishing features of different abstraction ***,matching heuristics are adopted to concatenate the learned features of enhancers and promoters,which pays attention to their potential interaction *** results on six cell lines indicate that EPIMR performs better than existing methods,with higher area under the precision-recall curve(AUPR)and area under the receiver operating characteristic(AUROC)results on benchmark and under-sampling ***,our model is pre-trained on all cell lines,which improves not only the transferability of cross-cell line prediction,but also cell line-specific prediction *** conclusion,our method serves as a valuable technical tool for predicting enhancer-promoter interactions,contributing to the understanding of gene transcription *** code and results are available at https://***/guofei-tju/EPIMR.

关键词： enhancer-promoter interactions Hilbert Curve multi-scale residual neural network(ResNet)

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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Using Hash Learning For a Multi-Model Fashion Recommendation System 5

Using Hash Learning For a Multi-Model Fashion Recommendation...

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5th International Conference on computer Vision and information technology, CVIT 2024

作者： Shaohua, Du Wei, Zhang Zeyu, He School of Computer Science Beijing Information Science and Technology University Beijing China Beijing Laboratory of National Economic Security Early-warning Engineering Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

ISBN: (纸本)9781510686755

In the fashion recommendation system, with the increase in the number of clothing, the combination of clothing matching is growing exponentially, which leads to the slowdown of training speed and excessive memory usage, and other problems are becoming more and more prominent. Hashing technology can effectively reduce memory usage and improve the recommendation speed. With more and more product images accompanied by text descriptions, multi-modal modeling has become a research hotspot in recommendation systems. Most of the past research adopts the method of processing different modal information separately and then weighting and averaging, but the correlation between the two modalities is not fully utilized. We believe that the visual and textual features of the same product are semantically consistent and have the same aesthetic characteristics. Therefore, we try new modeling approaches to mine the higher-order connections between the two modalities. Another issue is that visual information is much more important than textual information in fashion recommendation systems, so it is not reasonable to equate the two modalities. We model the hash-processed binary representation, optimize the network structure to include visual features in the ranking to compute the loss, and also use textual features to assist the image features for modeling. Tests on two larger datasets from Polyvore show that our model exhibits significant results compared to state-of-the-art models on key evaluation metrics such as user-personalized recommendation and compatibility modeling. © 2024 SPIE.

关键词： Recommender systems

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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

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Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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Color image restoration by filtering methods: a review

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Soft Computing 2024年第13-14期28卷 7755-7782页

作者： Salamat, Nadeem Missen, Malik Muhammad Saad Akhtar, Nadeem Mustahsan, Muhammad Surya Prasath, V.B. Department of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Department of Computer Science and IT The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Computer Science Pediatrics and Biomedical Informatics University of Cincinnati CincinnatiOH45221 United States

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks. © The Author(s) 2024.

关键词： Restoration

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Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

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IEEE Transactions on Cybernetics 2022年第6期52卷 5408-5418页

作者： Xu, Yesong Chen, Shuo Li, Jun Han, Zongyan Yang, Jian Nanjing University of Science and Technology PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Block-diagonal representation (BDR) is an effective subspace clustering method. The existing BDR methods usually obtain a self-expression coefficient matrix from the original features by a shallow linear model. However, the underlying structure of real-world data is often nonlinear, thus those methods cannot faithfully reflect the intrinsic relationship among samples. To address this problem, we propose a novel latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new dictionary for a linear model with block-diagonal regularization, which can ensure good performances for spectral clustering. Moreover, we theoretically prove that the learned features are located in the linear space, thus ensuring the effectiveness of the linear model using self-expression. Extensive experiments on various real-world datasets verify the superiority of our LBDR over the state-of-the-art subspace clustering approaches. © 2013 IEEE.

关键词： Decoding

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Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors 8

Deep Learning Approaches for Recognizing Daily Human Activit...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Sanpote, Worrakit Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Science University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on center for advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS-Cairo, CASAS-Milan and CASAS-Kyoto3 datasets, respectively. © 2023 IEEE.

关键词： Deep learning

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