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Regular Papers
Industrial Internet for intelligent manufacturing: past, present, and future Cover Article Enhanced Publication AI Introduction
“Reporting on the latest advancements in industrial Internet technology, experts have established a novel "thin waist" system that integrates sensing, communication, computing, and control, addressing key challenges in multi-dimensional collaborative sensing, end-to-end deterministic communication, and virtual computing and operation control.”Abstract：Industrial Internet, motivated by the deep integration of new-generation information and communication technology (ICT) and advanced manufacturing technology, will open up the production chain, value chain, and industry chain by establishing complete interconnections between humans, machines, and things. This will also help establish novel manufacturing and service modes, where personalized and customized production for differentiated services is a typical paradigm of future intelligent manufacturing. Thus, there is an urgent requirement to break through the existing chimney-like service mode provided by the hierarchical heterogeneous network architecture and establish a transparent channel for manufacturing and services using a flat network architecture. Starting from the basic concepts of process manufacturing and discrete manufacturing, we first analyze the basic requirements of typical manufacturing tasks. Then, with an overview on the developing process of industrial Internet, we systematically compare the current networking technologies and further analyze the problems of the present industrial Internet. On this basis, we propose to establish a novel "thin waist" that integrates sensing, communication, computing, and control for the future industrial Internet. Furthermore, we perform a deep analysis and engage in a discussion on the key challenges and future research issues regarding the multi-dimensional collaborative sensing of task–resource, the end-to-end deterministic communication of heterogeneous networks, and virtual computing and operation control of industrial Internet.Keywords：Intelligent manufacturing;Industrial Internet;Thin waist;Transparent service;Manufacturing as a service- 108
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Updated：2024-09-29Regular Papers
Recent progress on the applications of micro/nanofibers in ultrafast optics Enhanced Publication AI Introduction
“Ultrafast fiber lasers are essential in ultrafast optics, and their performance improvements are propelling the field forward. Micro/Nanofibers (MNFs), with their unique properties, are highly valuable for generating ultrashort pulses. This paper introduces the mode evolution and characteristics of MNFs and reviews recent advances in their use for ultrafast optics applications, including evanescent field modulation and control, dispersion and nonlinear management techniques, and nonlinear dynamical phenomenon exploration. It also discusses the potential application prospects of MNFs in ultrafast optics.”Abstract：Ultrafast fiber lasers are indispensable components in the field of ultrafast optics, and their continuous performance advancements are driving the progress of this exciting discipline. Micro/Nanofibers (MNFs) possess unique properties, such as a large fractional evanescent field, flexible and controllable dispersion, and high nonlinearity, making them highly valuable for generating ultrashort pulses. Particularly, in tasks involving mode-locking and dispersion and nonlinearity management, MNFs provide an excellent platform for investigating intriguing nonlinear dynamics and related phenomena, thereby promoting the advancement of ultrafast fiber lasers. In this paper, we present an introduction to the mode evolution and characteristics of MNFs followed by a comprehensive review of recent advances in using MNFs for ultrafast optics applications including evanescent field modulation and control, dispersion and nonlinear management techniques, and nonlinear dynamical phenomenon exploration. Finally, we discuss the potential application prospects of MNFs in the realm of ultrafast optics.Keywords：Micro/Nanofibers (MNFs);Nonlinear dynamics;Dispersion;Ultrafast optics- 51
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Updated：2024-09-29Regular Papers
OntoCSD: an ontology-based security model for an integrated solution of cyberspace defense Enhanced Publication AI Introduction
“In the field of cyberspace defense, a new integrated solution has been proposed. Expert xx established the OntoCSD system, which provides solutions to solve the issues in the field of cyberspace defense.”Abstract：The construction of an integrated solution for cyberspace defense with dynamic, flexible, and intelligent features is a new idea. To solve the problem whereby traditional static protection methods cannot respond to various network attacks or security demands in an adversarial network environment in time, and to form a complete integrated solution from “threat discovery” to “decision-making generation,” we propose an ontology-based security model, OntoCSD, for an integrated solution of cyberspace defense that uses Web ontology language (OWL) to represent the ontology classes and relationships of threat monitoring, decision-making, response, and defense in cyberspace, and uses semantic Web rule language (SWRL) to design the defensive reasoning rules. OntoCSD can discover potential relationships among network attacks, vulnerabilities, the security state, and defense strategies. Further, an artificial intelligence (AI) expert system based on case-based reasoning (CBR) is used to quickly generate a detailed and comprehensive decision-making scheme. Finally, through Kendall’s coefficient of concordance (W) and four experimental cases in a typical computer network defense (CND) system, which reasons on represented facts and the ontology, OntoCSD’s consistency and its feasibility to solve the issues in the field of cyberspace defense are validated. OntoCSD supports automatic association and reasoning, and provides an integrated solution framework of cyberspace defense.Keywords：Cyberspace defense;Integrated solution;Ontology;Case-based reasoning (CBR);Computer network defense (CND)- 39
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Updated：2024-09-29Regular Papers
Camouflaged target detection based on multimodal image input pixel-level fusion Enhanced Publication AI Introduction
“In the field of camouflaged target detection, a new technique called MIF-YOLOv5 has been developed. This method uses multimodal image fusion and a loss function optimized by K-Means++ clustering to enhance detection accuracy and robustness. The technique has achieved a detection accuracy of 96.5% and a recognition probability of 92.5%, setting a new standard in the industry.”Abstract：Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information, making target recognition extremely difficult. Most detection algorithms for camouflaged targets use only the target’s single-band information, resulting in low detection accuracy and a high missed detection rate. We present a multimodal image fusion camouflaged target detection technique (MIF-YOLOv5) in this paper. First, we provide a multimodal image input to achieve pixel-level fusion of the camouflaged target’s optical and infrared images to improve the effective feature information of the camouflaged target. Second, a loss function is created, and the K-Means++ clustering technique is used to optimize the target anchor frame in the dataset to increase camouflage personnel detection accuracy and robustness. Finally, a comprehensive detection index of camouflaged targets is proposed to compare the overall effectiveness of various approaches. More crucially, we create a multispectral camouflage target dataset to test the suggested technique. Experimental results show that the proposed method has the best comprehensive detection performance, with a detection accuracy of 96.5%, a recognition probability of 92.5%, a parameter number increase of 1×104, a theoretical calculation amount increase of 0.03 GFLOPs, and a comprehensive detection index of 0.85. The advantage of this method in terms of detection accuracy is also apparent in performance comparisons with other target algorithms.Keywords：Camouflaged target detection;Pixel-level fusion;Anchor box optimization;Loss function;Multispectral dataset- 56
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Updated：2024-09-29Regular Papers
Accurate estimation of 6-DoF tooth pose in 3D intraoral scans for dental applications using deep learning Enhanced Publication AI Introduction
“In the realm of digital dentistry, a new method for automatic tooth orientation estimation has been introduced. This breakthrough uses a deep neural network to analyze 3D point clouds of teeth, achieving high accuracy in tooth pose estimation. The model, trained on a large-scale dataset, offers a significant advancement for orthodontic treatment planning and simulation.”Abstract：A critical step in digital dentistry is to accurately and automatically characterize the orientation and position of individual teeth, which can subsequently be used for treatment planning and simulation in orthodontic tooth alignment. This problem remains challenging because the geometric features of different teeth are complicated and vary significantly, while a reliable large-scale dataset is yet to be constructed. In this paper we propose a novel method for automatic tooth orientation estimation by formulating it as a six-degree-of-freedom (6-DoF) tooth pose estimation task. Regarding each tooth as a three-dimensional (3D) point cloud, we design a deep neural network with a feature extractor backbone and a two-branch estimation head for tooth pose estimation. Our model, trained with a novel loss function on the newly collected large-scale dataset (10 393 patients with 280 611 intraoral tooth scans), achieves an average Euler angle error of only 4.780°–5.979° and a translation L1 error of 0.663 mm on a hold-out set of 2598 patients (77 870 teeth). Comprehensive experiments show that 98.29% of the estimations produce a mean angle error of less than 15°, which is acceptable for many clinical and industrial applications.Keywords：Artificial intelligence;Digital dentistry;Deep learning;Orthodontics;Tooth pose;Neural network- 126
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Updated：2024-09-29Regular Papers
Reversible data hiding in encrypted images based on additive secret sharing and additive joint coding using an intelligent predictor Enhanced Publication AI Introduction
“In the realm of secure data transmission, a new method for reversible data hiding in encrypted images has been developed. It utilizes an intelligent pixel predictor and an adaptive joint coding mechanism, enhancing both prediction accuracy and embedding capacity. This breakthrough ensures secure and lossless data extraction and image recovery, with a significant embedding capacity of 4.39 bpp demonstrated for the Lena image.”Abstract：Reversible data hiding in encrypted images (RDHEI) is essential for safeguarding sensitive information within the encrypted domain. In this study, we propose an intelligent pixel predictor based on a residual group block and a spatial attention module, showing superior pixel prediction performance compared to existing predictors. Additionally, we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space, outperforming single coding approaches. The image owner employs the presented intelligent predictor to forecast the original image, followed by encryption through additive secret sharing before conveying the encrypted image to data hiders. Subsequently, data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver. The receiver can extract secret data and recover the original image losslessly, with the processes of data extraction and image recovery being separable. Our innovative approach combines an intelligent predictor with additive secret sharing, achieving reversible data embedding and extraction while ensuring security and lossless recovery. Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity. For the Lena image, the number of prediction errors within the range of [-5, 5] is as high as 242 500 and our predictor achieves an embedding capacity of 4.39 bpp.Keywords：Reversible data hiding in encrypted images (RDHEI);Additive secret sharing;Adaptive joint coding;Intelligent predictor- 34
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Updated：2024-09-29
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