Hybrid Recurrent Neural Network Architecture-Based Intention Recognition for Human–Robot Collaboration

Gao, Xiaoshan; Liu, Yan; Wang, Gang; Gerada, Chris

doi:10.1109/tcyb.2021.3106543

Cited by 18 publications

(13 citation statements)

References 28 publications

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“…The ideal values of both are 99.61% and 33.40%, respectively. Their formula is as follows: −0.0025 −0.0300 0.0048 [17] −0.0022 −0.0009 0.0073 [28] 0.0035 −0.0032 0.0032 [33] −0.0017 0.0027 0.0023 [34] 0.0020 0.0005 0.0014 [35] −0.0003 0.0011 0.0013 [36] −0.0041 0.0062 0.0091 [30] −0.0006 0.0020 −0.0012…”

Section: Differential Attackmentioning

confidence: 99%

Holographic encryption algorithm based on the new integrated chaotic system and chaotic mask

Liang,

Chen,

Chen

et al. 2024

Phys. Scr.

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Image encryption algorithms based on chaos theory have rapidly developed in recent years, with many achieving encryption by confusion-diffusion structures. However, the security performance of these algorithms needs to be improved. This paper proposes a holographic encryption algorithm based on the new integrated chaotic system and chaotic mask. The improved Gerchberg-Saxton algorithm transforms plaintext images into pure-phase holograms. The chaotic masks generated by the new integrated chaotic system decompose holograms into sub-images. The sub-images are pixel-wise heterogeneous operations and are finally merged into the complete encrypted image. Compared with one-dimensional chaotic mapping, the generation of NICS is related to kinoform, which enhances the system's key sensitivity. The chaotic mask chunking method can generate random chunk locations and sizes, effectively preventing attacks against specific chunking rules. The performance analysis shows that the algorithm has a higher key space and security to resist stronger robustness attacks.

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Section: Differential Attackmentioning

confidence: 99%

Holographic encryption algorithm based on the new integrated chaotic system and chaotic mask

Liang,

Chen,

Chen

et al. 2024

Phys. Scr.

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“…[24][25][26][27][28][29] In juxtaposition, human activity prediction algorithms are honed for forecasting forthcoming human actions and enable automated systems to formulate their course based on historical and current data. They encompass intention prediction, [30][31][32] motion prediction, [33][34][35][36][37][38][39] and attention estimation. [40,41] Beyond collaborative tasks, human activity prediction holds efficacy for surveillance systems and social HRI.…”

Section: Human Activitymentioning

confidence: 99%

“…Body detection MobileNet-SSD; [18] Openpose þ SVM; [19] Bayesian Siamese neural network þ CVAE; [20] YOLO þ Bayesian DNN [21] Human following and autonomous navigation; Visual tracking for autonomous robots tasked with humans and environment interaction; Safe HRI and HRC Face recognition SSD þ FaceNetþKCF; [22] SFPD [23] Human following and autonomous navigation; Simultaneous face and person detection for real-time HRI Human activity Activity recognition Two-stream CNN; [24] 3D LRCN þ 3D CNN þ LSTM; [25] LSTM þ VAE þ DRL; [26] 3D-CNN; [27] STJ-CNN; [28] TCN [29] Collaborative assembly and packaging; Safe HRI and HRC; Companion robots; HRI and VR applications Intention prediction CNN; [30] ILSTM þ IBi-LSTM; [31] CNN þ VMM [32] Surveillance; Collaborative assembly Motion prediction RSSAC-Trajectronþþ; [33] RNN; [34,35] VAE; [36] CVAE þ LSTM; [37] Dynamic motion projection; [38] RNN þ RIMEDNet [39] Safe and efficient HRI and HRC; Collaborative manipulation and assembly; Human imitation; Social HRI; Handover tasks Attention estimation ANN; [40] LSTM [41] Attention level estimation; Blind 3D human attention inference Human pose Body pose recognition OpenPose þ Angle-based rules; [42] Fast-SCNN þ REDE; [43] PoseNet [44] Ergonomics in HRC; Handover task; Efficient and safe HRI and HRC…”

Section: Human Positionmentioning

confidence: 99%

“…Notably, two depth cameras frequently employed in HRI research are the Intel Realsense D415 (https://www.intelrealsense.com/depth-camera-d415/) [39,88,89] and Microsoft Kinect V2 (https://learn.microsoft.com/en-us/ windows/apps/design/devices/kinect-for-windows). [27,31,47,82,[90][91][92] Once processed by CV algorithms outlined in Table 1, images acquired from such depth cameras can accomplish a range of tasks in HRI outlined in Section 3.1, including detection, recognition, and prediction. Devices integrating several cameras are also prominently featured in HRI research.…”

Section: Vision-based Interfacementioning

confidence: 99%

See 1 more Smart Citation

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

Wang,

Zheng,

et al. 2023

Advanced Intelligent Systems

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Human–robot interaction (HRI) has escalated in notability in recent years, and multimodal communication and control strategies are necessitated to guarantee a secure, efficient, and intelligent HRI experience. In spite of the considerable focus on multimodal HRI, comprehensive disquisitions delineating various modalities and intricately analyzing their combinations remain elusive, consequently limiting holistic understanding and future advancements. This article aspires to bridge this inadequacy by conducting a profound exploration of multimodal HRI, predominantly concentrating on four principal modalities: vision, auditory and language, haptics, and physiological sensing. An extensive review encapsulating algorithmic dissection, interface devices, and applicative dimensions forms part of this discourse. This manuscript distinctively combines multimodal HRI with cognitive science, deeply probing into the three dimensions, perception, cognition, and action, thereby demystifying algorithms intrinsic to multimodal HRI. Finally, it accentuates the empirical challenges and contours preemptive trajectories for multimodal HRI in human‐centric smart manufacturing.

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“…Researchers also tried the combined models to improve the performance of their method. Xuan Zhao [37] proposed the Gaussian Hybrid Hidden Markov Model (GHMM) the Generalised Growth and Pruned Radial Basis Function Neural Network (GGAP-RBFNN) under this aim. A mixture of HMM and Gaussian mixture models (GMMs) [38] is also an effective method.…”

Section: Abstract Analysismentioning

confidence: 99%

Visualized Analysis of Intention Recognition Research Based on CiteSpace

2022

AJETS

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Intention recognition has improved the accuracy and speed of interactions between human and machine. However, due to the rapid development, the diversity of research content and methods, and the wide range of applications, researches in this field lack systematic integration. Based on 1234 selected papers, this paper applied visualization methods in CiteSpace to figure out significant country, institution, journal, reference and keywords to demonstrate the developments and research hotspots from 2011 to 2021. He Huang and Aaron J. Young are thought to be the most productive authors. According to the keywords analysis, "Hidden Markov Model", "networks", "feature extraction" are current hotspots. Furthermore, the deficiencies and future directions have been indicated by conducting this composite analysis.

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Hybrid Recurrent Neural Network Architecture-Based Intention Recognition for Human–Robot Collaboration

Cited by 18 publications

References 28 publications

Holographic encryption algorithm based on the new integrated chaotic system and chaotic mask

Holographic encryption algorithm based on the new integrated chaotic system and chaotic mask

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

Visualized Analysis of Intention Recognition Research Based on CiteSpace

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