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19th International Workshop on Digital Forensics and Watermarking, IWDW 2020

作者： Feng, Bingwen Liu, Zhiquan Wei, Kaimin Lu, Wei Lin, Yuchun College of Information Science and Technology Jinan University Guangzhou510632 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen518060 China School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Guangzhou Institute of Science and Technology Guangzhou510006 China

ISBN: (纸本)9783030694487

This paper presents a type of variable rate syndrome-trellis codes (VR-STC) for bursty channels. It can embed message bits with two different embedding rates. In the embedding, a cover vector is sliced into segments, and the embedding rates for each segment depend on the local channel distribution. The parities of stego segments are exploited to indicate the selected embedding rates according to a mapping function between parities and embedding rates. The core of the VR-STC is the parity-aware encoder, which can simultaneously output two candidate stego segments with different embedding rates and opposite parities, either of which can be used to constitute the final stego vector. A Viterbi algorithm is also suggested to find the closed stego segments. Besides, the mapping function between parities and embedding rates is designed by minimizing the embedding costs on a down sampled version of the cover vector. It can further improve the undetectability of the VR-STC. Experimental results on artificial signals and binary images suggest that the proposed VR-STC can provide high success rate of embedding and reduce the embedding cost on bursty channels. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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Research on intelligent Temperature Control Method of Closed-Loop Cooling System Based on Deep Q-Network

SSRN

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SSRN 2024年

作者： Liu, Yongchao Wang, Bo Zheng, Wei Wang, Zibo Li, Tong Cheng, Jiahao Niu, Zhenfeng Ke, Zhiwu Tan, Sichao Li, Jiangkuan Gao, Puzhen Tian, Ruifeng Zhu, Qidan Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment Harbin Engineering University Harbin150001 China Key Laboratory of Nuclear Safety and Advanced Nuclear Energy Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin150001 China Science and Technology Thermal Energy and Power Laboratory Wuhan430205 China Wuhan Second Ship Design and Research Institute Wuhan430205 China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China

As one of the most important subsystem types in the nuclear power system, the closed-loop cooling system is used for the heat transfer of the system. The control quality of its operating temperature is important for the safe operation of the entire nuclear power system. This paper proposes an intelligent temperature control method based on deep reinforcement learning (DRL). First, the closed-loop cooling system simulator is built and expanded to provide an experimental platform for the implementation of DRL. Secondly, a DRL interaction system is designed based on prior knowledge such as system thermal control characteristics and temperature control targets. Finally, the improved deep Q-network (DQN) algorithm is used to train the deep neural network agent, and the optimal temperature control strategy is approached and saved through self-learning interaction. The experimental results demonstrate that the DRL controller can realize automatic control operation and has good target tracking ability and robustness. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Fast and Accurate Mapping Method for OPGW Tower Based on M-OTDR

Fast and Accurate Mapping Method for OPGW Tower Based on M-O...

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International Conference on Optical Communications and Networks (ICOCN)

作者： Wang Ruofan Li Hongren Zhao Xinying Tong Shuai Zou Ningmu Zhang Xuping Liu Huanhuan Xiong Fei Zhang Yixin College of Engineering and Applied Sciences Nanjing University Nanjing China Key Laboratory of Intelligent Optical Sensing and Manipulation Nanjing University Nanjing China School of Information Science and Engineer Shandong University Qingdao China Shcnzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Inner Mongolia DT Electric Power Design Hohhot China

ISBN: (数字)9798350367652

ISBN: (纸本)9798350367669

This paper describes how distributed optical fiber sensing (DOFS) technology, combined with dark optical fibers within the current optical fiber composite overhead ground wire (OPGW), enables online monitoring and early warning of abnormalities in high-voltage overhead transmission lines. Achieving this goal requires an accurate mapping of the optical cable length to the actual GPS coordinates of the tower. The current tower positioning techniques have drawbacks like low identification rates, high positioning variation, and long calibration times. They are also restricted by particular weather conditions. This study presented a multi-parametric optical time domain reflectance sensing (M-OTDR) system that synchronizes the monitoring of temperature, strain, vibration, and other physical parameters along the fiber optic cable in order to address these problems. Additionally, the study proposed four reference positioning functions. An in-field test was carried out on a 220kV high-voltage transmission line in Fujian Province, PRC. The results showed that the proposed method could guarantee speedy and accurate placement of high-voltage overhead transmission line towers.

关键词： Optical fibers Integrated optics Vibrations Temperature sensors Optical fiber sensors Accuracy Poles and towers

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Surgical Gesture Recognition in Open Surgery Based on 3DCNN and SlowFast

Surgical Gesture Recognition in Open Surgery Based on 3DCNN ...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Yutao Men Jian Luo Zixian Zhao Hang Wu Feng Luo Guang Zhang Ming Yu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education(Tianjin University of Technology) Tianjin China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

In the field of computer-aided medicine, automated surgical gesture recognition is an important research direction. Its main applications include automated skills assessment, intraoperative guidance, and education and training systems. Current research has focused on robotic surgery, and there is less research on surgical gestures for open surgery. Therefore, in this study, a new simulated open surgery dataset was developed, and a series of surgical gestures for open surgery were defined for closure the abdomen surgery with the cooperation of professional doctors. In addition, this paper uses the classical algorithm 3DCNN and SlowFast model in the field of action recognition to perform offline recognition of surgical gestures for open surgery on the dataset. 3DCNN uses R3D and R(2+1)D models. The experimental results show that on the self-built open surgical dataset, the R3D model has the best surgical gesture recognition results, with an accuracy rate of 90.4%, a precision rate of 90.5%, and a recall rate of 90.0%.

关键词： Training Automation Accuracy Surgery Gesture recognition Medical services Information technology Robots Abdomen

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A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks

A Heuristic Sparsification Method to Improve the Performance...

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International Conference on Information Science and Technology (ICIST)

作者： Fangyu Zhang Yi Pan Yanjie Wei Jin Liu Zhenli Lin Centre for High Performance Computing Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China School of Computer Science and Engineering Central South University Changsha China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced.

关键词：

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WaveFormer: A Wavelet Transformer for Parkinson Disease’s Retinal Layer Segmentation in OCT

WaveFormer: A Wavelet Transformer for Parkinson Disease’s R...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yanlin Chen Xiaoqing Zhang Tianao Wang Chen Tang Haili Ye Jiang Liu Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Chinese Academy of Sciences Shenzhen China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou China The Oujiang Laboratory The Affiliated Eye Hospital Wenzhou Medical University Wenzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Pathology symptoms of Parkinson disease (PD) are different from those of retinal diseases in the retinal layers, which are subtle. However, segmenting pathology information of PD from retinal layers automatically based on optical coherence tomography (OCT) images has not been studied before. Although existing Transformer-based segmentation methods have achieved good segmentation results, they have limitations in capturing local context information. Convolutional neural networks (CNNs) can construct local context dependencies among pixels, which is complementary to Transformers. Particularly, edge information extraction is significant for accurate retinal layer segmentation, which is ignored by both Transformers and CNNs but can be captured by frequency domain learning methods. To fully leverage the advantages of Transformers, CNNs, and frequency domain learning methods, we propose a Wavelet Transformer (WaveFormer) for retinal layer segmentation based on OCT images. In the WaveFormer, we design a Wavelet Spatial Attention block to exploit the potential of frequency information. Based on these advantages, WaveFormer can be data-efficient in limited OCT images of PD. The experimental results on the OCT-PD segmentation dataset show that our WaveFormer outperforms existing Transformers and CNNs. For example, WaveFormer outperforms Swin-UNet by 3.41% of IoU.

关键词： Learning systems Image segmentation Pathology Wavelet domain Image edge detection Frequency-domain analysis Transformers

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design of Underwater Vehicle for Ship Draft Observation

Design of Underwater Vehicle for Ship Draft Observation

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Mechatronics and Robotics Engineering (ICMRE), International Conference on

作者： Jingyuan Zhu Meng Han Hongliang Zhang Lan Qi Lianyu Zhao Chenglin Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Deepinfar Ocean Technology Inc Tianjin China

Underwater Vehicles are very important underwater operation equipment, and a new underwater vehicle is designed in order to realize the measurement of the ship draft. The design is based on the ROV(Remote Operated Vehicle) structure, and the mobile power supply and the upper computer are mounted on the underwater vehicle body, aiming to accomplish the task of disconnecting the underwater vehicle from the cable under complex sea conditions. In this paper, the system communication and power supply scheme design, thruster arrangement, the center of gravity and floating center position design, and 3D model design are completed in turn to obtain a structural design scheme of the underwater vehicle based on the actual engineering background. Subsequently, the strength check of the overall frame was carried out based on ANSYS WORKBENCH, which proved that the material selection and structural design met the reliability requirements. Finally, the hydrodynamic characteristics of the robot were analyzed using ANSYS CFD.

关键词： Weight measurement Underwater cables Solid modeling Three-dimensional displays Power supplies Hydrodynamics Finite element analysis

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WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity

arXiv

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arXiv 2024年

作者： Wang, Qiyuan Liu, Yanzhe Zhao, Shang Liu, Rong Zhou, S. Kevin Anhui Hefei230026 China Suzhou Institute for Advanced Research USTC Jiangsu Suzhou215123 China Key Laboratory of Precision and Intelligent Chemistry USTC Anhui Hefei230026 China Institute of Computing Technology CAS Beijing100190 China Faculty of Hepato-Biliary-Pancreatic Surgery The First Medical Center Chinese PLA General Hospital Beijing China

For robotic surgical videos, instrument presence annotations are typically recorded with video streams, which offering the potential to reduce the manually annotated costs for segmentation. However, weakly supervised surgical instrument segmentation with only instrument presence labels has been rarely explored in surgical domain due to the highly underconstrained challenges. Temporal properties can enhance representation learning by capturing sequential dependencies and patterns over time even in incomplete supervision situations. From this, we take the inherent temporal attributes of surgical video into account and extend a two-stage weakly supervised segmentation paradigm from different perspectives. Firstly, we make temporal equivariance constraint to enhance pixel-wise temporal consistency between adjacent features. Secondly, we constrain class-aware semantic continuity between global and local regions across temporal dimension. Finally, we generate temporal-enhanced pseudo masks from consecutive frames to suppress irrelevant regions. Extensive experiments are validated on two surgical video datasets, including one cholecystectomy surgery benchmark and one real robotic left lateral segment liver surgery dataset. We annotate instance-wise instrument labels with fixed time-steps which are double checked by a clinician with 3-years experience to evaluate segmentation results. Experimental results demonstrate the promising performances of our method, which consistently achieves comparable or favorable results with previous state-of-the-art approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic surgery

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Experimental Study on Microstructure and Mechanical Properties of Dissimilar Cu/Al Welded by Hybrid Coaxial Blue-Ir Laser

SSRN

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SSRN 2024年

作者： Zhang, Mingjun Zhang, Yuxuan Zhang, Jian Wang, Hailong Li, Heqing Cheng, Bo Wu, Xiang Zhou, Dianwu Mao, Cong Hu, Yongle Li, Wen Hunan Provincial Key Laboratory of Intelligent Manufacturing Technology for High-performance Mechanical Equipment Changsha University of Science and Technology Changsha410114 China Institute of Metals College of Materials Science and Engineering Changsha University of Science & Technology Changsha410114 China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Hunan SANY Tower Lifting Machinery Co. Ltd. Changsha410600 China

A hybrid blue-infrared (IR) laser is adopted to weld 5052 Al with T2 Cu to investigate the impact of processing parameters on the quality of Cu/Al overlap joints in terms of microstructures, mechanical properties, and failure modes. The experiments show that combining an infrared laser and a blue laser has enlarged an opening channel of welds and stabilized the convention on edge of the weld pool. This eases the removals of bubbles and prevents the formations of pores. When a hybrid coaxial blue-IR laser with a power less than 500 W is used, the weld strengths of the specimens are increased significantly, and the plasticity is decreased slightly in comparison with the specimens welded by an infrared laser. When the power of the blue laser is increased to 800 W, the strength and plasticity of a lapped weld are decreased significantly. Moreover, the joints only welded by an infrared laser are fractured in a mixed fracture mode. When the blue laser power of the hybrid coaxial blue-IR laser is lower than 500 W, the weld is ruptured by a ductile fracture. However, a brittle fracture is observed as the blue laser power exceeding 800 W. © 2024, The Authors. All rights reserved.

关键词： Microstructure

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Temporal Information Reconstruction and Non-Aligned Residual in Spiking Neural Networks for Speech Classification

arXiv

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arXiv 2024年

作者： Zhang, Qi Wang, Huamin Shen, Hangchi Duan, Shukai Wen, Shiping Huang, Tingwen College of Artificial Intelligence Southwest University Chongqing400715 China Chongqing Key Laboratory of Brain Inspired Computing and Intelligent Chips Chongqing400715 China College of Information Engineering University of Technology Sydney Australian Institute of Artificial Intelligence Sydney2007 Australia Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Recently, it can be noticed that most models based on spiking neural networks (SNNs) only use a same level temporal resolution to deal with speech classification problems, which makes these models cannot learn the information of input data at different temporal scales. Additionally, owing to the different time lengths of the data before and after the sub-modules of many models, the effective residual connections cannot be applied to optimize the training processes of these models. To solve these problems, on the one hand, we reconstruct the temporal dimension of the audio spectrum to propose a novel method named as Temporal Reconstruction (TR) by referring the hierarchical processing process of the human brain for understanding speech. Then, the reconstructed SNN model with TR can learn the information of input data at different temporal scales and model more comprehensive semantic information from audio data because it enables the networks to learn the information of input data at different temporal resolutions. On the other hand, we propose the Non-Aligned Residual (NAR) method by analyzing the audio data, which allows the residual connection can be used in two audio data with different time lengths. We have conducted plentiful experiments on the Spiking Speech Commands (SSC), the Spiking Heidelberg Digits (SHD), and the Google Speech Commands v0.02 (GSC) datasets. According to the experiment results, we have achieved the state-of-the-art (SOTA) result 81.02% on SSC for the test classification accuracy of all SNN models, and we have obtained the SOTA result 96.04% on SHD for the classification accuracy of all models. Furthermore, on the non-spiking dataset GSC, we can achieve a test classification accuracy of 95.63%, which surpasses the original baseline SNN-Delays. Meanwhile, the method proposed in this paper can speed up the model’s processing speed and reduce its energy consumption. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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