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

作者： Yin, Qilin Wang, Jinwei Luo, Xiangyang Nanjing University of Information Science and Technology and Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China

ISBN: (纸本)9783030694487

With the development of information security, localization of image manipulations havs become a hot topic. In this paper, a hybrid loss network is proposed for the manipulated image forensics. First, the patch prediction module extracts corresponding features representing the discrepancy of the tampered region boundaries. Then, these features are constrained by the Pixel Normalization, thereby improving the classification performance for the tampered patches. Finally, multi-scale patch prediction masks and semantic information are fused to segment out the tampered regions. The experiments demonstrate the proposed model can achieve high performance. © 2021, Springer Nature Switzerland AG.

关键词： Pixels

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Multiple asymmetric couplings induced unconventional corner mode in topolectrical circuits

arXiv

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arXiv 2024年

作者： Jiang, Hengxuan Wang, Xiumei Chen, Jie Zhou, Xingping College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of Quantum Information and Technology Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China

We investigate the emergence of unconventional corner mode in a two-dimensional topolectrical circuits induced by asymmetric couplings. The non-Hermitian skin effect of two kinked one-dimensional lattices with multiple asymmetric couplings are explored. Then we extend to the two-dimensional model, derive conditions for the non-Hermitian hybrid skin effect and show how the corner modes are formed by nonreciprocal pumping based on one-dimensional topological modes. We provide explicit electrical circuit setups for realizing our observations via realistic LTspice simulation. Moreover, we show the time varying behaviors of voltage distributions to confirm our results. Our study may help to extend the knowledge on building the topological corner modes in the non-Hermitian presence. © 2024, CC BY.

关键词： Topology

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A Data Enhancement Strategy for Multi-Agent Cooperative Hunting based on Deep Reinforcement Learning

A Data Enhancement Strategy for Multi-Agent Cooperative Hunt...

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Industrial Cyber-Physical systems (ICPS)

作者： Zhenkun Gao Xiaoyan Dai Meibao Yao Xueming Xiao Intelligent Robotics Lab (IRL) School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China CVIR lab Changchun University of Science and Technology China Key Lab of Opto-electronic Measurement and Optical Information Transmission Technology Ministry of Education China

Cooperative hunting is a typical and significant scene to study multi-agent behaviors, where conventional control strategies are difficult to cope with, due to its high dimensionality of state space and locality of communication. Reinforcement learning provides a framework and a set of tools for this issue by trial-and-error interactions with the environment. Though promising, it often requires a large number of empirical sample data to learn effective hunting strategies, leading to low sample efficiency, understood as the training episodes required for the agent to learn effective behavior strategies. To improve the sampling efficiency, we propose a data enhancement strategy integrated in the execution (CTDE) training framework to train the multi-agent system. The data enhancement strategy is based on a state transfer dynamics model to generate additional predicted data, which we called dynamic prediction model, combined with the empirical data by interacting with the environment, for higher sample efficiency. The simulation results on the Webots platform show that our method outperforms some state-of-the-art methods, such as MAPPO, with high data sample efficiency.

关键词： Training Service robots Simulation Training data Reinforcement learning Predictive models Data models

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Design of Omnidirectional Mobile Robot Platform Controlled by Remote Visualization 5th

Design of Omnidirectional Mobile Robot Platform Controlled b...

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5th International Conference on Cognitive systems and Signal Processing, ICCSIP 2020

作者： Li, Kunhua Liang, Xiaotao Xu, Ming Hong, Zhouyang Li, Heng Sun, Fuchun Huang, Haiming College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China Peng Cheng Lab Shenzhen GD518055 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9789811623356

Mobile Robot can realize non-contact operation of medical and rehabilitation services in the outbreak of CoVID-19, and Mobile Robot Platform (MRP) is the key component in Mobile Robots. By using Mecanum wheels, the omnidirectional MRP (OMRP) of four-wheel layout is realized. Combined with raspberry PI and STM32 micro-controller, the OMRP is driven by motion control algorithm to moves and rotates in all directions. The remote visual control link is built through 4G data transmission and 4G diagram transmission to realize the 4G visual control mode. Also, combined with the digital control mode of RS-485 data transmission and the Bluetooth controller, the multi-mode control of OMRP is realized. Finally, a prototype of OMRP is made. Experiments of the prototype verify the feasibility of OMRP, and the effectiveness of the 4G remote visual control. Overall, this paper provides a new way of robot remote control in wireless communication for the upcoming 5G era. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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Fault Prediction of On-board Train Control system: An Data Balance-enhanced AdaBoost Approach

Fault Prediction of On-board Train Control System: An Data B...

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2021 China Automation Congress, CAC 2021

作者： Xu, Kang-Zhi Liu, Jiang Cai, Bai-Gen Wang, Jian School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of EMC GNSS Technology for Rail Transportation Beijing100044 China

ISBN: (纸本)9781665426473

This paper concerns the fault prediction modeling for Condition-based Maintenance (CBM) of on-board railway train control systems. Based on the field data from the CTCS2-200H on-board equipment, the imbalance problem of the fault and fault-free samples is investigated, which makes it difficult to realize effective model training for a fault prediction purpose. A data-driven system fault prediction framework is established. Based on that, a data balance-enhanced AdaBoost (Adaptive Boosting) approach is proposed, which uses an integrated Borderline-SMOTE method to improve the class distribution of the training samples. With this method, effective modeling using the AdaBoost algorithm can be enabled with enhanced datasets. Field data collected from the practical on-board train control equipment is utilized to demonstrate the proposed approach. Comparisons with the different dataset conditions and training algorithms illustrate the superior performance of the proposal and show its potential in the fault prediction-enabled intelligent maintenance systems. © 2021 IEEE

关键词： Forecasting

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A Framework of Online Learning and Experiment system Based on Affective Computing 3

A Framework of Online Learning and Experiment System Based o...

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3rd International Conference on E-Business, information Management and Computer science, EBIMCS 2020

作者： Zhou, Yinghui Tao, Xiaomei School of Information Science and Engineering Guilin University of Technology Guilin Guangxi Zhuang Autonomous Region China School of Information Science and Engineering Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin Guangxi Zhuang Autonomous Region China

ISBN: (纸本)9781450389099

With the development of online education, the problem of emotional deficiency has gradually emerged. In recent years, the research about emotional interaction in online education, and the application of Affective Computing in online education came into being. This paper introduces the impact of Affective Computing on online education, including the use of expressions, speech, eye movement, and physiological signals, as well as text and questionnaires and other modals, or multi-modal to infer students' emotional state and learning state in the learning process, and then to realize personalized learning support. The emerging technologies such as Artificial Intelligence, Big Data and Brain Computer Interface, can be applied to online education combined with Affective Computing technologies to improve the interaction between learners and the on-line learning system. This paper proposes a framework of online learning and experiment system based on Affective Computing supported by Artificial Intelligence, Big Data and Brain Computer Interface. The system can provide personalized education for students and help them to carry out experiments in a virtual reality environment. The framework can reduce the impact on students in the process of data collection, so that it can obtain more real data, and the framework uses emerging technologies to provide support and reference for emotion analysis and recognition, which will make the analysis and response more timely and emotion recognition more accurate. © 2020 ACM.

关键词： Students

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Improving subseasonal forecasting of East Asian monsoon precipitation with deep learning

引用

Atmospheric and Oceanic science Letters 2025年第03期 36-42页

作者： Jiahui Zhou Fei Liu School of Atmospheric Sciences Sun Yat-Sen University Key Laboratory of Tropical Atmosphere-Ocean System Ministry of Educationand Southern Marine Science and Engineering Guangdong Laboratory Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science and Technology

东亚夏季风(EASM)降水的准确次季节预报至关重要,因为它直接影响着数十亿人的生计.然而,最先进的次季节-季节(S2S)预测模型的预测技巧仍然有限.本研究开发了一种卷积神经网络(CNN)回归模型,通过利用动力预测模型预测的更可靠的环流场来... 详细信息

东亚夏季风(EASM)降水的准确次季节预报至关重要,因为它直接影响着数十亿人的生计.然而,最先进的次季节-季节(S2S)预测模型的预测技巧仍然有限.本研究开发了一种卷积神经网络(CNN)回归模型,通过利用动力预测模型预测的更可靠的环流场来提高EASM周降水的预测技巧.经过CNN模型的订正,在提前一周预测EASM降水指数时,11个S2S模式的平均距平相关系数从增加了14%,从0.30增加到0.35;均方根误差减少了22%,从3.22减少到2.52.在这些S2S模式中,通过CNN订正对预测技巧的提高程度取决于模式在准确预测大气环流变量方面的表现.对EASM降水指数的CNN订正只能订正模式的系统误差,与逐个网格订正还是整个区域平均指数订正无关,并且在不同的提前期内CNN的订正效果基本不变.此外,200hPa纬向风被认为是有效订正的最重要变量.

关键词：东亚季风降水次季节预测深度学习误差订正

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Refracting Reconfigurable Intelligent Surface Assisted URLLC for Millimeter Wave High-Speed Train Communication Coverage Enhancement

arXiv

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arXiv 2024年

作者： Liu, Changzhu He, Ruisi Niu, Yong Mao, Shiwen Ai, Bo Chen, Ruifeng The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China

High-speed train (HST) has garnered significant attention from both academia and industry due to the rapid development of railways worldwide. Millimeter wave (mmWave) communication, known for its large bandwidth is an effective way to address performance bottlenecks in cellular network based HST wireless communication systems. However, mmWave signals suffer from significant path loss when traversing carriage, posing substantial challenges to cellular networks. To address this issue, reconfigurable intelligent surfaces (RIS) have gained considerable interest for its ability to enhance cell coverage by reflecting signals toward receiver. Ensuring communication reliability, a core performance indicators of ultra-reliable and low-latency communications (URLLC) in fifth-generation systems, is crucial for providing steady and reliable data transmissions along railways, particularly for delivering safety and control messages and monitoring HST signaling information. In this paper, we investigate a refracting RIS-assisted multi-user multiple-input single-output URLLC system in mmWave HST communications. We propose a sum rate maximization problem, subject to base station beamforming constraint, as well as refracting RIS discrete phase shifts and reliability constraints. To solve this optimization problem, we design a joint optimization algorithm based on alternating optimization method. This involves decoupling the original optimization problem into active beamforming design and packet error probability optimization subproblem, and discrete phase shift design subproblems. These subproblems are addressed exploiting Lagrangian dual method and the local search method, respectively. Simulation results demonstrate the fast convergence of the proposed algorithm and highlight the benefits of refracting RIS adoption for sum rate improvement in mmWave HST networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile telecommunication systems

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Coded Distributed Computing for Hierarchical Multi-task Learning

arXiv

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arXiv 2022年

作者： Hu, Haoyang Li, Songze Cheng, Minquan Wu, Youlong School of Information Science and Technology ShanghaiTech University Shanghai201210 China Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China

In this paper, we consider a hierarchical distributed multi-task learning (MTL) system where distributed users wish to jointly learn different models orchestrated by a central server with the help of a layer of multiple relays. Since the users need to download different learning models in the downlink transmission, the distributed MTL suffers more severely from the communication bottleneck compared to the single-task learning system. To address this issue, we propose a coded hierarchical MTL scheme that exploits the connection topology and introduces coding techniques to reduce communication loads. It is shown that the proposed scheme can significantly reduce the communication loads both in the uplink and downlink transmissions between relays and the server. Moreover, we provide information-theoretic lower bounds on the optimal uplink and downlink communication loads, and prove that the gaps between achievable upper bounds and lower bounds are within the minimum number of connected users among all relays. In particular, when the network connection topology can be delicately designed, the proposed scheme can achieve the information-theoretic optimal communication loads. Experiments on real datasets show that our proposed scheme can reduce the overall training time by 17% ∼ 26% compared to the conventional uncoded scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： Hierarchical systems

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