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arXiv 2023年

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model’s uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Counting Mobile Devices With Physical Layer Measurements in the 2.4GHz ISM Band 19

Counting Mobile Devices With Physical Layer Measurements in ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhang, Zhaoyan Huang, Baoqi Zhang, Chaowei Jia, Bing Li, Wuyungerile Xu, Gang Engineering Research Center of Ecological Big Data Ministry of Education China Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Hohhot China Inner Mongolia University College of Computer Science Hohhot China Inner Mongolia Power Marketing Service and Operation Management Center China

ISBN: (纸本)9798350346558

In general, the more mobile devices are operating in space, the more wireless communications are produced, leading to significant changes in the wireless spectrum, which can be utilized to infer the number of mobile devices conversely. Inspired by this idea, we pioneered present a novel mobile device counting method of measuring the wireless signals of the 2. 4GHz ISM band in which most WiFi-and Bluetooth-enabled mobile devices operate. Precisely, the in-phase (I) and quadrature (Q) components from the wireless signals are measured through a software-defined radio (SDR) platform and are then converted into spectrograms. On this ground, we propose a dual attention network based on EfficientNet-B4 (EANet) to estimate the number of mobile devices with the aforementioned spectrograms as inputs. In addition, a cross-domain estimation method is proposed to enhance the generalizability of our model. Experiments are conducted in real environments to validate our method. It is shown that the proposed regression model EANet outperforms the models based on popular convolutional neural networks (CNNs), and the proposed cross-domain method can effectively reduce the training time without sacrificing the estimation accuracy. This study preliminarily verifies the idea's feasibility and the validity of the proposed methods, paving the way for future research. © 2022 IEEE.

关键词： Network layers

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An X-Band Circularly-Polarized Marine Radar Antenna Based on Spoof Surface Plasmon Polariton

An X-Band Circularly-Polarized Marine Radar Antenna Based on...

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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSRWTC)

作者： Dezhuang Zhang Zhongbao Wang Shipeng Zhao Hongmei Liu School of Information Science and Technology Dalian Maritime University Dalian Liaoning China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao Liaoning China

A novel X-Band circularly-polarized antenna for marine radar systems is proposed. Spoof surface plasmon polariton (SSPP) is used to convey slow waves as feeding transmission lines. A series of slotted circular metallic patches are used to radiate circularly-polarized wave. Since the top metallic patches are energized utilizing electromagnetic coupling, in order to achieve suppression of the side-lobe levels (SLL) lower than -20 dB, each patch is adjusted along the x-axis and the SSPP feed line. The simulation and measurement data both show that the antenna operating band is 9.1 to 9.65 GHz. In the marine radar antenna operating band (9.3-9.5 GHz), axial ratio is less than 3 dB and the gain is greater than 14.3 dBic. The proposed antenna has a wide range of applications in marine radars.

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Event-Triggered single-network Control for Nonlinear Continuous-Time Zero-Sum Games with Partially Unknown Dynamics

Event-Triggered single-network Control for Nonlinear Continu...

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Cyber-Energy Systems and intelligent Energy (ICCSIE), International Conference on

作者： Binbin Peng Xiaohong Cui Zimeng Wan Haijiao Yu The College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou China

This paper proposes an event-triggered integral reinforcement learning algorithm to solve the Hamilton-JacobiIsaacs (HJI) equation related to the zero-sum game problem. Then, we design a triggering rule to constrain the control updates. Secondly, the HJI partially differential equation is approximately solved using a single-critic neural network structure. The control law and disturbance law are modified when an event occurs. The system state and the network weights error are uniformly ultimately bounded (UUB) by Lyapunov theory. Finally, an engineering example is utilized to confirm the performance of the algorithm.

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Blockchain-Based Homomorphic Transaction Framework for Enhanced Consumer Security and Business Scalability

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IEEE Transactions on Consumer Electronics 2024年

作者： Guo, Tao Shang, Fengjun Dai, Xiangguang Liu, Qilie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Three Gorges University Key Lab. of Intelligent Info. Proc. and Contr. of Chongqing Munic. Institutions of Higher Education Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology School of Three Gorges Artificial Intelligence Chongqing400044 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

Energy trading in distributed microgrids represents an effective means of enhancing the utilization of renewable energy. However, the aggregation of large-scale consumption data may encounter business scalability issues and potential privacy exposure concerns, necessitating the development of novel secure and scalable frameworks for integrating consumption demands between prosumer and microgrid entities. we research focuses on providing a user-friendly transaction model for consumers and microgrid operators. Firstly, a privacy aggregation scheme for user resource requests is designed to ensure the security and decentralization of user data. In addition, the model employs a bidirectional heterogeneous resource allocation mechanism to support the optimal allocation of multiple resources, such as renewable energy, power assets, energy storage devices, and carbon credits, among different microgrids. Finally, a microgrid transaction prototype has been constructed on the Truffle development framework. In this context, smart contracts has been designed to ensure that the transactions are carried out automatically. Experimental results indicate that the scheme provides enhanced security for multi-resource trading in a cluster context relative to existing methodologies. The number of transactions increases linearly with the resource overhead, resulting in more efficient utilization of energy trading. Furthermore, the framework's scalability has been demonstrated through both temporal and spatial considerations. © 1975-2011 IEEE.

关键词： Decentralized finance

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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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SWCB: An Efficient Switch-Clustering of Bandit Model

SWCB: An Efficient Switch-Clustering of Bandit Model

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Chinese Control and Decision Conference, CCDC

作者： Xiangyu Xu Qifeng Zhou Qing Wang Langcai Cao Department of Automation Xiamen University Xiamen China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen China Department of Cognitive Service Foundations IBM T.J. Watson Research Center NY USA

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Bandit model is a general framework for solving the cold-start problem in recommender systems. In recent works, clustering strategies have been adopted in the bandit settings to further mitigate the lack of information for new users and items. Although these approaches prove to be effective, their performance may suffer from the clustering efficiency. On one hand, quickly identifying the underlying cluster to share the information has a significantly impact on retaining new users with low levels of interaction in the early stages of recommendation. On the other hand, problems like overfitting might result from repeatedly using the same clustering algorithm on the same set of users, which may decrease the recommended performance over an extended period in the later stages. To address these issues, we propose a Switch-Clustering of Bandit (SWCB), which utilizes the Bandit framework to enable online recommendation as well as new user clustering in the cold-start phase. In particular, the proposed model improves the efficiency of recommendations by switching the clustering algorithms automatically. SWCB can quickly identify the underlying clusters by splitting and merging operation. When clustering metric updated by Bandit reaches a certain threshold, SWCB alters the way of user clustering, thus speeding the information retrieval in the cold start stage and helping to retain new users of online recommendations. We evaluate the efficiency and effectiveness of the proposed model on one synthetic dataset and two real-world datasets. Compared with the baseline bandit models, SWCB gets higher rewards within the rounds of the cold-start phase.

关键词： Measurement Merging Clustering algorithms Process control Switches Information retrieval Control systems

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Optimal Partial Transport based Sentence Selection for Long-form Document Matching 29

Optimal Partial Transport based Sentence Selection for Long-...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Yu, Weijie Pang, Liang Xu, Jun Su, Bing Dong, Zhenhua Wen, Ji-Rong School of Information Renmin University of China China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China Institute of Computing Technology Chinese Academy of Sciences China Noah’s Ark Lab Huawei China

One typical approach to long-form document matching is first conducting alignment between cross-document sentence pairs and then aggregating all of the sentence-level matching signals. However, this approach could be problematic because the alignment between documents is partial — despite two documents as a whole are well-matched, most of the sentences could still be dissimilar. Those dissimilar sentences lead to spurious sentence-level matching signals which may overwhelm the real ones, increasing the difficulties of learning the matching function. Therefore, accurately selecting the key sentences for document matching is becoming a urgent problem. To address this issue, we propose a novel matching approach that equips existing document matching models with an Optimal Partial Transport (OPT) based component, namely OPT-Match, which selects the sentences that play a major role in matching. Enjoying the partial transport properties of OPT, the selected key sentences can not only effectively enhance the matching accuracy, but also be explained as the rationales for the matching results. Extensive experiments on four publicly available datasets demonstrated that existing methods equipped with OPT-Match consistently outperformed the corresponding underlying methods. Evaluations also showed that the sentences selected by OPT-Match were consistent with human-provided rationales. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

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Modelling and Optimising Process of Micro-Plasma Arc Freeform Fabrication

SSRN

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SSRN 2023年

作者： Hong, Bo Qu, Yuanyuan Hong, Yuxiang Li, Menglong Zou, Yeming School of Mechanical Engineering Xiangtan University Xiangtan411105 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

The main factors limiting the development of wire-based additive manufacturing (w-AM) are deposition process instability and deposition deviation caused by side feed. In this paper, the device tuning accuracy is introduced into the wire-arc deposition model for the first time, a wire-arc stable deposition model in micro-plasma arc directional energy deposition (mPA-DED) was established, and the process of the wire from contacting the micro-plasma arc (mPA) column to conducting stable metal transfer was carefully analyzed. A sensitivity coefficient describing the link between wire size and device adjustment precision in mPA-DED is proposed. The link between the sensitivity coefficient and the maximum meltable wire feeding speed and the threshold value of the maximum stable deposition speed was quantitatively analyzed, and the model was verified by cross-comparison experiments among the design wire size, arc size, and wire-arc position deviation. A well-formed 40-layer curved metal sample with a material area utilization ratio of 90.54 % was steadily deposited by using the proposed model and the optimized deposition parameters. This study offers theoretical guidance for designing an online control system for the deposition process to improve the stability of the fabrication process and deposition accuracy in micro-plasma arc freeform fabrication. © 2023, The Authors. All rights reserved.

关键词： 3D printers

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RELI11D: A Comprehensive Multimodal Human Motion dataset and Method

arXiv

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arXiv 2024年

作者： Yan, Ming Zhang, Yan Cai, Shuqiang Fan, Shuqi Lin, Xincheng Dai, Yudi Shen, Siqi Wen, Chenglu Xu, Lan Ma, Yuexin Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University China

Comprehensive capturing of human motions requires both accurate captures of complex poses and precise localization of the human within scenes. Most of the HPE datasets and methods primarily rely on RGB, LiDAR, or IMU data. However, solely using these modalities or a combination of them may not be adequate for HPE, particularly for complex and fast movements. For holistic human motion understanding, we present RELI11D, a high-quality multimodal human motion dataset involves LiDAR, IMU system, RGB camera, and Event camera. It records the motions of 10 actors performing 5 sports in 7 scenes, including 3.32 hours of synchronized LiDAR point clouds, IMU measurement data, RGB videos and Event steams. Through extensive experiments, we demonstrate that the RELI11D presents considerable challenges and opportunities as it contains many rapid and complex motions that require precise location. To address the challenge of integrating different modalities, we propose LEIR, a multimodal baseline that effectively utilizes LiDAR Point Cloud, Event stream, and RGB through our cross-attention fusion strategy. We show that LEIR exhibits promising results for rapid motions and daily motions and that utilizing the characteristics of multiple modalities can indeed improve HPE performance. Both the dataset and source code release publicly in http://***/reli11d/, fostering collaboration and enabling further exploration in this field. © 2024, CC BY.

关键词： Cameras

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