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2023 Cross Strait Radio Science and Wireless technology Conference, CSRSWTC 2023

作者： Hu, Peng Fei Leung, Kwok Wa School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou510006 China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Hong Kong Hong Kong Information and Communication Technology Centre CityU Shenzhen Research Institute Shenzhen518057 China

ISBN: (纸本)9798350358971

A dual-band polarization-diversity omnidirectional glass antenna is presented in this paper. A stepped glass dielectric resonator (DR) is fed by a probe and dual loops for vertical and horizontal polarizations, respectively. This antenna is designed in the 2.4 GHz and 5.8 GHz WiFi bands, with available bandwidths of 7.5% (2.31-2.49 GHz) and 6.5% (5.04-5.38 GHz). Two DR TM modes and two DR TE modes are used to obtain omnidirectional radiation patterns. © 2023 IEEE.

关键词： dielectric resonator antenna Glass antenna omnidirectional antenna

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A Quantitative Estimation Method of Migrating Insects Using Weather Radar by High Resolution Radar Joint Observation

A Quantitative Estimation Method of Migrating Insects Using ...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Jiayi Cui, Kai Hu, Cheng Wang, Rui Mao, Huafeng Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Laboratory School of Information and Electronic Beijing Institute of Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

The main migration animals in China are insects, and a large number of large-scale seasonal migration insects have a great impact on ecology. The establishment of a comprehensive early warning system for migratory insects necessitates the implementation of large-scale macro monitoring. Among the array of methodologies available, weather radar stands out as an efficacious tool for facilitating extensive macro-level monitoring. Nonetheless, several unresolved challenges persist in accurately quantifying the density of vast insect populations on a large scale. This article introduces an innovative method for insect quantification through the collaborative use of weather radar and high-resolution phased array radar for insect target observation and joint data analysis. This novel method harnesses the collective observational data derived from both weather radar and high-resolution radar systems, harmonizing the temporal and spatial dimensions of these datasets. By establishing a nuanced mapping correlation between insect density and weather radar reflectivity, this method enables the determination of insect density within specific geographical regions. In the final part of this article, joint observation experiments were conducted to examine the experimental data collected from operating weather radar and specialized phased array radar systems. A total of 244 days of data were analysed, and the proposed method was rigorously validated. © The Institution of Engineering & technology 2023.

关键词： Radar reflection

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Energy-Efficient Virtual Machine Migration Algorithm in Data Centers 3

Energy-Efficient Virtual Machine Migration Algorithm in Data...

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3rd International conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Wu, Xiaodong Zeng, Taisheng School of Mathmatics and Computer Science Quanzhou Normal University Fujian Province Quanzhou362000 China Fujian Provincial Key Laboratory of Intensive Computing Fujian Province Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Province Quanzhou362000 China

The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations. © Published under licence by IOP Publishing Ltd.

关键词： Electric power utilization

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A Microwave Sensor for Detection of Heavy Metals in Traditional Chinese Medicine 12

A Microwave Sensor for Detection of Heavy Metals in Traditio...

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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Wu, Jing Li, Hua-Feng Xu, Ming-Feng He, Jin-Peng Zhang, Zhong-Qi Chen, Min Chen, Rui-Sen Huang, Guan-Long Pang, Zi-Yu Tam, Kam-Weng Foshan University School of Electronic and Information Engineering Guangdong Foshan528225 China Foshan University Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology Foshan528225 China University of Macau Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350351019

This paper presents a microwave sensor for the detection of heavy metals in traditional Chinese medicine decoctions. The sensor is composed of interdigital capacitors (IDC), a split-ring resonators (SRR) coupled with a microstrip line on the substrate, and a test substance placed above the sensor. By altering the dielectric constant of the liquid, the sensor demonstrates a frequency shift, enabling the detection of heavy metal content. This design offers a high-sensitivity, non-invasive detection method suitable for real-time monitoring of heavy metals in traditional Chinese medicine decoctions. © 2024 IEEE.

关键词： Microwave sensors

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Method and system of power load classification and decomposition based on deep learning 4

Method and system of power load classification and decomposi...

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4th International Conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Sun, Hao Yuan, Hongtai Geng, Yudong Chen, Jing Zhang, Chuanfu Li, Wen Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350359619

This paper introduces a system for the classification and decomposition of electrical loads, addressing the shortcomings of current non-intrusive load decomposition methods that are prone to errors. We propose a method that first classifies electrical loads and then performs decomposition, along with the corresponding system workflow. Initially, we collect household electricity consumption data using a proprietary load collection device developed by the company, constructing an electrical load dataset. Subsequently, we train classification and decomposition methods using this dataset, conducting comparative experiments with various methods to select the most effective one. Based on the trained electrical load classification and decomposition models, we develop an electrical load inference program. The key implementation lies in applying the electrical load classification and decomposition inference program to edge devices. Leveraging the cloud server hosting a backend web service for the mobile app, we interact with users through the app, achieving precise decomposition and monitoring of electrical loads. Integrating edge computing and cloud computing, coupled with a mobile app serving as the interaction platform, this paper presents a high-precision, simplified, and highly scalable solution for electrical load analysis and management. In experimental comparisons, we find that using the Gated Recurrent Unit (GRU) method from deep learning and the Sequence-to-Point Neural Network (Seq2point) method outperform other methods in classification and decomposition, significantly improving the accuracy of electrical load classification. Therefore, we select the GRU method and Seq2Point method as the core techniques for the electrical load classification and decomposition system. This system not only demonstrates significant advantages in performance but also possesses high accuracy, model simplification, and scalability, providing an efficient and reliable solution for elec

关键词： Edge computing

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Compact Decoupled Antenna Based on Mode Cancellation Method and its Multibeam Application

Compact Decoupled Antenna Based on Mode Cancellation Method ...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Xu Li Yi-Ming Zhang Tian-Yan Lan Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-Sen University Guangzhou China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

In this paper, a compact decoupled antenna array for multibeam and millimeter-wave application working at 25GHz is proposed. The proposed antenna has been crafted by applying the mode cancellation theory for both common mode (CM) and differential mode (DM). By appropriately arranging the mirrors and adjusting the spacing between antennas, the proposed antenna can achieve self-decoupling without the need of additional decoupling structures. Simultaneously, the antenna array achieves full-duplex and multi-beam capabilities by utilizing directional couplers as part of the feed network. The simulation outcomes indicate that a remarkably low mutual coupling level of −40 dB has been attained. At the same time, the beam angle of the antennas array can switch −20° and 20°.

关键词： Mutual coupling Antenna theory Patch antennas Millimeter wave technology Full-duplex system Switches Directive antennas

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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Improving the Robustness of Low-Resource Neural Machine Translation with Adversarial Examples 1

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18th China Conference on Machine Translation, CCMT 2022

作者： Sun, Shuo Hou, Hongxu Wu, Nier Yang, Zongheng Wang, Yisong Wang, Pengcong Jian, Weichen Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9789811979606

ISBN: (纸本)9789811979590

Weak robustness and noise adaptability are major issues for Low-Resource Neural Machine Translation (NMT) models. That is, once some tiny perturbs are added to the input sentence, the model will produce completely different translation with high confidence. Adversarial example is currently a major tool to improve model robustness and how to generate an adversarial examples that can degrade the performance of the model and ensure semantic consistency is a challenging task. In this paper, we adopt reinforcement learning to generate adversarial example for low-resource NMT. Specifically, utilizing the actor-critic algorithm to modify the source sentence, the discriminator and translation model in the environment are used to determine whether the generated adversarial examples maintain semantic consistency and the overall deterioration of the model. Furthermore, we also install a language model reward to measure the fluency of adversarial examples. Experimental results on low-resource translation tasks show that our method highly aggressive to the model while maintaining semantic constraints greatly. Moreover, the model performance is significantly improved after fine-tuning with adversarial examples. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural machine translation

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Development prospects of metal-based two-dimensional nanomaterials in lithium-sulfur batteries

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Chinese Chemical Letters 2023年第1期34卷 102-113页

作者： Yuxue Mo Liling Liao Dongyang Li Rongwu Pan Yanhong Deng Yanliang Tan Haiqing Zhou College of Physics and Electronic Engineering Hengyang Normal UniversityHengyang 421002China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan ProvinceDepartment of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal UniversityChangsha 410081China Hunan Joint International Laboratory of Advanced Materials and Technology for Clean Energy Changsha 410082China Hunan Shengli High-tech Energy Technology Co. Ltd.Yongzhou 425000China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang 421002China

A lithium-sulfur(Li-S)system is an important candidate for future lithium-ion system due to its low cost and high specific theoretical capacity(1675 m Ah/g,2600 Wh/kg),which is greatly hindered by the poor conductivity of sulfur,large volume change and dissolution of lithium ***-dimensional(2D)materials with monolayers or few-layers usually have peculiar structures and physical/chemical properties,which can resolve the critical issues in Li-S ***,the metal-based 2D nanomaterials,including ferrum,cobalt or other metal-based composites with various anions,can provide high conductivity,large surface area and abundant reaction sites for restraining the diffusion for lithium *** this mini-review,we will present an overview of recent developments on metal-based 2D nanomaterials with various anions as the electrode materials for Li-S *** the main bottleneck for the Li-S system is the shuttle of polysulfides,emphasis is placed on the structure and components,physical/chemical interaction and interaction mechanisms of the 2D ***,the challenges and prospects of metal-based 2D nanomaterials for Li-S batteries are discussed and proposed.

关键词： Lithium-sulfur battery Two-dimensional materials Cathode materials Anode materials Anode protection

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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