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A Dynamic Task Assignment Strategy for Emitter Reconnaissance and Positioning through Use of UAV Swarms 11th

A Dynamic Task Assignment Strategy for Emitter Reconnaissanc...

引用

11th International Conference on computer Engineering and Networks, CENet2021

作者： Wang, Ruonan Gu, Yu Zhou, Zou Wang, Zhehao Xu, Fangwen Luo, Jian Ma, Lulu Qiu, Hongbing Provincial Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811665530

With three advantages of information sharing, system survival, and cost-effective exchange, Unmanned Aerial Vehicle (UAV) swarms can perform tasks at risk, such as emitter reconnaissance and positioning. Due to high risk, it’s impossible to maintain the expected effect of inter-individual cooperation among an UAV swarm. The loss of positioning UAVs and the spatiotemporal agility of a target model will also reduce the accuracy in positioning the target. Therefore, the reasonable and effective dynamic assignment of emitter reconnaissance and confrontation tasks has also become one new challenge. This paper proposes a dynamic swarm task assignment strategy to solve the above problem. For emitter reconnaissance and positioning, we study the multi-target cooperation among an UAV swarm, and take UAV relative position structure into consideration. This strategy uses the unique role conversion of the artificial bee colony algorithm to assign tasks. We also constructed a joint optimization model based on range difference and the Cramer-Rao Lower Bound with time, space and target adaptability. When the original flight trajectory does not change, the self-organizing and cooperative swarm can optimize positioning formation clustering. Simulation results show that this strategy can quickly achieve higher positioning accuracy and lower energy consumption with UAV loss rate of 8%. After 5 dynamic task assignments, the Root Mean Square Error is less than 50 m. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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A Patent Fast Recommendation Algorithm Based on User Requirements Combined with an Inverted Table

A Patent Fast Recommendation Algorithm Based on User Require...

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2024 lEEE International Conference on Advanced information, Mechanical Engineering, Robotics and Automation, AIMERA 2024

作者： Zheng, Wan Ding, Qingyan Li, Na Pan, Yu Dong, Xuecheng 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)9798350343335

Patent recommendation has always been an essential service for the innovation and development of all walks of life. The matching of demand and patent information can enable enterprises to quickly obtain the innovation and development trend of the industry. The current patent recommendation method has the problem of slow recommendation speed and insufficient comprehensive recommendation results. According to the existing problems, a patent fast recommendation algorithm based on user needs combined with inverted table is proposed. The algorithm using dual track recommended way for accurate recommendation and extensive recommendation, in the process of recommendation built by patent data inversion table and document list auxiliary recommendation to improve recommendation speed and accuracy, when accurate recommendation when using improved requirements based on GPT-2 compression model recommended after demand text compression, when widely recommended using TF-IDF mechanism for demand text of each paragraph after keyword extraction, finally generate widely recommendation list and accurate recommendation list. This recommendation method can not only realize the fast and accurate recommendation of patents, but also provide users with more innovative ideas for extensive recommendation. The experiments show that the proposed recommendation algorithm has higher speed and accuracy than the original recommendation algorithm. © 2024 IEEE.

关键词： Patents and inventions

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Enhancing Temperature Immunity of Digital Circuit Against Aging : the Standard Cell Subset Method 15

Enhancing Temperature Immunity of Digital Circuit Against Ag...

引用

15th IEEE International Conference on ASIC, ASICON 2023

作者： Zheng, Mingyue Chen, Wangyong Lyu, Yaoyang Chen, Haifeng Chen, Jiahui Cai, Linlin Sun Yat-sen University School of Microelectronics Science and Technology Guangzhou510275 China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510275 China

ISBN: (纸本)9798350312980

Temperature variation is a crucial design concern in advanced technology as it greatly impacts the stability of performance and reliability when circuits operate over the wide temperature range. Zero Temperature Delay (ZTD) point was proposed to mitigate performance volatilities of digital circuit induced by temperature variations. However, ZTD-based design approach suffers from the effects of aging, which reduces the effectiveness of the long-term temperature immunity of digital circuit. In this paper, we propose a standard cell subset-based design strategy to improve immunity to aging and temperature variation. The standard cell subset is generated based on the aging-aware ZTD determination for each standard cell. Utilizing the standard cell subset in the processes of logic synthesis, library characterization, and timing analysis enables to gain aging and temperature immunity simultaneously, which is demonstrated on typical benchmark circuits and is proven to be an effective solution. © 2023 IEEE.

关键词： aging BTI digital circuit standard cell library zero temperature delay

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MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning 24

MICM: Rethinking Unsupervised Pretraining for Enhanced Few-s...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Zhenyu Chen, Guangyao Zou, Yixiong Huang, Zhimeng Li, Yuhua Li, Ruixuan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

ISBN: (纸本)9798400706868

Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems. Unsupervised Few-Shot Learning (U-FSL) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases. In this work, we first quantitatively assess the impacts of Masked Image Modeling (MIM) and Contrastive Learning (CL) on few-shot learning tasks. Our findings highlight the respective limitations of MIM and CL in terms of discriminative and generalization abilities, which contribute to their underperformance in U-FSL contexts. To address these trade-offs between generalization and discriminability in unsupervised pretraining, we introduce a novel paradigm named Masked Image Contrastive Modeling (MICM). MICM creatively combines the targeted object learning strength of CL with the generalized visual feature learning capability of MIM, significantly enhancing its efficacy in downstream few-shot learning inference. Extensive experimental analyses confirm the advantages of MICM, demonstrating significant improvements in both generalization and discrimination capabilities for few-shot learning. Our comprehensive quantitative evaluations further substantiate the superiority of MICM, showing that our two-stage U-FSL framework based on MICM markedly outperforms existing leading baselines. The repository of this project is available at https://***/iCGY96/MICM. © 2024 ACM.

关键词： Contrastive Learning

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Investigation on the hemispherical array structure for VLC transceiver 2

Investigation on the hemispherical array structure for VLC t...

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2022 2nd International Conference on Artificial Intelligence, Automation and Algorithms, AI2A 2022

作者： Liu, Jiangnan Peng, Guojin Su, Jiapeng Provincial Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin541004 China

To achieve high quality, strong adaptability, and stability of visible light communication system, this paper plans to study the hemispherical array structure to realize the alignment of the light path. The structured surface covers multiple LED and PD detection arrays according to the longitude and weft direction, realizing the bidirectional sending and receiving model. It is demonstrated that the effective light intensity ratio of the hemispherical array is higher than that of the planar array, and the interference between channels of the hemispherical array is less than that of the planar array under the same conditions. In addition, the influence mechanism of optical mechanism parameters (transceiver hemisphere array radius, LED tilt angle, and PD tilt angle) on beam scattering is established, and the influence of relative position changes between LED array and PD array on communication performance is clarified. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Light emitting diodes

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A Multi-station Signal Sorting Method Based on Time Difference Pair and Azimuth information 12th

A Multi-station Signal Sorting Method Based on Time Differen...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Xiang, Zhexi Zhou, Zou Gu, Yu Bai, Xiao Liang, Caisheng Provincial Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811969003

Current sorting methods based on time difference of arrival (TDOA) can not concurrently realize the high sorting accuracy and low computational complexity with pulse loss and interference. This paper proposes a multi-station signal sorting method based on time difference pair and azimuth information. Firstly, the time difference window is introduced to determine the pulse range in which the pulse may be correctly matched with the pulse in the selected master station. According to the multi-parameters information, pulses in the range are accurately matched to get the real pulse pair and the time difference pair. Finally, the received signals are sorted by the time difference pair and azimuth information within the error tolerance. The simulation results show that the proposed method can effectively sort the interleaved signals with the pulse loss, interference, and the received pulses with few numbers. Furthermore, the computational complexity of the proposed method is greatly reduced. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time difference of arrival

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Lightweight Malicious Encrypted Traffic Detection Method Based on KL Image Compression 9

Lightweight Malicious Encrypted Traffic Detection Method Bas...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wen, Yuecheng Li, Xiaotong Lv, Haiqing Wang, Menglu Zuo, Wenbo 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)9798350391367

Timely and accurate detection of and response to potentially malicious encrypted traffic is crucial in cybersecurity strategies. Convolutional neural network-based approaches that convert traffic loads into images for analysis have made significant progress in malicious encrypted traffic detection, but they suffer from the challenges of large number of participants, large network size, and high demand for computational resources, especially when edge computing devices are limited. Therefore, there is an urgent need for a lightweight, efficient and accurate malicious encrypted traffic detection model. This paper proposes a malicious encrypted traffic detection model whose core idea is to utilize KL image compression technique. The model first converts the raw traffic data into grayscale images and applies KL transform to compress the images efficiently. Subsequently, a convolutional neural network model with lightweight design is used to classify the compressed image for fast and accurate malicious encrypted traffic detection. After validating on both the public traffic dataset and the lab's self-collected dataset, the results indicate that our method requires just 0.0216 seconds to process a single input sample, achieving an average classification accuracy of 98%. These findings highlight its practicality and excellence in addressing the real-world challenges of malicious encrypted traffic. © 2024 IEEE.

关键词： Cybersecurity

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An adaptive time-convolutional network online prediction method for ocean observation data 36

An adaptive time-convolutional network online prediction met...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Li, Enjing Li, Xiang Zhao, Zhigang Wu, Lu Wang, Yinglong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Deep learning is particularly important in the field of time series data analysis, and has been applied to tasks such as marine data prediction. However, there is a’concept drift’ problem in marine observation data, which leads to performance degradation and catastrophic forgetting of traditional deep learning models used in online scenarios. For this reason, this paper proposes OL-TCN, an adaptive temporal convolutional neural network online prediction deep learning model, which is more suitable for marine online learning and inference scenarios. We make the following innovations: Inside the model, model performance is enhanced by incorporating a multi-head attention mechanism and introducing an automatic machine learning enhancement in the residual cell. Outside the model, a model repository approach is used to effectively cope with the complexity and evolutionary challenges of data streams. The experimental results verify that the OL-TCN model is effective and feasible in marine time series processing. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Spatio-temporal data

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MTRNet: An Efficient Hybrid Network Model for Surface Defect Detection of Sheet Metal 36

MTRNet: An Efficient Hybrid Network Model for Surface Defect...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Hao, Huijuan Zhu, Sijian Chen, Yu Yi, Changle Zhao, Hongge Feng, Yue Yang, Rong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Addressing the challenges posed by the insufficient computational power of low-spec devices to achieve the inference efficiency of prevailing deep learning models, alongside the variability in type and shape within industrial metal sheet datasets, while also catering to the demand for high-precision detection, this paper proposes a novel single-stage detection architecture termed MTRNet. In this paper, we introduce three key enhancements: Firstly, to reduce the number of parameters, we propose a novel Partial Depth Convolution (PDC) structure. By minimizing redundant computations, we aim to enhance the efficiency of spatial feature extraction. Secondly, we propose a Convolution Transformer (CTR) structure that replaces the self-attention module with a convolutional module. This modification addresses the computational inefficiency inherent in the self-attention mechanism of existing Visual Transformers (ViT). Finally, we introduce a novel Attention Convolution Transformer (ACTR) structure to enhance the extraction of global and local feature information. This architecture seamlessly integrates the strengths of both attention and convolution mechanisms, working synergistically to improve performance. Our proposed MTRNet network demonstrates superior detection performance compared to existing industrial standards, achieving a detection accuracy of 81.5% on the NEU-DET dataset. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep learning

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Firzen: Firing Strict Cold-Start Items with Frozen Heterogeneous and Homogeneous Graphs for Recommendation 40

Firzen: Firing Strict Cold-Start Items with Frozen Heterogen...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： He, Hulingxiao He, Xiangteng Peng, Yuxin Shan, Zifei Su, Xin Wangxuan Institute of Computer Technology National Key Laboratory for Multimedia Information Processing Peking University Beijing China Tencent WeiXin Group Shanghai China Tencent WeiXin Group Shenzhen China

ISBN: (纸本)9798350317152

Recommendation models utilizing unique identities (IDs) to represent distinct users and items have dominated the recommender systems literature for over a decade. Since multi-modal content of items (e.g., texts and images) and knowledge graphs (KGs) may reflect the interaction-related users' preferences and items' characteristics, they have been utilized as useful side information to further improve the recommendation quality. However, the success of such methods often limits to either warm-start or strict cold-start item recommendation in which some items neither appear in the training data nor have any interactions in the test stage: (1) Some fail to learn the embedding of a strict cold-start item since side information is only utilized to enhance the warm-start ID representations;(2) The others deteriorate the performance of warm-start recommendation since unrelated multi-modal content or entities in KGs may blur the final representations. In this paper, we propose a unified framework incorporating multi-modal content of items and KGs to effectively solve both strict cold-start and warm-start recommendation termed Firzen, which extracts the user-item collaborative information over frozen heterogeneous graph (collaborative knowledge graph), and exploits the item-item semantic structures and user-user behavioral association over frozen homogeneous graphs (item-item relation graph and user-user co-occurrence graph). Furthermore, we build four unified strict cold-start evaluation benchmarks based on publicly available Amazon datasets and a real-world industrial dataset from Weixin Channels via rearranging the interaction data and constructing KGs. Extensive empirical results demonstrate that our model yields significant improvements for strict cold-start recommendation and outperforms or matches the state-of-the-art performance in the warm-start scenario. The code is available at https://***/PKU-ICST-MIPL/Firzen-ICDE2024. © 2024 IEEE.

关键词： Knowledge graph

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