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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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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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A Faster Parameterized Algorithm for Bipartite 1-Sided Vertex Explosion 16th

A Faster Parameterized Algorithm for Bipartite 1-Sided Vert...

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Liu, Yunlong Xiao, Guang Liu, Ao Wu, Di Huang, Jingui College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Xiangtan University Xiangtan411101 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9783031496134

Given a bipartite graph G = (T∪ B, E), the problem bipartite 1-sided vertex explosion is to decide whether there exists a planar 2-layer embedding of G after exploding at most k vertices of B. For this problem, which is known to be NP-complete, parameterized algorithms have received increasing attention more recently. In this paper, we focus on the problem parameterized by the number k of allowed exploded vertices of B and develop a faster algorithm for it. More specifically, we show that this parameterized problem admits a kernel of at most 10.5k vertices, and present a fixed-parameter tractable algorithm running in time O(2. 31 k· m), where m is the number of edges of G. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Embeddings

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Adaptive Occlusion Face Recognition Feature Fusion Network Based on Precise Guidance Mask Extraction

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Journal of Shanghai Jiaotong University (Science) 2025年 1-12页

作者： Ye, Jihua Zhang, Lei Wang, Chao Tu, Qixuan Zheng, Rehua Huang, Huiyuan Jiang, Aiwen School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal University Nanchang330022 China State International Science & Technology Cooperation Base of Networked Supporting Software Jiangxi Normal University Nanchang330022 China

The uncertainty in the position and size of occluding objects greatly affects the extraction of identity features in facial recognition, which is a challenge that existing methods fail to effectively address. To tackle this issue, this paper introduces a novel feature fusion network for occluded facial recognition that adaptively handles arbitrary occlusions and accurately extracts occlusion information masks. The network effectively mitigates the impact of occlusions on facial identity features through a feature fusion module. The proposed method employs an occlusion classification module and an occlusion classification loss to precisely guide the extraction of accurate occlusion masks. Subsequently, an occlusion-aware hybrid attention module is introduced, which focuses on the relationship between facial features and occlusion masks across channel and spatial dimensions. This fusion strategy enables the extraction of facial identity features that are resilient to occlusions. Experimental results indicate that the proposed method holds a significant advantage in handling facial recognition tasks with arbitrary occlusions. On the LFW and MF1 synthetic occlusion datasets, the average accuracy rates reached 93.19% and 72.57%, respectively, showing improvements of 1.7 and 2.39 percent points over mainstream methods. © Shanghai Jiao Tong University 2025.

关键词： A feature fusion occluded facial recognition occlusion mask occlusion perception TP391.41

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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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High-net-gain erbium-doped hybrid waveguide amplifiers on chalcogenide photonic platform 7

High-net-gain erbium-doped hybrid waveguide amplifiers on ch...

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7th IEEE International Symposium on Electromagnetic Compatibility, ISEMC 2023

作者： Wang, Chunxu Song, Jingcui Ao, Zhaohuan Li, Yan Li, Xueyang Li, Zhaohui School of Electronics and Information Technology and Guangdong Provincial Sun Yat-sen University Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China Peng Cheng Laboratory Shenzhen China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China

ISBN: (纸本)9798350333107

Chalcogenide glasses (ChGs) have been widely used in integrated photonics due to their excellent properties of low loss and high nonlinearity. However, the performance of the active waveguide amplifiers is merely adequate. Here, a hybrid waveguide amplifier in Ge25Sb10S65 planar waveguide by a highly erbium-doped Al2O3 layer is demonstrated. High-quality Er3+: Al2O3 thin film with high concentration of 9×1020 cm-3 is fabricated by atomic layer deposition technique. And the GeSbS waveguide is manufactured by thermal evaporation and plasma etching. Signal enhancement up to 11.93 ± 0.3 dB and internal net gain of approximately 1.28 dB/cm are obtained at 1533 nm in the 1.5-cm-long and 0.8-cm-long waveguides upon 1480 nm pumping, respectively. The hybrid configuration is proved to be an efficient strategy to implement ChG-based integrated waveguide amplifiers. © 2023 IEEE.

关键词： Germanium compounds

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A Radar Working Mode Recognition Algorithm with Approximate Coherent Metric and Multi-Agent PSR Model 12th

A Radar Working Mode Recognition Algorithm with Approximate ...

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

作者： Zhou, Zou Fei, Wenhao Zheng, Fei Luo, Jian Provincial Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811969003

Multifunctional radar(MFR) has non-cooperative, narrow beam, and flexible agile characteristics. The traditional radar feature parameters and syntactic modeling methods are difficult to make the information of radar working mode recognition complete and noise separation. The classical prediction state representation(PSR) model constructs a dynamic transition probability matrix by extracting temporal features. To achieve the mode estimation of radar transfer behavior, the probability distribution under each mode is calculated by the filter algorithm. However, the method is difficult to implement in actual confrontation scenarios because of the need to train numerous pulses. In this study, distributed sensors such as UAV clusters can effectively improve the incomplete information acquisition by the wide-area coverage sampling. The approximation coherent metric at the feature level is introduced, and propose a collective representation method of multi-agent perceptual data. This method achieves noise and dimensionality reduction while ensuring information integrity. For each mode of working, the probability distribution is obtained by training the PSR model using the collective representation information, and grid filter to track its dynamics. Determination of the mode is done by the maximum a posteriori estimator. The simulation results show that the proposed method has a good recognition effect. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Probability distributions

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Patent Recommendation Methods Based on Transformer Encoders and Regularization Strategies

Patent Recommendation Methods Based on Transformer Encoders ...

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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 service has become a major demand of all walks of life in the society. Patent data collection and analysis can not only help enterprises grasp the development status of the industry, but also provide decision support for them in the research and development process of science and technology services. In view of the problem of inaccurate patent recommendation caused by the inconsistency between the search text and the patent text length and the low accuracy of traditional patent recommendation, a word and sentence dual processing model based on Transformer encoder and regularization strategy is proposed to complete the patent recommendation task from the level of sentence granularity and word granularity. The sentence granularity feature extraction module uses the transformer encoder superimposed multi-head attention method to obtain the feature information between the sentence vector groups, so that the sentence vector fuses the feature information of each sentence, and solves the problem of low recommendation accuracy caused by the inconsistent length of the patent text and the search text;The word granularity feature extraction module uses the twin network model to extract the feature information of the sentence granularity vector using the Transformer encoder using the double splicing operation, improve the model performance and model accuracy;Introduce a strategy based on Dropout and symmetric JS divergence regularization, improve the model generalization ability and accuracy. Experiments have proved that it outperforms SBERT, BERT, MGMSN and SVM models in terms of performance. © 2024 IEEE.

关键词： Patents and inventions

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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