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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Zhang, Qing Gong, Miao Li, Gouqiong Ma, Xinyu Chen, Yiheng Zhao, Fei Zeng, Sanyou School of Computer Science Huanggang Normal University Huanggang438000 China Germany School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China Science and Technology on Blind Signal Processing Laboratory Chengdu610041 China

ISBN: (纸本)9789819743957

Antenna design is usually a complex optimization problem. It usually includes two stages. The first stage is to formulate it as an optimization problem. And then the second stage is to design an algorithm to solve the optimization problem. This paper aims to design unequally spaced antenna arrays for direction-finding, which requires cooperation among direction-finding users, antenna designers and optimizers. The paper combines the direction finding array with the optimization design and analyzes how to construct a reasonable optimization problem from the practical optimization design problem construction stage which has been neglected. A multi-objective problem is constructed by taking the direction-finding performance as the optimization objective. The mutual coupling effect causes the design of array antennas to require electromagnetic simulation to ensure the reliability of the results, which is a very time-consuming and expensive problem. Gaussian regression model is introduced into the multi-objective optimization algorithm to construct inexpensive surrogate optimization problems that reduce the number of accurate simulation evaluations. The experimental results show that the designed array meets all the requirements with good robustness while the number of simulations required is less than that of previous methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Antenna arrays

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An end-to-end algorithm for predicting missing load data based on waveGAIN

An end-to-end algorithm for predicting missing load data bas...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiang Zhang Gang Zhou Yuwei Feng Sen Yang Wanlin Wang Jiaqing Mo Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The accuracy of short-term load forecasting has improved with the development of deep learning on complete datasets. However, load data is often lost due to various complex factors in the power system, causing it is a challenge for incomplete load forecasting. To address this issue, this paper proposes an end-to-end load-completion and forecasting network. Specially, we designed the Wavelet-enhanced Generative Adversarial Imputation Net (waveGAIN) to improve the accuracy of data completion. Then, we propose an end-to-end training strategy that combines waveGAIN and Bidirectional Long Short-Term Memory (BiLSTM) networks to improve the accuracy of data filling and reduce the load forecasting error. The experimental results show that our proposed method improves the prediction accuracy in the case of different patterns and levels of missing data. And our method achieves good results even in case of 80% missing data.

关键词： Training Deep learning Accuracy Load forecasting Predictive models Data models Power systems

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LinWA:Linear Weights Attention for Time Series Forecasting

LinWA:Linear Weights Attention for Time Series Forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiang Li Jiwei Qin Dezhi Sun Fei Shi Daishun Cui Jiachen Xie School of Computer Science and Technology Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Transformer has achieved excellent results in the field of time series forecasting. However, some recent studies have pointed out that the existing Transformer models are ineffective in preserving the temporal order information. To solve this problem, we propose a Linear Weighted Attention mechanism (LinWA), a new, simple, and effective attention weights calculation method. Linwa calculates the linear weights and the attention weights, respectively, and combines them to reduce the proportion of the original attention weights to use the order information effectively and improve the forecasting performance. Additionally, we introduce an adaptive parameter adjustment mechanism to adjust the proportion between linear weights and attention weights dynamically. Experimental results on six publicly available datasets show that LinWA preserves temporal order well and improves performance on many Transformer models.

关键词： Adaptation models Attention mechanisms Time series analysis Neural networks Benchmark testing Transformers Forecasting

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Log anomaly detection based on parallel fusion of CNN and GRU

Log anomaly detection based on parallel fusion of CNN and GR...

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International Joint Conference on Neural Networks (IJCNN)

作者： Cheng Li Jiaqing Mo Gang Zhou Guangming Li Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the development of deep learning and big data technology, system log anomaly detection technology has received widespread attention. Although many methods have achieved good results in anomaly detection in logs, it remains an extremely difficult task mining the rich dependencies hidden within log data from both global and local perspectives. In this paper, we propose a multi-scale parallel fusion network based on GRU and CNN, which more comprehensively captures the overall dependencies of log data. Specifically, we employ BERT to semantize the preprocessed log sequences, subsequently feeding the semantic vectors into the neural networks of a dual-branch CNN and a variant GRU. This approach enables the capture of both local and global features of the log sequences. Finally, we parallelly fuse the outputs of the dual-branch CNN and variant GRU. We conduct extensive experiments and evaluate three well-known benchmark datasets, BGL, Thunderbird, and Spirit. The results show that our method is better than other methods.

关键词： Deep learning Fuses Neural networks Semantics Network architecture Big Data Feature extraction

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HiparaLog: Improving Log-based Anomaly Detection through Parameter Feature Integration

HiparaLog: Improving Log-based Anomaly Detection through Par...

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International Joint Conference on Neural Networks (IJCNN)

作者： Guangming Li Jiaqing Mo Gang Zhou Cheng Li Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Anomaly detection methods based on system logs are effective means of maintaining large-scale systems. In recent years, numerous excellent log anomaly detection methods have emerged. However, existing log anomaly detection methods identify anomalous logs either through the single template feature or by combining template features with the contextual information, ignoring the role of parameter value features in log messages. In this paper, we propose a novel log anomaly detection method named HiparaLog. HiparaLog converts each raw log message into a semantic vector, which incorporates both parameter features and template features. It then detects anomalies by employing a self-attention-enhanced GRU model, which has the ability to capture the global dependencies and contextual information within the log sequence. In this way, HiparaLog is able to detect abnormal logs from multiple dimensions. Our experimental results on two of the most challenging datasets demonstrate that HiparaLog achieves superior performance compared to current state-of-the-art methods.

关键词： Accuracy Semantics Neural networks Feature extraction Vectors Large-scale systems Anomaly detection

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Verification 14

LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Ver...

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14th International Symposium on Chinese Spoken Language processing, ISCSLP 2024

作者： Liu, Shuanghong Song, Zhida Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi China School of Intelligence Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798331516826

Due to its superior performance and fewer parameters, CAM++ has become the state-of-the-art model for speaker verification tasks. This model uses 2D convolutional blocks to extract front-end features, which are then fed into a densely connected time-delay neural network backbone to extract deep features. However, the simple stacking of 2D convolutions may lead to the generation of a significant amount of redundant features, which is detrimental to efficient feature extraction. Furthermore, although CAM++ already has a relatively small number of parameters, there is still room for further optimization. To address these issues, this paper first employs depthwise separable convolutions to replace the dilated convolutions in the backend network of CAM++, making the model more lightweight. Next, we introduce spatial and channel reconstruction convolution (SCConv) in the ResBlock module of CAM++ to reduce redundant features and optimize the feature extraction process. Finally, after SCConv, we apply squeeze and excitation attention mechanism to model the interdependencies between channels and recalibrate each channel, further enhancing the model’s representational capacity. We name the resulting model LE-CAM++. Our proposed model achieves an EER of 0.686 and a minDCF of 0.084 on the VoxCeleb1-O dataset. Compared to the baseline model CAM++, the EER is reduced by 11%, and the minDCF is reduced by 28%. Additionally, the model parameters are reduced by 8%. ©2024 IEEE.

关键词： Deep neural networks

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MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screening Attention Network for Urban Carbon Emission Prediction

MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screenin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ben Wang Xizhong Qin Jiwei Qin Xiaoyu Zhang Haodong Ma Xinjiang Key Laboratory of Signal Detection and Processing College of Computer Science and Technology Xinjiang University Ürümqi China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In order to cope with the increasingly severe global energy conservation and emission reduction problems, research on urban carbon emission prediction is of great significance. The existing methods mainly use time series analysis to predict urban carbon emission, but there is a strong spatial correlation between the carbon emission data of several cities. Therefore, this paper designs a multi-scale spatial-temporal feature screening attention network to predict target cities' future carbon emission data. Firstly, this paper combines the daily carbon emission data of the near-neighbouring cities and the daily homologous emission data of the target city to analyze the urban carbon emission data from a spatio-temporal perspective. Then, this paper designs a multi-scale spatial interactive convolution module and a multi-scale temporal convolution module to extract multi-scale spatio-temporal features effectively. In addition, the feature screening module is designed to reduce the adverse effects of redundant features. Finally, the multi-scale features are used to predict the future carbon emissions of the target city through a predictor. The experimental results show that our prediction model is superior to the existing methods in six datasets.

关键词： Correlation Convolution Urban areas Time series analysis Energy conservation Carbon dioxide Predictive models Feature extraction Cybernetics

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MUNet++: Multilevel wavelet nested UNet++ demoiréing residual network

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Displays 2024年 83卷

作者： Gao, Guxue Lai, Huicheng Jia, Zhenhong The College of Computer Science and Technology Xinjiang University Urumqi830017 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830017 China

When mobile phones and digital cameras are used to capture information on a screen or store scenes with rich textures, an unpleasant moiré phenomenon occurs, which seriously degrades the quality of the image and affects subsequent image processing tasks. Therefore, to remove the moiré patterns, we propose a multilevel wavelet nested UNet++ demoiréing residual network called MUNet++. Unlike these methods based on the RGB domain, our method is performed in the frequency domain. The multilevel discrete wavelet transform (DWT) and the inverse discrete wavelet transform (IDWT) are elegantly embedded into the UNet++ encoder-decoder structure and transformed to the multilevel frequency domain, which captures more moiré features and preserves details of the image. In addition, we design a dual branch efficient pixel fusion attention module (DBEPFAM) that combines multiscale information, channel attention and the proposed pixel attention fusion module (PAFM) to improve the representation of features. In the network layer, a simple nonlinear unit (NU) is designed and embedded in the middle of the network layer to improve the representation capability of the network. Experimental results on two public datasets show that MUNet++ can effectively remove the moiré patterns and outperform existing state-of-the-art techniques. © 2024

关键词： Pixels

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MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for Medical Image Segmentation

MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for M...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Diao, Liangjin Shen, Longfeng Chen, Jiacong Ge, Fangzhen Li, Mengwen College of Computer Science and Technology Huaibei Normal University Anhui Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui Hefei China

ISBN: (纸本)9798331517021

Recent advancements in CNNs for medical image segmentation have focused on the combination of CNN and Transformer architectures to capture both local and global features. However, challenges remain, such as effectively utilizing fused features at the encoder-decoder bottleneck and addressing blurred object edges in datasets. To tackle these issues, we introduce a multi-scale channel adaptive sensing module, enhancing the model's representational and perceptual capabilities by integrating multi-scale and channel-specific information. Additionally, we propose a cross-perception module and multi-scale segmentation head to improve global coordination and multi-scale information gathering. Experiments on the ACDC and Synapse datasets demonstrate our model's superiority. © 2024 IEEE.

关键词： Image segmentation

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