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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ren, Mengxi Liu, Jun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665410205

Thyroid nodules, which can manifest as solitary or multiple growths, are among the most prevalent diseases with a high incidence rate. When examining thyroid nodules, ultrasound is typically the first step. Ultrasound image segmentation of thyroid nodules can assist physicians in diagnosis. However, the following three factors limit the development of thyroid nodule segmentation: (1) most of the existing studies optimize the segmentation of single nodules, and the incidence of multiple nodules is higher, so the segmentation of multiple nodules is also very important;(2) the ultrasound image of thyroid nodules is difficult to segment because of its low resolution and fuzzy boundary;(3) Due to the small data set of thyroid nodule ultrasound, in such a small dataset, attention mechanisms may be prone to overfitting, resulting in poorer generalization performance. Therefore, the attention-based model may not be suitable for the segmentation of thyroid nodules. Hence, we propose Hybrid Convolutional U-Net (HCU-Net), a simple and strong fully convolutional neural network for the segmentation of single or multiple thyroid nodules. HCU-Net incorporates a large receptive field to ensure accurate and stable segmentation. Specifically, we design Hybrid Branch Block to enhance the network's feature extraction capability for single or multiple nodules in ultrasound images by integrating multi-scale features. Furthermore, we add Enhanced ConvNeXt Blocks to the U-Net's final layer, enabling the extraction of global contextual information for segmentation performance. The Experimental results show that our proposed method can effectively improve these issues on two publicly available datasets. In addition, compared to attention-based models, our network is simpler and more suitable for small datasets like ultrasound images. © 2024 IEEE.

关键词： Ultrasonic imaging

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Online Multi-Label Streaming Feature Selection by Label Enhancement and Fuzzy Synergistic Discrimination information

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zou, Ligeng Zhou, Tong Dai, Jianhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Online streaming feature selection is an effective approach for handling large-scale streaming data in real-world applications. However, many existing online streaming feature selection studies do not effectively leverage the correlation and hidden information in the label space. The measures used in streaming feature selection often face challenges such as computational complexity and insufficient information exploitation. To address these issues, we propose an online streaming feature selection method based on label enhancement and fuzzy synergistic discrimination information. Firstly, we define a novel label enhancement method based on label correlation, which does not require any prior knowledge of feature space. Secondly, we propose a new measure to evaluate the discriminative ability of multiple feature subsets for labels in fuzzy environment, called fuzzy synergistic discrimination information. It is bounded and has many interesting properties. The fuzzy synergistic discrimination information is computed directly based on fuzzy similarity relations, so the calculation is relatively simple. Thirdly, based on label enhancement and proposed fuzzy synergistic discrimination information, we construct a three-stage online multi-label streaming feature selection method, including online significance analysis, online relevance analysis and online redundancy analysis. Finally, comparative experiments on 12 datasets demonstrate the effectiveness and superiority of the proposed method. The influences of different feature sequences and these components on the algorithm's performance are further analyzed. © 1993-2012 IEEE.

关键词： information hiding

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Online Multi-label Streaming Feature Selection with Missing Features by Dual-Space Consistency information Measurement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Dai, Jianhua Wang, Jie Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Multi-label feature selection is critical to address the challenges of high dimensionality and computational complexity in multi-label learning. However, in some practical applications, a more complex challenge is the dynamic generation of features, known as streaming features. Although some scholars have studied multi-label streaming feature selection methods, the problem of missing features during dynamic feature generation in multi-label data is rarely considered. Hence, we propose an online multi-label streaming feature selection method for multilabel data containing missing features by dual-space consistency information measurement. Firstly, we construct the dual-space consistency information measurement, which can effectively capture the relationships between features and labels. Specifically, we consider it from the perspective of instance correlation, and propose a fuzzy α-tolerance relation in the feature space to directly deal with missing features, and introduce an improved neighbour similarity relation in the label space to capture the instance similarity between the label combinations. Secondly, based on the dual-space consistency information, we further introduce the consistency gain index to help identify those features with important information during the streaming feature selection process. Thirdly, by combining online significance analysis, online relevance analysis, and online redundancy analysis, a new streaming feature selection framework is constructed, which can comprehensively evaluate the newly arrived features in terms of strong consistency, weak consistency and irrelevance, so as to identify the most representative features in the continuously arriving feature streams. Finally, a series of comparative experiments on multiple benchmark datasets demonstrate the effectiveness and superiority of our proposed method. © 2025 IEEE.

关键词： Data consistency

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Cascading failure analysis of uniform double-layer hyper-networks based on the Couple Map Lattice model 24

Cascading failure analysis of uniform double-layer hyper-net...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Gao, Shujie Ma, Xiujuan Ma, Fuxiang Zhou, Bin School of Computer Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9798350319934

Cascading failures are a class of dynamical behaviors that are widely present in various types of complex systems. The study of cascading failures has also been a hot topic for researchers. The results have shown that the process of cascading failures varies greatly depending on the structure of the network. Based on the double-layer hyper-network structure, considering the types of hyper-networks and the way of inter-layer connection, we construct four types of 3 uniform double-layer hyper-networks. In addition, the Couple Map Lattice (CML) cascading failure model is proposed which is suitable for describing the cascading failure behavior of a double-layer hyper-network. By this model, we analyzed the cascading failure processes happen in hyper-edges of a uniform double-layer hyper-network. Furthermore, through theoretical analysis, an approximate solution for the external perturbation threshold is obtained, which makes the double-layer hyper-network reaches the global collapse. The results of this paper provide an important reference for optimizing the robustness of realistic double-layer hyper-networks. In practical applications, the robustness of the double-layer hyper-networks can be improved by reducing the inter-layer connectivity probability p12 and the coupling strength ϵ between hyper-edges. © 2022 IEEE.

关键词： Network layers

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Stress analysis of key components of finger rehabilitation exoskeleton robot based on finite element method 3

Stress analysis of key components of finger rehabilitation e...

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2021 3rd International Conference on Robotics, intelligent control and Artificial Intelligence, ICRICA 2021

作者： Zhang, Qiuyue Lu, Guangda Chen, Ying Guo, Tinghang Han, Yu Qie, Tongtong Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China

This paper analyzes the flexion and extension of three joints of a single exoskeleton mechanical finger. According to the motion characteristics of the finger, a finger rehabilitation exoskeleton robot is designed to assist patients with hand function for rehabilitation training. The three-dimensional model was established by SolidWorks, and the initial state of the robot was set under the action of gravity. The finite element analysis method was used to analyze the stress of the key components, including calculate and analyze its stress and strain. The simulation showed that the maximum stress of key components in rehabilitation training was 2140 (Pa), which is far less than the yield strength of the material 60 (MPa), and the maximum deformation was 0.092 (mm). The simulation result revealed that our design did not lead to failure of mechanical structure, which would provide a certain reference basis for its structural optimization design. © Published under licence by IOP Publishing Ltd.

关键词： Stress analysis

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A Multiband Frequency Reconfigurable Antenna for Wireless Communication

A Multiband Frequency Reconfigurable Antenna for Wireless Co...

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第43届中国控制会议

作者： Rongkai Liang Chuandong Li Youxun Su Weiwei Liang Menggang Chen Zhen Luo College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In order to enhance the adaptability of wireless devices across diverse applications,it becomes imperative for an antenna to embody a multitude of functionalities,including the capacity to dynamically reconfigure the operating frequency,polarization mode,and radiation *** paper introduces a frequency-reconfigurable antenna that incorporates an L-U shaped slot band with a Split-Ring Resonator(SRR).Fabricated on a 1.2 mm low-cost FR4 substrate using a partially grounded design approach,which measures 40 x 35 *** manipulating the bias states of the two PIN diodes,the radiation element current and field distributions can be altered,resulting in reconfigurable bands spanning 2.45 GHz,3.3 GHz,4.5 GHz,7.4GHz,and 9.4 GHz,with an expansive Ultra-Wide Bandwidth(UWB) of 4230 *** antenna design caters to a range of wireless applications,including WLAN(Wireless Local Area Network),WiMAX(Worldwide Interoperability for Microwave Access),Remote Radio,and X-band Satellite *** meticulous simulation optimization,four reconfiguration scenarios are thoroughly examined by presenting and analyzing the simulation results.

关键词： Frequency Bandwidth Reconfigurable Multi-Band Antenna Ultra Wide Band Antenna PIN Diode

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MSCTGAN: Conformer Improvements for Speech Enhancement

MSCTGAN: Conformer Improvements for Speech Enhancement

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International Conference on Signal processing Proceedings (ICSP)

作者： Zhengyu Wu Nengheng Zheng The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Convolution-augmented transformer (Conformer) has shown impressive performance in speech enhancement. However, it employs a single fixed large kernel convolutional block for local feature modeling, which limits its attention to local features. Also, modeling the entire frequency band in the same manner hinders the model from focusing on more critical sub band information, resulting in low performance. To address these two issues, we propose a GAN-based approach called Multi-Scale Convolution-augmented Transformer GAN (MSCTGAN) for speech enhancement. MSCTGAN incorporates several stacks of MSCT modules to better capture local features. Taking into account the varying importance of speech sub-bands, these features are introduced in frequency domain relative position encoding and loss function, which facilitated the fusion of both full-band and sub-band information. The objective evaluation demonstrates the competitive performance of MSCTGAN compared to models utilizing conventional Transformer and Conformer architectures.

关键词： Training Time-frequency analysis Convolution Focusing Speech enhancement Transformers Generative adversarial networks Encoding Kernel

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CAMG: Context-Aware Moment Graph Network for Multimodal Temporal Activity Localization via Language 12th

CAMG: Context-Aware Moment Graph Network for Multimodal Tem...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Hu, Yuelin Xu, Yuanwu Zhang, Yuejie Feng, Rui Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai200433 China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai200433 China School of Information Technology Deakin University Waurn PondsVIC3216 Australia

ISBN: (纸本)9783031446924

Temporal Activity Localization via Language (TALL) is a challenging task for language based video understanding, especially when a video contains multiple moments of interest and the language query has words describing complex context dependencies between the moments. Latest studies have proposed various ways to exploit the temporal context of adjacent moments, but two apparent limitations remained. First, only limited context information was encoded based on RNNs or 2-D convolutions, which highly depended on the pre-sorting of proposals and lacked flexibility. Second, semantically correlated content in different moments was ignored, i.e., semantic context. To address these limitations, we propose a novel GCN-based framework, i.e., Context-Aware Moment Graph (CAMG) network, to jointly model the temporal context and semantic context. Also, we design a multi-step fusion scheme to aggregate object, motion and textual features. A Query-Gated Integration Module is further designed to select queried objects and filter out noisy ones. Our model achieves superior performance to state-of-the-art methods on two widely-used benchmark datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Multi-Scale and Improved Skip Connections Network for Skin Lesion Segmentation 9

Multi-Scale and Improved Skip Connections Network for Skin L...

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9th International Conference on intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Botao Chen, Jun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350376548

To solve the problem that the current skin lesion segmentation networks have a poor segmentation accuracy when lesion areas are of different shapes and sizes and low contrast at the edge areas, based on U-Net, we present a skin lesion segmentation network based on multi-scale and improved skip connections. Firstly, a multi-scale feature enhancement module is designed to replace the U-Net's 3× 3 convolution blocks, which can improve the feature extraction ability of the network about lesion areas of different shapes and sizes. Secondly, a lesion attention module is introduced into the skip connection to improve the semantic expression of low-level features and the location attention of high-level features, which can improve the fine-grained information transmission efficiency at the skip connection. Finally, a new hybrid loss function is designed to calculate the label and the edge loss of lesion areas, which improves the ability of the network to segment lesion areas and edges. Experiments are conducted on the ISIC2017 and ISIC2018 datasets, and the results prove that our method have a higher segmentation accuracy in skin lesion segmentation. © 2024 IEEE.

关键词： Problem solving

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Two-Stage Acoustic Echo Cancellation Network with Dual-Path Alignment

Two-Stage Acoustic Echo Cancellation Network with Dual-Path ...

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

作者： Zhijian Jiang Haoming Li Nengheng Zheng The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China

Deep learning has become a popular approach for improving acoustic echo cancellation (AEC) in communication systems. However, existing systems mostly rely on traditional delay estimation methods, which often result in performance degradation due to inaccurate delay estimation. Furthermore, most deep learning-based methods use single-stage networks, the limited learning capability of which hinders the performance under harsh echo conditions. To address these challenges, this paper proposes a two-stage system with dual-path alignment. The two-stage strategy performs echo suppression on the magnitude spectrum in the first stage, followed by phase correction in the second one. In addition, the first stage processes two parallel features: the magnitude spectrum and its exponential compressed version, with which dual-path alignment is conducted to improve delay estimation. Experiment results demonstrate the effectiveness of the proposed system for echo suppression in challenging scenarios involving long delay, double-talk and nonlinear distortion.

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