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IET International Radar Conference (IRC 2023)

作者： Shujie Song Xiaolan Qiu Songtao Shangguan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing People's Republic of China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou People's Republic of China

ISBN: (数字)9781837240982

Fully-polarised (FP) array interferometric Synthetic Aperture Radar (FP-Array-InSAR) is an important technology in three-dimensional (3D) reconstruction and image interpretation of various scattering mechanisms (SMs) by exploiting the structural and polarisation properties of the targets. There has been a solid foundation for multi-baseline PolInSAR, but lack of systematic comparison and analysis for FP-Array-InSAR. Relying on the UAV-borne FP-Array-InSAR system developed by our research team, this paper applies two 3D imaging methods, one based on polarisation decomposition and the other based on multi-baseline (MB) polarimetric coherence optimization, to four-channel FP-array-InSAR datasets from Suzhou, China. Finally, this paper compares and analyses the experimental results of these two methods, and then gives their applicable scenarios. Such comparative experiments are positive for the rational selection and application of different methods in FP-array-InSAR.

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Improvement of GPSR routing protocol for TDMA-based UAV ad-hoc networks 4

Improvement of GPSR routing protocol for TDMA-based UAV ad-h...

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4th World Conference on Computing and Communication Technologies, WCCCT 2021

作者： Gao, Yanzhong Fu, Jielin Lu, Yi Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9780738144986

Compared with other routing protocols, the GPSR routing protocol has better performance in the UAV ad-hoc networks. However, in the TDMA-based UAV ad-hoc networks, whether the next-hop node can forward data packets in time depends on the 'time slot distance' between the next-hop node and the forwarding node, which may cause delay jitter. In order to better apply the GPSR routing protocol to the TDMA-based network and improve the latency performance, this paper proposes an improved GPSR routing protocol (TD-GPSR) based on the cross-layer thinking. In the TD-GPSR algorithm, when routing, in addition to considering the spatial distance between the next-hop node and the destination node, the 'time slot distance' between the next-hop node and the forwarding node is also considered. By weighting these two influencing factors separately and further calculating, a quantity called 'space-time distance' is obtained. The neighbor node with the smallest 'space-time distance' will be selected as the next-hop node. The simulation results show that the average end-to-end delay of TD-GPSR is effectively reduced. In addition, the packet loss rate of TD-GPSR is also significantly lower than that of GPSR. © 2021 IEEE.

关键词： Routing protocols

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RMSLRS: Real-Time Multi-terminal Sign Language Recognition System 13th

RMSLRS: Real-Time Multi-terminal Sign Language Recognition S...

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13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on information and Communication Technologies, WICT 2022

作者： Zhao, Yilin Zhang, Biao Ma, Kun Business School of Jinan University University of Jinan Jinan250022 China School of Computer and Information Hefei University of Technology Hefei230009 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9783031274985

Sign language is commonly used by deaf or speech-impaired people to convey meaning. Sign language recognition (SLR) aims to help users learn and use sign language by recognizing the signs from given videos. Although sign language has gained social acceptance, few sign language recognition systems have been developed with educational purposes. Current SLR system has a single form of expression and lacks real-time interaction and feedback with an expensive cost. And videos captured in real scenes are complex, which has impact on the performance of models. To this end, this paper has proposed a novel real-time multi-terminal sign language recognition system (RMSLRS). Specifically, a lightweight sign language recognition model based on MediaPipe Holistic is proposed to perform sign language inference by sensing multi-dimensional information such as pose, facial expression, and hand tracking, achieving near real-time performance on mobile and desktop devices. Then, a novel pre-processing module is proposed to reduce the adverse effects of background noise in videos through the YOLO model and OpenCv. Furthermore, a novel technical architecture for front-end and back-end separation and multi-terminal deployment is designed, including WeChat applet, desktop application and website. Finally, this system has been deployed with gratifying success in practice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Exploring Receptance Weighted key Value Model for Single-Channel Speech Enhancement

Exploring Receptance Weighted Key Value Model for Single-Cha...

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IEEE International Conference on information Communication and Signal processing (ICICSP)

作者： Yuanle Li Yi Zhou Hongqing Liu School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Signal and Information Processing CQUPT Chongqing China Intelligent Speech and Audio Research Lab (ISARL) CQUPT Chongqing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Speech enhancement has significantly benefited from advancements in deep learning, particularly in terms of intelligibility and perceptual quality. Traditional time-frequency (TF) domain methods rely on convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to predict TF masks or speech spectra. In this study, we introduce a novel network architecture, the Receptance Weighted key Value (RWKV) model integrated with the Deep Complex Convolution Recurrent Network (DC-CRN), aimed at effectively training complex targets. This model combines the strengths of RNNs and Transformers, redefining the attention mechanism for speech enhancement tasks to avoid the exponential growth in computational complexity typically associated with traditional Transformer models. Through a series of experiments on the VoiceBank+Demand dataset, the RWKV-DCCRN model has demonstrated superior performance, efficiency, and scalability in handling long-range dependencies compared to traditional DCCRN models. Our implementation showcases the potential to optimize computational efficiency and performance in real-time speech enhancement applications, making it particularly suitable for environments with varying noise conditions and for devices requiring low latency processing.

关键词： Performance evaluation Training Time-frequency analysis Computational modeling Scalability Refining Speech enhancement Transformers Real-time systems Computational efficiency

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Pancreas segmentation based on large convolution kernel and feature fusion 24

Pancreas segmentation based on large convolution kernel and ...

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Proceedings of the 2024 International Conference on Image processing, Multimedia technology and Maching Learning

作者： Jiali Cui Chenkun Xuan Han Zheng School of Information Science and Technology North China University of Technology Beijing China School of Information Science and Technology North China University of Technology Beijing China Key Laboratory of AI and Information Processing Education Department of Guangxi Zhuang Autonomous Region (Hechi University) Hechi Guangxi China

ISBN: (纸本)9798400713880

Accurate segmentation of the pancreas from CT images is important for the adjuvant treatment of pancreatic diseases. However, due to the complex shape of the pancreas, its small size, and the low contrast with surrounding tissues, achieving high-precision automated segmentation remains highly challenging. This paper proposes a pancreas segmentation method based on large convolution kernels and feature fusion. First, a Dilated Large Kernel (DLK) encoder is designed to achieve a receptive field similar to that of large convolution kernels without significantly increasing computational complexity. Second, a Feature Mix Module (FMM) is introduced to fuse shallow and deep features, enabling feature interaction between upper and lower layers. Finally, a DWLU decoder is developed to preserve local feature extraction capabilities while improving computational efficiency. In order to validate the effectiveness of the network, it was validated on the NIH dataset, and the experimental results showed that DSC, Jaccard and Recall achieved 85.6%, 74.9% and 87.1% respectively on the NIH dataset, which provided an effective solution for the pancreas segmentation task.

关键词： deep learning

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LSTM-A: A Cable Partial Discharge Detection Model with Circuit Big Data 12th

LSTM-A: A Cable Partial Discharge Detection Model with Circu...

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

作者： Yuan, Zhonghui Lu, Zhiqing Zhao, Jiyuan Lu, Lucong School of Information and Communication Guilin University of Electronic Science and Technology Guilin541004 China Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin University of Electronic Science and Technology Guilin541004 China

ISBN: (纸本)9789811969003

With the development of smart grid construction and the maturity of the Internet of Things, the application of big data in the smart grid has been further enhanced. Partial discharge is a common problem in cables, especially in aged. It brings a serious threat to power transmission and electricity safety. Using big data to solve the cable partial discharge problem can promote the construction of the smart grid, reduce the cost of human resources, and improve the safety of electricity. In this paper, a cable partial discharge detection method, named LSTM-A, using LSTM and attention mechanism is proposed. Based on LSTM-A, a line fault detection framework with circuit big data is proposed to judge the partial discharge of cables. As the main component of the model, LSTM is skilled in capturing timing features of the electrical signal, and attention mechanism can capture the long-term dependence of the electrical signal. Combining LSTM and attention mechanism, this paper firstly extracts the low-level features of the electrical signal through LSTM, then calculates the dependency relationship using attention structure, and finally output the prediction result after the full connection layer. To verify the effectiveness of the model, Matthews correlation coefficient was used to measure the results. The experiment is compared with common models such as LSTM and CNN, and the results show that LSTM-A is superior to other deep learning models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cables

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Radio-Frequency Interference Source Detection Based on Enhanced YOLOv8 for Synthetic Aperture Interferometric Radiometers

Radio-Frequency Interference Source Detection Based on Enhan...

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Cross Strait Quad-Regional Radio Science and Wireless technology Conference (CSRWTC)

作者： Shimeng Lu Mingjin Yang Huanyu Wang Fanxiang Li Dong Zhu School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Wuhan China

The presence of radio-frequency interference (RFI) contaminates data collected by synthetic aperture interferometric radiometers (SAIRs). The performances of current RFI detection methods are limited due to insufficient exploration of RFI characteristics. This paper proposes a new RFI source detection method based on a convolutional neural network (CNN) that exploits side-lobe characteristics of the array factor of SAIR systems. Based on the common characteristic observed in RFI images, namely the presence of hexagonal tailings extended from a local peak of the RFI source, this method incorporates the hexagonal tailing feature into the detection scope, thereby expanding the capability to identify RFI. By including the hexagonal tailing feature, the method enhances the comprehensiveness and accuracy of RFI detection. This inclusion leads to a more comprehensive and accurate detection range for RFI sources. Results using real SMOS satellite data demonstrate that the proposed method has improved detection performance compared with traditional approaches that are commonly used in RFI detection tasks for SAIRs.

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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EmoDamp: A Model for Speaker Emotion Inference in Dialogues with Bidirectional Emotion Damping Mechanism 6

EmoDamp: A Model for Speaker Emotion Inference in Dialogues ...

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6th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2023

作者： Jiang, Yueqi Sun, Xiao Wang, Jiamin AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400709203

Emotion analysis in conversation has been a popular research topic in the natural language processing field. While much of the existing research has focused on emotion recognition in conversation, the emotion inference task in conversation is more challenging due to its specific prerequisites. In this paper, we propose EmoDamp, a novel model for the conversational emotion inference task. By referring to psychological theories, a bidirectional emotion damping network(BED Net) is designed to model the emotion generation mechanism. Furthermore, external commonsense knowledge is also used to supplement the prediction results to make them more consistent with authentic logic. Finally, a conditional random field (CRF) is used to capture the potential constraints of the emotion transfer process in conversation and simulate emotion contagion. Experimental results on two benchmark datasets demonstrate that EmoDamp outperforms the baseline model, achieving improved results on each emotion category and showcasing its effectiveness. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Human computer interaction

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Dynamic SLAM System Using Histogram-based Outlier Score to Improve Anomaly Detection

Dynamic SLAM System Using Histogram-based Outlier Score to I...

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2021 China Automation Congress, CAC 2021

作者： Pei, Fujun Miao, Zhu Wang, Jinghui Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781665426473

It is well known that the traditional visual SLAM systems have performed well in a relatively ideal environment, but for the dynamic environment, there are some problems in the instance segmentation algorithm such as incomplete segmentation or wrong segmentation which will result in outliers. Aiming at this problem, this paper proposed a dynamic SLAM system using Histogram-based Outlier Score (HBOS) to improve the reprojection residual constraint to increase the robustness in removing outliers. In this method, HBOS method is used to process the reprojection residual, and the feature association data obtained after the segmentation mask processing is further filtered to improve the robustness. This method can combine the semantic segmentation network with traditional geometric methods to solve the data association problem caused by the wrong information generated from the dynamic objects. To verify the effectiveness of the proposed method, the experiment is carried out on the public TUM data set and the results show that the proposed method improves the accuracy of SLAM positioning in the dynamic environment. © 2021 IEEE

关键词： Statistics

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