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4th International Conference on Signal Processing and Communication technology, SPCT 2024

作者： Zhang, Xinrui Fu, Peng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710636

For semantic branching in a two-branch network structure, it is crucial to quickly improve the feeling field, in addition, the feature fusion interaction of two-branching needs to take into account the structural and multi-scale information fusion of features under different feeling fields. In this paper, in order to cope with this challenge, we propose to accumulate the receptive field in dense convolution using separable large kernel convolution through an adaptive semantic extraction module, and design a selectable large kernel attention module for the problem of mismatch of receptive field of feature information. Finally, this paper designs a multiscale feature extraction module that fuses local and global information, and uses large kernel dense separable convolution in the module with the ability to extract high-dimensional semantics serially and capture multiscale in parallel. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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Secure Transmission Scheme for Blocks in Blockchain-Based Unmanned Aerial Vehicle Communication Systems

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computers, Materials & Continua 2024年第11期81卷 2195-2217页

作者： Ting Chen Shuna Jiang Xin Fan Jianchuan Xia Xiujuan Zhang Chuanwen Luo Yi Hong School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer Science Qufu Normal UniversityRizhao276826China School of Information Science and Technology Beijing Forestry UniversityBeijing100083China

In blockchain-based unmanned aerial vehicle(UAV)communication systems,the length of a block affects the performance of the *** transmission performance of blocks in the form of finite character segments is also affected by the block ***,it is crucial to balance the transmission performance and blockchain performance of blockchain communication systems,especially in wireless environments involving *** paper investigates a secure transmission scheme for blocks in blockchain-based UAV communication systems to prevent the information contained in blocks from being completely eavesdropped during *** our scheme,using a friendly jamming UAV to emit jamming signals diminishes the quality of the eavesdropping channel,thus enhancing the communication security performance of the source *** the constraints of maneuverability and transmission power of the UAV,the joint design of UAV trajectories,transmission power,and block length are proposed to maximize the average minimum secrecy rate(AMSR).Since the optimization problem is non-convex and difficult to solve directly,we first decompose the optimization problem into subproblems of trajectory optimization,transmission power optimization,and block length ***,based on firstorder approximation techniques,these subproblems are reformulated as convex optimization ***,we utilize an alternating iteration algorithm based on the successive convex approximation(SCA)technique to solve these subproblems *** simulation results demonstrate that our proposed scheme can achieve secure transmission for blocks while maintaining the performance of the blockchain.

关键词： Unmanned aerial vehicles blockchain finite blocklength block transmission alternating optimization

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A Review of NILM Applications with Machine Learning Approaches

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computers, Materials & Continua 2024年第5期79卷 2971-2989页

作者： Maheesha Dhashantha Silva Qi Liu School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China

In recent years,Non-Intrusive LoadMonitoring (NILM) has become an emerging approach that provides affordableenergy management solutions using aggregated load obtained from a single smart meter in the power ***, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less ofa burden for the energy monitoring process. However, conducted research works have limitations for real-timeimplementation due to the practical issues. This paper aims to identify the contribution of ML approaches todeveloping a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed,along with the research works that have been conducted in the domain. Secondly, the contribution of machinelearning approaches in three aspects is discussed: Supervised learning, unsupervised learning, and *** highlights the limitations in the applicability of ML approaches in the field. Then, the challenges in the realtimeimplementation are concerned with six use cases: Difficulty in recognizing multiple loads at a given time,cost of running the NILM system, lack of universal framework for appliance detection, anomaly detection andnew appliance identification, and complexity of the electricity loads and real-time demand side ***, options for selecting an approach for an efficientNILMframework are suggested. Finally, suggestionsare provided for future research directions.

关键词： Non-intrusive load monitoring transfer learning machine learning feature extraction

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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

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computers, Materials & Continua 2023年第4期75卷 1-17页

作者： Xiaorui Zhang Rui Jiang Wei Sun Aiguo Song Xindong Wei Ruohan Meng Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Instrument Science and Engineering Southeast UniversityNanjing211189China School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

Robust watermarking requires finding invariant features under multiple attacks to ensure correct *** learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread *** existing methods use 3×3 small kernel convolution to extract image features and embed the ***,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the *** address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss *** uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel ***,the modification of the embedded watermarking on the cover image is extended to more *** the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight ***,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image *** experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

关键词： Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning

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Recent progresses in the development of tubular segmented-in-series solid oxide fuel cells:Experimental and numerical study

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International Journal of Minerals,Metallurgy and Materials 2024年第3期31卷 427-442页

作者： Shuo Han Tao Wei Sijia Wang Yanlong Zhu Xingtong Guo Liang He Xiongzhuang Li Qing Huang Daifen Chen School of Energy and Power Jiangsu University of Science and TechnologyZhenjiang 212003China College of Materials Science and Engineering Nanjing Forestry UniversityNanjing 210037China

Solid oxide fuel cells(SOFCs)have attracted a great deal of interest because they have the highest efficiency without using any noble metal as catalysts among all the fuel cell ***,traditional SOFCs suffer from having a higher volume,current leakage,complex connections,and difficulty in gas *** solve these problems,Rolls-Royce has fabricated a simple design by stacking cells in series on an insulating porous support,resulting in the tubular segmented-in-series solid oxide fuel cells(SIS-SOFCs),which achieved higher output *** work systematically reviews recent advances in the structures,preparation methods,perform-ances,and stability of tubular SIS-SOFCs in experimental and numerical ***,the challenges and future development of tubular SIS-SOFCs are also *** findings of this work can help guide the direction and inspire innovation of future development in this field.

关键词： solid oxide fuel cell segmented-in-series tubular experimental study numerical study

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SAF-SFT-SRAF-Based Signal Coherent Integration Method for High-Speed Target Detecting in Airborne Radar

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Progress in Electromagnetics Research C 2025年 154卷 183-190页

作者： Xu, Wenwen Wang, Yuhang Huang, Jidan Wang, Hao Cao, Jianyin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

In radar target detection, long-term coherent integration (LTCI) is widely employed to improve the signal-to-noise ratio (SNR) and enhance the detection capability for weak and small targets. Meanwhile, the airborne radar, with advantages of wide-area surveillance, high sensitivity, and strong maneuver ability, demonstrates significant superiority in detecting high-speed targets. However, during the flight of the airborne radar platform, motion errors and the relative motion of high-speed targets can cause significant range migration (RM) and Doppler frequency migration (DFM), degrading coherent integration performance. To this end, this paper proposes a coherent integration method for high-speed target in airborne radar based on the symmetric autocorrelation function, scaled Fourier transform, and sequence reversing autocorrelation function (SAF-SFT-SRAF). Detailed comparisons between SAF-SFT-SRAF and several typical methods demonstrate that the proposed method effectively balances computational complexity and detection performance. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Aircraft detection

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Residual diverse ensemble for long-tailed multi-label text classification

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science China(Information sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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Oxygen reduction reaction performance of Fe-N-C catalyst with dual nitrogen source

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Frontiers in Energy 2024年第6期18卷 841-849页

作者： Yuan Zhao Quan Wang Rongrong Hu Wenqiang Liu Xiaojuan Zhang Wei Wang Nicolas Alonso-Vante Dongdong Zhu School of Materials Engineering Jinling Institute of TechnologyNanjing 211169China School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing 210094China IC2MP UMR-CNRS 7285University of PoitiersF-86073 Poitiers CedexFrance Institute of Advanced Materials and Flexible Electronics School of Chemistry and Materials ScienceNanjing University of Information Science and TechnologyNanjing 210044China

Fe-N-C catalysts are potential substitutes to displace electrocatalysts containing noble chemical elements in the oxygen reduction reaction(ORR).However,their application is hampered by unsatisfactory activity and stability *** structures and morphologies of Fe-N-C catalysts have been found to be crucial for the number of active sites and local bonding *** this work,dicyandiamide(DCDA)and polyaniline(PANI)are shown to act as dual nitrogen sources to tune the morphology and structure of the catalyst and facilitate the ORR *** dual nitrogen sources not only increase the amount of nitrogen doping atoms in the electrocatalytic Fe-C-N material,but also maintain a high nitrogen-pyrrole/nitrogen-graphitic:(N-P)/(N-G)value,improving the distribution density of catalytic active sites in the *** a high surface area and amount of N-doping,the Fe-N-C catalyst developed can achieve an improved half-wave potential of 0.886 V(***)in alkaline medium,and a better stability and methanol resistance than commercial Pt/C catalyst.

关键词： Fe-N-C oxygen reduction reaction nitrogen-doped dual nitrogen source

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Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

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Digital Communications and Networks 2023年第1期9卷 138-145页

作者： Yueshen Xu Zhiying Wang Honghao Gao Zhiping Jiang Yuyu Yin Rui Li School of Computer Science and Technology Xidian UniversityXi'an710126China School of Computer Engineering and Science Shanghai UniversityShanghai200444China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China

A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually *** enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a *** emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of *** a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage ***,the question of how to determine the most suitable mashups from big data has become a challenging *** this paper,we propose a mashup recommendation framework from big data in mobile networks and the *** proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix *** employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the *** also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization *** then crawl through a real-world large mashup dataset and perform *** experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

关键词： Mashup recommendation Big data Machine learning Mobile networks Internet-of-Things

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Improved Discontinuous SVM Based Fault-Tolerant Control of AC/DC Converter for All-DC Offshore Wind Power System

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 184-195页

作者： Wei Qin Yingning Qiu Yanhui Feng School of Energy and Power Engineering of Nanjing University of Science and Technology Nanjing210094China

An improved discontinuous space vector modulation(IDSVM)approach is proposed through analysis of key factors to impact fault tolerance control performance for the AC/DC converter in an all-DC offshore wind power *** three main factors being studied are compensation ratios of active vectors,type of vectors used to resynthesize reference voltage vector and zero vectors distribution in time *** effects are discussed from perspective of energy input,transmission and loss in the AC/DC *** schemes using different compensation ratios for active vectors are compared,and scheme C using projections on current axis achieves better fault tolerance *** this paper,the distorted voltage vector is used to synthesize reference voltage vector and the healthy zero vector is redistributed in the twelve *** former is beneficial to reduce three-phase current spikes and the latter helps to stabilize DC-link *** of the improved modulation approach are verified on an experimental platform simulating the offshore wind turbine with single switch and multiple switch open-circuit *** robustness and high performance are thoroughly evaluated.

关键词： AC/DC converter All-DC offshore wind power system compensation ratios discontinuous SvM distorted voltage vector fault-tolerance control open-circuit faults

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