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Information Technology: IoT and Smart City (ITIoTSC), International Conference on

作者： Decai Wang Xia Chang Weibing Zhang Yuelin Gao The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province Yinchuan China School of Math and Information Science North Minzu University Yinchuan China

ISBN: (数字)9798350328349

ISBN: (纸本)9798350328356

In the field of biometric identification, fingerprints, as one of the most unique and persistent biometric features, are widely used for identification. With the popularity of fingerprint recognition in society, how to quickly retrieval fingerprints is a challenging task in large-scale databases. In order to ensure the accuracy of fingerprints retrieval , we propose a new retrieval model based on minutia features. We introduce fuzzy C-means clustering algorithm and Dijkstra *** C-means clustering is used to extract the detail point features in the fingerprint, Dijkstra algorithm to solve the shortest path of the minutia *** number of features and the shortest path distance are compared to determine whether the fingerprints are the same. In this way, our method can achieve fast retrieval. In addition, we conducted extensive experiments on a large-scale datasets and tested the correctness of the experiments by different filtering levels to demonstrate the superiority of our method for fingerprint retrieval.

关键词： Accuracy Filtering databases Smart cities Filtration Clustering algorithms Fingerprint recognition

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A modified iterative algorithm for solving split equality problems

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2021年第3期83卷 45-54页

作者： Fu, Yuanmin Zhu, Li-Jun Wei, Haicheng School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China Health Big Data Research Institute of North Minzu University Yinchuan750021 China Advanced Intelligent Perception Control Technology Innovative Team of NingXia North Minzu University Yinchuan750021 China

In this paper, we investigate the split equality problems in Hilbert spaces. We propose an iterative algorithm with self-adaptive step-size for solving split equality prob-lems. Convergent analysis of the suggested algorithm is proved under some suitable conditions. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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A Novel Band-Notched Balanced Bandpass Filter with Differential-Mode Reflectionless Characteristics

A Novel Band-Notched Balanced Bandpass Filter with Different...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Shipeng Zhao Zhongbao Wang Mingming Gao Hongmei Liu School of Information Science and Technology Dalian Maritime University Dalian Liaoning China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao Liaoning China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

A novel band-notched balanced bandpass filter (BPF) with differential-mode (DM) reflectionless characteristics is proposed in this paper. The original balanced BPF is composed of a parallel-coupled transmission line structure based on signal interference techniques, which can introduce two transmission zeros to improve the filtering selectivity. By utilizing the absorption networks, one notch band at the center of frequency and DM reflectionless characteristics can be achieved in the meanwhile. The common-mode (CM) rejection resistors loaded between the half-wavelength microstrip transmission lines enable suppression of CM noise. The operating frequency of the balanced BPF is located at 1.0 GHz with 3-dB fractional bandwidth of 61.5%. The CM rejection is greater than 20 dB in the filtering bandwidth.

关键词： Band-pass filters Wireless communication Resistors Radio frequency Power transmission lines Filtering Interference

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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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The State of Charge Estimation of Lithium-ion Battery Pack Based on PSO-KELM

The State of Charge Estimation of Lithium-ion Battery Pack B...

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Chinese Control and Decision Conference, CCDC

作者： Mengyuan Chen Hongyan Ma Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Accurately estimating the State of Charge (SOC) is of great significance for ensuring the safe operation of lithium-ion batteries and preventing overcharging or discharging. However, as SOC is the internal state of the battery unit and cannot be directly measured, it is difficult to obtain accurate SOC values. To improve the estimation accuracy of SOC, this paper establishes a prediction model using Kernel Extreme Learning Machine (KELM), and uses Particle Swarm Optimization (PSO) to optimize the kernel function parameter S and regularization coefficient C of KELM and determine the optimal values, a SOC prediction method for lithium-ion batteries based on PSO-KELM is proposed. The input of the PSO-KELM model is battery voltage, current, and temperature, the output is the actual SOC value. The results show that the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE) of SOC prediction are reduced to 2.76%, 2.18%, and 6.89%, increase coefficient of determination (R 2 ) to 0.9919. The PSO-KELM model improves prediction accuracy compared to ELM and KELM models, and has good convergence and generalization properties.

关键词： Lithium-ion batteries Accuracy Extreme learning machines Estimation Predictive models Battery charge measurement data models

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Rgmef: A Lightweight Multi-Exposure Image Fusion Network Based on Retinex and Gradient Guidance

SSRN

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SSRN 2024年

作者： Feng, Yunfei Zhang, Mei Zhu, Jinhui School of Automation Science and Engineering South China University of Technology Guangzhou510641 China School of Software Engineering South China University of Technology Guangzhou510641 China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education Guangzhou510641 China

The goal of the multi-exposure image fusion (MEF) is to create a high dynamic range (HDR) image with balanced exposures and more details by extracting and enhancing valid information from multiple low dynamic range (LDR) images with varied exposures. In recent years, deep learning has provided many solutions for MEF, and they have achieved remarkable effects. However, because of the high network complexity, these approaches often have low efficiency and limit the potential improvement of the effect. In order to fuse valuable information from different images, we introduce a lightweight but robust MEF network, which focus on the image internal characteristics rather than long-distance information. In this study, we first design a decomposed network inspired by Retinex theory to extract the illumination map from an image and obtain the gradient map through Sobel operator. Subsequently, we introduce two lightweight feature extraction modules to extract image features with the guidance of illumination and gradient maps respectively, and then fuse these features to produce an HDR result through three fusion blocks. Furthermore, we design a loss function to obtain a more suitable brightness range for the current scene. Numerous comparative experiments demonstrate the superiority of our method in terms of image texture, pixel intensity, and color retention. Moreover, some ablation studies indicate that our approach has a significant adaptability to source images. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Joint Multimodal Entity-Relation Extraction Based on Edge-Enhanced Graph Alignment Network andWord-Pair Relation Tagging 37

Joint Multimodal Entity-Relation Extraction Based on Edge-En...

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37th AAAI Conference on Artificial Intelligence (AAAI) / 35th Conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Yuan, Li Cai, Yi Wang, Jin Li, Qing South China Univ Technol Sch Software Engn Guangzhou Peoples R China MOE China Key Lab Big Data & Intelligent Robot SCUT Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Yunnan Univ Sch Informat Sci & Engn Kunming Yunnan Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781577358800

Multimodal named entity recognition (MNER) and multi-modal relation extraction (MRE) are two fundamental subtasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks independently, which ignores the bidirectional interaction between them. This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction (JMERE) task. Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for the JMERE task. Specifically, we first design a word-pair relation tagging to exploit the bidirectional interaction between MNER and MRE and avoid error propagation. Then, we propose an edge-enhanced graph alignment network to enhance the JMERE task by aligning nodes and edges in the cross-graph. Compared with previous methods, the proposed method can leverage the edge information to auxiliary alignment between objects and entities and find the correlations between entity-entity relationships and object-object relationships. Experiments are conducted to show the effectiveness of our model(1).

关键词： Alignment

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Detecting Adversarial data by Probing Multiple Perturbations Using Expected Perturbation Score 40

Detecting Adversarial Data by Probing Multiple Perturbations...

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40th International Conference on Machine Learning

作者： Zhang, Shuhai Liu, Feng Yang, Jiahao Yang, Yifan Li, Changsheng Han, Bo Tan, Mingkui South China Univ Technol Sch Software Engn Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Univ Melbourne Melbourne Vic Australia Beijing Inst Technol Beijing Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China

Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in high-dimension spaces. Recently, the gradient of log probability density (a.k.a., score) w.r.t. the sample is used as an alternative statistic to compute. However, we find that the score is sensitive in identifying adversarial samples due to insufficient information with one sample only. In this paper, we propose a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations. Specifically, to obtain adequate information regarding one sample, we perturb it by adding various noises to capture its multi-view observations. We theoretically prove that EPS is a proper statistic to compute the discrepancy between two samples under mild conditions. In practice, we can use a pre-trained diffusion model to estimate EPS for each sample. Last, we propose an EPS-based adversarial detection (EPSAD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples. We also prove that the EPS-based MMD between natural and adversarial samples is larger than that among natural samples. Extensive experiments show the superior adversarial detection performance of our EPS-AD.

关键词： Machine learning

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DDoS Detection and Defense Based on FLAD and SDN

DDoS Detection and Defense Based on FLAD and SDN

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jie Dong Wenyu Fang Wanling Zheng Jinkun Liu Yanhua Liu College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm.

关键词： Training data privacy Network topology Denial-of-service attack Feature extraction Real-time systems Topology Labeling Computer crime Long short term memory

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