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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Jin, Lixiao Huang, ruiyang Zhang, Xuanming Zhengzhou University School of Cyber Science and Engineering Zhengzhou China National Digital Switching System Engineering & Technological R&d Center Zhengzhou China

ISBN: (纸本)9798350314694

Website security detection is important for Internet security. Existing machine learning-based malicious UrL detection methods have a low accuracy and weak generalization ability. Thus, we proposed a new multi-feature fusion malicious website detection model BGresNet by integrating the advantages of Bidirectional Encoder representations from Transformers (BErT), Graph Convolutional Network (GCN), and residual neural network (resNet). The method integrates three features: Uniform resource Locator (UrL) creator, rule features, and website titles. First, we used BGresNet to process UrL characters and website titles separately, transforming them into vector representations. Then, these two vectors were fused with UrL rule vectors. Finally, we employed the softmax function to realize the detection of malicious websites. The experimental results showed that the proposed model exhibited significant superiority in detecting malicious websites, providing a new and effective method for malicious UrL detection in Internet security. © 2023 IEEE.

关键词： BErT features GCN resNet website security

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A BErT-GCN-Based detection Method for FBS Telecom Fraud Chinese SMS Texts 4

A BERT-GCN-Based Detection Method for FBS Telecom Fraud Chin...

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4th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Zhang, Xuanming Huang, ruiyang Jin, Lixiao Wan, Fangjie School of Cyber Science and Engineering Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China

ISBN: (纸本)9798350343113

In the past decade, Fake Base Stations (FBS) have been consistently employed by criminals to target mobile users through spam text messages. despite the introduction of several techniques to mitigate this problem, spam messages remain a persistent and challenging issue in some countries, such as China, resulting in billions of dollars in annual economic losses. Therefore, this paper proposes an algorithm named BErT-GCN that combines large-scale pre-training models with Graph Convolutional Networks (GCN) for multi-class detection of fraudulent base station telecommunications scams in Chinese text messages. First, BErT (Bidirectional Encoder representation from Transformers) is used to encode the corpus and generate word embeddings. Subsequently, the generated word embeddings are fed into GCN for training. Finally, a Softmax layer is employed to perform multi-class detection on fraudulent base station telecommunications scams in Chinese text messages. Experimental results demonstrate that the proposed model achieves favorable performance on the FBS-SMS-dataset and exhibits outstanding performance in the domain of multi-class detection. © 2023 IEEE.

关键词： Base stations

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downlink MMSE Beamforming for dynamic Metasurface Antennas 3

Downlink MMSE Beamforming for Dynamic Metasurface Antennas

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3rd International Conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Han, Zhiqiang Guo, Shuming Wang, Shifei Zhengzhou University School of Cyber Science and Engineering Zhengzhou China National Digital Switching System Engineering & Technological R&d Center Zhengzhou China

ISBN: (纸本)9798350316674

dynamic metasurface antenna (dMA) array is a new type of array structure with low complexity and low cost, which is emerging as a promising technique for next-generation wireless networks. In this paper, a downlink multi-user MISO system based on dMA arrays is investigated, and the mean square error (MSE) is chosen as a performance metric to characterize the transmission reliability. The beamforming performance of dMA arrays is investigated using the minimum mean square error (MMSE) criterion. For the multiuser scenario, the optimization problem minimizes MSE by jointly optimizing the transmit precoder and dMA weights while satisfying the transmit power constraint. Our numerical results show that the proposed MMSE scheme converges fast and performs close to full digital beamforming. © 2023 IEEE.

关键词： Beamforming component dMA manifold optimization (MO) MMSE

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Multi-Modal Named Entity recognition with Auxiliary Visual Knowledge and Word-Level Fusion

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Computers, Materials and Continua 2025年第3期83卷 5747-5760页

作者： Huansha Wang ruiyang Huang Qinrang Liu Xinghao Wang National Digital Switching System Engineering & Technological R&D Center Information Engineering University Zhengzhou 450001 China

Multi-modal Named Entity recognition (MNEr) aims to better identify meaningful textual entities by integrating information from images. Previous work has focused on extracting visual semantics at a fine-grained level, or obtaining entity related external knowledge from knowledge bases or Large Language Models (LLMs). However, these approaches ignore the poor semantic correlation between visual and textual modalities in MNEr datasets and do not explore different multi-modal fusion approaches. In this paper, we present MMAVK, a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion, which aims to leverage the Multi-modal Large Language Model (MLLM) as an implicit knowledge base. It also extracts vision-based auxiliary knowledge from the image for more accurate and effective recognition. Specifically, we propose vision-based auxiliary knowledge generation, which guides the MLLM to extract external knowledge exclusively derived from images to aid entity recognition by designing target-specific prompts, thus avoiding redundant recognition and cognitive confusion caused by the simultaneous processing of image-text pairs. Furthermore, we employ a word-level multi-modal fusion mechanism to fuse the extracted external knowledge with each word-embedding embedded from the transformer-based encoder. Extensive experimental results demonstrate that MMAVK outperforms or equals the state-of-the-art methods on the two classical MNEr datasets, even when the large models employed have significantly fewer parameters than other baselines.

关键词： Multi-modal named entity recognition large language model multi-modal fusion

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User Profile Construction Based on Multi-Channel Fusion 4

User Profile Construction Based on Multi-Channel Fusion

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4th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Miao, Chao Li, Shaomei Wan, Fangjie School of Cyber Science and Engineering Zhengzhou University Zhengzhou China R&D Center National Digital Switching System Engineering & Technological Zhengzhou China

ISBN: (纸本)9798350343113

Aiming at the shortcomings of existing user profile construction algorithms that do not fully utilize contextual structural information and multidimensional feature information., a user profile construction model based on the pre-training model NEZHA, deep Pyramidal Convolutional Neural Networks (dPCNN), Gated recurrent Unit (GrU), and attention mechanism is proposed, NCdGA. The method uses multiple channels to process the text, capturing different dimensions of information and constructing features to reduce the prediction bias that occurs when a single model captures a single feature. The experimental results show that the NCdGA model has better accuracy and effectiveness compared with other modeling methods. © 2023 IEEE.

关键词： User profile

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riskroot:Multi-dimensional root Causes Location Model Based on risk Score and Iterative Search 6

RiskRoot:Multi-Dimensional Root Causes Location Model Based ...

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6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Zhang, Kaiqin Zhang, dong Guo, Feng Wang, Xiaotong Ma, Hongchao duan, Yihai Qi, Kaiyuan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Inspur Electronic Information Industry Co. Ltd. System Software R&d Dept. Jinan China

ISBN: (纸本)9798350339994

For internet companies, multi-dimensional KPIs (Key Performance Indicators) are crucial monitoring data and it is essential for operations and maintenance personnel to rapidly and precisely identify the attribute combination causing anomalies in these KPIs. In this paper, a model named riskroot is proposed to address the problem of small fluctuations of anomalies or the interaction of two changes that make anomalies inconspicuous and thus difficult to locate the root cause, as well as the impact of too large search space on computational efficiency. First, all elements are divided and weighted, risk score is defined to identify root causes and non-obvious abnormalities are located through cumulative weights. Then, the hierarchical data is searched iteratively and each iteration is filtered using element pruning to ensure operational efficiency. Finally, we validate the effectiveness of riskroot on multiple datasets, the experimental results show that riskroot outperforms the state-of-the-art baseline. Its F1-score is on average 3.54 % higher than the next best method. In particular, the F1-score improves by 10.1 % in more challenging localization scenarios. © 2023 IEEE.

关键词： anomaly localization Multi-dimensional KPIs ripple effect root cause location

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Self-superviesed sleep staging model based on contrast learing 3

Self-superviesed sleep staging model based on contrast leari...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： ding, Huilong Zhang, dong Qi, Kaiyuan Wu, dong School of Electrical and Information Engineering Zhengzhou University Zhengzhou China System Software R&D Dept. Inspur Electronic Information Industry Co. Ltd. Jinan China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In the method of sleep staging task, the artificial sleep staging process is complicated, while the traditional machine learning only realizes feature classification and relies on manual feature extraction. This paper presents a deep learning method which can automatically extract and classify EEG features. At the same time, in view of the slow improvement of staging accuracy and high model complexity of existing studies, and the high cost of data due to the dependence on annotated data sets, this paper constructs the end-to-end self-supervised sleep staging model for these problems and continuously improves it. An automatic sleep staging model based on Inception structure and BILSTM was proposed. The convolutional neural network of Inception structure extracts the corresponding features from different time scales, combines the application of attention mechanism and residual structure to carry out the EEG signal characterization learning, and uses BILSTM to carry out the sequence information feature learning. Finally, a self-supervised learning method based on comparison is used to learn the potential feature representation of EEG data from a large number of unlabeled data. In the experiment, the sleep-EdF dataset was used, and the results showed that self-supervision improved the overall classification accuracy of the model. Meanwhile, the classification results of various categories were equalized, and the overall performance of the Sleep staging model was improved. © 2023 SPIE.

关键词： deep learning

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Fast Solving Algorithm Based on Priority ranking and rule Matching in Virtual Power Plant (VPP) 9

Fast Solving Algorithm Based on Priority Ranking and Rule Ma...

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9th Asia Conference on Power and Electrical Engineering, ACPEE 2024

作者： Hu, Jinshuang Yang, Jiayi Liu, Shuaishuai Wu, Shujun Shanghai Eneplus Intelligent Technology Co. Ltd Department of Power Market Shanghai China Software R&D Center Shanghai Eneplus Intelligent Technology Co. Ltd Shanghai China

ISBN: (纸本)9798350309638

Virtual Power Plant (VPP) calculation is a problem of searching optima in high-dimensional space. Its basic optimal solving methodology is to trim uninteresting regions of the search space where global optima are unlikely to be found by setting rules manually or automatically. A new fast solving algorithm based on priority ranking and rule matching of multidimensional responsive features of distributed Energy resources (dErs) is proposed and designed. Theoretical analysis and simulation result show that the new algorithm is with O(n2) time complexity, which means it is fast enough and can be put into practice in real-time VPP's engineering application scenarios. © 2024 IEEE.

关键词： Virtual Power Plants

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residual Signature Analysis for Wind Turbine Failure Prognosis

Residual Signature Analysis for Wind Turbine Failure Prognos...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUrOPE 2023

作者： Paul, Topon reddy, Vidhisha Ayyagari, Sai Prem Kumar Nishino, Kaneharu System AI Laboratory Corporate R&D Center Toshiba Corporation Kanagawa212-8582 Japan Pvt. Ltd KA Bangalore560034 India

ISBN: (纸本)9798350396782

In operation and maintenance of wind turbines, predictive maintenance is widely used to reduce the downtime and low operating efficiency due to failure of a wind turbine, in which the condition of a wind turbine is continuously monitored and measured by attaching a number of sensors, sign of near-future failure or degradation is detected early before the actual failure occurs, and faulty parts are replaced or maintenance is performed. In this context, automatic prognosis of failure by analyzing the time-series data of the attached sensors is very important. recently, various machine learning techniques are proposed where sign of failure is predicted based on the residual errors between the actual condition and predicted condition of a wind turbine with a prediction model built by using the time-series data. However, when the residual errors fluctuate due to external factors, this conventional approach may generate many false alarms and/or overlook potential points of failure prognosis. In this paper, we propose residual Signature Analysis (reSA) that generates anomaly scores by applying signal processing and clustering techniques on the segmented residual errors, which are generated by applying a prediction method on the time-series data. To show the effectiveness of the proposed method, we apply reSA to three wind turbines of two publicly available datasets and show the experimental results. Our experimental results suggest that our proposed method can detect sign of failure much earlier than actual time of failure with a very low rate ( © 2023 IEEE.

关键词： failure prognosis machine learning neural network operation and maintenance predictive maintenance signal processing Wind turbine

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design and Implementation of Security Protection for Service Call in New Generation dispatching Technology Support system 4

Design and Implementation of Security Protection for Service...

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4th International Conference on Information Science, Parallel and distributed systems, ISPdS 2023

作者： Yi, Qiang Wan, Shupeng Zhang, Ping Wang, Zhemin Tang, Yuhao Tao, rui Nari Technology Co. Ltd Nanjing China Nari Control System Co. Ltd Nanjing China Nari Technology Co. Ltd System R&d Center Nanjing China

ISBN: (纸本)9798350337181

To meet the needs of future power system development and safe, stable, and high-quality operation, it is proposed to build a new generation of dispatching technology support system. The new generation dispatching technology support system fully inherits the achievements of the smart grid dispatch control system, inherits the boundary security protection strategy of the original smart grid dispatch control system, and provides comprehensive security protection for all internal links of the system. This paper introduces the use of secure communication bus, security reinforcement based on authentication and encryption technology, and the use of signature verification, encryption and decryption, Anti-replay and other secure communication technologies for communication transmission between applications. © 2023 IEEE.

关键词： Smart power grids

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