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Flexibility Improvement of CHP Unit for Wind Power Accommodation

Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 731-742页

作者： Dongming Zhang Yong Hu Yaokui Gao School of Control and Computer Engineering North China Electric Power UniversityBeijingChina

Improving the flexibility of combined heat and power(CHP)units is an important way to solve the problem of wind power accommodation in northern ***,this paper analyzes the principle of an extraction-type CHP unit,calculates its safe operation range,and analyzes its contradiction between heating and ***,the safe operation ranges of the CHP unit with several flexibility modifications are further calculated,which involve two-stage bypass,low-pressure cylinder(LPC)removal,heat storage tank,and electric ***,based on the safe operation ranges,their effects on improving the capabilities of deep peak shaving and wind power accommodation are compared,and their adaptabilities to different wind scenarios are *** results show that:①all flexibility modifications can improve the deep peak shaving capability of the CHP unit,especially for the two-stage bypass and the electric boiler;②LPC removal modification can accommodate wind power to some extent,but most of wind power is still abandoned;③heat storage tank modification is unstable in different wind scenarios,which is determined by the surplus heating capability during the daytime.

关键词： Combined heat and power(CHP) flexibility bypass low-pressure cylinder(LPC)removal heat storage tank electric boiler

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Point Cloud Classification Based on Local Fusion and Dense Connection Structure 4

Point Cloud Classification Based on Local Fusion and Dense C...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Huang, Yansen Zhang, Lujian Bi, Yuanwei Liu, Yaowen Yantai University School of Computer and Control Engineering Shandong Yantai China

ISBN: (纸本)9798350394375

Due to the influence of objective factors such as scene noise and hardware equipment, compared to CAD modeling point clouds, the real point clouds collected by LiDAR often have strong non-uniformity and irregularity, which leads to the low ability of traditional point cloud classification models to extract point cloud features in real scenes and poor classification robustness. To address this issue, we have added a local feature fusion module and a dense connected feature fusion method to PointMLP, thereby encoding broader point cloud information into local features. The simple stacking of multiple MLP and Mixer blocks constitutes our network model-PointMM. The shallow features of the point cloud are used as input and enter the local feature fusion module to perform feature fusion within and between point sets. Finally, by maximizing the pooling layer and selecting points with stronger semantic features as inputs to the second stage feature extraction module. This method has been tested on the ScanObjectNN dataset and shows that it can approach SOTA with a classification accuracy of 0.8%, surpassing other mainstream models. © 2024 IEEE.

关键词： Classification (of information)

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ComMatch: A semi-supervised learning classification algorithm based on model calibration

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Journal of Intelligent and Fuzzy Systems 2025年第6期48卷 811-822页

作者： Li, Ye Zhou, Jingkang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China

Semi-supervised learning (SSL) aims to reduce reliance on labeled data. Achieving high performance often requires more complex algorithms, therefore, generic SSL algorithms are less effective when it comes to image classification tasks. In this study, we propose ComMatch, a simpler and more effective algorithm that combines negative learning, dynamic thresholding, and predictive stability discriminations into the consistency regularization approach. The introduction of negative learning is to help facilitate training by selecting negative pseudo-labels during stages when the network has low confidence. And ComMatch filters positive and negative pseudo-labels more accurately as training progresses by dynamic thresholds. Since high confidence does not always mean high accuracy due to network calibration issues, we also introduce network predictive stability, which filters out samples by comparing the standard deviation of the network output with a set threshold, thus largely reducing the influence of noise in the training process. ComMatch significantly outperforms existing algorithms over several datasets, especially when there is less labeled data available. For example, ComMatch achieves 1.82% and 3.6% error rate reduction over FlexMatch and FixMatch on CIFAR-10 with 40 labels respectively. And with 4000 labeled samples, ComMatch achieves 0.54% and 2.65% lower error rates than FixMatch and MixMatch, respectively. © 2025 – IOS Press. All rights reserved.

关键词： Semi-supervised learning

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Enhancing Resilience of LFC Systems Against False Data Injection Attacks via Active Disturbance Rejection control 43

Enhancing Resilience of LFC Systems Against False Data Injec...

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第43届中国控制会议

作者： Zhou Zheng School of Control and Computer Engineering North China Electric Power University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In response to the security threats faced by multi-area interconnected power systems, particularly various potential attacks, a security protection scheme based on an extended state observer is proposed. The proposed approach extends the state vector by introducing new variables, allowing disturbances and attacks to be treated as additional state variables within the system. Through the utilization of the extended state observer, separate estimations of interference and external attacks can be obtained, thereby enhancing the power system resilience against potential threats. Based on this, a threshold-based attack detection algorithm is proposed. In simulation experiments, the model of the multi-area interconnected power system is studied under various attacks such as step attacks, ramp attacks, and periodic attacks using active disturbance rejection control(ADRC).Based on the performance comparison between traditional PI control and ADRC, it has been demonstrated that the ADRC method exhibits significant advantages in improving system response speed and enhancing control accuracy.

关键词： False data injection attack load frequency control active disturbance rejection control attack detection

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Collaborative graph contrastive learning for recommendation

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Journal of Intelligent and Fuzzy Systems 2025年第1-2期48卷 33-46页

作者： Wei, Tao Yang, Changchun Zheng, Yanqi Zhang, Jingxue School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou University Jiangsu Changzhou China School of Microelectronics and Control Engineering Changzhou University Jiangsu Changzhou China

Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods. © 2025 – IOS Press. All rights reserved.

关键词： Self-supervised learning

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Medical Named Entity Recognition Based on Multiple Features and Dynamic Fusion 2

Medical Named Entity Recognition Based on Multiple Features ...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Ren, Yi Mao, Wei School of Computer and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350373110

To Address the challenges of unclear entity delineations and insufficient utilization of Semantic data, This study introduces a novel fusion approach leveraging multiple features for dynamic integration. To enrich the semantic representation of text, Textual model's embedding layer incorporates diverse techniques. Firstly, convolutional neural networks are used to implement font embedding, enriching the character representation of text through Chinese character fonts. Secondly, SoftLexicon is used to fuse word information from the dictionary and enhance entity boundary information. To achieve multi feature embedding, the word vector's semantic information is modeled using McBERT. In the feature extraction layer, long-distance inter-character semantic information is obtained by IDCNN, whereas contextual semantic information is obtained via BiLSTM. At the conclusion, the utilization of the dynamic fusion methodology is employed to accomplish the task of recognizing named entities, leveraging the conditional random field model. The model demonstrated an F1 score of 88.96% on the Chinese medical information evaluation dataset, surpassing the BERT BiLSTM CRF model by 3.81%, thereby validating its effectiveness. © 2024 IEEE.

关键词： Convolutional neural networks

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Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

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Big Data Mining and Analytics 2024年第1期7卷 142-155页

作者： Zhongyin Xu Xiujuan Lei Mei Ma Yi Pan School of Computer Science Shaanxi Normal UniversityXi'an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated *** Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization *** main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix *** intervals and state changes are then used to encode logD and solubility for subsequent *** the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,*** models are compared with the baseline models with respect to their abilities to generate molecules with specific *** show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

关键词： molecular optimization transformer Matched Molecular Pairs(MMPs) logD solubility

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A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

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computers, Materials & Continua 2023年第8期76卷 1385-1408页

作者： Yongcheng Cui Guohui Tian Xiaochun Cheng School of Control Science and Engineering Shandong UniversityJinanChina Computer Science Department Swansea UniversityWalesUK

Intelligent Space(IS)is widely regarded as a promising paradigm for improving quality of life through using service task *** the field matures,various state-of-the-art IS architectures have been *** of the IS architectures designed for service robots face the problems of fixedfunction modules and low scalability when performing service *** this end,we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture(SOA).Specifically,we first use the distributed deployment of functional modules to solve the problem of high computing resource ***,the Socket communication interface layer is designed to improve the calling efficiency of the function ***,the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing ***,we design and deploy an interactive system based on Browser/Server(B/S)architecture,which aims to display the status of the robot in real-time as well as to expand and call the robot *** system is integrated into the private cloud framework,which provides a feasible solution for improving the quality of ***,it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right *** results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service *** is an efficient way to transmit data and reduce the computational burden on the *** using our cloud detection module,the robot system can save approximately 25% of the averageCPUusage and reduce the average detection time by 0.1 s compared to the locally deployed system,demonstrating the reliability and practicality of our proposed architecture.

关键词： Service robot intelligent space cloud robot interactive robot system robot cloud service framework

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Leakage Localization of Gas Pipelines Based on Modal Acoustic Emission and Cross-Power Spectrum Peak Optimization

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IEEE Sensors Journal 2025年第11期25卷 19982-19992页

作者： Zhang, Wenbiao Gong, Kezheng Cui, Xiwang North China Electric Power University School of Control and Computer Engineering Beijing102206 China Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing100192 China

Leakage accidents in natural gas pipelines bring huge property losses and pose serious safety risks. Therefore, faster and more accurate leakage localization is of great significance. In this article, a new method based on modal acoustic emission (MAE) and peak optimization of cross-power spectrum is proposed to enhance the performance of leakage localization. When a pipeline leakage occurs, the sensors installed on the pipelines receive acoustic emission (AE) signals, which have various modes. Instead of extracting a single mode through complex algorithms, the proposed method directly extracts the cross-power spectrum of a specified mode by choosing the appropriate window function and window parameters to locate the leakage with velocity information from dispersion curves. Gaussian, Hamming, and Blackman window functions are compared in the extraction of the cross-power spectrum of L(0, 1) mode, among which the Blackman window function is found to be the most effective. The artificial fish swarm algorithm combined with the iteration criterion for peak optimization is used to obtain suitable window parameters. Finally, the localization error of the above method is less than 5.2%. In addition, the performance of the iteration criterion for peak optimization of cross-power spectrum combined with other intelligent swarm algorithms is investigated. The results show that the maximum errors of other intelligent methods are still less than 9%, demonstrating the great compatibility of the proposed method. © 2001-2012 IEEE.

关键词： Acoustic emissions

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An Attack on a Proposed Construction of Small-State Stream Ciphers and Proposals for New Constructions (Nov, bxac165, 2022)

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computer JOURNAL 2024年第2期67卷 806-806页

作者： Amin-Ghafari, Vahid Attari, Mahmoud Ahmadian School of Information and Control Engineering China University of Mining and Technology Jiangsu 221116 P. R. China Electrical and Computer Engineering School K. N. Toosi University of Technology Tehran *** Iran

Small-state stream ciphers (SSCs) idea is based on using key bits not only in the initialization but also continuously in the keystream generation phase. A time-memory-data tradeoff (TMDTO) distinguishing attack was successfully applied against all SSCs in 2017 by Hamann et al. They suggested using not only key bits but also initial value (IV) bits continuously in the keystream generation phase to strengthen SSCs against TMDTO attacks. Then, Hamann and Krause proposed a construction based on using only IV bits continuously in the packet mode. They suggested an instantiation of an SSC and claimed that it is resistant to TMDTO attacks. We point out that accessing IV bits imposes an overhead on cryptosystems that might be unacceptable in some applications. More importantly, we show that the proposed SSC remains vulnerable to TMDTO attacks 1. To resolve this security threat, the current paper proposes constructions based on storing key or IV bits that are the first to provide full security against TMDTO attacks. Five constructions are proposed for different applications by considering efficiency. Designers can obtain each construction’s minimum volatile state length according to the desirable keystream, key and IV lengths.

关键词： cryptography distinguishing attack IoT security lightweight encryption stream cipher time-memory-data tradeoff attack

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