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A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range

Applied Sciences 2025年第10期15卷 5628-5628页

作者： Hao Chen Zheng Dang Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an 710048 China School of Computer Science and Engineering Xi’an University of Technology Xi’an 710048 China

The Logical Range is a mission-oriented, reconfigurable environment that integrates testing, training, and simulation by virtually connecting distributed systems. In such environments, task-processing devices often experience highly dynamic workloads due to varying task demands, leading to scheduling inefficiencies and increased latency. To address this, we propose GCSG, a hybrid load forecasting model tailored for Logical Range operations. GCSG transforms time-series device load data into image representations using Gramian Angular Field (GAF) encoding, extracts spatial features via a Convolutional Neural network (CNN) enhanced with a Squeeze-and-Excitation network (SENet), and captures temporal dependencies using a Gated Recurrent Unit (GRU). Through the integration of spatial–temporal features, GCSG enables accurate load forecasting, supporting more efficient resource scheduling. Experiments show that GCSG achieves an R 2 of 0.86, MAE of 4.5, and MSE of 34, outperforming baseline models in terms of both accuracy and generalization.

关键词： logical range time-series data load forecasting

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EEG based Parkinson Detection through Supervised Information Enhanced Contrastive Learning

EEG based Parkinson Detection through Supervised Information...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jian Song Xiang Li Wenjing Jiang Chunxiao Wang Zhigang Zhao Jialiang Lv Bin Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Geriatric Neurology Key Laboratory of Cardiovascular Proteomics of Shandong Province Qilu Hospital of Shandong University Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study presents a novel Supervised Information Enhanced Contrastive Learning Algorithm for Parkinson’s Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks.

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Chinese named entity recognition method for the field of network security based on RoBERTa

Chinese named entity recognition method for the field of net...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Zhu, Xiaoyan Zhang, Yu Zhu, Lei Hei, Xinhong Wang, Yichuan Hu, Feixiong Yao, Yanni Xi'an Jiaotong University School of Computer Science and Engineering Xi'an710049 China School of Computer Science and Technology Xi'an University of technology Shaanxi Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi'an710048 China Shenzhen Tencent Computer System Co. Ltd Shenzhen518054 China

ISBN: (纸本)9781665441582

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively. © 2021 IEEE.

关键词： network security

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PCB Defect Detection Model with Convolutional Modules Instead of Self-Attention Mechanism

PCB Defect Detection Model with Convolutional Modules Instea...

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IEEE International Conference on Systems, Man and Cybernetics

作者： MingLe Zhou ChangLe Yi HongLin Wan Min Li Gang Li DeLong Han Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Physics and Electronic Science Shandong Normal University Jinan China

The detection of defects in printed circuit boards requires high accuracy and realtime performance. Existing industrial detection models generally adopt a pure convolutional structure for ease of deployment. However, the detection accuracy of these models is often insufficient to meet the requirements of the scene. To improve the detection model accuracy and ease of deployment, this paper proposes a convolutional merging Transformer network(CMTRNet). The CMTR-Net model proposes a backbone network (CNN-Former) that uses convolutional modules to replace self-attention, combining the Transformer architecture with a convolutional structure. This approach not only avoids the drawback of self-attention high computation complexity that is detrimental to deployment but also improves the model detection accuracy. Based on CNN-Former, this paper also proposes a feature fusion module that can better fuse the features extracted by CNN-Former. Further-more, based on the CMTRNet model and the characteristics of circuit board defects, this paper proposes a loss function called Melt-IoU, which can make the initial training phase smoother and further improve detection accuracy. Experiments have shown that CMTRNet outperforms existing advanced models on both datasets.

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Pulsar candidate recognition based on autoencoder and self-normalizing neural networks

Pulsar candidate recognition based on autoencoder and self-n...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Zhihao Wang Peng Fu Meng Guo Key Laboratory of Computing Power Network and Information Security of Ministry of Education Shandong Computer Science Center (National Supercomputing Center in Jinan) (Shandong Academy of Sciences) Qilu University of Technology Jinan Shandong P. R. China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong P. R. China

The identification of pulsar candidates is a crucial step in radio astronomy research. With the continuous improvement of modern radio telescope equipment and the increasing scale of pulsar sky survey, a pulsar survey will produce a large number of pulsar candidates. Identifying pulse signals from a large number of pulsar candidates is a prerequisite for discovering new pulsar. In the face of massive pulsar candidate data, it is a huge workload to identify pulsar candidates manually only by experts in relevant fields. In order to improve the efficiency of identifying pulsar candidates, a stacked autoencoder and self-normalizing neural network based pulsar candidate recognition framework AESNN is proposed, which does not need to consider the imbalance of the number of positive and negative samples in the pulsar data set. The pulsar candidate contains many features, but not all of them are important. The stacked autoencoder is used to extract the feature representation of the data, perform unsupervised dimensionality reduction, and input these features into the self-normalizing neural network for training. AESNN introduces self-normalizing properties by introducing 'scaled exponential linear units', and experiments show that AESNN has a high F1 score, precision, and recall, and does not require the use of complex data augmentation methods, making it more advantageous compared to other algorithms.

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An Algorithm for Detecting Surface Defects in Industrial Strip Steel based on Receptive Field and Feature Information Supplementation

An Algorithm for Detecting Surface Defects in Industrial Str...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qingyu Xu Jiguo Yu Anming Dong Zihao Shang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Data Intelligence Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the context of Industry 4.0 and the rise of intelligent manufacturing, the quality of industrial products is becoming more and more important. Strip steel surface defect detection, as a key link in industrial production, is crucial to ensure the quality of industrial products. However, due to the irregularity of the defect scale and the inconspicuous defect features in the surface defect image of strip steel, it is difficult for the existing detection algorithms to realize the effective detection of defects. In order to better extract the features of defects and improve the network’s ability to detect defects, this paper proposes an algorithm for detecting surface defects on industrial strip steel based on receptive field and feature information supplementation. First, we design a receptive field (RF) module to replace the residual structure in the C3 module, which we name C3RF. This module can effectively increase the network’s receptive field, allowing the network to fully capture irregular defect features without increasing the cost. Second, for the characteristics that defective features are not obvious and tend to lose detail information as the network deepens, an extra information supplemental branching feature fusion pyramid (EFPN) is proposed on the basis of the original PAFPN architecture to compensate for the detail information that is lost by the fragile features in deeper layers. Finally, convolutional block attention module (CBAM) is introduced to replace the spatial pooling pyramid (SPPF) in the baseline network, which enhances the contrast between defects and backgrounds, and improves the classification and localization ability of the network. Our network achieves an accuracy of 82.2% on the publicly available strip steel defect detection dataset, which is a 4.0% improvement over the baseline. The results show that the network constructed in this paper can realize effective defect detection.

关键词： Radio frequency Location awareness Strips Production Feature extraction Real-time systems Hardware

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Prompt-Based Few-Shot Text Classification with Virtual Class Representation

Prompt-Based Few-Shot Text Classification with Virtual Class...

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International Conference on Computer and Communications (ICCC)

作者： Zhengyu Ju Zhao Li Ye Li Shiwei Wu Canjun Wang Shandong Academy of Sciences Qilu University of Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Fundamental Research Center for Computer Science Jinan China Evay Info Jinan China

Over the past few years, "Pre-training + Fine-tuning" has been highly successful in Natural Language Processing (NLP) tasks, but it has limitations in few-shot tasks and zero-shot tasks. Recently, "Prompt-based Tuning + Pretrained Language models (PLMs)" has achieved great success in few-shot stage and zero-shot stage, including few-shot text classification. Prompt-based tuning wraps suggestive characters and [MASK] tokens into a raw input sequence. Additionally, Prompt learning uses a verbalizer to map the semantic information of the [MASK] position to the final classification prediction. The verbalizer contains manual verbalizer and automatic verbalizer, etc. However, manual verbalizers are too dependent on human experience and external knowledge, while automatic verbalizers still face significant challenges. In this work, we introduce a novel approach for constructing a verbalizer termed as “virtual class representation”. This method entails assigning a fixed-length sequence of digits to represent each label in the dataset. We train a small-scale randomly initialized embedding mechanism to achieve preliminary semantic information for each class when virtual class representation is introduced into the embedding mechanism. The semantic information of each category is then further extracted by a convolution operation, and the final model regression value is obtained by calculating the similarity between the last hidden state of PLM and the semantic vector of the class. Our extensive experiments demonstrate the effectiveness of the proposed method on few-shot text classification tasks.

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Research on the Design of Government Data Governance Evaluation System under the Perspective of Digital Government

Research on the Design of Government Data Governance Evaluat...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Zirui Xu Jinyang Sun Zhe Lu Delong Han Information Research Institute of Shandong Academy of Sciences Qilu University of Technology(Shandong Academy of Sciences) Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the context of the digital era, government data governance is a key factor in promoting the modernization of government governance, but there is a lack of a complete set of evaluation index system to standardize the content and scope of government data governance. This paper adopts the policy tool theory to code and analyze the 12 policies from the three policy tool dimensions of supply layer, demand layer, and environment layer, and proposes three first-level evaluation indicators of basic environment, actual effectiveness, and safeguard mechanism, on the basis of which the evaluation content is selected in a focused manner based on the integration of policy documents related to data governance. Meanwhile, a comprehensive, scientific and objective evaluation system of government data governance is constructed by adopting assessment means combining subjectivity and objectivity, and quantitative and qualitative. The system is designed to assess and guide the data governance practices of the domestic government, point out the direction for government departments at all levels to carry out data governance, and play an important role in promoting the transparency and efficiency of data management.

关键词： Codes Government Cyberspace Data science Data governance Indexes

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Signature Scheme Based on The SM2 Algorithm in Fabric

Signature Scheme Based on The SM2 Algorithm in Fabric

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International Conference on networking and network Applications (NaNA)

作者： Yaling Zhang Hongyun Wang Yichuan Wang Wenjiang Ji Lei Zhu School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of technology Xi’an China

Based on the analysis and research of the widely used Hyperledger Fabric alliance chain structure, this paper proposes a digital signature scheme based on the national cryptographic algorithm SM2 which can be applied to Hyperledger Fabric. Firstly, two elliptic curve public-key cryptography algorithms are analyzed, and the feasibility of the SM2 in Fabric system is studied. Secondly, the Fabric system using the SM2 algorithm is designed and implemented, and the generation process of the Fabric chain address using the SM2 algorithm is given. Finally, experimental verification of the availability and performance of the Fabric system after the replacement of national cryptography algorithms is carried out. The experimental results show that the SM2 interface of the new Fabric alliance chain has good usability, and the average latency is reduced by 51.8%, and the transaction throughput is increased by 33.1%.

关键词： Distributed ledger Elliptic curves Public key cryptography Throughput Fabrics Blockchains Usability

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Cross Aggregation network for Semantic Segmentation

SSRN

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SSRN 2022年

作者： Zhao, Minghua Zhi, Yuxing Du, Shuangli Hei, Xinhong Hu, Jing Shi, Cheng Li, Peng School of Computer Science and Engineering Xi’an University of Technology No.5 South Jinhua Road Shaanxi Xi’an710048 China Shaanxi Key Laboratory of Network Computing and Security Technology No.5 South Jinhua Road Shaanxi Xi’an710048 China

To achieve more accurate prediction, advanced semantic segmentation methods are explored in the way of context modeling. Images in real scenes usually contain multi-scale objects and contents. The feature propagation form in convolution networks is very important to capture multi-scale context and obtain accurate segmentation. This paper proposes a novel pattern of information flow aggregation to rich features expression, called Cross Aggregation Module (CRA), In CRA, the flows of different scales are transmitted to the context aggregation module (CAM) through a parallel-cross connection to generate a feature map containing more information to aggregate multi-scale features and capture remote context information. We further add the low-level features in the later stage in the encoder to enhance the features interaction. Based on these developments, we build a Context Cross Aggregation network (CRANet) which employs an asymmetric decoder to restore the scale of the predicted feature map. The proposed CRANet is evaluated on two challenging datasets, i.e., Cityscapes and Camvid. The experiments show that the proposed network achieves competitive performance. © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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