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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kumar, Sanjeev Sapru, Yudhishthira Purushottama Rao, K. Centre For Development of Advanced Computing Cyber Security Technology Division Mohali India CERT-In Indian Computer Emergency Response Team Delhi India Nit Department of Computer Applications Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350353068

Malware threats have become increasingly sophisticated and more complex, making detecting and preventing them difficult. Traditional malware detection methods, such as signature-based detection, are no longer effective against new malware variants. Deep learning, especially convolution neural networks, has evolved significantly as end-to-end malware detection without much requirement of domain expert analysis for feature engineering. This research paper proposes a new image visualization-based malware detection and classification using transfer learning and machine learning classifiers. At first, the network traffic dumps (PCAPs) are transformed into grayscale images. After that, pre-trained CNN models, including VGG16, VGG19, EfficientNetB0, EfficientNetB1, and Xception, are customized to extract the textural features via transfer learning by adding a fully connected layer of different dimensions. Feature fusion is performed by stacking the extracted feature maps from different pre-trained models to form a single feature map. Finally, the fused feature maps are fed to train three machine learning models to obtain the best classifier. The hyperparameter optimization of all classifiers is performed using grid-search and randomized search algorithms. This study performs intensive experiments using 913 public image datasets, and the proposed method obtained 94.71% accuracy, which is an excellent performance even using a smaller dataset. The experiment results reveal that the proposed method is well-suited to deal with live malware traffic in real-world industrial IoT environments. © 2024 IEEE.

关键词： Malware

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Carbon Emission Flow Analysis of New Power System Considering the Uncertainty of New Energy Sources 8

Carbon Emission Flow Analysis of New Power System Considerin...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Tang, Zhuang Chunhui, Ren Danni, Shi Jianchao, Lin Xinghua, Shi State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China State Grid Zhejiang Eletric Power Co Ltd Zhejiang310007 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The core feature of the new power system is that new energy replaces traditional thermal power as the main power source of the power system, and the basic development positioning is clean, low-carbon, safe and efficient. To analyze the low-carbon contribution of photovoltaic power generation to the power system, a method for analyzing the impact of uncertainty in photovoltaic injection power on the carbon emission flow of the power system is proposed. Firstly, by modeling the output of photovoltaic units and the power of system nodes, a functional expression between light intensity and the total carbon flow rate of system nodes is established. Secondly, the randomness of photovoltaic output is transferred to the balanced unit. Based on the carbon flow rate correlation matrix between the balanced unit and the system node branch, the impact factor of photovoltaic injection power on the node and branch is calculated, and the uncertain characteristics of carbon emissions in the power system under photovoltaic injection power are obtained. Finally, the correctness of the proposed method is verified through case analysis results, and the low-carbon contribution of photovoltaic access to the power system is quantitatively analyzed. © 2024 SPIE.

关键词： Flow rate

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Research on multi-party joint electric data value evaluation method based on optimized decision-making

Research on multi-party joint electric data value evaluation...

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2024 International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2024

作者： Wang, Xiaohui An, Ningyu Chen, Bin Yan, Li State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Information and Telecommunications Company State Grid Shandong Electric Power Company Shandong China

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

With the increasing intelligence and flexibility of the power system, various operators have joined the models of the power market and grid dispatch to predict future electricity demand or power generation, providing data support for safe and stable operation of the power system and load demand prediction. However, due to the complexity and uncertainty of the power system, as well as the heterogeneity and distribution of operators, there are significant errors in load forecasting results. As for to improving the performance of federated learning models, it is an important and difficult problem to evaluate the value of multi-party electricity data. In this paper, we propose a method based on gradient ascent model unlearning to evaluate datasets in probabilistic load forecasting scenarios for various flexible resource operating entities in the power grid. By checking new model performance after removing each client, we evaluate the contribution of each client dataset to the global model. It can effectively measure the impact of each client dataset on the global model while protecting client privacy. We conducted experiments on different datasets to verify the effectiveness and feasibility of our method. Our experimental results reflect the adaptability and sensitivity of our method in four electric datasets. © 2024 SPIE.

关键词： Federated learning

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Graph computing applications In Unit Commitment Historical Data Management

Graph Computing Applications In Unit Commitment Historical D...

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Sustainable Power and Energy Conference (iSPEC)

作者： Yishen Wang Fei Zhou Hong Zhu Siyan Liu Zhuang Tang He Yang State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Smart Grid Research Institute Co. Ltd Beijing China State Grid Jiangsu Electric Power Co. Ltd Nanjing China

The development of new types of power systems poses several key challenges for super-large-scale online analysis and optimization. These challenges include data merging, computation performance, and operation optimization. The uncertainty arising from renewable energy sources and load fluctuations has resulted in highly volatile power flow patterns compared to previous systems. As a result, the operating states of the system are more vulnerable to potential operational risks during critical periods. From the perspective of unit commitment, optimizing system resources involves increasingly complex constraints and variables. Graph computing has shown exceptional performance in online power system analysis, particularly in topology-related analysis. This paper explores the application of graph computing techniques to enhance security-constrained unit commitment historical data management. It offers new insights into the utilization of graph computing to improve unit commitment.

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Comparative study of DNN Models for Skin Cancer Detection 4

Comparative study of DNN Models for Skin Cancer Detection

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Kavitha, P. Jayalakshmi, V. Department of Computer Science Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Pallavaram Chennai India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Pallavaram Chennai India

ISBN: (纸本)9781665401180

Malignant melanoma, the most dangerous form of skin cancer, has become more common in recent years. The persistent global increase in this cancer, as well as the high medical costs and death rate, have made early detection of this malignancy a priority. Melanoma survival and cure are directly proportional to its thickness;if it can be found early, the survival rate will be enhanced. Despite the fact that significant progress has been achieved in the identification of skin malignancies, there are still significant issues. In comparison to the naked eye, computer-based detection systems can enhance the diagnosis rate of melanoma by 5-30 percent. Because visual perception frequently contains flaws, the need for a second viewpoint with greater precision and consistency is underlined. On the other side, it minimizes the tasks and responsibilities that physicians are responsible for. Many studies have been conducted in the area of automated melanoma detection. The potential benefits of such investigations are enormous and unquantifiable. Furthermore, the challenges are numerous, and fresh contributions in this field are greatly valued. However, it is widely known that more reliable and consistent detection methods necessitate a higher level of accuracy. The goal of this paper is to provide a skin cancer diagnosis system that can automatically identify lesions as malignant or benign. Preprocessing, segmentation, feature extraction and selection, and classification are some of the methods used in an automated skin cancer diagnosis. © 2022 IEEE

关键词： Deep neural networks

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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The positive mass theorem for asymptotically flat manifolds with isolated conical singularities

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Science China Mathematics 2024年

作者： Xianzhe Dai Yukai Sun Changliang Wang Department of Mathematics University of California Santa Barbara Key Laboratory of Pure and Applied Mathematics School of Mathematical SciencesPeking University School of Mathematical Sciences and Institute for Advanced Study Key Laboratory of Intelligent Computing and Applications (Ministry of Education)Tongji University

We prove the positive mass theorem for asymptotically flat(AF for short) manifolds with finitely many isolated conical singularities. We do not impose the spin condition. Instead, we use the conformal blow-up techniqu...

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Prediction Method of Transformer Oil Temperature Data Based on Informer 1

Prediction Method of Transformer Oil Temperature Data Based ...

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1st International Conference on Artificial Intelligence and Human-Computer Interaction, ArtInHCI 2023

作者： Fan, Xiaoyu Gan, Jinrui Guo, Zhenyu Ma, Huan Du, Peng Huang, Daojun State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China State Grid Anhui Ultra High Voltage Company HeFei AnHui230041 China

ISBN: (纸本)9781643685083

The transformer oil temperature is an important indicator that directly reflects the operational lifespan and load capacity of a transformer. Analyzing and predicting transformer oil temperature data is of significant importance in understanding the operational status and ensuring the safety of transformers. However, transformer oil temperature data belong to long sequential time series, and traditional time prediction models often encounter issues such as slow speed and convergence difficulties. Therefore, this paper proposes a transformer oil temperature prediction method based on the informer model, which enables the model to predict long sequential time series of oil temperature data. The effectiveness and accuracy of the model's predictions are demonstrated through real transformer oil temperature data. © 2024 The Authors.

关键词： Insulating oil

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Quantum integrable model for the quantum cohomology/K-theory of flag varieties and the double β-Grothendieck polynomials

arXiv

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arXiv 2025年

作者： Guo, Jirui School of Mathematical Sciences Institute for Advanced Study Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China

A GL(n) quantum integrable system generalizing the asymmetric five vertex spin chain is shown to encode the ring relations of the equivariant quantum cohomology and equivariant quantum K-theory ring of flag varieties. We also show that the Bethe ansatz states of this system generate the double β-Grothendieck polynomials. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomials

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Pre-trained Language Model for Chinese Frame Semantic Parsing based on Electric Power Dataset 4

Pre-trained Language Model for Chinese Frame Semantic Parsin...

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4th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Wang, Xiaohui Liang, Yundan Gui, Chenwei Wang, Boya Wang, Gaozhou State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Corporation of China Beijing China University of International Relations Lab of Intelligent Social Computing Beijing China State Grid Shandong Electric Power Company Information and Telecommunications Company Shandong China

ISBN: (纸本)9798350373233

In the operational landscape of the electric power industry, a wealth of events, operations, and fault reports generates substantial textual data. The application of frame semantic parsing to the power grid system presents challenges, especially given the distinctive characteristics of Chinese text, including entity ambiguity, word segmentation issues, and a misalignment with knowledge in the power systems field. In response to these challenges, we introduce an innovative pipeline specifically designed for Chinese semantic analysis based on an electric power dataset, showcasing significant enhancements over the baseline. This research strives to drive technological advancements, fostering a more sophisticated understanding of textual data within the intricate domain of the Chinese electric power industry, with a particular focus on natural language processing. © 2023 IEEE.

关键词： Pipelines

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