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GMA: Gap Imputing Algorithm for time series missing values

Journal of Electrical Systems and Information Technology 2023年第1期10卷 1-20页

作者： Khattab, Abd Alhamid Rabia Elshennawy, Nada Mohamed Fahmy, Mahmoud Computer and Automatic Control Departmaent Faculty of Engineering Tanta University Tanta Egypt

Data collected from the environment in computer engineering may include missing values due to various factors, such as lost readings from sensors caused by communication errors or power outages. Missing data can result in inaccurate analysis or even false alarms. It is therefore essential to identify missing values and correct them as accurately as possible to ensure the integrity of the analysis and the effectiveness of any decision-making based on the data. This paper presents a new approach, the Gap Imputing Algorithm (GMA), for imputing missing values in time series data. The Gap Imputing Algorithm (GMA) identifies sequences of missing values and determines the periodic time of the time series. Then, it searches for the most similar subsequence from historical data. Unlike previous work, GMA supports any type of time series and is resilient to consecutively missing values with different gaps distances. The experimental findings, which were based on both real-world and benchmark datasets, demonstrate that the GMA framework proposed in this study outperforms other methods in terms of accuracy. Specifically, our proposed method achieves an accuracy score that is 5 to 20% higher than that of other methods. Furthermore, the GMA framework is well suited to handling missing gaps with larger distances, and it produces more accurate imputations, particularly for datasets with strong periodic patterns.

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A Cooperative Feature Removal Mechanism for Cell Outage Detection in Wireless Telecommunication Networks 19th

A Cooperative Feature Removal Mechanism for Cell Outage Det...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Wrona, Andrea Gentile, Simone De Santis, Emanuele Giuseppi, Alessandro Pietrabissa, Antonio Delli Priscoli, Francesco Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome via Ariosto 25 Lazio Rome00185 Italy Department of Electrical and Information Engineering Polytechnic of Bari via Re David 200 Puglia Bari70125 Italy

ISBN: (纸本)9783031842597

In the domain of crisis management for telecommunications infrastructures, the autonomous detection of cell outages within cellular networks is of paramount importance for prompt identification and resolution in ensuring uninterrupted connectivity to users. Traditional methods usually involve data aggregation at the core network, which is responsible for identifying cell failures. Proposing a novel approach, we leverage a Machine Learning-based distributed and cooperative feature removal mechanism in order to preserve the privacy of data and avoid any degradation in classification performance. Simulations carried out on a dataset retrieved from a real 4G-LTE rollout demonstrate that the proposed approach, through a cooperation among agents, maintains or even slightly improves accuracy, precision, recall, and F1-score in outage prediction compared to other conventional methods, showcasing its efficacy for cell outage detection purposes while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Intelligent Decision Algorithm for Photovoltaic Power Generation control Based on Feature Neural Computing Modeling

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Tang, Mingze School of Control and Computer Engineering North China Electric Power University Beijing100096 China

The integration of renewable energy sources into the power grid, especially photovoltaic (PV) systems, has seen a significant upsurge due to the global push for sustainable energy. However, the variable nature of solar energy poses unique challenges in the effective management and control of PV power generation. This study introduces an innovative approach to intelligent decision-making in PV power generation control, leveraging deep learning techniques for temporal data feature modeling. By collecting and analyzing time-series data from PV systems, the proposed method utilizes feature neural computing to model the inherent characteristics of the data, facilitating effective temporal feature classification. The classification results are then employed to inform intelligent control strategies, optimizing the efficiency and reliability of PV power generation. This article proposes an end-to-end neural network structure that can simultaneously mine multi-scale local information and global temporal correlation information in sequences. This structure is primarily achieved through a module. The module consists of two parallel branches. One branch improves the InceptionTime module with depthwise separable convolution to extract multi-scale subsequence information. The other branch employs multi-head self-attention technology with added layer positional encoding to extract global temporal correlation information in sequences. © 2024 Mingze Tang, published by Sciendo.

关键词： Solar energy

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Automation Detection, Segmentation, and Classification for Ocean Reinforced Concrete Corrosion Based on Artificial Intelligence

SSRN

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SSRN 2024年

作者： Cui, Zhendong Jia, Zhitong Zhao, Yang Fu, Mingjiao School of Computer and Control Engineering Yantai University Yantai264000 China

Based on artificial intelligence technology, it is of great significance to automatically identify and determine the degree of corrosion damage for Ocean reinforced ***, an enhanced and comprehensive non-destructive testing framework was proposed for the automatic detection of corrosion in ocean concrete and the evaluation of its corrosion ***, a dataset containing 3,700 images of offshore ocean concrete corrosion has been established, which captured in various geographical locations, weather conditions, lighting situations, and engineering ***, a Faster-DeepLabV3+ model for damage segmentation and grade classification was present. An Efficient Channel Attention structure was introduced at the front of the inverted residual module of MobileNetV2 to reduce the model parameters and computation while maintaining high-accuracy segmentation results;a stick pooling layer was introduced in Atrous Spatial Pyramid Pooling to improve the model's ability to perceive damage at different scales;a combination of loss functions with Dice loss function and focal loss function was used to enhance the model's capacity to detect outlines and finer ***, different corrosion levels were identified to categorize cracks, spalling, and rebar corrosion in marine concrete into three conditions, and accurate delineation of corroded areas and rebar rusting was successfully ***, experiments are carried out on the constructed data set, and by comparing the intersection merging ratio, average pixel accuracy, pixel accuracy and frames per second, the proposed model is proved to be accurate and efficient. © 2024, The Authors. All rights reserved.

关键词： Nondestructive examination

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SSIG: Single-Sample Information Gain Model for Integrating Multi-Omics Data to Identify Cancer Subtypes

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Chinese Journal of Electronics 2021年第2期30卷 303-312页

作者： ZHANG Yuanyuan WANG Ziqi WANG Shudong KOU Chuanhua School of Information and Control Engineering Qingdao University of Technology College of Computer Science and Technology China University of Petroleum (East China)

Different living environments of cancer samples lead to different molecular mechanisms of cancer development, which in turn leads to different cancer subtypes. How to identify cancer subtypes is a key issue for the realization of precision medicine. With the development of high-throughput technologies, multi-omics data which can better understand different causes of cancer have emerged. However, the current methods of analyzing cancer subtypes using multi-omics data is mostly derived from population cancer sample data and ignores the differences between different cancer ***, the joint analysis of multi-omics based on a single sample may reveal more information about the differences between individual cancers. A strategy for identifying cancer subtypes is proposed based on Single-sample information gain(SSIG) which construct sample feature matrix by considering the heterogeneity of sample. Applying this strategy to current popular subtype identification methods, cancer subtypes can be identified more accurately and the mechanism of cancer can be found from the perspective of a single sample. By comparing different methods in different clustering measure, and using survival analysis, it is shown that SSIG is more suitable for cancer subtype identification than the original multi-omics data, and it is easier to mine the cancer subtype classification mechanism hidden behind the data.

关键词： identify cancer subtypes SSIG medical computing cancer subtype classification mechanism Single-sample information gain model different cancer subtypes original multiomics data genetics cancer subtype identification current popular subtype identification methods population cancer sample data different cancer samples data analysis cancer development genomics cancer sample feature matrix different molecular mechanisms different living environments bioinformatics individual cancers different clustering measure

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Recognition of Residual Current A+6 Waveform Based on Random Forest Algorithm 6

Recognition of Residual Current A+6 Waveform Based on Random...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Zhao, Pei Wang, Xiaohui Liu, Fanyu Ou, Ziye Jia, Yunshuo State Grid Hebei Electric Power Marketing Service Center Hebei Shijiazhuang China North China Electric Power University School of Control and Computer Engineering Hebei Baoding China

ISBN: (纸本)9798331541750

In the field of power system safety protection, residual current detection technology plays a vital role, which is essential to ensure the safe and stable operation of the system. Accurate identification of the waveform of the residual current is necessary for the residual current protection device to perform the disconnect operation correctly. At present, the recognition of specific waveforms such as F, A0, A90, A135, and SDC in the A+6 waveform is not very sufficient, especially in terms of compatibility and recognition efficiency at 50Hz and 60Hz frequencies. In this study, we propose a new residual current A+6 waveform recognition technology based on a random forest algorithm, which improves recognition accuracy by selecting 13 time-domain statistical features. The experimental results show that the accuracy of the proposed method for the recognition of alternating current, F, A0, A90, A135, and SDC waveforms and their amplitudes at 50Hz and 60Hz on the experimental bench reaches 98.6% and 99.3%, respectively. In addition, the generated decision trees can be easily integrated into embedded systems, showing high practical value. © 2024 IEEE.

关键词： Random forests

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Unpredictable Switching for Cyber-Physical Security Against Worst-case Attackers 63

Unpredictable Switching for Cyber-Physical Security Against ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Kanellopoulos, Aris Fotiadis, Filippos Vamvoudakis, Kyriakos G. Sandberg, Henrik Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden Georgia Institute of Technology Daniel Guggenheim School of Aerospace Engineering AtlantaGA30332 United States

ISBN: (纸本)9798350316339

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method. © 2024 IEEE.

关键词： Cyber attacks

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Improved Malaria Cells Detection Using Deep Convolutional Neural Network 5

Improved Malaria Cells Detection Using Deep Convolutional Ne...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Mahmood, Sarmad Nozad Mohammed, Swash Sami Ismaeel, Ayad Ghany Clarke, Hulya Gokalp Mahmood, Iman Nozad Aziz, Dlnya Abdulahad Alani, Sameer Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk Iraq Electrical and Electronics Engineering Ondokuz Mayis University Samsun Turkey Kirkuk General Education Directorate Kirkuk Iraq Computer Center University of Anbar Anbar Iraq

ISBN: (纸本)9798350337525

This research presents a deep convolutional neural network (CNN) as a solution for identifying malarial cells that are infected. The AI model suggested in this work comprises a three-layered CNN and a two-layered dense neural network. The model can capture both minor and significant features by utilizing CNN, thereby extracting a maximum amount of information from the input data. The model is trained over 20 epochs and evaluated using the binary cross entropy loss function and accuracy metric to assess its performance. Remarkably, the proposed model achieved an impressive accuracy of 96% and maintained a loss value below 0.2 for both the training and validation datasets. Ultimately, this research demonstrates promising potential for automating the detection of malaria through parasite cell counting. © 2023 IEEE.

关键词： Convolutional neural networks

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High Speed Machining for Linear Paths Blended with G3 Continuous Pythagorean-Hodograph Curves

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Journal of Systems Science & Complexity 2022年第4期35卷 1586-1607页

作者： ZHAO Kai LI Shurong College of Control Science and Engineering China University of Petroleum(East China)Qingdao266580China College of Computer Science and Information Engineering Anyang Institute of TechnologyAnyang455000China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

Previously,many studies have illustrated corner blend problem with different parameter *** a few of them take a Pythagorean-hodograph(PH)curve as the transition arc,let alone corresponding real-time interpolation *** this paper,an integrated corner-transition mixing-interpolation-based scheme(ICMS)is proposed,considering transition error and machine tool ***,the ICMS smooths the sharp corners in a linear path through blending the linear path with G3 continuous PH transition *** obtain optimal PH transition curves globally,the problem of corner smoothing is formulated as an optimization problem with *** order to improve optimization efficiency,the transition error constraint is deduced analytically,so is the curvature extreme of each transition *** being blended with PH transition curves,a linear path has become a blend ***,the ICMS adopts a novel mixed interpolator to process this kind of blend curves by considering machine tool *** mixed interpolator can not only implement jerk-limited feedrate scheduling with critical points detection,but also realize self-switching of two interpolation ***,two patterns are machined with a carving platform based on *** l results show the effectiveness of ICMS.

关键词： Bézier control polygon corner transition jerk-limited scheduling mixed interpolation pythagorean-hodograph curve

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Self-supervised Semantic Soft Label Learning Network for Deep Multi-view Clustering

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IEEE Transactions on Multimedia 2025年

作者： Yan, Weiqing Yang, Tingyu Tang, Chang Yantai University School of Computer and Control Engineering Yantai261400 China China University of Geosciences School of Computer Science Wuhan430074 China

Multi-view clustering, which identifies shared semantics from different perspectives and classifies data samples into distinct categories using unsupervised methods, is gaining increasing interest. This task primarily focuses on learning consistent multi-view feature representations and clustering labels. Current approaches for achieving consistent multi-view feature representations often use techniques such as cascading, weight fusion, and attention mechanism fusion. These methods reconstruct features based on original low-level features via encoder-decoder, which often contain visual private information, leading to misleading feature representations. Furthermore, in the clustering label learning process, many methods use a two-stage approach: first, they achieve consistent feature representations, and then they apply hard labeling methods like K-means or spectral clustering to obtain clustering labels. Single-stage methods typically derive consistent labels through a linear coding layer based on consistent representation learning. These methods do not fully utilize the multi-view view semantic information, and consistent representation learning may be impaired when some low-quality views are present, leading to the generation of inaccurate semantic labels. To address these issues, we propose a Self-supervised Semantic Soft Label Learning Network for Deep Multi-view Clustering. Specifically, we introduce a consensus high-level feature learning module that uses a shared MLP layer to transform low-level features into a high-level feature space. To enhance the consistency between high-level features from different views, we maximize mutual information between these features and introduce the U-Projection module, which improves the expressive power of the consensus feature via resampling the features and concatenating the fused features before and after sampling operations. Additionally, we propose a self-supervised semantic label learning module that employs a dual-br

关键词： Self-supervised learning

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