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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Xia, Chunyan Huang, Yifan Huang, Song Zheng, Changyou Wang, Yutong Wang, Yu College of Command and Control Engineering Army Engineering University of Pla China College of Computer and Information Technology Mudanjiang Normal University China Illinois Institute of Technology United States

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

With the rapid development of the information industry, intelligent software testing has become one of the hot research. This paper studies how to extract useful data from the original test set to test the modified model for the intelligent software. First, the initial test data are obtained according to the label classification of the original structured documents. Second, the non-dominated sorting genetic algorithm is used to prioritize the test data. Finally, the experimental results of intelligent software show that compared with the random method, the accuracy of the penalty_money prediction model is reduced by 0.04% 34.91 %, and the accuracy of the penalty_imprisonment prediction model is reduced by 0.08% 32.63 %, indicating that the proposed method can find more defects of the penalty prediction model earlier, to reduce the cost of intelligent software testing. © 2022 IEEE.

关键词： Multiobjective optimization

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The Most Powerful Unfalsified Linear Parameter-Varying Model with Shifted-Affine Scheduling Dependence

SSRN

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SSRN 2024年

作者： Markovsky, Ivan Verhoek, Chris Tóth, Roland Gran Capità Barcelona08034 Spain Control Systems Group Eindhoven University of Technology Eindhoven5600 Netherlands Control Systems Group Eindhoven University of Technology and Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

The paper considers exact identification of linear parameter-varying (LPV) systems from the behavioral perspective. In the case of linear time-invariant (LTI) systems, the notion of the most powerful unfalsified model (MPUM), i.e., the least complex exact model for a given data trajectory, is well established. The MPUM, however, has not been generalized for LPV systems. In order to do this, we define the notions of model complexity for LPV systems with shifted-affine scheduling dependence. The MPUM leads to identifiability conditions and a method for exact LPV system identification. The method is based on lifting the LPV system to a higher dimensional space and LTI embedding in the lifted space. It is made rigorous by proving a formal connection between the parameters of the LTI embedding and the original LPV system. © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Long Term Load Forecasting Using Adaptive Neural Fuzzy Inference System: Case Study on Woldia Power Distribution Systems

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SSRN 2024年

作者： Sinishaw, Getaye Yeshaneh Mitiku, Hunachew Moges Molla, Tesfahun Desale School of Electrical and Computer Engineering Electrical Power Woldia Institute of Technology Woldia University Woldia Ethiopia School of Electrical and Computer Engineering Industrial Control Woldia Institute of Technology Woldia University Woldia Ethiopia

Abstract: - Now a day’s power consumption in Woldia city is becoming increased. As a result, there is a need of load forecasting to know the electrical load consumption and be ready to supply the customers of woldia city. Therefore, load forecasting strengthens the power systems' dependability and energy efficiency. Power systems are highly distributed, highly complex graphically, and subject to numerous unforeseen events, making power system problems difficult to solve. It has considered the different demographic aspects, such as population growth, time, historical load, economic growth of the city and current load of the distribution system. In this thesis, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models have been used to determine the forecasted load using data collection obtained from woldia city and the results of the two models have been compared. The data sets cover a seven-year period (2016/17- 2022/23) on monthly basis and forecast for ten years (2023/24-2032/33). The performance evaluations of ANFIS model have been executed better when compared with ANN. The performance specification of load forecasting MAPE and RMSE in ANFIS were improve by 2.0196% and 2.859984 respectively as compared with ANN. The prediction performance of the ANFIS model for load forecasting based on the previous data trained was investigated and a good performance of future load forecasting has been recoded. It can be decided that the future load consumption of woldia city is directly related with the population growth and the GDP of the city. © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Range-Limited Pursuit-Evasion

Range-Limited Pursuit-Evasion

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Weintraub, Isaac E. Von Moll, Alexander Pachter, Meir Control Science Center Air Force Research Laboratory Wright-Patterson AFBOH United States Air Force Institute of Technology Electrical and Computer Engineering Wright-Patterson AFBOH United States

ISBN: (纸本)9798350338782

In pursuit-evasion the objective of the pursuer is to capture the evader. In this work, the faster pursuer is modeled to have limited range and therefore optimal strategies for the pursuer and evader change. Depending upon the range limits of the pursuer the evader may evade capture by the pursuer. This paper describes the optimal strategies and nuances that appear for point-capture or when the pursuer is endowed with a non-zero capture radius. © 2023 IEEE.

关键词： Multi agent systems

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Optimizing Electrocardiogram Signal Augmentation for Realistic Synthetic Data in Deep Learning Model

Optimizing Electrocardiogram Signal Augmentation for Realist...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Muhammad Farhan Safdar Piotr Pałka Ahmed Al Faresi Robert Marek Nowak Institute of Computer Science Warsaw University of Technology Poland Institute of Control and Computation Engineering Warsaw University of Technology Poland College of Information Technology United Arab Emirates University UAE

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

Electrocardiograms (ECG) are non-invasive signals and have proven useful in assessing the heart condition. Given the necessity for extensive datasets in ECG classification using deep learning (DL) models, there is a critical imperative to devise data augmentation methods capable of generating synthetic but realistic dataset suitable for training DL model. In this study, we propose a novel approach for augmenting ECG signals, aiming to produce realistic signals while optimizing memory usage and resource requirements. Building upon our previous work in ECG signal augmentation, we revisit the methodology to address limitations observed in the generation of synthetic signals. The existing method segmented ECG signals into fixed-length segments and combined them, occasionally resulting in unrealistic heart cycles within the signals in extreme condition. To address this issue, our proposed technique incorporates R peak detection, signal segmentation, and reordering based on the R-peaks information. We evaluated the proposed method using three benchmark datasets, including PTB-XL, Chapman-Shaoxin from PhysioNet, and the dataset from China Physiological Signal Challenge 2018 (CPSC-2018), for classifying atrial fibrillation from normal samples. Our approach achieved an accuracy of 0.83, sensitivity of 0.86, specificity of $0.80, F_{1}$-score of 0.83, and precision of 0.80. These results underscore the effectiveness and efficiency of our method in augmenting ECG signals for various applications in healthcare and biomedical research.

关键词： Training Heart Deep learning Biological system modeling Signal processing algorithms Electrocardiography Data augmentation Data models Classification algorithms Synthetic data

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A New Approach of Intrusion Detection with Command Sequence-To-Sequence Model 6th

A New Approach of Intrusion Detection with Command Sequence-...

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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Liu, Wei Mao, Yu Ci, Linlin Zhang, Fuquan School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China

ISBN: (纸本)9789811650352

Traditionally, researchers have focused on network level intrusion detection and program level intrusion detection to improve computer security. However, neither approach is foolproof. We argue that the internal and external security of a computer system are equally important. Typically, a successful attacker manifests in the form of the attacker becoming a user on the host either with elevated or normal user privileges. At this point, user-level intrusion detection attempts to deter and curtail an attacker even after the system has been compromised. In this work, we introduce a new approach of intrusion detection based on recurrent neural networks (RNNs) to solve the long sequential problem. We build a robust user command sequence-to-sequence model by semantic modeling command. Our model implements the prediction of user command sequence and the prophesying of user behaviors. The experimental results on data sets of Purdue University, SEA and self-collected data show that an accurate, effective and efficient detection can be achieved by using the proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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MAD-BA: 3D LiDAR Bundle Adjustment – From Uncertainty Modelling to Structure Optimization

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IEEE Robotics and Automation Letters 2025年第7期10卷 7254-7261页

作者： Krzysztof Ćwian Luca Di Giammarino Simone Ferrari Thomas Ciarfuglia Giorgio Grisetti Piotr Skrzypczyński Institute of Robotics and Machine Intelligence Poznan University of Technology Poznan Poland Department of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Rome Italy

The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems often prioritize pose optimization, with structure refinement either omitted or treated separately using implicit representations. This letter introduces a framework for simultaneous optimization of sensor poses and 3D map, represented as surfels. A generalized LiDAR uncertainty model is proposed to address less reliable measurements in varying scenarios. Experimental results on public datasets demonstrate improved performance over most comparable state-of-the-art methods. The system is provided as open-source software to support further research.

关键词： Laser radar Optimization Uncertainty Simultaneous localization and mapping Point cloud compression Measurement by laser beam Atmospheric modeling Accuracy Trajectory Data models

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Load balancing transmission technology of network communication based on phase spectrum compensation 7

Load balancing transmission technology of network communicat...

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2021 7th International Conference on Manufacturing technology and Applied Materials, ICAMMT 2021

作者： Zhai, Qian Lu, Xing Hua Huali College Guangdong University of Technology Institute of Computer Control Technology Guangzhou511325 China

In order to improve the load balance scheduling transmission ability of multi-frequency network communication in high-order nonlinear system, load balance design is needed. A load balance transmission method of multi-frequency network communication in high-order nonlinear system based on phase spectrum compensation is proposed. The time domain model of high-order nonlinear system multi-frequency network communication load is constructed by multi-frequency modulation phase spectrum compensation method. The feature detection of high-order nonlinear system multi-frequency network communication load is realized by high-order statistical feature connection identification and communication load balancing method. The autocorrelation constraint parameters of high-order nonlinear system multi-frequency network communication load are analyzed. The multi-dimensional array networking structure model of high-order nonlinear system multi-frequency network communication load is constructed by spatial spectral density analysis method. Combined with phase spectrum compensation and signal detection methods, the extracted high-order nonlinear system multi-frequency network communication load spectrum features are adaptively scheduled and filtered. In the wireless communication networking environment, load balance transmission of communication load in multi-frequency network of high-order nonlinear system is realized. The simulation results show that this method has better balance and lower error rate in multi-frequency network communication load balancing transmission, which improves the accuracy of multi-frequency network communication load balancing scheduling in high-order nonlinear systems. © Published under licence by IOP Publishing Ltd.

关键词： Time domain analysis

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Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

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IEEE Internet of Things Journal 2025年第11期12卷 17123-17136页

作者： Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. © 2014 IEEE.

关键词： Image reconstruction

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Solar Wind Density Forecasting with U-Net and LSTM-based Neural Networks 8

Solar Wind Density Forecasting with U-Net and LSTM-based Neu...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

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