检索结果-内蒙古大学图书馆

computer model of filter for virtual laboratory

学校读者我要写书评

暂无评论

Computer model of filter for virtual laboratory

2021 Radiation and Scattering of Electromagnetic Waves, RSEMW 2021

作者： Gubsky, Dmitry S. Daineko, Yevgeniya A. Ipalakova, Madina T. Lonkina, Daria V. Zemlyakov, Viacheslav V. Southern Federal University Faculty of Physics Rostov-on-Don Russia International IT University Radiengineering Electronic and Telecommunication Dept. Almaty Kazakhstan International IT University Computer Engineering and Information Security Dept. Almaty Kazakhstan

ISBN: (纸本)9781665425445

The paper considers the modeling of the H-waveguide filter. An analysis of its characteristics is carried out. The creation of a computer model for its inclusion in a virtual laboratory is described. In the new lab, the measuring instruments have a completely realistic interface and can be operated by pressing buttons on the front panel. © 2021 IEEE.

关键词： E-learning

Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction

学校读者我要写书评

暂无评论

Addressing Intermediate Verification Latency in Online Learn...

International Joint Conference on Neural Networks (IJCNN)

作者： Zixin Zhong Liyan Song Fengzhen Tang Bo Yuan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Faculty of Computing Harbin Institute of Technology Harbin China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In non-stationary data streams, the challenges of concept drift are further compounded by the issue of Intermediate Verification Latency (IVL), which can impede timely model adaptation. IVL refers to the finite delay between the arrival of data features and their corresponding labels. This delay could pose a significant challenge in adapting models to new concepts, ultimately hindering predictive performance. However, existing IVL approaches exhibit certain limitations. Some approaches passively wait for delayed labels, thereby overlooking temporarily unlabeled data. Other approaches employ pseudo-labeling for immediate model updates, but may risk losing valuable information when reverting model states to rectify previous pseudo-labeling mistakes. To overcome these limitations, we propose a novel approach called Micro-cluster based Immediate Pseudo-Labeling with Oriented Synthetic Correction (MIPLOSC). MIPLOSC leverages micro-cluster systems to effectively capture data distributions, thus facilitating its two core components: immediate pseudo-labeling and oriented synthetic correction. The immediate pseudo-labeling mechanism facilitates immediate utilization of temporarily unlabeled data, and the oriented synthetic correction mechanism enables finergrained rectification from previous erroneous pseudo-labels and concept drift, minimizing the loss of learned information. Experimental studies validated the effectiveness of MIPLOSC in addressing IVL, demonstrating its superiority over competing methods in both space consumption and predictive performance across varying degrees of label delay.

关键词： Adaptation models Costs Concept drift Noise Neural networks Predictive models Robustness

Improved Network Intrusion Classification with Attention-Assisted Bidirectional Lstm and Optimized Sparse Contractive Autoencoders

学校读者我要写书评

暂无评论

SSRN 2022年

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science Lyle School of Engineering Southern Methodist University DallasTX75205 United States

Accurately identifying network intrusion cannot only help individuals and enterprises better deal with network security problems, but also maintain the Internet environment. Currently, classification methods with autoencoders for feature learning have been proved to be suitable for the network intrusion detection. This work proposes a new hybrid classification method named SABD for network intrusion detection. SABD integrates Stacked sparse contractive autoencoders, Attention-based Bidirectional long-term and short-term memory (LSTM), and Decision fusion. SABD integrates the feature extraction of stacked sparse contractive autoencoders with the classification ability of attention-based bidirectional LSTM. Specifically, stacked sparse contractive autoencoders are used for extracting features, which are sent to the attention-based bidirectional LSTM for the classification. Besides, an improved optimization algorithm named genetic simulated-annealing-based particle swarm optimization is designed to optimize hyperparameters of SSCA. Finally, the decision fusion algorithm is adopted to integrate classification results of multiple classifiers and yield the final results. Experimental results based on two different types of real-life datasets including UNSW-NB15 and KDD’99 demonstrate that the proposed SABD outperforms its state-of-the-art peers in terms of classification accuracy. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

Accurate Water Quality Prediction with Attention-Based Bidirectional Lstm and Encoder-Decoder

学校读者我要写书评

暂无评论

SSRN 2022年

作者： Bi, Jing Chen, Zexian Yuan, Haitao Zhang, Jia School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science Lyle School of Engineering Southern Methodist University DallasTX75205 United States

Accurate prediction of water quality indicators can effectively prevent sudden water pollution events, and control pollution diffusion. Neural networks, e.g., Long Short-Term Memory (LSTM) and encoder-decoder network, have been widely used to predict time series data. However, as the water quality data increases, it becomes unstable and highly nonlinear. Accurate prediction of water quality becomes a big challenge. This work proposes a hybrid prediction method called VBAED to predict the water quality time series. VBAED combines Variational mode decomposition (VMD), Bidirectional input Attention mechanism, an Encoder with bidirectional LSTM (BiLSTM), and a Decoder with bidirectional temporal attention mechanism and BiLSTM. Specifically, VBAED first adopts VMD to decompose historical data of a predicted factor, and decomposed results are used as the input along with other features. Then, a bidirectional input attention mechanism is adopted to add weights to input features from both directions. VBAED adopts BiLSTM as an encoder to extract hidden features from input features. Finally, the predicted result is obtained by a BiLSTM decoder with a bidirectional temporal attention mechanism. Real-life data-based experiments demonstrate that VBAED obtains the best prediction results compared with other widely used methods. © 2022, The Authors. All rights reserved.

关键词： Variational mode decomposition

Multi-Indicator Water Quality Prediction with Attention-Assisted Bidirectional Lstm and Encoder-Decoder

学校读者我要写书评

暂无评论

SSRN 2022年

作者： Bi, Jing Zhang, Luyao Yuan, Haitao Zhang, Jia School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science Lyle School of Engineering Southern Methodist University DallasTX75205 United States

Accurate and real-time prediction of water quality not only helps to assess the environmental quality of water, but also effectively prevents and controls water quality emergencies. In recent years, neural networks represented by Bidirectional Long Short-Term Memory (BiLSTM) and Encoder-Decoder (ED) frameworks have been shown to be suitable for the prediction of time series data. However, traditional statistical methods cannot capture nonlinear characteristics of the water quality, and deep learning models often suffer from gradient disappearance and gradient explosion problems. This work proposes a hybrid water quality prediction method called SVABEG, which combines a Savitzky-Golay (SG) filter, Variational Mode Decomposition (VMD), an Attention mechanism, BiLSTM, an ED structure, and a hybrid algorithm called Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO). SVABEG first adopts the SG filter and VMD to remove noise and deal with nonlinear features in the original time series, respectively. Then, SVABEG combines BiLSTM, the ED structure, and the attention mechanism to capture bi-directional long-term correlations, realize dimensionality reduction and extract key information, respectively. Furthermore, SVABEG adopts GSPSO to optimize its hyperparameters. Experimental results with real-life datasets demonstrate that the proposed SVABEG outperforms current state-of-the-art algorithms in terms of prediction accuracy. © 2022, The Authors. All rights reserved.

关键词： Water quality

Label Cluster Chains for Multi-Label Classification

学校读者我要写书评

暂无评论

arXiv

arXiv 2024年

作者： Gatto, Elaine Cecília Nakano, Felipe Kenji Read, Jesse Ferrandin, Mauri Cerri, Ricardo Vens, Celine Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Faculty of Medicine Katholieke Universiteit Leuven West Flanders Kortrijk8500 Belgium École Polytechnique Polytechnic Institute of Paris Palaiseau Paris91120 France Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil Institute of Mathematical and Computing Sciences University of São Paulo SP São Carlos13566-590 Brazil

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), others learn all labels at once (global approach), and others combine several classifiers (ensemble approach). Regardless of the approach used, exploring and learning label correlations is important to improve the classifier predictions. Ensemble of Classifier Chains (ECC) is a well-known multi-label method that considers label correlations and can achieve good overall performance on several multi-label datasets and evaluation measures. However, one of the challenges when working with ECC is the high dimensionality of the label space, which can impose limitations for fully-cascaded chains as the complexity increases regarding feature space expansion. To improve classifier chains, we propose a method to chain disjoint correlated label clusters obtained by applying a partition method in the label space. During the training phase, the ground truth labels of each cluster are used as new features for all of the following clusters. During the test phase, the predicted labels of clusters are used as new features for all the following clusters. Our proposal, called Label Cluster Chains for Multi-Label Classification (LCC-ML), uses multi-label Random Forests as base classifiers in each cluster, combining their predictions to obtain a final multi-label classification. Our proposal obtained better results compared to the original ECC. This shows that learning and chaining disjoint correlated label clusters can better explore and learn label correlations. © 2024, CC BY.

关键词：

On deployment problem of multi-agent systems with delays

学校读者我要写书评

暂无评论

On deployment problem of multi-agent systems with delays

International Conference on Control, automation, Robotics and Vision (ICARCV)

作者： Katarzyna Topolewicz Carlos E. T. Dórea Sorin Olaru Ewa Girejko Faculty of Computer Science Bialystok University of Technology Białystok Poland Department of Computer Engineering and Automation Universidade Federal do Rio Grande do Norte UFRN-CT-DCA Natal RN Brazil Laboratory of Signals and Systems CentraleSupelec Univesity Paris-Saclay France

ISBN: (纸本)9781665476881

The paper analyzes the decentralized deployment policies for scalar multi-agent systems subject to delays. As a main contribution we obtain explicit conditions on the local feedback gains at the level of subsystems, which ensure safe deployment independent of the delays on the measurement of the positions of the neighboring agents. From the theoretic point of view, positive invariance of sets is used as a main concept for the proofs of the results.

关键词： Sufficient conditions Decentralized control Position measurement Gain measurement Robustness Delays Safety

Self-Adaptive Teaching-Learning-Based Optimizer with Improved Rbf and Sparse Autoencoder for High-Dimensional Problems

学校读者我要写书评

暂无评论

SSRN 2022年

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Zhou, MengChu School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science The Lyle School of Engineering Southern Methodist University DallasTX75205 United States Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Evolutionary algorithms are commonly used to solve many complex optimization problems in different fields. Yet, their performance is limited when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and they may easily trap into local optima. To solve this problem, this work proposes a Selfadaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-Learning-Based Optimizer (STLBO) is designed to dynamically adjust parameters for balancing exploration and exploitation abilities. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress search space into lower-dimensional one for more efficiently guiding population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate model to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate STORA by comparing it with several state-of-the-art algorithms through eight benchmark functions. We further test its actual performance by applying it to solve a real-world computation offloading problem. © 2022, The Authors. All rights reserved.

关键词： Radial basis function networks

Energy-Saving Practice of Variable Frequency Drive Technology in Data Center HVAC System

学校读者我要写书评

暂无评论

Energy-Saving Practice of Variable Frequency Drive Technolog...

International Conference on Power and Energy Technology (ICPET)

作者： Chuanxian Ai Jinpeng Yang Rui Chen Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Branch The National Computer Network Emergency Response Technical Team/Coordination Center of China Kunming China Power Dispatch Control Center Guizhou Power Grid Co. Ltd. China Southern Power Grid Co. Ltd Guiyang China

The variable-frequency drive technology (VFD) can modify the motor speed or torque by varying the frequency and voltage of the AC motor input power according to the load demand as a way to save energy. To analyse the energy saving effect of variable-frequency drive technology applied to data centre heating, ventilation, and air conditioning (HVAC) systems, this paper applies VFD technology to data centre fluorine pump-compressor composite air conditioning systems, implements monitoring and analysis of the operational energy efficiency of this system before and after retrofit, and combines the actual application of this technology. The findings indicate that the VFD technology can substantially reduce the number of compressor starts and stops, reduce the compressor start-up inrush current, and improve the power system’s stability. Furthermore, utilising this technology, the HVAC system can effectively synchronise with the load and fluctuations in outdoor temperature and humidity, resulting in accurate temperature regulation and optimal utilisation of outdoor cooling resources. This approach can lead to significant energy conservation of up to 42.95% when compared to extreme hot and cold weather conditions. The findings of the study indicate that the utilisation of VFD technology exhibits commendable adaptability and yields favourable outcomes in regions with varying temperature ranges. The minimum PUE value recorded was 1.228.

关键词：