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Satisfiability threshold of the random regular(s,c,k)-SAT problem

Frontiers of Computer Science 2022年第3期16卷 227-229页

作者： Xiaoling MO Daoyun XU Kai YAN Zaijun ZHANG College of Computer Science and Technology Guizhou UniversityGuiyang 550025China Agricultural Development Bank of China Beijing 100045China Key Laboratory of Complex Systems and Intelligent Computing and School of Mathematics and Statistics Qiannan Normal University for NationalitiesDuyun 558000China

1Introduction The satisfiability(SAT)problem is always a core problem in the field of computer *** theoretical and applied research have long been the common attention of many scholars in the field of artificial intelligence and mathematical *** the real world,all issues related to combinatorial optimization and coordination verification are closely related to SAT problem.

关键词： problem. problem combinatorial

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Intermittent control for nonlinear hybrid stochastic delayed systems with measurement error via dynamic event-triggered mechanism

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ISA Transactions 2025年

作者： Hairui Zhao Dongyan Chen Jun Hu Department of Applied Mathematics Harbin University of Science and Technology Harbin 150080 China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin 150080 China School of Automation Harbin University of Science and Technology Harbin 150080 China

This paper deals with the problem of intermittent control based on dynamic event-triggered mechanism (DETM) for nonlinear hybrid stochastic delayed systems (NHSDSs) with measurement error. The DETM and intermittent control strategy are employed to alleviate the network burden in feedback and forward channels, respectively. After giving the existence and uniqueness theorem of the solution to NHSDSs, the sufficient condition to guarantee -th moment exponential stability and almost surely exponential stability of NHSDSs is established by comparing with the constructed auxiliary system. On this basis, the intermittent control based on self-triggered mechanism is developed. In addition, it is also proved that neither the DETM nor the self-triggered mechanism can produce the Zeno phenomenon. Finally, a simulation example is given to verify the validity of the theoretical results.

关键词： Dynamic event-triggered mechanism Intermittent control Nonlinear hybrid stochastic delayed systems Self-triggered mechanism Stability

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An age-structured tuberculosis model with information and immigration:Stability and simulation study

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International Journal of Biomathematics 2023年第2期16卷 153-176页

作者： Lili Liu Jian Zhang Yazhi Li Xinzhi Ren Shanci Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Complea Systems Research Center Shanci University Taiyuan 030006P.R.China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province School of Mathematics and Statistics Qiannan Normal University for Nationalities Duyun 558000 P.R.China School of Mathematics and Statistics Southwest University Chongqing 400715P.R.China

This paper establishes an age-structured Tuberculosis(TB)model to investigate the joint impacts of information and immigration of population on the spread of TB ***,we show that the model is point dissipative,and the semi-flow generated by the model has the property of asymptotic smoothness,and then study the existence and global stability of positive steady state by the direct Lyapunov ***,by using Matlab software,we verify the theoretical results,and further explore the influence of information(including information coverage and disease-related memory delay)and immigration on the final size of TB *** simulation results show that both information coverage and immigration are positive correlated with the final size of disease,and disease-related memory delay can affect the arrival time of positive steady state,which implies us that improving information coverage,enlarging disease-related memory,and reducing the immigration of population(especially latent and infected individuals)can effectively control the progression of TB disease.

关键词： TB transmission model age-structured information-related immigration global stability.

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Prediction of photovoltaic power generation based on LSTM and transfer learning digital twin

Prediction of photovoltaic power generation based on LSTM an...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Yang, Heng Wang, Weisong School of Mathematics and Statistics Qiannan Normal University for Nationalities 558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou558000 China

This research offers a digital twin model for solar power production power prediction based on long short term memory network (LSTM), and then applies this model to other models with limited operational time and inadequate data through transfer learning. The prediction for the solar system's electrical output. Due to the effect of sun irradiation, temperature, and other random elements, photovoltaic power output is very intermittent and fluctuating, making it impossible to anticipate photovoltaic power with precision. Synchronization and real-time updating of physical entities, thereby obtaining more accurate forecasting results than traditional forecasting methods, while utilizing knowledge learned from PV systems with sufficient historical data to assist PV systems with limited historical data in establishing a digital twin of power generation forecasting model, not only can obtain accurate prediction results but also save training time for the model. In this study, the PV historical data of three distinct sites from the open source websites of Queensland University and Shanxi Jinneng Clean Energy Company are used to validate the validity of the suggested technique. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Roller Bearing Fault Diagnosis Using Deep Transfer Learning and Adaptive Weighting

Roller Bearing Fault Diagnosis Using Deep Transfer Learning ...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Liu, Yan Fan, Kaiyu School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou558000 China

A fault diagnosis approach for roller bearings utilizing deep transfer learning and adaptive weighting is suggested to address the issue that extra fault state samples in the target domain data of roller bearings impair the fault diagnostic accuracy. CNN-LSTM is a network model proposed by Lecun et al., which has good performance in image processing and image processing. It can effectively apply predictive local perception of time series and weight sharing of CNN, which can greatly reduce the number of networks and improve the efficiency of model learning. The method first establishes a feature extraction module, and uses a deep convolutional neural network to map bearing samples to a high-dimensional feature space. Secondly, uses the transfer learning concept to design a weighted domain discriminator, and adaptively weights the samples;and finally, through the confrontation in the feature space, the bearing samples are classified. Training to increase the domain similarity of the healthy state samples shared by the target domain and the source domain. Then measuring the similarity between these samples based on the sample weight size, and setting a threshold to label the additional fault state samples of the target domains as unknown. The suggested technique is validated using gearbox bearing data, roller bearing data from Case Western Reserve University, and locomotive wheel bearing data. The diagnostic accuracy of the samples is less than 80%, suggesting that the suggested approach can successfully overcome the effects of extra fault state samples and diagnose roller bearing faults. © Published under licence by IOP Publishing Ltd.

关键词： Failure analysis

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Analysis of the Research Status of Information Technology Education Literature in China and Abroad 24

Analysis of the Research Status of Information Technology Ed...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Yulin Liu, Kai Li, Junke School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu 223800 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

ISBN: (纸本)9798350319934

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad. © 2022 IEEE.

关键词： Informatization

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Pore extraction method of rock thin section based on Attention U-Net

Pore extraction method of rock thin section based on Attenti...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Li, Mingjiang Zhang, Pan Hai, Tao School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou558000 China

This paper proposes a solution to the shortcomings of traditional segmentation methods. The labeling method uses the incomplete labeling method in weakly supervised labeling to simplify labeling and combines transfer learning to initialize the weight of the network in advance. According to the above ideas, an end-to-end deep learning model is trained. The fine rock particles have a greater segmentation impact, and in addition to that, when compared with the popular deep learning semantic segmentation approaches, they also have a significant improvement. The next phase is to continue improving the network by optimizing the parameters, with the number of network layers and the total number of parameters remaining unaltered. This requirement must be satisfied before moving on to the next stage. The capability of generalization enhances the impact of segmentation on particles as well as their accuracy. Experiments show that this method is significantly better than the traditional method for segmenting rock flakes with manual operation and has better results in the segmentation and extraction of fine particles compared with the mainstream convolutional neural network. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Distributed Resilient Fusion Filtering for Nonlinear systems with Random Sensor Delays:Optimized Algorithm Design and Boundedness Analysis

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Journal of systems Science & complexity 2023年第4期36卷 1423-1442页

作者： HU Jun HU Zhibin DONG Hongli LIU Hongjian School of Automation Harbin University of Science and TechnologyHarbin 150080China Department of Mathematics Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318China School of Mathematics-Physics and Finance Anhui Polytechnic UniversityWuhu 241000China

This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical *** addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering *** aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample *** the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion ***,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is ***,a numerical example is provided,which can show the usefulness of the developed DRFF approach.

关键词： Distributed resilient fusion filtering matrix-weighted fusion nonlinear time-varying systems random sensor delays

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A Small Sample Photovoltaic Hot Spot Identification Method Based on Deep Transfer Learning

A Small Sample Photovoltaic Hot Spot Identification Method B...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Zhou, Jincheng Luo, Xiao School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun558000 China

A small-sample photovoltaic hot spot identification method based on deep transfer learning has been proposed as a solution to the problem that traditional deep learning models require a substantial amount of training data, whereas the number of hot spot effect samples is relatively low and difficult to collect. This issue can be resolved by the development of a solution that employs deep transfer learning. The Inception-v 4 model serves as the basis for building a deep transfer learning model. After finishing the training procedure using a small-sample hot spot dataset of negative sample multi-classification, a network model for hot spot detection is built. Experiments reveal that even when the number of accessible samples is restricted, the model trained using the deep transfer learning technique has great identification accuracy, a low false detection rate, and strong generalization capability. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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A Non-Intrusive Load Decomposition Method Based on Generative Adversarial Networks and Asynchronous Federated Learning

A Non-Intrusive Load Decomposition Method Based on Generativ...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Wang, Dan Fan, Kaiyu School of Mathematics and Statistics Qiannan Normal University for Nationalities Duyun Guizhou558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou558000 China

The non-intrusive load decomposition method is an essential approach of appreciating the water requirements of consumers. Traditional techniques of load breakdown have a number of drawbacks, including a poor level of water identification precision. In order to do this, a model for load decomposition based on generative adversarial networks has been developed. By applying a convolutional autoencoder to the total power noise, the generative network constructs the power breakdown sequence of the given home appliance. The discriminant network is then utilized to determine whether or not the created sequence is legitimate. In order to achieve a more natural and authentic sequence, the participants compete against one another. In light of the shortcomings of the centralized model training method, the network model implementation scheme based on asynchronous federated learning is utilized to replace the traditional convolution in order to realize the model's lightweight nature. This allows the model to be applied to terminal devices such as smart water meters, and it is carried out in a cloud-edge collaborative manner. In addition, the model is carried out in a manner that is collaborative between the cloud and the edge. Training not only eases the strain of communication transmission but also protects the privacy of users and the integrity of their data. © Published under licence by IOP Publishing Ltd.

关键词： Generative adversarial networks

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