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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Kalandarov, Palvan I. Ubaydulayeva, Shakhnoza R. Gaziyeva, Rano T. Nikolov, Nikola N. Alexsandrova, Mariela I. National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Department Automation and Control of Technology Process in Production Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna9010 Bulgaria

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

The article deals with the choice of criteria for efficiency, applicability in measuring technology, analytical and construction, on the basis of which a methodology for assessing the quality of structures of measuring devices of moisture of materials of the agro-industrial complex is proposed. The standard deviation of the random error characterizing the accuracy of measurements is shown. A complex efficiency criterion is discussed, including the dominant particular parameters: accuracy, reliability, cost, allowing to solve the problem of optimal design of humidity control devices for the materials under consideration. As an optimal option, a differential measuring device is analyzed, which practically eliminates random and systematic errors and makes it possible to design a multiparameter measurement method that considers the features of a heterogeneous environment, such as bulk grain materials of the agro-industrial complex © 2022 IEEE.

关键词： Random errors

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Analysis of a Data Set to Determine the Dependence of Airline Passenger Satisfaction 7th

Analysis of a Data Set to Determine the Dependence of Airlin...

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Tynchenko, V.S. Borodulin Kleshko, I.I. Nelyub, V.A. Rukosueva Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia

ISBN: (纸本)9783031535512

This paper presents an analysis of a data set to determine the factors influencing airline passenger satisfaction. The study examines various criteria such as gender, customer type, age, travel type, class, and range to assess their impact on passenger satisfaction. The dataset consists of 25 columns, including attributes like Wi-Fi availability, convenience of online booking, seat comfort, in-flight entertainment, baggage handling, and overall satisfaction. The sample is relatively balanced, with equal representation of men and women, predominantly repeat customers, and a majority flying for business purposes. Key findings include a strong correlation between departure and arrival delays, higher satisfaction among passengers in business class, and positive ratings for Wi-Fi service correlating with overall satisfaction. Correlation analysis reveals interdependencies between different attributes, such as the influence of cleanliness on seat comfort and food and beverage ratings. In addition, a neural network forecasting model is used to estimate the average ratings of passengers, although with low accuracy, which was later excluded. Finally, a decision tree algorithm is utilized to identify the most significant attributes affecting passenger satisfaction words. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Baggage handling

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Differentially Private Set-Based Estimation Using Zonotopes

Differentially Private Set-Based Estimation Using Zonotopes

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European control Conference (ECC)

作者： Mohammed M. Dawoud Changxin Liu Amr Alanwar Karl H. Johansson School of Computer Science and Engineering Constructor University Bremen Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden

For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise from disclosing sensitive measurement signals to a cloud estimator that predicts the system state. To solve this issue, we propose a differentially private set-based estimation protocol that preserves the privacy of the measurement signals. Compared to existing research, our approach achieves less privacy loss and utility loss using a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach. Zonotopes, a less conservative form of set representation, are used to represent estimation sets, giving set operations a computational advantage. The privacy-preserving noise anonymizes the centers of these estimated zonotopes, concealing the precise positions of the estimated zonotopes.

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Event-triggered leader-following formation control for multi-agent systems under communication faults: application to a fleet of unmanned aerial vehicles

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Journal of Systems Engineering and Electronics 2021年第5期32卷 1014-1022页

作者： VAZQUEZ TREJO Juan Antonio GUENARD Adrien ADAM-MEDINA Manuel PONSART Jean-Christophe CIARLETTA Laurent ROTONDO Damiano THEILLIOL Didier Research Center for Automatic Control of Nancy University of LorraineNancy F-54000France Electronic Engineering Department National Institute of Technology of MexicoCuernavaca Morelos 62490Mexico Lorraine Research Laboratory in Computer Science and Its Applications University of LorraineNancy F-54000France University of Stavanger Department of Electrical and Computer EngineeringStavanger 4021Norway

The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems(MASs)under communication *** the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the *** matrix inequalities(LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization *** on the closed-loop system,an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy *** proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles(UAVs)under communication faults.A comparison between a state-of-the-art technique and the proposed technique has been provided,demonstrating the performance improvement brought by the proposed approach.

关键词： event-triggered leader-following consensus communication fault formation control unmanned aerial vehicle(UAV) experimental result

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Differentially Private Set-Based Estimation Using Zonotopes

arXiv

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arXiv 2023年

作者： Dawoud, Mohammed M. Liu, Changxin Alanwar, Amr Johansson, Karl H. School of Computer Science and Engineering Constructor University Bremen Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden

关键词： Embedded systems

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The Preparation Method, Resistive Switching Mechanism and Application Prospects of Memristor

The Preparation Method, Resistive Switching Mechanism and Ap...

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2023 7th International Conference on Electrical, Mechanical and computer Engineering (ICEMCE)

作者： Jiao Li Ximei Zhao Tengfei Lei Lin Chai Shi Wang Yuna Yu Collaborative Innovation Center of Memristive and Computing Application Qilu Institute of Technology Jinan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Collaborative Innovation Center of Memristive and Computing Application Oilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

Memristors have also become one of the most promising research hotspots for solving data storage and computing problems faced in the big data era. As an emerging electronic device, memristor has potentially important applications in information storage and processing. This paper constructs a rod-shaped TiO 2 −x nanoscale memristor with FTO/TiO 2 −x/Cu structure, whose reliability is based on the memory behavior and its conversion mechanism. Research progress including materials, preparation methods, resistive switching mechanism and application prospects are described. First, the constituent materials of memristor are introduced, focusing on metal oxides and organic materials such as titanium oxide (TiO 2 ). The choice of these materials is critical to memristor performance and applications. Secondly, the advantages and limitations of the preparation methods are discussed. Then the resistance switching mechanism of memristor was studied in depth, focusing on the generation principle and adjustment mechanism of memristor effect. These include important concepts such as charge migration, Schottky barrier formation and oxygen vacancy drift, which help to understand the electrical properties of memristors. In addition, we explore the prospects of memristors in practical applications. Finally, past and current work in this field is summarized, and the prospects and challenges of memristorrelated research are prospected.

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Self-Triggered Ternary control for Resilient Consensus against Mobile Adversarial Agents

Self-Triggered Ternary Control for Resilient Consensus again...

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IEEE Conference on Decision and control

作者： Hiroki Matsume Yuan Wang Hideaki Ishii Xavier Défago Department of Computer Science Tokyo Institute of Technology Tokyo/Yokohama Japan Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we consider the problem of multi-agent consensus in the presence of mobile adversaries. Faulty agents try to prevent the coordination among the regular agents and moreover are mobile in the sense that they can change their identities over time. Our approach towards resilient consensus is to extend the so-called mean subsequence reduced (MSR) algorithms to reduce the necessary communication based on two measures: The information exchanged by the agents takes the form of ternary data in each message and furthermore self-triggered method is used to keep the transmission frequency limited. Certain features are introduced to address issues specific to the mobile nature of the adversarial agents. We verify the effectiveness of the proposed algorithm by means of a numerical example.

关键词： Frequency measurement Consensus protocol Behavioral sciences Resilience

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Molecular communication networks: drug target scalability based on artificial intelligence prediction techniques

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Journal of Nanoparticle Research 2021年第3期23卷 1-23页

作者： A. R. Junejo Xiang Li Hina Madiha Soha Mohamed School of Control Science and Control Engineering Harbin Institute of Technology Harbin China School of Computer Science &Technology Shaanxi Normal University Xian China School of Computer Science & Technology Harbin Institute of Technology Harbin China

With the continuous improvement of computer performance and related technology, the combination of molecular communication networks (MCN) and artificial intelligence (AI) can find the prediction to analyze target methods that are molecular diagnosis (MD) and more efficient. To overcome this issue, researchers have suggested the novel paradigm of the MD that combines nanoparticles-medicine (NPs-M) with tools from synthetic biology to provide re-engineering of biological computing devices. Here, in this work, the prediction model of the target is established by combining the drug calculation model of MCN and deep neural network (DNN) including classification performance for prediction. We developed a numerical analysis model to refer to drug target scalability (DTS), on a genome-wide scale based on protein to protein interaction (PPI) such as in-term of cardiac-disease. Finally, the prediction of the strength and direction of binding affinity between drugs and targets is achieved. Furthermore, to expand the application of the model, with a combination of genetic databases, the micro-processes related to genes are displayed. Then, to combine medicine, a multi-layer network is established based on the effect on/off mechanism. We establish a systematic MCN evaluation model that reveals the basis of MD of medicine and serves for screening, reorientation, drug development, and other fields of medical healthcare industries (MHI). The engineering finding demonstrates that the integration of phenotypic, chemical index in the NPs-M and PPI cannot only achieve scalability but also finds new applications for an existing drug.

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiang Yang Shuxin Zhang Suyu Dong Long Xu Weihe Dong Xiaokun Li Pengzhong Sun Feng Jiang Xianyu Zhang Gongning Luo College of Computer and Control Engineering Northeast Forestry University Harbin China School of Medicine and Health Harbin Institute of Technology Harbin China School of Computer Science and Technology Heilongjiang University Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Breast Surgery Harbin Medical University Cancer Hospital Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level.

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EARLY PREDICTION OF NATURAL GAS PIPELINE LEAKS USING THE MKTCN MODEL

arXiv

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arXiv 2024年

作者： Li, Xuguang Zuo, Zhonglin Dong, Zheng Yang, Yang School of Electrical and Automation Wuhu Institute of Technology Wuhu China School of Control Science and Engineering Zhejiang University Hangzhou China ByteDance Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Natural gas pipeline leaks pose severe risks, leading to substantial economic losses and potential hazards to human safety. In this study, we develop an accurate model for the early prediction of pipeline leaks. To the best of our knowledge, unlike previous anomaly detection, this is the first application to use internal pipeline data for early prediction of leaks. The modeling process addresses two main challenges: long-term dependencies and sample imbalance. First, we introduce a dilated convolution-based prediction model to capture long-term dependencies, as dilated convolution expands the model’s receptive field without added computational cost. Second, to mitigate sample imbalance, we propose the MKTCN model, which incorporates the Kolmogorov-Arnold Network as the fully connected layer in a dilated convolution model, enhancing network generalization. Finally, we validate the MKTCN model through extensive experiments on two real-world datasets. Results demonstrate that MKTCN outperforms in generalization and classification, particularly under severe data imbalance, and effectively predicts leaks up to 5000 seconds in advance. Overall, the MKTCN model represents a significant advancement in early pipeline leak prediction, providing robust generalization and improved modeling of the long-term dependencies inherent in multi-dimensional time-series data. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural gas pipelines

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