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Intelligent Systems conference, IntelliSys 2023

Intelligent Systems Conference, IntelliSys 2023

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Intelligent Systems conference, IntelliSys 2023

ISBN: (纸本)9783031477140

The proceedings contain 57 papers. The special focus in this conference is on Intelligent Systems. The topics include: A Computational Situationally Self-controlled Brain and Mind Interface Under Uncertainty;ethical Concerns About Personhood, Responsibility, and Privacy in Active and Passive Brain-Computer Interfaces;The VesselAI Methodology for AI-Powered Decision Support Systems for the Maritime Industry;An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA;Convergence of the Mini-Batch SIHT Algorithm;graph Autoencoder-Based Detection of Unseen False data Injection Attacks in Smart Grids;causal analysis of Artificial Intelligence Adoption in Project Management;comparative Lightweight Scheme for Individual Identification Through Hand-Vein Patterns;URL Classification with Intrusion Detection System;designing Cancellation Intervention System with Sliding Lead Times;NP4G: Network Programming for Generalization;Blockchain and AI for Optimizing Bank data Security;streamlining Conceptual modeling;E-Step control: Solution for processing and analysis of IS Users Activities in the Context of Insider Threat Identification Based on Markov Chain;Machine Learning Based Intelligent Irrigation System Using WSN;comparison of Artificial Neural Networks Algorithms on datasets with Different Characteristics;boosting Federated Multitask Learning: Transfer Effects in Cross-Domain Drug-Target Interaction Prediction;Surveying Impacts of AI in Education and Creative Practices;active Risk Mitigation for Unmanned Aerial Systems;Towards Explainable AI: Relationship Between Twitter Sentiment, User Behaviour, and Bitcoin Price Prediction;automatic Generation of a Portuguese Land Cover Map with Machine Learning;Production Portfolio Theory ii—First Steps Towards a General Portfolio Theory and Numerical Examplifications;AI as a Threat to Education: Contrasting GPT-3 and Google in Answering Questions Along Bloom’s Taxonomy of Educational Objectives.

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On the use of DPCA in process fault detection and identification

On the use of DPCA in process fault detection and identifica...

引用

International conference on control, Automation and Diagnosis (ICCAD)

作者： Mohamed Guerfel Hassani Messaoud Electronics Department Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Laboratory of Automatic Control Signal and Image Processing (LARATSI) National Engineering School of Monastir University of Monastir Monastir Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

This paper proposes an innovative Dynamic Principal Component analysis (DPCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method is proposed to fix the data lag and the number of principal components to retain. A new structuration method is introduced to identify the process fault. It is based on the computation of common angles between residual subspaces of different modes. This structuration yields a concise set of detection indices, specifically sensitive to certain process faults while remaining unsensitive to others. The proposed FDI scheme is successfully applied to a three tank affected by several process faults.

关键词： Fault diagnosis Automation Fault detection Computational modeling Systems modeling data models Principal component analysis

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Numerical control Machining Error Compensation Technology Based on Three-coordinate Calibration

Numerical Control Machining Error Compensation Technology Ba...

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2023 International conference on Computer Network Security and Software Engineering, CNSSE 2023

作者： Wang, Yihan Jiangsu Automation Research Institute Jiangsu Lianyungang China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

The machining error of parts is the result of the coupling action of various error sources, such as the spatial error of machine tools in the processing system. Machining error modeling is to establish the machining error relationship model according to the source of machining error or the measurement result of the machining error to compensate and improve the error. The accuracy of the machine tool itself is difficult to meet the requirements of the product process size. Therefore, it is necessary to carry out targeted process research to improve the processing accuracy and solve the problem of insufficient production capacity. In this paper, the data verification technology of CMM is adopted to preprocess point cloud, such as point cloud elimination, curvature analysis, point cloud division, regression analysis and point cloud regeneration. Firstly, the maximum allowable error position is found in the error model to carry out the subsequent compensation research. Finally, the proposed method is tested in a simulation environment to verify its effectiveness. The research tells that it plays an important role in improving the position accuracy, shape accuracy, surface integrity and consistency of machining. © 2023 SPIE.

关键词： Machine tools

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Signal Timing Optimization at Intersections Based on NSGA-ii

Signal Timing Optimization at Intersections Based on NSGA-II

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Machine Learning and Computer Application (ICMLCA), International conference on

作者： Gaihua Gui Yuyan Liang Qiqi Zhang Zhongchao Wang Zhanjiang Yuan Guangdong Polytechnic Of Science and Technology Zhuhai Guangdong China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

This study combines the methods of data analysis and mathematical modeling to explore an effective solution about traffic flow control in well-known scenic towns. Firstly, the missing and abnormal data of license plate number are eliminated through the open source dataset. Then, through hierarchical clustering analysis to divide the peak traffic flow hours, statistical traffic flow hour data, and using NSGA-ii algorithm to optimize the signal timing, and establish a service level model to analyze the results, and it is found that it can effectively improve the traffic efficiency and alleviate traffic congestion. The case used in this paper is the 2024 National Student Mathematical modeling Competition E question - traffic flow control of the topic for research and analysis, the results obtained and the actual review of the results of the information very much in line with the results.

关键词： Analytical models Flow production systems Reviews Roads Transportation Mathematical models data models Timing Resource management Traffic congestion

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Optimal Bayesian Biomarker Selection for Gene Regulatory Networks under Regulatory Model Uncertainty

Optimal Bayesian Biomarker Selection for Gene Regulatory Net...

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American control conference (ACC)

作者： Imani, Mahdi Imani, Mohsen Ghoreishi, Seyede Fatemeh Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Univ Calif Irvine Dept Comp Sci Irvine CA USA Northeastern Univ Dept Civil & Environm Engn Boston MA USA Northeastern Univ Khoury Coll Comp Sci Boston MA USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

Gene regulatory networks (GRNs) are large and complex dynamical systems often monitored through RNA sequencing or microarray technologies. Genomics studies often focus on a small subset of genes and analyze only these genes due to the huge cost and time-limit constraints. Therefore, selecting a small subset of genes that carries the highest information about the underlying process of these complex systems is highly desired. The existing biomarker selection techniques rely on unrealistic assumptions such as direct observability of genes' states as well as the availability of perfect knowledge about the modeling process. To address the aforementioned issues, this paper models GRNs with uncertain regulatory models with the signal model of partially-observed Boolean dynamical systems (POBDS) and derives the optimal Bayesian biomarker selection framework given the noisy available gene-expression data. The proposed framework is built on the multiple-model adaptive estimation (MMAE) framework and the optimal minimum mean-square error (MMSE) state estimator for POBDS, called Boolean Kalman smoother (BKS). The proposed framework is an optimal solution relative to the uncertainty class, and its high performance is demonstrated using the mammalian cell-cycle Boolean network model and the p53-MDM2 negative feedback loop observed through gene-expression data.

关键词： Adaptation models Sequential analysis Uncertainty Biological system modeling data models Bayes methods Negative feedback loops

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Application TRIKE Methodology When modeling Threats to APCs Information Security

Application TRIKE Methodology When Modeling Threats to APCs ...

引用

International Russian Automation conference (RusAutoCon)

作者： Chernov, D. Tula State Univ 92 Lenina Ave Tula 300012 Russia

ISBN: (纸本)9783030942021;9783030942014

In the modern world automation technologies of industrial processes have found wide application. Automated process control systems have become an important part of enterprises that operate in different economic fields and life support facilities all over the world. Nevertheless, the high growth of automation means raises the acute problem of providing APCs information security from external and internal threats. There are systematic reports in the media about new critical vulnerabilities in industrial equipment and attacks based on the exploitation of such vulnerabilities. When designing such systems it is necessary to estimate possible information security threats that already exist in the system or that are predicted to appear. That is why threat modeling is an important part of providing information security at industrial facilities using automation. In this work the author investigates the methodology of estimating threats to software security TRIKE. The research is aimed at finding general approaches to determine threat sources, tactics, and techniques for making implementation scenarios of threats to software information security, for applying them to provide cyber security of automated process control systems. In the frameworks of the subject main-streaming, the research gives a description of cyber-attacks at big industrial facilities, and their basic vulnerabilities are emphasized. In the article the analysis of TRIKE methodology has been done. It is aimed at determining basic threat modeling stages applicable to industrial automation systems. The approach of TRIKE methodology to generating a list of threats to information security is formalized. To achieve the aims the author constructs DFD data flow diagrams with the decomposition of peculiar elements of the algorithm for modeling threats to information security of automated process control systems using TRIKE methodology.

关键词： Automated process control systems Information security Threat Threat model TRIKE data flow diagrams Attack library

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Online Decision Mining and Monitoring in process-Aware Information Systems 41st

Online Decision Mining and Monitoring in Process-Aware Infor...

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41st International conference on Conceptual modeling (ER)

作者： Scheibel, Beate Rinderle-Ma, Stefanie Univ Vienna Fac Comp Sci Res Grp Workflow Syst & Technol Vienna Austria Tech Univ Munich Chair Informat Syst & Business Proc Management Garching Germany

ISBN: (纸本)9783031179952;9783031179945

Decision mining enables discovery of decision rules guiding the control flow in processes. Existing decision mining techniques deal with different kinds of decision rules, e.g., overlapping rules, or including data elements, for example, time series data. Though online process mining and monitoring are gaining traction, online decision mining algorithms are still missing. Decision rules can be, similarly to process models, subject to change during runtime due to, for example, changing regulations or customer requirements. In order to address these runtime challenges, this paper proposes an approach that i) discovers decision rules during runtime and ii) continuously monitors and adapts discovered rules to reflect changes. Furthermore, the concept of a decision rule history is proposed, enabling (manual) identification of change patterns. The feasibility and the applicability of the approach is evaluated based on three synthetic datasets, BPIC12, BPIC20 and sepsis data set.

关键词： Online decision mining Decision rule evolution Decision rule monitoring process-aware information systems

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6th International conference on Innovative Computing, IC 2023

6th International Conference on Innovative Computing, IC 202...

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6th International conference on Innovative Computing, IC 2023

ISBN: (纸本)9789819922864

The proceedings contain 235 papers. The special focus in this conference is on Innovative Computing. The topics include: Research on Visualization of Student Management System Based on Distributed Storage Technology;banking Fintech Capability Evaluation System Based on data Mining;technology Financial Service Platform Based on data Mining Algorithm;overlapping Region Extraction Method of 3D Laser Point Cloud Registration on Account of Artificial Intelligence Algorithm;smart Urban Carbon Emission Management Platform Based on Energy Big data;A Distribution Network Reconfiguration Method Considering Optimal Distribution of DG Output;a Study of Intersubjectivity in Translation Based on Communicative Behavior Theory and Computer Technology;analysis and Research on Children’s Cognitive Psychological System Based on Digital Learning Terminal;analysis of E-Commerce Live Broadcast Marketing Strategy Based on Big data Algorithm;analysis of Flow data of E-Commerce Project Incubation Park Based on Computer Algorithm;Research on the Application of BIM Technology in the Whole process Cost Management of Construction Project;analysis of Teaching Management data Based on Decision Tree Algorithm;analysis on Reasonable Planning and Design of Port Waters and Channels;ant Colony Algorithm Theory and Its Application in control Engineering;application and Optimization of Style Transfer Algorithm in Contemporary Cultural and Creative Products;application of Ant Colony Algorithm in Chinese Language and Literature Education;Application of BP Neural Network Algorithm in Teaching Evaluation and Learning Prediction;application of Computer Aided Technology in Kindergarten Interior Design Under the "New Crown" Epidemic;application of data Mining Algorithm in Computer Mathematical modeling;data Compression Algorithm of Power System.

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6th International conference on Innovative Computing, IC 2023

6th International Conference on Innovative Computing, IC 202...

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6th International conference on Innovative Computing, IC 2023

ISBN: (纸本)9789819920914

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iEMNN: An Iterative Integration Method for Single-Cell Transcriptomic data Based on Network Similarity Enhancement and Mutual Nearest Neighbors 20th

iEMNN: An Iterative Integration Method for Single-Cell Trans...

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20th International conference on Intelligent Computing (ICIC)

作者： Lin, Xuesheng Jiang, Yusheng Guan, Jinting Xiamen Univ Dept Automat Xiamen 361102 Fujian Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9789819756919;9789819756926

With the widespread application of single-cell RNA sequencing technology, integrating different batches of data has become a crucial step. Batch effects arise from non-biological variations such as different sequencing batches, sequencing protocols, sequencing depths, and so on. Batch effects introduce systematic biases and confound biological variations of interest, which have a detrimental impact on the validity of study findings. Eliminating batch effects can increase comparability and repeatability, prevent bias and confusion, and improve data quality and consistency. Currently, a great deal of batch effect removal techniques for single-cell transcriptome data has been developed based on finding mutual nearest neighbors (MNNs) across batches, the accuracy of which influences greatly the effect of data correction and the subsequent integrative analysis. To enhance the identification of MNNs, we propose an iterative integration method, called iEMNN, for single-cell transcriptome data by utilizing network similarity enhancement. iEMNN facilitates the detection of similar cells while separating distinct cells. iEMNN applies multiple iterations to improve the effectiveness of the integration process and can perform data correction in both high-dimensional and low-dimensional spaces. Through systematic experiments and comparisons with the existing methods, we demonstrate that iEMNN outperforms other approaches in improving batch correction removal and enhancing clustering performance across different scenarios. This study provides a new perspective and an effective solution for the field of single-cell transcriptomic data integration, with potential significance for a deeper understanding of cellular heterogeneity and dynamic changes.

关键词： Single-cell Transcriptomic data Mutual Nearest Neighbors Network Similarity Enhancement Batch Effect data Integration

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