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10th International conference on Modelling and Simulation for Autonomous Systems

作者： Reinisch, Fabian Strohal, Michael Stuetz, Peter Univ Bundeswehr Munich Inst Flight Syst Werner Heisenberg Weg 39 D-85577 Neubiberg Germany

ISBN: (纸本)9783031713965;9783031713972

Due to a rising number of entities and more advanced systems, modern air combat engagements are increasing in complexity. Therefore, to ensure success in pilot training and perform accurate threat evaluation using simulations, it is substantial to not only replicate and simulate the physical properties of the Computer-Generated forces (CGFs) to an adequate degree but also to provide them with sufficiently realistic, coordinated and situation-adaptive behavior. Additionally, most air combat research assumes that all aircraft information is known, however, in real-world scenarios, multiple factors, such as sensor performance limitations, can lead to missing or incorrect information about the position, altitude, or velocity of adversary aircraft. In this paper, we propose a Tactical Planning process as part of an overarching CGF Team Behavior Agent Function utilizing information such as threat risk and the enemy's intent from a Situation analysis created with realistically available data. This process is partitioned into two stages, Team Planning and Maneuver Selection. Team Planning consists of deciding whether the mission itself should be commenced or aborted, selecting Tactics to counter the threats, as well as performing a Targeting in which threat aircraft are assigned to the individual CGFs. Further, in Maneuver Selection, the own current risks are assessed and used to continuously decide the current task for each CGF with respect to its target. Following, the maneuver command itself is being selected and sent to the simulated aircraft. This is done within an evaluation of the own chances and risks by, in a first step, identifying suitable tactical maneuver types, in a second step, narrow down their parameters, so that, in the final step, predicted risks from the Situation analysis can be incorporated in the selection process as well. We employ Behavior Trees to guide the CGFs through these different tasks, while repeatedly assessing the developing risks to be ab

关键词： Computer Generated Forces Behavior modeling Tactical Planning Air Combat Pilot Training Threat Evaluation Behavior Trees

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SwiftMend: An Approach to Detect and Repair Activity Label Quality Issues in process Event Streams 30th

SwiftMend: An Approach to Detect and Repair Activity Label...

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30th International conference on Cooperative Information Systems, CoopIS 2024

作者： Kalukapuge, Savandi ter Hofstede, Arthur H. M. Wynn, Moe T. Queensland University of Technology Brisbane Australia

ISBN: (纸本)9783031813740

process mining (PM) techniques extract insights from event logs to discover, monitor, and improve business processes. The quality of input data significantly impacts the reliability and accuracy of these insights. Existing approaches to detect and repair these issues are limited to offline data pre-processing. Given the potential of real-time process analysis to provide valuable business-related insights, online PM has gained interest. However, process-data quality (PDQ) issues in process event streams (PES) beyond anomalous events or traces have not yet been addressed. Existing PDQ management approaches lack the adaptability and incremental processing capabilities necessary for streaming event data and evolving processes. This paper presents a novel approach for dynamically detecting and repairing synonymous, polluted, and distorted activity labels in PES, which are common issues affecting the quality of PM outcomes. By incrementally maintaining stabilised activity control-flow context using memory-efficient approximate data structures, the approach detects and merges similar labels or splits dissimilar labels on the fly. An incremental hierarchical clustering algorithm, incorporating decaying and forgetting mechanisms, is employed for the dynamic repair of similar activities, ensuring efficiency and adaptability. The approach is validated using publicly available real-life logs from two hospitals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distorted label Online process mining Polluted label process data quality Synonymous label

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Prompt gamma neutron activation analysis: A review of applications, design, analytics, challenges, and prospects

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RADIATION PHYSICS AND CHEMISTRY 2025年 234卷

作者： Abdelnour, Marina R. Liu, Juntao Hossny, K. Wajid, A. M. Li, Wenxin Liu, Zhiyi Lanzhou Univ Frontiers Sci Ctr Rare Isotopes Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Sch Nucl Sci & Technol Lanzhou 730000 Gansu Peoples R China Ain Shams Univ Fac Women Arts Sci & Educ Dept Phys Cairo Egypt KTH Royal Inst Technol Dept Phys Nucl Sci & Engn NSE Div Stockholm Sweden

Prompt gamma-ray neutron activation analysis (PGNAA) is a powerful, non-destructive technique widely used for multi-elemental analysis, valued for its rapid, on-site measurement capability and high sensitivity across diverse elements. Based on neutron capture reactions, PGNAA enables precise identification and quantification of elements by detecting characteristic prompt gamma emissions from neutron-captured nuclei. Recent advances in computational modeling, including Monte Carlo simulations, have revolutionized PGNAA setup design, allowing optimized configurations that enhance measurement accuracy and significantly reduce background noise. PGNAA's versatility has led to its adoption in critical applications, including food and agriculture, environmental monitoring, industrial process control, and security screening. This review covers PGNAA's setup, covering essential components such as neutron sources, moderators, collimators, and gamma detection, and highlights modern optimization techniques like machine learning and genetic algorithms. These transformative methods have boosted PGNAA's signal-to-noise ratio and enabled precise, efficient system designs. Additionally, parametric and sensitivity analyses, including the Morris method, are critical in refining system robustness under diverse operational conditions. Advanced data processing approaches, such as noise-mitigation preprocessing and post-processing, further improve the reliability of the information extracted. Despite its many strengths, PGNAA faces challenges, such as reducing background noise interference preserving high sensitivity and specificity, ensuring compact and deployable system designs, and meeting safety and regulatory standards are all crucial to the success of PGNAA detection systems. This review provides a comprehensive overview of PGNAA, addressing these practical criteria and identifying future directions to broaden its application potential in advanced analytical fields.

关键词： PGNAA Applications Design Analytics Machine learning

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Enhancing High-Speed Train Operations with the Random Forest Algorithm for Predictive control 11th

Enhancing High-Speed Train Operations with the Random Forest...

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11th International conference on Traffic and Transportation Studies, ICTTS 2024

作者： Zhao, Yang Huang, Susu Zhang, Miao Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796397

High-speed railways are vital infrastructure projects that significantly enhance regional connectivity and collaboration. This study presents an advanced data-driven predictive control methodology to optimize the precision of high-speed train operations. By analyzing operational data across various phases—startup, constant speed, and braking—this study identifies critical patterns and performance variations under different conditions, revealing optimization opportunities. The proposed predictive control system, based on a Random Forest algorithm model, incorporates key processes such as error quantification, predictive control, internal modeling, disturbance compensation, and feedback loops. The internal model utilizes Random Forest algorithms, involving detailed steps like training data analysis, random regression forest development, regression tree calculations, and aggregation of tree estimates to forecast future train states. Simulation and validation results demonstrate that integrating multiple parameters as predictive feature sets in the Random Forest model significantly improves forecast accuracy. Future research could further enhance this machine learning-based control method by incorporating more diverse data sources, thereby improving the accuracy and reliability of train predictions, advancing automation and intelligence in high-speed railways, and promoting sustainability and efficiency. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Predictive control systems

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Elderly Health Care data Integration Framework: Design and Implementation 4th

Elderly Health Care Data Integration Framework: Design and I...

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4th International conference on data and Information in Online Environments

作者： Zhong, Zhean Liu, Chenyu Zhao, Jing Xiao, Jianmao Gao, Qinghang Gao, Chao Ouyang, Chuying Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China Jiangxi Prov Engn Res Ctr Blockchain Data Secur & Nanchang 330022 Jiangxi Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Jiangxi Normal Univ Dept Phys Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9783031807121;9783031807138

The application of Internet of Things (IoT) detection devices in the field of elderly healthy generates a large amount of detection data, which faces problems such as diversified data sources and non-uniform parsing methods. The fragmentation and confusion of the process from data subscription to data analysis have become important challenges affecting the development of the healthy aging field. In this paper, we designed and proposed an elderly health care framework with a model-as-a-service orientation. The framework automates the integration of health data for the elderly, including subscription services. It handles Model Training analysis through a workflow-based system. Users can configure workflows using a low-code method provided by the platform. First, we abstracted and formalized the integration problem and workflow and defined a workflow description language. Then, we built an healthy aging unified modeling framework based on the problem model and provided a general introduction to the framework architecture. The module for analyzing is responsible for model training, persistence, and data analysis, enabling users to customize the selection of machine learning models and corresponding parametric. Ultimately, we built a model library and an external RESTful interface based on the automation option of Automated machine learning (AutoML). The experiments demonstrate this paper's proposed framework's feasibility and effectiveness.

关键词： data curation Elderly health care Model-as-a-Service Machine learning

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R-matrix analysis of ²²Ne(α, n)²⁵Mg and ²²Ne(α, γ)²⁶Mg reactions

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JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS 2025年第2期52卷 025201-025201页

作者： Santra, Rajkumar Roy, Subinit Ali, S. K. Mustak Univ Calcutta Dept Phys Kolkata 700009 India Michigan State Univ Facil Rare Isotope Beams E Lansing MI 48824 USA

The reaction rates of 22Ne(alpha, n)25Mg and its competing channel 22Ne(alpha, gamma)26Mg control the production of neutron flux for weak s-process nucleosynthesis in low mass asymptotic giant branch stars and in massive stars with M >= 10M circle dot. The temperature range of interest for these reactions lies between 0.2 and 0.4 GK. However, the rates of these reactions are poorly constrained at these temperatures due to uncertainties in the nuclear properties of several resonance states in the compound nucleus 26Mg, lying within the Gamow window. The present work reports a full R-matrix evaluation of the 22Ne(alpha, n)25Mg and 22Ne(alpha, gamma)26Mg reaction rates using updated nuclear data of 26Mg states. Previous rate evaluation by Adsley et al and R-matrix calculations of Wiescher et al were limited by using narrow resonance approximations and omission of the resonances below Er = 705 keV, respectively. In this work, the R-matrix fit to the available 22Ne(alpha, n)25Mg reaction data is performed by including the contributions of previously neglected resonances below Er = 705 keV and considering the interference effects. The (alpha, n) reaction rate from the present R-matrix evaluations is noticeably higher than the narrow resonance approximation calculations in the temperature range 0.1-0.3 GK. In particular, the present (alpha, n) reaction rate is significantly higher (7.5 - 4.5 times) compared to Adsley et al at 0.2-0.3 GK and approximate to 2 times greater than Wiescher et al at 0.3 GK. The estimated reaction rate ratio of (alpha, n) to (alpha, gamma) in the relevant temperature window 0.2-0.8 GK indicates that the production of neutrons for the s-process is more likely than the radiative alpha capture reaction, compared to the previous estimate by Adsley et al.

关键词： R-matrix modeling resonance and direct capture reaction He induced nuclear reactions reaction rate

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Distributed Photovoltaic Power Forecasting Considering Perceived data Quality

Distributed Photovoltaic Power Forecasting Considering Perce...

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2025 International conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Liu, Xiaodan Hou, Chunlei Du, Lishi Beijing FibrLINK Communications Co. Ltd. Beijing China

ISBN: (纸本)9798331506797

In distributed photovoltaic (PV) power generation systems, data quality plays a critical role in the accuracy of predictive models. However, the complexity of distributed PV sensor data, including issues such as missing values, noise and anomalous bias, often leads to data quality degradation, which seriously affects the reliability of the prediction. To address these challenges, this study proposes a novel forecasting method that integrates sensor data quality into the forecasting process to improve the reliability and robustness of forecasts. The method starts with a detailed analysis of distributed PV sensor data characteristics to identify patterns of data quality degradation and their impact on forecast accuracy. data recovery algorithm based on adaptive mean drift clustering with multi-paradigm optimisation is presented to effectively deal with data loss and reduce noise. In addition, an adversarial graphical neural network (GNN) is used for knowledge migration and spatio-temporal correlation modelling to make accurate predictions even in the presence of perceived data quality degradation. The GNN framework exploits spatial dependence and temporal dynamics to create robust prediction models. Simulation results show that the proposed method significantly improves the data quality and prediction accuracy of distributed PV power generation, providing a reliable basis for decision making in energy management systems. © 2025 IEEE.

关键词： adaptive mean drift clustering component distributed photovoltaic generation perceived data quality spatio-temporal correlation modeling style

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Machine Learning Serving the School Orientation process 3rd

Machine Learning Serving the School Orientation Process

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3rd International conference on Language processing and Knowledge Management, LPKM 2024

作者： Ben Fradj, Walid Turki, Mohamed Gargouri, Faiez MIRACL Laboratory ISIMS University of Sfax P.O. Box 242 Sfax3021 Tunisia University of Hail P.O. Box 2440 Hail81451 Saudi Arabia

ISBN: (纸本)9783031850660

Regardless of the spatial or temporal context, school orientation plays a pivotal role in determining the future of a high school student. Guiding the student to make the right choice in orientation requires significant effort from multiple stakeholders, including parents, friends, administration, teachers, and academic counselors. In many cases, the high school student may disregard the advice provided by these stakeholders, even though they lack sufficient knowledge to reconcile their desires with their abilities. Consequently, the final decision regarding orientation may be based on the student’s conviction, a random choice, imposed by parents, or an administrative decision. In this article, we propose a novel approach based on machine learning to predict the appropriate orientation choice for a new student based on their qualifications, results, and professional personality. In this context, we followed the standardized CRISP-DM methodology, widely used for data mining and data analysis projects. The data used were collected from students who have already undergone the orientation process. Subsequently, these data were carefully processed. During the modeling phase, we compared five algorithms, namely Decision Tree, KNN, Random Forest, SVM, and Logistic Regression. In the evaluation stage, we obtained promising results. Finally, according to our research, this approach represents an innovation in the field of secondary education, specifically in school orientation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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The Changes in Management control Systems of Moroccan Industrial Enterprises: An Empirical Study

The Changes in Management Control Systems of Moroccan Indust...

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International conference on Smart Business and technology, ICSBT 2024

作者： Othmane, Jaouhari Tissafi Fouad, Mohammed Amine Rabat Morocco

ISBN: (纸本)9783031866975

This study aims to examine the determinants of changes in management control systems (MCS) within industrial enterprises in Morocco. To address this topic, the concepts of management control systems and the changes in these systems were defined, and relevant theories and models were employed based on previous empirical studies. These elements were used to develop a conceptual research model describing the determinants of these changes, which have traditionally been positively associated. An empirical study was conducted in the Moroccan context, involving 186 companies operating in various industrial sectors across different cities of Morocco. For the analysis of collected data, a structural equation modeling approach using AMOS 26 was adopted. The results highlight divergences from previous studies. While variables such as size, competitive intensity, differentiation strategy, and organizational learning were found to significantly impact changes observed in management control systems within the surveyed companies, decentralization of the organizational structure did not prove to be a significant factor. This finding challenges the moderator role previously suggested by earlier studies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Enterprise resource management

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5th International conference on Electrical and Electronics Engineering, ICEEE 2024

5th International Conference on Electrical and Electronics E...

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5th International conference on Electrical and Electronics Engineering, ICEEE 2024

ISBN: (纸本)9789819790364

The proceedings contain 81 papers. The special focus in this conference is on Electrical and Electronics Engineering. The topics include: Design of a Single-Axis Solar Tracking System with a PI controller Implemented in MATLAB/Simulink;Reduce Multiple Power Quality Issues with UPQC by Using UVTG and PQ Current control Techniques Based on Snake Optimization;Utilize UPQC to Reduce Power Quality Issues by SRF control Strategies Based on Snake Optimization;Integrating Industrial PV-Battery Systems with Utility Grid: Techno Economic analysis for an Industry;AI Based Phase Identification to Integrate Distributed Energy Resources in Distribution Network;optimal Nearest Level control Modulation Technique for 25-Level Asymmetrical Hybrid Multilevel Inverter Topologies with Reduced Switch Stress;performance Investigation of a Vacuum-Enhanced Direct Contact Membrane Distillation Coupled with a Photovoltaic-Thermal System;utilization of Redundant Switching States in Asymmetric Cascaded H-bridge Multilevel Inverter;Modular DC-DC Converter for Battery Bank Interfacing in DC Microgrid;the Performance Effect of Seasonality Feature in Solar Photovoltaic Power Prediction Using Machine Learning;Hybrid Power System Simulation and modeling for PV and Wind;analysis of Different Energy Storage Characteristic Under Load Using BLDC Motor of Electric Scooter;advanced Federated Spectrum Busting: Federated Learning-Based Transmitter Identification System for Conventional Warfare and Counter Terrorism;Leveraging Synthetic data and LSTM Networks for Reliable RUl Estimation of EV battery;Thermal analysis of HTS Tape for SMES-Integrated Wind Turbines During Voltage Dips;maximizing Customer Satisfaction and Grid Resilience Through a Three-Tiered Energy Bidding Framework;Techniques to Rapid and Economic Prototyping of PWM Inverters;Analyzing the Impacts on Stability of IBR-Dominated Power Systems Under Evolving Composite Loads.

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