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18th International Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Mohamad, Mohamad Ponzio, Francesco Gassier, Maxime Pote, Nicolas Ambrosetti, Damien Descombes, Xavier Université Côte d’Azur INRIA CNRS I3S INSERM IBV Sophia Antipolis France Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Pathology Bichat Hospital Assistance Publique-Ĥopitaux de Paris Paris France Department of Pathology CHU Nice Université Côte d’Azur Nice France

In computational pathology, whole slide images represent the primary data source for AI-driven diagnostic algorithms. However, due to their high resolution and large size, these images undergo a patching phase. In this paper, we approach the diagnostic process from a pathologist’s perspective, modeling it as a Sequential decision-making problem using reinforcement learning. We build a foundational environment designed to support a range of whole slide applications. We showcase its capability by using it to construct a toy goal conditioned Navigation environment. Finally, we present an agent trained within this environment and provide results that emphasize both the promise of reinforcement learning in histopathology and the distinct challenges it faces. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Deep reinforcement learning

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A Machine Learning-Based Trading Strategy Integrating Technical analysis and Multi-agent Simulation 22nd

A Machine Learning-Based Trading Strategy Integrating Techni...

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22nd International conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Zouaghia, Zakia Kodia, Zahra Ben Said, Lamjed ISG Tunis SMART Lab University of Tunis Tunis Tunisia

ISBN: (纸本)9783031704147

This paper introduces TradeStrat-ML, a novel framework for stock market trading. It integrates various techniques: technical analysis, hybrid machine learning models, multi-agent-based simulations (MABS), and financial modeling for stock market analysis and future predictions. The process involves using a Convolutional Neural Network (CNN) to extract features from preprocessed financial data. The output of this model is then combined with three machine learning models (Gated Recurrent Unit (GRU), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR)) to predict future stock price indices. Subsequently, the models are evaluated, compared, and the most accurate model is selected for stock market prediction. In the final stage, the selected model, along with the Simple Moving Average (SMA) indicator, is used to develop an optimized trading strategy. The TradeStrat-ML system is organized into four main layers and validated using MABS simulations. Comparative analysis and simulation experiments collectively indicate that this new combination prediction model is a potent and practical tool for informed investment decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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analysis Methodology of Weather-Driven Airspace Flow Programs

Analysis Methodology of Weather-Driven Airspace Flow Program...

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Integrated Communications, Navigation and Surveillance conference, ICNS

作者： Gabriele Enea Tom G. Reynolds MIT Lincoln Laboratory Air Traffic Control & Weather Systems Group Lexington MA USA

ISBN: (数字)9798331534738

ISBN: (纸本)9798331534745

Weather impacts air traffic operations by reducing the airspace capacity that air traffic control can use to process traffic. In this paper, a general framework for systematic comparison of weather-related air traffic management challenges originally developed for airports is extended to en route airspace using Jacksonville air traffic control center as an example. A case day in June of 2023 is presented to illustrate the framework and to identify times of under-used airspace capacity from historical data. The role of advanced decision-support tools based on weather models to take advantage of potential missed opportunities for fully utilizing available capacity is also discussed in the paper.

关键词： Systematics Navigation Surveillance Atmospheric modeling process control FAA Traffic control Air traffic control Contracts Meteorology

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Development of a Virtual Ground Testing Model for Aerospace Electromechanical Actuator Design Performance Evaluation

Development of a Virtual Ground Testing Model for Aerospace ...

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International conference on Applied Nonlinear Dynamics, Vibration and control, ICANDVC 2024

作者： Yang, Shilin Li, Chuanyang Guo, Ning Xu, Chao Hu, Changhua School of Astronautics Northwestern Polytechnical University Xi’an710072 China Laboratory of Intelligent Control Rocket Force University of Engineering Xi’an 710025 China

ISBN: (纸本)9789819633166

The electromechanical actuator (EMA) has been widely applied in the aerospace field due to its high reliability and maintainability. As a key component of the flight control system of an aerospace vehicle, the performance of the electromechanical actuator directly affects the function of the entire vehicle. In this paper, detailed dynamic modeling of the servo controller, motor, and mechanical transmission mechanism is conducted considering the elastic stiffness of various mechanical components and the nonlinearity of multi-link clearances inside the actuator. The multidisciplinary coupled dynamic model of the electromechanical actuator-fin system is established through a modular modeling method to relieve ground testing efforts. Then, a series of ground tests are carried out based on the virtual prototype in digital space. In these virtual tests, simulation analysis of electromechanical actuator performance under different operating conditions enables the rapid acquisition of various performance indicators of the system. The test results are compared and analyzed with the performance requirements to verify that the system meets the requirements. The results show that the multidisciplinary coupled dynamics model proposed in this paper can quickly obtain the required performance parameters and give evaluated results. Compared with traditional ground testing, the virtual ground testing method significantly simplifies the test process, reduces the test cost and improves efficiency. In addition, this study lays the foundation for the fully digital design and optimization of high-performance electromechanical actuator systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Rotation

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Selective Miniature Circuit Breaker Operation Mechanism Motion process Mechanical Properties Optimization analysis

Selective Miniature Circuit Breaker Operation Mechanism Moti...

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Peng, Jin Wang, Xianbing Huang, Kecheng Peng, Jiyu School of Mechanical and Electrical Engineering Chuzhou Univesity Chuzhou239000 China School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China

ISBN: (纸本)9789819620418

A force characteristics mathematical modeling of motion process and optimization method was proposed to handle the selective miniature circuit breaker (SMCB) performance issue. The proposed solution resolved the collaboration of force characteristics among the operation mechanism components, and improved the miniature circuit breaker control characteristics. Based on the operation mechanism principle, it established the mechanical model of closing, breaking and tripping process. The Adams simulation platform was set up to analyze the operating mechanism motion process force energy distribution. Besides, it built the mathematical model for the operation mechanism structural parameters, and analyzed the topology optimization design via the quantum particle swarm algorithm. The optimized handle operating force was 7.4 N, and the operation mechanism achieved the optimal operation performance on the basis of reliable closing of miniature circuit breaker. The optimized tripping force was 0.48 N, which minimized the friction between the extension plate upper end and the lock catch upper end and achieved the miniature circuit breaker quick release. Finally, it designed the 100 A SMCB experiment prototype. The experimental results showed that the proposed prototype achieved good control performance, and the operation mechanism force characteristics collaborated well with the electromagnetic mechanism motion threshold characteristics under short-circuit. Thus, it met the selective protection goal. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Design of high impact control switch based on simply-supported beam electrode 24

Design of high impact control switch based on simply-support...

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Proceedings of the 2024 4th International conference on Computational modeling, Simulation and data analysis

作者： Shiyao Zhang Xinjie Wang Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (纸本)9798400711831

Aiming at the problem that the design of the high impact control switch is difficult to work stably and reliably under high impact environment, and the design is simple and the principle is simple, a new type of high impact control switch with a simple beam electrode core is designed. The working principle and working process of the model switch are defined, and the key parameters of the model switch are designed and checked. The function and reliability of the model switch are verified by simulation analysis. This model has the advantages of a small structure, simple principle, and small hysteresis time, and it can realize high precision control.

关键词： High impact

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Application Research of Intelligent Machinery Design Based on CAD System 14th

Application Research of Intelligent Machinery Design Based o...

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14th International conference on Frontier Computing, FC 2024

作者： Wang, Yinghui Guangdong Innovative Technical College Dongguan523960 China

ISBN: (纸本)9789819627974

With the development of artificial intelligence (AI) and machine learning (ML) technologies, CAD systems have evolved from simple drawing tools to complex design and analysis platforms. The system is now able to leverage AI algorithms to optimize the design process and assist designers through pattern recognition, automated design processes, and parameter optimization. Today's intelligent CAD systems can automatically identify design requirements and constraints to provide more accurate design solutions. Moreover, the intelligent CAD system can also carry out complex simulation and analysis, and the application research of intelligent mechanical design based on CAD system shows the great potential of this field, which not only improves the design quality and efficiency, but also opens new possibilities for the future development of the mechanical design industry. According to the experimental data, the success rate of intelligent mechanical design, the adaptation rate of material selection, the accuracy rate of manufacturing process and the cost control scheme of the CAD system combined with manual audit are all above 99.88%, and the error rate is below 0.12%. Moreover, with the continuous progress of technology, the application of intelligent CAD system in the field of mechanical design will be more extensive and in-depth in the near future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application Design Artificial Intelligence CAD Technology Cost control Mechanical Design

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Improving Organizational Knowledge control with the use of NLP

Improving Organizational Knowledge Control with the use of N...

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Intelligent control, Computing and Communications (IC3), International conference on

作者： Susovan Kumar Pan Ankita Tiwari Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Knowledge management has emerged as a key enabler of organisational learning and innovation in the digital era. Over data helps organizations to collect, transform and act upon these insights towards process innovation and decision victory. Rooted in a dynamic capability lens of organizational learning, this study extends theory and practice by illustrating how knowledge management systems, powered by NLP, can provide transformational impact in terms of guiding organizational learning to achieve competitive advantage in the midst of evolving business scopes. Failure to capture, share and innovate organizational learning allows an organisation to learn from its best practices and adequately adapt to change. Traditional KM approaches have been biased to the codification of explicit knowledge which, by and large, have focused on the codification of explicit knowledge, in general, they did not pay much attention to tacit knowledge embedded in extracted information from the organization communication, documents, and external data stores. NLP technologies help fill this vacuum by enabling organizations to work with both structured and unstructured data in a manner capable of uncovering previously opaque patterns, insights and knowledge. Leveraging essential NLP techniques such as sentiment analysis, topic modeling, entity recognition, and text summarization, organizations can generate insights from vast datasets. As a few examples, qualitative analysis can be used to to measure employee satisfaction or customer feedback using sentiment analysis etc. or topic modeling can work to surface new trends or areas of knowledge that are critical for strategic initiatives. Entity recognition also facilitates the identification of key players, relations and resources, which streamlines the decision-making process. When paired with machine learning algorithms, this kind of NLP tooling provides insight into key topics in real time, automating repetitive tasks and enabling adaptive lea

关键词： Sentiment analysis Analytical models Technological innovation Virtual assistants Transforms Market research Natural language processing Knowledge management Real-time systems data mining

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The effect of mineralogical, mechanical, physical, and dynamic properties on rock brittleness using statistical and soft computing methods

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EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-29页

作者： Xu, Cairui Zhang, Xinyu Yu, Jiuhong Hou, Kang Rastegarnia, Ahmad Gholipour, Sajjad Nanjing Tech Univ Coll Comp & Informat Engn Nanjing 211800 Peoples R China Jinling Univ Sci & Technol Sch Intelligent Sci & Control Engn Nanjing 211169 Peoples R China Ningbo Univ Finance & Econ Res Inst Data Intelligence Ningbo 315175 Peoples R China Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China Ferdowsi Univ Mashhad Fac Sci Dept Geol Mashhad Iran Urmia Univ Technol Engn Fac Khoy Dept Civil Engn Orumiyeh Iran

Brittleness is one of the most main strength characteristics of rocks that is extensively utilized in drilling operations. This study aimed to estimate the brittleness index (BI) of sandstones using the percentage of quartz, feldspar, fragments, point load value (PLV), water absorption, P-wave velocity (PW), density, and porosity. This study utilized a range of prediction models, namely simple and multivariate linear regression analysis (MVLRA), multilayer backpropagation neural network (MBPN) with diverse learning rates and training algorithms, random forest (RF), Gaussian process regression (GPR), support vector regression (SVR) with various kernel functions, and adaptive neuro-fuzzy inference system (ANFIS). The sandstones under investigation were classified into litharenite and feldspathic litharenite categories. The observation revealed that as the percentage of feldspar and quartz increased, while fragments decreased, there was a corresponding increase in the brittleness index. Quartz and carbonate fragments had the highest and lowest effects on brittleness, respectively. The statistical analysis revealed that PLV and PW have the greatest impact on the BI, while the percentage of minerals had the least influence. Comparing the results of different methods based on error value, performance index, statistical tests, and Taylor diagram, the SVR based on radial basis kernel function demonstrated the highest performance (R2 = 0.99 and RMSE = 0.04) in estimating the brittleness index of the studied sandstones. Tukey's test to check the significance of the mean BI obtained from modeling and measured in the laboratory displayed that there is no significant difference between the BI values obtained from these methods.

关键词： Brittleness index Mineralogy Machine learning Statistical methods Physical properties Mechanical properties P-wave velocity Sandstone

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Optimizing Battery Performance in Electric Vehicles Through Predictive modeling

Optimizing Battery Performance in Electric Vehicles Through ...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Kumar, C. Siva Sagar, P. Lakshmi Venkata Bhanu Pratap, B. Geethamadhuri, Gurramkonda Kumar, Banda Sai Ram Dinesh, Bommineni Mukesh, Pallaputhimmannagari Sree Vidyanikethan Engineering College Dept of Csse Andhra Pradesh Tirupati India Dept of Data Science Karakambadi A.P Tirupati India BORiTS Private Limited Telangana Hyderabad India Ug School Dept of Csbs Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9798331527495

The fast-paced electrification of the automobile sector has intensified an immediate need for reliable battery management systems capable of sustaining long-term performance. One of the most important indicators when it comes to battery health is State of Health (SOH), which indicates how much capacity is left and how long a certain battery will endure. Reliable estimation of SOH is critical to enable the optimal performance of vehicles, reduce service costs, avoid unanticipated failures. In this project, we propose a machine learning approach to predict the SOH of EV batteries based on the two widely used algorithms, XGBoost and LGBM. In order to cover and analyze as many process variables available as possible to build a database for the training and evaluation of the models, we measured multiple battery parameters, such as voltage, current, temperature and historical charge/discharge cycles. The train test split this dataset in such a way that the greater part of the dataset is used for the machine learning operations. Individual models, such as XGBoost and LGBM, are used to be train due to its strength in handling large data and complex data with a high precision. This study attempts to find out the better of the two algorithms for real-time SOH prediction in practice by analyzing the accuracy and performance metrics of both algorithms. Results of this analysis can help manufacturers and operators make data-informed decisions regarding battery usage, maintenance schedules and replacement, which will contribute to improved vehicle reliability and extended battery life. © 2025 IEEE.

关键词： Decision making

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