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From document-based to model-based system and software engineering: Experience report of a selective catalytic reduction system development 12

From document-based to model-based system and software engin...

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Joint 12th Educators Symposium and 3rd International Workshop on Open Source software for Model Driven engineering, EduSymp+OSS4MDE 2016

作者： Adedjouma, Morayo Thomas, Thibaud Mraidha, Chokri Gerard, Sebastien Zeller, Guillaume CEA LIST Department of System and Software Engineering Gif-sur-Yvette France Controlled Systems Department Plastic Omnium Auto Inergy Division Brussels Belgium

Plastic Omnium (PO) provides plastic fuel and emission reduction fluid systems for car manufacturers. These products, must be customizable to respond to different manufacturers needs while being compliant to ISO26262 and A-SPICE standards. To satisfy these needs, PO employed a development approach where system functional specifications are document-based, and software architecture design conforms to AUTOSAR. However, this approach requires to manually translate between textual specifications and software models, usually rising inconsistency and maintainability issues. This paper reports on the lessons learned with the introduction of a full model-based approach to solve the issues identified in its former approach. The proposed approach is evaluated on a real case study using the open-source Papyrus tool. Copyright © 2016 held by the author(s).

关键词： Specifications

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Evaluating machine learning models and feature selection for reference evapotranspiration estimation in semi-arid regions: a case study in Doukkala, Morocco

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Theoretical and Applied Climatology 2025年第6期156卷 1-24页

作者： Belarbi, Zaid EL Younoussi, Yacine Information System and Software Engineering Laboratory Abdelmalek Essaadi University Tetouan Morocco

Accurate estimation of reference evapotranspiration (ETo) is crucial for effective water resource management and irrigation planning, particularly in regions with significant agricultural activity, such as Doukkala, Morocco. This study explores the potential of machine learning (ML) models to estimate daily ETo using 18 years of climatic data collected from two meteorological stations in the region. The dataset includes key meteorological variables—temperature, humidity, solar radiation, wind speed, and rainfall—with ETo calculated using the FAO-56 Penman-Monteith (PM) equation as the reference. A range of ML models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), XGBoost, Random Forest (RF), k-Nearest Neighbors (KNN), Decision Trees (DT), and Multiple Linear Regression (MLR), were trained and compared. Additionally, deep learning architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) were evaluated for their ability to capture temporal dependencies within the data. Among the tested models, ANN demonstrated the highest performance, achieving an R² of 0.9868 and an MSE of 0.0440, followed closely by SVR and XGBoost, both of which also exhibited high predictive accuracy. In contrast, simpler models like MLR and DT showed comparatively lower performance, underscoring the advantages of more advanced ML techniques for ETo estimation. While LSTM displayed moderate success, CNN underperformed relative to other models. The inclusion of data from two stations enabled the evaluation of model generalizability across distinct locations within the region. However, this study has some limitations. While cross-site validation improves generalizability, the findings are based on two stations within the same semi-arid region, which may not fully represent ETo dynamics in more diverse climatic conditions. Additionally, while ML models demonstrated strong predictive capabilities, further improvements could be achieved b

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Towards Selection and Transition Between Behavior-Based Neural Networks for Automated Driving 24

Towards Selection and Transition Between Behavior-Based Neur...

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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Aslam, Iqra Anpilogov, Igor Rausch, Andreas Institute for Software and System Engineering Clausthal University of Technology Clausthal Zellerfeld Germany

ISBN: (纸本)9798400718182

Autonomous driving technology is progressing rapidly, largely due to complex End-To-End systems based on deep neural networks. While these systems are effective, their complexity can make it difficult to understand their behavior, which raises safety concerns. This paper presents a new solution: a Behavior Selector that uses multiple smaller artificial neural networks (ANNs) to manage different driving tasks, such as lane following and turning. Rather than relying on a single large network, which can be burdensome, require extensive training data and is hard to understand. The developed approach allows the system to dynamically select the appropriate neural network for each specific behaviour (i.e. turns) in real time. We focus on ensuring smooth transitions between behaviors and taking into account the vehicle’s current speed and orientation to improve stability and safety. The proposed system has been tested using the AirSim simulation environment, demonstrating its effectiveness. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Ant Colony Optimization with Genetic Algorithm for Solving the Job Shop Scheduling 1st

Ant Colony Optimization with Genetic Algorithm for Solving t...

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1st International Conference on Electrical systems and Smart Technologies, ICESST 2024

作者： Kriouich, Mohamed Sarir, Hicham Information System and Software Engineering ENSA Tetouan Abdelmalek Esaadi University Tetouan Morocco

ISBN: (纸本)9783031867040

In dynamic manufacturing contexts, the Job-Shop Scheduling Problem (JSP) presents considerable hurdles, particularly in light of uncertainties such as machine breakdowns and job reworks. In this article, multiple scheduling algorithms are evaluated in various scenarios, with a special emphasis on lowering makespan, or overall completion time. Advanced algorithms like Genetic Algorithm (GA) and Deep Reinforcement Learning (DRL) are contrasted with heuristic techniques like Shortest Processing Time (SPT), Longest Processing Time (LPT), and First-In-First-Out (FIFO). The research emphasizes the advantages of the hybrid Ant Colony Optimization with Genetic Algorithm (ACO_GA) solution that has been suggested. Although GA and DRL produce outcomes that are competitive, ACO_GA regularly performs better than them and, in most cases, comes quite close to the optimal solution. Even in the face of difficult, real-world problems, our hybrid approach yields more reliable and nearly optimal scheduling solutions by fusing the exploration potential of ACO with the refinement power of GA. The results show how adaptable and successful ACO_GA is, which makes it the best option for solving JSP in dynamic contexts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ant colony optimization

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PiVisionSort: Integrating Image Processing and Machine Learning for Material Recognition on Conveyor Belts 2

PiVisionSort: Integrating Image Processing and Machine Learn...

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2nd International Conference on Data, Information and Computing Science, CDICS 2024

作者： Kia, Shohreh Leiding, Benjamin Technische Universität Clausthal Institute for Software and System Engineering Clausthal-Zellerfeld Germany

ISBN: (纸本)9781643685762

This research aims to develop an advanced material detection system for conveyor belts, utilizing state-of-the-art image processing and machine learning techniques to automate the identification of various materials, thereby enhancing operational efficiency and accuracy in industrial settings. Current methods in material detection, such as traditional manual sorting and basic automated systems, often need more precision and adaptability in dynamic industrial environments. This paper identifies a gap in the practical and reliable detection of diverse materials under varying environmental conditions. The primary research questions addressed include: How can modern image processing and machine learning techniques improve material detection accuracy? What optimizations can be applied to ensure real-time processing efficiency? The proposed solution integrates a strategic camera setup with controlled lighting, robust image preprocessing algorithms (noise reduction, normalization, and resizing), and custom-designed detection algorithms using machine learning models. This system outperforms existing solutions by offering higher detection accuracy and adaptability to diverse industrial conditions. An interface is developed to display detection results and provide intuitive controls for system adjustments, ensuring practical use by industrial professionals. Rigorous testing and validation processes are implemented to enhance detection accuracy and processing speeds, with specific performance metrics established to measure efficacy. This paper provides a transformative impact on the manufacturing and processing industries by addressing environmental variability and material diversity. © 2025 The Authors.

关键词： Noise abatement

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Sparse Communication Mechanism for Federated Learning in IoT systems

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IEEE Internet of Things Journal 2025年第11期12卷 15261-15273页

作者： Zhu, Xi Wang, Junbo Liu, Zhi Zheng, Zibin Sun Yat-Sen University School of System Science and Engineering China Sun Yat-Sen University School of Intelligent Systems Engineering China University of Electro-Communications Japan Sun Yat-Sen University School of Software Engineering China

Federated learning (FL), an emerging distributed learning paradigm, addresses challenges in decentralized environments, particularly in internet of things (IoT) networks where numerous devices generate vast amounts of private data. A major concern in FL for IoT systems is communication overhead, especially in resource-constrained and wireless environments where frequent model uploads for aggregation create a critical bottleneck. As the complexity of neural networks increases, traditional FL methods demand substantial communication resources, which limits scalability in IoT applications. To address this, we propose a novel sparse communication mechanism for FL, called FedSC, that achieves high generalization performance and accuracy under extremely low communication frequencies, particularly for non-IID data. On the client side, we propose a multi-level model compression mechanism to capture and retain important information from the trained local model, which is then uploaded to the server. On the server side, we propose an iterative extraction mechanism to reconstruct models of uniform size based on the client models. Each extraction is followed by an aggregation step in an iterative process, ensuring effective generalization with non-IID data. As a result, clients need to upload their models only 1-5 times after completing local training, significantly reducing the communication overhead compared to traditional FL, which requires continuous uploads throughout the entire training process. Simulations on public datasets with popular deep learning models demonstrate that FedSC reduces the number of model uploads from hundreds to just 1-5 times while maintaining high accuracy, highlighting its potential to significantly enhance communication efficiency for FL in IoT systems. © 2014 IEEE.

关键词： Federated learning

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An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images

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Computers, Materials & Continua 2025年第4期83卷 1439-1458页

作者： Can Wu Wenyi Tang Yunbo Rao Yinjie Chen Hui Ding Shuzhen Zhu Yuanyuan Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Air Traffic Management System Nanjing210000China National Airspace Management Center Beijing100094China

Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).

关键词： Object segmentation uncertainty quantification bayesian convolutional neural network

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UAV-Mounted Intelligent Reflecting Surface in Maritime Wireless Powered Communication Network

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China Communications 2025年第3期22卷 254-269页

作者： Liu Yuan Han Fengxia Zhao Shengjie School of Information Science and Electrical Engineering Shandong Jiaotong UniversityJinan 250357China College of Electronic and Information Engineering Tongji UniversityShanghai 201804China School of Software Engineering Tongji UniversityShanghai 201804China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of EducationShanghai 200003China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804China

Intelligent reflecting surface(IRS)assisted with the wireless powered communication network(WPCN)can enhance the desired signal energy and carry out the power-sustaining problem in ocean monitoring *** this paper,we investigate a reliable communication structure where multiple buoys transmit data to a base station(BS)with the help of the unmanned aerial vehicle(UAV)-mounted IRS and harvest energy from the base station *** organically combine WPCN with maritime data collection scenario,a scheduling protocol that employs the time division multiple access(TDMA)is proposed to serve multiple buoys for uplink data ***,we compare the full-duplex(FD)and half-duplex(HD)mechanisms in the maritime data collection system to illustrate different performances under these two *** maximize the fair energy efficiency under the energy harvesting constraints,a joint optimization problem on user association,BS transmit power,UAV’s trajectory and IRS’s phase shift is *** solve the non-convex problem,the original problem is decoupled into several subproblems,and successive convex optimization and block coordinate descent(BCD)methods are employed obtain the near-optimal solutions *** results demonstrate that the UAV-mounted IRS can significantly improve energy efficiency in our considered system.

关键词： full-duplex IRS joint optimization TDMA UAV WPCN

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FBI-Net: Frequency Band Integration Network for Infrared Small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xin, Biqiao Li, Qiang Mao, Qianchen Wang, Jinbao Wang, Bingshu School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches. © 2025 IEEE.

关键词： Feature Fusion Infrared Small Target Segmentation Semantic Injection Wavelet Transformation

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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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