Hasil untuk "Systems engineering"

Peter Amorese, Morteza Lahijanian

Nonlinear ordinary differential equations (ODEs) are powerful tools for modeling real-world dynamical systems. However, propagating initial state uncertainty through nonlinear dynamics, especially when the ODE is unknown and learned from data, remains a major challenge. This paper introduces a novel continuum dynamics perspective for model learning that enables formal uncertainty propagation by constructing Taylor series approximations of probabilistic events. We establish sufficient conditions for the soundness of the approach and prove its asymptotic convergence. Empirical results demonstrate the framework's effectiveness, particularly when predicting rare events.

Megan S. Harris, Ehsanoddin Ghorbanichemazkati, Mohammad Mahdi Naderi et al.

Worsening global challenges demand solutions grounded in a systems-level understanding of coupled social and environmental dynamics. Existing environmental models encode extensive knowledge of individual systems, yet much of this information remains isolated within domain-specific formulations and data structures. This paper introduces a unified modeling framework that formalizes information from existing process models by asserting real-world physical relationships onto their underlying mathematical representations. By integrating Model-Based Systems Engineering (MBSE) with Hetero-functional Graph Theory (HFGT), the framework establishes a consistent ontology that explicitly defines system structure and behavior. Illustrative hydrological examples demonstrate implementation of the methodology, showing how relationships embedded in conventional process models can be made explicit and scalable. While simplified, these examples provide a first step toward applying the approach to complex environmental systems. More broadly, the methodology offers a foundation for future modeling of systems of systems within a shared computational architecture.

Dongming Jin, Zhi Jin, Linyu Li et al.

Recent advances in large pretrained models have led to their widespread integration as core components in modern software systems. The trend is expected to continue in the foreseeable future. Unlike traditional software systems governed by deterministic logic, systems powered by pretrained models exhibit distinctive and emergent characteristics, such as ambiguous capability boundaries, context-dependent behavior, and continuous evolution. These properties fundamentally challenge long-standing assumptions in requirements engineering, including functional decomposability and behavioral predictability. This paper investigates this problem and advocates for a rethinking of existing requirements engineering methodologies. We propose a conceptual framework tailored to requirements engineering of pretrained-model-enabled software systems and outline several promising research directions within this framework. This vision helps provide a guide for researchers and practitioners to tackle the emerging challenges in requirements engineering of pretrained-model-enabled systems.

Justus Bogner, Roberto Verdecchia

From its early foundations in the 1970s, empirical software engineering (ESE) has evolved into a mature research discipline that embraces a plethora of different topics, methodologies, and industrial practices. Despite its remarkable progress, the ESE research field still needs to keep evolving, as new impediments, shortcoming, and technologies emerge. Research reproducibility, limited external validity, subjectivity of reviews, and porting research results to industrial practices are just some examples of the drivers for improvements to ESE research. Additionally, several facets of ESE research are not documented very explicitly, which makes it difficult for newcomers to pick them up. With this new regular ACM SIGSOFT SEN column (SEN-ESE), we introduce a venue for discussing meta-aspects of ESE research, ranging from general topics such as the nature and best practices for replication packages, to more nuanced themes such as statistical methods, interview transcription tools, and publishing interdisciplinary research. Our aim for the column is to be a place where we can regularly spark conversations on ESE topics that might not often be touched upon or are left implicit. Contributions to this column will be grounded in expert interviews, focus groups, surveys, and position pieces, with the goal of encouraging reflection and improvement in how we conduct, communicate, teach, and ultimately improve ESE research. Finally, we invite feedback from the ESE community on challenging, controversial, or underexplored topics, as well as suggestions for voices you would like to hear from. While we cannot promise to act on every idea, we aim to shape this column around the community interests and are grateful for all contributions.

Yanni Xuan, Kun Yu, Hong Tian et al.

Particle adsorbents have gained significant traction in flue gas desulfurization applications, primarily attributed to their high structural homogeneity and large specific surface area. To address the multifaceted requirements of industrial sectors regarding the structural configurations and physicochemical properties of particle adsorbents while promoting sustainable manufacturing practices, this study systematically evaluates and critically appraises contemporary advancements in particle desulfurizing agent technologies. The synthesis of these findings establishes a theoretical framework to facilitate technological innovation and industrial progress within the particle desulfurizer domain. The research systems of particle adsorbents, encompassing active components, inert carriers, preparation methodologies, and gas–solid reaction models, were comprehensively reviewed. The advantages and current limitations of these systems were then systematically summarized. Finally, the fundamental principles and research trajectories in the application fields of distinct particle adsorbent research systems were elucidated. An analysis of the developmental trends indicated that enhancing the utilization efficiency of active components and improving the cyclic stability of adsorbents remained critical engineering challenges. It is posited that the pursuit of high reaction activity, thermal stability, mechanical strength, and superior anti-aggregation/sintering performance constitutes key directions for the advancement of particle adsorbents in China’s flue gas desulfurization industry.

R. Sujatha, Sushil Krishnan, Jyotir Moy Chatterjee et al.

Abstract Conventional techniques for identifying plant leaf diseases can be labor-intensive and complicated. This research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy to overcome the difficulty of the conventional methods. Our proposed method uses deep learning (DL) to extract features from photos of plant leaves and machine learning (ML) for further processing. To capture complex illness patterns, convolutional neural networks (CNNs) such as VGG19 and Inception v3 are utilized. Four distinct datasets—Banana Leaf, Custard Apple Leaf and Fruit, Fig Leaf, and Potato Leaf—were used in this investigation. The experimental results we received are as follows: for the Banana Leaf dataset, the combination of Inception v3 with SVM proved good with an Accuracy of 91.9%, Precision of 92.2%, Recall of 91.9%, F1 score of 91.6%, AUC of 99.6% and MCC of 90.4%, FFor the Custard Apple Leaf and Fruit dataset, the combination of VGG19 with kNN with an Accuracy of 99.1%, Precision of 99.1%, Recall of 99.1%, F1 score of 99.1%, AUC of 99.1%, and MCC of 99%, and for the Fig Leaf dataset with Accuracy of 86.5%, Precision of 86.5%, Recall of 86.5%, F1 score of 86.5%, AUC of 93.3%, and MCC of 72.2%. The Potato Leaf dataset displayed the best performance with Inception v3 + SVM by an Accuracy of 62.6%, Precision of 63%, Recall of 62.6%, F1 score of 62.1%, AUC of 89%, and MCC of 54.2%. Our findings explored the versatility of the amalgamation of ML and DL techniques while providing valuable references for practitioners seeking tailored solutions for specific plant diseases.

Lei You, Minghua Luo, Xinglin Zhu et al.

The belt conveyor is susceptible to longitudinal tearing, which poses a serious threat to the safety of coal mines. Traditional methods for detecting longitudinal tears have limitations such as poor image quality, limited applicability, and high hardware costs. An improved encoder-decoder network was proposed to solve the longitudinal tear detection problem. This method utilizes a line structured light system for image acquisition. The input images are downscaled using a sorting algorithm to extract the information of pixels with high grayscale values as the input feature map for the neural network. The reduced-dimensional encoder-decoder network then semantically segments the input feature map, and the resulting pixel segmentation is mapped to the location of the longitudinal tear. Finally, the position and length of the tear are calculated by back-projecting the semantic segmentation result to the world coordinate system. Experimental results demonstrate that this method effectively reduces hardware resource consumption and improves detection speed. The DICE and MIOU scores for the improved network are 97.69% and 95.47%, respectively, while the recall and precision for improved detection are 96.60% and 95.67%, respectively. Therefore, this method can successfully monitor longitudinal tear failures and ensure the safety of transportation.

Bashra Kadhim Oleiwi, Mohamed Jasim, Ahmad Taher Azar et al.

The position and trajectory tracking control of rigid-link robot manipulators suffers from problems such as poor accuracy, unstable performance, and response caused by unidentified loads and outside disturbances. In this paper, three control structures have been proposed to control a multi-input, multi-output coupled nonlinear three-link rigid robot manipulator (3-LRRM) system and effectively solve the signal chattering in the control signal. To overcome these problems, three hybrid control structures based on combinations between the benefits of fractional order proportional-integral-derivative operations (FOPID) and the benefits of neural networks are proposed for a 3-LRRM. The first hybrid control scheme is a neural network- (NN) like fractional order proportional-integral plus an NN-like fractional order proportional derivative controller (NN-FOPIPD) and the second control scheme is an NN plus FOPID controller (NN + FOPID). In contrast, the third control scheme is the Elman NN-like FOPID controller (ELNN-FOPID). The bat optimization algorithm (BOA) is applied to find the best parameter values of the proposed control scheme by minimizing the performance index of the integral time square error (ITSE). MATLAB software is used to carry out the simulation results. Using the simulation tests, the performance of the suggested controllers is compared without retraining the controller parameters. The robustness of the designed control schemes’ performance is assessed utilizing uncertainties in system parameters, outside disturbances, and initial position changes. The results show that the NN-FOPIPD structure demonstrated the best performance among the suggested controllers.

Sharifah Altalhi, Júnia Schultz, Tahira Jamil et al.

Abstract Background Hydrothermal vents along mid-ocean ridges host diverse microbial communities and are crucial to global elemental cycling. The Red Sea, known for its unique environmental conditions—including low nutrient levels, high year-round temperatures, bottom-water temperatures of 21 °C, and elevated salinity—hosts recently discovered active low-temperature hydrothermal vent fields at the axial Hatiba Mons volcano. These vents, characterized by large iron oxide mounds and abundant microbial mats, offer an extreme environment for studying the diversity and functions of prokaryotes involved in elemental cycling in this system. In this study, we used 16S rRNA sequencing and shotgun metagenomics to examine the microbial diversity and metabolic capabilities of precipitates and microbial mats from five vent sites. Results We recovered 314 non-redundant metagenome-assembled genomes (MAGs), including 250 bacterial and 64 archaeal MAGs, representing 34 bacterial and 11 archaeal phyla. Functional annotations revealed diverse nutrient and metal cycling potentials, with notable enrichment in iron redox genes. Key players include Bathyarchaeia and Chloroflexi in the precipitates (contributing to carbon, nitrogen, sulfur, and metal cycling potentials) and Pseudomonadota members in the microbial mats and upper precipitates (involved in iron and sulfur metabolism and carbon fixation through the CBB cycle). Carbon fixation in precipitate potentials primarily occurs through the Wood–Ljungdahl pathway. Sulfur and nitrogen cycling genes are distributed across various genomes, indicating collaborative cycling. Conclusion Our genome-resolved analysis positions the Hatiba Mons vents as an iron-rich system that provides new insights into oligotrophic hydrothermal environments, with potential relevance for understanding novel metabolic pathways, extremophilic adaptations, and their roles in element cycling and biotechnological applications.

Dinh Minh Vu, Thanh Son Nguyen

This paper addresses the fundamental problem of seed selection in semi-supervised clustering, where the quality of initial seeds has a significant impact on clustering performance and stability. Existing methods often rely on randomly or heuristically selected seeds, which can propagate errors and increase dependence on expert labeling. To overcome these limitations, we propose FA-Seed, a flexible and adaptive model that integrates active querying with self-guided adaptation within the framework of fuzzy hyperboxes. FA-Seed partitions the data into hyperboxes, evaluates seed reliability through measures of membership and association density, and propagates labels with an emphasis on label purity. The model demonstrates strong adaptability to complex and ambiguous data distributions in which cluster boundaries are vague or overlapping. The main contributions of FA-Seed include: (1) automatic estimation and selection of candidate seeds that provide auxiliary supervision, (2) dynamic cluster expansion without retraining, (3) automatic detection and identification of structurally complex regions based on cluster characteristics, and (4) the ability to capture intrinsic cluster structures even when clusters vary in density and shape. Empirical evaluations on benchmark datasets, specifically the UCI and Computer Science collections, show that our approach consistently outperforms several state-of-the-art semi-supervised clustering methods.

Marjolein Antoinette Jannyne Velthuizen, Erwin Hofman, Marcus Vinicius Pereira Pessôa

ABSTRACT Over the past decade, model‐based systems engineering (MBSE) has gained attention as an approach for managing system complexity, offering a shift from a document‐based to a model‐based approach. The International Council on Systems Engineering (INCOSE) sparked increasing interest when it introduced MBSE and its roadmap as part of its systems engineering vision for 2020. The MBSE roadmap spans 15 years and is the principal stimulus behind this study. This paper's main objective is to assess the current progress in the MBSE literature from 2007 to 2025 compared to the INCOSE MBSE roadmap and to identify areas needing further research attention. The paper uses bibliometric analysis techniques to identify past, present, and emerging themes within the MBSE research domain. The findings are compared to the INCOSE roadmap. The comparison shows that MBSE is increasingly applied for systems architecting. However, INCOSE's envisioned state has not been achieved. Key areas requiring further research attention are MBSE theories, standardized MBSE methodologies, MBSE standards, MBSE metrics, integration with hardware and software models, and tool interoperability. In addition, future research should focus on advancing and formalizing MBSE's role in digitalization, expanding model‐based safety analysis (MBSA) methods, exploring how MBSE can support sustainable system design, and formulating adoption strategies. Continued research efforts are essential for MBSE to achieve INCOSE's vision for widespread adoption and application.

Reginald Holmes, Hanumanthrao Kannan

Philipp Hehnle, Manfred Reichert

Different organisations often run similar digitised business processes to achieve their business goals. However, organisations often need to slightly adapt the business processes implemented in an information system in order to adopt them. Various approaches have been proposed to manage variants in process models. While these approaches mainly deal with control flow variability, in previous work we introduced an approach to manage implementation variants of digitised business processes. In this context Software Product Line (SPL) Engineering was applied to manage a set of common core artefacts including a process model from which Process-Aware Information Systems (PAIS) can be derived, which differ in the implementation of their process activities. When deriving a PAIS, implementations are selected for each process activity and then included in the PAIS at compilation time. One challenge that has not yet been solved is giving users of digitised business processes the option of selecting multiple implementations at runtime. This paper extends our previous work by not only allowing for the selection of activity implementations at compile time, but also at start time and runtime. Consequently, it becomes possible to defer the decision as to which implementation should be selected to start time and runtime. Furthermore, multiple implementations of a particular activity may be selected and executed concurrently. The presented approach also allows customising the input and output data of activities. Data from expert interviews with German municipalities suggests digitising business processes with varying implementations is a widespread challenge and our approach is a way to mitigate it.

Ahmed R. Sadik, Bram Bolder, Pero Subasic

A System of Systems (SoS) comprises Constituent Systems (CSs) that interact to provide unique capabilities beyond any single CS. A key challenge in SoS is ad-hoc scalability, meaning the system size changes during operation by adding or removing CSs. This research focuses on an Unmanned Vehicle Fleet (UVF) as a practical SoS example, addressing uncertainties like mission changes, range extensions, and UV failures. The proposed solution involves a self-adaptive system that dynamically adjusts UVF architecture, allowing the Mission Control Center (MCC) to scale UVF size automatically based on performance criteria or manually by operator decision. A multi-agent environment and rule management engine were implemented to simulate and verify this approach.

Simon Stock, Davood Babazadeh, Christian Becker et al.

While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification methods in identifying the power system dynamic behavior under measurement noise. This paper takes the next natural step and addresses the more significant challenge, exploring how BPINN perform in estimating power system dynamics under increasing uncertainty from many Inverter-based Resources (IBRs) connected to the grid. These introduce a different type of uncertainty, compared to noisy measurements. The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification. We explore the BPINN performance on a wide range of systems, starting from a single machine infinite bus (SMIB) system and 3-bus system to extract important insights, to the 14-bus CIGRE distribution grid, and the large IEEE 118-bus system. We also investigate approaches that can accelerate the BPINN training, such as pretraining and transfer learning. Throughout this paper, we show that in presence of uncertainty, the BPINN achieves orders of magnitude lower errors than the widely popular method for system identification SINDy and significantly lower errors than PINN, while transfer learning helps reduce training time by up to 80 %.

Dong Zhou, Bida Pei, Xueqin Li et al.

Abstract Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

Noshina Tariq, Amjad Alsirhani, Mamoona Humayun et al.

Abstract The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant privacy risks and extends the time required for intrusion detection. Training a high-quality IDS using Artificial Intelligence (AI) technologies on a single entity becomes particularly challenging when dealing with vast amounts of distributed data across the network. To address these concerns, this paper presents a novel approach: a fog-edge-enabled Support Vector Machine (SVM)-based federated learning (FL) IDS for SGs. FL is an AI technique for training Edge devices. In this system, only learning parameters are shared with the global model, ensuring the utmost data privacy while enabling collaborative learning to develop a high-quality IDS model. The test and validation results obtained from this proposed model demonstrate its superiority over existing methods, achieving an impressive percentage improvement of 4.17% accuracy, 13.19% recall, 9.63% precision, 13.19% F1 score when evaluated using the NSL-KDD dataset. Furthermore, the model performed exceptionally well on the CICIDS2017 dataset, with improved accuracy, precision, recall, and F1 scores reaching 6.03%, 6.03%, 7.57%, and 7.08%, respectively. This novel approach enhances intrusion detection accuracy and safeguards user data and privacy in SG systems, making it a significant advancement in the field.

Marjeta Marolt, Gregor Lenart, Mirjana Kljajić Borštnar et al.

This paper aims to explore the patterns in micro, small-, and medium-sized enterprises’ (MSMEs) digital transformation journey during recent years. Using an emergent concurrent mixed-methods approach, we combined insights from six in-depth interviews with MSME managers and owners with survey data from 66 MSMEs. The findings reveal major inhibitors and accelerators of MSMEs’ digital transformation and demonstrate how they cope with them by engaging in digitalisation actions. This study also provides insights into how inhibitors, accelerators, and digitalisation actions vary across MSME sizes. While an increased adoption of digital technology was observed among the participating MSMEs, this study identifies three distinct digital transformation paths: necessary, experimental, and committed. Each path is shaped by a unique combination of inhibitors and accelerators. From the practical perspective, this research provides insights for MSME managers and owners on how to tailor their digital transformation efforts to their unique inhibitors and accelerators. In addition, our insights can help policy makers to promote the digital transformation of MSMEs through appropriate measures and support mechanisms tailored to the specific needs of smaller enterprises.

Salma Majidah, Lavita Nuraviana Rizalputri, Eduardus Ariasena et al.

Integration of gold nanoparticles onto electrochemical biosensor electrodes has been widely conducted to improve the performance of biosensors. Gold nanospikes (AuNS), as one of the gold nanoparticle morphologies, can be integrated into biosensors through electrodeposition and has the potential to immobilize bioreceptor on biosensors using the self-assembled monolayer (SAM) method. This paper examines the potential of AuNS-deposited Screen-Printed Carbon Electrodes (SPCEs) on immobilizing enzymes as label-based electrochemical biosensor by evaluating the optimum parameter for glucose oxidase (GOx) enzyme immobilization on the SPCE that consists of incubation time and concentration of SAM molecule—L-cysteine—and GOx enzyme, then reviews its performances. The developed biosensor exhibits excellent performance in detecting glucose (linear range of 0.2–15 mM and limit of detection (LOD) of 116 µM), with good selectivity against uric acid, urea, ascorbic acid, dopamine, and lactic acid, and superiority towards gold nanosphere modified biosensor.

Ertugrul Basar

Noise: an enemy to be dealt with and a major factor limiting communication system performance. However, what if there is gold in that garbage? In conventional engineering, our focus is primarily on eliminating, suppressing, combating, or even ignoring noise and its detrimental impacts. Conversely, could we exploit it similarly to biology, which utilizes noise-alike carrier signals to convey information? In this context, the utilization of noise, or noise-alike signals in general, has been put forward as a means to realize unconditionally secure communication systems in the future. In this tutorial article, we begin by tracing the origins of thermal noise-based communication and highlighting one of its significant applications for ensuring unconditionally secure networks: the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange scheme. We then delve into the inherent challenges tied to secure communication and discuss the imperative need for physics-based key distribution schemes in pursuit of unconditional security. Concurrently, we provide a concise overview of quantum key distribution (QKD) schemes and draw comparisons with their KLJN-based counterparts. Finally, extending beyond wired communication loops, we explore the transmission of noise signals over-the-air and evaluate their potential for stealth and secure wireless communication systems.