Hasil untuk "Industrial directories"

Michail Takaronis, Athanasia Kollarou, Vyron Kampourakis et al.

It is well established that industrial control systems comprise the operational backbone of modern critical infrastructures, yet their increasing connectivity exposes them to cyber threats that are difficult to study and remedy safely under real-time operational conditions. In this paper, we present ICSSPulse, an open-source, modular, and extensible penetration testing platform designed for the security assessment of ICS communication protocols. To the best of our knowledge, ICSSPulse is the first web-based platform that unifies network scanning, protocol-aware Modbus and OPC~UA interaction, and Large Language Model (LLM)-assisted reporting within a single, lightweight ecosystem. Our platform provides a user-friendly graphical interface that orchestrates enumeration, exploitation, and reporting activities over simulated industrial services, enabling safe and reproducible experimentation. It supports protocol-level discovery, asset enumeration, and controlled read/write interactions, while preserving protocol fidelity and operational transparency. Experimental evaluation using synthetic Modbus test servers, a Factory I/O water treatment scenario, and a custom OPC~UA production-line model demonstrated ICSSPulse's potential to discover active industrial services, enumerate process-relevant assets, and manipulate process variables. A key contribution of this work lies in the integration of an LLM-assisted reporting module that automatically translates technical findings into structured executive and technical reports, with mitigation guidance informed by the ICS MITRE ATT&CK ICS matrix.

Talal Mousa Alshammari, Musab Rabi, Mazen J. Al-Kheetan et al.

Improving construction site safety remains a critical challenge in Saudi Arabia’s rapidly growing construction sector, where high accident rates and diverse labor forces demand evidence-based managerial interventions. This study investigated the influence of Managers’ Safety Perceptions and Practices (MSP) on Workers’ Safety Behaviors (WSB) in the Saudi construction industry, emphasizing the mediating roles of Workers’ Safety Awareness (WSA), Safety Competency (WSC), and Safety Actions (SA). The conceptual framework integrates these three mediators to explain how managerial attitudes and practices translate into frontline safety outcomes. A quantitative, cross-sectional design was adopted using a structured questionnaire distributed among construction workers, supervisors, and project managers. A total of 352 from 384 valid responses were collected, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. The findings revealed that MSP does not directly influence WSB but has significant indirect effects through WSA, WSC, and SA. Among these, WSC emerged as the most powerful mediator, followed by WSA and SA, indicating that competency is the most critical driver of safe worker behavior. These results provide robust empirical support for a multidimensional mediation model, highlighting the need for managers to enhance safety behaviors not merely through supervision but through fostering awareness and competency, providing technical training, and implementing proactive safety measures. Theoretically, this study contributes a novel and integrative framework to the occupational safety literature, particularly within underexplored Middle Eastern construction contexts. Practically, it offers actionable insights for safety managers, industry practitioners, and policymakers seeking to improve construction safety performance in alignment with Saudi Vision 2030.

Masatoshi Ishikawa, Ryoma Seto, Michiko Oguro et al.

Objectives: To elucidate the status of reduction in working hours following physician work-style reforms and factors associated with long working hours. Methods: A nationwide questionnaire survey was conducted among obstetricians and gynecologists (OB/GYNs) working in hospitals. The survey elucidated actual working conditions, including working hours and number of out-of-hour (OOH) shifts. To identify factors associated with long working hours, a multivariate logistic regression analysis was performed, with ≥60 or ≥80 working hours per week as dependent variables and OB/GYNs attributes (sex, age, job position, hospital type by ownership, total number of hospital beds, and regional characteristics) as independent variables. Results: Questionnaires were sent to 1,170 hospitals. Valid responses were obtained from 1164 OB/GYNs at 423 hospitals (response rate: 36%): 26.0% worked ≥60 hours per week, a reduction from 58.1% in 2019 (equivalent to over 960 hours of overtime annually), 5.4% worked ≥80 hours per week, a reduction from 41.2% in 2019 (equivalent to over 1,920 hours of overtime annually); and 46.9% worked OOH shifts ≥5 times per month. Factors significantly associated with long working hours per week included male sex, resident position, teaching duty, and number of OOH shifts. Conclusions: Although the working hours of OB/GYNs have decreased because of physician work-style reforms initiated in 2019, long working hours persist. To ensure health of OB/GYNs and patient safety, it is necessary to actively promote physician work-style reforms and advance measures aimed at the centralization of medical resources and addressing their maldistribution.

Stina Klein, Pooja Prajod, Katharina Weitz et al.

The integration of collaborative robots (cobots) in industrial settings raises concerns about worker well-being, particularly due to reduced social interactions. Avatars - designed to facilitate worker interactions and engagement - are promising solutions to enhance the human-robot collaboration (HRC) experience. However, real-world perspectives on avatar-supported HRC remain unexplored. To address this gap, we conducted a focus group study with employees from a German manufacturing company that uses cobots. Before the discussion, participants engaged with a scripted, industry-like HRC demo in a lab setting. This qualitative approach provided valuable insights into the avatar's potential roles, improvements to its behavior, and practical considerations for deploying them in industrial workcells. Our findings also emphasize the importance of personalized communication and task assistance. Although our study's limitations restrict its generalizability, it serves as an initial step in recognizing the potential of adaptive, context-aware avatar interactions in real-world industrial environments.

Jiao Chen, Weihua Li, Jianhua Tang

In dynamic Industrial Internet of Things (IIoT) environments, models need the ability to selectively forget outdated or erroneous knowledge. However, existing methods typically rely on retain data to constrain model behavior, which increases computational and energy burdens and conflicts with industrial data silos and privacy compliance requirements. To address this, we propose a novel retain-free unlearning framework, referred to as Probing then Editing (PTE). PTE frames unlearning as a probe-edit process: first, it probes the decision boundary neighborhood of the model on the to-be-forgotten class via gradient ascent and generates corresponding editing instructions using the model's own predictions. Subsequently, a push-pull collaborative optimization is performed: the push branch actively dismantles the decision region of the target class using the editing instructions, while the pull branch applies masked knowledge distillation to anchor the model's knowledge on retained classes to their original states. Benefiting from this mechanism, PTE achieves efficient and balanced knowledge editing using only the to-be-forgotten data and the original model. Experimental results demonstrate that PTE achieves an excellent balance between unlearning effectiveness and model utility across multiple general and industrial benchmarks such as CWRU and SCUT-FD.

Tom F. O'Hara, Ellen Player, Graham Ackroyd et al.

Triboelectrification of granular materials is a poorly understood phenomenon that alters particle behaviour, impacting industrial processes such as bulk powder handling and conveying. At small scales ($< 1 g$) net charging of powders has been shown to vary linearly with the total particle surface area and hence mass for a given size distribution. This work investigates the scaling relation of granular triboelectric charging, with small, medium ($< 200 g$), and large-scale ($\sim 400 kg$) laboratory testing of industrially relevant materials using a custom powder dropping apparatus and Faraday cup measurements. Our results demonstrate that this scaling is broken before industrially relevant scales are reached. Charge (Q) scaling with mass (m) was fitted with a function of the form $Q \propto m^b$ and $b$ exponents ranging from $0.68\ \pm\ 0.01$ to $0.86\ \pm\ 0.02$ were determined. These exponents lie between those that would be expected from the surface area of the bulk powder ($b = 2 / 3$) and the total particle surface area ($b = 1$). This scaling relation is found to hold across the powders tested and at varying humidities.

Aman Verma, Keshav Samdani, Mohd. Samiuddin Shafi

This paper presents the design, implementation, and evolution of a comprehensive multimodal room-monitoring system that integrates synchronized video and audio processing for real-time activity recognition and anomaly detection. We describe two iterations of the system: an initial lightweight implementation using YOLOv8, ByteTrack, and the Audio Spectrogram Transformer (AST), and an advanced version that incorporates multi-model audio ensembles, hybrid object detection, bidirectional cross-modal attention, and multi-method anomaly detection. The evolution demonstrates significant improvements in accuracy, robustness, and industrial applicability. The advanced system combines three audio models (AST, Wav2Vec2, and HuBERT) for comprehensive audio understanding, dual object detectors (YOLO and DETR) for improved accuracy, and sophisticated fusion mechanisms for enhanced cross-modal learning. Experimental evaluation shows the system's effectiveness in general monitoring scenarios as well as specialized industrial safety applications, achieving real-time performance on standard hardware while maintaining high accuracy.

Qishan Wang, Haofeng Wang, Shuyong Gao et al.

Industrial anomaly detection is a challenging open-set task that aims to identify unknown anomalous patterns deviating from normal data distribution. To avoid the significant memory consumption and limited generalizability brought by building separate models per class, we focus on developing a unified framework for multi-class anomaly detection. However, under this challenging setting, conventional reconstruction-based networks often suffer from an identity mapping problem, where they directly replicate input features regardless of whether they are normal or anomalous, resulting in detection failures. To address this issue, this study proposes a novel framework termed Collaborative Reconstruction and Repair (CRR), which transforms the reconstruction to repairation. First, we optimize the decoder to reconstruct normal samples while repairing synthesized anomalies. Consequently, it generates distinct representations for anomalous regions and similar representations for normal areas compared to the encoder's output. Second, we implement feature-level random masking to ensure that the representations from decoder contain sufficient local information. Finally, to minimize detection errors arising from the discrepancies between feature representations from the encoder and decoder, we train a segmentation network supervised by synthetic anomaly masks, thereby enhancing localization performance. Extensive experiments on industrial datasets that CRR effectively mitigates the identity mapping issue and achieves state-of-the-art performance in multi-class industrial anomaly detection.

Rustam Murasov , Yaroslav Melnyk

Сьогодні досить часто використовуються методи експертного оцінювання, що базується на залученні експертів із різних областей знань, які надають свої фахові оцінки стосовно ймовірності виникнення загроз та характеру їх впливу на об’єкти критичної інфраструктури. Метою статті є оцінювання загроз критичної інфраструктури методом експертного оцінювання для запобігання виникнення надзвичайних ситуацій, а в разі неможливості їхнього запобігання – мінімізації їх наслідків та оперативного ліквідування. Під час проведення дослідження застосовано наступні методи: методи аналізу під час аналізування існуючих джерел за напрямом досліджень, існуючих підходів оцінювання загроз, методи аналізу ризиків, методи математичного моделювання для оцінювання загроз та аналізу ризиків, методи машинного навчання такі як штучний інтелект, глибоке навчання та метод експертного оцінювання. Зазначений методологічний підхід дає змогу отримати результати оцінювання загроз критичної інфраструктури для раціонального розподілу засобів захисту, що використовуються складовими сил безпеки і оборони держави. У статті наведено існуючі підходи оцінювання загроз і розглянуто можливість використання методу експертного оцінювання для визначення загроз критичній інфраструктурі в сучасних умовах російсько-української війни. Також створено методологію оцінювання загроз критичній інфраструктурі, засновану на методі експертного оцінювання. Наведено результати оцінювання загроз для об'єктів критичної інфраструктури. Запропоновано порядок оцінювання загроз критичній інфраструктурі з пріоритизацією загроз. Пояснена суть методу експертного оцінювання та удосконалення цього методу завдяки введенню ідентифікаторів загроз критичної інфраструктури та укладенню таких визначень: усереднена експертна імовірність, окремі та сукупні деструктивні наслідки, імовірності загроз захисту критичної інфраструктури. Це дасть змогу зосередити зусилля на найбільш небезпечних загрозах і запобігти значним втратам критичної інфраструктури. Елементами наукової новизни статті є механізм пріоритизації ризиків (наслідків, сукупних деструктивних ефектів) з визначенням найнебезпечніших і відокремленні таких, що мають незначний ефект. Зроблено висновки стосовно можливостей та доцільності застосування методу експертного оцінювання з метою ідентифікації потенційних ризиків для об’єктів критичної інфраструктури та розроблення стратегій їх захисту. Проведено практичні розрахунки з висновками стосовно загроз і їх пріоритезації щодо критичної інфраструктури. До теоретичної значущості статті слід віднести вклад у розвиток методології оцінки загроз критичній інфраструктурі. Запропонований метод експертного оцінювання дозволяє отримати кількісні оцінки загроз, що є важливим для прийняття ефективних управлінських рішень та дозволяє враховувати різноманітні фактори, що впливають на рівень загрози в цілому. Практична значущість статті полягає в тому, що отримані результати дослідження можуть бути використані для: забезпечення безпеки критичної інфраструктури, формування пріоритетів захисту об'єктів критичної (військової) інфраструктури та розробки заходів щодо підвищення рівня безпеки критичної (військової) інфраструктури. Результати статті дають змогу здійснювати аналіз загроз критичної інфраструктури в умовах війни та ракетно-дронових ударів, формувати пріоритезований список загроз, відокремити незначні загрози з метою оптимального застосування наявних сил і засобів та мінімізації надзвичайних наслідків.

Budi Aswin, Willia Novita Eka Rini, Fajrina Hidayati

Introduction: Ergonomic work hazards are potential hazards that can negatively affect the health of farmers. One of the ergonomic hazards that farmers often experience is fatigue. This study aims to analyze the effectiveness of innovative ergonomic models and the preparation of balanced calorie needs in preventing work fatigue in rice farmers. Methods: The type of research used is a randomized controlled trial (RCT) design, which is the most powerful design to evaluate the intervention used, namely the effectiveness of innovative ergonomic models and the preparation of balanced calorie needs in preventing occupational fatigue in rice farmers. The population in this study were all farmers in Pudak Village, Kumpeh Ulu Subdistrict, Muaro Jambi Regency, totaling 238 people. The number of research samples was 68 farmers has taken using simple random sampling technique. Data were analyzed to determine the effectiveness of innovative ergonomic models using the ANOVA test with (α = 0.05). Result: There was a difference in the effectiveness of innovative ergonomic models in preventing work fatigue between at least two groups of rice farmers. Conclusion: the provision of stretching and snacks coupled with rest periods is most effective in preventing occupational fatigue. As for occupational fatigue, the provision of stretching, snacks, and rest time and the provision of simple education on the hazards of work ergonomics are effective in preventing occupational fatigue in rice farmers.

Aleksandr Sidorenko, Mahdi Rezapour, Achim Wagner et al.

The Industry 4.0 paradigm manifests the shift towards mass customization and cyber-physical production systems (CPPS) and sets new requirements for industrial automation software in terms of modularity, flexibility, and short development cycles of control programs. Though programmable logical controllers (PLCs) have been evolving into versatile and powerful edge devices, there is a lack of PLC software flexibility and integration between low-level programs and high-level task-oriented control frameworks. Behavior trees (BTs) is a novel framework, which enables rapid design of modular hierarchical control structures. It combines improved modularity with a simple and intuitive design of control logic. This paper proposes an approach for improving the industrial control software design by integrating BTs into PLC programs and separating hardware related functionalities from the coordination logic. Several strategies for integration of BTs into PLCs are shown. The first two integrate BTs with the IEC 61131 based PLCs and are based on the use of the PLCopen Common Behavior Model. The last one utilized event-based BTs and shows the integration with the IEC 61499 based controllers. An application example demonstrates the approach. The paper contributes in the following ways. First, we propose a new PLC software design, which improves modularity, supports better separation of concerns, and enables rapid development and reconfiguration of the control software. Second, we show and evaluate the integration of the BT framework into both IEC 61131 and IEC 61499 based PLCs, as well as the integration of the PLCopen function blocks with the external BT library. This leads to better integration of the low-level PLC code and the AI-based task-oriented frameworks. It also improves the skill-based programming approach for PLCs by using BTs for skills composition.

Huy Q. Ngo, Mingyu Guo, Hung Nguyen

Microsoft Active Directory (AD) is the default security management system for Window domain network. We study the problem of placing decoys in AD network to detect potential attacks. We model the problem as a Stackelberg game between an attacker and a defender on AD attack graphs where the defender employs a set of decoys to detect the attacker on their way to Domain Admin (DA). Contrary to previous works, we consider time-varying (temporal) attack graphs. We proposed a novel metric called response time, to measure the effectiveness of our decoy placement in temporal attack graphs. Response time is defined as the duration from the moment attackers trigger the first decoy to when they compromise the DA. Our goal is to maximize the defender's response time to the worst-case attack paths. We establish the NP-hard nature of the defender's optimization problem, leading us to develop Evolutionary Diversity Optimization (EDO) algorithms. EDO algorithms identify diverse sets of high-quality solutions for the optimization problem. Despite the polynomial nature of the fitness function, it proves experimentally slow for larger graphs. To enhance scalability, we proposed an algorithm that exploits the static nature of AD infrastructure in the temporal setting. Then, we introduce tailored repair operations, ensuring the convergence to better results while maintaining scalability for larger graphs.

Christof Tinnes, Alisa Welter, Sven Apel

Modeling structure and behavior of software systems plays a crucial role in the industrial practice of software engineering. As with other software engineering artifacts, software models are subject to evolution. Supporting modelers in evolving software models with recommendations for model completions is still an open problem, though. In this paper, we explore the potential of large language models for this task. In particular, we propose an approach, RAMC, leveraging large language models, model histories, and retrieval-augmented generation for model completion. Through experiments on three datasets, including an industrial application, one public open-source community dataset, and one controlled collection of simulated model repositories, we evaluate the potential of large language models for model completion with RAMC. We found that large language models are indeed a promising technology for supporting software model evolution (62.30% semantically correct completions on real-world industrial data and up to 86.19% type-correct completions). The general inference capabilities of large language models are particularly useful when dealing with concepts for which there are few, noisy, or no examples at all.

Lucas Lacasa, Abel Pardo, Pablo Arbelo et al.

Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aeronautical industries have recently developed a roadmap for concepts of design assurance and integration of neural network-related technologies in the aeronautical sector. This paper aims to contribute to this paradigm of AI-based certification in the context of supervised learning, by outlining a complete validation pipeline that integrates deep learning, optimization and statistical methods. This pipeline is composed by a directed graphical model of ten steps. Each of these steps is addressed by a merging key concepts from different contributing disciplines (from machine learning or optimization to statistics) and adapting them to an industrial scenario, as well as by developing computationally efficient algorithmic solutions. We illustrate the application of this pipeline in a realistic supervised problem arising in aerostructural design: predicting the likelikood of different stress-related failure modes during different airflight maneuvers based on a (large) set of features characterising the aircraft internal loads and geometric parameters.

Juliette Grosset, Alain-Jérôme Fougères, M Djoko-Kouam et al.

One of the challenges of Industry 4.0, is to determine and optimize the flow of data, products and materials in manufacturing companies. To realize these challenges, many solutions have been defined such as the utilization of automated guided vehicles (AGVs). However, being guided is a handicap for these vehicles to fully meet the requirements of Industry 4.0 in terms of adaptability and flexibility: the autonomy of vehicles cannot be reduced to predetermined trajectories. Therefore, it is necessary to develop their autonomy. This will be possible by designing new generations of industrial autonomous vehicles (IAVs), in the form of intelligent and cooperative autonomous mobile robots.In the field of road transport, research is very active to make the car autonomous. Many algorithms, solving problematic traffic situations similar to those that can occur in an industrial environment, can be transposed in the industrial field and therefore for IAVs. The technologies standardized in dedicated bodies (e.g., ETSI TC ITS), such as those concerning the exchange of messages between vehicles to increase their awareness or their ability to cooperate, can also be transposed to the industrial context. The deployment of intelligent autonomous vehicle fleets raises several challenges: acceptability by employees, vehicle location, traffic fluidity, vehicle perception of changing environments (dynamic), vehicle-infrastructure cooperation, or vehicles heterogeneity. In this context, developing the autonomy of IAVs requires a relevant working method. The identification of reusable or adaptable algorithms to the various problems raised by the increase in the autonomy of IAVs is not sufficient, it is also necessary to be able to model, to simulate, to test and to experiment with the proposed solutions. Simulation is essential since it allows both to adapt and to validate the algorithms, but also to design and to prepare the experiments.To improve the autonomy of a fleet, we consider the approach relying on a collective intelligence to make the behaviours of vehicles adaptive. In this chapter, we will focus on a class of problems faced by IAVs related to collision and obstacle avoidance. Among these problems, we are particularly interested when two vehicles need to cross an intersection at the same time, known as a deadlock situation. But also, when obstacles are present in the aisles and need to be avoided by the vehicles safely.

Bassir David, Lodge Hugo, Chang Haochen et al.

Quality control is very important aspect in Building Information Modelling (BIM) workflows. Whatever stage of the lifecycle it is important to get and to follow building indicators. The BIM it is very data consuming field and analysis of these data require advance numerical tools from image processing to big data analysis. Artificial intelligent (AI) and machine learning (ML) had proven their efficiency to deal with automate processes and extract useful sources of data in different industries. In addition to the indicators tracking, AI and ML can make a good prediction about when and where to provide maintenance and/or quality control. In this article, a review of the AI and ML application in BIM will be presented. Further suggestions and challenges will be also discussed. The aim is to provide knowledge on the needs nowadays into building and landscaping domain, and to give a wide understanding on how those technics would impact industries and future studies.

Rodríguez Cristhian C., Romero Quete Andrés A., Suvire Gastón O. et al.

This article proposed the use of multi-period mixed-integer linear programming method for investment planning to support decision-making processes in upgrading and managing street lighting systems. The technique incorporates a multi-variate model that maximizes energy-saving by considering budget constraints, the state of the lighting system, and the available technology in the market to replace the existing streetlights. This topic is novel because the complexity of the problem relies on the existence of several potentially large investments. As explained in this paper, the proposed method optimally considers the investments and returns as a combination that maximizes energy savings. The method was tested using actual data from an undisclosed public lighting system in Colombia. The results obtained revealed that multi-period investment optimization based on mixed-integer linear programming is an ideal investment plan, particularly in streetlight systems. Therefore, it forms an invaluable tool for street lighting systems' administrators and decision-makers in optimizing and facilitating the critical decision-making process in their work environment.

Thomas Decker, Michael Lebacher, Volker Tresp

Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industrial prediction problems is challenging as it is not immediately clear how to define and access appropriate concepts for individual use cases and specific data types. In this work, we investigate how to leverage established concept-based explanation techniques in the context of bearing fault detection with deep neural networks trained on vibration signals. Since bearings are prevalent in almost every rotating equipment, ensuring the reliability of intransparent fault detection models is crucial to prevent costly repairs and downtimes of industrial machinery. Our evaluations demonstrate that explaining opaque models in terms of vibration concepts enables human-comprehensible and intuitive insights about their inner workings, but the underlying assumptions need to be carefully validated first.

Lei Song, Chuheng Zhang, Li Zhao et al.

For industrial control, developing high-performance controllers with few samples and low technical debt is appealing. Foundation models, possessing rich prior knowledge obtained from pre-training with Internet-scale corpus, have the potential to be a good controller with proper prompts. In this paper, we take HVAC (Heating, Ventilation, and Air Conditioning) building control as an example to examine the ability of GPT-4 (one of the first-tier foundation models) as the controller. To control HVAC, we wrap the task as a language game by providing text including a short description for the task, several selected demonstrations, and the current observation to GPT-4 on each step and execute the actions responded by GPT-4. We conduct series of experiments to answer the following questions: 1)~How well can GPT-4 control HVAC? 2)~How well can GPT-4 generalize to different scenarios for HVAC control? 3) How different parts of the text context affect the performance? In general, we found GPT-4 achieves the performance comparable to RL methods with few samples and low technical debt, indicating the potential of directly applying foundation models to industrial control tasks.

Hamdani Hamid, Radi Bouchaïb, El Hami Abdelkhalak

The complexity challenges of mechatronic systems justify the need of numerical simulation to efficiently assess their reliability. In the case of solder joints in electronic packages, finite element methods (FEM) are commonly used to evaluate their fatigue response under thermal loading. Nevertheless, Experience shows that the prediction quality is always affected by the variability of the design variables. This paper aims to benefit from the statistical power of the R software and the efficiency of the finite element software ANSYS©, to develop a probabilistic approach to predicting the solder joint reliability in Mechatronic Packaging taking into account the uncertainties in material properties. The coupling of the two software proved an effective evaluation of the reliability of the T-CSP using the proposed method.