Hasil untuk "Industrial directories"

S. Kouro, M. Malinowski, K. Gopakumar et al.

Multilevel converters have been under research and development for more than three decades and have found successful industrial application. However, this is still a technology under development, and many new contributions and new commercial topologies have been reported in the last few years. The aim of this paper is to group and review these recent contributions, in order to establish the current state of the art and trends of the technology, to provide readers with a comprehensive and insightful review of where multilevel converter technology stands and is heading. This paper first presents a brief overview of well-established multilevel converters strongly oriented to their current state in industrial applications to then center the discussion on the new converters that have made their way into the industry. In addition, new promising topologies are discussed. Recent advances made in modulation and control of multilevel converters are also addressed. A great part of this paper is devoted to show nontraditional applications powered by multilevel converters and how multilevel converters are becoming an enabling technology in many industrial sectors. Finally, some future trends and challenges in the further development of this technology are discussed to motivate future contributions that address open problems and explore new possibilities.

Keno Moenck, Adrian Philip Florea, Julian Koch et al.

Autonomous vision applications in production, intralogistics, or manufacturing environments require perception capabilities beyond a small, fixed set of classes. Recent open-vocabulary methods, leveraging 2D Vision-Language Foundation Models (VLFMs), target this task but often rely on class-agnostic segmentation models pre-trained on non-industrial datasets (e.g., household scenes). In this work, we first demonstrate that such models fail to generalize, performing poorly on common industrial objects. Therefore, we propose a training-free, open-vocabulary 3D perception pipeline that overcomes this limitation. Instead of using a pre-trained model to generate instance proposals, our method simply generates masks by merging pre-computed superpoints based on their semantic features. Following, we evaluate the domain-adapted VLFM "IndustrialCLIP" on a representative 3D industrial workshop scene for open-vocabulary querying. Our qualitative results demonstrate successful segmentation of industrial objects.

Salim Fares

A growing number of publications address the best practices to use Large Language Models (LLMs) for software engineering in recent years. However, most of this work focuses on widely-used general purpose programming languages like Python due to their widespread usage training data. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, remains underexplored. This research aims to utilize and integrate LLMs in the industrial development process, solving real-life programming tasks (e.g., generating a movement routine for a robotic arm) and accelerating the development cycles of manufacturing systems.

Shahid Mumtaz, Ahmed Alsohaily, Zhibo Pang et al.

F. Civerchia, Stefano Bocchino, C. Salvadori et al.

Charity Owusu, Albert Ofori, Frank Adusei-Mensah et al.

Smokehouses play a vital role in several communities, while their environmental and health impacts remain largely unaddressed. Inside these confined spaces, the combination of intense heat, limited ventilation, and the use of firewood generates a complex mixture of hazardous air pollutants. While central to local economies, the health risks faced by workers in smokehouses are frequently overlooked. This study aimed to highlight these risks and to emphasize the urgent need for attention to these environments. Low-cost air quality monitors were deployed to monitor the levels of particulate matter (PM2.5), carbon monoxide, and ozone in 33 smokehouses. In addition, relative humidity and temperature were measured. The results revealed that PM2.5 concentrations ranged from 0.16 to 630.37 µg/m³ , with a mean concentration of 156.84 µg/m³ , and CO concentrations ranged from 2.37 parts per million (ppm) to 36.43 ppm, with a mean concentration of 17.29 ppm, all exceeding the World Health Organization's 24-hour guidelines. However, in the WHO guidelines, the ozone levels showed variability, ranging from 2.5 to 74.69 parts per billion (ppb). Temperature and relative humidity fluctuations were also significant, peaking at 46.29 °C and 81.59 %, respectively. This research spotlights the pressing need for enhanced air quality assessments in these environments and suggests innovative interventions that can ultimately protect public health and our fragile ecosystems.

Irfan Ullah, Ahmed Farid, Khaled Ksaibati

Intersections represent critical crash locations on road networks necessitating targeted safety interventions. This study employs a random parameters ordered logit (RPOL) model with heterogeneity in means to analyze injury severity contributing factors across 9108 Wyoming intersection crashes that occurred from 2007 to 2017. The analysis reveals that crashes on principal and minor arterial intersections are consistently associated with higher risks of severe/fatal injuries, while urban intersections exhibit less severe consequences, likely due to lower speeds and enhanced infrastructure. Adverse weather conditions, particularly snowy and icy road surfaces, increase the likelihood of property-damage-only (PDO) outcomes while reducing severe/fatal injuries. Temporal trends show a decline in crash severity over time, coinciding with advances in vehicle safety and policy improvements. Key behavioral factors, including left turn maneuvers and driver’s age heterogeneity, influence crash outcomes, whereas intersection sight distance (ISD) had no significant effect on crash severity underscoring data limitations requiring advanced analysis methods. This study’s findings prioritize the reconsideration of arterial intersection design, urban safety enhancements, and behavior-focused countermeasures for intersection safety.

Азаренко Е.И., Осадчая Л.И.

Jing Wu, Junru Ren, Ole Ravn et al.

The safety of robotics and automation technologies is a significant concern for stakeholders in Industry 5.0. Ensuring cost-effectiveness and inherent safety requires applying the defense-in-depth principle. This paper introduces a novel risk-informed design framework for functional safety, integrating function-centered hazard identification and risk assessment via fault tree analysis (FTA). Demonstrated in the design of a semi-automated agricultural vehicle, the framework begins with a function-centered hazard identification approach (F-CHIA) based on ISO 12100. It examined design intents, identified hazard zones, and conducted task and function identification. Foreseeable functional hazardous situations are analyzed, leading to functional requirements and the identification of relevant directives, regulations, and standards. The F-CHIA outputs inform the functional safety analysis, assessing the required performance level and deriving specific requirements for software, hardware, and human operators using FTA. The functional requirements derived from F-CHIA are more systematic than traditional methods and serve as effective inputs for functional safety analysis in human–robot collaboration applications. The proposed framework enables design teams to focus on enhancing factors that improve functional safety performance levels, resulting in a more thorough and effective safety design process.

Vyron Kampourakis, Christos Smiliotopoulos, Vasileios Gkioulos et al.

Traditional air gaps in industrial systems are disappearing as IT technologies permeate the OT domain, accelerating the integration of wireless solutions like Wi-Fi. Next-generation Wi-Fi standards (IEEE 802.11ax/be) meet performance demands for industrial use cases, yet their introduction raises significant security concerns. A critical knowledge gap exists regarding the empirical prevalence and security configuration of Wi-Fi in real-world industrial settings. This work addresses this by mining the global crowdsourced WiGLE database to provide a data-driven understanding. We create the first publicly available dataset of 1,087 high-confidence industrial Wi-Fi networks, examining key attributes such as SSID patterns, encryption methods, vendor types, and global distribution. Our findings reveal a growing adoption of Wi-Fi across industrial sectors but underscore alarming security deficiencies, including the continued use of weak or outdated security configurations that directly expose critical infrastructure. This research serves as a pivotal reference point, offering both a unique dataset and practical insights to guide future investigations into wireless security within industrial environments.

Imran Riaz Hasrat, Eun-Young Kang, Christian Uldal Graulund

Safety and reliability are crucial in industrial drive systems, where hazardous failures can have severe consequences. Detecting and mitigating dangerous faults on time is challenging due to the stochastic and unpredictable nature of fault occurrences, which can lead to limited diagnostic efficiency and compromise safety. This paper optimizes the safety and diagnostic performance of a real-world industrial Basic Drive Module(BDM) using Uppaal Stratego. We model the functional safety architecture of the BDM with timed automata and formally verify its key functional and safety requirements through model checking to eliminate unwanted behaviors. Considering the formally verified correct model as a baseline, we leverage the reinforcement learning facility in Uppaal Stratego to optimize the safe failure fraction to the 90 % threshold, improving fault detection ability. The promising results highlight strong potential for broader safety applications in industrial automation.

A. K. Sangaiah, D. Medhane, Tao Han et al.

Position-based services (PBSs) that deliver networked amenities based on roaming user's positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in PBSs. For effective PBSs, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques; however, only few deliberate the progress of position-based real-time applications considering significant tasks of PBSs. In this paper, a method for conserving position confidentiality of roaming PBSs users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor and estimates user destination along with the position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm, which will ensure the timely delivery of PBSs. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted, and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBSs.

Vadym Mashtalir , Oleksandr Huk , Igor Tolmachov et al.

З розвитком новітніх інформаційних технологій кіберпростір стає середовищем, у якому відбувається протиборство між суб’єктами міжнародних відносин у вигляді ведення кібервійн, а також інформаційних, мережецентричних, асиметричних, гібридних війн. З’являється тенденція використання стратегій асиметричних непрямих дій, заснованих на комбінації військових зусиль з політичними, економічними та інформаційно-психологічними методами впливу на супротивника для вирішення завдань, які раніше вирішувалися лише з використанням військової сили. В умовах цілеспрямованих інформаційно-технічних впливів і відсутності належних фахових знань про кіберпростір, розуміння цілей та характеру дій у ньому, а також динаміки змін означеного, виникла потреба розроблення методу прогнозування ступеню кібервпливу на гетерогенні інформаційні системи військового призначення. Основне завдання методу полягає у забезпеченні кібербезпеки держави за активного протистояння у кіберпросторі. Цей метод враховує сукупність факторів (загроз), що раніше не мали місця, а також еволюцію кібервпливів. Гетерогенні інформаційні системи є складними технічними системами та мають притаманні їм властивості, тому доцільно для їх опису застосовувати декомпозицію на окремі інформаційні системи. Метою статті є розроблення методу прогнозування ступеню кібервпливу на гетерогенні інформаційні системи військового призначення для забезпечення їх сталого функціонування в умовах кібервпливу. У статті застосовано аналітичний метод для розгляду останніх досліджень, публікацій та наукових джерел стосовно функціонування гетерогенних інформаційних систем військового призначення, цілочисельного програмування, максимального елементу та теорії оптимального розподілу ресурсів для прогнозування ступеню кібервпливу. Зазначений методологічний підхід дав змогу визначити набір засобів парирування зовнішніх впливів для кожного елементу гетерогенних інформаційних систем. Подано узагальнену структуру гетерогенних інформаційних систем, яка дозволяє формалізувати процес прогнозування ступеню кібервпливу. Розроблено метод прогнозування ступеню кібервпливу на гетерогенні інформаційні системи військового призначення та подано його формалізований математичний опис. Елементом наукової новизни є те що запропонований підхід базується на оптимальному розподілі засобів парирування зовнішніх впливів, які в свою чергу поділяються на види, за взаємопов’язаними елементами гетерогенних інформаційних систем. Сутність запропонованого підходу полягає у виборі для кожного з елементів системи та відповідного набору джерел кібервпливу, що діє на них з метою порушення сталого функціонування, оптимального розподілу типів засобів парирування зовнішніх впливів. Теоретична значущість дослідження полягає у тому, що на оcнові відомих математичних методів оптимального розподілу ресурсів під час синтезу складних систем, отримано новий підхід, що враховує еволюцію кібервпливів на гетерогенні інформаційні системи військового призначення. Практична цінність полягає у тому, що застосування зазначеного методу, є необхідним кроком для визначення придатності гетерогенних інформаційних систем військового призначення до виконання цільової функції, та дозволить на етапі створення гетерогенних інформаційних систем військового призначення визначити можливі уразливості.

Bruno Santos, Rogério Luís C. Costa, Leonel Santos

Unlocking the potential of Industry 5.0 hinges on robust cybersecurity measures. This new Industrial Revolution prioritises human-centric values while addressing pressing societal issues such as resource conservation, climate change, and social stability. Recognising the heightened risk of cyberattacks due to the new enabling technologies in Industry 5.0, this paper analyses potential threats and corresponding countermeasures. Furthermore, it evaluates the existing industrial implementation frameworks, which reveals their inadequacy in ensuring a secure transition from Industry 4.0 to Industry 5.0. Consequently, the paper underscores the necessity of developing a new framework centred on cybersecurity to facilitate organisations' secure adoption of Industry 5.0 principles. The creation of such a framework is emphasised as a necessity for organisations.

Wen Sun, Jiajia Liu, Yanlin Yue et al.

Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT.

Xin Luo, Mengchu Zhou, Shuai Li et al.

F. Tao, Jiangfeng Cheng, Qinglin Qi

Zhenyu Zhou, Chuntian Zhang, Chen Xu et al.

Industrial Internet of unmanned aerial vehicles (IIoUAVs) that enable autonomous inspection and measurement of anything anytime anywhere have become an essential component of the future industrial Internet of things (IIoT) ecosystem. In this paper, we investigate how to apply IIoUAVs for power line inspection in smart grid from an energy-efficiency perspective. First, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization, such as trajectory scheduling, velocity control, and frequency regulation, and the small-timescale optimization, such as relay selection and power allocation. Then, the original NP-hard problem is transformed into a two-stage suboptimal problem by exploring the timescale difference and the energy magnitude difference between the large-timescale and the small-timescale optimizations, and is solved by combining dynamic programming (DP), auction theory, and matching theory. Finally, the proposed algorithm is verified based on real-world map and realistic power grid topology.

Djabir Abdeldjalil Chekired, L. Khoukhi, H. Mouftah

Industry 4.0 or industrial Internet of things (IIoT) has become one of the most talked-about industrial business concepts in recent years. Thus, to efficiently integrate Internet of things technology into industry, the collected and sensed data from IIoT need to be scheduled in real-time constraints, especially for big factories. To this end, we propose in this paper a hierarchical fog servers’ deployment at the network service layer across different tiers. Using probabilistic analysis models, we prove the efficiency of the proposed hierarchical fog computing compared with the flat architecture. In this paper, IIoT data and requests are divided into both high priority and low priority requests; the high priority requests are urgent/emergency demands that need to be scheduled rapidly. Therefore, we use two-priority queuing model in order to schedule and analyze IIoT data. Finally, we further introduce a workload assignment algorithm to offload peak loads over higher tiers of the fog hierarchy. Using realistic industrial data from Bosch group, the benefits of the proposed architecture compared to the conventional flat design are proved using various performance metrics and through extensive simulations.

Kaira Devi, Priskila Hananingrum, Y. Denny A. Wahyudiono

Introduction:Work stress can occur in many professions, including nursing, which is inseparable from individual characteristics. Inpatient is one of the units at Ploso Regional Public Hospital, Jombang, which has time-consuming work that requires observation on an ongoing basis. This study aimed to understand the relationship between individual characteristics, such as age, gender, marital status, working period, and personality type, with the level of work stress experienced by the inpatient installation unit nurses at Ploso Regional Public Hospital, Jombang. Methods: Observational descriptive study was applied with a cross-sectional design. Age, gender, marital status, working period, and personality type were the independent variables used in this study, while the dependent variable was work stress. The sample used was the total accessible population of nurses in the inpatient unit with 33 respondents. The data collection method used was a general questionnaire for personal variables (age, gender, marital status, working period), Personality Type Questionnaire for personality type, and Health and Safety Executive (HSE) Questionnaire for work stress. Data were analyzed using chi-square correlation and spearman correlation test. Results: In the inpatient installation unit, most nurses were male between the ages of 24-37, had a working period of less than five years, were married, and had type A personality. The individual characteristics which had a moderate relationship with work stress were age (ρ = 0.419), marital status (ρ = 0.461), and working period (ρ = 0.359). Gender (ρ = 0.246) and personality type (ρ = 0.179) had a weak relationship with work stress. Conclusion: Age, marital status, and working period had a moderate relationship with work stress, while gender and personality type had a weak relationship.