Hasil untuk "Industrial directories"

Stefan Lenz, Sotiris Michaelides, Moritz Rickert et al.

Traditionally, industrial control systems (ICS) were designed without security in mind, prioritizing availability and real-time communication. As these systems increasingly become targets of powerful adversaries, security can no longer be neglected. Driven by flexibility and automation needs, ICS are transitioning from wired to 5G communication, introducing new attack surfaces and a less reliable communication medium, thereby exacerbating existing security challenges. Given their critical role in society, a comprehensive evaluation of their security is imperative. To this end, we introduce SWICS, a fully virtual testbed simulating an ICS in a realistic 5G environment, and study how this transition affects security under varying channel conditions. Our results show three key findings: under optimal channel conditions, industrial 5G networks can achieve resilience comparable to wired systems, while degraded channel conditions can amplify traditional attacks, threaten system stability, and undermine detection mechanisms based on predictable traffic patterns. We further demonstrate the inherent limits of securing 5G channels for ICS through eavesdropping and jamming on the open-air interface. Our work highlights the interplay between security and 5G channel conditions, showing that traditional security controls may no longer be sufficient and motivating further research.

Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang et al.

Standard Operating Procedures (SOPs) are essential for ensuring operational safety and consistency in industrial environments. However, retrieving and following these procedures presents unique challenges, such as rigid proprietary structures, condition-dependent relevance, and actionable execution requirement, which standard semantic-driven Retrieval-Augmented Generation (RAG) paradigms fail to address. Inspired by the Mixture-of-Experts (MoE) paradigm, we propose SOPRAG, a novel framework specifically designed to address the above pain points in SOP retrieval. SOPRAG replaces flat chunking with specialized Entity, Causal, and Flow graph experts to resolve industrial structural and logical complexities. To optimize and coordinate these experts, we propose a Procedure Card layer that prunes the search space to eliminate computational noise, and an LLM-Guided gating mechanism that dynamically weights these experts to align retrieval with operator intent. To address the scarcity of domain-specific data, we also introduce an automated, multi-agent workflow for benchmark construction. Extensive experiments across four industrial domains demonstrate that SOPRAG significantly outperforms strong lexical, dense, and graph-based RAG baselines in both retrieval accuracy and response utility, achieving perfect execution scores in real-world critical tasks.

Sergej Krasnikov, Lukas Meitz, Samineh Bagheri et al.

Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods validated under simplified conditions often fail to generalize to industrial settings. This work presents an empirical study on a unique dataset collected from fully operational industrial machinery, explicitly capturing pronounced process-induced variability. We evaluate which model classes are capable of capturing this complexity, starting with a classical Isolation Forest baseline and extending to multiple autoencoder architectures. Experimental results show that Isolation Forest is insufficient for modeling the non-periodic, multi-scale dynamics present in the data, whereas autoencoders consistently perform better. Among them, temporal convolutional autoencoders achieve the most robust performance, while recurrent and variational variants require more careful tuning.

Andrea Arcuri, Alexander Poth, Olsi Rrjolli et al.

REST APIs are widely used in industry, in all different kinds of domains. An example is Volkswagen AG, a German automobile manufacturer. Established testing approaches for REST APIs are time consuming, and require expertise from professional test engineers. Due to its cost and importance, in the scientific literature several approaches have been proposed to automatically test REST APIs. The open-source, search-based fuzzer EvoMaster is one of such tools proposed in the academic literature. However, how academic prototypes can be integrated in industry and have real impact to software engineering practice requires more investigation. In this paper, we report on our experience in using EvoMaster at Volkswagen AG, as an EvoMaster user from 2023 to 2026. We share our learnt lessons, and discuss several features needed to be implemented in EvoMaster to make its use in an industrial context successful. Feedback about value in industrial setups of EvoMaster was given from Volkswagen AG about 4 APIs. Additionally, a user study was conducted involving 11 testing specialists from 4 different companies. We further identify several real-world research challenges that still need to be solved.

Sankar Prasad Chaki, Melissa M. Kahl-McDonagh, Kurt A. Zuelke

The COVID-19 pandemic underscored the urgent need for rapid, reliable, and safe laboratory workflows that ensure both diagnostic accuracy and biosafety for laboratory personnel. We developed a comprehensive approach for SARS-CoV-2 detection using saliva-based RT-qPCR that spans the entire process from sample transfer to final disposal. This workflow integrates biosafety principles with efficient diagnostic procedures, ensuring safe handling, minimized exposure risks, and reliable molecular testing. Critical components included biosecurity, standardized protocols for sample receipt, secure transfer, safe processing, and environmentally responsible disposal. By applying a holistic safety framework, we not only protected laboratory staff during the pandemic but also established a model that can inform preparedness for future emerging infectious disease threats. This approach demonstrates how laboratory safety and diagnostic efficiency can be simultaneously achieved, offering a reference for institutions seeking to balance biosafety and public health needs in outbreak situations.

Jie Zhang, Changzai Pan, Kaiwen Wei et al.

Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information into reports remains a significant challenge for industrial applications. This task is plagued by two critical issues: 1) the complexity and diversity of tables lead to suboptimal reasoning outcomes; and 2) existing table benchmarks lack the capacity to adequately assess the practical application of this task. To fill this gap, we propose the table-to-report task and construct a bilingual benchmark named T2R-bench, where the key information flow from the tables to the reports for this task. The benchmark comprises 457 industrial tables, all derived from real-world scenarios and encompassing 19 industry domains as well as 4 types of industrial tables. Furthermore, we propose an evaluation criteria to fairly measure the quality of report generation. The experiments on 25 widely-used LLMs reveal that even state-of-the-art models like Deepseek-R1 only achieves performance with 62.71 overall score, indicating that LLMs still have room for improvement on T2R-bench.

Henning Femmer, Frank Houdek, Max Unterbusch et al.

Requirements quality is central to successful software and systems engineering. Empirical research on quality defects in natural language requirements relies heavily on datasets, ideally as realistic and representative as possible. However, such datasets are often inaccessible, small, or lack sufficient detail. This paper introduces QuRE (Quality in Requirements), a new dataset comprising 2,111 industrial requirements that have been annotated through a real-world review process. Previously used for over five years as part of an industrial contract, this dataset is now being released to the research community. In this work, we furthermore provide descriptive statistics on the dataset, including measures such as lexical diversity and readability, and compare it to existing requirements datasets and synthetically generated requirements. In contrast to synthetic datasets, QuRE is linguistically similar to existing ones. However, this dataset comes with a detailed context description, and its labels have been created and used systematically and extensively in an industrial context over a period of close to a decade. Our goal is to foster transparency, comparability, and empirical rigor by supporting the development of a common gold standard for requirements quality datasets. This, in turn, will enable more sound and collaborative research efforts in the field.

Annalena Daniels, Sebastian Kerz, Salman Bari et al.

Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Waste Electrical and Electronic Equipment (WEEE) materials, in which electric devices are disassembled and readied for the recovery of raw materials. Since devices may contain hazardous materials and their disassembly involves heavy manual labor, robotic disassembly is a promising venue. However, since devices may be damaged, dirty and unidentified, robotic disassembly is challenging since object models are unavailable or cannot be relied upon. This case study explores grasping strategies for industrial robotic disassembly of WEEE devices with uncertain vision data. We propose three grippers and appropriate tactile strategies for force-based manipulation that improves grasping robustness. For each proposed gripper, we develop corresponding strategies that can perform effectively in different grasping tasks and leverage the grippers design and unique strengths. Through experiments conducted in lab and factory settings for four different WEEE devices, we demonstrate how object uncertainty may be overcome by tactile sensing and compliant techniques, significantly increasing grasping success rates.

Heqiang Wang, Xiaoxiong Zhong, Kang Liu et al.

With the continuous improvement in the computational capabilities of edge devices such as intelligent sensors in the Industrial Internet of Things, these sensors are no longer limited to mere data collection but are increasingly capable of performing complex computational tasks. This advancement provides both the motivation and the foundation for adopting distributed learning approaches. This study focuses on an industrial assembly line scenario where multiple sensors, distributed across various locations, sequentially collect real-time data characterized by distinct feature spaces. To leverage the computational potential of these sensors while addressing the challenges of communication overhead and privacy concerns inherent in centralized learning, we propose the Denoising and Adaptive Online Vertical Federated Learning (DAO-VFL) algorithm. Tailored to the industrial assembly line scenario, DAO-VFL effectively manages continuous data streams and adapts to shifting learning objectives. Furthermore, it can address critical challenges prevalent in industrial environment, such as communication noise and heterogeneity of sensor capabilities. To support the proposed algorithm, we provide a comprehensive theoretical analysis, highlighting the effects of noise reduction and adaptive local iteration decisions on the regret bound. Experimental results on two real-world datasets further demonstrate the superior performance of DAO-VFL compared to benchmarks algorithms.

Vishal Verma, Vinod Reddy, Jaiprakash Ravi

The increasing need for causal analysis in large-scale industrial datasets necessitates the development of efficient and scalable causal algorithms for real-world applications. This paper addresses the challenge of scaling causal algorithms in the context of conducting causal analysis on extensive datasets commonly encountered in industrial settings. Our proposed solution involves enhancing the scalability of causal algorithm libraries, such as EconML, by leveraging the parallelism capabilities offered by the distributed computing framework Ray. We explore the potential of parallelizing key iterative steps within causal algorithms to significantly reduce overall runtime, supported by a case study that examines the impact on estimation times and costs. Through this approach, we aim to provide a more effective solution for implementing causal analysis in large-scale industrial applications.

Xuanchao Ma, Yanlin Jiang, Hongyan Liu et al.

Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types.

Houjia Xu, Yuntao Li, Taotao Zhou et al.

Emerging trends in digitization, intelligence, and low-carbon transition have significantly affected China's oil and gas pipeline development strategies. Emerging technology has resulted in significant cost savings but has also raised concerns about the safety of oil and gas pipelines. From the perspectives of infrastructure construction, safety assurance technology, and legal and regulatory frameworks, this study offers a survey and assessment of the current practices to identify the safety issues and challenges of oil and gas pipelines. Critical recommendations have been synthesized and discussed, focusing on the aspects of policy and technology, that is, the long-lifetime oil and gas pipeline safety assurance system with legal guidance and policy supplements, and the research and application of key technologies on the inherent safety design, resilience enhancement, intelligent safe operation and maintenance, and green and low-carbon transition. This study aims to identify the key issues addressed and challenges faced by research and development activities and to identify the gaps and opportunities for future research and development to ensure the safety of China's oil and gas pipelines.

Rizki Rahmawati, Mila Tejamaya

Introduction:In water treatment plants (WTP), chemicals play a crucial role. However, some of these chemicals are hazardous. This study aims to conduct a dermal risk assessment in the WTP of an ammonia and urea production facility. Methods: The study was performed in August 2023 and assessed dermal exposure risk for four hazardous chemicals: NaOCl (30%), HCl (60%), H2SO4 (98%), and NaOH (48%), utilizing the Tier 2 RISKOFDERM model. Intrinsic toxicity was evaluated using risk phrases and toxicity information. Potential dermal exposure rates (PERBODY and PERHANDS) were determined based on task group and exposure modifier, while actual dermal exposure rates (AERBODY and AERHANDS) were determined based on clothing type and activity time. Health risk was assessed using actual exposure scores and intrinsic toxicity levels, which were categorized into 10 different levels ranging from 1 to 10. Results: The risk phrases indicated that four chemicals possessed a high level of intrinsic toxicity in terms of local effect but no systemic effect. PERBODY and PERHANDS were high (NaOCl, HCl) and low (H2SO4, NaOH). The actual exposure scores were determined to be 1 (high) for NaOCl and HCI, 0.01 (low) for H2SO4, and 0.03 (medium) for NaOH. Health risk values were 8 for NaOCl and HCI, 5 for H2SO4, and 6 for NaOH. Conclusion: Health risks in NaOCl and HCl were assigned action priority (AP) 1, followed by NaOH at AP-2, and H2SO4 at AP-3. The study recommends the implementation of control measures encompassing engineering solutions, administration, and personal protective equipment.

Kumar Saurabh, Deepak Gajjala, Krishna Kaipa et al.

Industrial cyber-physical systems (ICPS) are gradually integrating information technology and automating industrial processes, leading systems to become more vulnerable to malicious actors. Thus, to deploy secure Industrial Control and Production Systems (ICPS) in smart factories, cyber threats and risks must be addressed. To identify all possible threats, Threat Modeling is a promising solution. Despite the existence of numerous methodological solutions for threat modeling in cyber-physical systems (CPS), current approaches are ad hoc and inefficient in providing clear insights to researchers and organizations involved in IIoT technologies. These approaches lack a comprehensive analysis of cyber threats and fail to facilitate effective path analysis across the ICPS lifecycle, incorporating smart manufacturing technologies and tools. To address these gaps, a novel quantitative threat modeling approach is proposed, aiming to identify probable attack vectors, assess the path of attacks, and evaluate the magnitude of each vector. This paper also explains the execution of the proposed approach with two case studies, namely the industrial manufacturing line, i.e., the Internet of Manufacturing (IoM), and the power and industry, i.e., the Internet of Production (IoP).

Huan Wang, Yan-Fu Li, Min Xie

Prognostics and health management (PHM) is essential for industrial operation and maintenance, focusing on predicting, diagnosing, and managing the health status of industrial systems. The emergence of the ChatGPT-Like large-scale language model (LLM) has begun to lead a new round of innovation in the AI field. It has extensively promoted the level of intelligence in various fields. Therefore, it is also expected further to change the application paradigm in industrial PHM and promote PHM to become intelligent. Although ChatGPT-Like LLMs have rich knowledge reserves and powerful language understanding and generation capabilities, they lack domain-specific expertise, significantly limiting their practicability in PHM applications. To this end, this study explores the ChatGPT-Like LLM empowered by the local knowledge base (LKB) in industrial PHM to solve the above limitations. In addition, we introduce the method and steps of combining the LKB with LLMs, including LKB preparation, LKB vectorization, prompt engineering, etc. Experimental analysis of real cases shows that combining the LKB with ChatGPT-Like LLM can significantly improve its performance and make ChatGPT-Like LLMs more accurate, relevant, and able to provide more insightful information. This can promote the development of ChatGPT-Like LLMs in industrial PHM and promote their efficiency and quality.

Motoko Nakatani, Sachie Inoue, Isao Kamae

Objective: Helicobacter pylori infections increase gastric cancer risk. Detecting and eradicating Helicobacter pylori infections and implementing a follow-up strategy should be considered by occupational health practitioners. This study aimed to evaluate the cost-effectiveness of adding an H. pylori antibody (HPA) test to current gastric cancer screening using upper gastrointestinal series (UGI) at the workplace in Japan. Methods: The data of Japanese people aged ≥40 years were collected from PubMed and evaluated in two cohorts: UGI (X-ray examination)+HPA test and UGI only. A Markov model was used for the cost-effectiveness analysis of the UGI+HPA test and UGI-only cohorts. The main outcomes were cost, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). The impact of uncertainty was assessed using one-way sensitivity analyses (OWSA) and probabilistic sensitivity analyses (PSA). Results: A base-case analysis showed that the UGI+HPA test strategy was less costly (−US＄1,039 and −US＄1,496) and more effective (0.415 and 0.437 QALYs) than the UGI-only strategy in the 40- and 50-year-old groups, respectively. The UGI+HPA test strategy was assessed as a dominant strategy. In the OWSA, the tornado diagram showed negative expected costs and positive QALY gains within the established ranges for all parameters. In the PSA, more than 95% of the simulations demonstrated ICER <5 million yen (US＄51,674; US＄1=96.76 yen)/QALY. Conclusions: This modeling study suggests that gastric cancer screening using UGI+HPA test followed by eradication and annual opportunistic screening, compared with UGI only, resulted in lower costs and greater QALY gains for both 40- and 50-year-old groups at the workplace.

Guy R. Aby, Salah F. Issa

Automated agricultural machinery has advanced significantly in the previous ten years; however, the ability of such robots to operate safely will be critical to their commercialization. This study provides a holistic evaluation of the work carried out so far in the field of automated agricultural machines’ safety, as well as a framework for future research considerations. Previous automated agricultural machines’ safety-related studies are analyzed and grouped into three categories: (1) environmental perception, (2) risk assessment as well as risk mitigation, and (3) human factors as well as ergonomics. The key findings are as follows: (1) The usage of single perception, multiple perception sensors, developing datasets of agricultural environments, different algorithms, and external solutions to improve sensor performance were all explored as options to improve automated agricultural machines’ safety. (2) Current risk assessment methods cannot be efficient when dealing with new technology, such as automated agricultural machines, due to a lack of pre-existing knowledge. Full compliance with the guidelines provided by the current International Organization for Standardization (ISO 18497) cannot ensure automated agricultural machines’ safety. A regulatory framework and being able to test the functionalities of automated agricultural machines within a reliable software environment are efficient ways to mitigate risks. (3) Knowing foreseeable human activity is critical to ensure safe human–robot interaction.

R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat et al.

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent results with applications in the process industry.

Dennis Müller, Michael März, Stephan Scheele et al.

Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisions are crucial. Therefore, we aim to extend the defect detection task towards an interactive human-in-the-loop approach that allows us to integrate rich background knowledge and the inference of complex relationships going beyond traditional purely data-driven approaches. We propose an approach for an interactive support system for classifications in an industrial quality control setting that combines the advantages of both (explainable) knowledge-driven and data-driven machine learning methods, in particular inductive logic programming and convolutional neural networks, with human expertise and control. The resulting system can assist domain experts with decisions, provide transparent explanations for results, and integrate feedback from users; thus reducing workload for humans while both respecting their expertise and without removing their agency or accountability.

Kosuke Sakai, Tomohisa Nagata, Masako Nagata et al.

Objectives: Presenteeism refers to the condition of working while having health problems and can be one of the perspectives to assess the incompatibility between workers and their jobs. The purpose of this survey was to find out what kind of occupational health issues can be detected by occupational physicians’ interviews focusing on presenteeism. Methods: We conducted interviews with workers suffering from presenteeism in a food manufacturing company. The Work Functioning impairment scale (WFun) was used as the indicator of presenteeism. We discussed the occupational health issues and the necessity of additional interventions. Results: Thirty-nine workers with WFun score of 21 or higher were interviewed, and we have found nine cases in need of support. The workplace issues were structured into four categories: (i) health problems that are difficult to identify through health checkups, (ii) health problems missed by the stress check program, (iii) health problems caused by workload that cannot be identified by workplace patrols, and (iv) health problems that are not considered because they do not require support. Conclusions: We discovered new workplace issues by interviewing workers suffering from presenteeism.