Hasil untuk "Industrial directories"

Andreas Rausch, Stefan Wittek

Different domains foster different architectural styles -- and thus different documentation practices (e.g., state-based models for behavioral control vs. ER-style models for information structures). Agentic AI systems exhibit another characteristic style: specialized agents collaborate by exchanging artifacts, invoking external tools, and coordinating via recurring interaction patterns and quality gates. As these systems evolve into long-lived industrial solutions, documentation must capture these style-defining concerns rather than relying on ad-hoc code sketches or pipeline drawings. This paper reports industrial experience from joint projects and derives a documentation systematics tailored to this style. Concretely, we provide (i) a style-oriented modeling vocabulary and a small set of views for agents, artifacts, tools, and their coordination patterns, (ii) a hierarchical description technique aligned with C4 to structure these views across abstraction levels, and (iii) industrial examples with lessons learned that demonstrate how the approach yields transparent, maintainable architecture documentation supporting sustained evolution.

Roberto José Hernández de la Iglesia, José L. Calvo-Rolle, Héctor Quintian-Pardo et al.

Ship repair is hazardous, often presenting unsuitable working areas and risks due to the ship’s configuration. Welding tasks are particularly dangerous due to the high temperatures generated, high enough to melt the metal in structural elements, bulkheads, linings, and tanks. This study investigates the consequences of temperature distribution during the welding of naval plates and proposes some accident prevention measures. Industry working conditions were reproduced, including the materials, procedures, and tools used, as well as the certified personnel employed. DH 36-grade naval steel, with a composition of C max. 0.18%, Mn 0.90–1.60%, P 0.035%, S 0.04%, Si 0.10–0.50%, Ni max 0.4%, Cr max 0.25%, Mo 0.08%, Cu max 0.35%, Cb (Nb) 0.05%, and V 0.1%, was welded via FCAW-G (Gas-Shielded Flux-Cored Arc Welding), selected for this study because it is one of the most widely practiced in the naval industry. The main sensor used in the experiments was an FLIR model E50 thermographic camera, and thermal waxes were employed. The results for each thickness case are presented in both graphical and tabular form to provide accurate and actionable guidelines, prioritizing safety. After studying the butt jointing of naval plates of various thicknesses (8, 10, and 15 mm), safe distances to maintain were proposed to avoid risks in the most unfavorable cases: 350 mm from the welding seam to avoid burn injuries to unprotected areas of the body and 250 mm from the welding seam to avoid producing flammable gases. These numbers are less accurate but easier to remember, which prevents errors in the face of hazards throughout a long working day.

Chris Mitrakas, Alexandros Xanthopoulos, Dimitrios Koulouriotis

This paper investigates the effectiveness and limitations of the traditional Fine-Kinney method for occupational risk assessment, emphasizing its shortcomings in addressing complex and dynamic work environments. To overcome these challenges, two advanced methodologies, Fine-Kinney10 (FK10) and Fuzzy Fine-Kinney10 (FFK10), are introduced. The FK10 employs a symmetric scaling system (1–10) for probability, frequency, and severity indicators, providing a more balanced quantification of risks. Meanwhile, FFK10 incorporates fuzzy logic to handle uncertainty and subjectivity in risk assessment, significantly enhancing the sensitivity and accuracy of risk evaluation. These methodologies were applied to a linemen workshop in an energy production and distribution company, analyzing various types of accidents such as falls from heights, exposure to electric currents, slips on surfaces, and more. The applications highlighted the practical benefits of these methods in effectively assessing and mitigating risks. A significant finding includes the identification of risks related to falls from heights of <2.5 m (SH1) and road traffic accidents (SH6), where all three methods yielded different verbal outcomes. Compared to the traditional Fine-Kinney method, FK10 and FFK10 demonstrated superior ability in distinguishing risk levels and guiding targeted safety measures. The study underscores that FK10 and FFK10 represent significant advancements in occupational risk management, offering robust frameworks adaptable across various industries.

Roshan Klein-Seetharaman, Daniel Rakita

Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-derive richer data, leading to redundant computations, fragmented implementations, and limited standardization. In this work, we introduce the Universal Robot Description Directory (URDD), a modular representation that organizes derived robot information into structured, easy-to-parse JSON and YAML modules. Our open-source toolkit automatically generates URDDs from URDFs, with a Rust implementation supporting Bevy-based visualization. Additionally, we provide a JavaScript/Three.js viewer for web-based inspection of URDDs. Experiments on multiple robot platforms show that URDDs can be generated efficiently, encapsulate substantially richer information than standard specification files, and directly enable the construction of core robotics subroutines. URDD provides a unified, extensible resource for reducing redundancy and establishing shared standards across robotics frameworks. We conclude with a discussion on the limitations and implications of our work.

Xiaoxue Wu, Xinyuan Chen, Yaohui Wang et al.

Shot transitions play a pivotal role in multi-shot video generation, as they determine the overall narrative expression and the directorial design of visual storytelling. However, recent progress has primarily focused on low-level visual consistency across shots, neglecting how transitions are designed and how cinematographic language contributes to coherent narrative expression. This often leads to mere sequential shot changes without intentional film-editing patterns. To address this limitation, we propose ShotDirector, an efficient framework that integrates parameter-level camera control and hierarchical editing-pattern-aware prompting. Specifically, we adopt a camera control module that incorporates 6-DoF poses and intrinsic settings to enable precise camera information injection. In addition, a shot-aware mask mechanism is employed to introduce hierarchical prompts aware of professional editing patterns, allowing fine-grained control over shot content. Through this design, our framework effectively combines parameter-level conditions with high-level semantic guidance, achieving film-like controllable shot transitions. To facilitate training and evaluation, we construct ShotWeaver40K, a dataset that captures the priors of film-like editing patterns, and develop a set of evaluation metrics for controllable multi-shot video generation. Extensive experiments demonstrate the effectiveness of our framework.

Tim Schreiter, Andrey Rudenko, Jens V. Rüppel et al.

Successful adoption of industrial robots will strongly depend on their ability to safely and efficiently operate in human environments, engage in natural communication, understand their users, and express intentions intuitively while avoiding unnecessary distractions. To achieve this advanced level of Human-Robot Interaction (HRI), robots need to acquire and incorporate knowledge of their users' tasks and environment and adopt multimodal communication approaches with expressive cues that combine speech, movement, gazes, and other modalities. This paper presents several methods to design, enhance, and evaluate expressive HRI systems for non-humanoid industrial robots. We present the concept of a small anthropomorphic robot communicating as a proxy for its non-humanoid host, such as a forklift. We developed a multimodal and LLM-enhanced communication framework for this robot and evaluated it in several lab experiments, using gaze tracking and motion capture to quantify how users perceive the robot and measure the task progress.

Anastasia Sergeeva, Claudia Negri-Ribalta, Gabriele Lenzini

Although extended reality(XR)-using technologies have started to be discussed in the industrial setting, it is becoming important to understand how to implement them ethically and privacy-preservingly. In our paper, we summarise our experience of developing XR implementations for the off-highway machinery domain by pointing to the main challenges we identified during the work. We believe that our findings can be a starting point for further discussion and future research regarding privacy and ethical challenges in industrial applications of XR.

Theofanis P. Raptis, Andrea Passarella, Marco Conti

Energy efficiency and reliability are vital design requirements of recent industrial networking solutions. Increased energy consumption, poor data access rates and unpredictable end-to-end data access latencies are catastrophic when transferring high volumes of critical industrial data in strict temporal deadlines. These requirements might become impossible to meet later on, due to node failures, or excessive degradation of the performance of wireless links. In this paper, we focus on maintaining the network functionality required by the industrial, best effort, low-latency applications after such events, by sacrificing latency guarantees to improve energy consumption and reliability. We avoid continuously recomputing the network configuration centrally, by designing an energy efficient, local and distributed path reconfiguration method. Specifically, given the operational parameters required by the applications, our method locally reconfigures the data distribution paths, when a network node fails. Additionally, our method also regulates the return to an operational state of nodes that have been offline in the past. We compare the performance of our method through simulations to the performance of other state of the art protocols and we demonstrate performance gains in terms of energy consumption, data delivery success rate, and in some cases, end-to-end data access latency. We conclude by providing some emerging key insights which can lead to further performance improvements.

Kazunari Takaya, Nobuyuki Shibata, Masayoshi Hagiwara et al.

Objectives: Ion-mobility spectrometry (IMS) is a promising system for on-site real-time monitoring of volatile organic compounds (VOCs). Calibration curves derived from shifts in nominal arrival-time spectra of chemical substances relative to those of water clusters enable quantitative analysis at high concentrations. Methods: This study investigated the adaptability of IMS to real-time monitoring of VOCs in the work environment, using toluene as a test case. Toluene concentrations were measured by IMS at one-minute intervals during a ten-minute simulated cleaning operation. Results: The arrival-time shift was lower at high concentrations because ion production saturates as the toluene concentration approaches the limit of ionizability, with a resulting decrease in slope of the calibration curve. The lower limit of quantification for toluene was assumed to be 13.3 ppm because no arrival-time shift was observed at lower concentrations. The time-averaged toluene concentration measured by IMS for 10 minutes of operation was 45.8 ppm, which is comparable to that measured by gas chromatography–mass spectrometry (GC–MS; 44.3 ppm) within ~3%. Conclusions: Our results indicate that the measurement of toluene concentrations is possible at one-minute intervals by IMS, making it possible to track rapid changes in workplace conditions. Therefore, IMS can measure exposure to VOCs in real-time with an accuracy similar to that of GC–MS.

Srinjoy Mitra, Jean-Pierre Raskin

This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and sustainability is self-evident. Today, the for-profit engineering/technology companies, some of which are among the largest in the world, also shape the narrative of engineering education and research in universities. As engineering graduates form the largest cohorts within STEM disciplines in Western countries, they become future professionals who will work, lead, or even establish companies in this industry. Unfortunately, the curriculum within engineering education often lacks a deep understanding of social realities, an essential component of a comprehensive university education. Here we establish this unusual connection with the industry that has driven engineering higher education for several decades and its obvious negative impacts to society. We analyse this nexus and highlight the need for engineering schools to hold a more critical viewpoint. Given the wealth and power of modern technology companies, particularly in the ICT domain, questioning their techno-solutionism narrative is essential within the institutes of higher education.

Wei Luo, Haiming Yao, Wenyong Yu et al.

Unsupervised visual anomaly detection is crucial for enhancing industrial production quality and efficiency. Among unsupervised methods, reconstruction approaches are popular due to their simplicity and effectiveness. The key aspect of reconstruction methods lies in the restoration of anomalous regions, which current methods have not satisfactorily achieved. To tackle this issue, we introduce a novel \uline{A}daptive \uline{M}ask \uline{I}npainting \uline{Net}work (AMI-Net) from the perspective of adaptive mask-inpainting. In contrast to traditional reconstruction methods that treat non-semantic image pixels as targets, our method uses a pre-trained network to extract multi-scale semantic features as reconstruction targets. Given the multiscale nature of industrial defects, we incorporate a training strategy involving random positional and quantitative masking. Moreover, we propose an innovative adaptive mask generator capable of generating adaptive masks that effectively mask anomalous regions while preserving normal regions. In this manner, the model can leverage the visible normal global contextual information to restore the masked anomalous regions, thereby effectively suppressing the reconstruction of defects. Extensive experimental results on the MVTec AD and BTAD industrial datasets validate the effectiveness of the proposed method. Additionally, AMI-Net exhibits exceptional real-time performance, striking a favorable balance between detection accuracy and speed, rendering it highly suitable for industrial applications. Code is available at: https://github.com/luow23/AMI-Net

Razin Farhan Hussain, Mohsen Amini Salehi

The Industry 4.0 revolution has been made possible via AI-based applications (e.g., for automation and maintenance) deployed on the serverless edge (aka fog) computing platforms at the industrial sites -- where the data is generated. Nevertheless, fulfilling the fault-intolerant and real-time constraints of Industry 4.0 applications on resource-limited fog systems in remote industrial sites (e.g., offshore oil fields) that are uncertain, disaster-prone, and have no cloud access is challenging. It is this challenge that our research aims at addressing. We consider the inelastic nature of the fog systems, software architecture of the industrial applications (micro-service-based versus monolithic), and scarcity of human experts in remote sites. To enable cloud-like elasticity, our approach is to dynamically and seamlessly (i.e., without human intervention) federate nearby fog systems. Then, we develop serverless resource allocation solutions that are cognizant of the applications' software architecture, their latency requirements, and distributed nature of the underlying infrastructure. We propose methods to seamlessly and optimally partition micro-service-based application across the federated fog. Our experimental evaluation express that not only the elasticity is overcome in a serverless manner, but also our developed application partitioning method can serve around 20% more tasks on-time than the existing methods in the literature.

Hyungmin Kim, Donghun Kim, Pyunghwan Ahn et al.

While deep neural networks have achieved remarkable performance, data augmentation has emerged as a crucial strategy to mitigate overfitting and enhance network performance. These techniques hold particular significance in industrial manufacturing contexts. Recently, image mixing-based methods have been introduced, exhibiting improved performance on public benchmark datasets. However, their application to industrial tasks remains challenging. The manufacturing environment generates massive amounts of unlabeled data on a daily basis, with only a few instances of abnormal data occurrences. This leads to severe data imbalance. Thus, creating well-balanced datasets is not straightforward due to the high costs associated with labeling. Nonetheless, this is a crucial step for enhancing productivity. For this reason, we introduce ContextMix, a method tailored for industrial applications and benchmark datasets. ContextMix generates novel data by resizing entire images and integrating them into other images within the batch. This approach enables our method to learn discriminative features based on varying sizes from resized images and train informative secondary features for object recognition using occluded images. With the minimal additional computation cost of image resizing, ContextMix enhances performance compared to existing augmentation techniques. We evaluate its effectiveness across classification, detection, and segmentation tasks using various network architectures on public benchmark datasets. Our proposed method demonstrates improved results across a range of robustness tasks. Its efficacy in real industrial environments is particularly noteworthy, as demonstrated using the passive component dataset.

Samira Kamali Poorazad, Chafika Benzaıd, Tarik Taleb

The industrial Internet of Things (IIoT) involves the integration of Internet of Things (IoT) technologies into industrial settings. However, given the high sensitivity of the industry to the security of industrial control system networks and IIoT, the use of software-defined networking (SDN) technology can provide improved security and automation of communication processes. Despite this, the architecture of SDN can give rise to various security threats. Therefore, it is of paramount importance to consider the impact of these threats on SDN-based IIoT environments. Unlike previous research, which focused on security in IIoT and SDN architectures separately, we propose an integrated method including two components that work together seamlessly for better detecting and preventing security threats associated with SDN-based IIoT architectures. The two components consist in a convolutional neural network-based Intrusion Detection System (IDS) implemented as an SDN application and a Blockchain-based system (BS) to empower application layer and network layer security, respectively. A significant advantage of the proposed method lies in jointly minimizing the impact of attacks such as command injection and rule injection on SDN-based IIoT architecture layers. The proposed IDS exhibits superior classification accuracy in both binary and multiclass categories.

Yuanyuan Chen, Zichen Chen, Sheng Guo et al.

Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often involve multiple industrial plants (possibly belonging to different companies or subsidiaries) with sensitive data collected and stored in a distributed manner, collaborative fault diagnostic model training often needs to leverage federated learning (FL). As the scale of the industrial fault diagnostic models are often large and communication channels in such systems are often not exclusively used for FL model training, existing deployed FL model training frameworks cannot train such models efficiently across multiple institutions. In this paper, we report our experience developing and deploying the Federated Opportunistic Block Dropout (FEDOBD) approach for industrial fault diagnostic model training. By decomposing large-scale models into semantic blocks and enabling FL participants to opportunistically upload selected important blocks in a quantized manner, it significantly reduces the communication overhead while maintaining model performance. Since its deployment in ENN Group in February 2022, FEDOBD has served two coal chemical plants across two cities in China to build industrial fault prediction models. It helped the company reduce the training communication overhead by over 70% compared to its previous AI Engine, while maintaining model performance at over 85% test F1 score. To our knowledge, it is the first successfully deployed dropout-based FL approach.

Muhammad Abbas, Ali Hamayouni, Mahshid Helali Moghadam et al.

Processing and reviewing nightly test execution failure logs for large industrial systems is a tedious activity. Furthermore, multiple failures might share one root/common cause during test execution sessions, and the review might therefore require redundant efforts. This paper presents the LogGrouper approach for automated grouping of failure logs to aid root/common cause analysis and for enabling the processing of each log group as a batch. LogGrouper uses state-of-art natural language processing and clustering approaches to achieve meaningful log grouping. The approach is evaluated in an industrial setting in both a qualitative and quantitative manner. Results show that LogGrouper produces good quality groupings in terms of our two evaluation metrics (Silhouette Coefficient and Calinski-Harabasz Index) for clustering quality. The qualitative evaluation shows that experts perceive the groups as useful, and the groups are seen as an initial pointer for root cause analysis and failure assignment.

Rindang Diannita

Introduction: One of the efforts to reduce the risk of occupational accident and occupational diseases is awareness regarding the importance of the safety and health of workers in hospitals, which is also a top priority in hospitals during a pandemic situation. The application of health protocols and the use of Personal Protective Equipment (PPE) are the main lines of defense against the risk of disease and occupational accident. So that the use of Personal Protective Equipment (PPE) is very important, especially for workers during a pandemic. The purpose of this study was to analyze the mapping of the use of Personal Protective Equipment (PPE) with the incidence of occupational accident. Methods: The research used was an analytic observational type using a cross sectional approach, besides that the researchers conducted a survey of the conditions in the hospital. With a sample of 179 respondents in all parts of the hospital. Results: There is an effect of the use of PPE on the incidence of work accidents and it is necessary to have a mapping of PPE, such as gloves, safety shoes, surgical glasses, surgical gown, apron, mask, face shield, head protection, safety helmet, safety shoes, body harness, fire-resistant clothing, fire-resistant helmet, fire-resistant goggles, and fire-resistant gloves. Conclusion: Control is needed in the form of procurement of Personal Protective Equipment at Hospital X, including face shields, aprons, gloves, masks, head protectors, and safety shoes.

ali PAHNABI, Solale RAMAZANI, Ehsan MOHAMMADI et al.

Introduction: Occupational skin diseases and hand contact dermatitis specifically are among the most common occupational diseases among the healthcare workers. Since surgical technologists have contact with allergens and irritant substances are more susceptible to hand contact dermatitis. Thus, the aim of this study was to evaluate the prevalence of occupational hand contact dermatitis and effective factors among surgical technologists in five educational centers affiliated to Mazandaran University of Medical Sciences. methods: The present cross-sectional study was conducted over 125 surgical technologists working in the hospitals affiliated with Mazandaran University of Medical Sciences who were selected via census sampling. Later, 97 participants who met the inclusion criteria were investigated. Data were collected by Nordic Occupational Skin questionnaire (NOSQ-2002) through interview and analyzed by SPSS software version 23. Results: The findings indicated that 68% of the examined technologists were female and 57.7% were over 37 years old. The prevalence of hand dermatitis was 45.4% (44 people). The highest prevalence was observed at the back of hands (24.7%) and between fingers (17.5%).  Contact hand dermatitis had a significant correlation with the participants’ gender (p = 0.002), work experience (p = 0.028), and frequency of hand washes (p = 0.021). Moreover, having a history of eczema and allergy (P-Value≤ 0.01) was significantly effective in increasing hand contact dermatitis. Conclusion: The prevalence of hand contact dermatitis is high among surgical technologists. Therefore, hospital managers are required to plan for preventive measures and control the current situation. Furthermore, future researchers are recommended to carry out more studies on allergic dermatitis.

Darius Sas, Ilaria Pigazzini, Paris Avgeriou et al.

Architectural Technical Debt (ATD) is considered as the most significant type of TD in industrial practice. In this study, we interview 21 software engineers and architects to investigate a specific type of ATD, namely architectural smells (AS). Our goal is to understand the phenomenon of AS better and support practitioners to better manage it and researchers to offer relevant support. The findings of this study provide insights on how practitioners perceive AS and how they introduce them, the maintenance and evolution issues they experienced and associated to the presence of AS, and what practices and tools they adopt to manage AS.