Hasil untuk "Industrial directories"

Leonard Kleinberger, Michael Gundall, Hans D. Schotten

Industrial 5G deployments using Time Division Duplex (TDD) networks face a critical challenge: existing schedulers rely on static configuration of Uplink (UL) to Downlink (DL) resource ratios, failing to adapt to dynamic asymmetric traffic demands. This limitation is particularly problematic in Industry 4.0 scenarios where traffic patterns exhibit significant asymmetry between directions and heterogeneous Quality of Service (QoS) requirements. We present FLEX, a novel QoS-aware scheduler that dynamically adjusts the UL/DL ratio in flexible TDD slots while respecting diverse QoS requirements. FLEX introduces DL buffer state estimation to prevent starvation of high-priority DL traffic, exploiting the deterministic nature of industrial traffic patterns for accurate predictions. Through extensive simulations of industrial scenarios using 5G LENA and ns-3, we demonstrate that FLEX achieves similar throughput compared to established scheduling while correctly enforcing QoS priorities in both traffic directions. For deterministic traffic patterns, FLEX maintains minimal latency overhead (less than 1 slot duration), making it particularly suitable for industrial automation applications.

Xueying Du, Jiayi Feng, Yi Zou et al.

Static analysis tools (SATs) are widely adopted in both academia and industry for improving software quality, yet their practical use is often hindered by high false positive rates, especially in large-scale enterprise systems. These false alarms demand substantial manual inspection, creating severe inefficiencies in industrial code review. While recent work has demonstrated the potential of large language models (LLMs) for false alarm reduction on open-source benchmarks, their effectiveness in real-world enterprise settings remains unclear. To bridge this gap, we conduct the first comprehensive empirical study of diverse LLM-based false alarm reduction techniques in an industrial context at Tencent, one of the largest IT companies in China. Using data from Tencent's enterprise-customized SAT on its large-scale Advertising and Marketing Services software, we construct a dataset of 433 alarms (328 false positives, 105 true positives) covering three common bug types. Through interviewing developers and analyzing the data, our results highlight the prevalence of false positives, which wastes substantial manual effort (e.g., 10-20 minutes of manual inspection per alarm). Meanwhile, our results show the huge potential of LLMs for reducing false alarms in industrial settings (e.g., hybrid techniques of LLM and static analysis eliminate 94-98% of false positives with high recall). Furthermore, LLM-based techniques are cost-effective, with per-alarm costs as low as 2.1-109.5 seconds and $0.0011-$0.12, representing orders-of-magnitude savings compared to manual review. Finally, our case analysis further identifies key limitations of LLM-based false alarm reduction in industrial settings.

Wahyu Rahmaniar, Kenji Suzuki

Traditional deep learning models often lack annotated data, especially in cross-domain applications such as anomaly detection, which is critical for early disease diagnosis in medicine and defect detection in industry. To address this challenge, we propose Multi-AD, a convolutional neural network (CNN) model for robust unsupervised anomaly detection across medical and industrial images. Our approach employs the squeeze-and-excitation (SE) block to enhance feature extraction via channel-wise attention, enabling the model to focus on the most relevant features and detect subtle anomalies. Knowledge distillation (KD) transfers informative features from the teacher to the student model, enabling effective learning of the differences between normal and anomalous data. Then, the discriminator network further enhances the model's capacity to distinguish between normal and anomalous data. At the inference stage, by integrating multi-scale features, the student model can detect anomalies of varying sizes. The teacher-student (T-S) architecture ensures consistent representation of high-dimensional features while adapting them to enhance anomaly detection. Multi-AD was evaluated on several medical datasets, including brain MRI, liver CT, and retina OCT, as well as industrial datasets, such as MVTec AD, demonstrating strong generalization across multiple domains. Experimental results demonstrated that our approach consistently outperformed state-of-the-art models, achieving the best average AUROC for both image-level (81.4% for medical and 99.6% for industrial) and pixel-level (97.0% for medical and 98.4% for industrial) tasks, making it effective for real-world applications.

Wei Luo, Haiming Yao, Wenyong Yu

Industrial anomaly detection plays a crucial role in ensuring product quality control. Therefore, proposing an effective anomaly detection model is of great significance. While existing feature-reconstruction methods have demonstrated excellent performance, they face challenges with shortcut learning, which can lead to undesirable reconstruction of anomalous features. To address this concern, we present a novel feature-reconstruction model called the \textbf{T}emplate-based \textbf{F}eature \textbf{A}ggregation \textbf{Net}work (TFA-Net) for anomaly detection via template-based feature aggregation. Specifically, TFA-Net first extracts multiple hierarchical features from a pre-trained convolutional neural network for a fixed template image and an input image. Instead of directly reconstructing input features, TFA-Net aggregates them onto the template features, effectively filtering out anomalous features that exhibit low similarity to normal template features. Next, TFA-Net utilizes the template features that have already fused normal features in the input features to refine feature details and obtain the reconstructed feature map. Finally, the defective regions can be located by comparing the differences between the input and reconstructed features. Additionally, a random masking strategy for input features is employed to enhance the overall inspection performance of the model. Our template-based feature aggregation schema yields a nontrivial and meaningful feature reconstruction task. The simple, yet efficient, TFA-Net exhibits state-of-the-art detection performance on various real-world industrial datasets. Additionally, it fulfills the real-time demands of industrial scenarios, rendering it highly suitable for practical applications in the industry. Code is available at https://github.com/luow23/TFA-Net.

Dafni M. Nalmpant-Sarikaki, Alexandros I. Theocharis, Nikolaos C. Koukouzas et al.

This work presents a structured methodology for multi-hazard risk assessment in post-mining coal areas, addressing the complex interactions between natural, mining, and technological hazards. The methodology provides a flexible, semi-quantitative mixed-methods framework designed to evaluate multi-hazard risk scenarios through a seven-step process, which includes identification of hazards, analysis of hazard interactions, and calculation of the Multi-Hazard Index (MHI), Vulnerability Index (VI), and Multi-Risk Value (MRV). The MHI assesses the cumulative intensity of hazard interactions, while the MRV quantifies the socio-economic impacts of various multi-hazard scenarios. The framework also incorporates vulnerability assessments, using social and physical vulnerability indices, to better understand the potential risks to communities. The methodology aims to enhance the safety of post-mining areas by mitigating the cascading effects of hazard interactions and by systematically increasing the knowledge of hazard interdependencies. This approach is adaptable to diverse post-mining contexts, offering a comprehensive framework for assessing and managing multi-hazard risks. It aligns with the broader objectives of the European Green Deal by promoting sustainable land management and addressing the transition of coal regions toward a carbon-neutral economy. It equips stakeholders with necessary tools to enhance resilience and ensure the long-term socio-economic and environmental stability and safety of post-mining areas.

Yanhui Lei, Jinye Zheng

China is the world’s largest emitter of agricultural methane (AGM). However, limited attention has been given to its spatial distribution, driving factors, and long-term temporal trends, which are crucial for the development of targeted emission control strategies and the achievement of carbon neutrality. This study employs the IPCC model to estimate AGM emissions in China from 2000 to 2020 in mainland China to explore the long-term trend and influencing factors using the q-statistic of Geodetector. Additionally, gravity theory is applied to analyze spatial shifts in AGM emissions over time. The results reveal that the AGM emissions in 2020 reached 18.38 Tg, with enteric fermentation as the largest contributor, accounting for 52.58 % of total emissions. Significant stratified heterogeneity is observed, with western provinces showing higher emissions. The primary socioeconomic drivers - the value of the primary industry and its share of GDP - influence AGM emissions by shaping land-use patterns, agricultural practices, and resource consumption, thereby altering emission dynamics. Gravity theory analysis shows a northwestward shift in the gravity center of AGM emissions from 2000 to 2020. Specifically, emissions from animal husbandry exhibited a consistent northwestward migration, while emissions from rice cultivation shifted northeastward. The findings reveal significant spatial heterogeneity in AGM emissions, with higher emissions in eastern regions driven by rapid economic growth and stricter agricultural policies, while emissions in the western regions are more influenced by natural environmental factors and traditional farming practices. This study pioneers the application of gravity theory to analyze AGM emission dynamics in China, identifying key socioeconomic drivers and providing a scientific basis for region-specific mitigation strategies to support carbon neutrality goals.

Wanli Ni, Hui Tian, Shuai Wang et al.

Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart factories where data privacy, communication efficiency, and device heterogeneity are critical concerns. In this article, we present a comprehensive study of FedSL frameworks tailored for resource-constrained robots in industrial scenarios. We compare synchronous, asynchronous, hierarchical, and heterogeneous FedSL frameworks in terms of workflow, scalability, adaptability, and limitations under dynamic industrial conditions. Furthermore, we systematically categorize token fusion strategies into three paradigms: input-level (pre-fusion), intermediate-level (intra-fusion), and output-level (post-fusion), and summarize their respective strengths in industrial applications. We also provide adaptive optimization techniques to enhance the efficiency and feasibility of FedSL implementation, including model compression, split layer selection, computing frequency allocation, and wireless resource management. Simulation results validate the performance of these frameworks under industrial detection scenarios. Finally, we outline open issues and research directions of FedSL in future smart manufacturing systems.

Dhaval Patel, Shuxin Lin, James Rayfield et al.

AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance scheduling, to minimize system downtime. While traditional AI/ML approaches solve narrow tasks in isolation, Large Language Model (LLM) agents offer a next-generation opportunity for end-to-end automation. In this paper, we introduce AssetOpsBench, a unified framework for orchestrating and evaluating domain-specific agents for Industry 4.0. AssetOpsBench provides a multimodal ecosystem comprising a catalog of four domain-specific agents, a curated dataset of 140+ human-authored natural-language queries grounded in real industrial scenarios, and a simulated, CouchDB-backed IoT environment. We introduce an automated evaluation framework that uses three key metrics to analyze architectural trade-offs between the Tool-As-Agent and Plan-Executor paradigms, along with a systematic procedure for the automated discovery of emerging failure modes. The practical relevance of AssetOpsBench is demonstrated by its broad community adoption, with 250+ users and over 500 agents submitted to our public benchmarking platform, supporting reproducible and scalable research for real-world industrial operations. The code is accesible at https://github.com/IBM/AssetOpsBench .

Roosa Risto, Mohit Sethi, Mika Katara

The Cyber Resilience Act (CRA) is a new European Union (EU) regulation aimed at enhancing the security of digital products and services by ensuring they meet stringent cybersecurity requirements. This paper investigates the challenges that industrial equipment manufacturing companies anticipate while preparing for compliance with CRA through a comprehensive survey. Key findings highlight significant hurdles such as implementing secure development lifecycle practices, managing vulnerability notifications within strict timelines, and addressing gaps in cybersecurity expertise. This study provides insights into these specific challenges and offers targeted recommendations on key focus areas, such as tooling improvements, to aid industrial equipment manufacturers in their preparation for CRA compliance.

Anwar Ahmed Khan, Indrakshi Dey

Efficient medium access control (MAC) is critical for enabling low-latency and reliable communication in industrial Machine-to-Machine (M2M) net-works, where timely data delivery is essential for seamless operation. The presence of multi-priority data in high-risk industrial environments further adds to the challenges. The development of tens of MAC schemes over the past decade often makes it a tough choice to deploy the most efficient solu-tion. Therefore, a comprehensive cross-comparison of major MAC protocols across a range of performance parameters appears necessary to gain deeper insights into their relative strengths and limitations. This paper presents a comparison of Contention window-based MAC scheme BoP-MAC with a fragmentation based, FROG-MAC; both protocols focus on reducing the delay for higher priority traffic, while taking a diverse approach. BoP-MAC assigns a differentiated back-off value to the multi-priority traffic, whereas FROG-MAC enables early transmission of higher-priority packets by fragmenting lower-priority traffic. Simulations were performed on Contiki by varying the number of nodes for two traffic priorities. It has been shown that when work-ing with multi-priority heterogenous data in the industrial environment, FROG-MAC results better both in terms of delay and throughput.

Peijian Zeng, Feiyan Pang, Zhanbo Wang et al.

Industrial Anomaly Detection (IAD) is critical for ensuring product quality by identifying defects. Traditional methods such as feature embedding and reconstruction-based approaches require large datasets and struggle with scalability. Existing vision-language models (VLMs) and Multimodal Large Language Models (MLLMs) address some limitations but rely on mask annotations, leading to high implementation costs and false positives. Additionally, industrial datasets like MVTec-AD and VisA suffer from severe class imbalance, with defect samples constituting only 23.8% and 11.1% of total data respectively. To address these challenges, we propose a reward function that dynamically prioritizes rare defect patterns during training to handle class imbalance. We also introduce a mask-free reasoning framework using Chain of Thought (CoT) and Group Relative Policy Optimization (GRPO) mechanisms, enabling anomaly detection directly from raw images without annotated masks. This approach generates interpretable step-by-step explanations for defect localization. Our method achieves state-of-the-art performance, outperforming prior approaches by 36% in accuracy on MVTec-AD and 16% on VisA. By eliminating mask dependency and reducing costs while providing explainable outputs, this work advances industrial anomaly detection and supports scalable quality control in manufacturing. Code to reproduce the experiment is available at https://github.com/LilaKen/LR-IAD.

Nurdan Baykuş, Aaron Anil Chadee, Nurgül Yalçın et al.

The construction sector is known to have the highest risks of occupational accidents. A rationale for this high occurrence of occupational risks can be related to legislative requirements to enforce safe construction practices within this sector. Within the context of excavation works in Turkey, this study investigates the leading risks for any compliance shortfalls and ultimately presents recommendations to mitigate occupational accidents’ occurrences during excavation works in the construction sector. Based on a quantitative methodology, a closed-ended survey consisting of 35 questions and based on legislative requirements was applied to project managers in the construction industry, such as site supervisors, occupational safety experts, auditors, and control personnel. A sample size of 277 responses was found to have stability and validity through a reliability analysis and an exploratory factor analysis, and was used for testing statistical significance via cross-tabulation analysis and chi-square tests. The findings revealed that the major deviation of safety in excavation works from legislative requirements is executing works during adverse weather conditions. Moreover, it was also noteworthy that protective curtains did not surround the excavation sites, and most of the employees encountered ground slippage during excavation work. Therefore, the findings revealed preliminary research that will contribute positively to providing incentives for a focus on and development of relevant security and technical measures. It also provided information to protect the safety and welfare of the workers involved in excavation works. Finally, though these findings may be considered context-specific, this research can be used for comparative purposes for similar studies into the safety practices of excavation works in different countries, where generalized findings can be later derived to inform academia and practice.

An-Chi Huang, Fang-Chao Cao, Xin-Yue Ma

This study offers a comprehensive evaluation of the effectiveness of expansible graphite (EG) and potassium bicarbonate (KHCO₃) in suppressing metal fires, which are known for their high intensity and resistance. Our assessment, utilizing thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM), revealed that compositions of EG–KHCO₃ can endure temperatures of up to 350 °C, indicating their thermal resilience. The 3:1 EG–KHCO₃ mixture demonstrated exceptional performance in fire suppression tests by extinguishing sodium flames in a mere 20 s, using approximately 50 g of the agent. This highlights a substantial improvement in efficiency. In addition, FTIR analysis identified important gaseous compounds released during decomposition, while XRD and SEM techniques confirmed the advantageous insertion of KHCO₃ into the EG matrix, enhancing its resistance to heat and chemical reactions. The mixture with a ratio of 3:1 also demonstrated a higher cooling rate of 2.34 °C/s within the temperature range of 350 to 200 °C. The results emphasize the potential of EG–KHCO₃ compositions, specifically in a 3:1 ratio, for efficient fire management by integrating fire suppression, heat resistance, and quick cooling. Subsequent investigations will prioritize the evaluation of these compositions across different circumstances and the assessment of their environmental and industrial viability.

Siti Rahmah Hidayatullah Lubis, Nuranisa Mu'minah

Introduction: Psychosocial hazards are significant threats to the safety and health of workers, particularly in industries such as production. Based on preliminary observations, assembly workers in the production setting, known for requiring precision, face increased risk, specifically when confronted with elevated demand. Therefore, this study aimed to analyze psychosocial hazards among production workers at PT. Sarandi Karya Nugraha. Method: Qualitative descriptive approach was utilized for a case study conducted at PT. Sarandi Karya Nugraha from July to October 2020. A total of 4 informants was selected by purposive methods, and data were collected through in-depth interviews, observations, and document reviews. Meanwhile, thematic analysis was used to analyze the details obtained. Result: The results showed that there are 6 psychosocial hazard themes within the production department. These comprised role ambiguity, role conflict, quantitative workload, qualitative workload, responsibility to others, and career development. In addition, issues related to role conflict, quantitative overload, and career development persisted in the company. Conclusion: Organizations should create plans, policies, and opportunities for workers in the production department to gain assurance regarding career advancement. Additionally, a detailed description of work process in situations comprising quantitative workload should be created.

Rustam Murasov , Anatolii Nikitin , Ivan Meshcheriakov et al.

В умовах широкомасштабного вторгнення російської федерації в Україну на рівні відсічі збройної агресії ворога виникла нагальна потреба захисту не тільки сил оборони держави, а й цивільних об’єктів, які не мають жодного відношення до військового сектора. Для країни-агресора цілями для ураження стали як місця проживання населення, так і об’єкти критичної інфраструктури, що забезпечують життєдіяльність на всій території України. Внаслідок одностороннього виходу російської федерації із зернової угоди, під ураження потрапили зерносховища та навколишня інфраструктура. Таким чином росія тероризує не лише Україну, але й призводить до недостачі харчових продуктів у багатьох країнах світу. Метою статті є удосконалення методики оцінювання загроз і ризиків для об’єктів критичної інфраструктури за сценаріями розвитку надзвичайних ситуацій для запобігання втрат населення та особового складу сил оборони. Під час проведення дослідження застосовано аналітичний метод для аналізу останніх досліджень і публікацій, метод оптимізації (за мінімальним і максимальним критеріями) та метод мінімаксу для вибору кращого варіанту дій та синтезу для досягнення мети дослідження. Зазначений методичний підхід дає змогу провести аналіз і декомпозицію існуючих методик оцінювання загроз для об’єктів критичної інфраструктури в зоні ведення бойових дій та оцінювання ризиків для об’єктів критичної інфраструктури внаслідок бойових дій. Удосконалено методику оцінювання загроз і ризиків для об’єктів критичної інфраструктури за сценаріями розвитку надзвичайних ситуацій. Зазначена методика включає п’ять послідовних блоків, що дають змогу приймати раціональні управлінські рішення для впровадження відповідних заходів безпеки і оборони, здійснення оптимального розподілу сил і засобів та мінімізації наслідків надзвичайних ситуацій, застосовуючи інформаційні технології. Теоретичною значущістю методики є те, що дає змогу оцінювати можливі ризики та визначати стратегії захисту об’єктів критичної інфраструктури. Удосконалена методика дає змогу практично визначати варіанти сценаріїв надзвичайних подій, оцінювати збитки та вибирати найгірші сценарії з метою їх запобігання та локалізації. Це мінімізує наслідки виникнення надзвичайних ситуацій в умовах обмеженості сил і засобів оборони об’єктів критичної інфраструктури.

Benjamin Alt, Florian Stöckl, Silvan Müller et al.

Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades.

Ronny Müller, Claudia De Lazzari, Fernando Chirici et al.

Information Reconciliation is a critical component of Quantum Key Distribution, ensuring that mismatches between Alice's and Bob's keys are corrected. In this study, we analyze, simulate, optimize, and compare the performance of two prevalent algorithms used for Information Reconciliation: Cascade and LDPC codes in combination with the Blind protocol. We focus on their applicability in practical and industrial settings, operating in realistic and application-close conditions. The results are further validated through evaluation on a live industrial QKD system.

Diego Vallarino

This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment, public investment, and access to credit. Machine learning models provide additional insights into nonlinear interactions between fiscal benefits and other macroeconomic factors, such as exchange rates, emphasizing the need for tailored fiscal policies. The findings have important policy implications, suggesting that fiscal incentives, when combined with broader economic reforms, can effectively promote industrial development in emerging economies.

Amy Moore, Vivek Sujan, Adam Siekmann et al.

Vehicular incidents, especially those involving tractor trailers, are increasing in number every year. These events are extremely costly for fleets, in terms of damage or loss of property, loss of efficiency, and certainly in terms of loss of life. Although the U.S. Department of Transportation (DOT) is responsible for performing inspections, and fleet managers are encouraged to maintain their fleet and participate in regular inspections, it is uncertain whether these inspections are occurring at a frequency that is necessary to prevent incidents. The Federal Motor Carrier Safety Administration (FMCSA) of the DOT manages and maintains the Motor Carrier Management Information System (MCMIS) dataset, which contains all incident and inspection data regarding commercial vehicles in the U.S. The purpose of this preliminary analysis was to explore the MCMIS dataset through spatiotemporal analyses, to uncover findings that may hint at potential improvements in the DOT inspection process and highlight location-specific trends in the dataset. These analyses are novel, as previous research using the MCMIS dataset only examined the data at the state or county level, not at a national scale. The results from the analyses pinpointed specific major metropolitan areas, namely Harris County (Houston), Texas, and three of the New York boroughs (Kings, Queens, and the Bronx), which were found to have increasing incident rates during the study period (2016–2020). An overview of potential causal factors contributing to this increase are provided as well as an overview of the inspection process, and suggestions for improvement relative to the highlighted locations in Texas and New York are also provided. Ultimately, it is suggested that the incorporation of advanced technology and automation may prove beneficial in reducing the occurrence of events that lead to incidents and may also help in the inspection process.

Omar Flor-Unda, Mauricio Fuentes, Daniel Dávila et al.

Technological advancements have allowed for the design and development of multiple intelligent devices that monitor the health and safety status of workers in the industry in general. This paper reviews and describes the alternative technologies and their potential for monitoring risk situations, vital signs, physical variables, worker positions, and behavioral trends of workers in their work activities in the workplace. A scoping review was conducted using PRISMA ScR in which information was extracted from 99 scientific articles related to these technological advances. The operational characteristics and utilities of devices whose primary function is to control better and monitor worker safety and health were identified. It was concluded that technology strongly improves the acquisition and sending of information. This information can be used to provide alerts and feedback to workers so that they act more safely and protect their health. In addition, technological developments have resulted in devices that eliminate operational risks by replacing manual activities with automated and autonomous tasks.