Hasil untuk "Industrial directories"

E. Monmasson, M. Cirstea

This paper reviews the state of the art of field- programmable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic.

Wei Xian, K. Yu, Fengling Han et al.

A revolution in advanced manufacturing has been driven by digital technology in the fourth industrial revolution, also known as Industry 4.0, and has resulted in a substantial increase in profits for the industry. In a new paradigm of Industry 5.0, advanced manufacturing will step further and be capable of offering customized products and a better user experience. A number of key enabling technologies are expected to play crucial roles in assisting Industry 5.0 in meeting higher demands of data acquisition and processing, communications, and collaborative robots in the advanced manufacturing process. The aim of this survey is to provide novel insights into advanced manufacturing in Industry 5.0 by summarizing the latest progress of key enabling technologies, such as artificial intelligence of things (AIoT), beyond 5G communications, and collaborative robotics. Finally, key directions for future research to enable this vision to become a reality, such as the industrial metaverse, are outlined.

Abdul Kadir, Surindar K. Dhesi, Vanisha Dwi Amalinda et al.

Occupational Health and Safety (OHS) in educational settings is a vital responsibility that is often inconsistently implemented. There is a need for research to bridge the gap between policy and practice. This study employed a cross-sectional mixed-methods design in six schools in the capital city of Indonesia to identify key implementation barriers, strategies, and compliance levels in OHS. Data were collected from 217 teachers using a structured KPAP (Knowledge, Attitudes, Perceptions, Practices) survey and from an additional 38 teachers via Focus Group Discussions (FGDs). Quantitatively, teachers showed highly positive attitudes (99.4% viewing OHS as a professional duty) and generally positive perceptions but implementation practices were sub-optimal (e.g., low participation in drills and PPE usage), showing a gap between awareness and action. Qualitatively, the main barriers identified were a lack of specific OHS regulation or guidance for schools, limited resources/infrastructure, and the perception of OHS as a low priority. Management strategies focused on external collaboration and ongoing in-school initiatives. In conclusion, a significant gap exists between OHS awareness and its integration into school management, highlighting the urgent need for strengthened governance, comprehensive policies, and sustained capacity-building to ensure a proactive, safe, and sustainable school environment for staff and students.

Sangbum Choi, Kyeongryeol Go, Taewoong Jang

Foundation models have revolutionized AI, yet they struggle with zero-shot deployment in real-world industrial settings due to a lack of high-quality, domain-specific datasets. To bridge this gap, Superb AI introduces ZERO, an industry-ready vision foundation model that leverages multi-modal prompting (textual and visual) for generalization without retraining. Trained on a compact yet representative 0.9 million annotated samples from a proprietary billion-scale industrial dataset, ZERO demonstrates competitive performance on academic benchmarks like LVIS-Val and significantly outperforms existing models across 37 diverse industrial datasets. Furthermore, ZERO achieved 2nd place in the CVPR 2025 Object Instance Detection Challenge and 4th place in the Foundational Few-shot Object Detection Challenge, highlighting its practical deployability and generalizability with minimal adaptation and limited data. To the best of our knowledge, ZERO is the first vision foundation model explicitly built for domain-specific, zero-shot industrial applications.

Sanjukta Ghosh

Industrial diagrams such as piping and instrumentation diagrams (P&IDs) are essential for the design, operation, and maintenance of industrial plants. Converting these diagrams into digital form is an important step toward building digital twins and enabling intelligent industrial automation. A central challenge in this digitalization process is accurate object detection. Although recent advances have significantly improved object detection algorithms, there remains a lack of methods to automatically evaluate the quality of their outputs. This paper addresses this gap by introducing a framework that employs Visual Language Models (VLMs) to assess object detection results and guide their refinement. The approach exploits the multimodal capabilities of VLMs to identify missing or inconsistent detections, thereby enabling automated quality assessment and improving overall detection performance on complex industrial diagrams.

Slavica Bogović

Snježana Kirin

Alireza Sanatkhah

Background and objective The role of public participation in managing natural crises, including floods, is evident, and the government will incur many costs without utilizing the potential of non-governmental organizations (NGOs). This study aims to explore the perceptions of NGOs in Iran regarding public participation in flood relief during the 2020 flood in Chabahar city, Sistan & Baluchistan Province, south of Iran. Method This is a qualitative study using the grounded theory. Participants were 25 members of NGOs who participated in relief operations during the 2020 flood in Chabahar city, who were selected using a purposive sampling method. A semi-structured interview was used to collect data. Member checking, analytical comparisons, and the auditing technique were used to determine the trustworthiness of the data. The data were analyzed using the grounded theory approach in three stages: open coding, selective coding, and axial coding. Results Causal factors included: Formation and support of NGOs, quality of flood management, operational transparency, and media culture-building. The intervening factors included: Quality and manner of information dissemination, quality of relief goods distribution and relief services, quality of institutional trust, and social-cultural conflicts. Contextual factors included: Regional public support for relief groups, cultural structure, and professional ethics of relief groups. Strategies included: Education and information on relief efforts, pragmatic/revolutionary approach to flood management, establishment of participatory platforms, and utilization of capacities. Consequences included: Lack of coordination in flood management, inapplicable policies, personal-psychological consequences, and institutional distrust. Conclusion Various causal, intervening, and contextual factors influence public participation in managing crises caused by floods in Iran.

Liu Shunli

The DM (digital museum) is based on the advantages of real-time feedback, multi-dimensionality, interactivity, and other characteristics of the information displayed by mobile devices, and fully combines the real three-dimensional entity with the modern network. It takes the route planning instructions as the center, connects the cultural explanation and the exhibition in series, and forms a complete digital guide process, so as to obtain the best auxiliary visiting information. However, there are still some information barriers between museums and the public under the new technology. In order to realize the development and upgrading of DM in the information technology and Internet environment, this paper uses the basic positioning algorithm in artificial intelligence to determine the specific location of visitors. User positioning is determined from the perspective of user research, and questionnaires and the overall architecture and functional process of the DM are formulated from the perspective of interaction design. The results show that in the DM based on artificial intelligence and user role model, the system innovation has increased by 19.81%, and the satisfaction of tourists has increased by 9.44%.

Qingping Chi, Hairong Yan, Chuan Zhang et al.

Nesreen Mufid

Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human control. Robots can be classified into three major kinds: industry robots, autonomous robots and mobile robots. Industry robots are used in industries and factories to perform mankind tasks in the easier and faster way which will help in developing products. Typically industrial robots perform difficult and dangerous tasks, as they lift heavy objects, handle chemicals, paint and assembly work and so on. They are working all the time hour after hour, day by day with the same precision and they do not get tired which means that they do not make errors due to fatigue. Indeed, they are ideally suited to complete repetitive tasks.

Chen Li, Ruijie Ma, Xiang Qian et al.

Addressing the challenge of data scarcity in industrial domains, transfer learning emerges as a pivotal paradigm. This work introduces Style Filter, a tailored methodology for industrial contexts. By selectively filtering source domain data before knowledge transfer, Style Filter reduces the quantity of data while maintaining or even enhancing the performance of transfer learning strategy. Offering label-free operation, minimal reliance on prior knowledge, independence from specific models, and re-utilization, Style Filter is evaluated on authentic industrial datasets, highlighting its effectiveness when employed before conventional transfer strategies in the deep learning domain. The results underscore the effectiveness of Style Filter in real-world industrial applications.

F. Adetunji, A. Karukayil, P. Samant et al.

This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing.

M. Henderson, J. P. Edelen, J. Einstein-Curtis et al.

Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.

Dmytro Chopa, Anatolii Derevianchuk , Denis Moskalenko et al.

В сучасних умовах артилерійський підрозділ у взаємодії з безпілотним авіаційним комплексом за своїми можливостями стає розвідувально-вогневим комплексом, основними компонентами якого є підсистеми розвідки, управління та ураження. Це дає змогу: виявляти ціль на місцевості, визначати її координати, ставити завдання обслузі вогневого засобу, наводити вогневий засіб на ціль, готувати до пострілу та здійснювати постріл, корегувати вогонь тощо. Широке застосування безпілотних авіаційних комплексів в інтересах виконання вогневих завдань артилерією свідчить про їх перевагу порівняно з іншими засобами розвідки. Тому інтеграція безпілотних авіаційних комплексів у бойові дії артилерійських підрозділів вимагає відповідної підготовки як особового складу вогневого підрозділу, так і екіпажу безпілотного авіаційного комплексу. Метою статті є проведення аналізу процесів функціонування мінометної обслуги та розгляд підходу стосовно створення віртуальних тренажерних комплексів (3D моделей) для практичної підготовки особового складу вогневого підрозділу у складі розвідувально-вогневого комплексу. Під час проведення дослідження застосовувались такі методи: аналіз, систематизація, вдосконалення, обґрунтування, 3D моделювання для практичної підготовки мінометної обслуги у складі розвідувально-вогневого комплексу. Зазначений методологічний підхід дав змогу: розробити схему алгоритму дії складової розвідувально-вогневого комплексу, а саме обслуги вогневого підрозділу, чітко визначити обсяг функціональних завдань основних суб’єктів вогневого підрозділу та здійснити віртуалізацію процесу кожної операції. Запропоновано послідовність підбору навчального контенту і його віртуалізації, що забезпечують якісну підготовку мінометної обслуги у стислі терміни. Отримані результати дослідження забезпечать: підвищення ефективності процесу підготовки особового складу розвідувально-вогневого комплексу, будуть сприяти покращенню рівня засвоєння навчального матеріалу, закріплення практичних навичок та приймання правильних рішень у позаштатних ситуаціях. Віртуальні тренажерні комплекси можуть використовуватися як у навчальних центрах підготовки підрозділів, так і в освітньому процесі вищих військових навчальних закладів та військових навчальних підрозділів закладів вищої освіти.

M. Cheminod, L. Durante, A. Valenzano

Yue Wang, Jinlong Peng, Jiangning Zhang et al.

2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields. Existing multimodal industrial anomaly detection methods directly concatenate the multimodal features, which leads to a strong disturbance between features and harms the detection performance. In this paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme: firstly, we design an unsupervised feature fusion with patch-wise contrastive learning to encourage the interaction of different modal features; secondly, we use a decision layer fusion with multiple memory banks to avoid loss of information and additional novelty classifiers to make the final decision. We further propose a point feature alignment operation to better align the point cloud and RGB features. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec-3D AD dataset. Code is available at https://github.com/nomewang/M3DM.

V. Vyatkin

M. Kazmierkowski

Yidan Feng, Biqi Yang, Xianzhi Li et al.

Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances. Particularly, industrial objects can have irregular shapes, that is, thin and concave, whereas in bin-picking scenarios, objects are often closely packed with strong occlusion. To address these challenges, we formulate a novel part-aware instance segmentation pipeline. The key idea is to decompose industrial objects into correlated approximate convex parts and enhance the object-level segmentation with part-level segmentation. We design a part-aware network to predict part masks and part-to-part offsets, followed by a part aggregation module to assemble the recognized parts into instances. To guide the network learning, we also propose an automatic label decoupling scheme to generate ground-truth part-level labels from instance-level labels. Finally, we contribute the first instance segmentation dataset, which contains a variety of industrial objects that are thin and have non-trivial shapes. Extensive experimental results on various industrial objects demonstrate that our method can achieve the best segmentation results compared with the state-of-the-art approaches.