Hasil untuk "Industrial directories"

Meng Fanyu

Recently, three-dimensional or visual design, dressing, and simulation programs have become prominent in the garment industry. Image processing technology is increasingly utilized in the online customization process to adapt to the growth and revolution of garment customization. The emergence of online sites for browsing and purchasing personalized garments has given consumers a new platform to choose their outfits. The major challenge is extracting garment data, general clothing portrayals, and automatic dimensional extractions. Hence, this article proposes the Image Processing Technology-assisted Garment Visual Design Element Recognition (IMT-GVDER) model for tailoring clothing throughout the early phases of unique design and product development. The series of cloth pictures can be given as input to the recognition model from datasets. This clothing style recognition aids in predicting clothes' features and patterns, which aids in classifying them using efficient feature extraction and classification models such as Convolutional Neural Network (CNN). It helps to automatically recognize cloth images and categorize clothes styles depending on style components and their salient visual feature. The image texture characteristic variables can be utilized to classify the defects. The experimental outcome demonstrates that the suggested IMT-GVDER model enhances the prediction accuracy ratio of 98.7%, the matching rate by 97.6%, the performance ratio of 96.7%, and the F1-score ratio of 94.56% and reduces the error rate by 0.9% compared to other existing methods in visual clothing design.

Ekky Imanjaya, Cynthia MF Pangabean

Introduction: As stipulated in the Indonesian Labor Law, every worker is entitled to work safety and health protection, including the film industry. This research focuses on two articles in the Health, Safety, and Environment (HSE) regulations and the Law of Film Year 2009. However, the Indonesian film industry has not officially implemented these laws. There have been several cases of HSE, which caused death or fatal injuries to film workers, without applying the regulations. Other HSE issues include the cases where only a few film producers gave insurance to the film workers, applied proper risk assessment, or provided first aid kits. The paper will overview HSE in the Indonesian film industry by mapping out the problems and potential solutions. Methods: By having in-depth interviews with key persons in the field, such as the workers and film producers, this research aims to map out such issues and answering why and how the laws on work health and safety are not implemented in the Indonesian film industry. Result: This research has resulted in maps of problems and recommendations for policymakers, film workers, and related institutes concerning HSE and the rights of film workers, including of the lack awareness of film workers on HSE and HSE-related curriculum in film education, as well as the need for stronger film associations and union. Conclusion: HSE in the Indonesian film industry must be evaluated to be more effective. Some factors to be reviewed include law enforcement in contracts, health insurance, the collaboration of various parties, HSE-related knowledge in the curriculum in Indonesian film education, and the application of Work Competency Standards (SKKNI) to all film professional associations.

Huang Chenglong, Lin Chi-Ho, Lee Suan

Traditional rule-based manual bridge inspection methods often suffer from low efficiency and poor accuracy, making them inadequate for the demands of industrial-scale production. This study aims to achieve rapid recognition and localization of virtual assembly components within bridge 3D point clouds by constructing an intelligent analytical framework that integrates supervoxel clustering with a Transformer architecture. Specifically, an improved supervoxel clustering algorithm is developed, deeply integrating geometric morphology, density distribution, and structural response features to generate multimodal voxel units, thereby enhancing the semantic representation of local features. A graph-based Transformer module is introduced to model spatial relationships and semantic associations among supervoxel nodes through a self-attention mechanism, effectively integrating global contextual information. Additionally, a voxel voting strategy within a pose estimation module is employed to optimize component localization accuracy, forming an end-to-end recognition and localization system. The proposed model demonstrates excellent performance across multiple datasets, including Stanford Large-Scale 3D Indoor Spaces Dataset, ETH Zurich Building Dataset, International Society for Photogrammetry and Remote Sensing Benchmark Dataset, and National Building Museum Point Cloud Dataset. Compared to baseline models, the proposed approach achieves improvements of over 21.5% in semantic segmentation Mean Intersection over Union, instance recognition accuracy, and pose regression precision. In complex multi-box girder bridge scenarios, the recognition accuracy for small-scale connectors improves by up to 37.1%. Computational efficiency increases by more than 18.7%, with inference time reductions of up to 31.5% when processing large-scale data. Overall improvements in bridge component recognition exceed 22.4%, with recognition accuracy for critical connection components increasing by up to 37.4%, and localization accuracy improving by over 26.2%, reaching up to 35.9% for key node localization. The results demonstrate that the proposed model effectively addresses critical challenges in processing bridge point cloud data through multimodal feature fusion and global structural reasoning, significantly enhancing component recognition accuracy and localization precision in complex scenes while maintaining a balance between algorithmic efficiency and model performance. This study provides an efficient solution for the digital delivery and quality control of intelligent bridge construction. By integrating finite element analysis with deep learning, the model enhances semantic understanding of bridge structural functions, contributing significantly to the advancement of intelligent bridge engineering.

Oscar Adamuz-Hinojosa, Felix Delgado-Ferro, Núria Domènech et al.

The digital transformation driven by Industry 4.0 relies on networks that support diverse traffic types with strict deterministic end-to-end latency and mobility requirements. To meet these requirements, future industrial automation networks will use time-sensitive networking, integrating 5G as wireless access points to connect production lines with time-sensitive networking bridges and the enterprise edge cloud. However, achieving deterministic end-to-end latency remains a challenge, particularly due to the variable packet transmission delay introduced by the 5G system. While time-sensitive networking bridges typically operate with latencies in the range of hundreds of microseconds, 5G systems may experience delays ranging from a few to several hundred milliseconds. This paper investigates the potential of configuring the 5G time division duplex pattern to minimize packet transmission delay in industrial environments. Through empirical measurements using a commercial 5G system, we evaluate different TDD configurations under varying traffic loads, packet sizes and full buffer status report activation. Based on our findings, we provide practical configuration recommendations for satisfying requirements in industrial automation, helping private network providers increase the adoption of 5G.

Anwar Ahmed Khan, Shama Siddiqui, Indrakshi Dey

Industrial Internet of Things (IIoT) promises to revolutionize industrial operations and productions through utilizing Machine-to-Machine (M2M) communications. Since each node in such environments generates various types of data with diverse service requirements, MAC protocol holds crucial importance to ensure efficient delivery. In this context, simple to complex MAC schemes are found in literature. This paper focuses on evaluating the performance of two major techniques "slot stealing" and "packet fragmentation" for the IIoT; representative protocols SS-MAC and FROG-MAC have been chosen from each category respectively. We conducted realistic simulations for the two protocols using Contiki. Delay and packet loss comparison for SS-MAC and FROG-MAC indicates the superiority of FROG-MAC due to reduction in the waiting time for urgent traffic. Thus, a simple fragmentation scheme could be deployed for efficient scheduling of heterogenous traffic in the industrial environments.

Karina M. Sindermann, Esma S. Gel, Nesim K. Erkip

Industrial Vending Machines (IVMs) automate the dispensing of a variety of supplies like safety equipment and tools at customer sites, providing 24/7 access while tracking inventory in real-time. Industrial distribution companies typically manage the replenishment of IVMs using periodic schedules, which do not take advantage of these advanced real-time monitoring capabilities. We develop two approaches to optimize the long-term average cost of replenishments and stockouts per unit time: a state-dependent optimal control policy that jointly considers all inventory levels (referred to as trigger set policy) and a fixed cycle policy that optimizes replenishment frequency. We prove the monotonicity of the optimal trigger set policy and leverage it to design a computationally efficient approximate online control framework. Unlike existing methods, which typically handle a very limited number of items due to computational constraints, our approach scales to hundreds of items while achieving near-optimal performance. Leveraging transaction data from our industrial partner, we conduct an extensive set of numerical experiments to demonstrate this claim. Our results show that optimal fixed cycle replenishment reduces costs by 61.7 to 78.6% compared to current practice, with our online control framework delivering an additional 4.1 to 22.9% improvement. Our novel theoretical results provide practical tools for effective replenishment management in this modern vendor-managed inventory context.

Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang et al.

Mission-critical industrial infrastructure, such as data centers, increasingly depends on complex management software. Its operations, however, pose significant challenges due to the escalating system complexity, multi-vendor integration, and a shortage of expert operators. While Robotic Process Automation (RPA) offers partial automation through handcrafted scripts, it suffers from limited flexibility and high maintenance costs. Recent advances in Large Language Model (LLM)-based graphical user interface (GUI) agents have enabled more flexible automation, yet these general-purpose agents face five critical challenges when applied to industrial management, including unfamiliar element understanding, precision and efficiency, state localization, deployment constraints, and safety requirements. To address these issues, we propose InfraMind, a novel exploration-based GUI agentic framework specifically tailored for industrial management systems. InfraMind integrates five innovative modules to systematically resolve different challenges in industrial management: (1) systematic search-based exploration with virtual machine snapshots for autonomous understanding of complex GUIs; (2) memory-driven planning to ensure high-precision and efficient task execution; (3) advanced state identification for robust localization in hierarchical interfaces; (4) structured knowledge distillation for efficient deployment with lightweight models; and (5) comprehensive, multi-layered safety mechanisms to safeguard sensitive operations. Extensive experiments on both open-source and commercial DCIM platforms demonstrate that our approach consistently outperforms existing frameworks in terms of task success rate and operational efficiency, providing a rigorous and scalable solution for industrial management automation.

John Oyekan, Christopher Turner, Michael Bax et al.

The rapid advancement of Large Language Models (LLMs) has resulted in interest in their potential applications within manufacturing systems, particularly in the context of Industry 5.0. However, determining when to implement LLMs versus other Natural Language Processing (NLP) techniques, ontologies or knowledge graphs, remains an open question. This paper offers decision-making guidance for selecting the most suitable technique in various industrial contexts, emphasizing human-robot collaboration and resilience in manufacturing. We examine the origins and unique strengths of LLMs, ontologies, and knowledge graphs, assessing their effectiveness across different industrial scenarios based on the number of domains or disciplines required to bring a product from design to manufacture. Through this comparative framework, we explore specific use cases where LLMs could enhance robotics for human-robot collaboration, while underscoring the continued relevance of ontologies and knowledge graphs in low-dependency or resource-constrained sectors. Additionally, we address the practical challenges of deploying these technologies, such as computational cost and interpretability, providing a roadmap for manufacturers to navigate the evolving landscape of Language based AI tools in Industry 5.0. Our findings offer a foundation for informed decision-making, helping industry professionals optimize the use of Language Based models for sustainable, resilient, and human-centric manufacturing. We also propose a Large Knowledge Language Model architecture that offers the potential for transparency and configuration based on complexity of task and computing resources available.

Dimitris Kallis, Moysis Symeonides, Marios D. Dikaiakos

The widespread adoption of IoT has driven the development of cyber-physical systems (CPS) in industrial environments, leveraging Industrial IoTs (IIoTs) to automate manufacturing processes and enhance productivity. The transition to autonomous systems introduces significant operational costs, particularly in terms of energy consumption. Accurate modeling and prediction of IIoT energy requirements are critical, but traditional physics- and engineering-based approaches often fall short in addressing these challenges comprehensively. In this paper, we propose a novel methodology for benchmarking and analyzing IIoT devices and applications to uncover insights into their power demands, energy consumption, and performance. To demonstrate this methodology, we develop a comprehensive framework and apply it to study an industrial CPS comprising an educational robotic arm, a conveyor belt, a smart camera, and a compute node. By creating micro-benchmarks and an end-to-end application within this framework, we create an extensive performance and power consumption dataset, which we use to train and analyze ML models for predicting energy usage from features of the application and the CPS system. The proposed methodology and framework provide valuable insights into the energy dynamics of industrial CPS, offering practical implications for researchers and practitioners aiming to enhance the efficiency and sustainability of IIoT-driven automation.

Vade Shah, Yohan John, Ethan Freifeld et al.

Industrial refrigeration systems have substantial energy needs, but optimizing their operation remains challenging due to the tension between minimizing energy costs and meeting strict cooling requirements. Load shifting--strategic overcooling in anticipation of future demands--offers substantial efficiency gains. This work seeks to rigorously quantify these potential savings through the derivation of optimal load shifting policies. Our first contribution establishes a novel connection between industrial refrigeration and inventory control problems with convex ordering costs, where the convexity arises from the relationship between energy consumption and cooling capacity. Leveraging this formulation, we derive three main theoretical results: (1) an optimal algorithm for deterministic demand scenarios, along with proof that optimal trajectories are non-increasing (a valuable structural insight for practical control); (2) performance bounds that quantify the value of load shifting as a function of cost convexity, demand variability, and temporal patterns; (3) a computationally tractable load shifting heuristic with provable near-optimal performance under uncertainty. Numerical simulations validate our theoretical findings, and a case study using real industrial refrigeration data demonstrates an opportunity for improved load shifting.

Francesc Sabater, Ouns El Harzli, Geert-Jan Besjes et al.

Optimization via decoded quantum interferometry (DQI) has recently gained a great deal of attention as a promising avenue for solving optimization problems using quantum computers. In this paper, we apply DQI to an industrial optimization problem in the automotive industry: the vehicle option-package pricing problem. Our main contributions are 1) formulating the industrial problem as an integer linear program (ILP), 2) converting the ILP into instances of max-XORSAT, and 3) developing a detailed quantum circuit implementation for belief propagation, a heuristic algorithm for decoding LDPC codes. Thus, we provide a full implementation of the DQI algorithm using Belief Propagation, which can be applied to any industrially relevant ILP by first transforming it into a max-XORSAT instance. We also evaluate the effectiveness of our implementation by benchmarking it against both Gurobi and a random sampling baseline.

Krivonogov Aleksei, Taranenko Pavel Alexandrovich, Khan Afrasyab

In connection to methods developed for determining of “liquid–gas” volume-mass parameters, research has been carried out by a group of scientists from South Ural State University and University of Dundee, where the current manuscript presents a new method for measuring a liquid and gas flow rate. Method able to measure a turbulent flow convective velocity through the pipeline wall and volumetric flow rate of a liquid and gas. A brief description of G. Taylor's “frozen turbulence” hypothesis is given on the basis of which the method works. Main scientific problems associated with its proof in relation to the problem of determining the convection velocity of turbulence are identified. Mathematical modeling was performed in the CFD computational fluid dynamics package using the hybrid eddy-resolving turbulence model SBES to determine an optimal configuration of the experimental setup. This model contains 2-D symmetry domain to decries simulation time. In this article describe correlation between 2-D symmetry model and full-scale tests. Result of experimental tests are presented. Therefore, novelty of this investigation is noninvasive method for flow measurement and experimental confirmation that it works.

Mike Allenspach, Michael Pantic, Rik Girod et al.

In real-world industrial environments, modern robots often rely on human operators for crucial decision-making and mission synthesis from individual tasks. Effective and safe collaboration between humans and robots requires systems that can adjust their motion based on human intentions, enabling dynamic task planning and adaptation. Addressing the needs of industrial applications, we propose a motion control framework that (i) removes the need for manual control of the robot's movement; (ii) facilitates the formulation and combination of complex tasks; and (iii) allows the seamless integration of human intent recognition and robot motion planning. For this purpose, we leverage a modular and purely reactive approach for task parametrization and motion generation, embodied by Riemannian Motion Policies. The effectiveness of our method is demonstrated, evaluated, and compared to \remove{state-of-the-art approaches}\add{a representative state-of-the-art approach} in experimental scenarios inspired by realistic industrial Human-Robot Interaction settings.

Zi Wang, Xingcheng Xu, Yanqing Yang et al.

We propose a deep learning framework, DL-opt, designed to efficiently solve for optimal policies in quantifiable general equilibrium trade models. DL-opt integrates (i) a nested fixed point (NFXP) formulation of the optimization problem, (ii) automatic implicit differentiation to enhance gradient descent for solving unilateral optimal policies, and (iii) a best-response dynamics approach for finding Nash equilibria. Utilizing DL-opt, we solve for non-cooperative tariffs and industrial subsidies across 7 economies and 44 sectors, incorporating sectoral external economies of scale. Our quantitative analysis reveals significant sectoral heterogeneity in Nash policies: Nash industrial subsidies increase with scale elasticities, whereas Nash tariffs decrease with trade elasticities. Moreover, we show that global dual competition, involving both tariffs and industrial subsidies, results in lower tariffs and higher welfare outcomes compared to a global tariff war. These findings highlight the importance of considering sectoral heterogeneity and policy combinations in understanding global economic competition.

Guido Alfaro Degan, Andrea Antonucci, Dario Lippiello

The ISO Standard 10819:2013 defines the method for evaluating the performances of antivibration (AV) gloves, but when used in real fields, the protection can be dissimilar to that labeled. This paper investigates the transmissibility, at the palm level, of three different types of AV gloves (air, gel, neoprene) and an ordinary leather glove, during the use of four similar electric hammers (average weight of 10 kg, and average impact energy of 18 J), in a limestone quarry plant. As the average triaxial transmissibility for all the hammers, results show very limited benefits in reducing the vibration (6%), with no significative differences among the different gloves. The working leather glove, instead, shows a transmissibility quite equal to the unit. Anyway, results can be different for the same glove when used among the different hammers, providing in some cases 19% of protection. Some differences can be found regarding the transmissibility through the three main axes for the same type of glove: the glove in gel seems to perform better in shear than in compression. The transmissibility in compression is around 20% higher than that provided by the manufacturers of the certified gloves. The usage of specific excitation curves during laboratory tests could help in providing a more accurate estimation of the transmissibility of the gloves when used with a specific tool.

Serhii Tsybulia, Artem Volokyta

У роботі розглянуті наявні підходи впливу на роботу алгоритмів штучного інтелекту, зокрема машинного навчання, що застосовуються в системах комп’ютерного зору для виявлення, класифікації та ідентифікації об’єктів. На даний час найпопулярнішою та найперспективнішою технологією розпізнавання образів є штучні нейронні мережі. Комп’ютерний зір застосовується у військовій справі для виявлення візуальних об’єктів певних класів: людей, озброєння та військової техніки, військових об’єктів тощо. Вхідними даними для аналізу можуть бути: фотографії, відеокадри чи відео потік реального часу, що отримані з космічних, повітряних або наземних засобів розвідки. Для боротьби з системами автоматичного виявлення об’єктів можливо застосовувати підходи, що здатні впливати на моделі машинного навчання, які використовуються у цих системах. Атака на моделі машинного навчання – це спеціальні дії щодо впливу на її елементи з метою досягти бажаної поведінки системи або перешкодити її коректній роботі. За результатами аналізу досліджень різних авторів визначено, що майже кожен алгоритм машинного навчання має певні вразливості. Під час виконання завдань інженерної підтримки військ щодо маскування військових об’єктів, найбільш доступними способами впливу на системи комп’ютерного зору, для введення їх в оману, є зміна фізичних властивостей об’єкта, що маскується, шляхом нанесення на його поверхню спеціальних покриттів і матеріалів. У якості покриттів можливо використовувати згенеровані змагальні патч-зображення, шляхом накладання або наклеювання їх на об’єкт та які здатні вносити завади в роботу алгоритмів засобу розвідки, прицілювання або наведення. Це особливо важливо в перспективі створення автономних систем зброї, які здатні виявляти, ідентифікувати цілі та самостійно приймати рішення на їх ураження.

Francesco Ragusa, Rosario Leonardi, Michele Mazzamuto et al.

ENIGMA-51 is a new egocentric dataset acquired in an industrial scenario by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and equipments (e.g., oscilloscope). The 51 egocentric video sequences are densely annotated with a rich set of labels that enable the systematic study of human behavior in the industrial domain. We provide benchmarks on four tasks related to human behavior: 1) untrimmed temporal detection of human-object interactions, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities. Baseline results show that the ENIGMA-51 dataset poses a challenging benchmark to study human behavior in industrial scenarios. We publicly release the dataset at https://iplab.dmi.unict.it/ENIGMA-51.

Johannes Vrana, Ripudaman Singh

The Fourth Industrial Revolution, ushered by the deeper integration of digital technologies into professional and social spaces, provides an opportunity to meaningfully serve society. Humans have tremendous capability to innovatively improve social well-being when the situation is clear. Which was not the case during the first three revolutions. Thus, society has been accepting lifestyle changes willingly and several negative consequences unwillingly. Since the fourth one is still in its infancy, we can control it better. This paper presents a unified model of the industrialized ecosystem covering value creation, value consumption, enabling infrastructure, required skills, and additional governance. This design thinking viewpoint, which includes the consumer side of digital transformation, sets the stage for the next major lifestyle change, termed Digital Transfiguration. For validation and ease of comprehension, the model draws upon the well-understood automobile industry. This model unifies the digital penetration of both industrial creation and social consumption, in a manner that aligns several stakeholders on their transformation journey.

Anisha Banu Dawood Gani, Yudi Fernando, Shulin Lan et al.

PurposeThis study aims to examine whether the cyber supply chain risk management (CSCRM) practices adopted by manufacturing firms contribute to achieving cyber supply chain (CSC) visibility. Studies have highlighted the necessity of having visibility across interconnected supply chains. Thus, this study examines the extent of CSCRM practices enabling CSC visibility to act as a mediator in achieving CSC performance.Design/methodology/approachA survey method was used to obtain data from the electrical and electronics manufacturing firms registered with the Federations of Malaysian Manufacturers directory. Data from 130 respondents were analysed using IBM SPSS and PLS-SEM.FindingsThis study empirically proves a dedicated governance team's integral role in setting the security tone within its CSC. The result also confirms the significant role that CSC visibility plays in achieving CSC performance. As theorised in the literature, there is also a strong direct relationship between CSC visibility and CSC performance, assuring manufacturing firms that investments and policies devised to improve CSC visibility are fruitful.Originality/valueThe significance of supply chain visibility in an integrated supply chain is recognised and studied using analytical models, behavioural techniques and case studies. Substantial empirical evidence on the CSCRM practices which contributes towards achieving supply chain visibility is still elusive. This study's major contribution lies in identifying CSCRM practices that can contribute towards achieving CSC visibility, and the mediating role CSC visibility plays in achieving CSC performance.

Arira Celia Virta Parawansa, Naomi Cimera, Ahmad Rido’i Yuda Prayogi et al.

Introduction: PT. Kerta Rajasa Raya is an industrial manufacturing company in the manufacturing of Woven Bags and Jumbo Bags. From 2012 to 2017, the total incidence of work accidents reached 844 cases. One of the divisions at PT. Kerta Rajasa Raya which often experiences work accidents is the extruder division. The most frequent accidents experienced by workers in the extruder division are being hit by a cutter and pinched by a roll on the machine. One of the causes of accidents is workers' non-compliance with the use of PPE. This study aims to analyze relationship between personal factors and non-compliance behavior in using PPE by workers. Methods: This study used a quantitative approach with an observational analytical method and a cross-sectional design. The population of this study was workers in the extruder division of PT. Kerta Rajasa Raya with sample of 79 workers, who were chosen through a random sampling technique. The data collection was conducted by the means of observation sheets and questionnaires using Kendall test analysis. Results: The results of the study showed that education level (r = 0.220), years of service (r = 0.216), attitude (r = -0.244) and knowledge (r = -0.210) had a weak relationship with the behavior of using PPE. Conclusion: There was an effect in the relationship between education level, years of servicee, attitude, and knowledge of workers in using personal protective equipement. Keywords: behavior, personal factors, personal protective equipment