Hasil untuk "Industrial directories"

Wencong Su, H. Rahimi-Eichi, Wente Zeng et al.

Xiaofeng Yuan, Jiao Zhou, Biao Huang et al.

Deep learning is a recently developed feature representation technique for data with complicated structures, which has great potential for soft sensing of industrial processes. However, most deep networks mainly focus on hierarchical feature learning for the raw observed input data. For soft sensor applications, it is important to reduce irrelevant information and extract quality-relevant features from the raw input data for quality prediction. To deal with this problem, a novel deep learning network is proposed for quality-relevant feature representation in this article, which is based on stacked quality-driven autoencoder (SQAE). First, a quality-driven autoencoder (QAE) is designed by exploiting the quality data to guide feature extraction with the constraint that the potential features should largely reconstruct the input layer data and the quality data at the output layer. In this way, quality-relevant features can be captured by QAE. Then, by stacking multiple QAEs to construct the deep SQAE network, SQAE can gradually reduce irrelevant features and learn hierarchical quality-relevant features. Finally, the high-level quality-relevant features can be directly applied for soft sensing of the quality variables. The effectiveness and flexibility of the proposed deep learning model are validated on an industrial debutanizer column process.

R. Addo-Tenkorang, P. Helo

David Romero, J. Stahre, M. Taisch

Humans are all makers of a sort. The tools we operate constantly leverage our human capabilities and evolve over history to take advantage of any innovation or a new source of power that emerges. Human-Technology Symbiosis has always been the basis for leaps in human prosperity. As we are presently in the Fourth Industrial Revolution, or Industry 4.0, it is important to focus on challenges and opportunities of contemporary work-life. Here we find the worker, the operator, benefitting from cyber-physical systems technology, connectivity, and global information networks while retaining human strengths and weaknesses. This special issue will describe the implications of a new breed of the manufacturing worker, “The Operator 4.0”. The 13 contributions in this special issue will take us from the early anthropocentric organisational models to the emerging connected and cyber-physically enhanced “Operator 4.0” in highly dynamic work environments. Methods and tools for development and analysis of complex work will support the scholar or practitioner that would like to dig deeper into the future of the potential work-life of the Operator 4.0.

Peiying Zhang, Chao Wang, Chunxiao Jiang et al.

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This article studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST, and CIFAR-10 datasets to represent the data generated by IIoT. During the experiment, we employ the deep neural network model to train the data, and experimental results show that the accuracy can reach more than 97%, which corroborates the effectiveness of the proposed algorithm.

Bin Pu, Kenli Li, Shengli Li et al.

Ruonan Liu, Guotao Meng, Boyuan Yang et al.

Muhammad Junaid Asif, Abdul Rehman, Asim Mehmood et al.

This research proposes an extensive technique for monitoring and controlling the industrial parameters using Internet of Things (IoT) technology based on wireless communication. We proposed a system based on NRF transceivers to establish a strong Wireless Sensor Network (WSN), enabling transfer of real-time data from multiple sensors to a central setup that is driven by ARDUINO microcontrollers. Different key parameters, crucial for industrial setup such as temperature, humidity, soil moisture and fire detection, are monitored and displayed on an LCD screen, enabling factory administration to oversee the industrial operations remotely over the internet. Our proposed system bypasses the need for physical presence for monitoring by addressing the shortcomings of conventional wired communication systems. Other than monitoring, there is an additional feature to remotely control these parameters by controlling the speed of DC motors through online commands. Given the rising incidence of industrial fires over the worldwide between 2020 and 2024 due to an array of hazards, this system with dual functionality boosts the overall operational efficiency and safety. This overall integration of IoT and Wireless Sensor Network (WSN) reduces the potential risks linked with physical monitoring, providing rapid responses in emergency scenarios, including the activation of firefighting equipment. The results show that innovations in wireless communication perform an integral part in industrial process automation and safety, paving the way to more intelligent and responsive operating environments. Overall, this study highlights the potential for change of IoT-enabled systems to revolutionize monitoring and control in a variety of industrial applications, resulting in increased productivity and safety.

Azmine Toushik Wasi, Enjamamul Haque Eram, Sabrina Afroz Mitu et al.

Industry 5.0 marks a new phase in industrial evolution, emphasizing human-centricity, sustainability, and resilience through the integration of advanced technologies. Within this evolving landscape, Generative AI (GenAI) and autonomous systems are not only transforming industrial processes but also emerging as pivotal geopolitical instruments. We examine strategic implications of GenAI in Industry 5.0, arguing that these technologies have become national assets central to sovereignty, access, and global influence. As countries compete for AI supremacy, growing disparities in talent, computational infrastructure, and data access are reshaping global power hierarchies and accelerating the fragmentation of the digital economy. The human-centric ethos of Industry 5.0, anchored in collaboration between humans and intelligent systems, increasingly conflicts with the autonomy and opacity of GenAI, raising urgent governance challenges related to meaningful human control, dual-use risks, and accountability. We analyze how these dynamics influence defense strategies, industrial competitiveness, and supply chain resilience, including the geopolitical weaponization of export controls and the rise of data sovereignty. Our contribution synthesizes technological, economic, and ethical perspectives to propose a comprehensive framework for navigating the intersection of GenAI and geopolitics. We call for governance models that balance national autonomy with international coordination while safeguarding human-centric values in an increasingly AI-driven world.

María Fernanda Trujillo-Guerrero, William Venegas-Toro, Danni De la Cruz-Guevara et al.

The ergonomic risks associated with posture in conventional office workstations have been extensively studied, but there is limited research available on these risks in the context of home-based work environments. Most available studies rely solely on questionnaire-based statistical analyses, leaving a gap in understanding the specific conditions of home-based work environments. This study focuses on evaluating the effects of workstation conditions on posture and muscular efforts across three anatomical segments: head-neck, trunk-upper trapezius, and arm-deltoid. The analysis is conducted by simulating workstation setups commonly associated with academic activities performed by students during the COVID-19 pandemic. The conditions examined in this study include inadequate desk height, the use of chairs without armrests, and the use of laptops. Eighteen volunteers, comprising nine women and nine men, participated in experiments conducted under scenarios designed using a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>2</mn><mi>k</mi></msup></semantics></math></inline-formula> statistical approach. In all experiments, participants completed questionnaires, and text-writing activities were performed to evaluate the effects of these conditions. This research introduces a new non-invasive technique for ergonomic assessment that integrates photogrammetry and surface electromyography (sEMG) to simultaneously evaluate posture and muscular effort. The developed methodology allows precise, contactless analysis of ergonomic conditions and can be adapted for various professional and academic teleworking environments. Significant effects were observed in the posture (°) of the trunk and head, with both small and large effects identified at significance levels of <i>p</i> < 0.001 under the furniture conditions studied. In terms of EMG activity, moderate effects were observed at <i>p</i> < 0.01 levels between table height and upper trapezius activation, while small effects were detected at <i>p</i> < 0.05 levels between the use of chairs without armrests and neck. Similarly, small to moderate effects were observed in the arm-deltoid segment under the same furniture conditions. These findings reveal information about the posture and muscular effort patterns associated with the studied tasks, offering knowledge that can be referenced for similar tasks in other technical fields where telematics activities are performed.

Patrick A. Darko, Hani S. Mitri

Rock fragmentation in hard rock mining has traditionally relied on explosives which raises significant environmental and safety concerns for both workers and local communities. In response, soundless chemical demolition agents (SCDAs) primarily composed of lime (CaO), offer safer and more sustainable alternative to traditional blasting due to their soundless, vibrationless, and fumeless properties. This study is focused on examining the effect of mixing water temperature on the rock breakage performance of two commercially available SCDA brands, namely Betonamit type R (BT-R) and Dexpan type 3 (DXP-3). The experiments were conducted using 15 cm cubic granite rock specimens as the host material under various ambient temperatures. The study revealed an increase in mixing water temperature significantly accelerates the mechanical performance of SCDAs, particularly in cold ambient conditions. For instance, increasing the mixing water temperature from 20°C to 40°C reduced the time to first crack (TFC) by 36 % for BT-R and 74 % for DXP-3 under an ambient temperature of 0°C. A corresponding reduction in minimum demolition time (MDT) was also observed. At higher ambient temperatures, the impact of mixing water temperature was found to be less pronounced for both SCDA types. It is concluded that high mixing water temperature would be highly recommended for cold climate applications in both open pit and underground mining.

Marijana Neuberg

Zhongquan Kang, Shengquan He, Xueqiu He et al.

Quantitative study of the temporal and spatial evolution of seismic wave characteristic parameters is of great significance for accurately identifying the P-wave and S-wave in seismic waves and improving the accuracy in localizing microseismic events. Based on the field-measured seismic wave data of the Wudong coal mine, this paper studies the temporal and spatial evolution law of seismic wave amplitude and frequency. The time-window sliding algorithm is proposed to process the amplitude and frequency content of the seismic wave. The obtained instantaneous change rates of amplitude and frequency can accurately characterize the arrival times of P-wave and S-wave, and assist in automatically pinpointing the corresponding seismic waves. The amplitude of the seismic wave recorded by seismic sensors located at different spatial locations from the source reveals that the attenuation decreases gradually with the increase of the propagation distance and that the attenuation rate gradually slows down. It is noted that there are some discrepancies in the amplitude and frequency of the seismic waves recorded by different seismic sensors yet with the same distance from the source. Based on the P-wave and S-wave component diagrams of the seismic displacement field of coal and rock rupture, combined with the laws and differences of the amplitude and frequency of the seismic wave signals at different spatial locations, a new method is proposed to roughly determine the rupture source direction. This study provides a new perspective that can be instrumental for a more comprehensive study of the rupture source direction generated by on-site microseismic events.

Fernando Barrios Aguirre, Diana Maritza Álvarez Ovalle, Nancy Milena Riveros Chávez et al.

The innovative performance of manufacturing and service companies can be impacted by the existing relationship between open innovation (OI) and the generation of confidentiality agreements (NDAs) as a tool for the protection of intellectual property. Based on the analysis of a cross-sectional sample of 6,798 industrial companies (2019–2020) and 9,304 companies in the service sector (2017–2019) that are part of the directory of the National Administrative Department of Statistics (DANE) in its Technological Innovation and Development Survey (EDIT and EDITS), it can be suggested that the interaction of these two variables (OI and NDAs) generate positive effects for the manufacturing industry but negative ones for the service sector. It could be deduced that the positive effect is due to the greater tradition of OI in the manufacturing industry and the negative effect to the caution that the service sector presents when collaborating with external actors.

D. Pratama, N. Sakti, W. Subroto

This scientific article is motivated by the description of the concept of entrepreneurship and industrial development applied to business actors in Java Island which is influenced by socio-cultural roles. The literature review in this scientific article has the following objectives: 1) to analyze the social role in the development of entrepreneurship and industry in Java Island; 2) to analyze the role of culture in the development of entrepreneurship and industry in Java Island; and 3) to analyze the concept of sociopreneur dimensions due to socio-cultural roles. This research is explained and presented with a comparative descriptive qualitative approach that focuses on the literature review scheme from the findings of 200 scientific articles discussing the socio-cultural role in the development of entrepreneurship and industry. The articles are divided into 10 articles that specifically represent the researcher's objectives to explore the socio-cultural role in Java Island. The criteria for selecting scientific articles look from the level of similarity of discussion topics, the quality of scientific articles, and the journal accreditation category from the Science and Technology Index (SINTA), Directory of Open Access Journals (DOAJ), and e-Journal of the National Library of the Republic of Indonesia. The results of the analysis show that the socio-cultural patterns that apply in the environment around the business are one of the main factors in seeing the existence of the development of the type of business that is established. So, businesses that are built from socio-cultural roles also have an impact on improving the economy in the environment around the business as feedback.

S. Ogu

This study assessed the extent to which Nigerian firms planed their marketing programmes and also the extent of formalization of marking planning. It was found that some forms of planning were in operation among many firms. However, only one-third of the firms of studied adopted a comprehensive, formalized, approach, and this was found to be a function of size of operation – large firms tended to do so. We further found that nearly half of the firms prepared forecasts and budgets in lieu of marketing plans, and this was attributed to the intellectual rigour involved in marketing planning. It has been recommended, among other measures that, experienced professionals be always appointed to head the marketing function in business organizations. The population for this study consisted of firms in Lagos employing 50 or more persons engaged in manufacturing or distributive trade. Only such firms listed in the 1986 edition of the Lagos state industrial Directory were included. For the purpose of this study, we classified the firms into categories, A, B, C, as small categories, D and E as medium, and categories F, G, H and I, as large. This implies that we focused attention on categories D to I while ignoring categories A to C. Our concentration on medium-large firms is borne out of our conviction that their size of operations may necessitate the need for elaborate planning.

Christian W. Frey

Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch

Olaf Sassnick, Georg Schäfer, Thomas Rosenstatter et al.

Industrial Operational Technology (OT) systems are increasingly targeted by cyber-attacks due to their integration with Information Technology (IT) systems in the Industry 4.0 era. Besides intrusion detection systems, honeypots can effectively detect these attacks. However, creating realistic honeypots for brownfield systems is particularly challenging. This paper introduces a generative model-based honeypot designed to mimic industrial OPC UA communication. Utilizing a Long ShortTerm Memory (LSTM) network, the honeypot learns the characteristics of a highly dynamic mechatronic system from recorded state space trajectories. Our contributions are twofold: first, we present a proof-of concept for a honeypot based on generative machine-learning models, and second, we publish a dataset for a cyclic industrial process. The results demonstrate that a generative model-based honeypot can feasibly replicate a cyclic industrial process via OPC UA communication. In the short-term, the generative model indicates a stable and plausible trajectory generation, while deviations occur over extended periods. The proposed honeypot implementation operates efficiently on constrained hardware, requiring low computational resources. Future work will focus on improving model accuracy, interaction capabilities, and extending the dataset for broader applications.

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani et al.

This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect detection accuracy through a cyclic data refinement strategy, iteratively removing misleading data points to improve model performance and robustness. We validate the IRP's effectiveness using two benchmark datasets, Kolektor SDD2 (KSDD2) and MVTec AD, covering a wide range of industrial products and defect types. Our experimental results demonstrate that the IRP consistently outperforms traditional anomaly detection models, particularly in environments with high noise levels. This study highlights the IRP's potential to significantly enhance anomaly detection processes in industrial settings, effectively managing the challenges of sparse and noisy data.

Hans Aoyang Zhou, Dominik Wolfschläger, Constantinos Florides et al.

Machine vision enhances automation, quality control, and operational efficiency in industrial applications by enabling machines to interpret and act on visual data. While traditional computer vision algorithms and approaches remain widely utilized, machine learning has become pivotal in current research activities. In particular, generative AI demonstrates promising potential by improving pattern recognition capabilities, through data augmentation, increasing image resolution, and identifying anomalies for quality control. However, the application of generative AI in machine vision is still in its early stages due to challenges in data diversity, computational requirements, and the necessity for robust validation methods. A comprehensive literature review is essential to understand the current state of generative AI in industrial machine vision, focusing on recent advancements, applications, and research trends. Thus, a literature review based on the PRISMA guidelines was conducted, analyzing over 1,200 papers on generative AI in industrial machine vision. Our findings reveal various patterns in current research, with the primary use of generative AI being data augmentation, for machine vision tasks such as classification and object detection. Furthermore, we gather a collection of application challenges together with data requirements to enable a successful application of generative AI in industrial machine vision. This overview aims to provide researchers with insights into the different areas and applications within current research, highlighting significant advancements and identifying opportunities for future work.