Hasil untuk "Industrial directories"

P. Aghion, M. Dewatripont, L. Du et al.

This paper argues that sectoral policy aimed at targeting production activities to one particular sector, can enhance growth and efficiency if it is made competition-friendly. First, we develop a model in which two firms can operate either in the same (higher growth) sector or in different sectors. To escape competition, firms can either innovate vertically or dif-ferentiate by choosing a different sector from their competitor By forcing firms to operate in the same sector, sectoral policy induces them to innovate ”vertically” rather than differentiate in order to escape competition with the other firm. The model predicts that sectoral targeting enhances average growth and productivity more when competition is more intense within a sector and when competition is preserved by policy. In the second part of the paper, we test these predictions using a panel of medium and large Chinese enterprises for the period 1998 through 2007. Our empirical results suggest that if subsidies are allocated to competitive sectors (as measured by the Lerner index) or allocated in such a way as to preserve or increase competition, then the net impacts of subsidies, tax holidays, and tariffs on total factor productivity levels or growth become positive and significant. We address the potential endogeneity of targeting and competition by using variations in targeting across Chinese cities that are exogenous to the individual firm.

Mohammad Aazam, S. Zeadally, Khaled A. Harras

Rapid technological advances have revolutionized the industrial sector. These advances range from automation of industrial processes to autonomous industrial processes, where a human input is not required. Internet of Things (IoT), which has emerged a few years ago, has been embraced by industry, resulting in what is known as the Industrial Internet of Things (IIoT). IIoT refers to making industrial processes and entities part of the Internet. Restricting the definition of IIoT to manufacturing yields another subset of IoT, known as Industry 4.0. IIoT and Industry 4.0, will consist of sensor networks, actuators, robots, machines, appliances, business processes, and personnel. Hence, a lot of data of diverse nature would be generated. The industrial process requires most of the tasks to be performed locally because of delay and security requirements and structured data to be communicated over the Internet to web services and the cloud. To achieve this task, middleware support is required between the industrial environment and the cloud/web services. In this context, fog is a potential middleware that can be very useful for different industrial scenarios. Fog can provide local processing support with acceptable latency to actuators and robots in a manufacturing industry. Additionally, as industrial big data are often unstructured, it can be trimmed and refined by the fog locally, before sending it to the cloud. We present an architectural overview of IIoT and Industry 4.0. We discuss how fog can provide local computing support in the IIoT environment and the core elements and building blocks of IIoT. We also present a few interesting prospective use cases of IIoT. Finally, we discuss some emerging research challenges related to IIoT.

Xiaokang Zhou, Wei Liang, Shohei Shimizu et al.

With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial cyber-physical systems (CPS). Intelligent anomaly detection for identifying cyber-physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this article, we propose a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve false alarm rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.

Yunlong Lu, Xiaohong Huang, Ke Zhang et al.

The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.

Zhihan Lv, Yang Han, A. Singh et al.

The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

Aamir Mahmood, Luca Beltramelli, Sarder Fakhrul Abedin et al.

Cellular networks are envisioned to be a cornerstone in future industrial Internet of Things (IIoT) wireless connectivity in terms of fulfilling the industrial-grade coverage, capacity, robustness, and timeliness requirements. This vision has led to the design of vertical-centric service-based architecture of 5G radio access and core networks. The design incorporates the capabilities to include 5G-AI-Edge ecosystem for computing, intelligence, and flexible deployment and integration options (e.g., centralized and distributed, physical, and virtual) while eliminating the privacy/security concerns of mission-critical systems. In this article, driven by the industrial interest in enabling large-scale wireless IIoT deployments for operational agility, flexible, and cost-efficient production, we present the state-of-the-art 5G architecture, transformative technologies, and recent design trends, which we also selectively supplemented with new results. We also identify several research challenges in these promising design trends that beyond-5G systems must overcome to support rapidly unfolding transition in creating value-centric industrial wireless networks.

Martin Serror, Sacha Hack, Martin Henze et al.

Given the tremendous success of the Internet of Things in interconnecting consumer devices, we observe a natural trend to likewise interconnect devices in industrial settings, referred to as industrial Internet of Things or Industry 4.0. While this coupling of industrial components provides many benefits, it also introduces serious security challenges. Although sharing many similarities with the consumer Internet of Things, securing the industrial Internet of Things introduces its own challenges but also opportunities, mainly resulting from a longer lifetime of components and a larger scale of networks. In this article, we identify the unique security goals and challenges of the industrial Internet of Things, which, unlike consumer deployments, mainly follow from safety and productivity requirements. To address these security goals and challenges, we provide a comprehensive survey of research efforts to secure the industrial Internet of Things, discuss their applicability, and analyze their security benefits.

Luís Fernando de Souza Cardoso, Flávia Cristina Martins Queiroz Mariano, E. Zorzal

Abstract This article aims to evaluate the impact of Augmented Reality (AR) applicability and usefulness on real industrial processes by employing a systematic literature review (SLR). The SLR was performed in five digital libraries to identify articles and reviews concerning the AR applicability from 2012 to 2018. A patent search in Google’s patents database was also conducted, for the same period. This paper describes how AR has been applied, which industries are most interested in the technology, how the technology has been developed to meet industry needs, as well as the benefits and challenges of AR. This survey concludes by providing a starting point for companies interested in integrating AR into their processes and proposing future directions for AR developers and researchers.

C. Gehrmann, M. Gunnarsson

The digital twin is a rather new industrial control and automation systems concept. While the approach so far has gained interest mainly due to capabilities to make advanced simulations and optimizations, recently the possibilities for enhanced security have got attention within the research community. In this article, we discuss how a digital twin replication model and corresponding security architecture can be used to allow data sharing and control of security-critical processes. We identify design-driving security requirements for digital twin based data sharing and control. We show that the proposed state synchronization design meets the expected digital twin synchronization requirements and give a high-level design and evaluation of other security components of the architecture. We also make performance evaluations of a proof of concept for protected software upgrade using the proposed digital twin design. Our new security framework provides a foundation for future research work in this promising new area.

Xin Liu, X. Zhai, Weidang Lu et al.

The traditional ground industrial Internet of Things (IIoT) cannot supply wireless interconnections anywhere due to its small-scale communication coverage. In this article, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses nonorthogonal multiple access (NOMA) for each beam to improve transmission rate. To guarantee Quality of Service (QoS) for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The NOMA transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node within the beam. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show higher transmission performance for the QoS-guarantee resource allocation.

Wen Sun, S. Lei, Lu Wang et al.

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article, we consider a new architecture of digital twin (DT) empowered Industrial IoT, where DTs capture the characteristics of industrial devices to assist federated learning. Noticing that DTs may bring estimation deviations from the actual value of device state, a trusted-based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning (DRL), to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.

Ying Chen, Z. Liu, Yongchao Zhang et al.

Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

Lianyong Qi, Chunhua Hu, Xuyun Zhang et al.

As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

Arianna Martinelli, Andrea Mina, M. Moggi

Technological revolutions mark profound transformations in socio-economic systems. They are associated with the development and diffusion of general-purpose technologies (GPTs) that display very high degrees of pervasiveness, dynamism and complementarity. This article provides an in-depth examination of the technologies underpinning the “factory of the future” as profiled by the Industry 4.0 paradigm. It contains an exploratory comparative analysis of the technological bases and the emergent patterns of development of Internet of Things, big data, cloud, robotics, artificial intelligence, and additive manufacturing. We qualify the “enabling” nature of these technologies. We then test whether, taken together and individually, they display the characteristics of generality, originality, and longevity associated with GPTs. Finally, we discuss key themes for future research on this topic from an industrial structural change perspective.

Xia Wu, Shijie Hu, Shibiao Su

This study addresses unreasonable capture velocities used in evaluating dust/toxin-removal efficiency of side-suction hoods adopted in occupational hazard control facilities. A combination of laboratory experiments and numerical simulations is employed to compare the diffusion patterns and capture velocities for side-suction hoods under different conditions. The results indicate that the initial dispersant velocities significantly impact the capture velocities of side-suction hoods. Specifically, the capture velocity for side-suction hoods was 0.12 m/s for an initial dispersant velocity of 0–0.40 m/s, 0.47 m/s for an initial velocity of 0.40–2.00 m/s, and 0.98 m/s for an initial velocity of 2.00–5.00 m/s, respectively. The dust capture efficiency of side-suction hoods was higher than that of chemical toxins, while the dust capture velocity was approximately 55.0 % that of chemical toxins. To address the challenge of undetectable capture velocities in certain scenarios, this study introduces the hood-face airflow velocity as a new indicator for evaluating the dust/toxin-removal capabilities of side-suction hoods based on an analysis of its relationship with the capture velocity. The results indicate that when the capture distance equals the equivalent hood diameter, the hood-face airflow velocity is evaluated as 0.66 m/s for an initial dispersant velocity of 0–0.40 m/s, 2.60 m/s for an initial velocity of 0.40–2.00 m/s, and 5.42 m/s for an initial velocity of 2.00–5.00 m/s, respectively. The findings of this study will provide useful theoretical guidance for the practical evaluation of the dust/toxin-removal performance of workplace side-suction hoods and for optimizing such evaluation methods.

Chenliang Liu, Kai Wang, Yalin Wang et al.

Due to the existence of complex disturbances and frequent switching of operational conditions characteristics in the real industrial processes, the process data under different operational conditions subject to different distributions, which means there exist different manifold structures under broad operations. Globally, the entire process data are distributed in a multimanifold structure. Nevertheless, the existing data-driven quality prediction methods do not consider the relationships among different manifolds of data and just treats the process data as a single manifold. How to extract effective multimanifold structure feature representation from complex process data and enhance online prediction ability are still challenging in the field of real industrial processes. To this end, in this article, a novel stacked multimanifold autoencoder (S-MMAE) is proposed for feature extraction and quality prediction. Especially, by introducing a new multimanifold regularization into the original loss function of stacked autoencoder at each layer, the intrinsic multimanifold structure information of data is utilized to guide the feature learning procedure. In this way, the learned features can offer a more comprehensive representation of original data and help enhance the prediction performance. At last, the application results in a practical hydrocracking process demonstrate that the proposed S-MMAE can achieve excellent prediction accuracy, which outperforms other state-of-the-art methods.

F. Ullah, Khan Muhammad, I. Haq et al.

Analyzing surveillance videos is mandatory for the public and industrial security. Overwhelming growth in computer vision fields has been made to automate the surveillance system in terms of human activity recognition, such as behavior analysis and violence detection (VD). However, it is challenging to detect and analyze the violent scenes intelligently to fulfill the notion of Industrial Internet of Things (IIoT)-based surveillance buoyed by constrained resources to reduce computational power. To tackle this challenge, in this article, an artificial intelligence enabled IIoT-based framework with VD-Network (VD-Net) is proposed. First, the input video frames are passed to light-weight convolutional neural network model for important information collection including humans or suspicious objects such as knives/guns. Upon suspicious object detection, an alert is generated as an earlier VD in IIoT network while the information is shared with concern departments. Only the frames with objects are forwarded to cloud for detail investigation where features are extracted using convolutional long short-term memory (ConvLSTM). The latter from ConvLSTM is propagated to gated recurrent unit for final VD. The conducted experiments and ablation study on the existing surveillance and nonsurveillance datasets empirically validate the effectiveness of the proposed VD-Net by improving 3.9% increase in the accuracy compared with the state-of-the-art VD methods.

Al-Baraa Abdulrahman Al-Mekhlafi, Noreen Kanwal, Mohammed Nasser Alhajj et al.

Safety culture plays a vital role in creating safer work environments, making its understanding important. This paper comprehensively analyzes safety culture research trends through a bibliometric study using the Scopus database. This study provided a full insight by analyzing 7058 papers published between 1978 and 2023, employing the PRISMA method and VOSviewer 1.6.19 for bibliometric mapping. The USA, England, China, and Australia are the leading contributors, with Johns Hopkins University being the most active institution. Approximately 75% of publications are co-authored, indicating strong collaboration in this field. Guldenmund (2000) is the most referenced work in safety culture research. Based on the results, this work identifies significant geographical gaps, particularly in Oceania, South America, the Middle East, Southeast Asia, and Africa, as well as underexplored sectors such as transportation, logistics, energy, sports, education, and construction. The COVID-19 pandemic has profoundly impacted research in this area, particularly healthcare, while potentially diverting attention from other critical sectors. This study contributes a fresh perspective on the trends of safety culture research, offering valuable insights for scholars and practitioners. Additionally, it highlights the importance of interdisciplinary collaboration in addressing the unique challenges faced by safety culture across diverse industries and regions.

Jian Shen, Maureen Hassall

Fatal and serious injury rates remain unacceptably high in the construction industry. Leadership plays a critical role in safety management and serious and fatal injury prevention. However, limited research has examined industry practitioners’ perceptions of leadership and how it influences safety outcomes, particularly in the prevention of serious and fatal injuries in the construction industry. Therefore, a theoretical model for capturing perceptions of safety leadership was developed from a systematic literature review. To ensure that the labels and language used in the model can be understood by industry practitioners, a Delphi study was conducted involving twelve experts. Over three iterative rounds, the model was refined to include five leadership styles, seventeen elements, and eighty-five descriptive statements spanning the range from laissez-faire to transformational leadership. The refined model provides a comprehensive framework for understanding safety leadership and serves as a foundation for future empirical testing with frontline construction workers.

Teppei Imai, Noriko Nishikido, Jiro Moriguchi et al.