Hasil untuk "Industrial directories"

Shen Yin, S. Ding, Xiaochen Xie et al.

Hanna Anisimova

Jacquelyn Schreck, Gerald Matthews, Jinchao Lin et al.

Nuclear power plants increasingly utilize digitalized systems, including computer-based procedures (CBPs) and automation. These novel technologies require human factors’ evaluation to ensure safety. Potentially, automation contributes to safety by reducing workload, but automation may also induce a loss of situation awareness and trust miscalibration. The current study investigated workload during a simulated nuclear power plant (NPP) emergency operation procedure (EOP) executed using a CBP supported by automation. Two levels of automation (LOA) were compared within subjects: management-by-consent (lower LOA) and management-by-exception (higher LOA). Subjective workload and trust were assessed, together with objective psychophysiological and performance-based workload measures. LOA effects varied across the different workload measures. The hypothesis that workload would be reduced at the higher LOA was confirmed for a behavioral measure (secondary task response time). However, other metrics, including instantaneous self-assessment (ISA) and heart rate variability (HRV), showed increased workload at the higher LOA. Different LOAs may produce differing operator strategies that require multivariate workload assessment to evaluate. Effect sizes for the impact of LOA on workload were indexed by Cohen’s <i>d</i>. Several of these effect sizes were in the 0.4–0.6 range, indicating effects of medium magnitude. In addition, subjective workload data were compared with those from a previous study that simulated conventional NPP operations. As anticipated, workload tended to be lower with the automated procedure. The study suggests future directions for human factors research on plant modernization.

Marwan Babiker

Golnush Jahromi Rajabi, Razieh KeshavarzFard

Background and objective Natural and human-made disasters have long-term, negative, and sometimes irreparable impacts and consequences. Proper response to these disasters requires effective management of relief services. Influential parties in such operations include humanitarian organizations, donors, and the government. One of the solutions to increase performance in emergency and humanitarian supply chains is to utilize the capacities of non-governmental organizations (NGOs). This study aims to present a collaborative model based on game theory that examines the cooperation between donors, humanitarian organizations, the government, and NGOs.  Method The collaborative model is designed based on the problem of maximum coverage of damaged areas caused by a natural disaster. By considering the number and diversity of players, it takes into account the extent of the disaster and the number of affected areas in relief assistance. It also considers the quality of relief services and the competence of NGOs. This can allow for a default order to prioritize qualified NGOs with the required capacities in the event of a disaster. The developed model was solved using a hypothetical numerical example and GAMS software, and underwent a sensitivity analysis. Results The results obtained from the model provide valuable insights into the optimal actions of each player in the humanitarian supply chain during a disaster. This model takes into account the capacities and capabilities of each player, as well as the quality of services provided by the private sector, allowing for more efficient allocation of resources and ensuring maximum coverage of affected areas. The model also considers qualitative assessments conducted in the pre-disaster period and allows for prioritization of qualified NGOs during a disaster. This can significantly improve the effectiveness of disaster management efforts. Conclusion The collaborative model is designed based on the problem of maximum coverage of damaged areas caused by a natural disaster. By considering the number and diversity of players, it takes into account the extent of the disaster and the number of affected areas in relief assistance. This can significantly improve the effectiveness of disaster management efforts. It also considers the quality of relief services and the competence of NGOs.

R. Iqbal, Tomasz Maniak, F. Doctor et al.

Automated fault detection is an important part of a quality control system. It has the potential to increase the overall quality of monitored products and processes. The fault detection of automotive instrument cluster systems in computer-based manufacturing assembly lines is currently limited to simple boundary checking. The analysis of more complex nonlinear signals is performed manually by trained operators, whose knowledge is used to supervise quality checking and manual detection of faults. We present a novel approach for automated Fault Detection and Isolation (FDI) based on deep learning. The approach was tested on data generated by computer-based manufacturing systems equipped with local and remote sensing devices. The results show that the approach models the different spatial/temporal patterns found in the data. The approach can successfully diagnose and locate multiple classes of faults under real-time working conditions. The proposed method is shown to outperform other established FDI methods.

Gang Sun, Zhu Xu, Hongfang Yu et al.

Guangjie Han, Li Liu, Jinfang Jiang et al.

Kim-Kwang Raymond Choo, S. Gritzalis, J. Park

Industrial Internet of Things (IIoT) is an emerging trend, including in nontraditional technological sector (e.g., oil and gas industry). There are, however, a number of research challenges such using cryptography and other techniques to ensure security and privacy in IIoT applications and services. In this special issue, we present existing state-of-the-art advances reported by the 21 accepted papers. We then conclude the special issue with a number of potential research agenda.

Xiaofeng Yuan, Yalin Wang, Chunhua Yang et al.

Qi Zhang, Chunjie Zhou, Yu-Chu Tian et al.

With the increasing deployment of data network technologies in industrial control systems (ICSs), cybersecurity becomes a challenging problem in ICSs. Dynamic cybersecurity risk assessment plays a vital role in ICS cybersecurity protection. However, it is difficult to build a risk propagation model for ICSs due to the lack of sufficient historical data. In this paper, a fuzzy probability Bayesian network (FPBN) approach is presented for dynamic risk assessment. First, an FPBN is established for analysis and prediction of the propagation of cybersecurity risks. To overcome the difficulty of limited historical data, the crisp probabilities used in standard Bayesian networks are replaced in our approach by fuzzy probabilities. Then, an approximate dynamic inference algorithm is developed for dynamic assessment of ICS cybersecurity risk. It is embedded with a noise evidence filter in order to reduce the impact from noise evidence caused by system faults. Experiments are conducted on a simplified chemical reactor control system to demonstrate the effectiveness of the presented approach.

K. Karunanithy, Bhanumathi Velusamy

Abstract Integrating Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) is to build a heterogeneous device network for connecting, sharing and storing their information in order to create the application environment as smart. It also helps in industrial automation to predict and build a fault-tolerant environment. The automation of industry can be made possible with the emergence of new developments in sensor manufacturing and communication. In this paper, a Cluster-Tree based Energy Efficient Data Gathering (CTEEDG) protocol is proposed to increase the lifetime and throughput of WSNs. The CTEEDG uses the Fuzzy logic to select the Cluster Head (CH) based on the information collected locally. In the inter-cluster communication phase, the tree topology is established between the clusters towards the Base Station (BS) which ensures the availability of the congestion free shortest path to the BS. From the simulation results, the proposed CTEEDG outperforms the FAMACROW and DL-LEACH by 28.81% and 38.28% in terms of throughput. And also, the proposed method has 29.26% and 49.29% reduction in average energy consumption when compared to the FAMACROW and DL-LEACH.

M. S. Egorov, E. V. Fominov

Introduction. At the present stage of technology development, special importance is given to improving the efficiency and durability of machine parts and especially parts of assemblies operating under increased loads. The problem of increasing reliability in the conditions of intensification of production, energy and resource conservation poses the task of introducing new technological processes and the use of modern materials into production. Often, the weakest element in the «material – working» environment system, which determines the operating conditions and the resource of mechanisms, is the surface of the material. Therefore, an important role in increasing its wear resistance is played by coatings that protect parts from the destructive influence of working environments.Problem Statement. The main objective of the research is the development of technological modes for applying thermal coatings to the belt pulleys of the TORUM 750 combine harvester by Rostselmash, as well as the selection of optimal coatings to increase the durability and wear resistance of the pulley surface.Theoretical Part. As a theoretical description, the application of various coating options is analyzed, as well as the mathematical processing of experimental data on the adhesion strength between the aluminum surface and the coating material is considered.Conclusions. The studies carried out by the authors have shown that it is advisable to conduct thermal spraying on aluminum alloys of belt coatings on a heated substrate (preheating temperature from 210°C). In the case of spraying coatings on the rubbing surfaces of pulleys, the surfaces should be regularly cooled.

И. А. Гущин, Д. В. Софронов, В. Д. Смольников, И. А. Рымбаев

Z. G. Farakhutdinova, E. I. Bakhonina, N. V. Shutov

Introduction. Major projects in the oil refining industry require the involvement of contractors. Cooperation with qualified and responsible contractors can be considered as a competitive advantage, allowing timely delivery of objects with the proper level of quality and safety.Significant injury rates in contracting organizations operating at oil refining facilities attract attention. This leads to an interest in safety of working conditions in the industry. One of the approaches to solving the problem is to use the occupational health and safety management system in the interactions of the customer and the contractor.Problem Statement. The objective of this study is to improve the mechanism of rating contractors from the point of view of occupational and industrial safety.Theoretical Part. The paper uses the official statistical reports on injuries and the data on injuries of employees of contracting organizations at an oil refinery of a large Russian company. A mechanism for rating contractors in this area has been developed. It includes three sections with their own criteria and weight coefficients. The automatically generated and periodically updated rating is proposed to be placed in open access on the website of the Federal Service for Labor and Employment.Conclusions. The use of the proposed rating will allow customers working in the field of oil refining to reasonably judge the risks generated by the industrial safety system of potential contractors.

Valerii Krainov , Roman Hrozovskyi, I Novikova

Питання створення комплексної системи інформаційної безпеки органів управління військового призначення – важлива частина концепції впровадження нових інформаційних технологій в військову справу. В умовах збройної агресії російської федерації значно вирослі загрози інформаційній безпеці держави. Ключове завдання системи інформаційної безпеки забезпечити створення передумов для розвитку такого потенціалу інформаційної сфери України, за якого забезпечується її випереджальний розвиток, а зовнішні негативні впливи не створюють реальних небезпек національній інформаційній безпеці держави. Тому в скрутній економічній обстановці, яка склалася на сьогоднішній день в Україні, виключно актуальною стала проблема розумного, науково обґрунтованого та ефективного використання вкрай обмежених матеріальних ресурсів та пошук нових можливостей забезпечення інформаційної безпеки органів управління військового призначення з урахуванням переходу від традиційної форми подання системи захисту інформації до широкого розуміння інформаційної безпеки держави у сфері оборони в умовах воєнного стану. Суть нового уявлення полягає у реалізації комплексного підходу до розуміння інформаційної безпеки як сукупності основних напрямків захисту інформації, захисту від інформації та формування безпекового середовища в умовах воєнного стану. Це стосується і робіт по створенню, впровадженню та експлуатації комплексної системи інформаційної безпеки органів управління військового призначення. Особливого значення набуває необхідність обґрунтування вимог  до комплексної системи інформаційної безпеки органів управління військового призначення як на етапе проектування так і при створенні, впровадженні та експлуатації.

Rhouas Sara, Bouchekourte Mustapha, El Hami Norelislam

Liquidity and volatility are the two barometers that allow stock markets to appreciate in terms of attractiveness, profitability and efficiency. Several macroeconomic and microstructure variables condition the level of liquidity that directly impact the asset allocation decisions of different investor profiles − institutional and individuals − and therefore the dynamics of the market as a whole. Volatility is the regulatory component that provides information on the level of risk that characterizes the market. Thus, the appreciation of these two elements is of considerable help to fund managers looking to optimize their equity pockets. In this work, we will use the liquidity ratio as a proxy variable for the liquidity of the Moroccan stock market, to estimate the indicators and factors that determine its short- and long-term variability. The appropriate econometric method would be to estimate an error correction vector model (ECVM) which has the property of determining the long- and short-term relationships between the variables. The volatility of the MASI index will be the subject of a second estimate to capture the shape of the function of its evolution.

Tie Qiu, Yushuang Zhang, D. Qiao et al.

Energy-efficient and robust-time synchronization is crucial for industrial Internet of things (IIoT). Some energy-efficient time synchronization schemes that achieve high accuracy have been proposed recently. However, some unsynchronized nodes namely isolated nodes exist in the schemes. To deal with the problem, this paper presents R-Sync, a robust time synchronization scheme for IIoT. We use a pulling timer to pull isolated nodes into synchronized networks whose initial value is set according to level of spanning tree. Then, another timer is set up to select backbone node and its initial value is related to the distance to parent node. Moreover, we do experiments based on simulation tool NS-2 and testbed based on wireless hardware nodes. The experimental results show that our approach makes all the nodes get synchronized and gets the better performance in terms of accuracy and energy consumption, compared with three existing time synchronization algorithms TPSN, GPA, STETS.

F. Núñez, Saúl Langarica, Pablo Diaz et al.

This article presents a real implementation of a neural network-based model predictive control scheme (NNMPC) to control an industrial paste thickener. The implementation is done over an Industrial Internet of Things (IIoT) platform designed using the seven layer reference model for IIoT systems. Modeling is achieved using an encoder–decoder with attention recurrent neural network, while MPC search is done using particle swarm optimization. An industrial evaluation is presented, which highlights the set-point tracking and disturbance rejection capabilities of the proposed NNMPC technique.

Gaolei Li, Jun Wu, Jianhua Li et al.

Recently, fog computing has gained increasing attention in processing the computing tasks of the industrial Internet of things (IIoT) with different service popularity. In task-diversified fog computing-enabled IIoT (F-IIoT), the mismatch between expected computing efficiency and partitioned resources on fog nodes (FNs) may pose serious traffic congestion even large-scale industrial service interruptions. The existing works mainly studied offloading which type of computing tasks into FNs, but few studies enabled smart resource partitioning of FNs. In this paper, a service popularity-based smart resources partitioning (SPSRP) scheme is proposed for fog computing-enabled IIoT. We first exploit Zipf's law to model the relationship between popularity ranks and computing costs of IIoT services. Moreover, we propose an implementation architecture of the SPSRP scheme for F-IIoT, which decouples the computing control layer from data processing layer of IIoT through a specified SPSRP controller. Besides, a mobility and heterogeneity-aware partitioning algorithm is presented for extending SPSRP scheme to seamlessly support cross-domain resources partitioning. The simulations demonstrate that the SPSRP scheme can bring notable performance improvements on delay time, successful response rate and fault tolerance for fog computing to deal with the large-scale IIoT services.