ABSTRACT
This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation;2) object detection;and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework's usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.
ABSTRACT
With the global outbreak of COVID-19, hundreds of pneumonias caused by cold chain products occurred worldwide, which seriously threatened the safety of people's lives and properties. To effectively prevent product quality problems caused by cold chain logistics, it is urgent to establish a cold chain logistics traceability system with interoperability of heterogeneous systems, to record, share and track the temperature, location, time, and other specific information. The traditional cold chain logistics traceability systems have many problems, such as broken cold chains, untrustworthy data, and data tampering and sharing, which hinder the coordination and interaction efficiency of cold chain logistics traceability data. This paper creatively proposes a cold chain logistics traceability system framework based on the identification and resolution system for the Industrial Internet. It establishes a general cold chain logistics traceability identification data model. The system framework and data model can effectively solve the difficulties of multi-code identification and multi-source heterogeneous system interaction, to improve the efficiency of cold chain logistics traceability, and ensure the quality of cold chain logistics products. © 2022 ACM.
ABSTRACT
Industrial Internet of Things (IIoT) is witnessing a steady increase in adoption by infrastructure and process industries. Industrial equipment manufacturers are one of the key stakeholders in this digitalization journey. The adoption of IIoT by the equipment manufacturers has been slower due to various valid reasons. The present pandemic COVID-19 created disruption in the factory operations in many parts of the world. This consequence has been hard on the manufacturing industry including the equipment manufacturers, and many of their strategic projects are slowing down or derailed. In India, a strict lockdown of three weeks which was later extended for another seven weeks was by far the longest lockdown effecting the industry and the equipment manufacturers. This study probes the impact of COVID-19 on the mindset of original equipment manufacturers (OEMs) towards adoption of IIoT. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
COVID-19 is not the last virus;there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims an HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. The proposed STRENUOUS system discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject's cross-path can be infected and suggest quarantine followed by testing. The proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.
ABSTRACT
In recent years, domestic robot industry is faced with a huge opportunity as well as severe challenges due to the four factors of age of a society, great power competition, COVID-19 and industrial upgrading. From the perspective of three elements of both pricing logic and promotion of industrial robot industry -economy, technology, talent and policy, taking EFORT industrial robots as an example, this paper analyzes the shortcomings and deficiencies in the current situation and the future development trend of industrial robot development, therefore finds the pain points of industrial robot enterprise development and makes a plan for the development of EFORT intelligent industrial robots. This paper summarizes the progress of EFORT intelligent robots and the training of robot application talents from the aspects of technology research and development, new application scenario development, business model innovation and the integration of the industrial chain and the education chain in. This paper has certain reference value for the current strategic decision-making of domestic robot industry enterprises. © 2022 SPIE.
ABSTRACT
Industrial Internet of Things and Industry 4.0 have been increasingly prevalent in the manufacturing sector since the mid-2010s, and the COVID19 pandemic has only strengthened this trend. However, there is a wide variation between companies in the degree of industrial digitalization adopted and the deployment context. This is particularly relevant in the case of the industrial maintenance sector, one of the business segments with the highest potential for digitization. This paper analyses the work environment by looking at the differences in the digitalization experiences of large domestically owned and international companies according to maintenance professionals in Hungary. The benefits and challenges of implementing Industry 4.0 will be examined, highlighting the differences between various types of companies. © 2022 IEEE.
ABSTRACT
Recently, the Healthcare Internet of Things (H-IoT) has been widely applied to alleviate the global challenge of the coronavirus disease 2019 (COVID-19) pandemic. However, security and limited energy capacity issues remain the two main factors that prevent the large-scale application of the H-IoT. Therefore, a permissioned blockchain and deep reinforcement learning (DRL)-empowered H-IoT system is presented in this research to address these two issues. The proposed H-IoT system can provide real-time security and energy-efficient healthcare services to control the propagation of the COVID-19 pandemic. To address the security issue, a permissioned blockchain method is adopted to guarantee the security of the proposed H-IoT system. As for handling the limited energy constraint, we employ the mobile edge computing (MEC) method to offload the computing tasks to alleviate the computational burden and energy consumption of the proposed H-IoT system. We also adopt an energy harvesting method to improve performance. In addition, a DRL method is employed to jointly optimize both the security and energy efficiency performance of the proposed system. The simulation results demonstrate that the proposed solution can balance the requirements of security and energy efficiency issues and hence can better respond to the COVID-19 pandemic.
ABSTRACT
Although the Internet of Things (IoT) has spawned a new breed of smart factories within supply chains, the latest pandemic has ushered in unparalleled supply chain disturbances. Following the challenges identified in the literature, we interview top experts to evaluate the significance of these challenges. We apply a multi-criteria decision analysis (MCDA) tool, analytical hierarchy process (AHP) in combination with interval-valued neutrosophic numbers (IVN). The critical part of this research is that we also perform a comparative analysis by focusing on before- and during- the pandemic periods individually to better assess the impact of the latest pandemic on the IoT challenges. Our study also includes a comprehensive, systematic literature review to bring the readers up-to-date.
ABSTRACT
Cybersecurity is one of the main challenges faced by companies in the context of the Industrial Internet of Things (IIoT), in which a number of smart devices associated with machines, computers and people are networked and communicate with each other. In this connected industrial scenario, personnel need to be aware of cybersecurity issues in order to prevent or minimise the occurrence of cybersecurity incidents and corporate data breaches, and thus to make companies resilient to cyber-attacks. In addition, the recent increase in smart working due to the COVID-19 pandemic means that the need for cybersecurity awareness is more relevant than ever. In this study, we carry out a systematic literature review in order to analyse how the existing state of the art deals with cybersecurity awareness in the context of IIoT, and to provide a comprehensive overview of this topic. Four areas of analysis are considered: (i) definitions of the concepts of cybersecurity awareness and information security awareness, with keyword extrapolation (e.g. cybersecurity control level, information and responsibility);(ii) the industrial context of the analysed studies (e.g. manufacturing, critical infrastructure);(iii) the techniques adopted to raise company awareness of cybersecurity (e.g. serious games, online questionnaires);and (iv) the main benefits of a large-scale campaign of cybersecurity awareness (e.g. the effectiveness of employees in terms of managing cybersecurity issues, identification of cyber-attacks). Practitioners and researchers can benefit from our analysis of the features of each area in their future research and applications.