Cyber-physical Data Research Articles

Learning from high-dimensional cyber-physical data streams: a case of large-scale smart grid

Learning from high-dimensional cyber-physical data streams: a case of large-scale smart grid

Mapping method for complex mechatronic equipment relationship data to contextual semantic-constrained knowledge graphs

Complex mechatronic equipment’s complete life cycle information model includes multidimensional data on structure, qualities, behavior, and limitations. The data are diverse, derived from multiple sources, and include both streaming and non-streaming cyber-physical data. The product data system’s structured relational model, which is represented by the XBOM (X Bill of Material), is primarily where this data is stored. However, with the growing amount of equipment information, this relatively isolated and redundant architecture poses challenges. Within relational models, the growing intricacy of inner-outer key joins impedes effective data mining and retrieval for dynamic digital twin business applications. In response, this study proposes a knowledge graph transformation method that takes into account the semantic restrictions of the business application context to translate relational data to entities and semantic expressions. The method builds a knowledge context constraint meta-model by classifying data according to context semantics into one-hop entities, multi-hop entities, and abstract classes. Additionally, an ontology-based data extraction transformation method and an ontology-based mapping algorithm based on relational schema transformation are introduced in this article. This strategy significantly minimizes knowledge redundancy and enhances retrieval performance, as demonstrated by experimental verification through the development of a relational data mapping tool software.

Cyber-Physical Data Fusion in Surrogate- Assisted Strength Pareto Evolutionary Algorithm for PHEV Energy Management Optimization

This article proposes a new form of algorithm environment for the multiobjective optimization of an energy management system in plug-in hybrid vehicles (PHEVs). The surrogate-assisted strength Pareto evolutionary algorithm (SSPEA) is developed to optimize the power-split control parameters guided by the data from the physical PHEV and its digital twins (DTs). By introducing a “confidence factor,” the SSPEA uses the fused data of physically measured and virtually simulated vehicle performances (energy consumption and remaining battery state of charge) to converge the optimization process. Gaussian noisy models are adopted to emulate the real vehicle system on the hardware-in-the-loop platform for experimental evaluation. The testing results suggest that the proposed SSPEA requires less R&D costs than the model-free method that only uses the physical information, and more than 44.6% energy can be saved during the R&D process. Driven by the SSPEA, the optimized energy management system surpasses other non-DT-assisted systems by saving more than 4.8% energy.

Digital twins as education support in construction: a first development framework based on the Reference Construction Site Aachen West

The Reference Construction Site Aachen West, located in Aachen, Germany, is a campus-based living lab for research and university education in construction. To ensure and improve the education quality, this paper researches a new study tool in the form of a digital twin framework powered by the Unreal Engine. By implementing MQTT, an IoT communication protocol, the digital twin realizes synchronicity of cyber-physical bi-directional data flows. Representing the virtual Reference Construction Site, an online multiplayer application was developed as a use case to answer the research questions: (1) how to design the digital twin and structure in an extendable and accessible development framework for different users and (2) how to support education in construction with this digital twin. To validate the concept, a group of students were invited online to explore the application and provide user feedback. Results show positive effects of the digital twin on enhancing the quality of the online education in construction with its real-time interaction and data from on-site machinery and processes. The digital twin is planned to be transferred to research and construction projects.

METHOD OF DATA TRANSMISSION PROTECTION IN CYBERPHYSICAL SYSTEMS

Wireless networks are used to transmit data in cyberphysical systems. This creates an opportunity for attackers to intercept information and then decrypt it and use it for malicious purposes. Consequently, the need to increase the protection of transmitted data in cyberphysical systems increases. This is made possible by new approaches to ensuring the cryptographic stability of the wireless security system. The purpose of ensuring the protection of data transmission in a cyberphysical system is to create a method of encryption that even when intercepting data by decryption was not possible. After sending the test sequence, the attacker hopes to obtain the key sequence that was used during the encryption as a result of decrypting this sequence (reverse operation the sum of module 2 to the encrypted sequence). Having a code sequence and intercepting the following blocks of information, their decryption will not be difficult. Using the coding method for cyberphysical system data before encryption allows you to change the statistical characteristics of the input sequence of elements (message length, data volume, entropy, probability of occurrence of characters). These are the statistical characteristics that an attacker needs to decrypt data. Using encryption before encryption does not allow you to select the input test sequence and obtain the key. The statistical characteristics of the input data and the data obtained as a result of encoding (compression) before encryption were determined; it is established that the use of the proposed method allows to obtain data that are statistically independent of each other in the input and coded sequence; it is shown that the method of protection of data transmission in the cyberphysical system is working and allows to protect data of different volumes.

False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

In the process of the detection of a false data injection attack (FDIA) in power systems, there are problems of complex data features and low detection accuracy. From the perspective of the correlation and redundancy of the essential characteristics of the attack data, a detection method of the FDIA in smart grids based on cyber-physical genes is proposed. Firstly, the principle and characteristics of the FDIA are analyzed, and the concept of the cyber-physical FDIA gene is defined. Considering the non-functional dependency and nonlinear correlation of cyber-physical data in power systems, the optimal attack gene feature set of the maximum mutual information coefficient is selected. Secondly, an unsupervised pre-training encoder is set to extract the cyber-physical attack gene. Combined with the supervised fine-tuning classifier to train and update the network parameters, the FDIA detection model with stacked autoencoder network is constructed. Finally, a self-adaptive cuckoo search algorithm is designed to optimize the model parameters, and a novel attack detection method is proposed. The analysis of case studies shows that the proposed method can effectively improve the detection accuracy and effect of the FDIA on cyber-physical power systems.

Cloud-BIM Enabled Cyber-Physical Data and Service Platforms for Building Component Reuse

While the Circular Economy in the built environment is often viewed in terms of recycling, more value can be obtained from buildings and physical components by their reuse, aided by stewardship and remanufacture, to ensure optimum performance capability. The use of cyber-physical information for online identification, examination and exchange of reusable components may improve their life-cycle management and circularity. To this end, a bi-directional data exchange system is established between physical building components and their virtual Building Information Modeling (BIM) counterparts, so that their life-cycle information—including history of ownership, maintenance record, technical specifications and physical condition—can be tracked, monitored and managed. The resultant prototype Cloud-based BIM platform is then adapted to support an ongoing product-service relationship between suppliers/providers and users/clients. A case study from a major new hospital, focusing upon an example of internal framed glazed systems, is presented for ”proof of concept” and to demonstrate the application of the proposed method. The result of the case study shows that, informed by the life-cycle data from the Cloud-BIM platform, a “lease with reuse” service option is able to deliver a lower total cost and less carbon intensity for each unit of frame-glazed module. This leads to a higher level of eco-efficiency, coupled with decreased consumption of material resources and reduced generation of waste. The research is expected to serve as a step forward in the era of Industry 4.0 and illuminate a more sophisticated way to manage building assets.

Ensuring Cybersecurity of Digital Production Using Modern Neural Network Methods

The transition from the information economy to the digital one presents new challenges for society associated with the development of disruptive technologies, a network of cyber-physical systems, artificial intelligence and big data. When creating digital platforms, a number of difficulties arise: the large size of the digital infrastructure and its heterogeneity, poorly established information interaction between segments, the lack of a unified approach to ensuring cybersecurity and a high dependence on the qualifications of personnel and equipment reliability. The introduction of the digital economy leads to an increase in the risk of cyber threats associated with access control problems between information flow regulation and control systems. To solve the problems of detecting cyber threats, it is proposed to use generative adversarial neural networks. Algorithms for learning and testing a neural network were presented. The results of the experiments have demonstrated that the proposed solution is highly accurate in detecting cyberattacks.

A 2020 perspective on “How to derive causal insights for digital commerce in China? A research commentary on computational social science methods”

Abstract Cyber-physical data from wearable and other data-sensing devices have been rapidly changing the landscape of opportunity for the conduct of computational social science (CSS) studies. We now have the opportunity to include in our research wearable healthcare data sensors, global positioning system (GPS) data, as well as a range of other digital data via mobile phones and other kinds of easily deployed sensors. The result is a dramatic new set of measurement opportunities for management scientists, marketing research staff, and policy analysts, who can now apply a range of approaches to such data capture and analysis, including machine learning of patterns, and causal inference methods for relevant policy analytics conclusions.

Identifying and Prioritizing the Futuristic Attributes of a Car in the Industry 4.0 Era

Industry 4.0 era is going to witness smart cars with embedded systems, which will use cyber-physical systems, big data and internet of things. In this research, the important desires of the future generation were identified from discussions, literature surveys and review of ongoing research in the field of automobile engineering. Ten futuristic attributes contributing towards driving safety, automation, optimised use of renewable energy, driver assistance, process control systems, etc. were selected. The opinions about these attributes were captured from a survey and fuzzy AHP was used to prioritise these. Long-term evolution, communication between vehicles and sheet thin batteries evolved as the most preferred attributes. Innovation always leads to a competitive advantage in the long-term. Connected cars and cloud-powered automotive industry are the major automotive industry trend and for automakers, embracing these smart technologies will offer both challenges and opportunities, thereby keeping them ahead in the competition.

A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives

With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.

Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems

Protection systems are one of the most critical components in the transmission system and are becoming more digital with ongoing automation. These digital systems are prone to vulnerabilities/attacks, and exploitation of these vulnerabilities may cause major impacts on the electric grid performance. Multiple alarms reported in the control center could be a result of the faults (expected operations) or failures in the protection system (anomalies/ unexpected operation). Situational awareness gained through sensors such as a phasor measurement unit (PMU) and data acquired through the cyber system provide an opportunity to develop continuous cyber-physical monitoring of the system. Note that relay data are not reported in the control center continuously. This paper presents a cyber-physical data analytics based technique to monitor transmission protection system and detect malicious activity. Initially, continuous monitoring of PMU data is utilized for data anomaly detection, which includes bad or missing data using long short-term memory (LSTM). Then, PMU data of interest are utilized for failure diagnosis, using a semisupervised deep autoencoder model. In this research, cyber anomalies are modeled by manipulating the setting/logic design of protective devices, and a ridge regression based classifier with a feature engineering pipeline is used to detect cyber anomalies. The results from the deep autoencoder model and ridge regression based classifier are then utilized for detailed investigation to find the root causes of the observed events assisted by the cyber log data from the protection devices. The algorithm is validated using a real-time simulation of the IEEE test system with industrial hardware relays and PMUs in the loop. Data analytics algorithm running on server utilizes these real-time data continuously for anomaly detection and classification for the developed use cases.

Askeri Personel İçin Akıllı Kıyafet Tasarımı

In this study, a system was established to provide interactive communication among the 9 military personnel through the equipment installed in their bodies. This communication is also provided so that it can be viewed by a specific central base and intervened if necessary. Interpretation of the realized cyber-physical system data is aimed at preventing the existing problems.

An ICT-enabled Product Service System for Reuse of Building Components

Abstract The paper outlines the development of a prototype cyber-physical data exchange system that connects RFID, BIM and the Cloud-based data platform by which reusable building components may be identified, tracked and managed. The digital platform is then adapted to support an ongoing Product-Service System (PSS) relationship between suppliers/providers and users/clients. This enables advanced services for reuse of building components at the highest value and with mutual benefits. Via an integration of the ICT-based data management and PSS, the types of life cycle, performance, cost and other data required for service relationships can be identified so that suppliers may manage building components over their extended life, retain their optimum value and find new profit centres. Clients can be assured that the quality and performance of reused components meet required service expectations, coupled with savings in cost and carbon. An example of internal glazed system is presented to demonstrate the application of the proposed method.

Extracting entity-based information in cyber-physical systems

This article proposes a framework [Formula: see text] for evaluating entity extraction problem over cyber-physical system data. As known by us, this article is the first work focusing on this problem, which is an important step for identifying entities in cyber-physical system data. Directed by the initial entities, utilizing the relaxation and verification techniques, [Formula: see text] provides a path expression–based solution for entity extraction problem, which has following characteristics. First, using path expressions, [Formula: see text] provides a condensed presentation for entity locations whose size may get very large when scaling up the data size. Second, requiring only one entity example to indicate the interests, using relaxation technique, [Formula: see text] can discover other similar entities automatically. Third, by adjusting the example given to [Formula: see text], users can specify their own interesting entities and control the entities discovered by [Formula: see text]. Besides, utilizing the idea of sharing computations, by extending previous automaton techniques, an efficient implementation of [Formula: see text] is provided. Experimental results are reported, which show that [Formula: see text] can provide an effective and efficient solution to the entity extraction problem.

Digital twin-driven product design, manufacturing and service with big data

Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.

Modeling and Analysis of the Thermal Properties Exhibited by Cyberphysical Data Centers

Data centers (DCs) contribute toward the prevalent application and adoption of the cloud by providing architectural and operational foundation. To perform sustainable computation and storage, a DC is equipped with tens of thousands of servers, if not more. It is worth noting that the operational cost of a DC is being dominated by the cost spent on energy consumption. In this paper, we model a DC as a cyberphysical system (CPS) to capture the thermal properties exhibited by the DC. All software aspects, such as scheduling, load balancing, and all the computations performed by the devices, are considered the “cyber” component. The supported infrastructure, such as servers and switches, are modeled as the “physical” component of the CPS. We perform detailed modeling of the thermal characteristics displayed by the major components of the CPS. Moreover, we propose a thermal-aware control strategy that uses a high-level centralized controller and a low-level centralized controller to manage and control the thermal status of the cyber components at different levels. Our proposed strategy is testified and demonstrated by executing on a real DC workload and comparing it with three existing strategies, i.e., one classical and two thermal-aware strategies. Furthermore, we also perform formal modeling, analysis, and verification of the strategies using high-level Petri nets, the Z language, the Satisfiability Modulo Theories Library (SMT-Lib), and the Z3 solver.