The Application of Mixed Reality Within Civil Nuclear Manufacturing and Operational Environments

This thesis documents the design and application of Mixed Reality (MR) within a nuclear manufacturing cell through the creation of a Digitally Assisted Assembly Cell (DAAC). The DAAC is a proof of concept system, combining full body tracking within a room sized environment and bi-directional feedback mechanism to allow communication between users within the Virtual Environment (VE) and a manufacturing cell. This allows for training, remote assistance, delivery of work instructions, and data capture within a manufacturing cell. The research underpinning the DAAC encompasses four main areas; the nuclear industry, Virtual Reality (VR) and MR technology, MR within manufacturing, and finally the 4 th Industrial Revolution (IR4.0). Using an array of Kinect sensors, the DAAC was designed to capture user movements within a real manufacturing cell, which can be transferred in real time to a VE, creating a digital twin of the real cell. Users can interact with each other via digital assets and laser pointers projected into the cell, accompanied by a built-in Voice over Internet Protocol (VoIP) system. This allows for the capture of implicit knowledge from operators within the real manufacturing cell, as well as transfer of that knowledge to future operators. Additionally, users can connect to the VE from anywhere in the world. In this way, experts are able to communicate with the users in the real manufacturing cell and assist with their training. The human tracking data fills an identified gap in the IR4.0 network of Cyber Physical System (CPS), and could allow for future optimisations within manufacturing systems, Material Resource Planning (MRP) and Enterprise Resource Planning (ERP). This project is a demonstration of how MR could prove valuable within nuclear manufacture. The DAAC is designed to be low cost. It is hoped this will allow for its use by groups who have traditionally been priced out of MR technology. This could help Small to Medium Enterprises (SMEs) close the double digital divide between themselves and larger global corporations. For larger corporations it offers the benefit of being low cost, and, is consequently, easier to roll out across the value chain. Skills developed in one area can also be transferred to others across the internet, as users from one manufacturing cell can watch and communicate with those in another. However, as a proof of concept, the DAAC is at Technology Readiness Level (TRL) five or six and, prior to its wider application, further testing is required to asses and improve the technology. The work was patented in both the UK (S. R EDDISH et al., 2017a), the US (S. R EDDISH et al., 2017b) and China (S. R EDDISH et al., 2017c). The patents are owned by Rolls-Royce and cover the methods of bi-directional feedback from which users can interact from the digital to the real and vice versa. Stephen Reddish Mixed Mode Realities in Nuclear Manufacturing Key words: Mixed Mode Reality, Virtual Reality, Augmented Reality, Nuclear, Manufacture, Digital Twin, Cyber Physical Syste

A hybrid method for haptic feedback to support manual virtual product assembly

The purpose of this research is to develop methods to support manual virtual assembly using haptic (force) feedback in a virtual environment. The results of this research will be used in an engineering framework for assembly simulation, training, and maintenance. The key research challenge is to advance the ability of users to assemble complex, low clearance CAD parts as they exist digitally without the need to create expensive physical prototypes. The proposed method consists of a Virtual Reality (VR) system that combines voxel collision detection and boundary representation methods into a hybrid algorithm containing the necessary information for both force feedback and constraint recognition. The key to this approach will be successfully developing the data structure and logic needed to switch between collision detection and constraint recognition while maintaining a haptic refresh rate of 1000 Hz. VR is a set of unique technologies that support human-centered computer interaction. Experience with current VR systems that simulate low clearance assembly operations with haptic feedback indicate that such systems are highly desirable tools in the evaluation of preliminary designs, as well as virtual training and maintenance processes. This work will result in a novel interface for assembly methods prototyping, and an interface that will allow intuitive interaction with parts based on a powerful combination of analytical, visual and haptic tools

Cloud intrusion detection systems: fuzzy logic and classifications

Cloud Computing (CC), as defned by national Institute of Standards and Technology (NIST), is a new technology model for enabling convenient, on-demand network access to a shared pool of configurable computing resources such as networks, servers, storage, applications, and services that can be rapidly provisioned and released with minimal management effort or service-provider interaction. CC is a fast growing field; yet, there are major concerns regarding the detection of security threats, which in turn have urged experts to explore solutions to improve its security performance through conventional approaches, such as, Intrusion Detection System (IDS). In the literature, there are two most successful current IDS tools that are used worldwide: Snort and Suricata; however, these tools are not flexible to the uncertainty of intrusions. The aim of this study is to explore novel approaches to uplift the CC security performance using Type-1 fuzzy logic (T1FL) technique with IDS when compared to IDS alone. All experiments in this thesis were performed within a virtual cloud that was built within an experimental environment. By combining fuzzy logic technique (FL System) with IDSs, namely SnortIDS and SuricataIDS, SnortIDS and SuricataIDS for detection systems were used twice (with and without FL) to create four detection systems (FL-SnortIDS, FL-SuricataIDS, SnortIDS, and SuricataIDS) using Intrusion Detection Evaluation Dataset (namely ISCX). ISCX comprised two types of traffic (normal and threats); the latter was classified into four classes including Denial of Service, User-to-Root, Root-to-Local, and Probing. Sensitivity, specificity, accuracy, false alarms and detection rate were compared among the four detection systems. Then, Fuzzy Intrusion Detection System model was designed (namely FIDSCC) in CC based on the results of the aforementioned four detection systems. The FIDSCC model comprised of two individual systems pre-and-post threat detecting systems (pre-TDS and post-TDS). The pre-TDS was designed based on the number of threats in the aforementioned classes to assess the detection rate (DR). Based on the output of this DR and false positives of the four detection systems, the post-TDS was designed in order to assess CC security performance. To assure the validity of the results, classifier algorithms (CAs) were introduced to each of the four detection systems and four threat classes for further comparison. The classifier algorithms were OneR, Naive Bayes, Decision Tree (DT), and K-nearest neighbour. The comparison was made based on specific measures including accuracy, incorrect classified instances, mean absolute error, false positive rate, precision, recall, and ROC area. The empirical results showed that FL-SnortIDS was superior to FL-SuricataIDS, SnortIDS, and SuricataIDS in terms of sensitivity. However, insignificant difference was found in specificity, false alarms and accuracy among the four detection systems. Furthermore, among the four CAs, the combination of FL-SnortIDS and DT was shown to be the best detection method. The results of these studies showed that FIDSCC model can provide a better alternative to detecting threats and reducing the false positive rates more than the other conventional approaches

5G-PPP Software Network Working Group:Network Applications: Opening up 5G and beyond networks 5G-PPP projects analysis, Version 2

It is expected that the communication fabric and the way network services are consumed will evolve towards 6G, building on and extending capabilities of 5G and Beyond networks. Service APIs, Operation APIs, Network APIs are different aspects of the network exposure, which provides the communication service providers a way to monetize the network capabilities. Allowing the developer community to use network capabilities via APIs is an emerging area for network monetization. Thus, it is important that network exposure caters for the needs of developers serving different markets, e.g., different vertical industry segments. The concept of “Network Applications” is introduced following this idea. It is defined as a set of services that provides certain functionalities to verticals and their associated use cases. The Network Applications is more than the introduction of new vertical applications that have interaction capabilities. It refers to the need for a separate middleware layer to simplify the implementation and deployment of vertical systems on a large scale. Specifically, third parties or network operators can contribute to Network Applications, depending on the level of interaction and trust. In practice, a Network Application uses the exposed APIs from the network and can either be integrated with (part of) a vertical application or expose its APIs (e.g., service APIs) for further consumption by vertical applications. This paper builds on the findings of the white paper released in 2022. It targets to go into details about the implementations of the two major Network Applications class: “aaS” and hybrid models. It introduces the Network Applications marketplace and put the light on technological solution like CAMARA project, as part of the standard landscape. <br/

Cloud intrusion detection systems: fuzzy logic and classifications

Cloud Computing (CC), as defned by national Institute of Standards and Technology (NIST), is a new technology model for enabling convenient, on-demand network access to a shared pool of configurable computing resources such as networks, servers, storage, applications, and services that can be rapidly provisioned and released with minimal management effort or service-provider interaction. CC is a fast growing field; yet, there are major concerns regarding the detection of security threats, which in turn have urged experts to explore solutions to improve its security performance through conventional approaches, such as, Intrusion Detection System (IDS). In the literature, there are two most successful current IDS tools that are used worldwide: Snort and Suricata; however, these tools are not flexible to the uncertainty of intrusions. The aim of this study is to explore novel approaches to uplift the CC security performance using Type-1 fuzzy logic (T1FL) technique with IDS when compared to IDS alone. All experiments in this thesis were performed within a virtual cloud that was built within an experimental environment. By combining fuzzy logic technique (FL System) with IDSs, namely SnortIDS and SuricataIDS, SnortIDS and SuricataIDS for detection systems were used twice (with and without FL) to create four detection systems (FL-SnortIDS, FL-SuricataIDS, SnortIDS, and SuricataIDS) using Intrusion Detection Evaluation Dataset (namely ISCX). ISCX comprised two types of traffic (normal and threats); the latter was classified into four classes including Denial of Service, User-to-Root, Root-to-Local, and Probing. Sensitivity, specificity, accuracy, false alarms and detection rate were compared among the four detection systems. Then, Fuzzy Intrusion Detection System model was designed (namely FIDSCC) in CC based on the results of the aforementioned four detection systems. The FIDSCC model comprised of two individual systems pre-and-post threat detecting systems (pre-TDS and post-TDS). The pre-TDS was designed based on the number of threats in the aforementioned classes to assess the detection rate (DR). Based on the output of this DR and false positives of the four detection systems, the post-TDS was designed in order to assess CC security performance. To assure the validity of the results, classifier algorithms (CAs) were introduced to each of the four detection systems and four threat classes for further comparison. The classifier algorithms were OneR, Naive Bayes, Decision Tree (DT), and K-nearest neighbour. The comparison was made based on specific measures including accuracy, incorrect classified instances, mean absolute error, false positive rate, precision, recall, and ROC area. The empirical results showed that FL-SnortIDS was superior to FL-SuricataIDS, SnortIDS, and SuricataIDS in terms of sensitivity. However, insignificant difference was found in specificity, false alarms and accuracy among the four detection systems. Furthermore, among the four CAs, the combination of FL-SnortIDS and DT was shown to be the best detection method. The results of these studies showed that FIDSCC model can provide a better alternative to detecting threats and reducing the false positive rates more than the other conventional approaches

Investigation of the Gap Between Traditional IP Network Security Management and the Adoption of Automation Techniques and Technologies to Network Security

Innenfor området nettverks- og nettverkssikkerhetsautomatisering avdekker denne oppgaven et komplekst landskap av utfordringer og muligheter. Forskningen som er gjennomført, belyser de intrikate elementene som er innebygd i automatiseringen av store IP-nettverk, noe som igjen fører til en transformasjon i perspektivene til organisatoriske ledere som har ansvaret for nettverksstyring. Den sentrale målsetningen med denne oppgaven er å undersøke og underbygge det eksisterende gapet i bruken av nettverksautomatiseringsteknikker og -teknologier innen nettverks- og nettverkssikkerhetssystemer. Det gir klarhet på flere dimensjoner innenfor det komplekse landskapet, og hjelper organisasjoner med beslutningstakingen når de vurderer løsninger for automatiseringsteknikker. For å oppnå dette målet, undersøker vi kritisk den samtidsorienterte litteraturen om nettverkssikkerhet og automatisering, og tilbyr en grundig gjennomgang av tradisjonelle nettverkssikkerhetsmetodikker, samt de potensielle automatiseringsteknikkene som er tilgjengelige for å forbedre nettverkssikkerheten. I tillegg analyserer vi faktorer som påvirker implementeringen av nettverkssikkerhetsautomatisering gjennom grundige undersøkelser og intervjuer. Våre funn understreker nettverksautomatiseringens mangfoldige natur og belyser det komplekse landskapet organisasjoner skal navigere når de vurderer automatiseringsløsninger. Ved å adressere gapet i dagens tilstand for nettverksautomatisering, bidrar vi til en dypere forståelse av dette stadig utviklende feltet. Valideringen av våre funn innebærer samhandling med to forskjellige grupper deltakere, akademiske fagpersoner innen IT-utdanning og erfarne IT-fagfolk som er ansvarlige for styring av omfattende nettverk. Deres innsikt, erfaringer, og forslag fungerer som en verdifull bekreftelse av vår forskning, og gir et helhetlig perspektiv på automatiseringen av nettverks- og nettverkssikkerhetsprosesser. Avslutningsvis fungerer vår forskning som en katalysator for endring innen feltet for nettverksautomatisering. Vi fremhever eksistensen av organisatoriske siloer og understreker viktigheten av å bryte ned disse barrierene for å oppnå det overordnede målet om å automatisere oppgaver innen nettverks- og nettverkssikkerhet. Vårt arbeid er i ferd med å øke bevisstheten blant interessenter og inspirere til meningsfulle endringer i nettverksstyringsparadigmer.In the realm of network and network security automation, this thesis unveils a complex landscape of challenges and opportunities. The research sheds light on the intricacies inherent in automating large IP networks, prompting a transformation in the perspectives of organizational leaders tasked with network management. The central objective of this thesis is to investigate and substantiate the existing gap in the application of network automation techniques and technologies within network and network security systems. It clarifies multiple dimensions within the complex landscape, assisting organizations in their decision-making when evaluating automation technique solutions. To achieve this goal, we critically examine the contemporary literature on network security and automation, offering a comprehensive review of traditional network security methodologies alongside the potential automation techniques available for enhancing network security. Additionally, we analyze factors influencing the adoption of network security automation through rigorous surveys and interviews. Our findings underscore the multifaceted nature of network automation, illuminating the complex landscape that organizations shall navigate when considering automation solutions. By addressing the gap in the current state of network automation, we contribute to a deeper understanding of this evolving field. The validation of our findings involves engaging with two distinct groups of participants, academic professionals in IT education and experienced IT professionals responsible for managing extensive networks. Their insights, experiences, and suggestions validate our research, providing a holistic perspective on the automation of network and network security processes. In conclusion, our research catalyzes change within the field of network automation. We highlight the existence of organizational silos and emphasize that breaking down these barriers is essential for achieving the overarching goal of automating network and network security tasks. Our work is poised to raise awareness among stakeholders and inspire meaningful shifts in network management paradigms

Topology optimization for additive manufacture

Additive manufacturing (AM) offers a way to manufacture highly complex designs with potentially enhanced performance as it is free from many of the constraints associated with traditional manufacturing. However, current design and optimisation tools, which were developed much earlier than AM, do not allow efficient exploration of AM's design space. Among these tools are a set of numerical methods/algorithms often used in the field of structural optimisation called topology optimisation (TO). These powerful techniques emerged in the 1980s and have since been used to achieve structural solutions with superior performance to those of other types of structural optimisation. However, such solutions are often constrained during optimisation to minimise structural complexities, thereby, ensuring that solutions can be manufactured via traditional manufacturing methods. With the advent of AM, it is necessary to restructure these techniques to maximise AM's capabilities. Such restructuring should involve identification and relaxation of the optimisation constraints within the TO algorithms that restrict design for AM. These constraints include the initial design, optimisation parameters and mesh characteristics of the optimisation problem being solved. A typical TO with certain mesh characteristics would involve the movement of an assumed initial design to another with improved structural performance. It was anticipated that the complexity and performance of a solution would be affected by the optimisation constraints. This work restructured a TO algorithm called the bidirectional evolutionary structural optimisation (BESO) for AM. MATLAB and MSC Nastran were coupled to study and investigate BESO for both two and three dimensional problems. It was observed that certain parametric values promote the realization of complex structures and this could be further enhanced by including an adaptive meshing strategy (AMS) in the TO. Such a strategy reduced the degrees of freedom initially required for this solution quality without the AMS

The value of personalised consumer product design facilitated through additive manufacturing technology

This research attempted to discover how Additive Manufacturing (AM) can best be used to increase the value of personalised consumer products and how designers can be assisted in finding an effective way to facilitate value addition within personalisable product designs. AM has become an enabler for end-users to become directly involved in product personalisation through the manipulation of three-dimensional (3D) designs of the product using easy-to-use design toolkits. In this way, end-users are able to fabricate their own personalised designs using various types of AM systems. Personalisation activity can contribute to an increment in the value of a product because it delivers a closer fit to user preferences. The research began with a literature review that covered the areas of product personalisation, additive manufacturing, and consumer value in product design. The literature review revealed that the lack of methods and tools to enable designers to exploit AM has become a fundamental challenge in fully realising the advantages of the technology. Consequently, the question remained as to whether industrial designers are able to identify the design characteristics that can potentially add value to a product, particularly when the product is being personalised by end-users using AM-enabled design tools and systems. A new value taxonomy was developed to capture the relevant value attributes of personalised AM products. The value taxonomy comprised two first-level value types: product value and experiential value. It was further expanded into six second-level value components: functional value, personal-expressive value, sensory value, unique value, co-design value, and hedonic value. The research employed a survey to assess end-users value reflection on personalised features; measuring their willingness to pay (WTP) and their intention to purchase a product with personalised features. Thereafter, an experimental study was performed to measure end-users opinions on the value of 3D-printed personalised products based on the two value types: product value and experiential value. Based on the findings, a formal added value identification method was developed to act as a design aid tool to assist designers in preparing a personalisable product design that embodies value-adding personalisation features within the product. The design method was translated into a beta-test version paper-based design workbook known as the V+APP Design Method: Design Workbook. The design aid tool was validated by expert designers. In conclusion, this research has indicated that the added value identification method shows promise as a practical and effective method in aiding expert designers to identify the potential value-adding personalisation features within personalisable AM products, ensuring they are able to fully exploit the unique characteristics and value-adding design characteristics enabled by AM. Finally, the limitations of the research have been explained and recommendations made for future work in this area