4,142 research outputs found

    A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models.

    Get PDF
    The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters

    Beam scanning by liquid-crystal biasing in a modified SIW structure

    Get PDF
    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

    Get PDF
    This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

    Image Diversification via Deep Learning based Generative Models

    Get PDF
    Machine learning driven pattern recognition from imagery such as object detection has been prevalenting among society due to the high demand for autonomy and the recent remarkable advances in such technology. The machine learning technologies acquire the abstraction of the existing data and enable inference of the pattern of the future inputs. However, such technologies require a sheer amount of images as a training dataset which well covers the distribution of the future inputs in order to predict the proper patterns whereas it is impracticable to prepare enough variety of images in many cases. To address this problem, this thesis pursues to discover the method to diversify image datasets for fully enabling the capability of machine learning driven applications. Focusing on the plausible image synthesis ability of generative models, we investigate a number of approaches to expand the variety of the output images using image-to-image translation, mixup and diffusion models along with the technique to enable a computation and training dataset efficient diffusion approach. First, we propose the combined use of unpaired image-to-image translation and mixup for data augmentation on limited non-visible imagery. Second, we propose diffusion image-to-image translation that generates greater quality images than other previous adversarial training based translation methods. Third, we propose a patch-wise and discrete conditional training of diffusion method enabling the reduction of the computation and the robustness on small training datasets. Subsequently, we discuss a remaining open challenge about evaluation and the direction of future work. Lastly, we make an overall conclusion after stating social impact of this research field

    An investigation into mild traumatic brain injury identification, management, and mitigation

    Get PDF
    Concussion is classified as a mild traumatic brain injury which can be induced by biomechanical forces such as a physical impact to the head or body, which results in a transient neurological disturbance without obvious structural brain damage. Immediate access to tools that can identify, diagnosis and manage concussion are wide ranging and can lack consistency in application. It is well documented that there are frequent incidences of concussion across amateur and professional sport such as popular contact sports like rugby union. A primary aim of this thesis was to establish the current modalities of ‘pitch side’ concussion management, identification, and diagnosis across amateur and professional sporting populations. Furthermore, the research sought to understand existing concussion management and concussion experiences by means of recording the player’s experiences and perceptions (retired professional rugby union players). These qualitative studies sought to gain insights into concussion experiences, the language used to discuss concussion and the duty of care which medical staff, coaching personnel, and club owners have towards professional rugby players in their employment. In addition, possible interventions to reduce the incidence of concussion in amateur and professional sports were investigated. These included a ‘proof of concept’ using inertial measurement units and a smartphone application, a tackle technique coaching app for amateur sports. Other research data investigating the use of neurological function data and neuromuscular fatigue in current professional rugby players as a novel means of monitoring injury risk were included in this research theme. The findings of these studies suggest that there is an established head injury assessment process for professional sports. However, in amateur sport settings, this is not the existing practice and may expose amateur players to an increased risk of post-concussion syndrome or early retirement. Many past professional rugby union players stated that they did not know the effects of cumulative repetitive head impacts. They discussed how they minimised and ignored repeated concussions due to peer pressure or pressure from coaches or their own internal pressures of maintaining a livelihood. These data suggest that players believed that strong willed medical staff, immutable to pressures from coaching staff or even athletes themselves, were essential for player welfare and that club owners have a long-term duty of care to retired professional rugby union players. However, there are anecdotal methods suggested to reduce concussion incidence. For example, neck strengthening techniques to mitigate against collision impacts. There is, no longitudinal evidence to suggest that neck strength can reduce the impacts of concussion in adult populations . Additionally, other factors such as lowering the tackle height in the professional and amateur game is currently being investigated as a mitigating factor to reduce head injury risk. The final theme of the thesis investigated possible methods to reduce injury incidence in amateur and professional athletes. The novel tackle technique platform could assist inexperienced amateur coaches on how to coach effective tackle technique to youth players. The findings from the neurological function data suggests that this may be an alternative way for coaches to assess and gather fatigue data on professional rugby union players alongside additional subjective measures and neuromuscular function data. Recently, the awareness of concussion as an injury and the recognition of concussion in many sports settings has improved. These incremental improvements have led to increased discussion regarding possible measures to mitigate the effects of concussion. There are many additional procedures to be implemented before a comprehensive concussion management is universally available, particularly in amateur and community sports. These necessary processes could be technological advances (e.g., using smart phone technology) for parents and amateur coaches to assist in the early identification of concussion or evidence-based concussion reduction strategies

    Tradition and Innovation in Construction Project Management

    Get PDF
    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Spatial-temporal domain charging optimization and charging scenario iteration for EV

    Get PDF
    Environmental problems have become increasingly serious around the world. With lower carbon emissions, Electric Vehicles (EVs) have been utilized on a large scale over the past few years. However, EVs are limited by battery capacity and require frequent charging. Currently, EVs suffer from long charging time and charging congestion. Therefore, EV charging optimization is vital to ensure drivers’ mobility. This study first presents a literature analysis of the current charging modes taxonomy to elucidate the advantages and disadvantages of different charging modes. In specific optimization, under plug-in charging mode, an Urgency First Charging (UFC) scheduling policy is proposed with collaborative optimization of the spatialtemporal domain. The UFC policy allows those EVs with charging urgency to get preempted charging services. As conventional plug-in charging mode is limited by the deployment of Charging Stations (CSs), this study further introduces and optimizes Vehicle-to-Vehicle (V2V) charging. This is aim to maximize the utilization of charging infrastructures and to balance the grid load. This proposed reservation-based V2V charging scheme optimizes pair matching of EVs based on minimized distance. Meanwhile, this V2V scheme allows more EVs get fully charged via minimized waiting time based parking lot allocation. Constrained by shortcomings (rigid location of CSs and slow charging power under V2V converters), a single charging mode can hardly meet a large number of parallel charging requests. Thus, this study further proposes a hybrid charging mode. This mode is to utilize the advantages of plug-in and V2V modes to alleviate the pressure on the grid. Finally, this study addresses the potential problems of EV charging with a view to further optimizing EV charging in subsequent studies

    Diagnosis, treatment, and control of bovine digital dermatitis in dairy cattle

    Get PDF
    This thesis focuses on the detection, prompt effective treatment, and control of digital dermatitis (DD) on dairy herds. In Chapter 2, an overview of the status quo on management of DD on dairy herds is given. In Chapter 3 we demonstrated that experienced scorers are well able to differentiate between photographs of feet affected by DD and photographs feet unaffected by DD. On the other hand, they were less able to identify specific lesion stages, including the M2 and M4 stage which are considered important stages regarding clinical impact and infection reservoir, respectively. In Chapter 4 we concluded that infrared thermography (IRT) is unlikely to be suited for automated identification of feet affected by DD due to the poor associations between maximum temperature of the plantar pastern region and the presence of M2 lesions or DD lesions in general. In Chapter 5 we demonstrated that topical treatment of active, often painful, DD lesions with a copper and zinc chelates gel (coppergel) and bandage outperformed an enzyme alginogel and bandage in M-score improvement, with the majority of lesions transitioning to the chronic, often not painful, state. In contrast, treatment with the alginogel achieved improved wound healing progress compared with the coppergel. However, neither the coppergel nor the alginogel achieved high cure rates to healthy skin. In Chapter 6 we illustrated that a standalone identification of risk factors for DD together with associated advice to control these risk factors is insufficient to decrease the prevalence of DD in dairy herds. Chapter 7 concludes this thesis with a general discussion providing a reflection on insights gained and suggestions for future research on the diagnosis, treatment, and control of digital dermatitis in dairy herds

    Radiation Response, Mechanical Property Changes, and Corrosion Behavior of Molten Salt Reactor Materials

    Get PDF
    Corrosion related failures pose risk to the integrity of routinely cycled and permanent reactor components long before radiation damage alone adversely impact reactor performance. Compared to Light Water Reactors (LWRs), Molten Salt Reactors (MSRs) have not enjoyed a history of continuous engineering development and refinement. Hastelloy N, a nickel superalloy developed at ORNL explicitly for molten fluoride salt conditions, and 316SS, a widely used austenitic alloy, are among the leading candidates for immediate deployment in MSR systems. Data collected during initial development of Hastelloy N suffered from limitations in available microscopy and spectroscopy techniques, obfuscating the role of radiation damage and mechanical stress in the microstructural evolution of the alloy. 316SS, considered a more economical alternative to the nickel superalloy, is restricted by lower corrosion resistance and strength at high temperature. The present work bridges some of the nuanced gaps in knowledge related to Hastelloy N microstructural evolution, as well as evaluating the feasibility of coating systems for enhanced corrosion resistance for 316SS. Hastelloy N was exposed to light ion irradiation, micromechanical testing, and immersion corrosion using FLiNaK molten salt after either irradiation or static strain mounting using the three-point bending technique. 316SS, either coated using a modified cathodic cage plasma nitriding technique or mounted under static strain, was exposed to heavy ion irradiation. Several evaluation techniques were used including scanning electron microscopy (SEM), electron dispersive x-ray spectroscopy (EDS), electron backscatter diffraction (EBSD), transmission electron microscopy (TEM), and micromechanical pillar compression testing. The results show that, low-dose irradiation and subsequent elemental segregation and embrittlement, as well as tensile mechanical stress loading, have a deleterious effect on the corrosion resistance of Hastelloy N. Nickel coating on 316SS is demonstrated as highly radiation tolerant. Combination of irradiation and the three-point bending technique demonstrates a feasible pathway for further evaluation of alloys and coating systems for MSR applications
    • …
    corecore