Benchmarking heuristic search and optimisation algorithms in Matlab

With the proliferating development of heuristic methods, it has become challenging to choose the most suitable ones for an application at hand. This paper evaluates the performance of these algorithms available in Matlab, as it is problem dependent and parameter sensitive. Further, the paper attempts to address the challenge that there exists no satisfied benchmarks to evaluation all the algorithms at the same standard. The paper tests five heuristic algorithms in Matlab, the Nelder-Mead simplex search, the Genetic Algorithm, the Genetic Algorithm with elitism, Simulated Annealing and Particle Swarm Optimization, with four widely adopted benchmark problems. The Genetic Algorithm has an overall best performance at optimality and accuracy, while PSO has fast convergence speed when facing unimodal problem

An intelligent intrusion detection system for external communications in autonomous vehicles

Advancements in computing, electronics and mechanical systems have resulted in the creation of a new class of vehicles called autonomous vehicles. These vehicles function using sensory input with an on-board computation system. Self-driving vehicles use an ad hoc vehicular network called VANET. The network has ad hoc infrastructure with mobile vehicles that communicate through open wireless channels. This thesis studies the design and implementation of a novel intelligent intrusion detection system which secures the external communication of self-driving vehicles. This thesis makes the following four contributions: It proposes a hybrid intrusion detection system to protect the external communication in self-driving vehicles from potential attacks. This has been achieved using fuzzification and artificial intelligence. The second contribution is the incorporation of the Integrated Circuit Metrics (ICMetrics) for improved security and privacy. By using the ICMetrics, specific device features have been used to create a unique identity for vehicles. Our work is based on using the bias in on board sensory systems to create ICMetrics for self-driving vehicles. The incorporation of fuzzy petri net in autonomous vehicles is the third contribution of the thesis. Simulation results show that the scheme can successfully detect denial-of-service attacks. The design of a clustering based hierarchical detection system has also been presented to detect worm hole and Sybil attacks. The final contribution of this research is an integrated intrusion detection system which detects various attacks by using a central database in BusNet. The proposed schemes have been simulated using the data extracted from trace files. Simulation results have been compared and studied for high levels of detection capability and performance. Analysis shows that the proposed schemes provide high detection rate with a low rate of false alarm. The system can detect various attacks in an optimised way owing to a reduction in the number of features, fuzzification

Absolute calibration of radiometric partial discharge sensors for insulation condition monitoring in electrical substations

Measurement of partial discharge (PD) is an important tool in the monitoring of insulation integrity in high voltage (HV) equipment. Partial discharge is measured traditionally using galvanic contact techniques based on IEC 60270 standard or near field coupling [1]. Freespace radiometric (FSR) detection of PD is a relatively new technique. This work advances calibration method for FSR measurements and proposer a methodology for FSR measurement of absolute PD intensity. Until now, it has been believed that absolute measurement of partial discharge intensity using radiometric method is not possible. In this thesis it is demonstrated that such measurement is possible and the first ever such absolute measurements are presented. Partial discharge sources have been specially constructed. These included a floating electrode PD emulator, an acrylic cylinder internal PD emulator and an epoxy dielectric internal PD emulator. Radiated signals are captured using a wideband biconical antenna [1]. Free-space radiometric and galvanic contact measurement techniques are compared. Discharge pulse shape and PD characteristics under high voltage DC and AC conditions are obtained. A comparison shows greater similarity between the two measurements than was expected. It is inferred that the dominant mechanism in shaping the spectrum is the band-limiting effect of the radiating structure rather than band limiting by the receiving antenna. The cumulative energies of PD pulses in both time and frequency domains are also considered [2]. The frequency spectrum is obtained by FFT analysis of time-domain pulses. The relative spectral densities in the frequency bands 50 MHz – 290 MHz, 290 MHz – 470 MHz and 470 MHz – 800 MHz are determined. The calibration of the PD sources for used in the development of Wireless Sensor Network (WSN) is presented. A method of estimating absolute PD activity level from a radiometric measurement by relating effective radiated power (ERP) to PD intensity using a PD calibration device is proposed and demonstrated. The PD sources have been simulated using CST Microwave Studio. The simulations are used to establish a relationship between radiated PD signals and PD intensity as defined by apparent charge transfer. To this end, the radiated fields predicted in the simulations are compared with measurements. There is sufficient agreement between simulations and measurements to suggest the simulations could be used to investigate the relationship between PD intensity and the field strength of radiated signals [3]

Predicting potential customer needs and wants for agile design and manufacture in an industry 4.0 environment

Manufacturing is currently experiencing a paradigm shift in the way that products are designed, produced and serviced. Such changes are brought about mainly by the extensive use of the Internet and digital technologies. As a result of this shift, a new industrial revolution is emerging, termed “Industry 4.0” (i4), which promises to accommodate mass customisation at a mass production cost. For i4 to become a reality, however, multiple challenges need to be addressed, highlighting the need for design for agile manufacturing and, for this, a framework capable of integrating big data analytics arising from the service end, business informatics through the manufacturing process, and artificial intelligence (AI) for the entire manufacturing value chain. This thesis attempts to address these issues, with a focus on the need for design for agile manufacturing. First, the state of the art in this field of research is reviewed on combining cutting-edge technologies in digital manufacturing with big data analysed to support agile manufacturing. Then, the work is focused on developing an AI-based framework to address one of the customisation issues in smart design and agile manufacturing, that is, prediction of potential customer needs and wants. With this framework, an AI-based approach is developed to predict design attributes that would help manufacturers to decide the best virtual designs to meet emerging customer needs and wants predictively. In particular, various machine learning approaches are developed to help explain at least 85% of the design variance when building a model to predict potential customer needs and wants. These approaches include k-means clustering, self-organizing maps, fuzzy k-means clustering, and decision trees, all supporting a vector machine to evaluate and extract conscious and subconscious customer needs and wants. A model capable of accurately predicting customer needs and wants for at least 85% of classified design attributes is thus obtained. Further, an analysis capable of determining the best design attributes and features for predicting customer needs and wants is also achieved. As the information analysed can be utilized to advise the selection of desired attributes, it is fed back in a closed-loop of the manufacturing value chain: design → manufacture → management/service → → → design... For this, a total of 4 case studies are undertaken to test and demonstrate the efficacy and effectiveness of the framework developed. These case studies include: 1) an evaluation model of consumer cars with multiple attributes including categorical and numerical ones; 2) specifications of automotive vehicles in terms of various characteristics including categorical and numerical instances; 3) fuel consumptions of various car models and makes, taking into account a desire for low fuel costs and low CO2 emissions; and 4) computer parts design for recommending the best design attributes when buying a computer. The results show that the decision trees, as a machine learning approach, work best in predicting customer needs and wants for smart design. With the tested framework and methodology, this thesis overall presents a holistic attempt to addressing the missing gap between manufacture and customisation, that is meeting customer needs and wants. Effective ways of achieving customization for i4 and smart manufacturing are identified. This is achieved through predicting potential customer needs and wants and applying the prediction at the product design stage for agile manufacturing to meet individual requirements at a mass production cost. Such agility is one key element in realising Industry 4.0. At the end, this thesis contributes to improving the process of analysing the data to predict potential customer needs and wants to be used as inputs to customizing product designs agilely

New multiphase flow measurements for slug control.

Severe slug flow is undesirable in offshore oil production systems, particularly for late-life fields. Active control through choking is one of the effective approaches to mitigating/controlling severe slug flow in oil production pipeline-riser systems. However, existing active slug control systems may limit oil production due to overchoking. Another problem in most active control systems is their dependency on information obtained from subsea measurements such as riser base pressure for active slug flow control. Both of these control challenges have been satisfactorily solved through the introduction of new multiphase flow topside measurements that are reliable and efficient in providing flow information for active slug control systems. By using Venturi multiphase flow topside measurements and Doppler ultrasonic measurements, an active slug flow control system is proposed to suppress severe slug flows without limiting oil production. Experimental and simulated results demonstrate that under active slug control, the proposed system is able not only to suppress slug flow but also to increase oil production compared to manual choking. Another objective of this research was to assess the applicability of continuous-wave Doppler ultrasonic (CWDU) techniques for accurate identification of gas-liquid flow regimes in pipeline-riser systems. Firstly, flow regime classification using the kernel multi-class support-vector machine (SVM) approach from machine learning (ML) was investigated. For a successful industrial application of this approach, the feasibility of conducting principal component analysis (PCA) for visualising the information from intrinsic flow regime features in two-dimensional space was also investigated. The classifier attained 84.6% accuracy on test samples and 85.7% accuracy on training samples. This approach showed the success of the CWDU, PCA-SVM, and virtual flow regime maps for objective two-phase flow regime classification on pipeline-riser systems, which would be possible for industrial application. Secondly, an approach that classifies the flow regime by means of a neural network operating on extracted features from the flow’s ultrasonic signals using either discrete wavelet transform (DWT) or power spectral density (PSD) was proposed. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thereby showing the superiority of the DWT in feature extraction of flow regime classification. This approach demonstrates the employment of a neural network and DWT for flow regime identification in industrial applications, using CWDU. The scheme has significant advantages over other techniques in that it uses a non-radioactive and non-intrusive sensor. The two investigated methods for gas-liquid two-phase flow regime identification appear to be the first known successful attempts to objectively identify gas-liquid flow regimes in an S-shape riser using CWDU. The CWDU approaches for flow regime classification on pipeline-riser systems were successful and proved possible in industrial applications.PhD in Energy and Powe

Multiphase flow instability and active slug control solutions.

Slugging as a flow assurance challenge is an upsetting condition to the oil and gas industry due to the instabilities it poses on the system. The negative repercussions associated with slug flow stems from the inlet through to the topside facilities where processing is done. Active control has been established as one of the best techniques to eradicate slug and its accompanying challenges however the controller robustness and some setbacks make improvement a necessity. In that vein, the Inferential slug controller which uses a combination of topside measurement signals to produce a single control variable which is more sensitive to slug variations hence can effectively be used to control slug, was invented. Again the robustness of this controller has been in question. This study presents a comprehensive and systematic analysis of the Inferential slug controller design for system stability analysis and maximising throughput from unstable riser pipeline system configurations in the quest to advance this technology. The inferential slug controller’s robustness was assessed by implementing this technique on several pipeline riser systems including U-shape and S-shape riser configurations. Prior to that, the flow behaviour for a wide range of flow conditions was investigated, highlighting the impact of geometry on unstable slug flow through the OLGA flow simulator (modelling) and experiments. New and unused measurement signals from the topside of either the riser/platforms were deployed in the inferential slug control technology to make the controller more sensitive and robust. A simplistic nevertheless robust procedure for designing the inferential slug controller was proposed. Unstable slug flow conditions were observed to stabilise at a relatively larger valve opening compared with that seen in open loop. The inferential slug controller technology is further extended to deal with systems with variable time delay using a proposed modified Smith predictor model. The modified Smith predictor was recorded to improve and stabilise a pipeline riser system which has deteriorated in control performance due to time delay in the system, a resultant of large stroke time in the valve. This in practicality means an increased production through the system. In advancing the ISC technology to be deployed on offshore fields in conjunction with other passive slug mitigation techniques, the slug mitigation potential of pseudo spiral tube (PST) was assessed when installed at the topside of the riser system. The analysis showed that the PST pipe section (spiral and wavy piece) when installed at the topside of the riser system, possesses some mitigation potential. Four different slug regions was identified for the entire pipeline system. The first region being a slug flow occurrence in the system with and without the PST whiles the second region is the region where slugging occurs in the system but disappears when coupled with the PST and the opposite describes the third region. Lastly, the fourth region is described as that region where slugging flow exist for the system coupled with the spiral pipe section and without any PST (plain) but slugging flow disappears when the system is coupled with the wavy pipe section. The wavy or spiral pipe section coupled with the S-shape riser system have slug mitigation capabilities when they are installed at the top of the riser although its effectiveness of slug mitigation depends on the flow condition. This is evident in the significant reduction in the riserbase pressure oscillation magnitude and the significant reduction in the slug envelope (region) when the system was coupled with the wavy or spiral pipe section relative to the plain system.PhD in Energy and Powe

Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

Journal of Telecommunications and Information Technology, 2019, nr 2

kwartalni

Neolithic land-use in the Dutch wetlands: estimating the land-use implications of resource exploitation strategies in the Middle Swifterbant Culture (4600-3900 BCE)

The Dutch wetlands witness the gradual adoption of Neolithic novelties by foraging societies during the Swifterbant period. Recent analyses provide new insights into the subsistence palette of Middle Swifterbant societies. Small-scale livestock herding and cultivation are in evidence at this time, but their importance if unclear. Within the framework of PAGES Land-use at 6000BP project, we aim to translate the information on resource exploitation into information on land-use that can be incorporated into global climate modelling efforts, with attention for the importance of agriculture. A reconstruction of patterns of resource exploitation and their land-use dimensions is complicated by methodological issues in comparing the results of varied recent investigations. Analyses of organic residues in ceramics have attested to the cooking of aquatic foods, ruminant meat, porcine meat, as well as rare cases of dairy. In terms of vegetative matter, some ceramics exclusively yielded evidence of wild plants, while others preserve cereal remains. Elevated δ15N values of human were interpreted as demonstrating an important aquatic component of the diet well into the 4th millennium BC. Yet recent assays on livestock remains suggest grazing on salt marshes partly accounts for the human values. Finally, renewed archaeozoological investigations have shown the early presence of domestic animals to be more limited than previously thought. We discuss the relative importance of exploited resources to produce a best-fit interpretation of changing patterns of land-use during the Middle Swifterbant phase. Our review combines recent archaeological data with wider data on anthropogenic influence on the landscape. Combining the results of plant macroremains, information from pollen cores about vegetation development, the structure of faunal assemblages, and finds of arable fields and dairy residue, we suggest the most parsimonious interpretation is one of a limited land-use footprint of cultivation and livestock keeping in Dutch wetlands between 4600 and 3900 BCE.NWOVidi 276-60-004Human Origin

Taphonomy, environment or human plant exploitation strategies?: Deciphering changes in Pleistocene-Holocene plant representation at Umhlatuzana rockshelter, South Africa

The period between ~40 and 20 ka BP encompassing the Middle Stone Age (MSA) and Later Stone Age (LSA) transition has long been of interest because of the associated technological change. Understanding this transition in southern Africa is complicated by the paucity of archaeological sites that span this period. With its occupation sequence spanning the last ~70,000 years, Umhlatuzana Rock Shelter is one of the few sites that record this transition. Umhlatuzana thus offers a great opportunity to study past environmental dynamics from the Late Pleistocene (MIS 4) to the Late Holocene, and past human subsistence strategies, their social organisation, technological and symbolic innovations. Although organic preservation is poor (bones, seeds, and charcoal) at the site, silica phytoliths preserve generally well throughout the sequence. These microscopic silica particles can identify different plant types that are no longer visible at the site because of decomposition or burning to a reliable taxonomical level. Thus, to trace site occupation, plant resource use, and in turn reconstruct past vegetation, we applied phytolith analyses to sediment samples of the newly excavated Umhlatuzana sequence. We present results of the phytolith assemblage variability to determine change in plant use from the Pleistocene to the Holocene and discuss them in relation to taphonomical processes and human plant gathering strategies and activities. This study ultimately seeks to provide a palaeoenvironmental context for modes of occupation and will shed light on past human-environmental interactions in eastern South Africa.NWOVidi 276-60-004Human Origin