9,045 research outputs found
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures
In this work we present a non-parametric online market regime detection
method for multidimensional data structures using a path-wise two-sample test
derived from a maximum mean discrepancy-based similarity metric on path space
that uses rough path signatures as a feature map. The latter similarity metric
has been developed and applied as a discriminator in recent generative models
for small data environments, and has been optimised here to the setting where
the size of new incoming data is particularly small, for faster reactivity.
On the same principles, we also present a path-wise method for regime
clustering which extends our previous work. The presented regime clustering
techniques were designed as ex-ante market analysis tools that can identify
periods of approximatively similar market activity, but the new results also
apply to path-wise, high dimensional-, and to non-Markovian settings as well as
to data structures that exhibit autocorrelation.
We demonstrate our clustering tools on easily verifiable synthetic datasets
of increasing complexity, and also show how the outlined regime detection
techniques can be used as fast on-line automatic regime change detectors or as
outlier detection tools, including a fully automated pipeline. Finally, we
apply the fine-tuned algorithms to real-world historical data including
high-dimensional baskets of equities and the recent price evolution of crypto
assets, and we show that our methodology swiftly and accurately indicated
historical periods of market turmoil.Comment: 65 pages, 52 figure
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
A review of abnormal behavior detection in activities of daily living
Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend
Customer Segmentation: An application to dental medicine patients
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceCustomer segmentation allows to divide a company’s customers into multiple market segments, enabling the development of customized marketing actions based on each segment’s characteristics. This work describes the application of a customer segmentation approach to the patients of a Portuguese dental company. The approach taken to select the feature subset for the final model was mostly based on the LRFM (length, recency, frequency, and monetary) model, and the monetary variable was split into multiple variables according to the treatment category where the amount was spent. K-Means and Self-organizing maps were used to cluster the company’s patients using these variables, and the results returned by both algorithms are compared. The final solution was obtained with K-Means, and 7 clusters of patients were identified. An overview of the 7 clusters is provided, and possible marketing actions are suggested based on their main characteristics. The results allowed the company to understand how its turnover was distributed across segments, and to develop an initiative to contact the patients belonging to a segment where most of them did not have an appointment in one of the company’s clinics for a long time
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
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Co-teaching and the Development of Pupil Identity in the Bilingual Primary Classroom: A case study of a Hong Kong School
This research study explores pupil experiences of co-teaching within a bilingual international primary school in Hong Kong. The majority of pupils are first language Cantonese speakers, studying in an English and Putonghua bilingual programme. Within Hong Kong, the historical context and political situation following the change of sovereignty, has shaped educational policy and views about valued languages, shaping the practices of the school and therefore pupil experiences of language learning. The school supports pupil language development through a bilingual programme (one teacher, one language), supported by co-teaching (in English and Putonghua). The aims of the study were to understand how co-teaching is understood and enacted by co-teachers to support pupil language learning identities.
Two co-taught classrooms were the focus of the research (one in Year 3 (Y3) and one in Year 5 (Y5)). Research data for this case study were collected through formal classroom observations over a five-month period, pupil interviews (three from each class) and interviews with the two co-teachers of each class. A broad sample of pupil language experiences was collected as part of a questionnaire to all pupils in Y3 and Y5. Questionnaire data served as an opportunity for data triangulation and rich, detailed analysis. Qualitative data analysis consisted of open coding to find emerging themes, these themes then becoming the focus of the discussion.
The findings of the study revealed that enactment of co-teaching is shaped by teacher perceptions of their classroom roles. An identity of a co-teacher, for example, was found to facilitate mutual understanding and respect for shared practices that further supported pupil language development. Co-teaching enactment, perceived teacher language valuations and English language dominance within the classroom were all found to shape pupil language learning identities. The study also found that the co-teaching model, Team Teaching, was effective in teaching and learning because of the opportunities for pupil translanguaging and because it positions the classroom languages with equal status, supporting pupil investment and motivation to learn. Since co-teaching model effectiveness was not the focus of this study, it is recommended as an area of further research
A Secure and Privacy-Preserving E-Government Framework using Blockchain and Artificial Immunity
Electronic Government (e-Government) systems constantly provide greater services to people, businesses, organisations, and societies by offering more information, opportunities, and platforms with the support of advances in information and communications technologies. This usually results in increased system complexity and sensitivity, necessitating stricter security and privacy-protection measures. The majority of the existing e-Government systems are centralised, making them vulnerable to privacy and security threats, in addition to suffering from a single point of failure. This study proposes a decentralised e-Government framework with integrated threat detection features to address the aforementioned challenges. In particular, the privacy and security of the proposed e-Government system are realised by the encryption, validation, and immutable mechanisms provided by Blockchain. The insider and external threats associated with blockchain transactions are minimised by the employment of an artificial immune system, which effectively protects the integrity of the Blockchain. The proposed e-Government system was validated and evaluated by using the framework of Ethereum Visualisations of Interactive, Blockchain, Extended Simulations (i.e. eVIBES simulator) with two publicly available datasets. The experimental results show the efficacy of the proposed framework in that it can mitigate insider and external threats in e-Government systems whilst simultaneously preserving the privacy of information
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
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