Structuring of Ranked Models

Prognostic procedures can be based on ranked linear models. Ranked regression type models are designed on the basis of feature vectors combined with set of relations defined on selected pairs of these vectors. Feature vectors are composed of numerical results of measurements on particular objects or events. Ranked relations defined on selected pairs of feature vectors represent additional knowledge and can reflect experts' opinion about considered objects. Ranked models have the form of linear transformations of feature vectors on a line which preserve a given set of relations in the best manner possible. Ranked models can be designed through the minimization of a special type of convex and piecewise linear (CPL) criterion functions. Some sets of ranked relations cannot be well represented by one ranked model. Decomposition of global model into a family of local ranked models could improve representation. A procedures of ranked models decomposition is described in this paper

Selected Works in Bioinformatics

This book consists of nine chapters covering a variety of bioinformatics subjects, ranging from database resources for protein allergens, unravelling genetic determinants of complex disorders, characterization and prediction of regulatory motifs, computational methods for identifying the best classifiers and key disease genes in large-scale transcriptomic and proteomic experiments, functional characterization of inherently unfolded proteins/regions, protein interaction networks and flexible protein-protein docking. The computational algorithms are in general presented in a way that is accessible to advanced undergraduate students, graduate students and researchers in molecular biology and genetics. The book should also serve as stepping stones for mathematicians, biostatisticians, and computational scientists to cross their academic boundaries into the dynamic and ever-expanding field of bioinformatics

Virtual inertia for suppressing voltage oscillations and stability mechanisms in DC microgrids

Renewable energy sources (RES) are gradually penetrating power systems through power electronic converters (PECs), which greatly change the structure and operation characteristics of traditional power systems. The maturation of PECs has also laid a technical foundation for the development of DC microgrids (DC-MGs). The advantages of DC-MGs over AC systems make them an important access target for RES. Due to the multi-timescale characteristics and fast response of power electronics, the dynamic coupling of PEC control systems and the transient interaction between the PEC and the passive network are inevitable, which threatens the stable operation of DC-MGs. Therefore, this dissertation focuses on the study of stabilization control methods, the low-frequency oscillation (LFO) mechanism analysis of DC-MGs and the state-of-charge (SoC) imbalance problem of multi-parallel energy storage systems (ESS). Firstly, a virtual inertia and damping control (VIDC) strategy is proposed to enable bidirectional DC converters (BiCs) to damp voltage oscillations by using the energy stored in ESS to emulate inertia without modifications to system hardware. Both the inertia part and the damping part are modeled in the VIDC controller by analogy with DC machines. Simulation results verify that the proposed VIDC can improve the dynamic characteristics and stability in islanded DC-MG. Then, inertia droop control (IDC) strategies are proposed for BiC of ESS based on the comparison between conventional droop control and VIDC. A feedback analytical method is presented to comprehend stability mechanisms from multi-viewpoints and observe the interaction between variables intuitively. A hardware in the loop (HIL) experiment verifies that IDC can simplify the control structure of VIDC in the promise of ensuring similar control performances. Subsequently, a multi-timescale impedance model is established to clarify the control principle of VIDC and the LFO mechanisms of VIDC-controlled DC-MG. Control loops of different timescales are visualized as independent loop virtual impedances (LVIs) to form an impedance circuit. The instability factors are revealed and a dynamic stability enhancement method is proposed to compensate for the negative damping caused by VIDC and CPL. Experimental results have validated the LFO mechanism analysis and stability enhancement method. Finally, an inertia-emulation-based cooperative control strategy for multi-parallel ESS is proposed to address the SoC imbalance and voltage deviation problem in steady-state operation and the voltage stability problem. The contradiction between SoC balancing speed and maintaining system stability is solved by a redefined SoC-based droop resistance function. HIL experiments prove that the proposed control performs better dynamics and static characteristics without modifying the hardware and can balance the SoC in both charge and discharge modes

Programming by Feedback

International audienceThis paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves one learning and one optimization components; the learning component estimates the user's utility function and accounts for the user's (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems

Optimisation of racing car suspensions featuring inerters

Racing car suspensions are a critical system in the overall performance of the vehicle. They must be able to accurately control ride dynamics as well as influencing the handling characteristics of the vehicle and providing stability under the action of external forces. This work is a research study on the design and optimisation of high performance vehicle suspensions using inerters. The starting point is a theoretical investigation of the dynamics of a system fitted with an ideal inerter. This sets the foundation for developing a more complex and novel vehicle suspension model incorporating real inerters. The accuracy and predictability of this model has been assessed and validated against experimental data from 4- post rig testing. In order to maximise overall vehicle performance, a race car suspension must meet a large number of conflicting objectives. Hence, suspension design and optimisation is a complex task where a compromised solution among a set of objectives needs to be adopted. The first task in this process is to define a set of performance based objective functions. The approach taken was to relate the ride dynamic behaviour of the suspension to the overall performance of the race car. The second task of the optimisation process is to develop an efficient and robust optimisation methodology. To address this, a multi-stage optimisation algorithm has been developed. The algorithm is based on two stages, a hybrid surrogate model based multiobjective evolutionary algorithm to obtain a set of non-dominated optimal suspension solutions and a transient lap-time simulation tool to incorporate external factors to the decision process and provide a final optimal solution. A transient lap-time simulation tool has been developed. The minimum time manoeuvring problem has been defined as an Optimal Control problem. A novel solution method based on a multi-level algorithm and a closed-loop driver steering control has been proposed to find the optimal lap time. The results obtained suggest that performance gains can be obtained by incorporating inerters into the suspension system. The work suggests that the use of inerters provides the car with an optimised aerodynamic platform and the overall stability of the vehicle is improved

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

The UVES Spectral Quasar Absorption Database (SQUAD) Data Release 1: The first 10 million seconds

We present the first data release (DR1) of the UVES Spectral Quasar Absorption Database (SQUAD), comprising 467 fully reduced, continuum-fitted high-resolution quasar spectra from the Ultraviolet and Visual Echelle Spectrograph (UVES) on the European Southern Observatory's Very Large Telescope. The quasars have redshifts $z=0$-5, and a total exposure time of 10 million seconds provides continuum-to-noise ratios of 4-342 (median 20) per 2.5-km/s pixel at 5500 \AA. The SQUAD spectra are fully reproducible from the raw, archival UVES exposures with open-source software, including our UVES_popler tool for combining multiple extracted echelle exposures which we document here. All processing steps are completely transparent and can be improved upon or modified for specific applications. A primary goal of SQUAD is to enable statistical studies of large quasar and absorber samples, and we provide tools and basic information to assist three broad scientific uses: studies of damped Lyman-$\alpha$ systems (DLAs), absorption-line surveys and time-variable absorption lines. For example, we provide a catalogue of 155 DLAs whose Lyman-$\alpha$ lines are covered by the DR1 spectra, 18 of which are reported for the first time. The HI column densities of these new DLAs are measured from the DR1 spectra. DR1 is publicly available and includes all reduced data and information to reproduce the final spectra.Comment: 21 pages, 18 figures. Accepted by MNRAS. All final quasar spectra, reduced contributing exposures, and supplementary material available via https://github.com/MTMurphy77/UVES_SQUAD_DR

Roster-Based Optimisation for Limited Overs Cricket

The objective of this research was to develop a roster-based optimisation system for limited overs cricket by deriving a meaningful, overall team rating using a combination of individual ratings from a playing eleven. The research hypothesis was that an adaptive rating system accounting for individual player abilities, outperforms systems that only consider macro variables such as home advantage, opposition strength and past team performances. The assessment of performance is observed through the prediction accuracy of future match outcomes. The expectation is that in elite sport, better teams are expected to win more often. To test the hypothesis, an adaptive rating system was developed. This framework was a combination of an optimisation system and an individual rating system. The adaptive rating system was selected due to its ability to update player and team ratings based on past performances. A Binary Integer Programming model was the optimisation method of choice, while a modified product weighted measure (PWM) with an embedded exponentially weighted moving average (EWMA) functionality was the adopted individual rating system. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process. The model constraints were objectively obtained by identifying the player’s role and performance outcomes a limited over cricket team must obtain in order to increase their chances of winning. Utilising a random forest technique, it was found that players with strong scoring consistency, scoring efficiency, runs restricting abilities and wicket-taking efficiency are preferred for limited over cricket due to the positive impact those performance metrics have on a team’s chance of winning. To define pertinent individual player ratings, performance metrics that significantly affect match outcomes were identified. Random Forests proved to be an effective means of optimal variable selection. The important performance metrics were derived in terms of contribution to winning, and were input into the modified PWM and EWMA method to generate a player rating. The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established rating methods for cricket teams. Applying the Bradley-Terry method to the team ratings, generated through the adaptive system, we calculated the probability of teami beating teamj. The adaptive rating system was applied to the Caribbean Premier League 2015 and the Cricket World Cup 2015, and the systems predictive accuracy was benchmarked against the New Zealand T.A.B (Totalisator Agency Board) and the CricHQ algorithm. The results revealed that the developed rating system outperformed the T.A.B by 9% and the commercial algorithm by 6% for the Cricket World Cup (2015), respectively, and outperformed the T.A.B and CricHQ algorithm by 25% and 12%, for the Caribbean Premier League (2015), respectively. These results demonstrate that cricket team ratings based on the aggregation of individual player ratings are superior to ratings based on summaries of team performances and match outcomes; validating the research hypothesis. The insights derived from this research also inform interested parties of the key attributes to win limited over cricket matches and can be used for team selection