Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

On the execution of collaborative business processes

Management of collaborative business processes that span multiple business entities has emerged as a key requirement for business success. These processes are embedded in sets of rules describing complex message-based interactions between parties such that if a logical expression defined on the set of received messages is satisfied, one or more outgoing messages are dispatched. The execution of these processes presents significant challenges since each contentrich message may contribute towards the evaluation of multiple expressions in different ways and the sequence of message arrival cannot be predicted. These challenges must be overcome in order to develop an efficient execution strategy for collaborative processes in an intensive operating environment with a large number of rules and very high throughput of messages. In this paper, we present a discussion on issues relevant to the evaluation of such expressions and describe a basic query-based method for this purpose, including suggested indexes for improved performance. We conclude by identifying several potential future research directions in this area. © 2010 IEEE. All rights reserved

Optimum switching point assessment of parallel braking resistor and serial current limiter for power system stability enhancement

A new control algorithm using parallel braking resistor (BR) and serial fault current limiter (FCL) for power system transient stability enhancement is presented in this paper. The proposed control algorithm can prevent transient instability during first swing by immediately taking away the transient energy gained in faulted period. It can also reduce generator oscillation time and efficiently make system back to the post-fault equilibrium. The algorithm is based on a new system energy function based method to choose optimal switching point. The parallel BR and serial FCL resistor can be switched at the calculated optimal point to get the best control result. This method allows optimum dissipation of the transient energy caused by disturbance so to make system back to equilibrium in minimum time. Case studies are given to verify the efficiency and effectiveness of this new control algorithm.

The effective management of geological risk in long-term production scheduling of open pit mines

Investment in mining projects, like most business investment, is susceptible to risk and uncertainty. The ability to effectively identify, assess and manage risk may enable strategic investments to be sheltered and operations to perform closer to their potential. In mining, geological uncertainty is seen as the major contributor to not meeting project expectations. The need to assess and manage geological risk for project valuation and decision-making translates to the need to assess and manage risk in any pertinent parameter of open pit design and production scheduling. This is achieved by taking geological uncertainty into account in the mine optimisation process. This thesis develops methods that enable geological uncertainty to be effectively modelled and the resulting risk in long-term production scheduling to be quantified and managed. One of the main accomplishments of this thesis is the development of a new, risk-based method for the optimisation of long-term production scheduling. In addition to maximising economic returns, the new method minimises the risk of deviating from production forecasts, given the understanding of the orebody. This ability represents a major advance in the risk management of open pit mining.

Investigation of healthy and infected newborn saliva: the role of proteins and lipids as potential inflammatory diagnostic tools

We have carried out a discovery proteomics investigation aimed at identifying disease biomarkers present in saliva, and, more specifically, early biomarkers of inflammation. The proteomic characterization of saliva is possible due to the straightforward and non-invasive sample collection that allows repetitive analyses for pharmacokinetic studies. These advantages are particularly relevant in the case of newborn patients. The study was carried out with samples collected during the first 48 hours of life of the newborns according to an approved Ethic Committee procedure. In particular, the salivary samples were collected from healthy and infected (n=1) newborns. Proteins were extracted through cycles of sonication, precipitated in ice cold acetone, resuspended and resolved by 2D-electrophoresis. MALDI TOF/TOF mass spectrometry analysis was performed for each spot obtaining the proteins’ identifications. Then we compared healthy newborn salivary proteome and an infected newborn salivary proteome in order to investigate proteins differently expressed in inflammatory condition. In particular the protein alpha-1-antitrypsin (A1AT), correlated with inflammation, was detected differently expressed in the infected newborn saliva. Therefore, in the second part of the project we aimed to develop a robust LC-MS based method that identifies and quantifies this inflammatory protein within saliva that might represent the first relevant step to diagnose a condition of inflammation with a no-invasive assay. The same LC-MS method is also useful to investigate the presence of the F allelic variant of the A1AT in biological samples, which is correlated with the onset of pulmonary diseases. In the last part of the work we analysed newborn saliva samples in order to investigate how phospholipids and mediators of inflammation (eicosanoids) are subject to variations under inflammatory conditions and a trend was observed in lysophosphatidylcholines composition according to the inflammatory conditions.

The 'moving targets' training algorithm

A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The algorithm resembles back-propagation in that an error function is minimized using a gradient-based method, but the optimization is carried out in the hidden part of state space either instead of, or in addition to weight space. Computational results are presented for some simple dynamical training problems, one of which requires response to a signal 100 time steps in the past.

The n-tuple classifier: too good to ignore

The n-tuple pattern recognition method has been tested using a selection of 11 large data sets from the European Community StatLog project, so that the results could be compared with those reported for the 23 other algorithms the project tested. The results indicate that this ultra-fast memory-based method is a viable competitor with the others, which include optimisation-based neural network algorithms, even though the theory of memory-based neural computing is less highly developed in terms of statistical theory.

A mathematical model for dynamic efficiency using data envelopment analysis

In this paper we propose a data envelopment analysis (DEA) based method for assessing the comparative efficiencies of units operating production processes where input-output levels are inter-temporally dependent. One cause of inter-temporal dependence between input and output levels is capital stock which influences output levels over many production periods. Such units cannot be assessed by traditional or 'static' DEA which assumes input-output correspondences are contemporaneous in the sense that the output levels observed in a time period are the product solely of the input levels observed during that same period. The method developed in the paper overcomes the problem of inter-temporal input-output dependence by using input-output 'paths' mapped out by operating units over time as the basis of assessing them. As an application we compare the results of the dynamic and static model for a set of UK universities. The paper is suggested that dynamic model capture the efficiency better than static model. © 2003 Elsevier Inc. All rights reserved.

Non-rigid object tracking in complex scenes

A recently proposed colour based tracking algorithm has been established to track objects in real circumstances [Zivkovic, Z., Krose, B. 2004. An EM-like algorithm for color-histogram-based object tracking. In: Proc, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 798-803]. To improve the performance of this technique in complex scenes, in this paper we propose a new algorithm for optimally adapting the ellipse outlining the objects of interest. This paper presents a Lagrangian based method to integrate a regularising component into the covariance matrix to be computed. Technically, we intend to reduce the residuals between the estimated probability distribution and the expected one. We argue that, by doing this, the shape of the ellipse can be properly adapted in the tracking stage. Experimental results show that the proposed method has favourable performance in shape adaption and object localisation.

Expert systems for fault tree synthesis

Hazard and operability (HAZOP) studies on chemical process plants are very time consuming, and often tedious, tasks. The requirement for HAZOP studies is that a team of experts systematically analyse every conceivable process deviation, identifying possible causes and any hazards that may result. The systematic nature of the task, and the fact that some team members may be unoccupied for much of the time, can lead to tedium, which in turn may lead to serious errors or omissions. An aid to HAZOP are fault trees, which present the system failure logic graphically such that the study team can readily assimilate their findings. Fault trees are also useful to the identification of design weaknesses, and may additionally be used to estimate the likelihood of hazardous events occurring. The one drawback of fault trees is that they are difficult to generate by hand. This is because of the sheer size and complexity of modern process plants. The work in this thesis proposed a computer-based method to aid the development of fault trees for chemical process plants. The aim is to produce concise, structured fault trees that are easy for analysts to understand. Standard plant input-output equation models for major process units are modified such that they include ancillary units and pipework. This results in a reduction in the nodes required to represent a plant. Control loops and protective systems are modelled as operators which act on process variables. This modelling maintains the functionality of loops, making fault tree generation easier and improving the structure of the fault trees produced. A method, called event ordering, is proposed which allows the magnitude of deviations of controlled or measured variables to be defined in terms of the control loops and protective systems with which they are associated.

Amino acids in oral drug delivery:salts, ion-pairs and transcriptomics

Oral drug delivery is considered the most popular route of delivery because of the ease of administration, availability of a wide range of dosage forms and the large surface area for drug absorption via the intestinal membrane. However, besides the unfavourable biopharmaceutical properties of the therapeutic agents, efflux transporters such as Pglycoprotein (P-gp) and multiple resistance proteins (MRP) decrease the overall drug uptake by extruding the drug from the cells. Although, prodrugs have been investigated to improve drug partitioning by masking the polar groups covalently with pre-moieties promoting increased uptake, they present significant challenges including reduced solubility and increased toxicity. The current work investigates the use of amino acids as ion-pairs for three model drugs: indomethacin (weak acid), trimethoprim (weak base) and ciprofloxacin (zwitter ion) in an attempt to improve both solubility and uptake. Solubility was studied by salt formation while creating new routes for uptake across the membranes via amino acids transporter proteins or dipeptidyl transporters was the rationale to enhance absorption. New salts were prepared for the model drugs and the oppositely charged amino acids by freeze drying and they were characterised using FTIR, 1HNMR, DSC, SEM, pH solubility profile, solubility and dissolution. Permeability profiles were assessed using an in vitro cell based method; Caco-2 cells and the genetic changes occurring across the transporter genes and various pathways involved in the cellular activities were studied using DNA microarrays. Solubility data showed a significant increase in drug solubility upon preparing the new salts with the oppositely charged counter ions (ciprofloxacin glutamate salt exhibiting 2.9x103 fold enhancement when compared to the free drug). Moreover, permeability studies showed a 3 fold increase in trimethoprim and indomethacin permeabilities upon ion-pairing with amino acids and more than 10 fold when the zwitter ionic drug was paired with glutamic acid. Microarray data revealed that trimethoprim was absorbed actively via OCTN1 transporters while MRP7 is the main transporter gene that mediates its efflux. The absorption of trimethoprim from trimethoprim glutamic acid ion-paired formulations was affected by the ratio of glutamic acid in the formulation which was inversely proportional to the degree of expression of OCTN1. Interestingly, ciprofloxacin glutamic acid ion-pairs were found to decrease the up-regulation of ciprofloxacin efflux proteins (P-gp and MRP4) and over-express two solute carrier transporters; (PEPT2 and SLCO1A2) suggesting that a high aqueous binding constant (K11aq) enables the ion-paired formulations to be absorbed as one entity. In conclusion, formation of ion-pairs with amino acids can influence in a positive way solubility, transfer and gene expression effects of drugs.

Binocular fusion, suppression and diplopia for blurred edges

Purpose: (1) To devise a model-based method for estimating the probabilities of binocular fusion, interocular suppression and diplopia from psychophysical judgements, (2) To map out the way fusion, suppression and diplopia vary with binocular disparity and blur of single edges shown to each eye, (3) To compare the binocular interactions found for edges of the same vs opposite contrast polarity. Methods: Test images were single, horizontal, Gaussian-blurred edges, with blur B = 1-32 min arc, and vertical disparity 0-8.B, shown for 200 ms. In the main experiment, observers reported whether they saw one central edge, one offset edge, or two edges. We argue that the relation between these three response categories and the three perceptual states (fusion, suppression, diplopia) is indirect and likely to be distorted by positional noise and criterion effects, and so we developed a descriptive, probabilistic model to estimate both the perceptual states and the noise/criterion parameters from the data. Results: (1) Using simulated data, we validated the model-based method by showing that it recovered fairly accurately the disparity ranges for fusion and suppression, (2) The disparity range for fusion (Panum's limit) increased greatly with blur, in line with previous studies. The disparity range for suppression was similar to the fusion limit at large blurs, but two or three times the fusion limit at small blurs. This meant that diplopia was much more prevalent at larger blurs, (3) Diplopia was much more frequent when the two edges had opposite contrast polarity. A formal comparison of models indicated that fusion occurs for same, but not opposite, polarities. Probability of suppression was greater for unequal contrasts, and it was always the lower-contrast edge that was suppressed. Conclusions: Our model-based data analysis offers a useful tool for probing binocular fusion and suppression psychophysically. The disparity range for fusion increased with edge blur but fell short of complete scale-invariance. The disparity range for suppression also increased with blur but was not close to scale-invariance. Single vision occurs through fusion, but also beyond the fusion range, through suppression. Thus suppression can serve as a mechanism for extending single vision to larger disparities, but mainly for sharper edges where the fusion range is small (5-10 min arc). For large blurs the fusion range is so much larger that no such extension may be needed. © 2014 The College of Optometrists.

Using genetic algorithms for improved discrete sequence prediction

A statistics-based method using genetic algorithms for predicting discrete sequences is presented. The prediction of the next value is based upon a fixed number of previous values and the statistics offered by the training data. According to the statistics, in similar past cases different values occurred next. If these values are considered with the appropriate weights, the forecast is successful. Weights are generated by genetic algorithms.

Cerebellar morphology in developmental dyslexia

Recent evidence has suggested cerebellar anomalies in developmental dyslexia. Therefore, we investigated cerebellar morphology in subjects with documented reading disabilities. We obtained T1-weighted magnetic resonance images in the coronal and sagittal planes from 11 males with prior histories of developmental dyslexia, and nine similarly-aged male controls. Proton magnetic resonance spectra (TE=136 ms, TR=2.4 s) were obtained bilaterally in the cerebellum. Phonological decoding skill was measured using non-word reading. Handedness was assessed using both the Annett questionnaire of hand preference and Annett’s peg moving task. Cerebellar symmetry was observed in the dyslexics but there was significant asymmetry (right grey matter>left grey matter) in controls. The interpretation of these results depended whether a motor- or questionnaire-based method was used to determine handedness. The degree of cerebellar symmetry was correlated with the severity of dyslexics’ phonological decoding deficit. Those with more symmetric cerebella made more errors on a nonsense word reading measure of phonological decoding ability. Left cerebellar metabolite ratios were shown to correlate significantly with the degree of cerebellar asymmetry (P<0.05) in controls. This relationship was absent in developmental dyslexics. Cerebellar morphology reflects the higher degree of symmetry found previously in the temporal and parietal cortex of dyslexics. The relationship of cerebellar asymmetry to phonological decoding ability and handedness, together with our previous finding of altered metabolite ratios in the cerebellum of dyslexics, lead us to suggest that there are alterations in the neurological organisation of the cerebellum which relate to phonological decoding skills, in addition to motor skills and handedness.

Correlating liposomal adjuvant characteristics to in-vivo cell-mediated immunity using a novel Mycobacterium tuberculosis fusion protein:a multivariate analysis study

Objective In this study, we have used a chemometrics-based method to correlate key liposomal adjuvant attributes with in-vivo immune responses based on multivariate analysis. Methods The liposomal adjuvant composed of the cationic lipid dimethyldioctadecylammonium bromide (DDA) and trehalose 6,6-dibehenate (TDB) was modified with 1,2-distearoyl-sn-glycero-3-phosphocholine at a range of mol% ratios, and the main liposomal characteristics (liposome size and zeta potential) was measured along with their immunological performance as an adjuvant for the novel, postexposure fusion tuberculosis vaccine, Ag85B-ESAT-6-Rv2660c (H56 vaccine). Partial least square regression analysis was applied to correlate and cluster liposomal adjuvants particle characteristics with in-vivo derived immunological performances (IgG, IgG1, IgG2b, spleen proliferation, IL-2, IL-5, IL-6, IL-10, IFN-γ). Key findings While a range of factors varied in the formulations, decreasing the 1,2-distearoyl-sn-glycero-3-phosphocholine content (and subsequent zeta potential) together built the strongest variables in the model. Enhanced DDA and TDB content (and subsequent zeta potential) stimulated a response skewed towards a cell mediated immunity, with the model identifying correlations with IFN-γ, IL-2 and IL-6. Conclusion This study demonstrates the application of chemometrics-based correlations and clustering, which can inform liposomal adjuvant design.