A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - a review

Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.

Chalk and cheese: Grounded theory case studies of the introduction and usage of activity-based information in two British banks

During the conduct of a research project into influences on the use of management accounting information, the use of activity-based techniques and information in two British banks was studied by the application of grounded theory principles. Juxtaposition of these two case studies reveals insights about the managers' significantly different experiences of ongoing applications, and the different outcomes of implementation that may arise, despite commonality in the organization and industry environment. This paper presents these two case studies, highlights the similarities and differences between them, and draws some conclusions about the causes of the differences. Factors that can be managed to achieve a greater use of these particular management accounting techniques, and the information they generate, are revealed. In particular, the findings suggest that the introduction of transfer charging between the bank's internal units highlights the need for activity-based techniques, and that education, communication and implementor support are vital, both for implementation success and for the widespread continuing use of the resultant applications. Further, between the two cases the greatest consensus was found in a common concern about the amount of detail in the databank and reports.

Fingerprint recognition system using hybrid matching techniques

With an increasing emphasis on the emerging automatic person identification application, biometrics based, especially fingerprint-based identification, is receiving a lot of attention. This research developed an automatic fingerprint recognition system (AFRS) based on a hybrid between minutiae and correlation based techniques to represent and to match fingerprint; it improved each technique individually. It was noticed that, in the hybrid approach, as a result of an improvement of minutiae extraction algorithm in post-process phase that combines the two algorithms, the performance of the minutia algorithm improved. An improvement in the ridge algorithm that used centre point in fingerprint instead of reference point was also observed. Experiments indicate that the hybrid technique performs much better than each algorithm individually.

Data-based dynamic haptic interaction model with deformable 3D objects

The data-based modeling of the haptic interaction simulation is a growing trend in research. These techniques offer a quick alternative to parametric modeling of the simulation. So far, most of the use of the data-based techniques was applied to static simulations. This paper introduces how to use data-based model in dynamic simulations. This ensures realistic behavior and produce results that are very close to parametric modeling. The results show that a quick and accurate response can be achieved using the proposed methods.

A naïve, salience-based method for speaker identification in fiction books

This paper presents a salience-based technique for the annotation of directly quoted speech from fiction text. In particular, this paper determines to what extent a naïve (without the use of complex machine learning or knowledge-based techniques) scoring technique can be used for the identification of the speaker of speech quotes. The presented technique makes use of a scoring technique, similar to that commonly found in knowledge-poor anaphora resolution research, as well as a set of hand-coded rules for the final identification of the speaker of each quote in the text. Speaker identification is shown to be achieved using three tasks: the identification of a speech-verb associated with a quote with a recall of 94.41%; the identification of the actor associated with a quote with a recall of 88.22%; and the selection of a speaker with an accuracy of 79.40%.

A hybrid graphical password based system

In this age of electronic connectivity, where we all face viruses, hackers, eavesdropping and electronic fraud, there is indeed no time when security is not critical. Passwords provide security mechanism for authentication and protection services against unwanted access to resources. A graphical based password is one promising alternatives of textual passwords. According to human psychology, humans are able to remember pictures easily. In this paper, we have proposed a new hybrid graphical password based system, which is a combination of recognition and recall based techniques that offers many advantages over the existing systems and may be more convenient for the user. Our scheme is resistant to shoulder surfing attack and many other attacks on graphical passwords. This resistant scheme is proposed for small mobile devices (like smart phones i.e. ipod, iphone, PDAs etc) which are more handy and convenient to use than traditional desktop computer systems.

Efficient tensor based face recognition

This paper addresses the limitation of current multilinear PCA based techniques, in terms of pro- hibitive computational cost of testing and poor gen- eralisation in some scenarios, when applied to large training databases. We define person-specific eigen-modes to obtain a set of projection bases, wherein a particular basis captures variation across light- ings and viewpoints for a particular person. A new recognition approach is developed utilizing these bases. The proposed approach performs on a par with the existing multilinear approaches, whilst sig- nificantly reducing the complexity order of the testing algorithm.

Robustness enhancement of quantization based audio watermarking method using adaptive safe-band

This paper presents a novel adaptive safe-band for quantization based audio watermarking methods, aiming to improve robustness. Considerable number of audio watermarking methods have been developed using quantization based techniques. These techniques are generally vulnerable to signal processing attacks. For these conventional quantization based techniques, robustness can be marginally improved by choosing larger step sizes at the cost of significant perceptual quality degradation. We first introduce fixed size safe-band between two quantization steps to improve robustness. This safe-band will act as a buffer to withstand certain types of attacks. Then we further improve the robustness by adaptively changing the size of the safe-band based on the audio signal feature used for watermarking. Compared with conventional quantization based method and the fixed size safe-band based method, the proposed adaptive safe-band based quantization method is more robust to attacks. The effectiveness of the proposed technique is demonstrated by simulation results. © 2014 IEEE.

Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular networks or pathways. Drug discovery processes should delineate new drugs targeting the intracellular networks and immune-related pathways.

Sexing little penguins eudyptula minor using bill measurements

In Little Penguins Eudyptula minor there are no reliable plumage or body size differences that can be used visually to distinguish the sex of individuals. However, sexual dimorphism of morphometric measures has been noted, with males always being a little larger than females. In this study, differences between E. minor sexes at eight colonies in south-eastern Australia were determined statistically via discriminant function analysis (DFA) and through the utilization of DNA-based techniques developed for non-ratite birds. The DFA correctly determined gender in 91.1% of cases and molecular methods were 100% accurate. Our DFA success rate of classification is similar to that previously published for Little Penguins in Victoria. In this study statistically significant differences in mean bill depths and lengths were found between Little Penguin colonies at St Kilda, Phillip Island and Gabo Island, compared to colonies at Kangaroo Island, Granite Island, Middle Island and London Bridge. As birds in eastern populations (St Kilda, Phillip Island, Gabo Island) exhibit statistically significantly smaller beaks (bill depth and bill length), separate discriminant functions were investigated for each phenotypically distinct geo-spatial cohort. Interestingly, cluster analysis for bill length identified three groups: western (Kangaroo Island and Granite Island), eastern (St Kilda, Phillip Island and Middle Island) and the London Bridge Little Penguin colony, which constituted a separate group. We conclude that while there is a slight increase in DF power for colonies west of Cape Otway and for some specific colonies, colony-specific DFA is not required to identify the sex of Little Penguins in south-eastern Australia.

Reduction of vividness and associated craving in personalized food imagery

This study aimed to extend recent experimental work on the efficacy of visuo-spatial working memory-based techniques for reducing food cravings by adopting a more naturalistic methodology. Fifty undergraduate women formed images of their favorite foods while performing a visuo-spatial task across six successive trials. Vividness and craving intensity were rated for each food image. Concurrent visuo-spatial processing reduced the vividness of, and craving reactivity to, personally relevant food images. Forehead tracking, a novel self-administered task, proved to be as effective in reducing vividness and craving ratings as the established visuo-spatial working memory laboratory tasks of eye movements, dynamic visual noise, and spatial tapping, and thus presents a simple, accessible technique potentially applicable in the home environment. All four tasks maintained their reducing effect over multiple trials. Individual differences in imaging ability and habitual food craving did not impact upon their effectiveness, indicating that visuo-spatial tasks can be successfully used to reduce food cravings across a range of people.

Generic and diabetes-specific well-being in the AT.LANTUS follow-on study : further psychometric validation of the W-BQ28 indicates its utility in research and clinical practice in type 2 diabetes in the UK

Aims  To undertake further psychometric validation of the W-BQ28 to determine its suitability for use in adults with Type 2 diabetes in the UK using data from the AT.LANTUS follow-on study.

Methods  A total of 353 people with Type 2 diabetes participated in the AT.LANTUS Follow-on study, completing measures of well-being (W-BQ28), treatment satisfaction (DTSQ) and self-care (SCI-R). Confirmatory factor analyses was used to confirm the W-BQ28 structure and internal consistency reliability was assessed. Additional statistical tests were conducted to explore convergent, divergent and known-groups validity. Minimal important differences were calculated using distribution and anchor-based techniques.

Results  Structure of the W-BQ28 (seven four-item subscales plus 16-item generic and 12-item diabetes-specific scales) was confirmed (comparative fit index = 0.917, root mean square error of approximation (RMSEA) = 0.057). Internal consistency reliability was satisfactory (four-item subscales: alpha = 0.73–0.90; 12/16-item scales: α = 0.84–0.90). Convergent validity was supported by expected moderate to high correlations (rs = 0.35–0.67) between all W-BQ28 subscales (except Energy); divergent validity was supported by expected low to moderate correlations with treatment satisfaction (rs = −0.03–0.52) and self-care (rs = 0.02–0.22). Known-groups validity was supported with statistically significant differences by sex, age and HbA1c for expected subscales. Minimal important differences were established (range 0.14–2.90).

Conclusions  The W-BQ28 is a valid and reliable measure of generic and diabetes-specific well-being in Type 2 diabetes in the UK. Confirmation of the utility of W-BQ28 (including establishment of minimal important differences) means that its use is indicated in research and clinical practice.

Fast growing self organizing map for text clustering

This paper presents an integration of a novel document vector representation technique and a novel Growing Self Organizing Process. In this new approach, documents are represented as a low dimensional vector, which is composed of the indices and weights derived from the keywords of the document.

An index based similarity calculation method is employed on this low dimensional feature space and the growing self organizing process is modified to comply with the new feature representation model.

The initial experiments show that this novel integration outperforms the state-of-the-art Self Organizing Map based techniques of text clustering in terms of its efficiency while preserving the same accuracy level.

The biology and ecology of the ocean sunfish Mola mola : a review of current knowledge and future research perspectives

Relatively little is known about the biology and ecology of the world’s largest (heaviest) bony fish, the ocean sunfish Mola mola, despite its worldwide occurrence in temperate and tropical seas. Studies are now emerging that require many common perceptions about sunfish behaviour and ecology to be re-examined. Indeed, the long-held view that ocean sunfish are an inactive, passively drifting species seems to be entirely misplaced. Technological advances in marine telemetry are revealing distinct behavioural patterns and protracted seasonal movements. Extensive forays by ocean sunfish into the deep ocean have been documented and broad-scale surveys, together with molecular and laboratory based techniques, are addressing the connectivity and trophic role of these animals. These emerging molecular and movement studies suggest that local distinct populations may be prone to depletion through bycatch in commercial fisheries. Rising interest in ocean sunfish, highlighted by the increase in recent publications, warrants a thorough review of the biology and ecology of this species. Here we review the taxonomy, morphology, geography, diet, locomotion, vision, movements, foraging ecology, reproduction and species interactions of M. mola. We present a summary of current conservation issues and suggest methods for addressing fundamental gaps in our knowledge.