Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

Deep hole drilling with twist drills: aspects of the CNC process and its real time monitoring and adaptive control

Deep hole drilling is one of the most complicated metal cutting processes and one of the most difficult to perform on CNC machine-tools or machining centres under conditions of limited manpower or unmanned operation. This research work investigates aspects of the deep hole drilling process with small diameter twist drills and presents a prototype system for real time process monitoring and adaptive control; two main research objectives are fulfilled in particular : First objective is the experimental investigation of the mechanics of the deep hole drilling process, using twist drills without internal coolant supply, in the range of diarneters Ø 2.4 to Ø4.5 mm and working length up to 40 diameters. The definition of the problems associated with the low strength of these tools and the study of mechanisms of catastrophic failure which manifest themselves well before and along with the classic mechanism of tool wear. The relationships between drilling thrust and torque with the depth of penetration and the various machining conditions are also investigated and the experimental evidence suggests that the process is inherently unstable at depths beyond a few diameters. Second objective is the design and implementation of a system for intelligent CNC deep hole drilling, the main task of which is to ensure integrity of the process and the safety of the tool and the workpiece. This task is achieved by means of interfacing the CNC system of the machine tool to an external computer which performs the following functions: On-line monitoring of the drilling thrust and torque, adaptive control of feed rate, spindle speed and tool penetration (Z-axis), indirect monitoring of tool wear by pattern recognition of variations of the drilling thrust with cumulative cutting time and drilled depth, operation as a data base for tools and workpieces and finally issuing of alarms and diagnostic messages.

The development of calibration and adaptive filtering procedures for neuromagnetic studies of human visual function

This thesis first considers the calibration and signal processing requirements of a neuromagnetometer for the measurement of human visual function. Gradiometer calibration using straight wire grids is examined and optimal grid configurations determined, given realistic constructional tolerances. Simulations show that for gradiometer balance of 1:104 and wire spacing error of 0.25mm the achievable calibration accuracy of gain is 0.3%, of position is 0.3mm and of orientation is 0.6°. Practical results with a 19-channel 2nd-order gradiometer based system exceed this performance. The real-time application of adaptive reference noise cancellation filtering to running-average evoked response data is examined. In the steady state, the filter can be assumed to be driven by a non-stationary step input arising at epoch boundaries. Based on empirical measures of this driving step an optimal progression for the filter time constant is proposed which improves upon fixed time constant filter performance. The incorporation of the time-derivatives of the reference channels was found to improve the performance of the adaptive filtering algorithm by 15-20% for unaveraged data, falling to 5% with averaging. The thesis concludes with a neuromagnetic investigation of evoked cortical responses to chromatic and luminance grating stimuli. The global magnetic field power of evoked responses to the onset of sinusoidal gratings was shown to have distinct chromatic and luminance sensitive components. Analysis of the results, using a single equivalent current dipole model, shows that these components arise from activity within two distinct cortical locations. Co-registration of the resulting current source localisations with MRI shows a chromatically responsive area lying along the midline within the calcarine fissure, possibly extending onto the lingual and cuneal gyri. It is postulated that this area is the human homologue of the primate cortical area V4.

Design and evaluation of a framework for cooperative and adaptive QoS control of DSRC network for road safety applications

Dedicated short-range communications (DSRC) are a promising vehicle communication technique for collaborative road safety applications (CSA). However, road safety applications require highly reliable and timely wireless communications, which present big challenges to DSRC based vehicle networks on effective and robust quality of services (QoS) provisioning due to the random channel access method applied in the DSRC technique. In this paper we examine the QoS control problem for CSA in the DSRC based vehicle networks and presented an overview of the research work towards the QoS control problem. After an analysis of the system application requirements and the DSRC vehicle network features, we propose a framework for cooperative and adaptive QoS control, which is believed to be a key for the success of DSRC on supporting effective collaborative road safety applications. A core design in the proposed QoS control framework is that network feedback and cross-layer design are employed to collaboratively achieve targeted QoS. A design example of cooperative and adaptive rate control scheme is implemented and evaluated, with objective of illustrating the key ideas in the framework. Simulation results demonstrate the effectiveness of proposed rate control schemes in providing highly available and reliable channel for emergency safety messages. © 2013 Wenyang Guan et al.

Stochastic control strategies and adaptive critic methods

Adaptive critic methods have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, nonlinear and nonstationary environments. In this study, a novel probabilistic dual heuristic programming (DHP) based adaptive critic controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) adaptive critic method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterized by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the critic network is then calculated and shown to be equal to the analytically derived correct value.

Static, dynamic, and adaptive heterogeneity in distributed smart camera networks

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

The immune system as drug target

The immune system is perhaps the largest yet most diffuse and distributed somatic system in vertebrates. It plays vital roles in fighting infection and in the homeostatic control of chronic disease. As such, the immune system in both pathological and healthy states is a prime target for therapeutic interventions by drugs-both small-molecule and biologic. Comprising both the innate and adaptive immune systems, human immunity is awash with potential unexploited molecular targets. Key examples include the pattern recognition receptors of the innate immune system and the major histocompatibility complex of the adaptive immune system. Moreover, the immune system is also the source of many current and, hopefully, future drugs, of which the prime example is the monoclonal antibody, the most exciting and profitable type of present-day drug moiety. This brief review explores the identity and synergies of the hierarchy of drug targets represented by the human immune system, with particular emphasis on the emerging paradigm of systems pharmacology. © the authors, publisher and licensee Libertas Academica Limited.

Modelling Apoptotic cell clearance within the atherosclerotic plaque

The aged population have an increased susceptibility to infection, therefore function of the innate immune system may be impaired as we age. Macrophages, and their precursors monocytes, play an important role in host defence in the form of phagocytosis, and also link the innate and adaptive immune system via antigen presentation. Classically-activated 'M1' macrophages are pro-inflammatory, which can be induced by encountering pathogenic material or pro-inflammatory mediators. Alternatively activated 'M2' macrophages have a largely reparative role, including clearance of apoptotic bodies and debris from tissues. Despite some innate immune receptors being implicated in the clearance of apoptotic cells, the process has been observed to have a dominant anti-inflammatory phenotype with cytokines such as IL-10 and TGF-ß being implicated. The atherosclerotic plaque contains recruited monocytes and macrophages, and is a highly inflammatory environment despite high levels of apoptosis. At these sites, monocytes differentiate into macrophages and gorge on lipoproteins, resulting in formation of 'foam cells' which then undergo apoptosis, recruiting further monocytes. This project seeks to understand why, given high levels of apoptosis, the plaque is a pro-inflammatory environment. This phenomenon may be the result of the aged environment or an inability of foam cells to elicit an anti-inflammatory effect in response to dying cells. Here we demonstrate that lipoprotein treatment of macrophages in culture results in reduced capacity to clear apoptotic cells. The effect of lipoprotein treatment on apoptotic cell-mediated immune modulation of macrophage function is currently under study.

Dealing with cell death – an ageing problem?

The aged population have an increased susceptibility to infection, therefore function of the innate immune system may be impaired as we age. Macrophages, and their precursors monocytes, play an important role in host defence in the form of phagocytosis, and also link the innate and adaptive immune system via antigen presentation. Classically-activated ‘M1’ macrophages are pro-inflammatory, which can be induced by encountering pathogenic material or pro-inflammatory mediators. Alternatively activated ‘M2’ macrophages have a largely reparative role, including clearance of apoptotic bodies and debris from tissues. Despite some innate immune receptors being implicated in the clearance of apoptotic cells, the process has been observed to have a dominant anti-inflammatory phenotype with cytokines such as IL-10 and TGF-ß being implicated. The atherosclerotic plaque contains recruited monocytes and macrophages, and is a highly inflammatory environment despite high levels of apoptosis. At these sites, monocytes differentiate into macrophages and gorge on lipoproteins, resulting in formation of ‘foam cells’ which then undergo apoptosis, recruiting further monocytes. This project seeks to understand why, given high levels of apoptosis, the plaque is a pro-inflammatory environment. This phenomenon may be the result of the aged environment or an inability of foam cells to elicit an anti-inflammatory effect in response to dying cells. Here we demonstrate that lipoprotein treatment of macrophages in culture results in reduced capacity to clear apoptotic cells. The capability of lipoprotein treated macrophages to respond to inflammatory stimuli is also shown. Monocyte recruitment to the plaque is currently under study, as is apoptotic cell-mediated immune modulation of human monocyte-derived macrophages.

The role of apoptotic cell clearance in a high-lipid environment:links to ageing

Individuals within the aged population show an increased susceptibility to infection, implying a decline in immune function, a phenomenon known as immunosenescence. Paradoxically, an increase in autoimmune disease, such as rheumatoid arthritis, is also associated with ageing, therefore some aspects of the immune system appear to be inappropriately active in the elderly. The above evidence suggests inappropriate control of the immune system as we age. Macrophages, and their precursors monocytes, play a key role in control of the immune system. They play an important role in host defence in the form of phagocytosis, and also link the innate and adaptive immune system via antigen presentation. Macrophages also have a reparative role, as professional phagocytes of dead and dying cells. Clearance of apoptotic cells by macrophages has also been shown to directly influence immune responses in an anti-inflammatory manner. Inappropriate control of macrophage function with regards to dead cell clearance may contribute to pathology as we age. The aims of this study were to assess the impact of lipid treatment, as a model of the aged environment, on the ability of macrophages to interact with, and respond to, apoptotic cells. Using a series of in vitro cell models, responses of macrophages (normal and lipid-loaded) to apoptotic macrophages (normal and lipid-loaded) were investigated. Monocyte recruitment to apoptotic cells, a key process in resolving inflammation, was assessed in addition to cytokine responses. Data here shows, for the first time, that apoptotic macrophages (normal and lipid-loaded) induce inflammation in human monocyte-derived macrophages, a response that could drive inflammation in age-associated pathology e.g. atherosclerosis. Monoclonal antibody inhibition studies suggest the classical chemokine CX3CL1 may be involved in monocyte recruitment to apoptotic macrophages, but not apoptotic foam cells, therefore differential clearance strategies may be employed following lipid-loading. CD14, an important apoptotic cell tethering receptor, was not found to have a prominent role in this process, whilst the role for ICAM-3 remains unclear. Additionally, a small pilot study using macrophages from young (<25) and mid-life (>40) donors was undertaken. Preliminary data was gathered to assess the ability of primary human monocyte-derived macrophages, from young and mid-life donors, to interact with, and respond to, apoptotic cells. MØ from mid-life individuals showed no significant differences in their ability to respond to immune modulation by apoptotic cells compared to MØ from young donors. Larger cohorts would be required to investigate whether immune modulation of MØ by apoptotic cells contribute to inflammatory pathology throughout ageing.

Shaping strategic action through the rhetorical construction and exploitation of ambiguity

This paper extends existing understandings of how actors' constructions of ambiguity shape the emergent process of strategic action. We theoretically elaborate the role of rhetoric in exploiting strategic ambiguity, based on analysis of a longitudinal case study of an internationalization strategy within a business school. Our data show that actors use rhetoric to construct three types of strategic ambiguity: protective ambiguity that appeals to common values in order to protect particular interests, invitational ambiguity that appeals to common values in order to invite participation in particular actions, and adaptive ambiguity that enables the temporary adoption of specific values in order to appeal to a particular audience at one point in time. These rhetorical constructions of ambiguity follow a processual pattern that shapes the emergent process of strategic action. Our findings show that (1) the strategic actions that emerge are shaped by the way actors construct and exploit ambiguity, (2) the ambiguity intrinsic to the action is analytically distinct from ambiguity that is constructed and exploited by actors, and (3) ambiguity construction shifts over time to accommodate the emerging pattern of actions.

Strategy as practice:recursiveness, adaptation, and practices-in-use

In this article, a social theory framework is developed to explain the common themes of recursive and adaptive practice underpinning the existing strategic management literature. In practice, there is a coexistent tension between recursive and adaptive forms of strategic action that spans multiple levels from macro-institutional and competitive contexts to within-firm levels of analysis to individual cognition. This tension may be better understood by examining how management practices are used to put strategy into practice. Such practices span multiple levels of context and are adaptable to their circumstances of use, serving to highlight both general characteristics and localized idiosyncrasies of strategy as practice. The article develops the concept of management practices-in-use into a research agenda and nine broad research questions that may be used to investigate empirically strategy as practice.

Models@run.time:foundations, applications, and roadmaps

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.

Temperature management for heterogeneous multi-core FPGAs using adaptive evolutionary multi-objective approaches

Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionary algorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objective optimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.

Using ancillary statistics in on-line learning algorithms

Neural networks are usually curved statistical models. They do not have finite dimensional sufficient statistics, so on-line learning on the model itself inevitably loses information. In this paper we propose a new scheme for training curved models, inspired by the ideas of ancillary statistics and adaptive critics. At each point estimate an auxiliary flat model (exponential family) is built to locally accommodate both the usual statistic (tangent to the model) and an ancillary statistic (normal to the model). The auxiliary model plays a role in determining credit assignment analogous to that played by an adaptive critic in solving temporal problems. The method is illustrated with the Cauchy model and the algorithm is proved to be asymptotically efficient.