A linked data approach to multi-scale energy modelling

Interactions between built infrastructure are complex and nuanced; changes to any one component can have disproportionate effects on the system as a whole. For instance, adoption of heat pumps or electric vehicles by a significant proportion of a population in an urban centre would place new demands on both electricity transmission and distribution networks. It is essential that planners – both national and local – can understand and share information about the resource demands that this type of change places on national and local infrastructure. Access to integrated sources of information – from building component to national levels – is key to supporting policy makers and decision takers. However, over time, information – and as a consequence, the software that manages it – has evolved into functional silos; this has, in turn, affected the definition of data exchange standards. This limits the ability of experts in functional areas to exchange data and implement broader decision support systems. This paper describes the use of linked data approaches to permit queries across large, diverse information sources to provide reasoning about complex questions at multiple scales. The methodology defines a central context to which various external sources can be attached. These distributed sources are, in themselves, registered in a central catalogue; they remain, however, under the control of their source organisations. In this way a large, extensible, interconnected network of distributed data describing, for example, a built environment or electricity transmission network; this network of data resources can be queried centrally to provide customised views of subsets of the data, and so provide a richer view than one source in isolation. The approach was applied to prepare and integrate information about Ireland’s transmission grid and administrative boundaries, along with domestic housing stock into a single data source. The resulting data network is queried by a scenario exploration tool. This tool successfully allows analysis, at a national level by economists, of the effects of the adoption of new technologies on the national grid of Ireland.     

CACHE RP : A novel dynamic cache size tuning model working with relative object popularity for fast web information retrieval

The CACHE_RPframework leverages the relative object popularity as the sole parameter for dynamic cache size tuning. In the process it consistently maintains the prescribed cache hit ratio on the fly by deriving the popularity ratio from the current statistics. As a result the accuracy of the statistical CACHE_RPoperation is independent of changes in the Internet traffic pattern that may switch suddenly. The contribution by the novel CACHE_RPframework is that by adaptively maintaining the given hit ratio it effectively reduces the end-to-end information retrieval roundtrip time (RTT) and frees more bandwidth for sharing. This bandwidth would otherwise be consumed in transferring large amounts of data from remote data sources to the proxy server before it is given to the client or requestor.     

On the importance of cache tuning in a cache-aware algorithm: A case study

In the present paper we describe and analyse a sieving algorithm for determining prime numbers. This external memory algorithm contains several parameters which are related to the sizes of the levels in the memory hierarchy. We examine how we should choose the values of these parameters in order to obtain an optimal running time. We compare the running times obtained by varying the parameters. We conclude that in this specific problem fine tuning pays off as we got a speed-up of almost 40%.     

A new approach to static output feedback stabilization of linear dynamic systems

The topic of this study is output feedback control of linear control system with output. Use of condensed forms of the linear system under static output feedback (SOF) control is made to derive sufficient conditions for stabilization. A minimal-order control-free observer is presented.     

Cacheminer: A runtime approach to exploit cache locality on SMP

Exploiting cache locality of parallel programs at runtime is a complementary approach to a compiler optimization. This is particularly important for those applications with dynamic memory access patterns. We propose a memory-layout oriented technique to exploit cache locality of parallel loops at runtime on Symmetric Multiprocessor (SMP) systems. Guided by application-dependent and targeted architecture-dependent hints, our system, called Cacheminer, reorganizes and partitions a parallel loop using the memory-access space of its execution. Through effective runtime transformations, our system maximizes the data reuse in each partitioned data region assigned in a cache, and minimizes the data sharing among the partitioned data regions assigned to all caches. The executions of tasks in the partitions are scheduled in an adaptive and locality-presented way to minimize the execution time of programs by trading off load balance and locality. We have implemented the Cacheminer runtime library on two commercial SMP servers and an SimCS simulated SMP. Our simulation and measurement results show that our runtime approach can achieve comparable performance with the compiler optimizations for programs with regular computation and memory-access patterns, whose load balance and cache locality can be well optimized by the tiling and other program transformations. However, our experimental results show that our approach is able to significantly improve the memory performance for the applications with irregular computation and dynamic memory access patterns. These types of programs are usually hard to optimize by static compiler optimizations     

A column dependency-based approach for static and dynamic recovery of databases from malicious transactions

Even state of the art database protection mechanisms often fail to prevent occurrence of malicious attacks. Since in a database environment, the modifications made by one transaction may affect the execution of some of the later transactions, it leads to spreading of the damage caused by malicious (bad) transactions. Following traditional log-based recovery schemes, one can rollback (undo) the effect of all the transactions, both malicious as well as non-malicious. In such a scenario, even the unaffected transactions are also rolled back. In this paper, we propose a column dependency-based approach to identify the affected transactions which need to be compensated along with the malicious transactions. To ensure durability, committed non-malicious transactions are then re-executed in a manner that retains database consistency. We present a static recovery algorithm as well as an on-line version of the same and prove their correctness. A detailed performance evaluation of the proposed scheme with TPC-C benchmark suite is also presented.     

Auto tuning for new energy dispatch problem: A case study

The new energy dispatch problem has aroused more and more attention. In this paper, we investigate the problem of determining the optimal usage of generating power during a scheduling period. A set of MIP formulations are adopted for precise modeling of the variety of power systems (different power generation units) and the actual situation in china. Based on these formulations, we construct a new energy dispatch model which includes many MIP sub-problems. An auto-tuning MIP solver CMIP is given to effectively improve the performance of solving the proposed model. The CMIP focuses on optimizations for presolver, the LP solver for corresponding relaxation problem, and the primal heuristics. Actual predict data is used in performance experiments. Computational results conform to the viability of optimization. Our optimizations further reduce 27.6% of the average execution time compared to CPLEX.     

AspectC++: An integrated approach for static and dynamic adaptation of system software

Modern computer systems require an enormous amount of flexibility. This is especially the case in low-level system software, from embedded devices to networking services. From literature and practice, various approaches to modularize and integrate adaptations have been investigated. However, most of this work is implemented with dynamic languages that offer extensive run-time support and enable easy integration of such approaches. System software is written in languages like C or C++ in order to minimize utilization of system resources and maximize efficiency. While for these languages highly optimized and reliable compilers are available, the support for static and dynamic adaptation is rather limited. In order to overcome these limitations, we present an adaptation approach that is based on a sophisticated combination of static and dynamic aspect weaving for aspects written in AspectC++. This facilitates the incremental evolution and deployment of system software that has to be “always on”. We demonstrate the feasibility of our approach and its applicability to two pieces of system software, namely the Squid web proxy and the eCos operating system, which is used in the domain of resource-constrained deeply embedded systems.     

HED-FL: A hierarchical,energy efficient,and dynamic approach for edge Federated Learning

The increasing data produced by IoT devices and the need to harness intelligence in our environments impose the shift of computing and intelligence at the edge, leading to a novel computing paradigm called Edge Intelligence/Edge AI. This paradigm combines Artificial Intelligence and Edge Computing, enables the deployment of machine learning algorithms to the edge, where data is generated, and is able to overcome the drawbacks of a centralized approach based on the cloud (e.g., performance bottleneck, poor scalability, and single point of failure). Edge AI supports the distributed Federated Learning (FL) model that maintains local training data at the end devices and shares only globally learned model parameters in the cloud. This paper proposes a novel, energy-efficient, and dynamic FL-based approach considering a hierarchical edge FL architecture called HED-FL, which supports a sustainable learning paradigm using model parameters aggregation at different layers and considering adaptive learning rounds at the edge to save energy but still preserving the learning model’s accuracy. Performance evaluations of the proposed approach have also been led out considering model accuracy, loss, and energy consumption.     

A new approach to dynamic bandwidth allocation in Quality of Service networks: Performance and bounds

Efficient dynamic resource provisioning algorithms are necessary to the development and automation of Quality of Service (QoS) networks. The main goal of these algorithms is to offer services that satisfy the QoS requirements of individual users while guaranteeing at the same time an efficient utilization of network resources.In this paper we introduce a new service model that provides per-flow bandwidth guarantees, where users subscribe for a guaranteed rate; moreover, the network periodically individuates unused bandwidth and proposes short-term contracts where extra-bandwidth is allocated and guaranteed exclusively to users who can exploit it to transmit at a rate higher than their subscribed rate.To implement this service model we propose a dynamic provisioning architecture for intra-domain Quality of Service networks. We develop a set of dynamic on-line bandwidth allocation algorithms that take explicitly into account traffic statistics and users’ utility functions to increase users’ benefit and network revenue.Further, we propose a mathematical formulation of the extra-bandwidth allocation problem that maximizes network revenue. The solution of this model allows to obtain an upper bound on the performance achievable by any on-line bandwidth allocation algorithm.We demonstrate through simulation in realistic network scenarios that the proposed dynamic allocation algorithms are superior to static provisioning in providing resource allocation both in terms of total accepted load and network revenue, and they approach, in several network scenarios, the ideal performance provided by the mathematical model.     

Static and dynamic minimum energy broadcast problem in wireless ad-hoc networks: A PSO-based approach and analysis

In this paper, we address the minimum energy broadcast (MEB) problem in wireless ad-hoc networks (WANETs). The researches in WANETs have attracted significant attentions, and one of the most critical issues in WSNs is minimization of energy consumption. In WANETs the packets have to be transported from a given source node to all other nodes in the network, and the objective of the MEB problem is to minimize the total transmission power consumption. A hybrid algorithm based on particle swarm optimization (PSO) and local search is presented to solve the MEB problem. A power degree encoding is proposed to reflect the extent of transmission power level and is used to define the particle position in PSO. We also analyze a well-known local search mechanism, r-shrink, and propose an improved version, the intensified r-shrink. In order to solve the dynamic MEB problem with node removal/insertion, this paper provides an effective simple heuristic, Conditional Incremental Power (CIP), to reconstruct the broadcast network efficiently. The promising results indicate the potential of the proposed methods for practical use.     

Dimensionality reduction in data mining: A Copula approach

The recent trends in collecting huge and diverse datasets have created a great challenge in data analysis. One of the characteristics of these gigantic datasets is that they often have significant amounts of redundancies. The use of very large multi-dimensional data will result in more noise, redundant data, and the possibility of unconnected data entities. To efficiently manipulate data represented in a high-dimensional space and to address the impact of redundant dimensions on the final results, we propose a new technique for the dimensionality reduction using Copulas and the LU-decomposition (Forward Substitution) method. The proposed method is compared favorably with existing approaches on real-world datasets: Diabetes, Waveform, two versions of Human Activity Recognition based on Smartphone, and Thyroid Datasets taken from machine learning repository in terms of dimensionality reduction and efficiency of the method, which are performed on statistical and classification measures.     

Modelling storage organizations: A dynamic approach

This paper discusses an approach to modelling storage organizations of future computer systems. Our motivation comes from two general uncharted areas of computer performance analysis. The first is that of dynamic or time-dependent effects: certain parameters critically affect the behaviour of the system, and as they vary over time it becomes important to understand the transient behaviour and the stabilities of the system. The second area concerns the interaction between distinct intelligent components of the system, particularly in a stressed environment. Our approach, which is different from the more usual queuing approach, is that of Dynamics Systems Theory. We describe it with a particular, though very simple, example that focuses on the relationship between a CPU and an I/O Processor. By exploring this example, we are able to introduce a number of important concepts, to derive qualitative insights, and to identify barometers of performance and control mechanisms.     

A methodology for transparent knowledge specification in a dynamic tuning environment

The increasing use of parallel/distributed applications demands a continuous support to take significant advantages from parallel power. This includes the evolution of performance analysis and tuning tools which automatically allows for obtaining a better behavior of the applications. Different approaches and tools have been proposed and they are continuously evolving to cover the requirements and expectations of users. One such tool is MATE (Monitoring Analysis and Tuning Environment), which provides automatic and dynamic tuning for parallel/distributed applications. The knowledge used by MATE to analyze and take decisions is based on performance models which include a set of performance parameters and a set of mathematical expressions modeling the solution of the performance problem. These elements are used by the tuning environment to conduct the monitoring and analysis steps, respectively. The tuning phase depends on the results of the performance analysis. This paper presents a methodology to specify performance models. Each performance model specification can be automatically and transparently translated into a piece of software code encapsulating the knowledge to be straightforwardly included in MATE. Applying this methodology, the user does not have to be involved in the implementation details of MATE, which makes the usage of the tool more transparent. Copyright © 2011 John Wiley & Sons, Ltd.     

A static analysis of cryptographic processes: the denotational approach

This paper presents a non-uniform static analysis for detecting the term-substitution property in infinite cryptographic processes specified by the language of the spi calculus. The analysis is fully compositional following the denotational approach throughout. This renders the implementation of the analysis straightforward in functional programming. The results are then used to detect certain security breaches, like information leakage and authenticity breaches. As an example of its applicability, we apply the analysis to the SPLICE/AS protocol and the FTP server.     

On accurate stress calculation in static and dynamic problems using the natural factor approach

The natural factor approach was introduced during the last few years as a tool for more accurate computations of displacements [1]. This paper presents an economic iterative procedure for the precise determination of stresses in numerically critical large scale problems. In addition, it furnishes a corresponding error analysis. Furthermore, we discuss the stress (and displacement) computations for unsupported structures under self-equilibrating loads as they may arise in dynamic analysis. Finally, some examples are given.     

Non-linear dynamic system identification: A multi-model approach

We are concerned with models which are able to describe multiple-input multiple-output (MIMO) non-linear dynamic systems. These models are represented in the form of rules and are known as Tagaki-Sugeno models. An identification algorithm for these models based on input and output data is presented. Parameter estimation is based on the calculation of model sensitivity functions with respect to their parameters. Some aspects of structure identification are also tackled, i.e. determination of local model orders and number of rules.     

Iterative motion feedforward tuning: A data-driven approach based on instrumental variable identification

Feedforward control can significantly enhance the performance of motion systems through compensation of known disturbances. This paper aims to develop a new procedure to tune a feedforward controller based on measured data obtained in finite time tasks. Hereto, a suitable feedforward parametrization is introduced that provides good extrapolation properties for a class of reference signals. Next, connections with closed-loop system identification are established. In particular, instrumental variables, which have been proven very useful in closed-loop system identification, are selected to tune the feedforward controller. These instrumental variables closely resemble traditional engineering tuning practice. In contrast to pre-existing approaches, the feedforward controller can be updated after each task, irrespective of noise acting on the system. Experimental results confirm the practical relevance of the proposed method.     

An improved PSO-based approach with dynamic parameter tuning for cooperative multi-robot target searching in complex unknown environments

Target searching in complex unknown environments is a challenging aspect of multi-robot cooperation. In this paper, an improved particle swarm optimisation (PSO) based approach is proposed for a team of mobile robots to cooperatively search for targets in complex unknown environments. The improved cooperation rules for a multi-robot system are applied in the potential field function, which acts as the fitness function of the PSO. The main improvements are the district-difference degree and dynamic parameter tuning. In the simulation studies, various complex situations are investigated and compared to the previous research results. The results demonstrate that the proposed approach can enable the multi-robot system to accomplish the target searching tasks in complex unknown environments.     

Querying linked data graphs using semantic relatedness: A vocabulary independent approach

Linked Data brings inherent challenges in the way users and applications consume the available data. Users consuming Linked Data on the Web, should be able to search and query data spread over potentially large numbers of heterogeneous, complex and distributed datasets. Ideally, a query mechanism for Linked Data should abstract users from the representation of data. This work focuses on the investigation of a vocabulary independent natural language query mechanism for Linked Data, using an approach based on the combination of entity search, a Wikipedia-based semantic relatedness measure and spreading activation. Wikipedia-based semantic relatedness measures address existing limitations of existing works which are based on similarity measures/term expansion based on WordNet. Experimental results using the query mechanism to answer 50 natural language queries over DBpedia achieved a mean reciprocal rank of 61.4%, an average precision of 48.7% and average recall of 57.2%.