A characterization of partial blocks in weighted graphs

Some of the classical connectivity concepts in Graph theory are generalized in this article. Strong and strongest strong cycles are introduced. Partial blocks are characterized using strongest paths. Some necessary and sufficient conditions for a weighted graph to be a partial block are also presented.     

DISCWorld: an environment for service-based matacomputing

We describe our DISCWorld system for wide-area, high-performance metacomputing in which we adopt a high-level, service-based approach. Users’ client programs request combinations of services from a set of server nodes which communicate at a peer-based level. DISCWorld is a constrained metacomputing system, running only the service operations its participating resource administrators have chosen to provide and advertise, and provides a common integration environment for clients to access these services and developers to make them available. We discuss our software architecture and experiences building DISCWorld using Java and CORBA components, and the associated research issues for metacomputing that we are addressing.     

Scalable approach to uncertainty quantification and robust design of interconnected dynamical systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks.     

CDUL: Class directed unsupervised learning

A novel neural network called Class Directed Unsupervised Learning (CDUL) is introduced. The architecture, based on a Kohonen self-organising network, uses additional input nodes to feed class knowledge to the network during training, in order to optimise the final positioning of Kohonen nodes in feature space. The structure and training of CDUL networks is detailed, showing that (a) networks cannot suffer from the problem of single Kohonen nodes being trained by vectors of more than one class, (b) the number of Kohonen nodes necessary to represent the classes is found during training, and (c) the number of training set passes CDUL requires is low in comparison to similar networks. CDUL is subsequently applied to the classification of chemical excipients from Near Infrared (NIR) reflectance spectra, and its performance compared with three other unsupervised paradigms. The results thereby obtained demonstrate a superior performance which remains relatively constant through a wide range of network parameters.     

Geometric Representation of Graphs in Low Dimension Using Axis Parallel Boxes

An axis-parallel k-dimensional box is a Cartesian product R ₁×R ₂×???×R _k where R _i (for 1≤i≤k) is a closed interval of the form [a _i ,b _i ] on the real line. For a graph G, its boxicity box?(G) is the minimum dimension k, such that G is representable as the intersection graph of (axis-parallel) boxes in k-dimensional space. The concept of boxicity finds applications in various areas such as ecology, operations research etc. A number of NP-hard problems are either polynomial time solvable or have much better approximation ratio on low boxicity graphs. For example, the max-clique problem is polynomial time solvable on bounded boxicity graphs and the maximum independent set problem for boxicity d graphs, given a box representation, has a $\lfloor 1+\frac{1}{c}\log n\rfloor^{d-1}An axis-parallel k-dimensional box is a Cartesian product R ₁×R ₂×⋅⋅⋅×R _k where R _i (for 1≤i≤k) is a closed interval of the form [a _i ,b _i ] on the real line. For a graph G, its boxicity box (G) is the minimum dimension k, such that G is representable as the intersection graph of (axis-parallel) boxes in k-dimensional space. The concept of boxicity finds applications in various areas such as ecology, operations research etc. A number of NP-hard problems are either polynomial time solvable or have much better approximation ratio on low boxicity graphs. For example, the max-clique problem is polynomial time solvable on bounded boxicity graphs and the maximum independent set problem for boxicity d graphs, given a box representation, has a ?1+\frac1clogn?^d-1\lfloor 1+\frac{1}{c}\log n\rfloor^{d-1} approximation ratio for any constant c≥1 when d≥2. In most cases, the first step usually is computing a low dimensional box representation of the given graph. Deciding whether the boxicity of a graph is at most 2 itself is NP-hard.     

Energy optimization for CAN bus and media controls in electric vehicles using deep learning algorithms

The Journal of Supercomputing - This study offers a neural network-based deep learning method for energy optimization modeling in electric vehicles (EV). The pre-processed driving cycle is...     

Split-window and multi-angle methods of sea surface temperature determination: An analysis

A physical model of split-window and multi-angle algorithms for the retrieval of sea surface temperature has been obtained. It is shown that optical thickness in the thermal infrared window region is approximately represented by a separable function of wavelength and atmospheric variables which greatly simplify the radiative transfer model. In modelling the temperature retrieval algorithms we exploit the correlation that exists between the sea surface temperature and the average temperature of the atmosphere. It is shown that, to the extent this correlation is maintained, there is a simple relationship between surface temperature and the brightness temperatures measured in two split-window or multi-angle channels. The different atmospheric conditions which cause spread in this relationship are briefly discussed. It is shown that measurements at three optical thicknesses instead of two as in the case of the split-window method improve the SST retrieval accuracy since it can take care of non-equilibrium conditions such as inversions, surface instabilities, etc., prevailing in the atmosphere.     

Business analytics and business value: A comparative case study

With growing adoption of business analytics, it is important for investing firms to understand how business value is created from investments. Studies in IT domain have highlighted how higher investment in technology may not bring more returns, rather how IT as an organizational capability acts as a key mediator in value creation. This research extends the model to business analytics, to identify elements of analytics technology assets and business analytics capability and to understand the mechanism of business value creation using multiple case studies. We capture how analytics resources contribute to business performance by developing operational and organizational performance measures.