Some Quantum Dynamical Semi-groups with Quantum Stochastic Dilation

We consider the GNS Hilbert space $\mathcal{H}$ of a uniformly hyper-finite $C^*$- algebra and study a class of unbounded Lindbladian arises from commutators. Exploring the local structure of UHF algebra, we have shown that the associated Hudson-Parthasarathy type quantum stochastic differential equation admits a unitary solution. The vacuum expectation of homomorphic co-cycle, implemented by the Hudson-Parthasarathy flow, is conservative and gives the minimal semi-group associated with the formal Lindbladian. We also associate conservative minimal semi-groups to another class of Lindbladian by solving the corresponding Evan-Hudson equation

A Study Of Quality Management In Small Organizations Providing Services Directed At People

This paper reports on a study of managerial perceptions of the implementation of total quality management (TQM). Results of a survey covering small firms in northeastern Indiana providing services directed at people are presented. Aspects discussed include the unique nature of this category of service firms, TQM deployment, tools used, successes, failures, benefits, and problems encountered. The majority of respondents indicated their firms’ commitment to TQM but a significantly smaller proportion demonstrated notable engagement with and actual implementation of a formal TQM program. Even smaller percentages had benchmarked internal quality standards, used TQM tools and quality-enhancing activities, rewarded employees for successful quality performance, and involved suppliers in their quality programs. Strategic implications of these findings are considered

A Deep Learning Approach Towards Generating High-fidelity Diverse Synthetic Battery Datasets

Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the number of vehicles powered by fossil fuels. Lithium-ion based batteries are presently dominating the electric automotive sector. Energy research efforts are also focussed on accurate computation of State-of-Charge of such batteries to provide reliable vehicle range estimates. Although such estimation algorithms provide precise estimates, all such techniques available in literature presume availability of superior quality battery datasets. In reality, gaining access to proprietary battery usage datasets is very tough for battery scientists. Moreover, open access datasets lack the diverse battery charge/discharge patterns needed to build generalized models. Curating battery measurement data is time consuming and needs expensive equipment. To surmount such limited data scenarios, we introduce few Deep Learning-based methods to synthesize high-fidelity battery datasets, these augmented synthetic datasets will help battery researchers build better estimation models in the presence of limited data. We have released the code and dataset used in the present approach to generate synthetic data. The battery data augmentation techniques introduced here will alleviate limited battery dataset challenges.Comment: Accepted at IEEE Transactions on Industry Application

Characterization of unitary processes with independent increments

In this paper, we study unitary Gaussian processes with independent increments with which the unitary equivalence to a Hudson-Parthasarathy evolution system is proved. This gives a generalization of results in [11] and [12] in the absence of the stationarity condition